The extraction methods for carotenoids, xanthophylls and chlorophylls are essential processes involved in the recovery of bioactive compounds from a wide variety of natural sources, including plants, marine organisms, microorganisms and animals. These bioactive compounds, characterized by their significant functional and structurally active properties, play a crucial role in various industries, such as pharmaceuticals, nutraceuticals and food. Due to their presence in small quantities and conjugated form in nature, the extraction of these compounds can be laborious and often requires meticulous purification steps. Interestingly, the primary and secondary metabolites present in plants contribute significantly to the production of these bioactive compounds, adding to their complexity and importance. Notably, carotenoids, as natural pigments with antioxidant and nutritional properties, are known for offering numerous health benefits and are instrumental in various biological processes, including the photoprotection of plants and the prevention of chronic degenerative diseases. It is fascinating to observe that by-products generated from agricultural activities and food processing serve as abundant sources of these bioactive compounds, aligning with the principles of the circular economy and sustainable resource management. Given the diverse health benefits and multifaceted applications of these natural pigments, there is a growing interest in not only extracting but also utilizing them for industrial, nutritional and pharmacological purposes.

In this paper, we prove the existence of three solutions for a $p$-Kirchhoff problem with $\psi$-Hilfer fractional derivative. To be more precise, we use the variational method and we prove that the associated functional energy admits a critical point in each of the three constructed sets, these critical points are weak solutions for a studied problem. Moreover, by definition of these sets, one of these solutions is positive, the second is negative, and the third one change sign. At the end of this work, we present an example to validate our main results.

The 5-hydroxymethylfurfural (HMF) is a six-carbon heterocyclic organic compound with two substituents an aldehyde group and an alcohol group. It is formed from saccharides as a result of two reactions: the Maillard reaction and the caramelization of saccharides. It has both a negative and a positive effect on the human body, which makes it, even more, an attractive research object. The objective of the study was to determine HMF content in honey samples available on the Polish market using three varied methods and compare the results obtained. The methodology used included: two spectrophotometric methods (by White and Winkler) and the HMF determination method based on the LC-MS/MS technique developed uniquely for this study. The determined HMF content in all tested honey samples did not exceed the applicable standard. All results are within the scope of the European Council Directive 2001/110/EC of 20 December 2001, i.e. they do not exceed HMF content in honey, which is 40 mg/kg.

Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

Global warming and climate change have been the major concern for multiple disciplines. Population growth, urbanization, and industrialization are major contributing factors to such a problem by escalating the use of fossil fuels to meet their energy demands. The building sector has the largest share in using the total globally produced energy and emitting tons of greenhouse gas emissions. Emerging eco-friendly technologies such as solar and wind energy harvesting are being extensively explored but still, they are insufficient. Nature-inspired technologies can be the solution to our problem. For instance, algae are microorganisms that use water, light, and CO2 to produce energy and sustain life. Exploiting such characteristics in the built environment is termed algae building technology, a very efficient and green application for a sustainable future. Algae-integrated façades show great versatility through biomass and energy production, wastewater treatment, shading, and thermal and acoustic insulation. In this paper, algae will be introduced as a robust tool toward a greener and more sustainable future, The algae-building technology and its implementation will be demonstrated. Steps for applying this sustainable approach in Egypt will be discussed.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. Maintaining water quality is crucial for environmental well-being. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, and evaluate associated health risks pertaining to nitrate and flouride in AlHassa region, KSA. The physiochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021-4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate Health Quotient (HQ) values indicated chronic human health risks ranging from low to severe, with newborns being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The Hazard Index (HI) yielded moderate to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the Total Hazard Index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources and vulnerable human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

The European Union's recent decision to renew the authorization for the use of glyphosate until 15 December 2033 stimulates the scientific discussion all around the world regarding its toxicity or otherwise for humans. Glyphosate is a chemical used by millions of tons in the last 50 years worldwide to drying weeds in cultivated fields, greenhouses, roadsides. Concern has been raised in many quarters about the possible presence in the food chain and the consequent adverse effects on health: both aspects that argue in favor of toxicity and those that instead may indicate limited toxicity of glyphosate are here discussed. The great debate that has been generated requires further investigations and field measurements to understand its fate once dispersed in the environment and its concentration in the food chain. Hence the need for validated analytical methods that are available to analysts in the field. In the present review, methods for the analytical determination of glyphosate and its main metabolite, AMPA, are discussed, with a specific focus on chromatographic techniques applied to cereal products. The experimental procedures are explained in any detail, including cleanup, derivatization, and instrumental conditions to give the laboratories involved enough information to proceed with the implementation of this line of analysis. The prevalent chromatographic methods used are LC-MS/MS, GC-MS/SIM, and GC-MS/MS but sufficient indications are also given to those laboratories that wish to use the simpler HPLC-FLD, HPLC-UV, GC-NPD or GC-FPD techniques for screening purposes. Concentrations from literature measured in wheat, corn, barley, rye, oats, soybean, and cereal-based foods are reported together with the regulatory status in various parts of the world and the accumulation mechanism. As for the accumulation in cereals, available data show that glyphosate tends to accumulate more in wholemeal flours than in refined ones, that its concentration in the product strictly depends on the treatment period (the closer it is to the time of harvesting, the higher the concentration) and that in cold climates the herbicide tends to persist in the soil for a long time.

Hydrogen is undoubtedly one of the most promising alternatives for generating energy with low environmental impact. However, most hydrogen produced today is derived from fossil fuels, which negatively impacts the environment. In this context, biomass conversion technologies emerge as a sustainable and environmentally friendly alternative to conventional fossil-based processes for hydrogen production. The main objective of this study is to underscore the potential of biomass-derived hydrogen, as one of the key sustainable energy solutions. This work reviews diverse biological and thermochemical pathways for biomass-derived hydrogen production, noting challenges in technology and feedstock diversity. Biomass offers local availability and economic benefits, potentially yielding negative emissions. Biological methods like bio-photolysis and fermentation promise low-energy, waste-recycling solutions despite operational constraints. Thermochemical processes, influenced by biomass type and energy content, favor economies of scale and conventional gasification for economic viability. Hydrothermal gasification shows promise for high-moisture feedstocks. Overall, while thermochemical processes suit centralized large-scale hydrogen production, biological pathways offer decentralized options, necessitating continued innovation for integration into future energy strategies.

This study uses the Scopus and Web of Science databases to review the forecast of electricity consumption from 2015 to 2024. Based on the keywords used in the article title, which are associated with electricity consumption forecasts, the study retrieved 821 docu-ments for additional analysis using various techniques. For frequency analysis, we used Mi-crosoft Excel; for data visualisation, we used VOSviewer; and for citation metrics and analysis, we used Harzing's Publish or Perish. Standard bibliometric variables, including publication growth, authorship patterns, collaboration, prolific authors, national contribution, most active institutions, favourite journals, and highly cited articles, are used in this study's reporting of the findings. Our analysis indicates that over ten years, starting in 2015, publications on the forecasting of electricity consumption have continued to increase. China was the country that contributed the most to research on electricity consumption. Most of the research publications on electricity consumption were published in the Journal of Energy.

Pelagic stingray (Pteroplatytrygon violacea) is the only species of stingray (Dasyatidae) that utilizes both pelagic and demersal habitats. It is the main bycatch species in pelagic longline fisheries targeting bluefin tuna in the Gulf of Lions. In the Mediterranean Sea, their stock structure, behavioural ecology and movements are unknown. For the first time in the Mediterranean, 17 individuals (39 to 60 cm disc width) were tagged with pop-up satellite archival transmitting tags using a novel method of tag attachment, to investigate horizontal and vertical movements. The tags were attached for between two and 60 days. Between the months of July and October, pelagic stingray occupied a temperature range of 12.5 to 26.6°C, and a depth range extending from the surface to 480 m. Monthly trends in catch-per-unit-effort (CPUE) of pelagic stingray peaked in August and decreased by late autumn. Pelagic stingray may aggregate on the continental shelf during summer, and move southwards in early autumn, and this movement pattern is considered in relation to the reproductive cycle and overwintering. Future work and options for bycatch mitigation are discussed.

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

Recent advances in organoid technology have revolutionized cancer biology and thera-peutic interventions, offering personalized immunotherapy treatment. Organoids, three-dimensional cell cultures derived from patient tumors, accurately replicate the tumor microenvi-ronment, providing unprecedented insights into tumor-immune interactions and therapeutic re-sponses. In this literature-based study, we discuss various culture methods for the diverse appli-cations of organoids in cancer immunotherapy, including drug screening, personalized treatment strategies, and mechanistic studies. Additionally, we address the technological challenges asso-ciated with these methods and propose potential future solutions to accelerate the development of novel immunotherapeutic approaches. This review highlights the transformative potential of or-ganoid models in advancing preclinical cancer immunotherapy modeling, screening, and evalua-tion, paving the way for more effective and personalized cancer treatments.

Attention to the work-related musculoskeletal disorders (WMSDs) involve also statistical surveys showing an increasing trend in the incidence of WMSDs. Technological development retrieves new tools and methods for the assessment of physical load at work. In addition to the conventional questionnaires and scaling assessment systems, these methods are mostly based on the direct sensing of appropriate parameters, which allows more precise quantification. The aim of this paper is to compare the assessment of ergonomic risk through current legislative regulations. A Captiv wireless sensor system was used at a car headlight quality control assembly workplace for sensing, data acquisition and data processing. During the evaluation of the postures and movements at work, we discovered differences in the applicable standards: Decree 542/2007 Coll. (Slovak legislation), STN EN 1005-4+A1, French standards default in the Captiv system. Standards define the thresholds for hazardous postures with significant differences in several evaluated body segments, which also affects the final evaluation of the measurements. Based on the results of our research, we found that respondents were in an acceptable position for approximately 63% of the total duration of the work activity in terms of the Legislative method, 44% with Captiv and 27% with Standards method.

