New advances in creating effective and affordable copper-based catalysts for water splitting represent a promising strategy for driving sustainable energy technologies. However, ma- terials containing copper are susceptible to corrosion and agglomeration. In this study, we describe a synthesis route to produce olamine-functionalized CuO nanoparticles using the Hot Injection method. Nanoparticles were obtained and subsequently deposited on FTO substrates, undergoing thermal treatment at 500°C and 600°C in atmospheres containing O2 and N2, aiming to enhance the adhesion of the active material to the substrate and modify its oxidation state. Microstructural properties were analyzed using XRD, SEM, and TEM, while electrochemical properties were studied using CV, LSV, and EIS. Electrodes treated thermally in an N2 atmosphere at 600°C exhibited higher ESCA, indicating a greater area of active sites exposed to surface reactions and, therefore, superior catalytic activity in Hy- drogen Evolution Reaction (HER). These findings show an interesting strategy for avoiding rapid electron-hole recombination and, consequently, CuO corrosion. For Oxygen Evolution Reaction (OER), samples treated thermally in O2 atmosphere at 500°C and 600°C appear to be more effective.

This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement learning frameworks show significant advantages in optimizing decision making, predictive perception, path planning, and controller design, exceeding the limitations of traditional supervised learning methods. The paper highlights the critical role of components such as scene understanding, positioning, and map making in autonomous driving systems, which provide reliable environmental awareness through deep learning and sensor fusion technologies to support intelligent decision-making in complex urban environments. In addition, the paper discusses innovative approaches to safety reinforcement learning to reduce risk in autonomous driving and ensure that systems adhere strictly to safety constraints while maximizing expected rewards. These findings provide an important theoretical and practical basis for further improving algorithm robustness, managing multi-agent interactions, and integrating ethical considerations in the future.

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

We demonstrate that multiple well-known metrics, such as the Schwarzschild metric and the extremal solution of the Reissner-Nordstr ̈om metric, can be expressed in what we will call thermodynamic form. These formulations appear to be valid for all Rs = ct growing black holes and, therefore, also within the framework of Rh = ct growing black hole cosmology. However, the metrics can also be applied to non-black hole gravitational objects as long as one calculates the CMB and Hawking temperature of the hypothetical black hole from the gravitational mass.

Springs play a significant role in maintaining hydrological balance in the mountain ecosystem. The communities living in the mountain tops rely entirely on spring water for their daily needs. In recent decades, spring water discharges have decreased drastically in the Himalayan regions and become seasonal. Remote sensing and geographical information system techniques have proved to be essential for understanding the sustainable development of groundwater, particularly where people rely solely on spring water in the mountainous regions of the Himalayas. This study employs the Analytical Hierarchy Process (AHP) to identify groundwater potential zones. It involves overlaying seven thematic layers concerning elements of physical features and land-use/land-cover of the region, using the weighted overlay toolbox in ArcGIS, with each layer being assigned a weight based on its importance. Based on the results, groundwater potential zones have been classified into five categories: poor, fair, good, very good, and excellent. The study found that poor groundwater potential zones cover 1.1% of the area, fair zones cover 27.8%, good zones cover 23.3%, very good zones cover 26.6%, and excellent zones cover 21.2% of the total area. The excellent to very good potential zones are associated with perennial springs that have higher discharge (4.30-30.92 l/m), lineaments, rainfall, forested land, and porous soils, favoring the potentiality of the springs. The fair and poor groundwater potential zones are associated with seasonal flowing springs, consisting of barren land, less rainfall, and low fracture density are influencing the potentiality groundwater. The validation of the groundwater potential zones indicated an Area Under Curve (AUC) value of 0.88, which shows good agreement with observed spring water locations. The identified groundwater potential zones are valuable for prioritizing the area for the sustainable development of spring water. Particularly, it is helpful for the stakeholder departments to involved in developing strategies for sustainable water management at the micro-watershed level and safeguarding spring water resources using integrated watershed management.

