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Microfluidic paper-based analytical devices (µPADs) are a promising technology to enable accurate and quantitative in situ assays. Paper's inherent hydrophilicity drives the fluids without the need for external pressure sources. However, controlling the flow in the porous medium has remained a challenge. This study addresses this problem from the nature of the paper substrate and its design. A computational fluid dynamic model has been developed, which couples the characteristics of the porous media (fiber length, fiber diameter and porosity) to the fluidic performance of the diffusion-based µPAD sensor. The numerical results showed that for a given porous membrane, the diffusion, and therefore the sensor performance is affected not only by the substrate nature but also by the inlets' orientation. Given a porous substrate, the optimum performance is achieved by the lowest inlets' angle. A diffusion-based self-referencing colorimetric sensor was built and validated according to the design. The device is able to quantify the hydronium concentration in wines by comparison to 0.1-1.0 M tartaric acid solutions with a 41.3 mM limit of detection. This research showed that by proper adjustments even the simplest µPADs can be used in quantitative assays for agri-food applications.

High cost and low electrochemical stability of the interconnection in Proton Exchange Membrane Fuel Cell (PEMFC) in the presence of H2SO4 are one of the main issues hindering the commercialization of these devices. This manuscript presents the utilization of cost-effective steel in an attempt to minimize the PEMFC interconnection costs with a thin-film solid oxide coating (TFSOC) providing sufficient corrosion resistance for efficient long-term operation. Novel Ti0.50-y/2Si0.50-y/2Nby1,2O2 as TFSOC was deposited on the C45E steel as a metal interconnect utilizing a sol–gel process at various annealing temperatures. The analysis of the phase and surface morphology demonstrates that lower annealing temperatures developed nanometric crystallite size of 68 nm, more uniform structure and higher corrosion resistance. Under standard test conditions, the TFSOC demonstrated high polarization resistance (1.3 kΩ cm2) even after 720 hours (h). Electrical conductivity of the TFSOC as low as 1.4 × 10−2 (Ω m)−1 and activation energy of 0.20 eV were achieved, which helps to maintain the PEMFC output power. [ABSTRACT FROM AUTHOR]

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks. [ABSTRACT FROM AUTHOR]

Umbilical cord mesenchymal stem cell-derived extracellular vesicles (UC-EVs) are valuable in nanomedicine as natural nanocarriers, carrying information molecules from their parent cells and fusing with targeted cells. miRNA-126, specific to endothelial cells and derived from these vesicles, supports vascular integrity and angiogenesis and has protective effects in kidney diseases. Objective: This study investigates the delivery of miRNA-126 and anti-miRNA-126 via UC-EVs as natural nanocarriers for treating nephrotoxic injury in vitro. Method: The umbilical cord-derived mesenchymal stem cell and UC-EVs were characterized according to specific guidelines. Rat kidney proximal tubular epithelial cells (tubular cells) were exposed to nephrotoxic injury through of gentamicin and simultaneously treated with UC-EVs carrying miRNA-126 or anti-miRNA-126. Specific molecules that manage cell cycle progression, proliferation cell assays, and newly synthesized DNA and DNA damage markers were evaluated. Results: We observed significant increases in the expression of cell cycle markers, including PCNA, p53, and p21, indicating a positive cell cycle regulation with newly synthesized DNA via BrDU. The treatments reduced the expression of DNA damage marker, such as H2Ax, suggesting a lower rate of cellular damage. Conclusions: The UC-EVs, acting as natural nanocarriers of miRNA-126 and anti-miRNA-126, offer nephroprotective effects in vitro. Additionally, other components in UC-EVs, such as proteins, lipids, and various RNAs, might also contribute to these effects. [ABSTRACT FROM AUTHOR]

Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in "unfair" outcomes. Bias in the "outside world" and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models. [ABSTRACT FROM AUTHOR]

Recognising the urgency of addressing climate change and the imperative to mitigate its effects, the European Union (EU) has embarked on a transformative journey to reshape its energy landscape, with a pronounced emphasis on harnessing renewable energy sources (RESs) and augmenting their contribution to electricity generation. To propel Europe towards sustainable development through its energy transition, EU member states are encouraged to foster collaborative efforts on a European scale, inviting neighbouring countries to participate in joint ventures aimed at leveraging RESs for electricity generation. Consequently, it becomes imperative to evaluate the potential depth of cooperation among these nations, assessing how such partnerships can align with Europe's overarching objectives while fostering mutually beneficial conditions. This paper seeks to undertake a thorough analysis and evaluation of the potential impacts of such cooperation, both in advancing RES objectives and in promoting broader cooperation goals concerning the countries involved. The appropriate methodological framework has been developed, utilising and implementing the fuzzy PROMETHEE multicriteria decision analysis method, to address the problem's multidimensional character, intending to implement an appropriate action plan and promote production from RESs. The methodology has been applied to assess alternative strategies in two case study countries, Morocco and Egypt, while important outcomes have emerged towards the successful implementation of cooperation mechanisms promoting RES. [ABSTRACT FROM AUTHOR]

Background: Sleep is essential for health, especially during adolescence. However, most adolescents do not obtain the recommended 8 to 10 h of sleep, and their health is significantly affected. While both physical activity (PA) and diet have been shown to help improve the sleep quality and duration, the combined association of these two factors with sleep has yet to be analysed. Objectives: Therefore, the main objective of this study was to assess the evidence on the combined association of PA and diet with the quality and duration of sleep in adolescents. Secondary objectives were to analyse the evidence on the single association of PA with the quality and duration of sleep in adolescents and to analyse the single association of diet with the quality and duration of sleep in adolescents. Methods: To this end, a scoping review was conducted with a structured search in four online databases (PubMed, Scopus, Web of Science and ERIC). Results: The findings suggest that the amount of PA (time/week) and healthy dietary patterns, characterised by meal regularity and high consumption of fruits and vegetables, favour a better quality and a longer duration of sleep. Conversely, less weekly PA and the intake of less healthy foods, such as ultra-processed foods, are associated with decreasing the sleep quality and duration. Conclusions: In conclusion, the results underscore the importance of considering PA and diet as an appropriate approach to investigating sleep quality and duration in adolescents. Studies analysing the interplay between PA, diet and sleep in adolescents are scarce. [ABSTRACT FROM AUTHOR]

