We are experiencing an unprecedented urbanization process that alongside with physical, social and economic developments is having a significant impact on population’s health. Due to higher apprehensions of pollution, violence and poverty, our modern cities no longer ensure a good quality of life so they become unhealthy environments. This study aims to measure the socio-environmental and hematologic profile of residents of Santo André’s landfill – “Bairro Espírito Santo” by using the contextualization of the studied area. The research method is Observational type and from Retrospective cohort and by convenience sampling in Santo André in the Greater ABC region. The study determined a socio environmental profile and the hematologic diseases screening related to a close location to the landfill. The disease manifests itself within a broad spectrum of symptoms that causes changes in blood count parameters. The full blood counts analysis was performed, indicating that the blood counts of residents living near the landfill led to positive hematological changes and diseases like Leukopenia, Anemia, Neutropenia and lymphocytosis were the most common changes. However it is considered that the proof of the relation of cause- effect to environmental exposures that may trigger chronic manifestations in humans requires specific studies that are often complicated.

People with aphantasia exhibit the inability to voluntarily generate or form mental imagery in their minds. Since the term “aphantasia” was proposed by Zeman et al. in 2015 to describe this, it has gained increasing attention from psychiatrists, neuroscientists, and clinicians. Previous studies have mainly focused on definition, prevalence, and measurement, its impacts on individuals’ cognitive and emotional processing, and theoretical frameworks synthesizing existing findings, which have contributed greatly to our understanding of aphantasia. However, there are still some debates regarding the conclusions derived from existing research and the theories that were constructed from various sources of evidence. Building upon existing endeavors, the current systematic review emphasizes that future research is much needed to refine definition and diagnosis of aphantasia, strengthen empirical investigations at behavioral and neural levels, and more importantly, develop or update theories. These multiple lines of efforts could lead to a deeper understanding of aphantasia and further guide researchers in future research directions.

Various measures have been attempted to prevent infectious diseases in calves, such as envi-ronmental improvement and vaccine administration. Probiotics are commonly used to im-prove the body condition of newborn calves and prevent disease. In our previous research, Lactiplantibacillus plantarum RGU-LP1 (LP1) inhibited the expression of inflammatory cyto-kines in PBMC from cows fed LP1. In this study, we evaluated the effect of LP1 on calf growth and disease control. Methods: Twenty-six one-week-old Holstein bull calves were divided into two groups (13 each), LP1 group (LP1 treated) and CN group (no LP1 fed), and tested as follows. The LP1 group was fed lyophilised LP1 (10⁹ CFU/head/day) in milk replacer for 40 days. The CN group was fed the same diet only. Calves were followed for 63 days. During this period, the weight and treatment times of the calves were recorded. Faeces and blood were collected from each calf during this period. Faeces were examined for gut microbiota, and blood for immune assay and cytokine gene expression. The LP1-treated group showed a de-crease in disease incidence and an increase in body mass compared to controls. Results: The average treatment cost during the observation period was significantly reduced compared to the CN group. The expression of TGFβ and IL10, inhibitory cytokines of inflammation, was sig-nificantly increased. The simultaneous expression of this set of inhibitory molecules resulted in low serum IL1β levels during the growth period. Conclusions: The Th1-type cytokine IFNγ was also significantly increased in LP1-treated calves. By reducing the number of disease treatment times and increasing body weight, LP1 is effective in preventing infectious diseases in calves. In addition, the increase in IFNγ by LP1 indicates improved Th1-type immunity in calves. These results show that LP1 has effects on the regulated inflammatory response and growth of calves.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

Introduction: Pharmaceutical tablets are solid forms administrated orally and they constitute 80% of the market because of its best stability and ease of large-scale production, adherence and treatment. The Brazilian Legislation establishes that for a drug to be registered as a similar or generic drug is necessary to verify the pharmaceutical equivalence in relation to the reference drug, indicated by ANVISA, through quality control tests, ensuring that the drug is approved with satisfactory quality. Objective: to evaluate the reference, generic and similar glibenclamide tablets through quality control tests, comparing the results with each other and relate clinical impacts on the patient in the face of changes in the quality control of medicines. Methodology: tests of average weight, friability and disintegration were done, according to the Brazilian Pharmacopoeia VI Edition, in uncoated tablets of Glibenclamide 5mg Reference (Daonil), Generic (Glibenclamide) and Similar (Glionil), and the results obtained were compared with the that advocates the scientific literature. Results: Data referring to the average weight: Reference 0.1591g, Generic 0.1002g and Similar 0.0996g; For friability: Reference value 0.54%, Generic value 0.49% and Similar value 0.28%. The disintegration tests showed the following times: Reference 35 seconds, Generic 29 seconds and Similar 33 seconds. Conclusion: the tests to which the glibenclamide tablets were submitted showed results within the parameters established by the Brazilian Pharmacopoeia.

Background: Old age and illnesses can limit physical activity (PA). Objectives: To assess the level of PA and its limitations. Design: Cross-sectional study, January and June 2024. Setting: Diabetes Center. Subjects: Patients (N=207) >64 years with diabetes (DM). Methods: Surveying using: IPAQ, Accompanying Survey (AS). The significance was assessed by: Student’s t-test, Mann-Whitney U test, Pearson’s test. The relationships between the IPAQ and the AS results were analyzed based on a meta-analysis model for variable effects, OR with a 95% CI. ROC curve was used to determine the threshold values for age, BMI. Correlations between selected key variables were evaluated using the PCA. Results: The median age: 72 years (65–87), BMI: 28.70 kg/m2 (18.61–49.69). The median PA level: 1,837 MET-min/week (0–9,891). The individuals who obtained insufficient (n=28), sufficient (n=75), and high levels of PA (n=53) were 17.95%, 48.08%, and 33.97%, respectively. Diseases were the main factor limiting PA (53,54%) in patient’s opinion, followed by the lack of a companion or motivation. The meta-analysis found no diseases linked to insufficient PA, but those with BMI > 33.3 kg/m² and over 75 years old more often had insufficient PA. PCA revealed several characteristics that predispose individuals to insufficient PA. Conclusions: People over 75 years of age tend to avoid PA more than younger seniors, leading to its insufficient level, similarly like higher BMI. The individual with DM who has an insufficient level of PA is typically a single, woman, over 75, BMI>33.

Inflammation is associated with cardiovascular disease (CVD) in people with HIV (PWH). Co-existent rheumatic disease (RD) may exacerbate inflammation and CVD events. We assessed the impact of RD therapies (RDT) on CVD outcomes retrospectively in 3930 veterans with HIV. The primary outcome was first occurrence of a CVD event. RD was associated with CVD events (OR = 2.67; p

Soil stabilization using Portland cement is a widely adopted technique. Previous research has demonstrated that molasses, which contains sugars, enhances the reaction between cement and aggregates. This study investigates the impact of adding molasses to soil stabilized with Ordinary Portland Cement (OPC) on geotechnical properties. Expansive clay soil samples from Taru Jabba, District Nowshera, Pakistan, were treated with various combinations of molasses and cement. The concentrations of each stabilizer were varied at 0%, 4%, 8%, and 12% by dry weight of the soil. Additionally, the soil was treated with constant molasses contents of 4%, 8%, and 12%, while varying the cement content at 4%, 8%, and 12% by dry weight. Geotechnical tests, including Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and swelling potential, were conducted to assess the effects of the stabilizers. The results indicated that the addition of molasses improved soil strength, mitigated shrinkage cracks, and reduced brittleness. Specifically, the CBR value increased from 3.2% in the native soil to 12.3% with 12% molasses and 12% cement. The Plasticity Index (PI) decreased from 14.23% to 8.12%, and the CBR swell value reduced from 9.66% to 3.82%. Furthermore, the UCS of the stabilized soil increased by 64.7% compared to the untreated soil after a 7-day curing period.

With the increased global attention to environmental protection, social responsibility and corporate governance (ESG) standards, integrating ESG into supply chain management (SCM) is becoming a strategic requirement for companies to achieve competitiveness and sustainable development. This study reviews the impact factors of ESG practices on supply chain competitiveness, and empirically examines the mechanism of ESG performance on supply chains through a questionnaire survey with a sample of Chinese SMEs. It was found that normative pressure, coercive pressure and imitative pressure in SMEs have a significant positive effect on cognition and attitude and perceived behavioral control; good corporate cognition and attitude, perceived behavioral control and corporate pressure also enhance the level of ESG practices. Further analysis shows that cognition and attitude and perceived behavioral control play a mediating role in the impact of ESG practices, and ESG performance promotes sustainable development of SMEs. The article puts forward the following suggestions: (1) close supply chain partnerships to enhance green innovation; (2) the government should give more resource support to ESG-invested enterprises and support the development of advantageous enterprises; (3) improve the corporate disclosure mechanism and strengthen the supervision; and (4) strengthen the training of professionals in supply chain ESG management.

This study analyzes groups about game performance elements and sport class composition in each country by utilizing official records provided by major international wheelchair basketball games. To achieve the objective of this study, major game factors were explored by analyzing descriptive statistics in each year regarding the latest trend of sport class composition and playing time of each sport class and conducting the difference test between the groups. To analyze performance details of international wheelchair basketball games, this study selected official records of 209 games of 24 teams that participated in major international wheelchair basketball games. A test for difference also was conducted to examine the sport class composition, playing time, and game details of each group. First, scoring factors directly affecting the game results were compared between the groups. The result of this difference test shows that the success rate of 2-point and 3-point shots was significantly different except for the free throw success rate. The numbers of assists, defense rebounds, and steals were significantly different. Second, the medal group was compared with the other group, the average class of the other group's participation in quarters was higher than that of the medal group and the difference was significant. Regarding the difference between groups in terms of game performance, the difference in playing time between the medal group and the other group was in the order of 2.5 points, 4.0 points, 3.0 points, and 1.5 points. Third, given the general trend in 2012 to 2022 international events, the sport class composition and para-athlete played minutes in quarterly participation changes.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

Advances in diagnostic procedures have led to an increasing rate of diagnosis of autoimmune encephalitis or paraneoplastic neurological syndrome (AE/PNS) among patients with progressive supranuclear palsy (PSP)-like manifestations. The antibodies involved in these conditions include anti-IgLON5, -Ma2, and -Ri antibodies, each of which has a characteristic clinical presentation. The steps in the diagnosis of AE/PNS in patients with PSP-like manifestations include (i) suspicion of AE/PNS based on clinical presentations atypical of PSP and (ii) antibody detection measures. Methods used to identify antibodies include a combination of tissue-based assays and confirmatory tests. The primary confirmatory tests include cell-based assays and immunoblotting (line or western blot). Treatments can be divided into immunotherapy and tumor therapies, the former of which includes acute and maintenance therapies comprising corticosteroids, intravenous immunoglobulins, plasmapheresis, rituximab, and cyclophosphamide. One of the major challenges of diagnosis is that existing reports on PSP-like patients with AE/PNS include only case reports, with the majority discussing antibodies other than anti-IgLON5 antibody. As such, more patients need to be evaluated to establish the relationship between antibodies and PSP-like manifestations.