Detecting and evaluating pain in people with cognitive impairment, especially dementia, is a challenge. Their ability to accurately report pain and comprehend related questions decreases the reliability of their self-report. This has contributed to the fact that pain is underdiagnosed and undertreated in this population, which can lead to involuntary ageism. The design and assessment of this training capsule under the context of the European COST-Action TD1005 responds to the need for education and training in "Pain Assessment In Cognitive Impairment, especially dementia" and the development of the internationally agreed observational PAIC15 scale. The 3-hour capsule, intended for healthcare workers and caregivers, consists of theoretical-practical levels: 1) basic concepts and challenges in pain in aging, cognitive impairment/dementia, 2) available scales and PAIC15, 3) video presentation of clinical cases illustrating a 3×2 matrix of situations (rest, transfer and experimental pain) and stages of disease (mild and moderate). The objectives focused on training the ability to observe, detect, and assess the signs of pain using PAIC15 and its feasibility. The pre-post analysis in a Master educational setting showed agreement with the results obtained in the Spanish national survey on 1) scarce knowledge of observational tools and their use, and 2) uncertainty in the identification of signs of pain and feasibility in moderate/severe scenarios. The capsule improved the ability to identify and evaluate the pain items, especially facial expressions, and their confidence on observational tools. Debriefings highlighted the relevance of observational scales in dementia and the gain obtained using the new observational instrument.

The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach ("VarioCNN"). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image data sets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. With reference to the industrial sector, the greatest need is the decarbonisation of industries energy-intensive, such as producers of iron and steel, aluminium, cement, petrochemicals, paper, etc. In particular, the steel industry is a hard to abate high-emissivity industry which due to chemical processes and high thermal demands (often above 500 °C) contributed to 7.4% of global CO₂ in the year 2021. This paper has explored the potential applications of hydrogen in the hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing how although there is potential for its inclusion as an element for the decarbonization, it is necessary to evaluate the energy balances of the various processes and the methods of hydrogen production very carefully, in order not to risk obtaining a marginal effect.

Cucurbitacin phytonematicides consistently suppress population densities of plant nematodes, but have no effect against bacteria, fungi and insects. This study investigates the mechanism through which cucurbitacin phytonematicides consistently exert nematicidal properties on nematodes. Post 72-h exposure of second-stage juveniles (J2) of Meloidogyne enterolobii to geometric concentration of Nemafric-BL phytonematicide, J2 total proteins were measured using TruspecCHNS Macro. Total proteins versus phytonematicide exhibited a negative quadratic relation (R² = 0.97), with the minimum total proteins achieved at x = 4.9% phytonematicide. Beyond the minimum, total proteins increased gradually, depicting the existence of both isoprenylation (breakdown) and farnesylation (biosynthesis) of total proteins. In conclusion, cucurbitacins and nematode cuticles are both chemically structured, which provides the mechanism through which the test chemicals consistently decrease population densities of plant nematodes.

Abstract: Background/Objectives: Schizophrenia (SZ) is a severe psychiatric disorder characterized by a complex interplay of genetic, developmental, and environmental factors that significantly increase the risk of suicidal ideation (SI) and suicide attempts (SAs). This systematic review synthesizes current research on the developmental predictors of SI in individuals with SZ, aiming to delineate the multifactorial etiology of suicide within this population. Methods: A comprehensive search across Medline, PsycINFO, and EMBASE databases identified 23 eligible studies, emphasizing the varied methodological approaches and the global distribution of research efforts. Results: These studies demonstrate a robust association between early life adversities, particularly childhood trauma such as physical neglect, emotional abuse, and sexual abuse, and the increased prevalence of SI and SAs in SZ. This review also highlights the significant genetic factors associated with development of suicidality in SZ, suggesting that polymorphisms in inflammation-related genes and neurodevelopmental abnormalities may influence susceptibility to SI. Notably, family history of psychiatric conditions may exacerbate the risk of SI through both hereditary and environmental mechanisms. Environmental factors, including socioeconomic status and social support, are also implicated, underscoring the role of broader socio-environmental conditions influencing outcomes. Conclusions: This review supports the integration of biopsychosocial models in understanding SI in SZ, advocating for interventions addressing the complex interplay of risk factors and the need for longitudinal studies to elucidate the dynamic interactions between risk factors over time. This comprehensive understanding is crucial for developing targeted preventative strategies and enhancing the clinical management of SZ, aiming to reduce suicidality in this vulnerable population.

Grass pea (Lathyrus sativus L.) is a protein-rich crop that is resilient to various abiotic stresses, including drought. However, it is not cultivated widely for human consumption due to the neurotoxin β-N-oxalyl-L-α, β-diaminopropionic acid (β-ODAP), and its association with neurolathyrism. Though some varieties with low β-ODAP have been developed through classical breeding, the β-ODAP content is increasing due to genotype x environment interactions. This review covers grass pea nutritional quality, β-ODAP biosynthesis, mechanism of paralysis, traditional ways to reduce β-ODAP, candidate genes for boosting sulfur-containing amino acids, and the potential and targets of gene editing to reduce β-ODAP content. Recently, two key enzymes (β-ODAP synthase and β-cyanoalanine synthase) have been identified in the biosynthetic pathway of β-ODAP. We proposed four strategies through which the genes encoding these enzymes can be targeted and suppressed using CRISPR/Cas9 gene editing. Compared to its homology in Medicago truncatula, the grass pea β-ODAP synthase gene sequence and β-cyanoalanine synthase showed 62.9% and 95% similarity, respectively. The β-ODAP synthase converts the final intermediate L-DAPA into toxic β-ODAP whist β-cyanoalanine synthase converts O-Acetylserine into β-isoxazolin-5-on-2-yl-alanine. Since grass pea is low in methionine and cysteine amino acids, improvement of these amino acids is also needed to boost its protein content.

