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Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI's meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members' (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI's greatest benefit, while students and faculty members' lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation. [ABSTRACT FROM AUTHOR]

Urtica species is an angiosperm plant in the Urticaceae family. It serves as a traditional food and medicinal herb, possessing high nutritional value and various bioactive compounds, including polysaccharides, flavonoids, and polyphenolic compounds. In the realm of animal feeds, Urtica spp. can replace traditional protein feed sources and high-quality forage, thereby reducing feed costs. Moreover, Urtica spp. extract exhibits antioxidant and anti-inflammatory properties and boosts immune regulation. Hence, Urtica spp. plays a beneficial role in enhancing animal performance and improving their immune function. Recently, with the development of sustainable farming techniques, the demand for feed additives that prioritize safety, the absence of drug residues, and environmental friendliness have grown. Consequently, Urtica spp. and its extracts have received widespread attention in animal production. This article summarizes the biological functions of Urtica spp. and its application in animal husbandry while also outlining future prospects for its application. It will provide a scientific basis and reference point for the application of Urtica spp. in animal health and breeding. [ABSTRACT FROM AUTHOR]

In this article, a game theoretic non‐cooperative dynamic target tracking algorithm that empowers defensive unmanned aerial vehicles (UAVs), with directional sensing capabilities to track and collect information on intrusive UAVs, is proposed. Specifically, defenders aim to maximize the collection of identity information from intruders possessing anti‐tracking and evading capabilities, while simultaneously preventing their entry into protected areas. Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. The probability perception model is utilized for evaluating the dynamic target tracking capability and designing a tracking merit function to assess tracking performance, taking into account both the target's position and the perception relative angle. Furthermore, considering the dynamic interactive behaviors between intruders and defenders, the iterative linear quadratic game (ILQG) algorithm is employed to solve the Nash equilibrium of the non‐cooperative target tracking game. Through simulation experiments, the effectiveness of the proposed algorithm in accomplishing multi‐agent dynamic target tracking tasks is demonstrated and the performance of the algorithm under varying parameters in the intruder's cost function is evaluated, which represent different intrusion intentions. [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]

This study aims to capture the stance of YouTube video content creators regarding the use of generative AI for language learning. Opinion mining and sentiment analysis techniques were employed to analyse the content, comments, and transcriptions of 66 YouTube videos published from December 2022 to October 2023. The findings revealed that most videos focused on speaking (n = 40) and writing skills (n = 24), with fewer videos addressing listening (n = 3) and reading (n = 19) skills. Sentiment analysis showed that videos predominantly conveyed optimistic (n = 42) and analytical (n = 17) sentiments, indicating a generally positive stance towards generative AI for language learning. Clustering analysis identified four thematic clusters: "language development and practices" (n = 33), "basic expression skills" (n = 25), "intercultural communication skills" (n = 6), and "language structure and meaning" (n = 2), representing different approaches to language learning with generative AI. Cross-sectional analyses revealed fluctuations in video counts and sentiment scores over time, with higher expectations for generative AI in writing and speaking skills, and relatively lower interest in listening skills. The findings suggest that YouTube video creators generally view generative AI as a promising tool for language learning, with a focus on developing practical communication skills, fostering intercultural understanding, and facilitating language development. These insights can inform the design and implementation of AI-supported language learning materials and practices. [ABSTRACT FROM AUTHOR]