Privacy protection is a fundamental guarantee for secure data flows and serve as a basic requirement for data security. A reasonable privacy protection system acts as a catalyst for unlocking the financial value of data. The current legislative framework for privacy protection in data flows in China, adhering to the principle of proportionality, establishes the “informed-consent” rule for data collection and processing, data classification and grading management measures, and remedies for data leakage and other risks. In addition, in judicial practice, typical disputes regarding personal information protection and privacy rights have promoted to clarify the scope of collecting user personal information and biometric data. Despite ongoing improvements needed in legislative, judicial and technical approaches to data privacy protection, China’s commitment and practice in privacy protection of data flows are noteworthy. The existing legislation, law enforcement and technical practices are playing an increasingly vital role in realizing the financial value of data and are essential for international cooperation on data privacy protection. Furthermore, it is crucial to actively explore cooperation mechanism for cross-border data flows under the principle of data sovereignty, participate in developing international rules for cross-border data flows, and formulate different management norms for cross-border circulation of data in different industries.

The integration of genetic technologies into neurological diagnostics has significantly enhanced our ability to understand and manage neurological disorders. This article discusses the functional analysis of key genetic tests, including microarray analysis, variant sequencing, exome sequencing, RNA sequencing, and whole genome sequencing. Each of these technologies offers unique advantages and challenges, providing a comprehensive toolkit for diagnosing and understanding the genetic basis of neurological conditions. The functional insights gained from these tests not only detect genetic abnormalities but also elucidate their functional consequences, aiding in the diagnosis, prognosis, and management of neurological disorders. As these technologies continue to evolve, they are expected to play an increasingly important role in the field of neurological diagnostics, paving the way for personalized medicine and improved patient outcomes.

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and free of charge Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) Machine Learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated classification. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living Above-Ground Biomass (AGB) was estimated with an accuracy of -1.0% (± 5.9%). In conclusion, the proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Epigenetics, the study of heritable changes in gene expression that do not involve alterations to the DNA sequence, is revolutionizing our understanding of psychiatric disorders. This article provides a comprehensive overview of the role of epigenetics in psychiatry, discussing its implications for genomics, evolution, inheritance patterns, and the development of biomarkers. Epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNAs, are explored in the context of psychiatric conditions such as depression, schizophrenia, and post-traumatic stress disorder (PTSD). The discussion highlights how epigenetic modifications can influence brain dynamics, as measured by electroencephalography (EEG), and how artificial intelligence (AI) can accelerate the development of treatment options. The potential for epigenetic biomarkers to improve diagnosis, prognosis, and treatment is emphasized, along with the role of behavioral interventions in inducing beneficial epigenetic modifications. Future directions in epigenetic research, including the development of epigenetic therapies and the elucidation of molecular mechanisms underlying epigenetic inheritance, are also discussed. This article underscores the transformative potential of epigenetics in advancing our understanding of mental health and disease, paving the way for novel therapeutic interventions.

In the increasingly competitive SVOD market, platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquir-ing/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pi-rates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms.

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

This comprehensive analysis aims to offer valuable insights into the safety profiles of these glucocorticoids, facilitating the identification of compounds with potentially reduced adverse effects. This study investigates the toxicity profiles of various glucocorticoids, utilizing predictions from the pKCSM Server. Among the glucocorticoids examined, Dexamethasone, Triamcinolone, Hydrocortisone hemisuccinate, Paramethasone, Fluprednisolone, and Flumethasone emerge as potentially less toxic, displaying favorable scores across diverse parameters such as Max. Tolerated Dose (human, MRT), Oral Rat Acute Toxicity (LD50), and Oral Rat Chronic Toxicity (LOAEL). Notably, Hydrocortisone hemisuccinate and Triamcinolone stand out for their high tolerability capacity based on the Max. Tolerated Dose (human, MRT), suggesting enhanced safety profiles. These findings contribute insights into selecting glucocorticoids with optimal safety and tolerability for potential therapeutic applications.Further research and clinical investigations are warranted to validate and refine these findings, ensuring the responsible and effective use of glucocorticoids in medical practice.

This paper critically reviews the convergence between Business Ecosystem Theory and Social Network Theory in sustainability studies. While both frameworks view organizations as part of larger, interconnected systems, they can be differentiated by six key dimensions: unit of analysis, focus, decomposability, types of relationships, market segment, and worldview. To better reflect real-world phenomena, this paper argues for a new stream of theoretical convergence that is practical, reliable, generalizable, and reproducible. Specifically, it proposes shifting from interoganizational networks to interfunctional networks, which reduces complexity, enhances stability over time, and provides a clearer theoretical distinction. This is illustrated through a case study of Tesla Inc.’s ecosystem, built from data presented in the company’s 10K form, which also serves as an example of the emergence of a new strategic construct named the Business Ecosystem Footprint. This construct could assist managers in understanding where their organization stands within the network of functions, guiding them in making informed decisions about resource allocation and diversification, specifically aimed at supporting financial goals as well as sustainability and decarbonization objectives. The article concludes by suggesting potential research agendas, such as automating ecosystem mapping, exploring the constraints of the new construct, and testing new hypotheses related to firm performance.

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% ac-curacy in verbal abuse detection. Additionally, a disaster psychological recovery support module was included, which demonstrated a 30% improvement in users' stress reduction rates in simulated disaster scenarios. The results of this study suggest that such digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues and provide effec-tive support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies.

Late-onset Alzheimer’s disease (LOAD) is a dementia category disease that starts after the age of 65. Progressive problems in thinking, behaviour, learning etc. are its characteristics. Due to its progressive nature, early diagnosis is crucial. A complex pathway mechanism with different risk factors is involved in LOAD progression. A comprehensive study is done on factors causing LOAD, biomarkers used for diagnosis in clinical and preclinical stage of the disease, ML (Machine Learning) techniques to identify disease stage. We reviewed research papers on clinical and preclinical diagnosis of LOAD using ML techniques from biomarker data: MRI, PET image, MMSE, Genome / Gene expression, DNA / RNA binding sites with protein, Epigenetic expression /DNA, methylation, Speech and text. We have listed improvement scope after finding the gaps and scope of using Generative AI and SMOTE (Synthetic Minority Oversampling Technique) for data augmentation. At the end, we have elaborated current use of DL (Deep Learning) and CNN (‘Convolution Neural Network’) in Alzheimer’s disease (AD) diagnosis.

In an era where digital technologies are integral to daily life, developing higher-order thinking skills with appropriate information and communication technology (ICT) support is crucial. The purpose of this study was to examine synergies of systems thinking and technology-enhanced learning from the perspective of flow theory. For that purpose, we surveyed more than 65 pre-service preschool teachers engaged in a design, technology, and engineering (DTE) course at the University of Ljubljana. Mapping of systems thinking revealed that pre-service preschool teachers needed support with regard to feedback and understanding the interrelationship dimen-sion of systems thinking. Predictive and mediation analyses yielded some interesting results. Those who participated in the ICT-enhanced DTE course showed higher cognitive and social engage-ment, which is correlated with better systems thinking, while their aesthetic engagement was lower. Experiencing a flow state during ICT activities positively influenced systems thinking, par-ticularly in terms of clear goals and autotelic experiences. Flow theory seems to be a solid and ap-propriate framework to use for studying synergies in technology-enhanced systems thinking for sustainable education. These findings underscore the importance of integrating systems thinking into curricula to enhance learning outcomes and prepare students for future challenges toward the achievement of Sustainable Development Goals.

This study investigates the use of flours derived from the leaves of Pereskia aculeata and Pereskia grandifolia, commonly known as ora-pro-nóbis, in pasta products. The motivation stems from the search for nutritious and affordable dietary alternatives that promote food security. The objective is to produce flours from the leaves of these species through convection drying and to assess their physicochemical quality and potential application in spaghetti-type pasta. The leaves were dehydrated and processed into flour, which was then incorporated into pasta formulations in varying concentrations from 1% to 7%. The resulting samples underwent physicochemical, antioxidant, toxicity, and mineral composition analyses. Significant differences were observed between the flours of P. aculeata and P. grandifolia in terms of protein, lipid, carbohydrate, and bioactive compound content. P. grandifolia was noted for its higher protein and mineral content, while P. aculeata exhibited superior antioxidant activity. The pasta enriched with these flours demonstrated improvements in nutrient content and changes in colorimetric properties. The study concludes that ora-pro-nóbis flours possess high nutritional value and are viable for application in pasta products, contributing to a healthier and more diversified diet without compromising the sensory characteristics of the products.

Porcine metabolomics research has been extensively conducted, primarily focusing on conventional cohorts or animal models. However, the composition and functions of fecal metabolites in pigs at various ages, particularly in the elderly, remain poorly understood. In the present study, an untargeted metabolomics approach was employed to analyze the fecal metabolomes of pigs at three distinct age stages, with the aim of elucidating age-associated changes in metabolite composition and functionality under standardized rearing conditions. Through the untargeted metabolomic analysis of fecal samples from young (one year), middle-aged (four years), and elderly (eight years) pig cohorts, a variety of age-related metabolites were identified. For example, L-methionine sulfoxide was observed to increase with age, while cytidine-5-monophosphate exhibited a concomitant gradual decrease during the aging process. These metabolites were found to undergo alterations across multiple biological pathways, including energy metabolism, pyrimidine metabolism, lipid metabolism, and amino acid metabolism. Furthermore, the age-dependent metabolic changes were associated with known senescence biomarkers, offering new insights into the mechanistic understanding of aging in pigs.

In this paper, we study the asymptotic properties of slowly varying functions of one real variable in the sense of Karamata. We establish analogs of fundamental theorems on uniform convergence and integral representation for slowly varying functions with remainder depending on the types of remainder. We also prove several important theorems on the asymptotic representation of integrals of Karamata functions. Under certain conditions, we observe a “narrowing” of classes of slowly varying functions concerning the types of remainder. At the end of the paper, we discuss the possibilities of application of slowly varying functions in the theory of stochastic branching systems. In particular, under the condition of the finiteness of the moment of the type Exlnx for the particle transformation intensity, it is established that the property of slow variation with remainder is implicitly present in the asymptotic structure of a non-critical Markov branching random system.

Pressmud, a by-product of sugarcane processing, is typically disposed of through incineration or landfilling, though it has considerable potential in organic agriculture. This study explored the com-posting of pressmud through bioaugmentation using specific bacterial strains. Two experimental setups were created: E2 with a cellulolytic and phosphorus-solubilizing strain, Bacillus amyloliquefa-ciens-ASK11, and E3 with a nitrogen-fixing strain, Bacillus megaterium-ASNF3. A control setup (E5) was also maintained without bacterial augmentation. Nitrogen (N) and phosphorus (P) contents were measured using the Kjeldahl and Olsen methods, respectively, while Fourier-Transform Infra-red (FTIR) spectroscopy confirmed compost maturity. Results indicated that the Bacillus-enhanced composts in E2 and E3 significantly increased the nitrogen(49.27% and 91.23%) and phosphorus contents(129% and 83.85%), respectively, compared to the control with only 13.62% N and 65% P contents after 60 days of the pressmud compost maturity.. Additionally, organic matter decomposi-tion improved by 49-50% in the bioaugmented setups after 60 days. FTIR analysis revealed organic phosphate peaks and P-O-C stretching bands at 1025 cm-1 in the E2 compost, while a nitrogen vi-bration band at 3849 cm-1 in E3 indicated significantly higher nitrogen content compared to the control. The Bacillus-enriched pressmud compost not only accelerated the composting process but also enhanced nutrient levels, positioning it as a promising biofertilizer for rehabilitating barren lands.