The analysis of aggregate data has received increasing attention in the statistical discipline over the past 20 years. Much of this attention has been focused on estimating the cells frequencies of a 2×2 contingency table given only the marginal totals; the analyses proposed have been received with missed reviews. More recently, the focus has shifted toward analysing the overall association structure rather than on the estimation of the cell frequencies. This article provides some insight int how informative the aggregate data of a single 2×2 contingency table is for assessing the association. The information contained in the margins of the table is quantified using the squared deviation of the expected cell frequencies over the possible range of values, where the row and column totals are known and fixed. The new measure we discuss to quantify this association is referred to as the aggregate informative index (AII) and is shown to be the standardized area under the squared deviation curve.

Given the increased provision of international exchange programmes in higher education, this paper examines students’ perceptions of their learning process in one Erasmus+ programme in Food Innovation and Product Design. Using evaluation model of international exchange programme and triple helix model as the analytical framework, students’ testimonials from 2015–2021 were analysed using a critical discourse analysis. EU and non-EU students had similar perceptions of learning outcomes related to knowledge of the food innovation industry, but non-EU students had a stronger sense of awareness of both the international context and multicultural interaction.

Pathogens such as bacteria, fungi and viruses are important components of soil and aquatic communities, where they can benefit from decaying and living organic matter, and may opportunistically infect human and animal hosts. One-third of human infectious diseases is constituted by sapronotic disease agents that are natural inhabitants of soil or aquatic ecosystems. They are capable of existing and reproducing in the environment outside of the host for extended periods of time. However, as ecological research on sapronosis is infrequent and epidemiological models are even rarer, very little information is currently available. Their importance is overlooked in medical and veterinary research, as well as the relationships between free environmental forms and those that are pathogenic. Here, using dynamical models in realistic aquatic metacommunity systems, we analyze sapronosis transmission, using the human pathogen Mycobacterium ulcerans that is responsible for Buruli ulcer. Our work constitutes the first set of metacommunity models of sapronotic disease transmission, and is highly flexible for adaptation to other types of sapronosis. The importance of sapronotic agents on animal and human disease burden needs better understanding and new models of sapronosis disease ecology to guide the management and prevention of this important group of pathogens.

Sublingual vaccines offer the benefits of inducing mucosal immunity to protect against respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza, while also enabling needle-free self-administration. Previously, we developed a sublingual vaccine formulated with the SARS-CoV-2 receptor binding domain antigen and Poly(I:C) adjuvant in nonhuman primates, cynomolgus macaques. This study examined the immune and inflammatory responses elicited by the sublingual influenza vaccine containing hemagglutinin (HA) antigen and Poly(I:C) adjuvant and assessed the safety of this vaccine in nonhuman primates. The Poly(I:C)-adjuvanted sublingual vaccine induced both mucosal and systemic immunities. Specifically, the sublingual vaccineproduced HA-specific secretory IgA antibodies in saliva and nasal washings, and HA-specific IgA and IgG were detected in the blood. This vaccine appeared to be safe, as judged from the results of blood tests and plasma C-reactive protein levels. Notably, sublingual vaccination neither increased the production of inflammation-associated cytokines—IFN-alpha, IFN-gamma, and IL-17—in the blood, nor upregulated the gene expression of proinflammatory cytokines—IL12A, IL12B, IFNA1, IFNB1, CD69, and granzyme B—in white blood cells. Moreover, DNA microarray analyses revealed that sublingual vaccination evoked both enhancing and suppressing expression changes in genes associated with immune-related responses in cynomolgus monkeys. Therefore, the sublingual vaccine with Poly(I:C) adjuvant is safe and creates a balanced state of enhancing and suppressing the immune-related response.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Scholars and policymakers see sustainability as depending on the interrelationships between the three pillars of the environment, society and the economy. However, it remains less clear how key stakeholders with a responsibility for educating the next generation perceive the concept of sustainability and act accordingly. In order to gain new insights, this research scrutinises participant perceptions on sustainability and climate change in two eco-schools in England for primary children (aged 5-11 years). Our case study involved individual interviews with classroom teachers and headteachers, group interviews with fourth- and fifth-year students (ages 10-12 years), and in-class observations. We also analysed data from student exercise books and photographs of school grounds to understand participants’ self-reported knowledge and perceptions of sustainability and climate change. Within a framework drawing on theories of science communication and transformative learning (a learning approach based on having challenging experiences), the results show that the integration of sustainability into the curriculum was limited and problematic. That is despite the fact that all students and teachers were aware of the environmental dimensions of sustainability, like climate change and the overuse of natural sources. These findings suggest that schools are no different to other institutional settings when it comes to dealing with the challenges of integrating sustainability into daily practice. We conclude that there is a need for in-service teacher education programmes to enable and motivate teachers to provide richer teaching-learning environments so as to enable effective learning in schools about sustainability and climate change.