The association between vitamin D concentrations and the occurrence of diabetic foot ulcers (DFUs) remains a topic of ongoing debate. In order to provide a comprehensive and updated review, we conducted this meta‐analysis to further investigate the relationship between vitamin D concentrations and DFUs occurrence. The following databases, including Cochrane Library, EMBASE, Web of Science, PubMed, CBM, CNKI, WANFANG DATA and VIP Database, were systematically searched for studies published up to Dec. 20th, 2023. The combined estimation was calculated using both fixed-effects and random‐effects models. The overall effect size was reported as a weighted mean difference (WMD) with a corresponding 95% confidence interval (95%CI). Data analysis was performed utilizing Review Manager 5.4 and Stata 14. The Protocol has been registered in PROSPERO CRD42024503468. This updated meta-analysis, incorporating thirty-six studies encompassing 11,298 individuals with or without DFUs, demonstrated a significant association between vitamin D deficiency/insufficiency and an elevated risk of DFUs occurrence (< 25 nmol/L, OR 3.28, P < 0.00001; < 50 nmol/L, OR 2.25, P < 0.00001; < 75 nmol/L, OR 1.67, P = 0.0003). Vitamin D concentrations were significantly lower in individuals with DFUs compared to those without DFUs (P < 0.00001). Subgroup analyses consistently demonstrated this trend among the older population (> 50 years, P < 0.00001), individuals with long duration of diabetes (> 10 years, P < 0.00001), and those with poor glycemic control (mean HbA1c 8%-9% and > 9%, P < 0.00001). [ABSTRACT FROM AUTHOR]

Groundwater arsenic poisoning has posed serious health hazards in the exposed population. The objective of the study is to evaluate the arsenic ingestion from breastmilk among pediatric population in Bihar. In the present study, the total women selected were n = 513. Out of which n = 378 women after consent provided their breastmilk for the study, n = 58 subjects were non-lactating but had some type of disease in them and n = 77 subjects denied for the breastmilk sample. Hence, they were selected for the women health study. In addition, urine samples from n = 184 infants' urine were collected for human arsenic exposure study. The study reveals that the arsenic content in the exposed women (in 55%) was significantly high in the breast milk against the WHO permissible limit 0.64 µg/L followed by their urine and blood samples as biological marker. Moreover, the child's urine also had arsenic content greater than the permissible limit (< 50 µg/L) in 67% of the studied children from the arsenic exposed regions. Concerningly, the rate at which arsenic is eliminated from an infant's body via urine in real time was only 50%. This arsenic exposure to young infants has caused potential risks and future health implications. Moreover, the arsenic content was also very high in the analyzed staple food samples such as rice, wheat and potato which is the major cause for arsenic contamination in breastmilk. The study advocates for prompt action to address the issue and implement stringent legislative measures in order to mitigate and eradicate this pressing problem that has implications for future generations. [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]

Background and Aims: The purpose of this meta‐analysis was to investigate the effect of vitamin D supplementation on hemoglobin A1C (HbA1C), fasting blood sugar (FBS), low‐density lipoprotein (LDL), high‐density lipoprotein (HDL), systolic blood pressure (SBP), and the total vitamin D level in patients with Type 2 diabetes (T2DM). Methods: A systematic search was conducted in databases such as PubMed (Medline), Scopus, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov using relevant keywords from January 1990 to January 2024. After screening and extracting data, a qualitative evaluation of articles was performed using the Cochrane risk‐of‐bias tool for randomized trials (RoB 2). Results: The findings revealed that vitamin D supplementation significantly decreased the mean HbA1C (SMD: −0.15; 95% CI: −0.29, −0.20; Isquare: 79.76%; p value < 0.001) and mean FBS (SMD: −0.28; 95% CI: −0.40, −0.15; Isquare: 70.13%; p value < 0.001), lowered SBP (SMD: −0.06; 95% CI: −0.16, −0.05; Isquare: 39.63%; p value = 0.23), and reduced LDL (SMD: −0.11; 95% CI: −0.28, −0.05; Isquare: 73.66%; p value < 0.001). Furthermore, vitamin D supplementation increased the average HDL (SMD: 0.13; 95% CI: 0.04, 0.29; Isquare: 79.33%; p value < 0.001) and vitamin D levels (SMD: 1.78; 95% CI: 1.53, 2.04; Isquare: 91.92%; p value < 0.001) in patients with T2DM. Subgroup analyses showed that weight gain, BMI, and duration of the disease could reduce the effect of vitamin D supplementation on diabetes control in affected patients. Conclusion: The results also indicated that taking vitamin D supplements in the amount of 50,000 IU had a significant effect on reducing the indicators related to diabetes control. Based on the combined evidence, the findings of this meta‐analysis suggest that vitamin D supplementation can significantly improve glycemic control and reduce the risk of complications associated with T2DM, especially cardiovascular diseases (CVDs). [ABSTRACT FROM AUTHOR]