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-β motif of parallel or antiparallel β-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena can occur in blood from individuals with circulating inflammatory molecules (and also some originating from viruses and bacteria). Such pathological clotting can result in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We thus provide evidence against such a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant quantitative overlap with the normal plasma proteome and no correlation between plasma protein abundance and their presence in fibrinaloid microclots. Notably, abundant plasma proteins like α-2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand factor are well represented. Using bioinformatic tools, including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-β elements into amyloid structures. This integration likely contributes to the microclots' resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders. [ABSTRACT FROM AUTHOR]

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model's accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm's lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the mAP@0.5 metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size. [ABSTRACT FROM AUTHOR]

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students' performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes. [ABSTRACT FROM AUTHOR]

Inflammation contributes to the pathogenesis of type 2 diabetes (T2DM). This study sought to document how the systemic biomarkers of inflammation varied based on food choices among patients with T2DM. This cross-sectional study enrolled ambulatory patients with T2DM. Demographic and clinical information was collected. Five drops of fingerstick blood were collected using an absorbent paper device (HemaSpot HFR). C-reactive protein (CRP), serum amyloid A protein (SAA), and fibrinogen were measured using a Luminex assay. Patient-generated 7-day food diaries were analyzed using a validated food processor software. Data were analyzed by Pearson's correlation tests, linear regression and logistic regression with the significance level set at 0.05. Among the 71 participants, 43 (60.6%) were females. The average age and duration of T2DM were 64.1 ± 10.3 and 15.8 ± 9.1 years, respectively. In a simple linear regression run with selected micronutrients, iron [F (1, 53) = 5.319, p < 0.05, adj. R2 = 0.074] significantly predicted plasma CRP. This significance was lost with multiple linear regressions including age, gender, BMI, T2DM duration, T2DM complications, glycohemoglobin A1c (HbA1c) and other micronutrients. The average intake of most micronutrients by the participants was below the recommended daily intake. A higher intake of iron-rich foods was associated with higher levels of systemic inflammation in a simple linear regression model, but the association was not present after adjusting for patient factors like age, gender, BMI and T2DM-related variables. This relationship needs to be explored further given the key role of inflammation in the pathogenesis of T2DM and its associated complications. [ABSTRACT FROM AUTHOR]

In this research, we introduce the intuitionistic hesitant fuzzy rough set by integrating the notions of an intuitionistic hesitant fuzzy set and rough set and present some intuitionistic hesitant fuzzy rough set theoretical operations. We compile a list of aggregation operators based on the intuitionistic hesitant fuzzy rough set, including the intuitionistic hesitant fuzzy rough Dombi weighted arithmetic averaging aggregation operator, the intuitionistic hesitant fuzzy rough Dombi ordered weighted arithmetic averaging aggregation operator, and the intuitionistic hesitant fuzzy rough Dombi hybrid weighted arithmetic averaging aggregation operator, and demonstrate several essential characteristics of the aforementioned aggregation operators. Furthermore, we provide a multi attribute decision-making approach and the technique of the suggested approach in the context of the intuitionistic hesitant fuzzy rough set. A real-world problem for selecting a suitable worldwide partner for companies is employed to demonstrate the effectiveness of the suggested approach. The sensitivity analysis of the decision-making results of the suggested aggregation operators are evaluated. The demonstrative analysis reveals that the outlined strategy has applicability and flexibility in aggregating intuitionistic hesitant fuzzy rough information and is feasible and insightful for dealing with multi attribute decision making issues based on the intuitionistic hesitant fuzzy rough set. In addition, we present a comparison study with the TOPSIS approach to illustrate the advantages and authenticity of the novel procedure. Furthermore, the characteristics and analytic comparison of the current technique to those outlined in the literature are addressed. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) continue to be a major global health concern, representing a leading cause of morbidity and mortality. This review provides a comprehensive examination of CVDs, encompassing their pathophysiology, diagnostic biomarkers, advanced imaging techniques, pharmacological treatments, surgical interventions, and the emerging role of herbal remedies. The review covers various cardiovascular conditions such as coronary artery disease, atherosclerosis, peripheral artery disease, deep vein thrombosis, pulmonary embolism, cardiomyopathy, rheumatic heart disease, hypertension, ischemic heart disease, heart failure, cerebrovascular diseases, and congenital heart defects. The review presents a wide range of cardiac biomarkers such as troponins, C-reactive protein, CKMB, BNP, NT-proBNP, galectin, adiponectin, IL-6, TNF-α, miRNAs, and oxylipins. Advanced molecular imaging techniques, including chest X-ray, ECG, ultrasound, CT, SPECT, PET, and MRI, have significantly enhanced our ability to visualize myocardial perfusion, plaque characterization, and cardiac function. Various synthetic drugs including statins, ACE inhibitors, ARBs, β-blockers, calcium channel blockers, antihypertensives, anticoagulants, and antiarrhythmics are fundamental in managing CVDs. Nonetheless, their side effects such as hepatic dysfunction, renal impairment, and bleeding risks necessitate careful monitoring and personalized treatment strategies. In addition to conventional therapies, herbal remedies have garnered attention for their potential cardiovascular benefits. Plant extracts and their bioactive compounds, such as flavonoids, phenolic acids, saponins, and alkaloids, offer promising cardioprotective effects and enhanced cardiovascular health. This review underscores the value of combining traditional and modern therapeutic approaches to improve cardiovascular outcomes. This review serves as a vital resource for researchers by integrating a broad spectrum of information on CVDs, diagnostic tools, imaging techniques, pharmacological treatments and their side effects, and the potential of herbal remedies. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model's computational complexity is significantly reduced, making it suitable for deployment on resource-constrained edge devices. Additionally, Dynamic UpSampling and Coordinate Attention mechanisms enhance the model's ability to capture multi-scale features and focus on relevant regions, improving detection accuracy for small-scale fires. The Distance-Intersection over Union loss function further optimizes the model's training process, leading to more accurate bounding box predictions. Experimental results on a comprehensive dataset demonstrate that our proposed model achieves a 41% reduction in parameters and a 54% reduction in GFLOPs, while maintaining a high mean Average Precision (mAP) of 99.0% at an Intersection over Union (IoU) threshold of 0.5. The proposed model offers a promising solution for real-time forest fire monitoring, enabling a timely detection of, and response to, wildfires. [ABSTRACT FROM AUTHOR]