The purpose of this manuscript is to examine the extent to which Polish SMEs use digital marketing tools in the context of their impact on economic performance. The choice of the objective is due to the research gap related to the lack of studies on the impact of the use of digital marketing tools on economic performance by SME companies in Poland. In order to achieve the objective, a two-stage approach was used. The first stage identified the degree of use of digital marketing tools by Polish SMEs compared to EU countries based on secondary data. The second stage analyzed the impact of the use of selected digital marketing and sales communication tools on the economic performance of Polish SMEs. The analysis was based on primary data, which were the results of surveys conducted on a sample of 574 Polish SMEs in the industrial sector. The analysis used statistical methods such as difference significance tests (Mann-Whitney U test, ANOVA Kruskal-Wallis test and Dunn's post-hoc tests), Kendall's rank correlation coefficient and Multivariate Adaptive Regression Splines (MARSplines). The results obtained have theoretical and practical values. The theoretical implications are the enrichment of the marketing concept of customer value creation taking into account the digital customer, which is also the originality of the research. Practical implications refer to indicating to SME managers the need for, as well as ways of implementing and financing digital technologies within the framework of various EU programs for digital transformation of member states.

The direct detection of dark matter constituents, in particular the weakly interacting massive particles (WIMPs), is central to particle physics and cosmology. In this paper we develop the formalism for WIMP-Nucleus induced transitions from isomeric nuclear states. We specialize it in the case of the experimentally interesting target 180 Ta

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

Enteric infections are a major public health concern. Detecting risk areas requires a strong surveillance system for pathogenic viruses in possible sources such as wastewater. To build an environmental surveillance system, we aimed to identify group A rotavirus (RVA) and human adenovirus (HAdV) in wastewater. We also focused on three different methods of viral concentration to determine suitability for a regular surveillance system. We screened 20 wastewater samples for HAdV and RVA using quantitative polymerase chain reaction (qPCR) and conventional polymerase chain reaction (cPCR). Sixteen posi-tives (18/20) and ten (14/20) positives for HAdV and RVA, respectively, were obtained on qPCR, representing a prevalence of 90% and 70%. Twelve (12) positives were successfully amplified. Bag-mediated filtration system (BMFS) and skimmed milk (SM) flocculation were the most consistent viral concentration methods for HAdV and RVA, respectively. Human adenovirus was identified with nucleotide identity range of 98.48% to 99.53%. Statistical analysis of positives showed that viral burden differed by site (P

This study explores the complexities involved in the extraction and purification of catechins, prominent bioactive compounds found in plant extracts and tea. It addresses challenges arising from the intricate molecular structure of catechins and their inherent variability across botanical sources. Despite these obstacles, catechins present significant potential, particularly in healthcare. Their potent antioxidant properties have been extensively investigated and associated with numerous health benefits, positioning them as promising therapeutic agents for combating chronic diseases. Furthermore, catechins have diverse applications across various industries, including food and beverage, cosmetics, agriculture, and materials science. This abstract emphasizes the importance of continued research and innovation in catechin extraction and utilization, which hold promise for advancing human health and technological progress across multiple domains.

The distribution of noble metal nanoparticles in hydrogels are influencing their nanoplasmonic response and signal used for biosensor purposes. By controlling the particle distribution, it is possible to obtain new nanoplasmonic features with new sensing modalities. Particle distributions can be characterized by using volume-imaging methods such as the focused ion beam-scanning electron microscope (FIB-SEM) and the serial block-face scanning electron microscopy (SBFSEM) techniques . Since the pore structure of hydrogels is contained by the water absorbed in the polymer network it may pose challenges for volume-imaging based on electron microscope techniques since the sample must be in a vacuum chamber. The structure of hydrogels can be conserved by choosing appropriate preparation methods, which also depends on the composition of the hydrogel used. In this paper, we have prepared low-weight percentage hydrogels, with and without gold nanorods (GNR) for conventional SEM imaging by using two different drying techniques; (1) the critical point drying (CPD) technique and (2) hexamethyldisilazane (HMDS) drying of hydrogels. A qualitative characterization of the GNR-hydrogels was carried out to study the GNRs positioned in the polymer network. The effect of the two different drying methods on the hydrogel morphology were also compared. The use of HMDS as an alternative to the CPD has several advantages involving less parametrical variables for drying, involving less effort, being cost-effective, and requires no equipment use. In addition, choosing an optimized sample preparation method for SEM with optimized imaging parameters is highly important for obtaining accurate information about materials that is not correlated to artifacts. Hence, the results obtained from the preparation methods and SEM imaging parameters in this paper are useful for developing methods for mapping the metal particle distributions in micro-hydrogels by using FIB-SEM and SBFSEM techniques.

(Background) Phytotoxicity refers to the capacity of chemical substances or environmental factors to have a negative impact on plants. This is a crucial issue in both the context of crop cultivation and environmental protection. (Material and Methods) The research results were based on a 3-year field experiment conducted at the experimental station in Jadwisin (52°28′ N, 21°02′ E) on loamy soil. The experiment was set up using a randomized sub-block design in a split-split-plot arrangement, with three replications. The first-order factor consisted of potato cultivars, while the second-order factors were weed control methods: 1) Control – without protection; 2) mechanical weed control, extensive mechanical treatments to close rows; 3) Sencor 70 WG – pre-emergence (PRE) of potatoes; 4) Sencor 70 WG + Titus 25 WG + Trend 90 EC – PRE of potatoes; 5) Sencor 70 WG – post-emergence (POST) of potatoes; 6) Sencor 70 WG + Titus 25 WG + Trend 90 EC – POST of potatoes; 7) Sencor 70 WG + Fusilade Forte 150 EC – POST of potatoes; 8) Sencor 70 WG + Apyros 75 WG + Atpolan 80 SC – POST of potatoes. The phytotoxic effects of herbicides on potato plants and weeds were assessed every 7 days, starting from the date when the first signs of damage appeared, until they stabilized or disappeared. (Results:) Phytotoxic damage to potato and weed plants were caused by the chemical weed control methods used. The response of potato plants to herbicides was significantly related to the genetic traits of the cultivars and meteorological conditions in the years of research. (Conclusion:) Phytotoxicity is an important aspect in both agriculture and environmental protection. Research on its mechanisms and impact will enable the development of effective plant protection strategies and the preservation of ecosystem balance.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

The primary objective of this study was to identify a rapid and noninvasive methodology for effectively differentiating various herbal species cultivated in Eastern Poland. Typically, a plant's chemical profile is obtained using chromatographic methods; however, spectroscopic methods can serve as a complementary or independent tool for determining similarities in the analyzed samples. The investigation centered on leaves of plant species from the Lamiaceae family, encompassing basil, lavender, oregano, sage, and thyme. UV-VIS and ATR-FTIR spectra of dried plant samples' powder were recorded and subsequently analyzed using chemometric tools. This paper assesses the relative performance of different discrimination methods, including both traditional methods based on machine learning and those dealing with specimens associated with a specific category. Among the methodologies tested, SIMCA showed the best performance, particularly effective for small training sets.

3D surface scans were carried out to determine the shapes of the upper sections of (skeletal) crania of adult Eurasian otters (Lutra lutra) from Great Britain. Landmark points were placed on these shapes by using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) could be found by using the landmark points. These “GUI-based” distances were shown to be accurate and reliable in comparison to physical measurements taken on the crania directly by using a digital calliper. The crania of males were 6.85mm, 5.44mm, 1.66mm larger in terms of length, width and height, respectively, than females in our sample (P < 0.001), i.e., male otters had significantly larger skulls than females. Significant differences in size occurred also by geographical area in Great Britain (P < 0.05). Multilevel Principal Components Analysis (mPCA) indicated that sex and geographical area explained 31.1% and 9.6% of shape variation in “unscaled” shape data and that they explained 17.2% and 9.7% of variation in “scaled” data. The first mode of variation at level 1 (sex) correctly reflected size changes between males and females for “unscaled” shape data. Modes at level 2 (geographical area) also showed possible changes in size and shape. Clustering by sex and geographical area was observed in standardised component scores. Such clustering in cranial shape by geographical area might reflect genetic differences that are known to occur in otter populations in Great Britain, although other potentially confounding factors (e.g. population age-structure, diet, etc.) might also drive regional differences. Furthermore, sample sizes per group were small for geographical comparisons. However, this work provides a successful first test of the effectiveness of 3D surface scans and multivariate methods such as mPCA to study the cranial morphology of otters.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, the coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Cervical Cancer affects a large part of female population that makes the prediction of this disease based on Machine Learning (ML) by outmost importance. ML algorithms can be integrated in complex intelligent agent-based systems that can offer decision support to the resident medical doctors or even to experienced medical doctors. For instance can be mentioned the situation when an experienced medical doctor diagnose a case but he/she needs expertise support that is related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influencing effect in making predictions using ML algorithms. Cancer data generally and cervical cancer data particularly are frequently imbalanced. Based on this fact the study of data imbalance impact on diverse state-of-the-art ML prediction algorithms is important. This research subject is also motivated by the fact that in many research are presented experimental evaluations of algorithms without characterization of the data on that they have been applied. Such characterizations could give clear indication to other researchers regarding the applicability of the algorithms on their specific data. Specifically, if the data have the respective characteristics than are expectable the same performance evaluation results like those in the reported research. For the study we chosed a messy real-life Cervical Cancer dataset available in a recognized data repository included a large amount of missing and noisy values. To identify the best imbalanced technique for this medical dataset, it is compared the performance of eleven important resampling methods combined with the following state-of-the-art ML models: K-Nearest Neighbours (with k values of 2 and 3), binary Logistic Regression, and Random Forest, that are frequently applied in prediction types of researches in healthcare. The studied resampling methods includes seven undersampling methods namely Condensed Nearest Neighbour, Tomek Links, Edited Nearest Neighbours, Repeated Edited Nearest Neighbours, All K-Nearest Neighbours, NearMiss, Neighbourhood Cleaning Rule, and Instance Hardness Threshold, and four oversampling methods namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning SMOTE, Support Vector Machine SMOTE and Borderline SMOTE. In the case of the dataset for the confidence interval with 95% confidence level was between 9.23 and 16.22, while the imbalance ratio is 12.73. The obtained results show that resampling methods help the learning models to improve the classification ability of cervical cancer. The applied oversampling techniques generally showed better results than undersampling methods. In the case of Logistic Regression classifier had the highest impact on balanced techniques, while Random Forest had promising performance, even before balancing techniques and KNN2 was generally better than KNN3.