Today's wireless environments, soft robotics, and space applications demand delicate design of devices with tunable performances and simple fabrication processes. Here we show strain-based adjustability of RF/microwave performance by applying frequency-selective patterns of conductive Ti3C2Tx MXene coatings on low-cost acetate substrates under ambient conditions. The tailored performances were achieved by applying frequency-selective patterns of thin Ti3C2Tx MXene coatings with high electrical conductivity as a replacement to metal on low-cost flexible acetate substrates under ambient conditions. Under quasi-axial stress, the Kirigami design enables displacements of individual resonant cells, changing the overall electromagnetic performance of a surface (i.e., array) within a simulated wireless channel. Two flexible Kirigami-inspired prototypes were implemented and tested within the S, C, and X (2-4 GHz, 4-8 GHz, and 8-12 GHz) microwave frequency bands. The resonant surface, having ~1/4 of the size of a standard A4 paper, was able to steer a beam of scattered waves from each resonator by ~25°. Under a strain of 22%, the resonant frequency of the wired co-planar resonator was shifted by 400 MHz, while the reflection coefficient changed by 158%. Deforming the geometry impacted the spectral response of the components across three arbitrary frequencies in the 4-10 GHz frequency range. With this proof of concept, we anticipate implementing thin films of MXenes on technologically relevant substrates, achieving multi-functionality through cost-effective and straightforward manufacturing. Modern communication applications may demand devices with tunable performances and simple fabrications. Here, we show strain dependent, adjustable RF/microwave performance by applying patterns of conductive Ti3C2Tx MXene coatings on low-cost acetate substrates in a straightforward coating process. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students' performance. This paper presents an advanced EDM system tailored for classifying and improving tertiary students' programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, and the integration of Explainable Artificial Intelligence (XAI) to elucidate model decisions. Through rigorous experimentation, including an ablation study and evaluation of six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others in accuracy, precision, recall, f1-score, ROC curve, and McNemar test. Leveraging XAI tools, we provide insights into model interpretability. Additionally, we propose a system for identifying skill gaps in programming among weaker students, offering tailored recommendations for skill enhancement. [ABSTRACT FROM AUTHOR]

We aimed to investigate the preventive effect of vitamin D2 on COVID-19 and the improvement of symptoms after COVID-19 infection. The study recruited 228 health care workers who tested negative PCR or antigen for COVID-19. Subjects were randomly allocated to vitamin D2 or non-intervention at a ratio 1:1. Subjects recorded PCR or antigen tests and the symptoms of COVID-19 twice a week during the follow-up visit. The concentration of serum 25-hydroxyvitamin D (25(OH)D), C-reaction protein (CRP), complement component C1q and inflammatory cytokines were measured. The rates of COVID-19 infection were 50.5% in the vitamin D2 group and 52.4% in the non-intervention group (P = 0.785). There was no difference in the COVID-19 symptoms between the two groups. The mean 25(OH)D level significantly increased from 14.1 to 31.1 ng/mL after administration (P < 0.001). The difference between the two groups was not significant for the concentrations of CRP, C1q and inflammatory cytokines on the thirtieth day of the trial. According to the second level of vitamin D, there was a 14.3% difference in positive infection rates between the vitamin D adequate (> 30 ng/mL) and deficient groups (< 20 ng/mL). Adequate vitamin D had a tendency to prevent COVID-19. Trial registration: ClinicalTrials.gov NCT05673980, dated: 12/2022. [ABSTRACT FROM AUTHOR]