This research proposes an optimized method for identifying high-quality vegetation areas, with a focus on forest ecosystems, using an improved Vegetation Health Index (VHI). The study introduces the Land Cover Vegetation Health Index (LCVHI), which integrates the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) with land cover data. Utilizing MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery and Google Earth Engine (GEE), the study assesses the impact of land cover changes on vegetation health, with particular attention to forested areas. The application of the LCVHI demonstrates that forests exhibit a VHI approximately 25% higher than that of croplands, and wetlands show an 18% higher index compared to grasslands. Analysis of data from 2012 to 2022 in Hubei Province, China, reveals an overall upward trend in vegetation health, highlighting the effectiveness of environmental protection and forest management measures. Different land cover types, including forests, wetlands, and grasslands, significantly impact vegetation health, with forests and wetlands contributing most positively. These findings provide important scientific evidence for regional and global ecological management strategies, supporting the development of forest conservation policies and sustainable land use practices. The research results offer valuable insights into the effective management of regional ecological dynamics. [ABSTRACT FROM AUTHOR]

This research explores the impact of urban design performance qualities on pedestrian behavior in a cultural heritage site designated by UNESCO. The study employs a multi-method approach, including a questionnaire survey, empirical observation of pedestrian activities, and empirical axial line and visibility graph analysis using the space syntax technique. The first part of the study involved a questionnaire formatted as a polling sheet to gather expert assessments of spatial performance measures. The second part used a pilot survey to capture the perspectives of end users regarding the study's objectives and their perceptions of the site. Pedestrian flow was observed using a technique called "gate counts", with observations recorded as video clips during specific morning and afternoon periods across three pedestrian zones. The study also examined the behavioral patterns of pedestrians, including their movement patterns. Finally, the ArcGIS 10.3.1 software was employed to evaluate the reliability of the results. The main finding of this research is that pedestrian behavior and walkability in the historical areas are significantly influenced by landmark integration, wayfinding behavior, and the socio-economic functions of heritage sites. This study highlights the importance of using cognitive and syntactic analysis, community engagement, and historical preservation to enhance walkability, accessibility, and social interaction in heritage contexts. In addition, it identifies the need for improvements in urban design to address inconsistencies between syntactic maps and actual pedestrian flow, emphasizing the role of imageability and the impact of environmental and aesthetic factors on pedestrian movement. This research provides valuable insights for urban designers and planners, environmental psychologists, architects, and policymakers by highlighting the key elements that make urban spaces walkable, aiming to enhance the quality of public spaces. [ABSTRACT FROM AUTHOR]

Cyber–physical systems have become critical across industries. They have driven investments in education services to develop well-trained engineers. Education services for cyber–physical systems require the hiring of expert tutors with multidisciplinary knowledge, as well as acquiring expensive facilities/equipment. In response to the challenges posed by the need for the equipment and facilities, a metaverse-based education service that incorporates digital twins has been explored as a solution. However, the issue of recruiting expert tutors who can enhance students' achievements remains unresolved, making it difficult to effectively cultivate talent. This paper proposes a reference architecture for a learner-centric educational metaverse with an intelligent tutoring framework as its core feature to address these issues. We develop a novel explainable artificial intelligence scheme for multi-class object detection models to assess learners' achievements within the intelligent tutoring framework. Additionally, a genetic algorithm-based improvement search method is applied to the framework to derive personalized feedback. The proposed metaverse architecture and framework are evaluated through a case study on drone education. The experimental results show that the explainable AI scheme demonstrates an approximately 30% improvement in the explanation accuracy compared to existing methods. The survey results indicate that over 70% of learners significantly improved their skills based on the provided feedback. [ABSTRACT FROM AUTHOR]

Interactive multimedia experiences (IME) can be a pedagogical resource that has a strong potential to enhance learning experiences in early childhood. Learning analytics (LA) has become an important tool that allows us to understand more clearly how these multimedia experiences can contribute to the learning processes of these students. This article proposes a work route that defines a set of activities and techniques, as well as a flow for their application, by taking into consideration the importance of including LA guidelines when designing IMEs for elementary education. The work route's graphical representation is inspired by the foundations of the Essence standard's graphical notation language. The guidelines are grouped into five categories, namely (i) a data analytics dashboard, (ii) student data, (iii) teacher data, (iv) learning activity data, and (v) student progress data. The guidelines were validated through two approaches. The first involved a case study, where the guidelines were applied to an IME called Coco Shapes, which was aimed at transition students at the Colegio La Fontaine in Cali (Colombia), and the second involved the judgments of experts who examined the usefulness and clarity of the guidelines. The results from these approaches allowed us to obtain precise and effective feedback regarding the hypothesis under study. Our findings provide promising evidence of the value of our guidelines, which were included in the design of an IME and contributed to the greater personalized monitoring available to teachers to evaluate student learning. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a common endocrine disorder that impacts women of reproductive age. In addition to reproductive and psychological complications, women with PCOS are also at a higher risk of developing metabolic diseases such as obesity, type 2 diabetes and cardiovascular disease. While weight reduction can help manage these complications in overweight or obese women, many weight loss interventions have been ineffective due to weight stigma and its psychological impact on women with PCOS. Therefore, exploring alternative dietary strategies which do not focus on weight loss per se is of importance. In this regard, omega-3 polyunsaturated fatty acids of marine origin (n-3 PUFAs), which are known for their hypotriglyceridemic, cardioprotective and anti-inflammatory effects, have emerged as a potential therapy for prevention and reversal of metabolic complications in PCOS. Several clinical trials showed that n-3 PUFAs can improve components of metabolic syndrome in women with PCOS. In this review, we first summarize the available clinical evidence for different dietary patterns in improving PCOS complications. Next, we summarize the clinical evidence for n-3 PUFAs for alleviating metabolic complications in PCOS. Finally, we explore the mechanisms by which n-3 PUFAs improve the metabolic disorders in PCOS in depth. [ABSTRACT FROM AUTHOR]