Background: Breastfeeding is essential for optimal infant health, offering numerous benefits such as infection prevention and enhanced bonding. Despite its importance, challenges exist, particularly for mothers of preterm and very low birth weight (VLBW) infants, necessitating effective lactation support strategies. Initiatives like the Baby Friendly Hospital Initiative (BFHI) and Kangaroo Mother Care (KMC) aim to promote breastfeeding success, while donor human milk (DHM) serves as an alternative bridge while the mother is supported to build up her supply. Adequate lactation support, counseling, and education are vital antenatally, during NICU stays and after discharge to improve breastfeeding rates and infant health outcomes, ultimately contributing to global efforts to reduce neonatal mortality and achieve sustainable development goals. This study explores existing research on factors influencing mothers’ ability to produce breast milk and sustain breastfeeding, with the aim of enhancing lactation support and improving infant health outcomes.Methods/Design: A systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Searches will be performed in the following databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE Ovid via EBSCOhost, Google Scholar, and WILEY online Library) to identify and incorporate primary research articles related to the topic published between 2014 and 2024. Authors will search peer-reviewed articles, gray literature sources, and reference lists to identify eligible studies. Independent abstract and full-text screening by two reviewers will follow the title screening. The analysis of data will be carried out using Review Manager software (version 5.3.5). The I² test will be used to quantify the amount of unexplained heterogeneity. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger’s test, Begg’s test, and Trim and Fill analysis.Discussion: Our review represents a singular exploration into exclusive breastfeeding among very low birth weight (VLBW) infants in low- and middle-income countries (LMICs), focusing on breastfeeding assistance methods. With VLBW defined as less than 1500 g, and most low birth weight (LBW) babies born in LMICs, there is a critical need to address feeding challenges and promote optimal nutrition. Despite updated WHO guidelines in 2022, there is still a lack of comprehensive advice for this vulnerable population. Peer support interventions and health worker counseling have shown promise in increasing breastfeeding continuation rates in LMICs, but challenges persist due to workforce shortages and inadequate documentation. Promoting early postpartum milk expression is crucial for milk production and infant health. Our review aims to fill the gap in understanding VLBW mothers’ breastfeeding experiences in LMICs, shedding light on the effectiveness of breastfeeding support methods and identifying areas for further research. Systematic review registration: CRD42024547864

The focus was on developing machine learning models to predict the IC50 values of the compound OTX015 using gene expression levels of cancer cell lines. Through regression-based machine learning models, particularly the SVM model, trained on pre-laboratory data, consistent and generalizable performance with low error scores was achieved. These models underwent enhancement via hyperparameter optimization and were tested on validation data. The results indicate the utility of machine learning in drug discovery processes and personalized medicine, offering rapid and cost-effective predictions of IC50 values. However, for enhanced reliability, further model development, data gathering, and evaluation with diverse compounds are recommended. In conclusion, this research demonstrates the potential of machine learning approaches to optimize experimental processes, reduce costs, and contribute to personalized treatments in healthcare by accurately predicting compound responses.

Cell viability refers to the proportion of living, healthy cells within a given population. This information is crucial in any cell-based research, especially in toxicology. Over the past century, numerous methods for assessing cell viability have been developed. These methods can be categorized into five main types: non-invasive cell structure damage, invasive cell structure damage, cell growth, cellular metabolism, and cell membrane potential. Each method operates on different principles and has its own advantages and disadvantages, making it essential for researchers to choose the method that best fits their experimental design. Despite the variety of cell viability methods available, most rely on indirect measurements of cell viability, which may compromise their accuracy. In this sense, there is insufficient information regarding their accuracy, highlighting the need for researchers to investigate the accuracy of both existing and future methods.

Successful geophysical exploration works in Gucheng-Yaxi area located in Gaochun District, Jiangsu province, China, have been limited partly due to complex geological condition of the area and high artificial noise in data acquired using electrical and electromagnetic methods. In this work, we deployed the new anti-interference spread spectrum induced polarization method (SSIP) and audio magnetotellurics (AMT) method to detect a copper-polymetallic deposit in the area. Two-dimensional (2D) inversion results at Gucheng-Yaxi Area revealed a high chargeability anomalous zone on SSIP profile that coincided with a zone of moderate resistivity located between two resistor bodies on AMT profile. A follow up 1200 meters drill hole was established at this high chargeability-moderate resistivity zone which encountered polymetallic (copper, lead, zinc, gold and silver) mineralization at the depth ≥ 400 m. Drill hole data analysis showed that mineralization occurs interspaced in marble rock mass at varies depth. Furthermore, several low resistivity-weak chargeability sections were revealed and inferred attributed to Cretaceous sediments and faults. These faults are thought to have played critical role in the polymetallic mineralization genesis. In summary, this work demonstrated the successful of application of SSIP and AMT at detecting a metallic deposit in an area with high artificial noise. Hence the geophysical prospection potential of Gucheng-Yaxi area is great.

Graph neural networks (GNNs) have been effectively implemented in a variety of real-world applications, while their underlying work mechanisms remain a mystery. To unveil this mystery and advocate trustworthy decision-making, many GNN explainers have been proposed. However, existing explainers often face significant challenges, such as: 1) explanations being tied to specific instances; 2) limited generalizability to unseen graphs; 3) potential generation of invalid graph structures; and 4) restrictions to particular tasks (e.g., node classification, graph classification). To address these challenges, we propose a novel explainer, GAN-GNNExplainer, which employs a generator to produce explanations and a discriminator to oversee the generation process, enhancing the reliability of the outputs. Despite its advantages, GAN-GNNExplainer still struggles with generating faithful explanations and underperforms on real-world datasets. To overcome these shortcomings, we introduce ACGAN-GNNExplainer, an improved approach that improves upon GAN-GNNExplainer by using a more robust discriminator that consistently monitors the generation process, thereby producing explanations that are both reliable and faithful. Extensive experiments on both synthetic and real-world graph datasets demonstrate the superiority of our proposed methods over existing GNN explainers.

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge in safeguarding saffron quality and authenticity. Advanced techniques like Time of Flight Secondary Ion Mass Spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three levels of inclusion (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through Principal Component Analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

This review paper presents a comprehensive synthesis of current knowledge on the contamination of water systems by polycyclic aromatic hydrocarbons (PAHs). PAHs, organic contaminants characterized by two or more fused aromatic rings, are notably problematic due to their persistence, bioaccumulation, and significant health risks. The review commences with an examination of various sources of PAHs in aquatic environments, encompassing both anthropogenic activities, such as industrial discharges, urban runoff, and oil spills, and natural phenomena including volcanic eruptions and wildfires. Subsequently, the discussion shifts to the distribution patterns of PAHs within different aquatic ecosystems, with an analysis of factors that influence their transport. Considerable focus is placed on the entry mechanisms of PAHs into water systems and their interactions with diverse environmental matrices. Additionally, the ecotoxicological effects of PAHs on aquatic life are scrutinized, with particular emphasis on molecular, individual, and ecosystemic impacts. This includes an exploration of PAH bioaccumulation in aquatic organisms and their potential mutagenic and carcinogenic consequences. The review further addresses the challenges associated with detecting and quantifying PAHs in aquatic settings, reflecting on the advancements in analytical methodologies and the complexities introduced by their varied physicochemical properties. The paper concludes with a summary of existing regulatory frameworks and guidelines governing PAH contamination in water systems, underscoring the imperative for global collaboration in the monitoring and management of PAH pollution.