Background and Aim: Increased oxidative stress within the airways is associated to epithelial damage and amplification of inflammatory responses that in turn contribute to Chronic Obstructive Pulmonary Disease (COPD) progression. This study was aimed to identify whether a new formulation of N-acetylcisteine (NAC), carnitine, curcumin and B2 vitamin could counteract oxidative stress and downstream pro-inflammatory events promoted by cigarette smoke extract (CSE) exposure in primary bronchial epithelial cells (PBEC), both submerged/ undifferentiated (S-PBEC) and cultured at the air-liquid interface (ALI-PBEC). Methods: PBEC were exposed to CSE with/without the new formulation or NAC alone and ROS production, IL-8 and IL-6 gene expression and protein release were evaluated. Results: CSE increased ROS, IL-8 and IL-6 gene expression and protein release and the new formulation counteracted these effects. NAC alone was not effective on IL-8 and IL-6 release. The effects of a similar nutraceutical formulation were evaluated in COPD patients treated for six months. The results showed that the treatment reduced the concentration of IL-8 in nasal wash and improved quality of life. Conclusion: The tested formulation, exerting antioxidant and anti-inflammatory effects, can preserve airway epithelial homeostasis and improve clinical symptoms in COPD. [ABSTRACT FROM AUTHOR]

Introduction: Metabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, laboratory, dietary, and genetic factors to predict the incidence of metabolic syndrome in Koreans are limited. In the present study, we propose a genome-wide polygenic risk score for the prediction of metabolic syndrome, along with other factors, to improve the prediction accuracy of metabolic syndrome. Methods: We developed 7 machine learning-based models and used Cox multivariable regression, deep neural network (DNN), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RAF), Naïve Bayes (NBA) classifier, and AdaBoost (ADB) to predict the incidence of metabolic syndrome at year 14 using the dataset from the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung. Results: Of the 5440 patients, 2,120 were considered to have new-onset metabolic syndrome. The AUC values of model, which included sex, age, alcohol intake, energy intake, marital status, education status, income status, smoking status, dried laver intake, and genome-wide polygenic risk score (gPRS) Z-score based on 344,447 SNPs (p-value < 1.0), were the highest for RAF (0.994 [95% CI 0.985, 1.000]) and ADB (0.994 [95% CI 0.986, 1.000]). Conclusions: Incorporating both gPRS and demographic, clinical, laboratory, and seaweed data led to enhanced metabolic syndrome risk prediction by capturing the distinct etiologies of metabolic syndrome development. The RAF- and ADB-based models predicted metabolic syndrome more accurately than the NBA-based model for the Korean population. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the effectiveness of the use of biostimulants, such as humic acid preparation, Seaweed algae extract and microbial preparation BaikalEM, on plant growth and yield. The impact of biostimulants on plant development and crop yields was studied on sugar beet (Beta vulgaris) and maize (Zea mays). To achieve this goal, field studies were conducted to compare different biostimulants in terms of germination and yield (total crop weight, weight of a single fruit, sugar, starch and protein content). The study was conducted in April-August 2023 in the Sumy district of the Sumy region. Standard agronomic methods, including soil cultivation, measurement of plant growth and yield parameters, and statistical processing of the data were used in the study. The results showed that humic acids and algae extract, when applied separately, provided the highest seed germination and yield. Among all the variants of combined application, the most significant increase in germination rates for beetroot was provided by treatment with a combination of Seaweed and Baikal-EM – 91.7%. For maize, Seaweed with humic acids and Seaweed with Baikal-EM are 92% each. The combination of Seaweed and humic acids had the best effect on the yield of both crops: 460.9 c/ha for beetroot (compared to 325 c/ha without treatment) and 61 c/ha for corn (41.5 c/ha without treatment). The microbial preparation Baikal also demonstrated a positive effect, but its results were lower, and it proved effective in combination with humic acids. The results obtained indicate the feasibility of using humic acids and algae extract to increase plant productivity, while Baikal can be useful for improving the general condition of soil and plants in combination with other fertilisers. [ABSTRACT FROM AUTHOR]