Background: The lateral antebrachial cutaneous nerve (LACN) is the terminal sensory branch of musculocutaneous nerve and is rarely entrapped or injured. This study describes the electrodiagnostic (EDX) findings and etiologies of LACN neuropathy. Methods: This is a review of 49 patients with pain and/or paresthesia of the forearm who underwent EDX studies. The diagnosis of LACN neuropathy was based on clinical and sensory conduction abnormalities. Results: The most common etiology of LACN neuropathy was iatrogenic injury in 30 (61.2%) patients, primarily due to biceps tendon repair at the elbow (11 [36.7%]) and phlebotomy (5 [16.7%]. Fifteen (30.6%) patients sustained a non-iatrogenic injury at the proximal forearm/elbow, consisting of 6 (60%) laceration injuries and 5 (33.3%) stretch injuries. Four (8.2%) patients comprised the “other” etiology category, including 2 mass lesions causing LACN compression. Pain, paresthesia, and/or numbness in the LACN distribution were reported in 33 (67.3%), 27 (55.1%), and 23 (46.9%) patients, respectively. Hypoesthesia was detected in 45 (91.8%) patients, and dysesthesia in 7 (14.3%). The sensory nerve action potentials (SNAPs) of the LACN on the symptomatic side were absent in 44 (89.8%) patients. Of the 5 patients whose SNAPs of the LACN were detected, all had a decreased amplitude and 2 had increased sensory latency. Conclusion: The most common etiology for LACN neuropathy in this series was iatrogenic injury; repair of biceps tendon at the elbow was the most frequent provoking cause. Protection of the LACN during surgical procedures at the elbow and forearm is vital to prevent iatrogenic injury.

This study investigated the effects of two types of plastic flooring, with and without nanotechnological antimicrobial additives, as complete or partial replacements for wood shavings on broiler chickens' performance, yield, meat quality, and litter microbiology up to 42 days of age. A total of 1,500 Ross 408® male broiler chicks were randomly assigned to five treatments: wood shavings (WS), plastic flooring (PF), a 50/50 mix of plastic flooring and wood shavings (PF+WS), plastic flooring with antimicrobial additive (PFA), and a 50/50 mix of antimicrobial plastic flooring and wood shavings (PFA+WS). The study assessed liver, heart, spleen, gizzard biometrics, Eimeria lesion severity, microbiological profiles, performance indices, and meat quality. Results showed that plastic flooring, mainly when used alone, posed challenges, such as increased intestinal lesions associated with coccidiosis and a higher presence of Salmonella. Additionally, birds raised on plastic flooring exhibited poorer feed conversion, lower body weight, and reduced carcass yield than wood shavings. Meat quality was also affected, with plastic flooring leading to less desirable fillet characteristics. Overall, using plastic flooring negatively impacted the performance and health of broiler chickens.

Acquisition traits are those that depend for their evolutionary (fitness) value on the acquisition of something that gradually accumulates during an organism’s life but is not transmitted genetically to descendants. This situation causes these traits including language, immunity, intelligence, and social status to represent a special need for evolvability and programmed aging. Programmed aging refers to the idea that mammals and some other multiparous sexually reproducing organisms evolved complex biological mechanisms that cause gradually increasing fitness deterioration and internally limit individual organism lifetimes. The rationale is that this process enhances the survival (non-extinction) of a species population. Evolvability theories propose that mammals have evolved design characteristics that aid their ability to evolve, that is, reduce the time required for a particular increment of evolutionary adaptation and/or increase the precision with which the adaptation can be performed. Because an increase in evolvability aids a population in escaping extinction, evolution selects the associated design characteristics. Both concepts conflict with traditional (Darwinian) theory regarding details of the nature of evolution. However, more recent genetics discoveries have exposed rich detail regarding the nature of the evolution process including the fact that biological inheritance involves the transmission of organism design information from parent to descendant in digital form. These discoveries have acted to support both programmed aging and evolvability theories.

Cats, including companion cats, inflict extensive harm on wildlife. Using a national survey of 2000 households in New Zealand, we investigated the influence of involvement (an indicator of motivation) on the willingness of cat owners to keep their cats indoors at night. We found that respondents’ intentions to protect wildlife, and the frequency with which respondents with cats kept them indoors at night, was influenced by their involvement with cat welfare and their involvement with protecting wildlife, in addition to their attitudes and subjective norms. We also found that keeping cats indoors at night could be characterised as involving approach-avoidance conflict. Our findings have implications for efforts to increase the adoption by cat owners of keeping cats inside at night regarding the attentiveness of cat owners to promotional activities. Our findings suggest that such activities will not be particularly effective in the absence of cat-friendly, inexpensive, practical, and easily maintained devices that enable cats to be kept inside. Importantly, when the adoption of keeping cats inside at night is appropriately characterised as approach-avoidance conflict, our results suggest that promotional activities seeking to persuade cat owners that pet cats cause much greater harm to wildlife than they might believe are most likely to have a limited and likely temporary effect and may even be counter-productive.

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex — the smallest quantized object in superconductors — can enable drastic miniaturization to the nanometer scale. In this work, we present numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultra-fast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultra-low energy per operation, ∼10−19 J.

Tianjin is currently in the phase of urban stock renewal, yet its industrial heritage encounters significant challenges, including constrained spatial development, a misalignment between industrial progress and the reuse of industrial heritage, and an understated cultural representation. This study applies geospatial analysis to delineate the distribution characteristics of Tianjin's industrial heritage across time, space, and typology. Moreover, the geographic detector is employed to assess the impact of various factors on the development of Tianjin's industrial heritage, ultimately elucidating the correlations between existing challenges and objective patterns. The conclusion of this paper presents three strategic approaches for the renewal of industrial heritage, emphasizing historical culture, environmental space, and sustainable development. These strategies are designed to provide a solid research foundation for protecting and revitalizing Tianjin's industrial heritage resources.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

Hepatitis B Virus (HBV) infection carries a significant health burden worldwide. It is a leading cause of acute and chronic liver disease resulting in cirrhosis and hepatocellular carcinoma (HCC). This virus affects millions globally, and despite several medical advancements, research, and efforts to develop curative treatment, the persistence of HBV in infected individuals by cccDNA and integration pose formidable challenges. This review aims to provide a broad-based overview of HBV persistence, integration and the implications for chronic infection and tumorigenesis. By considering these points, we bring forth insights into the advancement of therapies that aim to disrupt the chronicity of HBV infection and improve clinical outcomes.

Background/Objectives: Determining the outcome of epilepsy is crucial for making proactive and timely treatment decisions, as well as for counseling patients. Recent research efforts have focused on using various imaging techniques and EEG for prognostication; however, there is insufficient evidence regarding the role of blood parameters. Our study aimed to investigate the additional prognostic value of routine blood parameters in predicting epilepsy outcomes. Methods: We analyzed data from 1,782 patients who underwent routine blood tests within 90 days of their first visit and had a minimum follow-up duration of three years. The etiological types were structural (35.1%), genetic (14.2%), immune (4.7%), infectious (2.9%), and unknown (42.6%). The outcome was defined as the presence of seizures in the last year. Results: Initially, a multivariate analysis was conducted based on clinical variables, MRI, and EEG data. This analysis revealed that sex, age of onset, epileptiform discharge, structural etiology, and the number of antiseizure medications were related to the outcome, with an area under curve (AUC) of 0.708. Among the blood parameters, fibrinogen, bilirubin, uric acid, and aPTT were significant, with AUCs of 0.602, 0.597, 0.455, and 0.549, respectively. Including these blood parameters in the analysis slightly improved the AUC to 0.713. Conclusions: Some blood parameters were found to be related to the final outcome, potentially paving the way to understanding the mechanisms of epileptogenesis and drug resistance.

Current treatments for medial meniscus lesions in association with CCL ruptures [1–5] have shown disappointing long-term outcomes [6]. Meniscal suturing may improve the outcome, but their clinical application remains limited [7–9]. This study aims to assess the practicability of a minimally invasive DNT to facilitate meniscal suturing in dogs. Ten stifles of eight client-owned dogs with arthroscopically confirmed CCL disease and medial meniscal tears in the abaxial third of the meniscus were included. The described suture technique was applied under joint distraction using the Titan joint distractor.[8] Surgical stabilization of all stifles was then accomplished through a X-Porous TTA procedure [10]. The DNT allowed for the precise placement of meniscal sutures. Minor intraoperative complications included reduced arthroscopic visibility (n=2) and suture breakage during its passage through the meniscus (n=3). No complications related to the meniscal sutures were noted throughout the six-month follow-up. The described DNT proved to be a viable and effective method for suturing lesions of the caudal horn of the medial meniscus, provided there is adequate visualization during the procedure. Appropriate stifle stabilization postoperatively is mandatory for protecting the suture and avoiding concomitant meniscal lesions due to joint instability.

Introduction: Immune checkpoint inhibitors (ICI) are used to treat various malignancies. They block the inhibitory signals of tumor cells and enhance the inflammatory cascade which results in tumor killing. However, this can lead to unchecked inflammation throughout the body, leading to various adverse effects. A rare gastrointestinal adverse effect of ICI therapy is the development of immune-mediated celiac disease. This entity has a similar clinical presentation to the more common ICI-induced enterocolitis. Our study aims to determine the clinical characteristics and optimal treatment strategies for this rare ICI toxicity, and differentiate it from ICI-induced enterocolitis. Methods and Material: We conducted a retrospective analysis of eight cases of ICI-induced celiac disease and 24 cases of ICI-induced enterocolitis from the literature. Data on patient demographics, clinical history, therapeutic interventions and outcomes were collected. Comparative analysis was performed to identify the key differences between the two groups. Results: Patients with ICI-induced celiac disease were more likely to have a pre-existing autoimmune condition and HLA-DQ2 positivity. Significant differences in clinical manifestations, histological findings, and treatment outcomes were observed. Notably, weight loss, nutritional deficiencies and electrolyte abnormalities were more commonly associated with ICI-CD. On pathology, duodenal villous blunting was noted more commonly with ICI-induced celiac disease. Initiating a gluten-free diet led to rapid improvement in patients with ICI-induced celiac disease, while immunosuppressive therapy did not have an impact. Conclusion: ICI-induced celiac disease is a rare and underrecognized gastrointestinal adverse effect of ICI therapy, often misdiagnosed as ICI-induced enterocolitis. Early recognition and treatment with a gluten-free diet can lead to rapid symptom resolution, sparing patients from unnecessary systemic immunosuppression and discontinuation of antineoplastic immunotherapy.

Since ancient times, music has been instrumental in giving life to the stories we build our identities and cultures around. Music perpetuates myth by storytelling, retelling and updating old stories, and creating new ones. Many musicals, such as The Phantom of the Opera, Les Miserables, Wicked, and Hamilton, have been based on novels or epic poems. I will examine adaptations on a smaller scale, of rock songs based on novels, poems, and/or myths, and look at how, in our time, music creates myth by creating its own heroes and heroines through capital and the star system. In traditional literary and cultural analysis, a distinction was drawn between the natural and the supernatural when discussing literary mythology (Frye, 1957); in the twentieth century, an equivalent distinction was made in works of art that, in Baudrillard’s terminology, make use of the realms of the real and the “hyperreal” (1981). In today’s mythmaking, the supernatural has been largely replaced by the technological, and recent developments blur the line between science fiction and fantasy; tech has become megalotech. Examples can be seen in a number of recent and current songs, as well as films and television shows about music, musicians, and the music business.

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, trustworthy, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including balancing interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends and providing valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, promoting the development of transparent, trustworthy, and effective AI-driven solutions.