Environmental factors have been identified as having a direct relationship with physical activity (PA) and sedentary behavior. The main aim of this study was to investigate the relationship between microscale built-environment attributes and the levels of PA and sedentary behavior in young people. This study included 465 adolescents (55% girls) between 14 and 18 years from Valencia, Spain. Accelerometers and self-reported questionnaires were used to measure PA, including active commuting, and sedentary behavior, and the MAPS (Microscale Audit of Pedestrian Streetscapes)-Global tool was used for microscale variables. Mixed-effects regression models were used for data analysis. Higher levels of moderate-to-vigorous activity were identified when more positive elements were found in the street characteristics. Greater active commuting in the neighborhood had a positive relationship not only with more positive elements of land use and destinations but also with the overall score of the MAPS-Global tool. The sedentary levels were higher when higher levels of negative aesthetics and social characteristics were identified, and the participants were less sedentary when more bike facilities were observed. The main results of this study provide us with evidence of the relationship between the microscale variables of the built environment and both PA and sedentary behavior. [ABSTRACT FROM AUTHOR]

The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions on teaching and learning. Three cycles of design science research and multiple small loops were implemented to develop the dashboard. The tool was then deployed and evaluated in real time with the authentic courses. Five courses from two Swedish universities were included in this study. The co-design with teachers is crucial to the applicability of this dashboard, while letting teachers use the tool during their courses is more important to help them to recognize the features they actually use and the tool's usefulness for their teaching practices. TEADASH can address the prior matters, align with the learning design, and meet teachers' needs. The technical and co-design aspects, as well as the advantages and challenges of applying TEADASH in practice, are also discussed here. [ABSTRACT FROM AUTHOR]

Obesity disrupts glucose metabolism, leading to insulin resistance (IR) and cardiometabolic diseases. Consumption of cow's milk and other dairy products may influence glucose metabolism. Within the complex matrix of cow's milk, various carbohydrates, lipids, and peptides act as bioactive molecules to alter human metabolism. Here, we summarize data from human studies and rodent experiments illustrating how these bioactive molecules regulate insulin and glucose homeostasis, supplemented with in vitro studies of the mechanisms behind their effects. Bioactive carbohydrates, including lactose, galactose, and oligosaccharides, generally reduce hyperglycemia, possibly by preventing gut microbiota dysbiosis. Milk-derived lipids of the milk fat globular membrane improve activation of insulin signaling pathways in animal trials but seem to have little impact on glycemia in human studies. However, other lipids produced by ruminants, including polar lipids, odd-chain, trans-, and branched-chain fatty acids, produce neutral or contradictory effects on glucose metabolism. Bioactive peptides derived from whey and casein may exert their effects both directly through their insulinotropic effects or renin-angiotensin-aldosterone system inhibition and indirectly by the regulation of incretin hormones. Overall, the results bolster many observational studies in humans and suggest that cow's milk intake reduces the risk of, and can perhaps be used in treating, metabolic disorders. However, the mechanisms of action for most bioactive compounds in milk are still largely undiscovered. [ABSTRACT FROM AUTHOR]

Polymer composites are widely used in various fields of production and life, and the study of preparing environmentally friendly and flame retardant clay/polymer composites has gradually become a global research hotspot. But how to efficiently surface modify clay and apply it to the field of flame retardant polymers is still a potential challenge. One of the most commonly used surface modification methods is the modification of clay with silane coupling agents. The hydrolysable groups of the silane coupling agent first hydrolyze to generate hydroxyl groups. These hydroxyl groups then undergo a condensation reaction with the hydroxyl groups on the surface of the clay, allowing for organic functional groups to be grafted onto the clay surface. The organic functional groups and polymer matrix react to generate chemical bonds so that the composite material's interface is more closely combined. Thus, the dispersion of clay in the organic polymer material and the compatibility of the two is better, which improves the flame retardant effect of the composite material. This paper introduces the classification of a silane coupling agent and the mechanism and process of silane coupling agent-modified clay, outlines the mechanism of silane coupling agent-modified clay flame retardant polymers, reviews the research results on flame retardant polymers of various clays after surface treatment with silane coupling agents in recent years, and highlights the synergistic flame retardant effect of clay and flame retardant organized by silane coupling agents. Finally, it is found that the current research in the field of silane coupling agent-modified clay in flame retardants is focused on the modification of montmorillonite, sepiolite, attapulgite, and kaolinite by KH-550, KH-560, and KH-570, and the development trends in this field are also prospected. [ABSTRACT FROM AUTHOR]