This study aimed to evaluate the discrepancies between genetic and visual coat color assignment in the Sarcidano Horse and to elucidate potential reasons. Individual DNA from 90 Sarcidano Horses was used for genetic assignment of coat color to explore the correspondence with individual forms containing phenotypical traits. The MC1R exon 1 and ASIP exon 3 have been genotyped and sequenced to obtain a picture of the coat color distribution in this breed. Surprisingly, once we compared the genetic results with the individual forms reporting the phenotypic data for each subject, a certain degree of non-correspondence between the phenotypic and genetic data in relation to coat color emerged. From the genetic analysis, Chestnuts (n = 58) resulted the most common Sarcidano Horse (n = 58), followed by a quite large number of Blacks (n = 28) and a very small number of Bays (n = 4), whereas phenotypic distribution resulted in 38 Chestnuts, 40 Bays, only 2 Blacks, and 10 Grays (without the possibility of recognizing the true color they carried). Chestnut resulted a very representative coat color, while many horses that visually identified as Bays were genetically Blacks. This discrepancy, that could be due to a variety of individual and external factors, including age, time of year, living situation and dietary condition, suggesting the importance of accurate coat color identification to ensure adequate features registration and reliable prediction of offspring’s coat color.

Chikungunya (CHIKV) and Mayaro (MAYV) viruses are arthritogenic alphaviruses that promote an incapacitating and long-lasting inflammatory muscle-articular disease. Despite studies pointing out the importance of skeletal muscle (SkM) in viral pathogenesis, the long-term consequences on its physiology after acute injury and the mechanism of persistence of symptoms are still poorly understood. Combining molecular, morphological, Nuclear Magnetic Resonance Imaging, and histological analysis, we conduct a temporal investigation of CHIKV and MAYV replication in a wild-type mice model, focusing on the impact on SkM composition, structure, and repair in the acute and late phases of infection. We found that viral replication and induced-inflammation promote fast loss of muscle mass and reduction of fibers cross sectional area, by inducing muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin expression, that trigger protein breakdown, resulting in acute SkM fibers atrophy. After temporal reduction of inflammation and clearance of infectious viral particles, SkM atrophy persists until the late phase of infection. The genomic CHIKV and MAYV RNAs were still detected in SkM at the late phase, along with increased levels of chemokines and anti-inflammatory cytokines expression. In agreement with the involvement of inflammatory mediators on induced atrophy, neutralization of TNF and the reduction of oxidative stress using monomethyl fumarate, an agonist of Nrf2, decreases atrogens expression and atrophic fibers, while increasing weight gain in treated mice. Taken together, these data indicate that arthritogenic alphavirus infection could chronically impact body SkM composition and also harm repair machinery, contributing to a better understanding of mechanisms of arthritogenic alphaviruses pathogenesis and with description of potentially new targets of therapeutic intervention.

Given the widespread use of AI language models such as ChatGPT, there is an urgent need 1 to understand their political biases in different languages. This study aims to uncover such biases. To 2 investigate this issue, we applied IDRLabs’ Political Coordinates Test to ChatGPT based on GPT-4 3 in four languages - English, French, German, and Italian. The results confirm previous studies that 4 identified a left-libertarian bias in ChatGPT. There was remarkable consistency in political orientation 5 across all four languages, although minor variations were found. Differences in ChatGPT response 6 distributions across languages could indicate culturally determined differences in interpretation 7 of political questions. The results highlight the need to diversify and optimize data collection and 8 model training methods to ensure greater fairness and neutrality and minimize the influence of bias. 9 Our study highlights the importance of cultural differences in the processing and interpretation of 10 political information by AI models.