Background: The COVID-19 pandemic has disproportionately affected people from more deprived communities. The experience of Long Covid is similarly distributed but very few investigations have concentrated on the needs of this population. The aim of this project was to co-produce an acceptable intervention for people with Long Covid, living in communities recognised as more deprived. Methods: The intervention was based on a multi-disciplinary team using approaches from sport and exercise medicine and functional rehabilitation. The co-production process was undertaken with a stakeholder advisory group and patient public involvement representation. This study identified participants by postcode and the indices of multiple deprivation (IMD); recruitment and engagement were supported by an existing health and wellbeing service. A virtual ‘clinic’ was offered with a team of professional practitioners who met participants three times each; to directly consider their needs and offer structured advice. The acceptability of the intervention was based on the individual’s participation and their completion of the intervention. Results: Ten participants were recruited with eight completing the intervention. The partnership with an existing community health and wellbeing service was deemed to be an important way of reaching participants. Two men and six women ages ranging from 38 to 73 were involved and their needs were commonly associated with fatigue, anxiety and depression with overall de-conditioning. None reported serious hardship associated with the pandemic although most were in self-employment/part-time employment or were not working due to retirement or ill-health. Two older participants lived alone, and others were single parents and had considerable challenges associated with managing a household alongside their Long Covid difficulties. Conclusions: This paper presents the needs and perspectives of eight individuals involved in the process and discusses the needs and preferences of the group in relation to their support for self- managed recovery from Long Covid.

This study analyzed methodologies for identifying β-casein genotypes in dairy cows and the purity of A2 fermented milk. The research was justified by the need for quick and accurate techniques for herd screening and A2 milk quality control. The methodology included the development of an ELISA test to detect β-casein A1 in fermented milk and the evaluation of a commercial Lateral Flow Immuno Assay (LFIA) to identify A2A2 genotypes and the purity of A2 milk. The results showed that the ELISA test had high sensitivity and specificity (100%) in detecting β-casein A1. The LFIA test was 100% effective in identifying A2A2 genotypes and verifying the purity of A2 milk with a minimum detectable contamination of 5% for raw milk and 10% for fermented milk. Compared to traditional genotyping, these methods proved to be highly reliable and more practical for large-scale screening. It was concluded that the ELISA and LFIA were valuable tools for ensuring the quality and authenticity of A2 milk, meeting the demands of producers and consumers for safe and healthy dairy products.

5th Generation (5G) cellular networks have been adopted around the world since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). 5G also introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brought with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core have to be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize Post Quantum Cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for TLS v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications on a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM’s benefits far outweigh any downsides when integrated into 5G and 6G core services.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method to study wildfire contribution to PM2.5 using satellite-derived smoke plume indicators and PM2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy.

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of credit worthiness.

Currently, urban renewal activities in China are booming. And promoting the renovation of public buildings is the key due to its large scale, high cost and significant impact to the nature and social environment. To reduce the ambiguity and uncertainty in evaluating the potential for renewal of existing public buildings, a renewal potential evaluation model combining game theory combination weighting method and cloud model theory is proposed. The paper constructs a comprehensive evaluation index system based on relevant standards and literature. Game theory is used to optimize the weights obtained by AHP and entropy weight methods to obtain a combined weight. MATLAB programming is used to calculate the comprehensive cloud parameters of the evaluation index for the potential renewal of existing public buildings and therefore generate cloud Graphs. Through case study in Nanjing of China, it has been demonstrated that the combination empowerment cloud model can objectively reflect the relationship between the fuzziness and randomness of evaluation indicators for public building renewal potential. The expression of cloud Graphs can intuitively reflect the magnitude of renewal and renovation potential and the degree of uncertainty in evaluation results. The research result provides useful references for the sustainable utilization of building resources in the era of building.

Muon tomography leverages cosmic muons’ exceptional penetration capabilities to reconstruct detailed tomographic images via multiple Coulomb scattering interactions, making it invaluable for detecting High-Z materials critical to homeland security. This study introduces the Slope Intercept (SI) algorithm, a novel approach for rapid and precise tomographic image reconstruction. Implemented using Geant4 simulations in a standard-sized container tomography station, SI demonstrates superior performance in speed and simplicity compared to traditional methods. While the imaging results of SI and the Point of Closest Approach (POCA) algorithm are nearly identical, the SI algorithm is simpler to implement, requires shorter CPU time, and exhibits a smaller margin of error. Its computational efficiency and simplicity make it a robust alternative to the POCA algorithm. These findings highlight SI’s suitability for applications demanding robust imaging and efficient data processing, which are crucial for advancing security measures and scientific research.

This study presents a critical literature review on the models of users' adoption of blockchain applications in supply chains. This review pertaining to blockchain technology adoption by supply chain stakeholders is significant as it is a pre-requisite for the technical sustainability of blockchain technology in supply chains. Through a critical literature review, the previous studies related to technology adoption models were analyzed to explore practices brought about by blockchain applications for supply chain management. This study examined the suitability of the existing models (e.g., the technology-organization-environment (TOE) framework, task technology fit (TTF), unified theory of acceptance and use of technology (UTAUT)) of technology adoption for blockchain applications in supply chains and came up with a new unified model, namely, TOE-TTF-UTAUT. This review provides implications for a more appropriate research design using mixed methods. When adopting the TOE-TTF-UTAUT model in a study, two phases of research using mixed methods can be performed. The first phase is an exploratory sequential design in which a qualitative approach to explore the TOE and supply chain task constructs is followed by a quantitative approach using a survey and quantitative analyses to investigate the factors affecting blockchain adoption in supply chains. The second phase adopts explanatory sequential design in which the quantitative findings from the first phase are reviewed for follow-up by qualitative interviews to obtain explanations.

The increase in greenhouse gas emissions fosters the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, the paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the “research through design” research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in 4 phases, aligning with 3 general objectives, 12 specific research objectives, and 24 tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the Industrial Engineering and Design disciplinary sectors.

This article articulates the significance of conceptual analysis (CA) methods for the postgraduate philosophy scholar at the G-CAR institution. In order to clarify the significance, first the concepts were defined, their context examined and the problems posed by the concepts looked at. The activity of scholars who are bent on researching for concepts meaning, was highlighted and the methods scholars use in determining meaning were carefully examined. Three major methods discussed by philosophers, namely, constructive, reductive and detective were explained as well as their significance in helping philosophy scholars do qualitative research in meaning determination. Meaning was found to be a discovery of the observer’s relationship to the objective reality as depicted by concepts. The common factor in these three methods was the dominant role both the observer and the human subjects played in determining meaning – a constructive engagement. It was clarified too, that the ability to explicate concepts’ meaning is the tool kit or skill sets that all scholars need to navigate the semantic jungle in the 21^st Century.

Determining precise boundaries and protective zones for historical and cultural objects enables their effective preservation. This is particularly crucial in the face of increasing tourism, urbanization, and natural disasters. Accurately defining boundaries and protective zones can aid in the planning of scientific research, archaeological excavations, and other studies aimed at exploring the history and cultural significance of these objects. The article presents the framework for establishing protective zones around historical and cultural heritage sites existing in Ukraine, using the example of the Citadel defensive complex located in Lviv (Ukraine). It proposes general and detailed conceptual models for the combined application of remote and non-invasive methods for investigating historical and cultural heritage sites. This conceptual article introduces the theory of integrating radar satellite imaging with ground-based georadar imaging. Additionally, it presents a software module that has been developed to analyze collected data on two-dimensional historical and cultural heritage objects, refine their boundaries, and establish protective zones around them.

This paper proposes a lightweight joint with bamboo culm cross connection, which is constructed by screwing one side of the screw into the other side of the wooden plug, its simple construction makes it easy to manufacture and cost-effective. This form connects adjacent bamboo culms to form corner joints, and bolt connects adjacent bamboo culm frames to form edge joints, to explore the mechanical properties of the combination joints formed by corner joints and edge joints, by combining experiments and numerical simulations, the method discovered and explained the relationship and mechanism between the number and position of bolts and the stiffness of edge joints. A calculation model for frame-unit bamboo culm members considering semi-stiffness of joints is proposed, and the conversion equations between linear stiffness of corner and edge joint and their rotational stiffnesses are derived. By establishing the series and parallel relationship between the corner and edge joints in the assembled members, a generalized relational equation between the total stiffness of the assembled joint and the stiffness of the corner and edge joints is derived. A numerical simulation method for frame-unit bamboo culm members with semi-rigid joints is proposed, which can obtain the stiffness calculation formula of edge joints and the average rotational stiffness value of semi-rigid corner joints. A finite element analysis method based on semi-rigid joints is proposed for frame-unit bamboo culm members, and the internal force sketches and calculation methods for bamboo culms and joints are presented, which can be used for the internal force analysis and design of frame-unit bamboo culm lattice structures.

It is shown that the 5D geodetic equations and 5D Ricci identities give us a way to create a new viewpoint on some problems of Modern Physics, Astrophysics, and Cosmology. Specifically, the application of the 5D geodetic equations in (4+1) and (3+1+1) splintered forms obtained with the help of the monad and dyad methods made it possible to introduce a new, effective generalized concept of the rest mass of the elementary particle. The latter leads one to novel connections between the General Relativity and quantum field theories, as well as all of that, including the (4+1) splitting of the 5D Ricci identities, brings about a better understanding of the magnetic monopole problem and the vital difference in the origins of the Maxwell equations and gives rise to surprising connections between them. The obtained results also provide new insight into the mechanism of the 4D Universe’s expansion and its following acceleration.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Keywords: liquid alloys, microstratification, dispersed particles, spontaneous dispersion, criteria for true (entropic) spontaneous dispersion, mechanisms of quasi-spontaneous dispersion, methods and results for assessing size of dispersed particles.