In the future, smart cities will provide key services including seamless communication, intelligent transport systems, advanced healthcare platforms, urban and infrastructure management, and digital services for local and regional government. Therefore, a new service and networking paradigm, called the Intelligent Edge Environment, has been specified. As a key part of this system, a new secure service ecosystem must be developed to provide the secure real-time movement of services on different High-Performance Edge Cloud Systems. This paper explores these issues by introducing the following mechanisms: a Resource Allocation Algorithm, a Resource Allocation Secure Protocol and finally a Secure Service Protocol. These systems were integrated into the Basic Capability System Library and a multithreaded FUSE client connected to the Service Management Framework. Docker was used as the migration mechanism. A prototype was developed and implemented using a FUSE-Network Memory System in which the Network Memory Server was migrated as users moved around. The result shows that this approach was safe and could be used to develop new applications and services for smart cities. [ABSTRACT FROM AUTHOR]

Since their conception with the smallpox vaccine, vaccines used worldwide have mitigated multiple pandemics, including the recent COVID-19 outbreak. Insightful studies have uncovered the complexities of different functional networks of CD4 T cells (T helper 1 (Th1); Th2, Th17) and CD8 T cells (T cytotoxic; Tc), as well as B cell (BIgM, BIgG, BIgA and BIgE) subsets, during the response to vaccination. Both T and B cell subsets form central, peripheral, and tissue-resident subsets during vaccination. It has also become apparent that each vaccination forms a network of T regulatory subsets, namely CD4+ CD25+ Foxp3+ T regulatory (Treg) cells and interleukin-10 (IL-10)-producing CD4+ Foxp3− T regulatory 1 (Tr1), as well as many others, which shape the quality/quantity of vaccine-specific IgM, IgG, and IgA antibody production. These components are especially critical for immunocompromised patients, such as older individuals and allograft recipients, as their vaccination may be ineffective or less effective. This review focuses on considering how the pre- and post-vaccination Treg/Tr1 levels influence the vaccination efficacy. Experimental and clinical work has revealed that Treg/Tr1 involvement evokes different immune mechanisms in diminishing vaccine-induced cellular/humoral responses. Alternative steps may be considered to improve the vaccination response, such as increasing the dose, changing the delivery route, and/or repeated booster doses of vaccines. Vaccination may be combined with anti-CD25 (IL-2Rα chain) or anti-programmed cell death protein 1 (PD-1) monoclonal antibodies (mAb) to decrease the Tregs and boost the T/B cell immune response. All of these data and strategies for immunizations are presented and discussed, aiming to improve the efficacy of vaccination in humans and especially in immunocompromised and older individuals, as well as organ transplant patients. [ABSTRACT FROM AUTHOR]

Nutritional epigenomics is exceptionally important because it describes the complex interactions among food compounds and epigenome modifications. Phytonutrients or bioactive compounds, which are secondary metabolites of plants, can protect against osteoarthritis by suppressing the expression of inflammatory and catabolic mediators, modulating epigenetic changes in DNA methylation, and the histone or chromatin remodelling of key inflammatory genes and noncoding RNAs. The combination of natural epigenetic modulators is crucial because of their additive and synergistic effects, safety and therapeutic efficacy, and lower adverse effects than conventional pharmacology in the treatment of osteoarthritis. In this review, we have summarized the chondroprotective properties of bioactive compounds used for the management, treatment, or prevention of osteoarthritis in both human and animal studies. However, further research is needed into bioactive compounds used as epigenetic modulators in osteoarthritis, in order to determine their potential value for future clinical applications in osteoarthritic patients as well as their relation with the genomic and nutritional environment, in order to personalize food and nutrition together with disease prevention. [ABSTRACT FROM AUTHOR]

The objective of this work was to design and implement a system based on reconfigurable hardware as a study tool for the synchronization of chaotic circuits. Mathematical models were established for one circuit, two synchronized, and multiple synchronized Chua circuits. An ordinary differential equation solver was developed applying Euler's method using the Verilog hardware description language and synthesized on a Spartan 3E FPGA (Field-Programmable Gate Array) equipped with a 32-bit RISC processor, 64 MB of DDR SDRAM, and 4 Mb of PROM. With a step size of 0.005 and a total of 10,000 iterations, the state equations for one and three Chua circuits were solved at a time of 0.2 ms and a frequency of 50 Mhz. The logical resources used by the system did not exceed 4%. To verify the operation, a numerical simulation was carried out using the Octave V9.1.0 calculation software on an Intel(R) Core i7-9750H CPU 2.59 GHz computer, obtaining the same results but in a time of 493 ms and 3.177 s for one and three circuits, respectively. [ABSTRACT FROM AUTHOR]

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples. [ABSTRACT FROM AUTHOR]

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). [ABSTRACT FROM AUTHOR]

This study explores the impacts of climate change on the number of dry days and very heavy precipitation days within Iran's metropolises. Focusing on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, the research utilizes the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Circulation Models (GCMs) to predict future precipitation conditions under various Shared Socioeconomic Pathways (SSPs) from 2025 to 2100. The study aims to provide a comprehensive understanding of how climate change will affect precipitation patterns in these major cities. Findings indicate that the SSP126 scenario typically results in the highest number of dry days, suggesting that under lower emission scenarios, precipitation events will become less frequent but more intense. Conversely, SSP585 generally leads to the lowest number of dry days. Higher emission scenarios (SSP370, SSP585) consistently show an increase in the number of very heavy precipitation days across all cities, indicating a trend towards more extreme weather events as emissions rise. These insights are crucial for urban planners, policymakers, and stakeholders in developing effective adaptation and mitigation strategies to address anticipated climatic changes. [ABSTRACT FROM AUTHOR]

Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges. Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers' behavioral intentions, employing structural equation modeling for validation. The findings reveal the following: (1) designer trust is the primary factor influencing their behavioral choices; (2) different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks; (3) only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated; and (4) only by effectively safeguarding designers' legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions. This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of the typical Mediterranean diet (TMD), low-carbohydrate Mediterranean diet (LCMD), and low-fat Mediterranean diet (LFMD) on biochemical findings, fatty liver index (FLI), anthropometric measurements, and body composition in individuals with obesity with non-alcoholic fatty liver disease (NAFLD) and insulin resistance. This study included 63 participants with obesity with insulin resistance diagnosed with NAFLD by ultrasonography to investigate the effects of an 8-week energy-restricted TMD, LCMD, and LFMD on biochemical findings, FLI, fibrosis-4 index (FIB-4), anthropometric measurements, and body composition. Patients were randomized into three groups and were interviewed face-to-face every week. According to the food consumption records (baseline end), the difference in the amount of sucrose and total fat consumed in the TMD group; the difference in energy intake from sucrose, monounsaturated fatty acids, and oleic acid in the LCMD group; and the difference in energy intake from fiber, sucrose, monounsaturated and polyunsaturated fatty acids, and cholesterol in the LFMD group showed significant correlations with liver enzymes and FLI (p < 0.05). In conclusion, although it has a different macronutrient composition, the Mediterranean diet may positively affect biochemical parameters and FLI in individuals with NAFLD, albeit in different ways. [ABSTRACT FROM AUTHOR]

Hypertension contributes to the increase in health care spending in Canada through two primary mechanisms. First, it directly increases costs, as individuals with hypertension require medical care to manage the condition. Second, it indirectly raises expenses by serving as a risk factor for numerous chronic diseases, leading to increased health care utilization among those affected. Therefore, reducing hypertension prevalence could alleviate its resulting strain on the Canadian health care system. Clinical trials have demonstrated that daily flaxseed consumption effectively lowers both systolic and diastolic blood pressure. This study employs a four-step cost-of-illness analysis to estimate the potential health care cost-savings from a flaxseed-based treatment for hypertension. The analysis begins by assessing the proportion of individuals with hypertension likely to adopt the flaxseed regimen. It then evaluates the impact of flaxseed consumption on systolic and diastolic blood pressure. Next, data from the Canadian Health Measures Survey, Cycles 5 and 6, are used to estimate the prevalence of hypertension and the expected reduction in prevalence due to the flaxseed treatment. Finally, the potential reduction in health care spending is calculated. To incorporate uncertainty, partial sensitivity analysis and Monte Carlo simulations were utilized, varying the intake success rate and other model parameters, respectively. The most conservative estimate suggests a potential health care cost-savings of CAD 96,284,344 in Canada for the year 2020. [ABSTRACT FROM AUTHOR]

Aims: Human recombinant insulin is currently the only therapy for children and adolescents with type 1 diabetes (T1D), although not always efficient for the glycemic control of these individuals. The interrelation between the gut microbiome and the glycemic control of apparently healthy populations, as well as various populations with diabetes, is undeniable. Probiotics are biotherapeutics that deliver active components to various targets, primarily the gastrointestinal tract. This systematic review and meta-analysis examined the effect of the administration of probiotics on the glycemic control of pediatric and adolescent individuals with T1D. Materials and Methods: Randomized controlled trials employing the administration of probiotics in children and adolescents with T1D (with ≥10 individuals per treatment arm), written in English, providing parameters of glycemic control, such as mean glucose concentrations and glycosylated hemoglobin (HbA1c), were deemed eligible. Results: The search strategy resulted in six papers with contradictory findings. Ultimately, five studies of acceptable quality, comprising 388 children and adolescents with T1D, were included in the meta-analysis. Employing a random and fixed effects model revealed statistically significant negative effect sizes of probiotics on the glycemic control of those individuals, i.e., higher concentrations of glucose and HbA1c than controls. Conclusions: Children and adolescents with T1D who received probiotics demonstrated worse glycemic control than controls after the intervention. Adequately powered studies, with extended follow-up periods, along with monitoring of compliance and employing the proper strains, are required to unravel the mechanisms of action and the relative effects of probiotics, particularly concerning diabetes-related complications and metabolic outcomes. [ABSTRACT FROM AUTHOR]

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. [ABSTRACT FROM AUTHOR]

The increasing prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) underscores the need for better understanding of its complex pathogenesis. Lipid accumulation in hepatocytes is among principal mechanisms contributing to MASLD development. While routine lipid parameters are well studied, the profile of circulating fatty acids in MASLD patients remains less explored. This study aimed to assess relative proportions of individual fatty acids in plasma of MASLD patients and to explore their associations with other biochemical markers of MASLD. Ninety-one patients and 48 healthy individuals were enrolled. The relative proportions of fatty acids in plasma were determined using gas chromatography with FID detection. Proportions of total n-6 polyunsaturated fatty acids (PUFAs) and linoleic acid (LA) in plasma were lower in MASLD patients (p = 0.001 and p = 0.004, respectively), with no differences observed in n-3 PUFAs. Total plasma n-6 PUFAs correlated negatively with body mass index, hepatic steatosis indices, triglyceride concentration and coronary risk index. Decreased prevalence of n-6 PUFAs in plasma was independently associated with higher odds of MASLD (OR = 0.769; CI: 0.611–0.968; p = 0.025). Our findings indicate an altered circulatory fatty acid distribution in MASLD, characterized by a reduced amount of n-6 PUFAs, particularly LA, which may have significant implications for the prevention and treatment of MASLD. [ABSTRACT FROM AUTHOR]

This review is a fundamental tool for researchers and engineers involved in the design and optimization of fiber-reinforced composite materials. The aim is to provide a comprehensive analysis of the mechanical performance of composites with epoxy matrices reinforced with carbon nanofibers (CNFs). The review includes studies investigating the static mechanical response through three-point bending (3PB) tests, tensile tests, and viscoelastic behavior tests. In addition, the properties of the composites' resistance to interlaminar shear strength (ILSS), mode I and mode II interlaminar fracture toughness (ILFT), and low-velocity impact (LVI) are analyzed. The incorporation of small amounts of CNFs, mostly between 0.25 and 1% by weight was shown to have a notable impact on the static and viscoelastic properties of the composites, leading to greater resistance to time-dependent deformation and better resistance to creep. ILSS and ILFT modes I and II of fiber-reinforced composites are critical parameters in assessing structural integrity through interfacial bonding and were positively affected by the introduction of CNFs. The response of composites to LVI demonstrates the potential of CNFs to increase impact strength by reducing the energy absorbed and the size of the damage introduced. Epoxy matrices reinforced with CNFs showed an average increase in stiffness of 15% and 20% for bending and tensile, respectively. The laminates, on the other hand, showed an increase in bending stiffness of 20% and 15% for tensile and modulus, respectively. In the case of ILSS and ILFT modes I and II, the addition of CNFs promoted average increases in the order of 50%, 100%, and 50%, respectively. [ABSTRACT FROM AUTHOR]