The adoption of the Internet of Things (IoT) in supply chain operations has emerged as a pivotal advancement, offering the potential to significantly enhance operational efficiency, visibility, and responsiveness. This study explores the multifaceted impact of IoT integration in supply chains, emphasizing both the opportunities and challenges associated with its implementation. Through a combination of semi-structured interviews, case studies, and a comprehensive literature review, the research uncovers how IoT technologies contribute to improved asset tracking, real-time inventory management, and optimized logistics. Key benefits include enhanced operational efficiency, predictive maintenance capabilities, and better supply chain collaboration, which collectively lead to increased customer satisfaction and cost savings. However, the study also identifies several challenges, including the high costs of IoT infrastructure, difficulties in integrating new technologies with existing systems, and concerns over data security and privacy. These challenges pose significant barriers to adoption, particularly for smaller organizations. Despite these obstacles, the study highlights the substantial opportunities presented by IoT, such as fostering innovation, developing new business models, and enhancing supply chain resilience. The findings suggest that while the path to successful IoT adoption may be complex and resource-intensive, the potential benefits make it a valuable investment for organizations seeking to remain competitive in a rapidly evolving market. Addressing the identified challenges while leveraging the advantages of IoT will be crucial for companies aiming to optimize their supply chain operations and achieve long-term success.

Maritime surveillance technology faces challenges of astronomical size in this vast ocean world—challenges vital to global security and environmental protection alike. Such as these may be reversible or not. Remotely sensed monitoring also uses photos taken from satellites, synthetic aperture radar (SAR) equipment, and unmanned aerial vehicles (UAVs) to fully monitor maritime activities but also ferret out suspicious behaviour like piracy, bootleg shipments, and illegal fishing. This mixed-method approach--quantitatively and qualitatively——measures the efficiency of difficulties associated with, and legal implications behind aerospace-monitored maritime surveillance. The results show that remote sensing provides a great deal of sea-level situational awareness; however, there are also many complex technical, operational, and legal issues, including information overcrowding, complications in integration, privacy issues, as well as the need for worldwide legal harmonisation. It ends with recommendations on ways to ultimately increase capacity, how to write standard procedures for secondary development and proposals for revising laws to absorb these new technologies. Its goal, therefore, is to benefit the global anti-maritime crime effort. Ultimately, though, it dreams that we might construct an entirely marine environment where all marine activities are conducted both safely and freely, without fear.

Leishmaniasis, comprising cutaneous and mucocutaneous forms, is a parasitic disease caused by Leishmania species via sandfly bites, leading to skin ulcers and mucosal damage. Traditional treatments include antiparasitic drugs such as pentavalent antimonials, amphotericin B, and miltefosine, with immunotherapy and chemotherapy being explored for enhanced efficacy and reduced relapse. Mucocutaneous leishmaniasis (ML) balances IFN-γ and IL-10, affecting severity, while cutaneous leishmaniasis (CL) shows varied IFN-γ/IL-10 ratios influencing prognosis. Cytokines and chemokines are diagnostic and prognostic markers in CL. Research into neutrophils and the NLRP3 inflammasome informs Th1/Th2 response regulation. Live attenuated LdCen-/- parasites and genetically modified vaccines show promise, with miR-21 as a potential efficacy biomarker. Understanding cytokine networks is crucial for new treatment strategies.

In this study, we propose a scalable deep learning approach to automated phenotyping using UAV multispectral imagery, exemplified by yellow rust detection in winter wheat. We adopt a high granularity scoring method (1 to 9 scale) to align with international standards and plant breeders’ needs. Using a lower spatial resolution (60m flight height at 2.5cm GSD), we reduce the data volume by a factor of 3.4, making large-scale phenotyping faster and more cost-effective while obtaining results comparable to those of the state-of-the-art. Our model incorporates explainability components to optimise spectral bands and flight schedules, achieving top-3 accuracies of 0.87 for validation and 0.67 and 0.70 on two separate test sets. We demonstrate that a minimal set of bands (EVI, Red, and GNDVI) can achieve results comparable to more complex setups, highlighting the potential for cost-effective solutions. Additionally, we show that high performance can be maintained with fewer time steps, reducing operational complexity. Our interpretable model components improve performance through regularisation and provide actionable insights for agronomists and plant breeders. This scalable and explainable approach offers an efficient solution for yellow rust phenotyping and can be adapted for other phenotypes and species, with future work focusing on optimising the balance between spatial, spectral, and temporal resolutions.

This review stresses the need for geoscience and mining engineering students to be educated during their undergraduate studies in social and sustainability issues so they would have the skills and knowledge in their work after graduation, as professionals, to engage Indigenous People for mutual benefit. Large - scale mining development often takes place on Indigenous land and these educated mining professionals in their contact with these vulnerable communities must act in a responsible way. This includes the important mining policy issues and standards of corporate social responsibility (CSR), social license to operate and free, prior and informed consent from the Indigenous People before mining. For example, these policy standards issues would have been important to consider in Indonesia where the Indigenous Muluy Dayak community in East Kalimantan tried to resist large-scale mining operations by depicting their small-scale mining as part of their Indigenous customs and identity and in Australia where there was the recent destruction of an Aboriginal sacred site during large scale mining operations.

Chronic stress is a pervasive issue that significantly impacts both physical and mental health. Unlike acute stress, which is short-lived and can be beneficial, chronic stress persists over extended periods, leading to a range of adverse health outcomes. This paper explores the myriad physical, mental, and behavioral consequences of chronic stress, emphasizing the underlying physiological mechanisms and potential long-term health implications. Through a comprehensive literature review and data analysis utilizing Python, we aim to provide an in-depth understanding of chronic stress and its effects. Key findings include the critical roles of cortisol and other stress-related chemicals in exacerbating health issues, and the potential for the normalization of high stress levels as an adaptive yet harmful response.

In the Euclidean form of the theory of gravity, where there is no dedicated time parameter, a generalized canonical form of the principle of least action is proposed. On its basis, the quantum principle of least action is formulated, in which the “dynamics” of the universe in the Origin is described by the eigenvector of the action operator - the wave functional on the space of 4D Riemannian geometries and configurations of matter fields in some compact region of 4D space. The corresponding eigenvalue of the action operator determines the initial state for the world history of the universe outside this region, where the metric signature is Lorentzian and thus the time parameter exists. The boundary of the Origin region is determined by the condition that the rate of change of the determinant of the 3D metric tensor is zero on it. A modification of quantum theory is proposed that eliminates the uncertainty of the sign of the Hilbert-Einstein action in the Euclidean region of the Origin. On this basis, we formulate a minimum principle for the eigenvalue of the action operator, similar to the principle of minimum energy for the ground state in quantum mechanics. It has been suggested that in the initial state the universe contains a certain distribution of its own mass, which is not directly related to the fields of matter.

Background: School nursing plays a crucial role in promoting the health and well-being of students in educational settings. Previous research has highlighted its protective effect across various health domains, including substance use, mental health, violence, and sexual health. Despite its importance, the job satisfaction of school nurses and the factors influencing it have been insufficiently addressed in scientific research, underscoring the need to better understand their challenges and barriers. Therefore, the objective of this research is to evaluate the perceived job satisfaction and professional quality of life of school nurses at a national level in Spain. Methods: A descriptive cross-sectional study was conducted using the Font-Roja Job Satisfaction Questionnaire and the CPV-35 Professional Quality of Life Questionnaire. The sample included 553 school nurses from different geographical regions in Spain, selected through school nursing groups and associations. Data were collected from November 2023 to February 2024. Results: The majority of the nurses were women (68%), with a median age of around 40 years and predominantly less than 5 years of work experience (56%). Significant differences were observed in job satisfaction and professional quality of life depending on the employing entity (healthcare and educational settings). Factors such as workload, institutional support, and work experience influenced the perception of job satisfaction. Conclusions: We affirm that this study, being one of the first of its kind at a national level in Spain, highlights the need to develop effective strategies to improve the job satisfaction and professional quality of life of school nurses. School nurses in Spain present a moderately high level of job satisfaction, although there are significant differences in factors such as status and job monotony depending on the sector of employment (Healthcare, Education, Others). Nurses in educational contexts report lower levels of job satisfaction, influenced by the nature of the work, interpersonal relationships, opportunities for professional development, and institutional support. Additionally, work experience impacts satisfaction, being more positive for nurses with greater experience. We also affirm that the professional quality of life of school nurses is homogeneous, with an average score of 6.000 on the CPV-35. Although professional competence is uniformly perceived, work pressure is a common concern. Working more than 37.5 hours per week is associated with higher professional quality of life, although also with lower job satisfaction. Only half of the schools have a school nurse, highlighting the need for policies that ensure equity in service provision. There is also a recognized need for health training for teachers, which could improve support for nurses and student health. Active interaction with the educational community reinforces the integral role of school nurses and emphasizes the importance of the socioeconomic context in interventions and resource allocation. Keywords: School nursing, job satisfaction, professional quality of life, work factors, educational settings.

Objectives: The aim of this study is to understand the rationale for the lack of interest to perform regular opportunistic oral cancer screening (OCS) by primary care providers (PCPs) and the challenges they have in incorporating OCS into their annual examination protocol. Method: Sequential exploratory mixed methods were used. Both qualitative and quantitative data from PCPs working at Nebraska Medicine were obtained. One-on-one in-depth interviews were performed to identify PCP's perceptions and challenges in performing OCS. The themes identified in the qualitative study were used as a guide in the development of survey questionnaires. The survey was emailed to all the PCPs (N=100), whose information was available on the Nebraska Medicine publicly accessible website. Results: The response rate was 34%. Approximately 60% of the participants mentioned that they currently perform oral cancer screening, but only 3.8% of the providers answered that they were performing comprehensive oral cancer screening every time. Forty-eight percent of the providers identified time as the biggest challenge. Over 35.3% of the providers answered that they perform screenings for patients with a history of tobacco (i.e., smoking) or alcohol use. Conclusion: Around half of the participants identified lack of time as their primary challenge in performing OCS and lack of knowledge as their second main obstacle. Very few provided comprehensive OCS and visual examination of the oral cavity is the only step performed often.

Aspergillosis is a fungal disease caused by the inhalation of fungal spores of the genus Aspergillus spp. This fungus mainly affects the lungs but can spread and infect the maxillofacial region through the bloodstream. The disease has nonspecific clinical manifestations, which hinders early diagnosis. Even though the Polymerase Chain Reaction (PCR) technique holds promising potential to aid in diagnosing aspergillosis, anatomopathological analysis services have not yet routinely adopted the method. Therefore, the present study aims to evaluate the applicability and standardize the techniques of preparation of biological samples and PCR for the detection of the species Aspergillus niger, Aspergillus fumigatus and Aspergillus flavus. Six samples of formalin-fixed, paraffin-embedded tissue (FFPE), with histopathological diagnosis suggestive of aspergillosis, were investigated by PCR. Five of the six samples evaluated were positive for the fungus Aspergillus spp, 2/5 of which were co-infected by A. fumigatus and A. flavus species, and the others were positive for A. flavus species. The findings of this study suggest that PCR may be an auxiliary method in diagnosing aspergillosis. However, the expansion of the sample size and the evaluation of PCR in comparison with other diagnostic tests for aspergillosis is essential to determine the accuracy of the method.