Background/Objectives: Thyroid cancer, particularly well-differentiated thyroid cancer, is one of the most prevalent endocrine malignancies, with a rising incidence. Although it generally has a favorable prognosis, recurrence is common. Accurate prediction of recurrence is crucial for optimizing treatment plans and improving patient outcomes. This study aimed to advance the state-of-the-art in thyroid cancer recurrence prediction by refining feature engineering techniques and exploring a diverse ensemble of machine learning algorithms and an artificial neural network, using the differentiated thyroid cancer dataset from the UCI Machine Learning Repository; Methods: Various unsupervised data engineering methods, such as dimensionality reduction and clustering, were employed to enhance feature quality and mitigate noise, using stratified 10-fold cross-validation. The best-performing dimensionality reduction techniques were used to build classification model pipelines employing each of several machine learning models and an artificial neural network. The performance of these classification pipelines were assessed using metrics sensitive to class imbalance; Results: Principal Component Analysis and Truncated Singular Value Decomposition achieved superior clustering performance and moderate variances in their first principal components. Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Feedforward Neural Network models all achieved high performance, with Logistic Regression pipelines demonstrating balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision—all greater than 0.93 on the test set and slightly lower in 10-fold cross-validation. Gradient Boosting classification pipelines performed the lowest, though still with respectable metrics; Conclusions: This study shows that employing feature engineering techniques like Principal Component Analysis or Truncated Singular Value Decomposition in Logistic Regression, Random Forest, Feedforward Neural Networks, Support Vector Machine, and K-Nearest Neighbors classification pipelines can improve thyroid cancer recurrence prediction accuracy and reliability, supporting more personalized treatment strategies in post-treatment patients.

Falls are a frequent and serious problem for older adults and especially those living with Parkinson’s. The relationship between falls and physical activity is complex and people often restrict activity following a fall. Exercise is an important aspect of re-ducing further risk of a fall and a key component of the management of Parkinson’s. The aim of this study was to understand the types of activity that are engaged in, the environments in which they take place and the experience of people with and without Parkinson’s who have fallen. 17 people with Parkinson’s and 13 older adults, who had experienced at least one fall in the previous year, were recruited to this mixed methods study. Activity levels were captured over one week using accelerometers and body worn cameras, allowing the type and location of activity to be recorded and analysed. This information informed an interview. Findings showed that although both groups often achieved up to 10,000 steps per day, this was in very short bouts of activity. Sedentary activity such as watching television dominated the findings. Participants were aware of the benefits of being active but described many barriers to achieving the level of activity they would like to.

Deep inhalation breath hold (DIBH) has become a valuable technique in left breast cancer radiotherapy, offering the possibility to reduce radiation exposure to organs at risk (OARs) and minimize the risk of cardiac complications. The treatment method involves patients stopping breathing during irradiation to temporarily distance the heart from the radiation field, reducing potential cardiac and other complications. Research shows that DIBH reduces the radiation dose to the heart and lungs. However, the effectiveness of DIBH is determined by a variety of factors, including the patient's training, cooperation, anatomical features, age and choice of radiotherapy technique. Additionally, cardiovascular risk factors: diabetes, smoking, hypertension can affect the effectiveness and potential complications of DIBH. Moreover, lack of consent, fear, anxiety, depression and comorbid psychiatric disorders disqualify some patients from the DIBH treatment method. Careful patient selection, comprehensive training and optimization of treatment parameters are essential to maximize the benefits of DIBH while minimizing potential side effects. DIBH enhancement techniques, such as IMRT or VMAT, also have an important role to play. The purpose of this review article is to summarize the factors affecting the efficacy and side effects of DIBH in radiation therapy for left breast cancer, with the aim of optimizing its clinical application while minimizing side effects.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Precise classification of sarcomas is crucial to drive optimal clinical management. In this prospective, multicenter, observational study within the Hellenic Group of Sarcoma and Rare Cancers (HGSRC) we assessed the effect of expert pathology review, coupled with the application of molecular diagnostics, on diagnosis and management of sarcoma patients. Newly diagnosed sarcoma patients were addressed by their physicians to one of the two sarcoma pathologists of HGSRC for histopathological diagnostic assessment. RNA next-generation sequencing was per-formed to all samples, using a platform targeting 86 sarcoma gene fusions. Additional molecular methods were performed on the opinion of the expert pathologist. Therefore, the expert pathologist provided a final diagnosis based on the histopathological findings and, when necessary on molecular tests. In total, 128 specimens from 122 patients were assessed. Among the 119 cases in which there was a preliminary diagnosis by a non-sarcoma pathologist, there were 37 modifications in diagnosis (31.1%) by the sarcoma pathologist, resulting in 17 (14.2%) modifications in management. Among the 110 cases in which molecular tests were performed, there were 29 modifications in diagnosis (26.4%) through the genomic results, resulting in 12 (10.9%) modifications in management. Our study confirms that expert pathology review is of utmost importance for the optimal sarcoma diagnosis and management and should be assisted by molecular methods in selected cases.

Understanding and measuring rock characteristics is crucial for scientific and practical applications. This study investigates stone quarries in Morelia, Michoacán, Mexico, evaluating ten quarries (both volcanic and crushed) using diverse analytical methods. Physical properties such as density were measured alongside mechanical tests like uniaxial compressive strength and ultrasonic pulse velocity. Results demonstrate that crushed materials exhibit superior physical and mechanical properties compared to volcanic stones. Advanced techniques including high-magnification scanning electron microscopy and X-ray fluorescence provided insights into surface morphology and elemental composition. Additionally, a novel finite element model was constructed, leveraging dynamic modulus of elasticity (Ed) derived from ultrasonic pulse velocity and maximum load data from uniaxial compressive strength tests. This model facilitated the classification of quarries based on their engineering characteristics. The findings underscore the model’s potential applicability to quarry assessments globally, offering valuable insights into optimizing stone resource utilization and enhancing structural integrity in construction projects.

Violence against healthcare workers (HCWs) is a widespread, underreported and inadequately prevented problem. Only a few companies have efficient systems for assessing the extent of the phenomenon. In 2005, the health surveillance service of a public health company introduced a system that monitored violence experienced by HCWs by means of three items from the Violent Incident Form (VIF), integrated with departmental in-depth analyses using the participatory ergonomics group technique. In 2005, the annual rate of physical assaults was 8.2%, that of threats 12.0%, while the harassment rate was 19.6%. Over the past twenty years of observation (2005-2024), the percentage of workers who reported experiencing a physical attack in the previous year at their periodic medical examination, has fluctuated between 5.8% and 11.1%, except for the years 2020 and 2021 when, during the COVID-19 pandemic, the rate was 3.9% and 3.2%, respectively. During the same pandemic period, the annual threat rate that ranged from 9.4% to 20.1%, dropped to 7.7%, while the prevalence of harassment, that was between 13.5 and 19.6, fell to 7.2%. HCWs believe that (i) limiting visitor access (ii) a better balance of the demand for services and (iii) a better attitude towards HCWs were the causes of the reduced rate of violence during the pandemic. Recording the violence experienced during health surveillance is an economical, reliable and sustainable risk assessment method.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

The study of different forms of preconditioners for solving a system of nonlinear equations, by using Newton’s method, is presented. The preconditioners provide numerical stability and rapid convergence with reasonable computation cost, whenever chosen accurately. Different families of iterative methods can be constructed by using a different kind of preconditioners. The multi-step iterative method consists of a base method and multi-step part. The convergence order of base method is quadratic and each multi-step add an additive factor of one in the previously achieved convergence order. Hence the convergence of order of an m-step iterative method is m + 1. Numerical simulations confirm the claimed convergence order by calculating the computational order of convergence. Finally, the numerical results clearly show the benefit of preconditioning for solving system of nonlinear equations.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

With the increase in technological advancements, architects and engineers over the past couple of decades have been trying to achieve high efficiency within construction projects. Their goals also include reducing global warming effects from construction and building new construction faster for the growing population in urban cities. This requires using a construction method that is less harmful to the environment and faster than other traditional methods. Modular construction, also known as modular construction, is the method that has the potential to make this happen. This review paper discusses what modular construction is and how it is different from the traditional on-site construction. Environmental impacts of using modular construction and what leads to it being more efficient and faster than on-site construction. The study doesn’t suggest that modular construction is the solution to all the problems being faced in the construction industry today, but it is a potential solution that if used in the right situation and correct manner, can have a huge positive impact on the construction industry.

Accurate Quantitative Precipitation Estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The obtained results indicate that merging radar and rain gauge data outperform both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. The study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing evidence for the greater reliability of merging methods

The paper explores the features of numerical simulation to analyze the dynamic behavior of drives controlled by pulse-width modulators. Modern motor control systems widely use pulse width modulation. Effective numerical modeling of such systems has specific features because the models employed are continuous-event and have hybrid behavior due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. The paper shows how these problems are solved when modeling an electric drive with a DC motor in the SimInTech software.

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of the electrical equipment. In particular, modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of Deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. Particularly in the paper, different Neural networks used in scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modelling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

Background/objectives: The health benefits of resistant starch are well known, but there have not been many studies available documenting the effect of basmati rice products containg resistant starch upon blood glucose level and lipid profile in vivo efficacy. Methods: Common cooking methods were used to prepare basmati rice products including two boiling methods viz. boiling 1 (boiling by absorption), boiling 2 (boiling in extra amount of water), frying and pressure cooking. The cooked rice were held at various temperatures and times: it was made fresh (T1), kept at room temperature (20–22°C) for 24 h (T2), kept at 4°C for 24 h (T3), and then reheated after being kept at 4°C for 24 h(T4). Next, the products' proximate composition, total dietary fibre (soluble and insoluble), resistant starch (RS), and In vitro starch digestion rate (rapidly and slowly digested starch) were examined. The effect of RS on blood glucose and lipid profile were measured in humans and rats. Histopathological study of liver and pancreas in rats were also observed. Results: The basmati rice prepared by boiling 1 and stored with T3 was found more effective to control blood glucose and lipid profile. Similarly in rats, the blood glucose level, cholesterol, triglycerides and LDL were reduced about 29.7, 37.9, 31.3 and 30.5% respectively, after consumption of basmsti rice prepared by boiling 1 and stored at T3. Conclusion: Rice products stored at 4⁰C have a high amount of resistant starh, insoluble dietary fibre, slowly digestible starch and low glycemic index. Thus helps to inhibit glycogen and lipid synthesis.