This study provides a comprehensive comparison of the Peter Chew Sum method and Newton's Sum in Quadratic Root Function Computations. Both methods offer significant advancements by alleviating the need for memorization of multiple formulas. However, when confronted with higher-order quadratic root functions such as the calculation of power 33. Newton's Sum method proves cumbersome due to its requirement of all previous answers for final computation, leading to increased steps and error risks. In contrast, the Peter Chew Sum method simplifies the computational process by requiring only a few previous answers, resulting in a more straightforward series of operations. We contextualize our analysis within the historical progression of mathematical techniques, highlighting the transformative leap represented by the Peter Chew Sum method in quadratic root function computations. Peter Chew Sum addressing the limitations of Newton's Sum , especially concerning higher-order functions such as the calculation of power 256. Furthermore, study demonstrates that the innovative approach of the Peter Chew Sum method not only simplifies complex computations but also fosters enhanced conceptual understanding among students. In conclusion, study findings underscore the profound impact of the Peter Chew Sum method on quadratic root function computations.

Strong-aroma Baijiu is a traditional Chinese spirit distilled from grains that undergo solid-state fermentation. At the end of each fermentation cycle, a significant quantity of Huangshui accumulates at the bottom of the fermentation pit, constituting one of the primary by-products in the brewing process of strong-flavor liquor. In this report, the chemical component analysis of Huangshui obtained from a strong-flavor liquor factory revealed a diverse array of organic acids, alcohols, esters, starch, reducing sugars, amino acids, and other compounds present in Huangshui. Additionally, two specialized solid-state fermentation models were developed to investigate the formation process of Huangshui. Our findings demonstrate a positive correlation between the yield of Huangshui and the initial moisture content of fermented grains. It is concluded that Huangshui is the percolate produced during the solid-state brewing process of strong-flavor liquor, with its water content originating from the initial moisture of fermented grains.

Coastal resources in Lian, Batangas, have undergone significant degradation due to a combination of human activities and natural factors, including sea-level rise, changes in sea-surface temperature, fluctuations in rainfall, increased storm frequency, and sedimentation. This has led to adverse effects on key ecosystems such as corals, mangroves, and seagrasses. To assess the extent of climate change hazards on these coastal resources, a Vulnerability Assessment was conducted in the municipality of Lian, Batangas. The assessment employed a combination of the Coastal Integrity Vulnerability Assessment Toolkit (CIVAT) and Integrated Coastal Sensitivity, Exposure, and Adaptive Capacity to Climate Change (ICSEA-C) guidelines. Additionally, the analysis incorporated detailed mapping using Light Detection and Ranging (LiDAR) data for mangroves and LandSat 8 imagery for corals and seagrasses. The findings at the municipal scale revealed that a staggering 90% of mangroves in Lian, Batangas, were highly vulnerable. This vulnerability was attributed to a high score in intrinsic characteristics and a low adaptive capacity. Similarly, a majority of seagrasses in the area were classified as medium and highly vulnerable at the municipal level. Only 10–20% of seagrasses received low to medium vulnerability scores. Shockingly, almost 100% of the coral reefs in Lian, Batangas, were deemed highly vulnerable, indicating a critical threat to this crucial marine ecosystem.

Major depressive disorder (MDD) and substance use disorder (SUD) are of immense medical and social concerns. Although significant progress on neuronal involvement in mood and reward circuitries has been achieved, only relatively recently the role of glia in these disorders have attracted attention. Detail understanding of the glial function in these devastating diseases could offer novel interventions. Here, following a brief review of circuitries involved in mood regulation and reward perception, specific contribution of the neurotrophic factors, neuroinflammation and gut microbiota to these diseases are highlighted. In this context, the role of specific glial cells (e.g., microglia, astroglia, oligodendrocytes and synantocytes) on phenotypic manifestation of MDD or SUD are emphasized. In addition, potential use of this knowledge in developing novel therapeutics is touched upon.

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar el-bow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corre-sponding forearm by hook and loop velcro and thus, helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation and thus, the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm, but through a prismatic pair which allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, that is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mecha-nism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage, by giving a first mechanical de-sign and a 3D printed prototype.