The advent of the digital era has catalyzed transformative shifts in educational paradigms, seamlessly integrating sustainable education with innovative pedagogical approaches that cater to a broader spectrum of learners and educators. As the academic landscape evolves into an information-dense environment, traditional models of one-on-one feedback often fall short, resulting in delays and a lack of diverse intellectual engagement. This shift underscores the growing importance of peer feedback and asynchronous post-lesson feedback mechanisms, which have emerged as vital, process-oriented educational activities. Such mechanisms not only address the immediacy and diversity of feedback but also foster a sustainable, collaborative, and engaging learning environment that bolsters student autonomy and interaction. This study conducts a mapping review and synthesis of the literature on peer interaction, assessment, and participatory innovations in online and mobile learning within higher education, covering the period from 1993 to 2022. By employing topic modeling techniques to analyze a corpus of 485 articles, the literature was initially segmented into 19 distinct issues. These were subsequently consolidated into three major thematic models, thereby highlighting significant themes, research focal points, and prospective trends. This analytical process not only enriches the understanding of peer dynamics in digital learning settings but also integrates sustainable educational practices by promoting resource efficiency and inclusivity. The findings serve as a robust reference for future researchers aiming to explore the intersections of technology, peer interaction, and sustainability in educational settings. [ABSTRACT FROM AUTHOR]

Metabolic dysfunction-associated steatotic liver disease/steatohepatitis (MASLD/MASH) is a major disease worldwide whose effective treatment is challenging. Peroxisome proliferator-activated receptors (PPARs) belong to the nuclear receptor superfamily and function as ligand-activated transcription factors. To date, three distinct subtypes of PPARs have been characterized: PPARα, PPARβ/δ, and PPARγ. PPARα and PPARγ are crucial regulators of lipid metabolism that modulate the transcription of genes involved in fatty acid (FA), bile acid, and cholesterol metabolism. Many PPAR agonists, including natural (FAs, eicosanoids, and phospholipids) and synthetic (fibrate, thiazolidinedione, glitazar, and elafibranor) agonists, have been developed. Furthermore, recent advancements in nanoparticles (NPs) have led to the development of new strategies for MASLD/MASH therapy. This review discusses the applications of specific cell-targeted NPs and highlights the potential of PPARα- and PPARγ-targeted NP drug delivery systems for MASLD/MASH treatment. [ABSTRACT FROM AUTHOR]

The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality. [ABSTRACT FROM AUTHOR]

The future of education relies on the integration of information technologies, emphasizing the importance of equity and inclusiveness for quality education. Teacher education programs are essential for fostering qualified educators for the future. Integrating AI in education is crucial to ensure inclusivity and comprehensive services for all. This study aims to evaluate student teachers' perceptions of using AI in learning and teaching, and to provide suggestions for enhancing sustainable education through information technologies. A qualitative research design was adopted to gather perceptions and experiences from 240 student teachers who participated in a seminar on AI usage and completed self-reflection tasks. These student teachers, enrolled in various teaching methods and principal courses, contributed to the thematic analysis. The study reveals that AI should be carefully planned and incorporated into lesson plans to enhance personalized learning. Student teachers reported that AI supports and motivates the learning process, effectively transforming students' needs and learning experiences. However, they also noted potential drawbacks, such as AI imposing restrictions on the teaching profession, replacing teachers, and producing biased results. The study suggests that capacity-building strategies for student teachers should be enriched across different courses to raise awareness about AI's applications. [ABSTRACT FROM AUTHOR]

Simple Summary: In the present meta-analysis of randomized controlled trials, probiotic fermented milk supplementation appeared to be beneficial in lowering the levels of fasting plasma glucose, HbA1c, total cholesterol, and C-reactive protein. While these findings are encouraging, they should be interpreted cautiously, as considerable limitations of the included trials and analyses precluded solid conclusions. Modulating gut microbiota composition through probiotic administration has been proposed as a novel therapy for type 2 diabetes mellitus (T2DM), and fermented milk is arguably the most common and ideal probiotic carrier. The present meta-analysis was performed to assess the effects of probiotic fermented milk supplementation on glucose and lipid metabolism parameters and inflammatory markers in patients with T2DM using published data from randomized controlled trials (RCTs). The PubMed, Web of Science, and Cochrane Library databases were searched for relevant RCTs. A random-effects model was used to generate the weighted mean difference (WMD) and 95% confidence interval (95% CI). Probiotic fermented milk supplementation reduced the levels of fasting plasma glucose (MD = −17.01, 95% CI −26.43, −7.58 mg/dL; n = 7), hemoglobin A1c (MD = −0.47, 95% CI −0.74, −0.21%; n = 7), total cholesterol (MD = −5.15, 95% CI −9.52, −0.78 mg/dL; n = 7), and C-reactive protein (MD = −0.25, 95% CI −0.43, −0.08; n = 3) but did not significantly affect the levels of HOMA-IR (MD = −0.89, 95% CI −2.55, 0.78; n = 3), triglyceride (MD = −4.69, 95% CI −14.67, 5.30 mg/dL; n = 6), low-density lipoprotein cholesterol (MD = −4.25, 95% CI −8.63, 0.13 mg/dL; n = 7), high-density lipoprotein cholesterol (MD = 1.20, 95% CI −0.96, 3.36 mg/dL; n = 7), and tumor necrosis factor-alpha (MD: −0.58, 95% CI −1.47, 0.32 pg/mL; n = 2). In summary, the present findings provide a crude indication of the potential benefits of probiotic fermented milk supplementation in improving glucose and lipid metabolism and inflammation in patients with T2DM. However, more robust evidence is needed to determine the clinical significance of probiotic fermented milk in the management of T2DM. [ABSTRACT FROM AUTHOR]