The term immersive technology refers to various types of technologies and perspectives that are constantly changing and developing. It can be used for different purposes and domains such as: education, healthcare, entertainment, arts, and engineering. This research paper aims to examine which training method is more effective between the traditional approach, using PowerPoint and the immersive technology approach, through mixed reality using a HoloLens 2 device. The experiment involves comparing two groups of students who received different training methods: the first group saw a PowerPoint slide with an image of the human muscular system, while the second group saw a 3D hologram of the human body that showed the same muscle groups as in PowerPoint. The researchers wanted to test whether mixed reality devices could enhance students' cognitive functions and to investigate if the IQ of the participants had any influence in the effectiveness of the teaching method. The main finding of this study is that the instructions given with mixed reality technology, via the Microsoft HoloLens 2 device, are more appropriate for participants who have an IQ score below 100 points. For the other participants, with an IQ score above 100 points, the PowerPoint support was more suitable.

Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

The maritime environment is the setting for a variety of economic activities, such as offshore wind energy, aquaculture, salt extraction, and oil and gas platforms. While some of these activities have a long-term presence, others require decarbonisation as they head towards their demise. In this context, the aim of this study is to develop a methodology to replace the electrical energy from high-emission industrial processes offshore with clean electricity generated by offshore wind energy. The proposal is structured in three phases: Initiation, which involves the collection of quantitative, technical and geospatial information of the study area; Indicators, where the main indicators are calculated and the best alternative is selected using multi-criteria evaluation methods; and finally, short, medium and long-term scenarios are proposed. The methodology is evaluated in Spain, the best alternative with a nominal power of 225 MW, is capable of avoiding up to 1.44 MtCO₂ by 2050.

Ship hull is a complex design where various types of complex loading conditions , like as Hydrostatic, Hydrodynamic, Wind, Wave and external internal bending forces. Ensuring structural integrity under these varying loads is essential for the ship structural safety and environmental protection. This review discusses the application of finite element methods(FEM) for the safety of ship hulls and analysis of ship hulls , emphasizing two significant studies that leverage ANSYS SOFTWARE to evaluate stress concentrations and deformation of hull under the various loading operation including the wave and wind forces and the stress. The findings Underscore the importance of analyzing both local and global aspects of ship structures to improve design practices and enhance safety standards. In this paper we mostly focused on a general cargo ships hull structure, Grounding of a ship.

In this paper, a new full-Newton step feasible interior point method for convex quadratic programming is presented and analyzed. The idea behind this method is to replace the complementarity condition with a non-negative variable weight vector and use the algebraic equivalent transformation for the obtained equation. Under the selection of appropriate parameters, the quadratic rate of convergence of the new algorithm is established. In addition, the iteration complexity of the algorithm is obtained. Finally, some numerical results are presented to demonstrate the practical performance of the proposed algorithm.

The microfluidic industry recognizes the lack of sensitive and standardized testing methods as one of the major hurdles to overcome before fulfilling its revolutionary promises. Highly ranked upon the list of things in need of testing methods are the internal dimensions of microfluidic structures after chip assembly. This work, performed under the MFMET project, aims to address this challenge by presenting and comparing multiple protocols for measuring the dimensions of internal microfluidic channels of fully assembled transparent chips. The protocols include optical profilometry, optical microscopy and tiled digital imagery. Several standardized chip designs out of two different materials commonly used in microfluidics (D263©bio and TOPAS© COC) were measured by the different protocols. A consistency analysis through normalized error statistics revealed optical profilometry as the preferred method due to its low uncertainty compared to the other protocols and its higher consistency with nominal geometry values. All protocols showed difficulty with vertical depth measurements of internal structures. Associated possible directions of future research are considered including further study of the influence of multiple refractive surfaces on optical profilometry measurements and further investigation of confocal microscopy as a potential measurement method showing promising preliminary results. The influence of the different materials appears to be minimal on optical profilometry, but other materials and method would merit looking into before such an observation might be generalized. In conclusion, the results of this work directly address the current lack of measurement methods for internal microfluidic structures by providing a comprehensive comparison of different protocols, ultimately suggesting a preferred option for immediate application within the microfluidic industry. Additionally, this work offers valuable directions for future research, serving as an initial step in overcoming a significant challenge that impedes the microfluidic industry from realizing its revolutionary potential.

The mechanical properties of the materials stored inside agricultural silos influence the loads generated inside them. The different silo calculation methodologies allow predicting these loads under static and dynamic conditions if the mechanical properties of the stored materials are known. It has been known for many years that these properties are highly dependent on the moisture content of the material. However, to date there are not many studies focused on its determination. The goal of this research is the determination of the angle of internal friction, apparent cohesion, dilatancy angle and apparent specific weight of maize subjected to different moisture contents. Direct shear and oedometer assay devices were used for this purpose. In addition, the maize samples used were moistened using a climatic chamber. Moisture contents applied to maize sample ranged from 9.3% to 17.4%. The results obtained for the angle of internal friction and apparent cohesion, as well as those obtained for the apparent specific weight, were similar to those reported in the literature. However, the values obtained for the dilatancy angle of maize as a function of moisture content could not be compared because nothing has been published so far. The values obtained for this parameter overlap with those published for this material under ambient conditions. In addition, no direct relationship between the values obtained for this parameter and the moisture content of the sample could be found. Finally, the apparent specific weight increased with increasing moisture content of the samples, which differed from that published in the literature. The values provided here can be used for the optimization of storage and handling structures for granular agricultural materials using numerical methods.

Physical activity (PA) benefits are well-established, yet many US adults fail to meet PA guidelines. This is especially true for minorities facing social inequities. This study explored barriers and facilitators to PA among urban Midwestern minorities using a mixed-methods approach framed on the socio-ecological model. A cross-sectional survey was conducted among community-dwelling minorities. Participants were grouped as completing low (LLPA) or high (HLPA) weekly leisure-time PA for comparison. Quantitative analysis included MANOVA, follow-up ANOVAs, and calculation of effect sizes. Qualitative data were assessed using inductive thematic analysis. Twenty-nine adults (44.83% Black, 41.37% Latino) participated in the study. The HLPA group (n = 18) reported higher leisure-time PA (p = 0.001, d = 2.21) and total PA (p = 0.02, d = 1.00) compared to the LLPA group (n = 11). LLPA participants faced more personal barriers to PA (p = 0.02, d = -0.92). Common barriers identified in the interviews included a lack of time and financial costs. Facilitators included social support and available PA facilities. Both groups achieved USPA guidelines through different PA domains. Increasing social support and lowering PA-related costs could enhance participation. Addressing barriers and leveraging existing facilitators are crucial to increasing PA among minorities.

Healthcare crises occur occasionally are very disruptive and resource consuming. The Covid-19 pandemic provided an example of inefficient wasteful resource management that would be unsustainable in the long term. This paper presents a framework for sustainable crisis management that will be applicable for future crises. The methodology is also applicable to other pandemics, wars, multi-casualty natural disasters, industrial disasters, etc. Attention focuses on rapid crisis resolution such as lockdowns. Despite unprecedented investments in healthcare, lack of capacity and timeliness are still problems affecting every country, even the wealthier ones. During the Covid19 pandemic, some healthcare systems around the world have collapsed. For example, Brazil, Spain, Italy, and more. This is due to the lack of a clear methodology for handling such crises. This paper presents a resource-based methodology to cope with healthcare crises considering three scenarios: “Peace”, “War” and “Tsunami”. The paper outlines a methodology for identifying the system bottlenecks for coping with the crisis in each scenario by using established simple tools. These tools assure significant resource frugality that will enhance sustainability. The paper also analyses where the system constraint should be and prescribes how to prepare for such scenarios. A large medical center case study demonstrates our approach. During “peace” times, evolutionary strategies should be used exclusively. During “war” times, a hybrid – evolutionary/disruptive (revolutionary) strategy. During the “Tsunami” scenario, one should rely only on the disruptive (revolutionary) strategy. This methodology can be extended to other scenarios and other lines of research.

This paper briefly reviews the state of the art in artificial intelligence including inductive and deductive methods. Deep learning and ensemble machine learning lie in inductive methods while automated reasoning implemented in deductive computer languages (Prolog, Otter, and Z3) is based on deductive methods. In the inductive methods, intelligence is inferred by pseudorandom number for creating the sophisticated decision trees in Go (game), Shogi (game), and quiz bowl questions. This paper demonstrates how to wisely use the pseudorandom number for solving coin-weighing puzzles with the deductive method. Monte Carlo approach is a general purpose problem-solving method using random number. The proposed method using pseudorandom number lies in one of Monte Carlo methods. In the proposed method, pseudorandom number plays a key role in generating constrained solution candidates for coin-weighing puzzles. This may be the first attempt that every solution candidate is solely generated by pseudorandom number while deductive rules are used for verifying solution candidates. In this paper, the performance of the proposed method was measured by comparing with the existing open source codes by solving 12-coin and 24-coin puzzles respectively.