Docking results suggest that Amentoflavone has a higher affinity for investigated Serine/threonine-protein kinases, indicating potential interactions that may influence the activities of these kinases. The negative values of the binding energies signify strong and stable interactions, suggesting that Amentoflavone could be a key molecule in modulating the functions of these kinases, which play crucial roles in cellular processes such as cell cycle regulation and apoptosis.

or the first time, this communication is intended to perform the possible biological role of natural compound Hypericin, in the Crystal Structure of dual-specificity tyrosine phosphorylation regulated kinase 2 (DYRK2), by Molecular and Dynamic Simulation. From Docking analysis by Autodock Vina and Autodock 4, Hypericin shows a significant ability to binds to DYRK2 protein, an important receptor for cell growth and development. Indeed, from docking studies Hypericin reports a binding energy value of about -13 kcal/mol and Estimated Inhibition Constant (Ki)of ca 1nM . Regarding MD simulation.From our results, this theoretical research it could has excellent future implications in the fight against cancer disease.

Epithelial barrier damage plays a central role in the development and maintenance of allergic inflammation. The rise in airways epithelial barrier permeability alters the tissue homeostasis and allows the allergen and other external agents penetration. Different factors contribute to barrier impairment, such as eosinophilic infiltrate and allergens proteases action: the eosinophilic cationic proteins and the allergens' proteolytic activity have a central role in contributing to epithelial damage. In the airways, the allergens proteases degrade the epithelial junctional proteins allowing the allergen penetration and the uptake by dendritic cells. The increase in allergen-immune system interactions induce the release of the alarmins and the activation of type 2 inflammatory pathways causing, or worsening, the main symptoms at skin, bowel and respiratory levels. We aim to highlight the molecular mechanisms underlying allergenic proteases-induced epithelial barrier damage and the role of immune response in allergic asthma onset, maintenance and progression. We further will explore clinical and radiological potential biomarkers of airways remodeling in allergic asthma patients.

Neisseria gonorrhoeae is a bacterium that causes the sexually transmitted infection gonorrhea. This study employed molecular docking simulations to investigate potential interactions between natural molecules and the Fe(3+) ions import ATP-binding protein fbpC in Neisseria gonorrhoeae FA 1090. Among the screened molecules, Hypericin exhibited a remarkable binding energy of approximately -13.2 kcal/mol, indicating a robust and favorable binding affinity with the target protein. This promising outcome suggests that Hypericin could serve as a potential candidate for further exploration as a therapeutic agent against Neisseria gonorrhoeae infections. However, it is imperative to emphasize that these in silico findings necessitate experimental validation through in vitro and in vivo studies to ascertain the actual efficacy and safety of Hypericin as a treatment option.

Background: Atopic dermatitis (AD), a common dermatological condition, is often associated with significant economic and social burdens. Despite extensive studies globally, there is a gap in understanding the comprehensive impact of this condition in Romania. Objectives:This study evaluated the economic burden and quality of life impact of AD in Romania, considering both direct and indirect costs. Materials & Methods: A mixed methods cost of illness study was conducted from April to October 2023. A patient survey, expert reviews, and national health data analysis were used to assess direct medical costs (treatments, medical services), direct non-medical costs (transportation, accommodation), and indirect costs (productivity losses). Quality of life was evaluated using the EuroQol EQ-5D-5L instrument. Results: In 2022, the total cost of atopic dermatitis in Romania was 32.983.498 EUR for adults and 31.573.968 EUR for children. Adult costs included 24.534.603 EUR in direct and 8.429.884 EUR in indirect medical costs, with an average annual cost per patient of 447 EUR. Pediatric costs involved 15.026.781 EUR in direct and 16.516.410 EUR in indirect costs, averaging 290 EUR per patient annually. Conclusion: Care expenditure with AD management in Romania is substantially lower than in other countries, posing a significant economic burden, primarily driven by indirect costs. This study provides a foundation for future policy interventions and highlights the need for targeted management strategies for AD.

Background : Probiotics are composed of microorganisms that have positive effects on health when consumed in prescribed quantities. Lately, studies have attempted to establish this role of probiotics in enhancing health. Aim : The aim of this study was to compare the antifungal activity of 3 different probiotics against Candida albicans.Methodology :This was an in-vitro study. 3 types of commercially available probiotics - Bifilac, Pre pro HS and VSL-3 were cultured separately with Candida albicans and were placed under incubation. The maximum zone of inhibition was recorded for each of the probiotic solutions.Results : In this study, it was found that among the three probiotic products, Bifilac exhibited a maximum zone of inhibition of 13mm at concentration of 100µL against C. albicans, while Pre pro HS and VSL-3 showed zone of inhibition of 11mm at concentrations of 50µL and 100µL, respectively.Conclusion : Within the limits of the present study, This study could prove that probiotics can be considered as an alternative and safe treatment option for treatment of Oral candidiasis infections.