The hepatoprotective effects of natural products have been a significant focus in recent decades due to the growing demand for the help in the treatment of hepatic impairments. This review specifically delves into the findings of clinical trials involving 13 selected natural products, namely plants and their derived compounds (e.g., artichoke, berberine, and turmeric), algae (e.g., spirulina), probiotics, and other products like phospholipids and vitamin D. A literature search was performed in the Scopus database, PubMed, and Google Scholar, covering all articles found up to June 2024. Artichoke, berberine, chlorella, chicory, green tea, probiotics, phospholipids, schisandra, silymarin, spirulina, and vitamin D caused a decrease in liver enzymes, while for cinnamon and turmeric such an effect was either not observed or not convincing. The presented results indicate that some natural products might satisfactorily improve hepatic outcomes in NAFLD, NASH, and other liver disorders; however, further studies and metanalyses are needed to clearly demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

This survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As Artificial Intelligence (AI) continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. This survey examines the methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based Decision Support Systems. Through an in-depth analysis of current research and practical implementations, this article aims to guide researchers, practitioners, and decision-makers in navigating the intricate landscape of XAI-based DSSs. These systems assist end-users in their decision-making, providing a full picture of how a decision was made and boosting trust. Furthermore, a methodical taxonomy of the current methodologies is proposed and representative works are presented and discussed. The analysis of recent studies reveals that there is a growing interest in applying XDSSs in fields such as medical diagnosis, manufacturing, and education, to name a few, since they smooth down the trade-off between accuracy and explainability, boost confidence, and also validate decisions. [ABSTRACT FROM AUTHOR]

This study investigated the effects of certain roselle (Hibiscus sabdariffa Linnaeus) extraction methods on various functional properties, including the antioxidant and antiglycation capacities and bacterial growth inhibition. Roselle anthocyanins were extracted using water and ethanol solvents at different temperatures and concentrations. The results revealed that the extraction rate increased with higher temperatures and ethanol concentrations (p < 0.05). Ethanol extracts exhibited higher total organic acid and total anthocyanin contents compared to water extracts, while water extracts showed higher total saccharide, total polyphenol, and total flavonoid contents (p < 0.05). Furthermore, the water extracts demonstrated superior Trolox equivalent antioxidant capacity (TEAC) values, while the ethanol extracts exhibited better 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability, antiglycation capacity, and bacterial growth inhibition. A Pearson correlation analysis revealed strong associations between specific components and functional properties, such as a positive correlation between the total anthocyanin content and antiglycation capacity (R2 = 0.9862). A principal component analysis and agglomerative hierarchical clustering highlighted distinct clusters of water and ethanol extracts, indicating solvent-dependent variations in functional properties. This study assessed roselle extraction models for antioxidant, antiglycation, and antibacterial activities, which could be used for the development of functional alcoholic or non-alcoholic beverages. [ABSTRACT FROM AUTHOR]

Epoxy resins were reinforced with different ZnO nanofillers (commercial ZnO nanoparticles (ZnO NPs), recycled ZnO and functionalized ZnO NPs) in order to obtain ZnO–epoxy composites with suitable mechanical properties, high adhesion strength, and good resistance to corrosion. The final properties of ZnO–epoxy composites depend on several factors, such as the type and contents of nanofillers, the epoxy resin type, curing agent, and preparation methods. This paper aims to review the preparation methods, mechanical and anti-corrosion performance, and applications of ZnO–epoxy composites. The epoxy–ZnO composites are demonstrated to be valuable materials for a wide range of applications, including the development of anti-corrosion and UV-protective coatings, for adhesives and the chemical industry, or for use in building materials or electronics. [ABSTRACT FROM AUTHOR]

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named "Convolutional and Attention-based Cloud Mask Net (CACM-Net)", was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results. [ABSTRACT FROM AUTHOR]

Glucose control in postmenopausal women is influenced by many factors, such as hormones, lifestyle variables, and genetics. Limited data exist on the effect of whole flaxseed on glucose status in postmenopausal Native American women. The aim of this study was to investigate the glucose management effect of a flaxseed dietary intervention on postmenopausal Native American women. In this study, 55 Native American postmenopausal women (aged 47–63 years) with borderline hyperglycemia (>100 and <126 mg/dL) and mild to moderate hypercholestorolemia (≥200 to ≤380 mmol/L), who were not on hormone replacement therapy, were enrolled. Participants were randomly assigned to one of the three dietary regimens (control, flaxseed, and flaxseed + fiber) for three months, receiving interventions in the form of bread, muffins, and flaxseed powder. Despite daily consumption of flaxseed across diverse food formats, no significant changes in glucose (p = 0.3, p = 0.2), insulin levels (p = 0.59, p = 0.9), or HOMA-IR (p = 0.84, p = 0.66) were observed compared to their respective baseline values within the flaxseed and flaxseed + fiber groups, respectively. Conversely, the control group showed a significant rise in final glucose values from baseline (p = 0.01). However, the incorporation of ground flaxseed into low-glycemic foods holds potential for beneficial effects through maintaining glucose status among postmenopausal Native American women. This research provides critical insights into the effects of flaxseed, emphasizing the need for continued exploration to understand its role in supporting glucose management among postmenopausal Native American women. Further exploration is required to investigate the potential long-term impact and the use of flaxseed in managing glucose levels in this demographic. [ABSTRACT FROM AUTHOR]