The use of braces equipped with dissipative devices for the seismic strengthening of seismically prone reinforced concrete frames is among the most widespread methods, as it allows for high reductions in seismic vulnerability with inexpensive, quickly executed interventions, which can often be carried out mainly by the exterior, resulting in interruptions of use limited both in time and to only small portions of the building. The design methods of dissipative devices are based on the extensive use of pushover analyses capable of highlighting the structural deficiencies of the building and comparing the performances achievable by developing designs according to different methods and sizing criteria. In the present work, with reference to a case study represented by a four-story spatial frame having characteristics representative of design and construction common practice of the 1970s in Southern European countries, the performance of three different design methods is compared. The examined procedures differ for: methods for estimating the peak displacement response of the nonlinear systems, namely the well-known Equal Displacement Rule and the Equivalent (secant) stiffness and Damping rule; and criteria for distributing stiffness and strength of the bracing along the height, namely the distribution of stiffness and strength proportionally to those of the frame, and methods that vary the stiffness and strength along the height in order to minimize the eventual irregularity in elevation of the bare frame. The effectiveness of the procedures is checked by static pushover analysis and nonlinear response history analysis, performed by unidirectional and bidirectional input. Pros and cons of each procedure are summarized, all of them able to provide bracing designs that meet the performance requirements set during the design phase.

When discussing the Diophantine quintic equation $p(a^5 + b^5) = q(c^5 + d^5)$ where p is a prime and q is an integer, there is a clear gap between the mathematical literature and online forums. Because of the intrinsic complexity of parameterizing fifth-degree equations, this equation re- mains largely unexplored. In this work, we approach this quintic problem through algebraic methods with the goal of providing numerical solutions that illuminate its properties and behaviors.Our research uncovers in- triguing patterns and connections that shed light on the enigmatic nature of Diophantine quintic equations in this particular form.

The emergence of prosumers, who actively participate in designing and producing goods, has generated interest in domestically manufactured products. Factors such as innovation, design methods, component reuse, prototyping, and digital fabrication empower prosumer designers to realize their ideas and enhance products to meet their specific design requirements. This article presents a case that combines bioinspired design methods with prosumer characteristics from the perspective of a designer who uses biological research results acting as a prosumer. By analyzing prosumer needs, bioinspired design, and domestic fabrication requirements, designers define the methods that best suit their possibilities. As a result of using biological information, the geometry of Samara seeds, propeller sizing, wind tunnel tests, and subsequent improvements, the custom design of a bioinspired microgenerator has been refined to the final design of a bicycle flashlight. The aim is to popularize a working approach that facilitates the interaction of design methods and reinforces domestic manufacturing processes. Implementing bioinspired design methods within the prosumer context offers a methodological alternative to transform the current economic model, promoting systemic sustainability and the development of proprietary technologies.

Ecosystem surveillance monitoring is critical to managing natural resources and especially so under changing environments. Despite this importance, the design and implementation of monitoring programs across large temporal and spatial scales has been hampered by the lack of appropriately standardised methods and data streams. To address this gap, we outline a surveillance monitoring method based on permanent plots and voucher samples suited to rangeland environments around the world that is repeatable, cost-effective, appropriate for large-scale comparisons and adaptable to other global biomes. The method provides comprehensive data on vegetation composition and structure along with soil attributes relevant to plant growth, delivered as a combination of modules that can be targeted for different purposes or available resources. Plots are located in a stratified design across vegetation units, landforms and climates to enhance continental and global comparisons. Changes are investigated through revisits. Vegetation is measured to inform on composition, cover and structure. Samples of vegetation and soils are collected and tracked by barcode labels and stored long-term for subsequent analysis. Technology is used to enhance the accuracy of field methods, including differential GPS r plot locations, instrument based Leaf Area Index (LAI) measures, and three dimensional photo-panoramas for advanced analysis. A key feature of the method is the use of electronic field data collection to enhance data delivery into a publicly-accessible database.Our method is pragmatic, whilst still providing consistent data, information and samples on key vegetation and soil attributes. The method is operational and has been applied at more than 704 field locations across the Australian rangelands as part of the Ecosystem Surveillance program of the Terrestrial Ecosystem Research Network (TERN). The methodology enables continental analyses, and has been tested in communities broadly representative of rangelands globally, with components being applicable to other biomes. Here we also recommend the consultative process and guiding principles that drove the development of this method as an approach for development of the method into other biomes. The consistent, standardised and objective method enables continental, and potentially global analyses than were not previously possible with disparate programs and datasets.

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

Empirical Colebrook equation from 1939 is still accepted as an informal standard to calculate friction factor during the turbulent flow (4000 < Re < 10⁸) through pipes from smooth with almost negligible relative roughness (ε/D→0) to the very rough (up to ε/D = 0.05) inner surface. The Colebrook equation contains flow friction factor λ in implicit logarithmic form where it is, aside of itself; λ, a function of the Reynolds number Re and the relative roughness of inner pipe surface ε/D; λ = f (λ, Re, ε/D). To evaluate the error introduced by many available explicit approximations to the Colebrook equation, λ ≈ f(Re, ε/D), it is necessary to determinate value of the friction factor λ from the Colebrook equation as accurate as possible. The most accurate way to achieve that is using some kind of iterative methods. Usually classical approach also known as simple fixed point method requires up to 8 iterations to achieve the high level of accuracy, but does not require derivatives of the Colebrook function as here presented accelerated Householder’s approach (3^rd order, 2^nd order: Halley’s and Schröder’s method and 1^st order: Newton-Raphson) which needs only 3 to 7 iteration and three-point iterative methods which needs only 1 to 4 iteration to achieve the same high level of accuracy. Strategies how to find derivatives of the Colebrook function in symbolic form, how to avoid use of the derivatives (Secant method) and how to choose optimal starting point for the iterative procedure are shown. Householder’s approach to the Colebrook’s equations expressed through the Lambert W-function is also analyzed. One approximation to the Colebrook equation based on the analysis from the paper with the error of no more than 0.0617% is shown.

The electromagnetic inference is an issue from decades, where working for a better shielding material is still on-going. The purpose of this study is to review the existing methods in the formation of graphene, metal and polymer-based composites. Study indicates that in graphene and metal-based composites, the utilization of alternating deposition method showed the highest shielding effectiveness, whereas, in polymer-based composite, the utilization of chemical vapor deposition method showed highest shielding effectiveness. However, this review reveals that still there is a gap in the literature in terms of the selection of the method. Although there are various available methods which researchers adopt as per their convenience, none of the studies makes a comparison of the methods to form a similar composite. Therefore, as a future gap researcher needs to adopt various methods to form a single composite and then make a comparison of shielding effectiveness. This act will be useful for future researchers to select the appropriate method.

Objective: This analysis investigates patient experiences regarding pain management during gynecological procedures through a comprehensive survey, aiming to address the lack of clear guidelines in the clinical landscape and emphasize the importance of effective pain management strategies in enhancing patient care and satisfaction. Design: A cross-sectional survey with 258 respondents assessed pain management experiences during gynecological procedures. The survey was distributed via Qualtrics, and 258 responses were obtained. Participants/Materials, Setting, Methods: Participants provided demographic information and responded to questions regarding contraceptive methods, procedures undergone, pain management discussions, anxiety levels, and willingness to accept pain management interventions. Confidentiality and informed consent protocols were strictly observed throughout the study. Results: Most female respondents reported diverse contraceptive methods and procedures. Pain management discussions and provisions varied, with a significant portion reporting never being offered pain management. Many experienced anxiety and pain during procedures, with varying intensities, that were not addressed or discussed by their treating provider. Most expressed willingness to accept pain management interventions. Limitations: This study relies on self-reported data, potentially introducing recall bias, and has limited generalizability due to its cross-sectional design. Conclusions: Findings reveal a significant gap in pain management provision and discussion during gynecological procedures. Improved guidelines and practices are necessary to address patient discomfort effectively. Standardized pain management protocols are crucial to enhance patient experiences and outcomes, with further research needed to tailor strategies to individual patient needs.

Both soybean variety and processing affect product characteristics. This study’s objective was to characterize the effect of four extraction methods (variations of soaking and grinding) combined with cooking on the content and composition of phenolic substances, antioxidant and anti-DU145 prostate cancer cell proliferative properties of soymilk prepared from a yellow soybean of the ProSoy variety, which is a high protein variety. Results showed soymilk processing yield was the greatest by Method 4, but Method 2 gave the highest solids and protein yields by about 14 and 12%, respectively. Method 4, a two-step grinding also increased yields (8 and 7%, for solids and protein, respectively), and in all but one instance produced higher total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC) and total isoflavone content in both raw and cooked soymilk as compared to Method 1. Cooking soymilk reduced 14-17% of total phenolic substances. Cooking reduced anticancer capacity of Method-4-prepared soymilk phenolic extract by increasing IC50 value from about 4.9 mg/mL to 6.8 mg/mL. The increases in phenolic compounds and antioxidants produced in Prosoy soymilk by Methods 2 and 4 with simultaneous increases in product solids yield are significant to benefit the soymilk industry and consumer health. Keywords: ProSoy yellow soybean, soymilk processing, phenolics, isoflavones, antioxidant capacity, anti-DU145 prostate cancer proliferation

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple one-sample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio (GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential analysis and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence their effects need to be properly assessed before deployment to obtain a good level of expected performance and during the operation in order to avoid dangerous unexpected results. In this paper we propose a methodology to support design and deployment of dependable WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool allows to generate automatically formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime. During the runtime we can check the behavior of theWSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of theWSN at runtime

Stability studies remain a crucial aspect of power systems dynamic analysis, and are typically explored in three main categories: numerical methods, linearization techniques, or direct methods, which utilize Lyapunov energy functions. This paper belongs to the third category, and highlights the usefulness of the Popov stability criterion in the analysis of nonlinear power system models. The main advantage of this criterion is that it provides conditions for global asymptotic stability of an equilibrium point, for a nonlinear dynamic system. We show a general method to apply this stability criterion, and examine its uses in several specific applications and case-studies. The results are demonstrated by analyzing the stability of a system that includes a grid-connected storage device and a renewable energy source.