Abstract: Founders of quantum theory have pointed out the irreducibility of living systems to quantum mechanics. Quantum mechanics is never violated they rightfully argue but works under a set of active macroscopic constraints that selectively order the behavior of the micro components and these constraints are not reducible to the reversible, causally closed formalism of quantum mechanics. The whole of biological evolution is characterized by the production of increasingly more highly-ordered levels of such macroscopic constraints and biological evolutionary theory has no account for this either. It is assumed outside its theoretical first principles. In fact this generative macroscopic ordering, the production of increasingly more highly differentiated levels of order—more things, more kinds of things, cannot be explained within biological theory because it is generic to the universe itself. Irreducible to quantum mechanics this begs the question of fundamentality in physics. Fundamental laws in a universe defined by active macroscopic ordering, level-building, and differentiation must be able to causally cross levels, and more importantly explain why there is any level-building at all to begin with. The laws of thermodynamics are fundamental laws in this sense, and 4th Law path selection explains the generative universal ordering and differentiation (“complexification”).

The impact of financial risks extends beyond the normal operation and survival of industrial and commercial enterprises and financial institutions. They also have the potential to impede the stable development of a country's and even the world's financial economy. This is clearly demonstrated by the severe consequences of the frequent financial crises that have occurred in recent years. It thus follows that the prevention of financial risks has become one of the core tasks of the operation and management of industrial and commercial enterprises and financial institutions. Portfolio optimization techniques are employed in the construction of a variety of asset allocation models, with reinforcement learning algorithms used to dynamically adjust investment strategies with the objective of maximizing returns and minimizing risks. The preliminary construction of an asset allocation model is achieved through the utilization of a genetic algorithm. Genetic algorithms emulate the processes of natural selection and genetic variation to identify the optimal portfolio of assets that will yield the greatest returns at a pre-established level of risk. Subsequently, the Deep Q-Learning algorithm is introduced to facilitate dynamic adjustments and optimization of the asset allocation, based on the initial construction. Deep Q-learning employs deep neural networks to forecast the prospective returns of disparate investment strategies, thereby optimizing the decision-making process through continuous learning and updating. The combination of genetic algorithms and deep Q-learning enables the system to identify the optimal investment strategy under a diverse range of initial conditions and to adapt it in real-time to respond to market fluctuations and uncertainties. The experimental analysis demonstrates that the proposed method exhibits an exemplary capacity for risk control and a robust ability to generate stable income growth across diverse market environments. In simulation experiments, portfolios constructed using this method demonstrate lower volatility and higher average returns than those generated through traditional methods.

Sustainability is a concept which engages the weighted balance between economic, environmental and social domains for all life forms and man. Within the frame of sustainability, it is important to realize that coexistence is part of a single life equation. Damage to the environment, social spectrum, and economic sectors (through deviant practices) reap havoc upon all life forms. Sustainability insures the existence of all within the single equation of life. Unsustainable, colonial practices prevail upon the present and the future of life.Sustainability should advocate for healthy environs for all life forms (including, but not exclusive to humans) to sustain themselves. The neglect and/or omission of concern for this mandate will lead to the deuteriation and eventual expiration of any such life forms. Life cannot be sustained in an imposed, artificial domain which is suited solely for mercantile purposes. History has taught us that the cost of dismissing the errors of the past and forging on results in error. This article proposes to explore the impact of the exploitation of agricultural practices, food availability, deforestation, dislocation, and "voluntary tourism" upon "Others" stemming from colonial occupation.

This research aims to explore the integration method of deep learning and large language models in speech recognition to improve the system’s recognition accuracy and ability to handle complex contexts. Deep neural network (DNN), convolutional neural network (CNN), long short-term memory network (LSTM) and Transformer-based large language model are used to build an integrated acoustic and language model framework. Experiments on TIMIT, LibriSpeech and Common Voice datasets show that the ensemble model shows significant improvements in both word error rate (WER) and real-time factor (RTF) compared to traditional models. Especially in terms of adaptability to multiple languages and accent changes, the model shows superior performance. The conclusion shows that through technology integration, the performance of the speech recognition system in complex environments can be effectively improved, providing a new direction for the future development of speech recognition technology.

Maxwell's equations accurately describe the propagation of electromagnetic (EM) waves. However, the conventional formulation implies invariance to observers, posing challenges in explaining how the wave is perceived by different observers. For example, Doppler effect shows that observers perceive the same EM wave differently.By employing mathematical transformations, we derive a general form of Maxwell’s wave equations by incorporating a "time scaling" factor to account for observer perception. The original form is shown to be a special case when static. The Doppler effect and Sagnac effect are directly derived from Maxwell’s equations. All the results are consistent with established experiments.Our findings offer a fresh perspective, promising a deeper understanding and unification of EM phenomena. We extend Maxwell’s equations to describe not only the propagation of an EM wave, but also how it will be measured by different observers, say, antennas.

We investigate perovskite oxides from different aspects, namely their pseudoharmonic dynamical properties, their dynamical properties when strong anharmonicity exists and the intriguing functionalities arising from domain walls. Taking these viewpoints together yields a rather complex picture of this material class which was not anticipated in previous approaches. It opens pathways to novel applications and reveals the rich ground states beyond the fictitious belief in ‘simplicity of perovskites and such structures’.

The risk factors and positive factors of teleworking have been widely analyzed. Emotional exhaustion has been one of the most studied among them. However, the impact of ethical leadership in virtual environments has remained completely unnoticed. There is no clear answer to whether the ethical leader is a factor that buffers or accelerates stress-related processes in teleworking. Furthermore, the role of perceived work autonomy as a theoretical mechanism that protects against specific adverse psychological outcomes requires more scientific evidence because the results obtained so far are confusing and contradictory. Therefore, this study explores how and when ethical leadership influences emotional exhaustion through work autonomy and teleworking intensity (TI). The sample consisted of 448 professionals from six organizations in the Colombian electricity sector. The mediation effect was evaluated through a four-step procedure, and the moderated mediation model was analyzed using statistical tools such as PROCESS and AMOS macros for SPSS. Surprisingly, our results revealed a positive relationship between ethical leadership and emotional exhaustion, with work autonomy potentially mediating this relationship. However, teleworking intensity (TI) was found to moderate this mediation process. Specifically, the negative impact of ethical leadership on emotional exhaustion was nullified when TI was high. Moreover, a high level of TI strengthened the relationship between ethical leadership and work autonomy, while weakening the positive relationship between work autonomy and emotional exhaustion. In conclusion, our study suggests a potential positive relationship between ethical leadership and emotional exhaustion from the follower's perspective. However, the number of teleworking days can influence this trend, potentially even reversing it. In other words, teleworking intensity (TI) is a resource as its intensity increases, and it can alter the direction of work autonomy and reconfigure the relationship between ethical leadership and emotional exhaustion. This study, to the best of our knowledge, is the first to analyze these variables, significantly contributing to our understanding of the attributes of ethical leadership and its impact on employee well-being.

The inability to delineate a unified physical ontology that accounts simultaneously for the laws of special relativity and the results of quantum experiments has been a defining problem in physics for more than 100 years. This analysis addresses the problem by positing a novel mixed ontology composed of a “discrete” 4D spacetime and a physical, ultra-high dimensional (3 x N) “Planck Space.” Together, Planck Space and the three spatial dimensions of 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the “Dual Ontology”).Critically, the Dual Ontology’s structure replaces 1) the continuous, differentiable manifold of 4D spacetime with a discrete, background-independent 4D spacetime that is intrinsically curved and 2) mathematical spaces with a physical (3 x N) Planck Space. Moreover, the Dual Ontology is structurally and dynamically predicated on the one-to-one mapping and identity between the discrete spatial units that simultaneously form 4D spacetime and Planck Space. The one-to-one mapping and identity of the discrete spatial units support an integrated quantum dynamics based upon the dynamic evolution of single and N-body quantum states in 4D spacetime in full compliance with the laws of special relativity and the instantaneous collapse of all quantum states in an ontic Planck Space, where special and general relativity and more generally, 4D spacetime’s laws of physics, do not apply.

Previous studies found that corilagin inhibits the growth of many cancer cells. Ovarian cancer is the deadliest gynecological malignancy, but research on the effects of corilagin on ovarian cancer is limited. This experiment aims to explore the impact of corilagin on ovarian cancer A2780 cell apoptosis and its mechanism of action to evaluate the prospects of corilagin in treating ovarian cancer. Corilagin significantly affects the A2780 cell cycle, reducing the proportion of G0/G1 and G2/M phase cells and blocking them in the S phase. Corilagin at low concentrations can cause A2780 cell apoptosis, mitochondrial membrane potential decline, and calcium influx. After the cells were stained with Acridine Orange, autophagosomes were observed under a fluorescence microscope and increased with the increase in concentration. The results of transcriptome sequencing showed that the effect of corilagin could cause differential expression of apoptosis-related genes in A2780 cells and concentrated on the PI3K-AKT pathway. The qPCR results showed that the expression of genes P53 and Bax in cells increased while the expression of BCL-2 decreased. The expressions of apoptotic factors caspase-9, caspase-3, p53-upregulated modulator of apoptosis (PUMA), and cytochrome C increased after the action of corilagin, indicating that corilagin can induce cell apoptosis. Corilagin can effectively inhibit the proliferation of A2780 cells, cause cell cycle arrest, and induce cell apoptosis. Moreover, it can trigger autophagy and has in vitro antitumor effects. It is a promising drug for treating ovarian cancer through the PI3K/P53 pathway.

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.

Senolytic and senomorphic therapies gain more and more attention in the last decade. This kind of therapy is based on the killing of cellular senescent cells without harming the “normal” cells. Ageing is not a disease. Clinical studies on healthy people will be difficult to conduct. Therefore, one possibility is to draw on the large repertoire of medicinal plants and use their sneolytic properties to provide mild, anti-ageing therapies. Chamomile, goldenrod, reishi and green tea were tested for their ability to trigger senolysis quercetin was used as control substance. Cellular senescence was induced with 25µM etoposide in human dermal fibroblasts and established for at least 14 days. The plant extracts were tested for their antioxidant potential (DPPH assay) and their polyphenol content. Senolysis was determined by presto blue assay of young and etoposide-induced senescent cells, and ß-Gal assays were also performed. The senomporphic properties of the plants were investigated using IL-6 ELISA and qPCR. It turned out that chamomile triggers a kind of cytokine storm and causes the cytokine values in the Elisa and in the qPCR to rise extremely, and other senescence-associated phenotype (SASP) markers were also elevated. Goldenrod and quercetin tend to have a senolytic and senomorphic effect respectively. Regarding the senolytic and senomorphic properties of herbs, we have found that all tested herbs can have a senolytic effect, and a senomorphic effect of quercetin has also been uncovered. With regard to the effect of chamomile, however, we can say that seemingly harmless tea products may have harmful effects, especially in combination with chemotherapy, at least in cell culture experiments.

Gigantism in theropods represents one of the most intriguing phenomena in dinosaur evolution, showcasing extreme adaptations in body size and ecological roles. This literature review focuses on Giganotosaurus carolinii, one of the largest known theropods to have ever existed, to explore its unique aspects of gigantism and the subsequent ecological impacts. By analyzing features such as its skeletal morphology, comparative anatomy, and paleoenvironmental data, we assess the evolutionary pressures and biological constraints that shaped the gigantism observed in Giganotosaurus. We discover that the species exhibited a combination of physiological and ecological adaptations that allowed it to thrive as an apex predator in its Cretaceous environment. We will also further examine the implications of its size on prey dynamics, competition with other large theropods, and the overall ecosystem structure. Through a thorough, multidisciplinary approach, this research enhances our understanding of how gigantism influenced theropod behavior, interspecies interactions, and the broader paleoecological landscape.