Dark pigmentation can be observed in various parts of the plant, ranging from foliage to petals to berries. The molecular basis of black pigmentation appears to vary among species, with anthocyanins playing a significant role, although specific anthocyanin types and their mechanisms differ. These findings suggest that the development of phenotypes is species-specific or varies between larger taxonomic groups; this is further supported by the polyphyletic nature of dark pigmentation. Additionally, several different regulatory mechanisms have been described for the occurrence of dark pigmentation. Firstly, the repression or knockout of the competing flavone biosynthesis has been shown to lead to darker pigmentation while another mechanism is based on the activation and upregulation of the anthocyanin biosynthesis genes in the presence of MYB transcription factors. Potential ecological functions of dark pigmentation were identified as the protection of the photosynthesis apparatus, camouflage against herbivores, and the attraction of pollinators. Promising industrial applications include microbial factories for the production of natural food colourants, induction of novel phenotypes for the ornamental plant industry and lastly the increase of anthocyanins within agriculturally relevant crops.

We used light, electron microscope and teased fiber autoradiography to show that myelinating Schwann cells synthesize phospholipids and proteins in perinuclear cytoplasm and superficial cytoplasmic channels or Cajal bands (SCC/CB). Neither phospholipid nor protein syntheses occur in paranodal loops, Schmidt-Lanterman incisures or adaxonal cytoplasm. Autoradiographic studies also show that in support of local protein synthesis, RNAs move from the nucleus into perinuclear cytoplasm and out along SCC/CB. Unlike other phospholipids, phosphatidylinositol synthesis occurs extensively in axons. This finding indicates differences in the distributions of phospholipid synthesizing enzymes in smooth endoplasmic reticulum of Schwann cell processes and axoplasm.Autoradiographs of nerves at longer survival times show that tritiated phosphatidylcholine and fucose-labeled glycoproteins move from synthetic sites to outermost myelin lamellae. From there, phosphatidylcholine spreads inward reaching innermost lamellae of thicker sheaths in days. Coupled with retention of fucose glycoproteins in outer myelin lamellae is the presence of fucose glycoprotein synthesized during early myelination in inner lamellae of thicker sheaths, a result consistent with growth of myelin sheaths from inner lamellae out. In this review, we revisit these studies because they provide temporal and spatial perspectives needed in understanding dynamic aspects of myelinogenesis and how myelinating cells respond to injury and from altered gene expression.

This paper presents a methodology for the integration of Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to assess the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the global increase in the aging population, there is an increased demand for housing solutions tailored to the needs of elderly individuals. Focusing on the importance of the early phase of design, this study aims to improve the process of making efficient decisions by providing a comprehensive assessment of the life cycle costs associated with AIP homes. The study introduces a semi-automated model for the economic evaluation of AIP homes, enabling the estimation of costs throughout the houses’ entire life cycle, from design and construction to operation, maintenance, and eventual renovation or disposal. The said model facilitates the exploration of the long-term economic feasibility of design’s related decisions with an emphasis on the importance of considering the life cycle costs early during the design process to optimize the functionality and economic viability. By investing in accessible and universal design features upfront, the initial costs for modifications can lead to long-term savings by reducing the need for extensive retrofits. The model can easily do comparison between different design alternatives in terms of their lifecycle costs, allowing designers to assess the financial impact of using important features in their design such as wider doorways, accessible bathrooms, and elevators. Overall, this study provides valuable insights for designers and homeowners about the economic aspects of designing AIP homes as a support for efficient decision-making during the early stages of the design process.

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e. sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago the ratio between emitted and sequestered carbon has varied considerably with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with domestication of crops and livestock leading to urbanisation and development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance it is important to understand the roles of high-capacity carbon sequestration systems, such as natural forests and tropical croplands, and to devise strategies to facilitate net CO₂ uptake.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

Emerging molecular and computational techniques may allow construction and computational treatment of dynamic digital twins on the scale of populations to individuals. This approach may pave the way for predictive, preventive, and personalized treatment. Because digital twins are independent of geographical location, this may also contribute to global equitable health.

Based on our recent thermodynamic formulation of the Friedmann equation, we demon- strate how the age of the universe must be approximately 14.6 billion years. This is about 800 million years older than predicted by the Λ-CDM model and therefore greatly alleviates the early galaxy formation problem.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban-rural transition affects PM2.5 are warranted within the framework of urban-rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban-rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban-rural transformation and PM2.5. The results are as follows: (1) Over time, there was a discernible upward tendency in the change in urban-rural areas. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban-rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative-positive-negative changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. Currently, an inverted "U-shaped" curve forms the left side of the impact of population transition (PT) on PM2.5. Land development (LT) has a "U-shaped" curve for its effect on PM2.5. This research provides a new perspective on the topic of PM2.5 and its connection to urban-rural integration, which is crucial to understanding the dynamics of this shift. To achieve its goal of high-quality development, it supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, it can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.