An extremely dangerous side effect of type I and type II diabetes is diabetic nephropathy (DN). From the early microproteinuria to end-stage renal failure, it progresses. About one in three diabetics in the US suffer from diabetic nephropathy. Chronic hyperglycemia is the primary cause of diabetic ketoacidosis. Hyperglycemia (HG) has the potential to cause humoral mediators and cytokines to be produced by both resident and non-resident renal cells. These substances may interfere with cell growth, alter renal cell and tissue phenotype and function, interact with proteins, produce advanced glycation end products (AGEs), damage tubules and glomeruli, and ultimately cause kidney disease. Poor blood glucose management is thus a significant risk factor for the onset of DN. An alternate course of treatment for DN may use extracts from herbal remedies. Medicinal plants' bioactive components stop DN from progressing. Attention has to be paid to the role that traditional herbs and medications play in the treatment of diabetic nephropathy, particularly in India where several fruits and herbs are believed to provide health benefits. Natural compounds influence the KEAP1/Nrf2/ARE and NFB pathways in addition to having antioxidant and anti-inflammatory properties. The efficacy of entire herbs, plants, or seeds, together with their active components, in treating diabetic nephropathy was investigated in preclinical research. Natural compounds are biologically active substances that come from natural sources and are beneficial for treating specific illnesses. Numerous natural substances, such as glycosides, polysaccharides, terpenoids, alkaloids, flavonoids, and polyphenols, have been shown to enhance DN. The exorbitant expenses associated with contemporary medications suggest that other approaches are necessary for improved DN treatment. Future research on herbal remedies may provide a natural key to open a pharmacy for diabetologists. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic changes in the composition of tropical forests are expected to alter soil properties. Thus, an understanding of the effects on changes in crop/plant composition on soil properties is needed to choose better land-use options. So, the primary goal of this study was to examine the impacts of land-use change (from shifting cultivation to teak and rubber plantation) on some physico-chemical features (available P, K, N, pH, moisture content (MC), bulk density, organic matter (OM)) of soil. Soil physico-chemical properties were compared to a shifting cultivation land with a 14-year-old teak plantation and a 16-year-old rubber plantation that had previously been used for shifting cultivation. Results showed that soil properties change with the changes of crop/plant composition, such as phosphorus (P), K, and OM was significantly high in teak and rubber plantation than the shifting cultivation area both in surface (0–10 cm) and sub-surface (10–20 cm) soil layers. Teak (3.5689 mg/kg) and rubber (3.5478 mg/kg) plantations exhibited significantly higher mean P content (p < .01) compared to shifting cultivation (3.4678 mg/kg). Shifting cultivation had the maximum K levels (0.2233 meq/100 g), while teak (0.2833 meq/100 g) and rubber (0.2733 meq/100 g) plantations showed significantly higher values (p < .05). Moisture content was 1.13% higher in teak and 1.2% higher in rubber plantations than in shifting cultivation. Furthermore, OM content was significantly higher in teak and rubber plantations (5.2744%, 5.2567%) compared to shifting cultivation (4.5067%). On the other hand, total nitrogen (N) and pH levels showed no significant difference among the land-use types. Finally, the study showed that conversion to teak and rubber plantation could lead to positive change in the soil properties. Hence it suggested that this can be undertaken/chosen as a better land cover option for shifting cultivated land in Chittagong Hill Tracts (CHTs) from soil conservation point of view. [ABSTRACT FROM AUTHOR]