Pipelines are widely used globally, particularly in Nigeria, to transport raw and processed hydrocarbon fluids, including crude oil, from the point of production to the shore for export, processing facilities and distribution of refined products to depots for sales and consumption. Although pipelines are designed to withstand environmental and physical shocks, intermittently, pipelines are exposed to natural or human-induced conditions that damage pipelines and disrupt their functionality. These disruptions could result from operational defects or vandalism, causing an economic loss of up to US$ 658.5 million as observed in the Niger Delta from 1990 to 2022. Furthermore, product loss of about 705,100 barrels observed over the same period has caused environmental and social impacts, damaging the natural environment, destroying livelihoods supported by the ecosystem, and deteriorating the health of residents as documented in several literatures. To mitigate these issues and ensure the safe and reliable conveyance of crude oil in pipelines, measures must be taken to timely detect spillages and intervene during oil spillages to minimize damage and loss. Such detection methods include external, visual/ biological and internal/computation-based approaches. This paper uses a mixed-method approach to review the literature on the causes and impact of crude oil pipeline leakage in the Niger Delta region of Nigeria and highlights some methods that could be applied to detect pipeline leakage, as well as their strengths and inefficiencies. This paper further assesses the joint investigation visit mechanisms for oil spill response used in the oil and gas industry in Nigeria and identified that between 2006 to 2021, it took an average of 4 days to report oil spill occurrence, and 21 days to respond. Also, for 18 percent of the spill occurrences in that period, there were no JIV. This study suggests that the gap in the JIV mechanism can be addressed by integrating technology to enhance detectability and minimize the cost of monitoring. The paper also guides the way forward to develop and improve the reliability of pipeline leak detection systems.

This article aims to analyze the problem of the identity and persistence of objects based on the hylomorphic doctrine, that is, the doctrine according to which objects are mereological compositions of the elements of form and matter. After introducing the contemporary discussion on the problem of identity and persistence and identity of objects, we propose a brief formalism of the logic of the relationship between part and whole or mereology to define the solution to the problem under study that defines a possible solution for it.

Familial pancreatic cancer (FPC) represents a significant yet underexplored area in pancreatic cancer research. Basic research efforts are notably limited, and when present, they are predominantly centered on BRCA1 and BRCA2 mutations due to the scarcity of other genetic variants associated with FPC, leading to a limited understanding of the broader genetic landscape of FPC. This review examines the current state of FPC research, focusing on the molecular mechanisms driving pancreatic ductal adenocarcinoma (PDAC) progression. It highlights the role of homologous recombination (HR) and its therapeutic exploitation via synthetic lethality with PARP inhibitors in BRCA1/2-deficient tumors. The review discusses various pre-clinical models of FPC, including conventional two-dimensional (2D) cell lines, patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and genetically engineered mouse models (GEMMs) as well as new advancements in FPC research.

We have synthesized and examined a series of calix[4]arene-based chemosensors containing thiosemicarbazone or thiourea as a binding site and a pyridine-chromophore unit in various conformations. These conformations include pinched cone (I), cone (III and IV), and 1,3-alternate (II and V). Our objective was to assess the binding abilities of these sensors towards anions and cations using different spectroscopic techniques. All receptors demonstrate the ability to recognize cations, with notable distinctions between them. Receptor I demonstrates a broad spectrum of ion recognition, encompassing Ag+, Cu2+, Co2+, Ni2+, and Zn2+. Receptors IV and V also exhibit complex formation with Co2+, Ni2+, and Cu2+ ions. Sensors II and III demonstrate exceptional selectivity for recognizing the copper (II) ion. When studying anions, only receptor I yielded satisfactory results. This is due to the thiosemicarbazone bridge and its spatial orientation in the pinched cone conformation, which allows for an interaction to occur. It is worth noting that sensors IV and V, which are fluorescent molecules due to the triazolo pyridine unit in their structure, exhibit particularly noteworthy behavior. The highest association constant is observed for cobalt (II) complexes, with values of 6.59 and 2.38 (x1012 mol/L)-2 for receptors IV and V, respectively. Our findings indicate that there are no significant differences in selectivity and sensitivity between ca-lix[4]arene receptors in 1,3-alternate and cone conformations.

The transition to renewable energy sources is critical for sustainable development, particularly in rural regions with limited access to electricity. Solar-powered microgrids offer a promising solution for rural electrification by providing reliable, clean energy that can enhance economic opportunities and improve quality of life. This paper presents a techno-economic analysis of solar-powered microgrids for rural areas, evaluating their feasibility, costs, and benefits. The analysis encompasses technological aspects, such as solar panel efficiency, battery storage, and grid management, alongside economic factors including initial investment, maintenance costs, and potential returns. Additionally, the study assesses the social impact of solar-powered microgrids, exploring their effects on local communities, including improvements in education, health, and economic activities. The findings indicate that solar microgrids can be a viable and impactful solution for rural electrification, with significant long-term benefits for both economic development and social well-being.

Pomegranate extract has potential benefits for women's reproductive health, including fertility enhancement, menstrual cycle regulation, pregnancy support, and polycystic ovary syndrome (PCOS) treatment. It possesses antioxidant properties, reducing oxidative stress and improving fertility. Phytoestrogens in pomegranate extract may help regulate hormonal imbalances and promote regular menstrual cycles. The extract's rich nutrient profile supports placental development and fetal growth and may reduce the risk of preterm birth. Additionally, pomegranate extract shows promise in improving insulin sensitivity and reducing inflammation and oxidative damage in PCOS. Some studies suggest its potential anticancer properties, particularly against breast cancer. However, further research, including human clinical trials, is necessary to establish its effectiveness and safety. The current evidence is limited and primarily based on in vitro studies, animal studies, and clinical trials. This review provides a comprehensive summary of the benefits of pomegranate extract for women's reproductive health and breast cancer, serving as a reference for future research.

The Blue Economy represents a sustainable development model emphasizing the responsible use of ocean resources to foster economic growth while preserving marine ecosystems. This approach aims to balance economic prosperity with environmental conservation, recognizing the oceans' crucial role in supporting life on Earth. Key sectors within the Blue Economy include fisheries, tourism, renewable energy, maritime transportation, and aquaculture. As global populations rise and climate change impacts intensify, the Blue Economy is increasingly seen as essential for addressing global challenges and promoting sustainable development. Responsible fisheries management, such as implementing fishing quotas and creating marine protected areas, is crucial to combat overfishing and ensure food security. Sustainable aquaculture practices further alleviate pressure on wild fish stocks. Marine tourism, or ecotourism, promotes responsible practices that benefit local communities and the marine environment. Activities like beach clean-ups, wildlife conservation, and responsible diving practices are integral to this sector, supporting local economies and conservation initiatives. The Blue Economy significantly impacts the global economy, with the potential to double its contribution to the world's GDP by 2030. Sustainable marine sectors create jobs, drive economic growth, and contribute to energy security by harnessing renewable ocean-based energy sources. Additionally, the Blue Economy fosters innovation and technological advancements, enhancing competitiveness across various industries. Despite its potential, the Blue Economy faces challenges like overfishing, pollution, and climate change, which threaten marine biodiversity and ecosystems. Addressing these issues requires international cooperation, effective governance, and public awareness to promote sustainable practices and protect marine resource.

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.

This article underscores the immense potential for substantial economic growth and development that can be harnessed through effective participation in global value chains (GVCs). It emphasises the role of policymakers in adeptly navigating GVCs, prioritising tasks, exploring different forms of GVC governance, and fostering a conducive environment for foreign investments. By effectively managing power dynamics and supply chain risks, countries can attract valuable foreign investors, enhance market connectivity, and improve infrastructure and services, leading to significant economic growth. The potential benefits of GVC participation are vast, and policymakers can shape the situation by understanding and addressing strategic inquiries, laying the foundation for a prosperous future. Furthermore, the article explores the potential for a country to enhance its involvement in GVCs and progress to more lucrative activities by strengthening existing connections between GVCs and the local economy. By enhancing the capacity of local stakeholders to acquire knowledge, policymakers can play a crucial role in maximising the benefits from GVC spillovers, positively impacting a country's economic development.

Cancer remains a leading cause of death despite advancements in research and treatment, with traditional therapies often causing significant side effects and resistance. Oxyhydrogen gas, a mixture of 66% molecular hydrogen (H₂) and 33% molecular oxygen (O₂) has shown exceptional promise as a novel therapeutic agent due to its ability to modulate oxidative stress, inflammation, and apoptosis. H2, a key component of oxy-hydrogen gas, neutralises reactive oxygen and nitrogen species, enhancing existing treatments and reducing harmful oxidative states in cancer cells. H₂ also lowers pro-inflammatory mediators including chemokines, cytokines and interleukins, inhibiting cancer cell proliferation and boosting the effectiveness of conventional therapies. Additionally, hydrogen can induce apoptosis in cancer cells by modulating pathways such as MAPK and inhibiting the PI3K/Akt phosphorylation cascade. Preclinical and clinical evidence supports oxyhydrogen gas's potential in treating various cancers. In lung cancer models, it inhibits cell proliferation, induces apoptosis, and enhances chemotherapy sensitivity. Similar results have been observed in breast cancer, where patients reported improved quality of life. In colorectal cancer, oxyhydrogen gas suppresses tumour growth, induces apoptosis and improves intestinal microflora dysbiosis. The unique properties of oxyhydrogen gas make it a promising adjunctive or standalone cancer treatment. However, further research is needed to understand H₂'s mechanisms, optimise treatment protocols, and evaluate long-term safety and efficacy in human patients.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine-tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via Reinforcement Learning (RL) methodologies. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model's ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

Logistic Regression and Random Forest are used to identify risk factors that influence traffic accident fatalities in the United Kingdom. The mean decrease accuracy was used to measure variable importance. The speed limit, police attendance and quarter had an increasing influence on accident fatalities. They had a mean decrease of 102.1669, 221.5322, and 120.894 respectively. The speed limit, had a parameter estimate of 0.0046902 and a standard deviation of 0.0004875. Light Conditions: Night had a parameter estimate of 1.2657635 and a standard deviation of 0.0118409. Road Type Round About had a parameter estimate of -0.4055796 and a standard deviation of 0.0210848. Police Attendance classified as Yes had a parameter of 0.8546232 and a standard deviation of 0.0151043. The best predictors were speed limit, police attendance and quarter since they had p values that were less than 0.05. The findings of the study indicated that logistic Regression had a higher accuracy rate 79.85% as compared to 64.00% for Random Forest. A split test was used and a standard deviation of 0.0010486 was obtained for the Logistic Regression model.

This review details the extraction, characterization and utilization of seaweed-derived biopolymers for future packaging applications. The review is contextualized within the broader scope of the challenge of plastic pollution and the current urgent need for more sustainable packaging materials. Macroalgae (or seaweed) has been highlighted as a promising source of biopolymers, most commonly sodium alginate, agar and carrageenan, for reasons such as a rapid growth rate and decreased environmental impact when compared to terrestrial plant-life. Extraction methods detailed include the traditional solvent-based extraction to more sustainable developments such as ultrasound assisted extraction, microwave assisted extraction and bead milling. The review additionally presents the characterization techniques most pertinent in determining the applicability of these biopolymers in packaging applications. Properties of key importance to the development of sustainable packaging materials such as thermal properties, mechanical strength, barrier properties and biodegradability are highlighted in comparison to conventional petroleum-based plastics. This review concludes by realistically identifying the challenges faced by implementing seaweed-based biopolymers into packaging structures, such as cost-effectiveness, scalability, performance while suggesting future directions to mitigate these issues and improve the commercial viability of these materials for the packaging industry.