The study aimed to manage and to analyse the results of the laboratory methods, using liver samples, preparing and analizing in pathology service. This mentioned, are known as routinally practice for hepatitis C diagnosis, which conduct to hepatic cirrhosis. In this study direction, chronic liver diseases stimulate a degree of hepatocyte injury. So previously mentioned modifications, alters thet affect the liver architecture and finally ends in cirrhosis are well analyse on microscopic preparates.

Keratin extraction from chicken feathers provides a sustainable way to valorise poultry by-products. This study aimed to optimize keratin yield and antioxidant properties by compar-ing two pre-treatment methods: the typical (T-method), involving washing, drying, cutting, grinding, and defatting, and the simplified (S-method), involving only washing, drying, and cut-ting, under various alkaline hydrolysis conditions. These included different feather mass to NaOH ratios (1:3, 1:6, or 1:9), hydrolysis times (16, 24, or 32 h), and shaking speeds (150, 175, or 200 rpm). Under the most intensive hydrolysis conditions (1:9 ratio, 32 hours, 200 rpm), the T-method achieved the highest keratin yield (72%), DH of 2 mmol NH2/g protein, and an average molecular weight of 5.0-5.9 kDa. The S-method had a lower yield (57%) and a higher molecular weight (7.4 kDa). The highest antioxidant activity was observed under 1:3 feather mass to NaOH ratio, 32 h of hydrolysis time, and 175 rpm of shaking speeds. The T-method is optimal for maximum yield, while the S-method is suitable for large-scale applications due to its simplicity and high antioxidant activity.

Important current limitations of the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of new technologies and interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot – or only partially – be applied in rehabilitation. Well powered high quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drugs’ validation studies, limited funding’s since not sponsorized by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high level of evidence is sparse. The current limitations of EBP in rehabilitation are presented in this paper and innovative solutions are suggested such as: technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the limitations to increase the quality of the research in rehabilitation. The development and implementation of new technologies should increase the quality of research and the level of evidence supporting rehabilitation provided some adaptation in our research methodology.

Extracellular vesicles (EVs) are enclosed by a lipid-bilayer membrane and secreted by all types of the cells under various physiological conditions. EVs play important roles in intercellular communication and crosstalk between tissues in the body. They are classified into three groups, such as apoptotic bodies, microvesicles, and exosomes. Exosomes were isolated from biofluids including blood, urine, milk, and cell culture media. Exosomes have significant potential for drug delivery and diagnosis. However, the method of isolation affects the physical and biological properties of exosomes. Several methods based on different principles have been developed for exosome isolation. These include ultrafiltration, precipitation, ultracentrifugation, size-exclusion chromatography, and microfluidics. In this study, we applied three common methods, such as ultrafiltration, precipitation, and ultracentrifugation, to isolate exosomes from the cell culture medium and investigated the effects of these different isolation methods on the size distribution and quality of the isolated exosomes. Field emission scanning electron microscopy (FESEM) images, size distribution, total protein content, and the effect of exosomes on the viability of hypoxic cells were analyzed in this study. The analysis revealed that compared to other methods, the ultracentrifugation method can isolate exosomes with smaller diameter (ranging from 20 to 80 nm), lower total protein content (50 µg/ml), and causing the increased viability of the hypoxic cells. The precipitation method does not require special equipment and is inexpensive, if the quality and purity of this method are solved, and it can be used as the best method for exosome isolation. This study can serve as a guide for choosing the best exosome isolation method for applications in medicine according to the needs, time, cost, and equipment.

Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These Convolutional Neural Network algorithms, in addition to requiring huge amounts of data, can identify higher and lower-level features that are significant while classifying tumors into benign or malignant. However, these algorithms have limitations concerning explainability and identifying the exact features that are of importance and contributing to the final classification of tumors as benign or malignant. In this paper, we implement several explainable AI techniques, namely, LIME, Saliency Map, Occlusion Analysis, Grad-CAM, SHAP, and Smooth Grad to interpret the results of a custom ResNet60 classifier in the classification of ovarian tumors as benign or malignant. The ResNet60 model attained an accuracy of 97.50% for the test dataset.

Advancements in biological and medical science are intricately linked to the biological central dogma. In recent years, gene editing techniques, especially CRISPR/Cas systems, have emerged as powerful tools for modifying genetic information, supplementing the central dogma and holding significant promise for disease diagnosis and treatment. Extensive research has been conducted on the continuously evolving CRISPR/Cas systems, particularly in relation to challenging diseases, such as cancer and HIV infection. Consequently, the integration of CRISPR/Cas-based techniques with contemporary medical approaches and therapies is anticipated to greatly enhance healthcare outcomes for human. This review begins with a brief overview of the discovery of the CRISPR/Cas system. Subsequently, using CRISPR/Cas9 as an example, a clear description of the classical molecular mechanism underlying the CRISPR/Cas system was given. Additionally, the development of the CRISPR/Cas system and its applications in gene therapy and high-sensitivity disease diagnosis were discussed. Furthermore, we address the prospects for clinical applications of CRISPR/Cas-based gene therapy, highlighting the ethical consideration associated with altering genetic information. This brief review aims to enhance understanding of the CRISPR/Cas macromolecular system and provide insight into the potential of genetic macromolecular drugs for therapeutic purposes.

The rapid development of the photovoltaic (PV) industry will result in an increase in the amount of electrical and electronic waste from used PV panels. Only in Poland, the total capacity installed in photovoltaic sources in Poland in May 2020 exceeded 1,950 MW, and the installation weight was approximately 120,000. tone. The problem arises in the recycling or management of this waste. This work presents methods used to recycle waste into photovoltaic modules. The authors investigated the possibility of mechanical and chemical processing of crystalline silicon. A method of thermal treatment of the panel was also proposed. As a result of the research, it was found that the stage of separating materials (crystalline cream, EVO foil, aluminum frame) plays an important role in the recycling process, which is not easy. Chemical treatment allows the silicon to become plastic and the temperature melts the back layer of the panel.

The mining industry, critical for global resource supply, has historically been linked to significant environmental degradation and social challenges including habitat disruption, water and soil contamination as well as fatalities. But recently the industry has been undergoing a transformative change to meet evolving environmental and societal expectations. Operations Research (OR) provides essential tools and techniques to optimize decision-making processes in this context. This paper presents a comprehensive review of different OR methods and their applications in balancing economic, social and environmental objectives in surface mine planning. By evaluating the strengths and limitations of these techniques, this review offers valuable insights for researchers and practitioners aiming to improve production efficiency and sustainability through advanced planning strategies.

The identification and quantitative determination of wool and fine animal fibers are of great interest in the textile field because of significant price differences between them and common adulterations in raw and processed textiles. Since animal fibers have remarkable similarities in their chemical and physical characteristics, specific identification methods have been studied and proposed following advances in analytical technologies. The identification methods of wool and fine animal fibers are reviewed in this paper and the results of relevant studies are listed and summarized, starting from classical microscopy methods which are still used today not only in Small to Medium Enterprises but also in large industries, research studies and quality control laboratories. Particular attention has been paid to image analysis, Nir spectroscopy and proteomics which constitute the most promising technologies of quality control in the manufacturing and trading of luxury textiles and can find application in forensic science and archeology.

The permanent protection of groundwater and surface sources of drinking water in the required raw water quality from contamination is the primary foundation of preserving the life of society and the entire engineering infrastructure operability not only in the Czech Republic but also in other countries of the world. One of the basic ways of contamination prevention enhancement and risk mitigation of putting a water source out of operation is the identification of the given risk as well as soil contamination monitoring. The purpose of this study is to understand specific aspects of contamination threats to drinking water sources and determine possible solutions to eliminate the risk, shown in the example case study in the Czech Republic conditions. Based on the findings, a proposal to enhance the prevention of water source contamination has been elaborated in the form of recommendations regarding the ways and means used to monitor the risk areas and to implement technical measures so as to eliminate or terminate the specified risk. A complex approach based on the integration of interdisciplinary factors and methodology is required to minimize the threats of aquatic ecosystems contamination in the Czech Republic.

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

Aquatic macroinvertebrates play a crucial role in freshwater ecosystems, serving as reliable indicators of water quality and ecosystem health. To effectively monitor and assess these ecosystems, it is essential to employ appropriate sampling methods that capture the diversity and abundance of macroinvertebrate communities. The objective of this study was to provide a comprehensive overview and comparative analysis of different sampling methods for aquatic macroinvertebrates, highlighting their advantages, limitations, and applicability in ecological research was carried out. The results showed that the most commonly used methods include Kick-net Sampling, Surber Sampler, D-frame Net, Bottle Sampling, and Pitfall Traps. Researchers should select methods based on study goals, environmental conditions, and target organisms. Integrating multiple techniques enhances understanding of macroinvertebrate communities and their responses to environmental changes, contributing to improved assessments of water quality, ecosystem health, and conservation in freshwater ecosystems.