Introduction Water resources are the common aspect of the goals and challenges of sustainable development, the lack of which is one of the big multidimensional problems of the current century and is one of the main reasons for positive and negative developments in the world. Therefore, the water poverty index (WPI) is one of the indices defined for this purpose. This index shows the effect of the combination of effective factors on the scarcity and stress of water resources. It provides the conditions for prioritizing and developing management versions in different regions. To determine water scarcity and poverty in each region, attention should be paid to the conditions of water resources in the studied region, the ability to calculate the index and the existence of information and data in the studied region, as well as the selection of selected criteria and components in that region. In this study, the water poverty index is used to investigate the shortage and tension of water resources and for its influence on drought, its relationship with univariate drought indices based on precipitation including standardized precipitation index (SPI) and Z score index (ZSI), and variable indices based on precipitation and evapotranspiration including standardized precipitation evapotranspiration index (SPEI) and reconnaissance drought index (RDI) were searched. Materials and Methods The study area in this research is the Hashem-Abad meteorological station in Gorgan Township, and the statistical period for calculating the water poverty index based on the data available in the study area was considered to be 13 years (2003-2015). The water poverty index in this research is calculated based on five main components, which include the resource (groundwater loss), meteorological (temperature and precipitation), consumption (water need), capacity (river discharge), and environmental (salinity). Each of the components must be weighed after calculating to calculate the water poverty index. For this purpose, the AHP hierarchical technique was used. First, a questionnaire was prepared and the components were scored based on the opinion of regional water experts and university professors, then, using Expert Choice software, the weight of the main components of the water poverty index was determined, and finally, the WPI for the study area in this research was also estimated. Then, in the next step, drought indices SPI, SPEI, RDI, and ZSI were calculated in 6-month and 12-month time windows. To calculate the drought indices, the precipitation and temperature data at the Hashem-Abad meteorological station for a period of 30 years (1990-2019) were considered, which were sorted monthly and the coding necessary to calculate the SPI and SPEI indices in time windows 6 and 12 months was done by R programming and statistical software. Also, two indicators, RDI and ZSI, were calculated in the Excel software. Finally, the relationship between drought indices and the water poverty index was searched based on simple one-to-multivariate correlations. Results and Discussion The results of the water poverty index’s components showed that the resources and environment component had the highest value in 2009 and 2010 and the lowest value in 2010 and 2016, respectively. About meteorological, capacity, and consumption components, the highest values were in the years 2010, 2004, and 2009, respectively, and the lowest values occurred in the years 2010, 2016, and 2016, respectively. Questionnaire analysis of WPI components with AHP showed that resources and environment components had the highest and lowest weights with values of 0.354 and 0.041, respectively. However, by multiplying these weights by their related components, it was found that the components of consumption, environment, resources, meteorology, and capacity had the greatest effect in calculating the water poverty index. The range of WPI changes during the years (2004-2016) varies from 26 to 82, so 2014, which is one of the driest years, the region was in the poorest state of water resources and the year 2008 had the best conditions. Considering the average WPI of about 55, out of the 13 years studied, the WPI was lower than the average in 8 years. In the next step, due to the lack of data, there was no possibility of non-linear modeling, therefore, simple one-to-multivariate correlations were used. The results of these correlations showed that the use of the multivariate linear regression method by considering the drought index in a 12-month time window along with two six-month time windows related to the first and second half of the year increases their correlation with the water poverty index. Examining the effect of the time window considered for the drought index on the water poverty index shows that the 12-month time window has a higher correlation than the sixmonth time window. Also, among the six-month time windows, in the SPEI index, the first six months of the year, which includes the spring and summer seasons, had a higher correlation with the water poverty index. Correlation results between drought indices and WPI showed that the annual time interval is more suitable than the 6-month time one. And among the 4 indices studied, the SPEI index with R2=0.90 had the highest correlation while the ZSI index with R2=0.81 had the lowest correlation with WPI. Conclusion Based on the results of the components of the water poverty index in this research, it was observed that the consumption component in the Gorgan region had the biggest role in the WPI estimation, so water conservation can have a great contribution to solving water poverty. Due to the high volume of water consumption in the agricultural sector, some measures should be taken to manage water consumption and choose the appropriate cultivation patterns. The high correlation of WPI with drought indices, especially the SPEI variable index, makes the importance of creating a drought monitoring and forecasting system more tangible, and due to global warming and climate change in the future, which this region is not exempt from, it can make the problems of water poverty and lack of water more severe in this region. [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]

The Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three‐dimensional mesoscale‐resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data‐driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of ∼ ${\sim} $0.74 and ∼ ${\sim} $0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability. Plain Language Summary: Monitoring changes in the strengths of Southern Ocean current systems is challenging due to their vast size and the region's relative inaccessibility. This study explores the potential for remotely monitoring these currents via satellite measurements. Neural networks are used to "learn" the relationship between satellite‐measurable ocean properties and the strengths of Southern Ocean currents, using a simplified simulation as a test case. A key question is whether the circulation can be inferred from satellite measurements when the ocean hosts a vigorous field of mesoscale eddies—horizontal swirls of fluid that reach hundreds of kilometers in diameter. Three neural network (NN) frameworks are trained to predict the simulated ocean circulation strength from the simulated satellite measurements, and then their performance is evaluated using a separate segment of the simulation data. It is shown that this approach yields accurate predictions of all of the targeted components of the Southern Ocean circulation strength, provided that the NNs use a "convolutional" filter, which enhances their ability to identify spatial patterns in the simulated satellite measurements, and thus to infer movements of ocean water induced by the eddies. These findings serve to guide future indirect approaches to observing the Southern Ocean using remote sensing. Key Points: Deep Learning methods link sea surface height and ocean bottom pressure to transport variability in an eddying Southern Ocean channel modelConvolutional neural network captures sub‐annual and interannual variance in both circumpolar transport and overturning strengthPredicting overturning variability requires convolutional kernel to capture eddy‐induced meridional transports [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]

In order to achieve this goal, the OLI sensor images of Landsat-8 satellite were used in July 2022. After calculating the land surface temperature (LST) using the split window method, three indices of vegetation cover, the index of built-up areas or impervious surfaces and the index of barren lands were calculated and using Pearson's correlation at the confidence level of 0.01 were investigated with the temperature of the earth's surface, the results of the correlation of temperature with the mentioned indices showed that the barren areas index (NDBaI) has a positive correlation with the temperature of the earth's surface and its R2 value is equal to 0.488. The builtup area index (NDBI) has a negative correlation with the temperature of the earth's surface, and its R2 value is equal to -0.642, and the vegetation cover index has a non-linear positive correlation with the temperature of the earth's surface, the non-linear reason The existence of vegetation cover is scattered and limited in different parts of Khuzestan province. The significance and effectiveness of the three mentioned indicators were investigated using the least square regression method. The results of the investigation of heat islands in Khuzestan province using the local Moran index showed that the heat islands are concentrated outside the urban area and in barren areas, and the temperature of urban areas is lower than the areas outside the city. And cool islands are concentrated on water areas and vegetation and have a much smaller area than thermal islands in Khuzestan province. [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]

In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein's theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI. [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]

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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]

Background: Neuroblastoma (NB) is a heterogeneous embryonal malignancy and the deadliest tumor of infancy. It is a complex disease that can result in diverse clinical outcomes. In some children, tumors regress spontaneously. Others respond well to existing treatments. But for the high-risk group, which constitutes approximately 40% of all patients, the prognosis remains dire despite collaborative efforts in basic and clinical research. While its exact cellular origin is still under debate, NB is assumed to arise from the neural crest cell lineage including multipotent Schwann cell precursors (SCPs), which differentiate into sympatho-adrenal cell states eventually producing chromaffin cells and sympathoblasts. Methods: To investigate clonal development of neuroblastoma cell states, we performed haplotype-specific analysis of human tumor samples using single-cell multi-omics, including joint DNA/RNA sequencing of sorted single cells (DNTR-seq). Samples were also assessed using immunofluorescence stainings and fluorescence in-situ hybridization (FISH). Results: Beyond adrenergic tumor cells, we identify subpopulations of aneuploid SCP-like cells, characterized by clonal expansion, whole-chromosome 17 gains, as well as expression programs of proliferation, apoptosis, and a non-immunomodulatory phenotype. Conclusion: Aneuploid pre-malignant SCP-like cells represent a novel feature of NB. Genetic evidence and tumor phylogeny suggest that these clones and malignant adrenergic populations originate from aneuploidy-prone cells of migrating neural crest or SCP origin, before lineage commitment to sympatho-adrenal cell states. Our findings expand the phenotypic spectrum of NB cell states. Considering the multipotency of SCPs in development, we suggest that the transformation of fetal SCPs may represent one possible mechanism of tumor initiation in NB with chromosome 17 aberrations as a characteristic element. [ABSTRACT FROM AUTHOR]