This study focuses on the creation and implementation of a platform to improve personal financial management for scholarship students of the National Program of Scholarships and Educational Credit (Pronabec) in Peru. Using a mixed methodology that combines qualitative and quantitative techniques, the effectiveness and user satisfaction with the platform were evaluated. The results indicated that 50\% of users found the tool useful, 55\% considered it easy to use, 70\% reported a positive impact on their personal budget planning, and 85\% expressed high overall satisfaction. In addition to providing tools for financial recording and analysis, the platform promotes financial education among its users, significantly contributing to improving their financial management skills.

The advent of 5G technology represents a transformative leap in the realms of retail marketing and supply chain operations, offering unprecedented enhancements and posing unique challenges. This study explores the multifaceted impacts of 5G, focusing on its influence on customer experiences, operational efficiency, and supply chain management. The integration of 5G enables advanced technologies such as augmented reality (AR) and virtual reality (VR), significantly enhancing the interactivity and personalization of the shopping experience. Real-time data collection and analysis, facilitated by 5G, allow for more precise inventory management and optimized store operations, leading to improved customer satisfaction and reduced operational costs. In the supply chain domain, 5G technology offers enhanced visibility and control through real-time tracking and environmental monitoring, which improves logistics efficiency and product quality management. Despite these advancements, the adoption of 5G technology involves considerable challenges, including high infrastructure costs, data security concerns, and global disparities in technology deployment. Businesses must navigate these obstacles while leveraging the benefits of 5G to achieve competitive advantage. The study also highlights the role of 5G in integrating with other advanced technologies, such as artificial intelligence (AI) and blockchain, to drive further innovation. Additionally, 5G supports sustainability efforts by optimizing energy use and reducing waste. Overall, the successful implementation of 5G requires strategic investment and adaptability, but it holds the potential to revolutionize retail and supply chain operations, enhancing efficiency, customer engagement, and sustainability in a rapidly evolving digital landscape.

Objective: To evaluate audiological outcomes, quality of life, and complications in patients implanted with Active middle ear implants (AMEI). The secondary objective is to investigate the required duration after implantation to reach satisfactory outcomes. Methods: This retrospective study included 31 patients implanted with Active middle ear implants (AMEI) with different methods of floating mass transducer attachment. Patients with incomplete medical records and those who did not respond to postoperative follow-up were excluded. Patients were assessed preoperatively, and at one, three, and six months postoperatively. The assessment included Pure Tone Average (PTA4), Speech Reception Threshold (SRT) and Speech Discrimination Score (SDS). The Speech Spatial and Qualities of Hearing scale (SSQ12) was also used to evaluate levels of satisfaction. Result: There are no significant differences found in PTA and SRT between the 3-, 6-, and 12-month visits. The speech reception threshold (SRT) showed a statistically significant improvement at 3, 6, and 12 months post-operative measures compared to pre-operative. Additionally, the SDS exhibited a significant increase only after 12 months, compared to the 3-month time point. However, satisfaction levels did not significantly differ between the 6-month and 12-month measurements following surgery. Conclusion: Vibrant Sound bridge improves subjective satisfaction scores and audiological test scores in patients with different types of hearing loss. AMEI has a low risk of medical or surgical complications, the ease of using a hearing implant, and the social benefits of good hearing and communication.

Heat pumps are recognized as a key tool in the energy transition toward a carbon-neutral society, enabling the electrification of the heating sector at least for low- and medium-temperature heat demands. In recent years, natural refrigerants have been reconsidered due to their low environmental impact: among them, CO2 is a safe option without impact on the ozone layer and low global warming potential compared to synthetic fluids. However, as a consequence of its thermophysical properties, its thermodynamic cycle is transcritical and is particularly suitable for specific end-user temperature profiles. This paper analyzes in a systematic and thorough way the most significant modifications to the reference cycle that have been proposed in the literature to improve the performance, finding how the optimum configurations change with a change in the rated operating conditions (inlet temperature and temperature glide of the heat demand, and ambient temperature). Exergy analysis explains why there is an optimal gas cooler pressure and why its trend with the average temperature is split into two distinct regions, clearly recognizable in all cycle layouts. The maximum coefficient of performance (COP) of the reference cycle varies in the 1.52–3.74 range, with a second-law efficiency of 6.436.1, for an optimal gas cooler pressure of up to 15.45MPa, depending on ambient temperature and end-user temperature profile. The most effective modification is the cycle with ejector and internal heat exchanger, which raises the COP to 1.84–4.40 (second-law efficiency 8.745.56). The presented results provide an extensive guide to understanding the behavior of a transcritical CO2 cycle and predict its performance in heat pump applications.

Flares, sometimes accompanied by coronal mass ejections (CMEs), are the result of sudden changes in the magnetic field of stars with high energy release through magnetic reconnection, which can be observed across a wide range of the electromagnetic spectrum from radio waves to the optical range to X-rays. In our review, we attempt to collect some fundamental new results, which can largely be linked to the Big data era that has arrived due to the expansion of space photometric observations of the last two decades. We list the different types of stars showing flare activity, their observation strategies, and discuss how their main stellar properties relate to the characteristics of the flares (or even CMEs) they emit. Our goal is to focus, without claiming to be complete, on those results that may in one way or another challenge the "standard" flare model based on the solar paradigm.

Deep cavity configurations are common in various industrial applications, including automotive windows, sunroofs, and many other applications in aerospace engineering. Flows over such a geometry can result in an Aero-Acoustic coupling between the cavity shear layer and the acoustic modes of the surrounding. This phenomenon can lead to significant noise and may cause damage to structures due to resonance caused by high-pressure fluctuations produced nearby the cavity. The aims when employing a passive control device is to disturb the cavity flow in order to reduce or eliminate the involved resonance. An experimental set up was developed to study the effectiveness of using either a cylinder or a profiled cylinder positioned upstream from the cavity. A decrease of as high as 30 dB was obtained in the Sound Pressure Levels (SPL). Additionally, Particle Image Velocimetry (PIV) technique was employed to get the kinematic fields of the flow past the cavity for both controlled and non-controlled configurations. A Snapshot Proper Orthogonal Decomposition (POD) was applied to better understand the cavity flow dynamics in both controlled and non-controlled cases. Furthermore, the interaction of the wake of the control mechanisms with the shear layer of the cavity and its consequence on the Aero-Acoustic coupling was investigated and new flow physics is revealed.

The concept of Digital Twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber-physical systems. Despite the various foundations, standards, and platforms in Systems Engineering, there are ongoing challenges with Verification and Validation methodology. This study aims to establish a generic framework that addresses the various aspects of Digital Twinning. The multifaceted nature of the problem requires raising the abstraction level in both the Real (Actual) and Virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the Quintuple Helix model with the Problem and Operational Domains of a Real (Actual) Twin, the Solution and Implementation Domains of a Virtual Twin, and the Execution Domain as the bridge that links them. Verification and Validation dimensions follow the Meta Object Facility abstraction layers (Instance, Model, Meta-model, and Meta-meta-model) mapping over five Helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios.

Observation of the distribution of the velocity of galactic rotation curves differed from their expected centripetal form and lead to the notion of Dark Matter or modifications to Newtonian and General Relativity, such as MOND, TeVeS and the like and even Quantised Inertia. We aim to show that General Relativity with Dark Energy/the Cosmological Constant is all that is needed, with the proviso that the Cosmological Constant can increase in the presence of a gravitational field and become gravitating to account for the hypothesis of Dark Matter Haloes.

We propose a theoretical Photocurrent - Voltage characteristic (PC-V) model to assess the interfacial phenom-ena for a photo-electrochemical cell (PEC). The origin of voltage deficits and the distribution of the photocur-rent across the semiconductor-electrolyte interface (SEI) are reported for the cell. The model predicts the hole exchange current parameter to extract the cell polarization data at the SEI. The potential drop across the SEI across the cell is mapped for the n-type cells. The simulation results of the Pc-V model are used to differentiate the effect of the bulk and space charge region (SCR) recombination in the semiconductor cells. A deep neural network model is developed to assess the electron-hole transfer mechanism using the Pc-V characteristic curve. The applicability of the model is tested and validated with the real time data. The results show good agreement with the experimental data.

The purpose of this disquisition is to analyze bullfighting, its advances and setbacks at a global and local level in terms of animal law. It was developed as a descriptive documentary research with a non-experimental design, using the observation and interpretation of documents related to the subject, from the global level, to the local, the national, specific to the regions and communities, being interpreted through the hermeneutic method. In the analysis, results were reached such as: every day more countries are willing to assume that bulls, because they are living beings that feel, are being attended to from the legal and the ethical, propitiating changes in the way of thinking of people in terms of wanting to consider bullfighting, a sport, art and hobby considered cultural heritage of many countries. For this reason, different actions have been generated from surveys, to define the acceptance or not of these events. It is concluded that there are important advances in this regard, as bullfighting is being prohibited in different parts of the world, taking into account the protection that protects animals, but the laws do not establish specific prohibition in this regard worldwide.

The objective of this research was to analyze the evolution and trend of the generation gap and nursing education from 1968 to 2021 through bibliometric indicators. The method used is a bibliometric, descriptive and retrospective study of documents in Scopus, with descriptors “Nursing educat*”, “generation* and the Boolean operator “AND”, N= 653 web documents from 1968 to 2021, and filters of the type bib, csv. The search was carried out on October 9, 2021, using the Bibliometrix R Package software and the Biblioshiny tool. The Results indicate high scientific production in 2020 (54 articles disclosed). This study demonstrates patterns and lines of research in nursing education, such as educational models, interpersonal relationships, and organizational innovation.

In the dynamic realm of HPC (High-performance computing) and cloud-native applications, ensuring optimal network performance for Kubernetes Container Network Interfaces (CNIs) is critical. Traditional manual methods for evaluating network bandwidth and latency are prone to errors, time-consuming, and lack consistency. This paper introduces a novel approach that leverages Ansible to automate and coordinate network performance tests across diverse CNIs, performance profiles, and configurations. By automating these processes, we eliminate the potential for human errors, ensure the ability to replicate the process, and significantly reduce the time required for comprehensive testing. The use of Ansible playbooks facilitates the efficient and scalable deployment, configuration, and execution of performance tests, enabling comprehensive and standardized benchmarking across various environments. The set of Ansible playbooks, which we will make available online, has a significant real-world impact by providing DevOps teams with robust and reliable tools for consistently monitoring and enhancing network performance. This, in turn, enhances the stability and efficiency of cloud-native applications, accelerates the development cycle, and ensures that Kubernetes-based infrastructures can meet the demanding performance requirements of modern applications.

Machine reading comprehension is a crucial area of research in natural language processing, aiming to enable machines to read and understand text as humans do, and to answer questions related to the text content. The pre-trained language model, represented by BERT, has achieved superior results compared to traditional models in many NLP tasks, leading to the rise of the pre-trained paradigm in the field of natural language processing. This paper addresses the issue that the pre-trained language model lacks the ability to extract long-distance semantic relations and make efficient use of linguistic features. Firstly, the recent developments of the pre-trained language model are described. Then, two feature maps are used to intuitively express the structured long-distance semantic correlation features, and the traditional sequence structure features are integrated. The influence of different feature map construction methods on machine reading comprehension is compared. Finally, the application of the pre-trained language model in machine reading comprehension is summarized and prospected. By fusing graph structure features, the model can learn more abundant language knowledge, thus further improving its reasoning ability.

We introduce the notion of p-adic equiangular lines and derive the first fundamental relation between common angle, dimension of the space and the number of lines. More precisely, we show that if {τj} n j=1 is p-adic γ-equiangular lines in Qd p , then (1) |n| 2 ≤ |d| max{|n|, γ2 }. We call Inequality (1) as the p-adic van Lint-Seidel relative bound. We believe that this complements fundamental van Lint-Seidel [Indag. Math., 1966] relative bound for equiangular lines in the p-adic case. Keywords: Equiangular lines, p-adic Hilbert space.

Wicked problems, such as climate change, systemic poverty, and healthcare inequalities, rep- resent the most pressing and intractable challenges facing society today. We propose a novel approach that leverages complex systems theory as a comprehensive framework for understanding and tackling these challenges. By applying concepts such as state space analysis, attractors, and nonlinear dynamics, we offer a sophisticated toolkit for mapping the intricate web of interactions defining wicked problems and exploring potential interventions. Our approach moves beyond tradi- tional reductionist methods, embracing the inherent complexity and dynamism of these issues. We demonstrate how this framework can lead to more nuanced, adaptive, and effective strategies for addressing persistent societal problems. Despite the prevalence of wicked problems and the avail- ability of complex systems theory, practical applications of this approach remain uncommon. This paper aims to bridge this gap, illustrating the potential of complex systems theory to transform our engagement with intractable problems and pave the way for more robust, holistic solutions. By synthesizing insights from mathematics, physics, and social sciences, we present a transdisci- plinary approach that promises to revolutionize how we conceptualize and address society’s most challenging issues.

This study investigates the anthropogenic activities, water quality, and ichthyofaunal diversity of the Navotas Riverine Ecosystem. It examines the anthropogenic influences, analyzes the physicochemical and microbiological properties of water, identifies fish species, evaluates the water quality index, and determines ichthyofaunal diversity using various indices. Water samples and fish collections were conducted from January to April 2024 at three sites. Key findings indicate that water quality parameters such as temperature (23.20-37.5°C), dissolved oxygen (6.22-7.84 mg/L), phosphate concentration (0.55-3.72 mg/L), and fecal coliform (350-160,000 MPN/100 mL) failed to meet DENR standards for Class C water. The TDS (1153.33-10700 ppm) and conductivity (17653.33-21333.33 µS/cm) were also elevated, indicating significant pollution. The study identified low ichthyofaunal diversity, with six native and one invasive fish species. A total of 269 fish were collected: 161 from Site 1, 46 from Site 2, and 62 from Site 3. The most abundant species was Scatophagus argus (19% occurrence), while the invasive Sarotherodon melanotheron ranked third (17% occurrence). Notably, pH (r = 0.753) correlated highly with nitrate concentration. S. melanotheron significantly correlated with pH (r = 0.602, p = 0.019) and nitrates (r = 0.555, p = 0.031). Lutjanus argentimaculatus also had significant correlations with pH (r = 0.527, p = 0.039) and nitrates (r = 0.651, p = 0.011). Conversely, S. argus had a negative moderate correlation with temperature (r = -0.529, p = 0.039) and conductivity (r = -0.536, p = 0.036). These correlations suggest that the water parameters significantly influence the diversity and abundance of fish in the river. The study concludes that the river's water quality is heavily influenced by anthropogenic activities which negatively affect ichthyofaunal diversity. Recommendations include continuous monitoring, gut and heavy metal analysis of fish, assessment of avifauna diversity, and strict implementation of conservation regulations.

The use of low cost sensors has dramatically increased in recent years in all the engineering sectors. In the field of buildings and automotive, low cost sensors open very interesting perspectives, because they allow to monitor temperature and humidity distributions together with air quality, in a widespread and punctual way and allow control of all energy parameters. The main issue still open remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems have been set to measure Particulate air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity and GPS position. These systems have been calibrated by the comparison with standard and certified sensors used by the regional authority of Emilia-Romagna region (ARPAE, Italy) for characterising the air-quality. The innovation of this approach lies in both the in-field comparison of low-cost and high quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors performance is given, with a focus on effects of time-granularity, relative humidity, mass conversion from particle counts and size detection response. The results show that the low-cost sensors measurements of air temperature, relative humidity and particle number concentration are in a good agreement with high quality sensors measurements, with a strong impact of relative humidity on performance indicators. Overall, a good quality and consistency of the data among the sensors has been achieved.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

Cooperation is essential in social life, involving collaborative efforts for mutual benefit. Individual differences in the cooperativeness trait are pivotal in these interactions. Previous research suggested that Duchenne smiles signal cooperative intent, and human-like eyes are theorized to enhance cooperation evolutionarily. However, the relationships between cooperative behavior, cooperativeness and possible behavioral cues signaling cooperative behavior are yet to be studied. In the current study, we explored the relationships among cooperativeness, Duchenne smiling with gaze, IBS during conversation, and their impacts on cooperative behavior. We hypothesized Duchenne smiling with gaze would mediate cooperativeness’ impact on cooperative behavior. Additionally, using functional near-infrared spectroscopy (fNIRS), we evaluated inter-brain synchrony (IBS) in the left prefrontal region, expecting it to predict cooperative behavior. The results demonstrated that cooperativeness significantly predicted Duchenne smiling with gaze and cooperative behavior, however, Duchenne smiling with gaze did not mediate the relationship between them. We further conducted a path analysis to examine the role of IBS during conversation in successive cooperative behavior. The path analysis result showed that cooperativeness directly affected cooperative behavior. Cooperativeness significantly predicted Duchenne smiling with gaze, however, neither Duchenne smiling with gaze nor IBS during conversation predicted successive cooperative behavior. These results suggest dispositional factors like cooperativeness may play a more decisive role than momentary expressional cues or neural synchrony in naturalistic unstructured communication in shaping cooperative behavioral outcomes after the communication. The study highlights how personality traits like cooperativeness shape nonverbal communication and social interactions.

Abstract: Little is known about landbird group dynamics in response to predation. Here, we describe the interactions between the Helmeted Guineafowl (Numida meleagris) and its predators regarding the survival advantages grouping behaviour may provide. Livestream webcam observations were conducted in Madikwe Game Reserve (South Africa) from August 2020 to December 2021. Emphasis was placed on predator–prey interaction and its effect on group size and structure in a spatial framework. Although Black-Backed Jackals (Lupulella mesomelas) are not adept at hunting birds, they might negatively impact guineafowl by curtailing their feeding time during winter with constant harassment. We found that these canids often approach larger and denser groups in the morning and late evenings. Surprisingly, guineafowl most often responded by standing nearby. On the contrary, a higher predator-induced response was associated with the presence of predatory birds, arguably as part of an evolutionary stable prey-predator system. This line of thought is also supported by the lack of a precise defensive mechanism elicited by jackal attacks, unlike those of aerial predators. Nevertheless, we contend that grouping in Helmeted Guineafowl is a defensive strategy to respond to predator attacks while individuals engage in daily activities.

Due to the genotype-dependent effects of water deficit (WD) on coffee physiology, climate change raises the risk of coffee yield. The aim of the study was to assess the effect of WD on physiological and growth traits in arabica coffee (Coffea arabica L.). Water status, photosynthetic response to in-tercellular concentration of CO2 (A/Ci curves) and growth parameters were evaluated in seedlings of four genotypes (Catimor ECU 02, Cavimor ECU, red Caturra and Sarchimor 4260). Most of the physiological traits evaluated differed significantly among genotypes. Between control and WD plants, there were significant differences in A/Ci parameters, showing a wide range of values for photosynthetic rate, stomatal conductance, and water use efficiency, with decreases ranging from 4 to 74%. Maximum rate of carboxylation by RuBisCO, maximum electron transport rate through photosystem II, and triose phosphate utilization rate were all strongly reduced by WD 61 % (red Caturra and Sarchimor 4260), followed by Cavimor ECU (35 %) and 24 % in Catimor ECU 02. Differences in response to WD among genotypes suggest possible genotypic differences in toler-ance. The results indicated that Catimor ECU 02 and Cavimor ECU were less sensitive to WD, while red Caturra and Sarchimor 4260 were the most susceptible.

Background: Subclinical myocardial injury (SCMI) is associated with an increased risk of poor cardiovascular disease (CVD) outcomes. Understanding the underlying risk factors for SCMI is crucial for the prevention and management of CVD. We hypothesized that atherogenic dyslipidemia, a combination of high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C), is associated with an increased risk of SCMI. Methods: This analysis from the third National Health and Nutrition Examination Survey (NHANES-III) included 7,093 participants (age 59.3±13.4 years, 52.8% women, and 49.4% White) free of CVD. Atherogenic dyslipidemia was defined as TG ≥150 mg/dL and HDL-C <40 mg/dL in men or <50 mg/dL in women. A validated electrocardiographic-based cardiac infarction injury score (CIIS) ≥10 was considered positive for SCMI. Multivariable logistic regression analysis was used to examine the association of different combinations of TG and HDL-C groups, including atherogenic dyslipidemia with SCMI. Results: About 22.5% (n=1,594) of participants had atherogenic dyslipidemia, and 26.3% (n=1862) had SCMI. Compared to participants with normal TG and normal HDL-C, those with atherogenic dyslipidemia had a higher prevalence of SCMI (31.2% vs. 23.9%, p-value <0.001). In a multivariable logistic regression model, atherogenic dyslipidemia was associated with the highest odds of SCMI followed by high TG/normal HDL-C, then low HDL-C/normal TG [OR (95% CI): 131 (1.14,1.52), 1.13 (0.97,1.33), and 1.01(0.86,1.20), respectively). Conclusions: Atherogenic dyslipidemia is associated with a higher risk of SCMI, which highlights the role of nontraditional risk factors in the development of subclinical CVD.

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the uncertainty in outputs of process-based hydrological models and the scarcity of gauge flow data. We develop a Bayesian hierarchical model (BHM) based on the Generalized Extreme Value (GEV) distribution for regional flood frequency analysis at high resolution across North America. Our model leverages annual maximum flow data from ≈20000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include daily gauge discharge data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improveable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield satisfying agreement when compared with available design peak discharge from various government sources. Assessment of the covariates’ contributions to the model also informs on the most relevant underlying factors influencing flood-inducing peak flows. The key covariates in our model are temperature-related bioindicators, the catchment drainage area and geographical location.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

This paper presents the design, modelling, and comparative analysis of two internal MEMS vibrating ring gyroscopes for harsh environmental conditions. The proposed designs investigate the symmetric structure of the vibrating ring gyroscopes that operate at the identical shape of wine glass mode resonance frequencies for both driving and sensing purposes. This approach improves the gyroscope’s sensitivity and precision in rotational motion. The analysis starts with the investigation of the dynamic behaviour of the vibrating ring gyroscope with the detailed derivation of motion equations. A comprehensive evaluation of the design geometry, meshing technology, and simulation results presented on two internal vibrating ring gyroscopes. The two designs are distinguished by their support spring configurations and internal ring structures. Design I comprises eight semicircular support springs and Design II comprises with sixteen semicircular support springs. These designs were modelled and analyzed using finite element analysis (FEA) in Ansys software. The paper further evaluates static and dynamic performance, emphasizing mode matching and temperature stability. The results reveal that Design II, with additional support springs, offers better mode matching, higher resonance frequencies, and better thermal stability compared to Design I. Additionally, electrostatic, modal, and harmonic analyses highlight the gyroscope's behaviour under varying DC voltages and environmental conditions. Furthermore, the study investigates the impact of temperature fluctuations on performance, demonstrating the robustness of the designs within a temperature range of -100°C to 100°C. These research findings suggest that the internal vibrating ring gyroscopes are highly suitable for harsh conditions such as high-temperature applications and space applications.