Optimizing warehouse operations and leveraging data insights are driving forces behind the rise of automation. This research explores the development of the SWIF warehouse management system (WMS) using the Agile methodology, a departure from traditional software development approaches. Agile’s iterative and step-by-step approach facilitated a streamlined development process. It allowed for quick adjustments based on evolving needs and feedback from stakeholders. This resulted in a substantial decrease in development time and a noticeable improvement in the final product’s quality. The study emphasizes the positive effect of Agile on teamwork and customer satisfaction. Developers reported enhanced communication and collaboration, leading to a stronger sense of ownership and engagement. Customers expressed greater satisfaction with the features and functionalities delivered in stages. The successful implementation of Agile in SWIF’s development showcases its effectiveness in managing complex and ever-changing software development projects. The study encourages the adoption of Agile methodology for similar projects to boost responsiveness, quality, and overall project success.

The Jakarta LARP community, established in March 2020 and primarily active in Gelora Bung Karno, fosters inclusivity, diversity, and physical role-playing activities. It adheres to a policy of excluding ultra-nationalist ideologies and integrates new members despite language barriers. Bruhn (2011) defines this kind of community sharing the same goals, values, and lifestyles, while McMillan & Chavis (1986) outline four aspects of the sense of community.The research discovered that communities endure by utilizing solidarity built on the basis of their peculiarities. This study employed ethnographic methods alongside grounded research to identify the community's culture and their efforts to sustain and utilize it as community solidarity.

The presence of the mite, Varroa destructor, requires the use of acaricide treatments in most honeybee colonies. Amitraz is one of the most common acaricide substances used by beekeepers. Some Varroa mite populations have developed resistance to amitraz leading to a loss of efficacy of amitraz-based treatments. Two products, Apivar and Supatraz were applied in the same apiary. Both treatments are amitraz-based but have a different galenics. Thanks to field data, a dynamic model was used to simulate the action of Apivar and Supatraz on the mite population. We consider two parameters to compare the products: daily mortality rate and treatment duration. In the field, the percentage of efficacy between the two products was not significantly different but Supatraz kills mite faster and decreases 90% of the mite infestation in 28.4 days compared to 50.9 days for Apivar. Thanks to modelling, we showed the daily impact of the two different products on mite population. Supatraz has a higher daily mortality rate during the first two weeks than Apivar. Supatraz requires a lower efficacy to stabilize the varroa mite population due to its faster release of active ingredient than Apivar, needing a shorter period to achieve the same result. Depending on the model, Supatraz conserve a good efficacy for low resistant mites but not for high resistant mites. These results show that the comparison of the efficacy of two products with different characteristics (duration of treatment and daily mortality rate) should be analyzed with caution.

This study centered on examining 60 different antibiotics by assessing their primary toxicity parameters through the pkCSM database. The parameters included Max. Tolerated Dose (Human) (log mg/kg/day), Oral Rat Acute Toxicity (LD50) (mol/kg), Oral Rat Chronic Toxicity (LOAEL) (log mg/kg_bw/day), and Minnow Toxicity (LC50) (log mM). The results highlight four potential antibiotics—cefotaxime, ceftriaxone, imipenem, and meropenem—that exhibited lower toxicity effects compared to other compounds. These findings imply a relatively favorable safety profile for these antibiotics concerning the specified toxicity parameters. However, it is crucial to interpret these outcomes within the context of the specific clinical application and to consider additional factors for a comprehensive safety assessment. It's essential to acknowledge that evaluating toxicity is intricate, and the determination of what is considered "less toxic" can rely on specific criteria and the context of use. Furthermore, individual responses to antibiotics may vary, and the selection of an antibiotic should be based on diverse factors, including the specific infection being treated, the patient's health, and potential side effects.

Gradual disability worsening in Multiple Sclerosis (MS) is indicative of confirmed progression that mostly involves progression independent of relapse activity (PIRA) and is associated with neurodegeneration. Insights from pathology reveal grey matter involvement (neocortex, hippocampus, spinal cord, and deep grey matter structures) and microglia activation in early stages of MS. Cortical lesions result in thinning and atrophy and are considered accurate predictors of disability progression and cognitive decline.Neuroimaging biomarkers, such as high-resolution 7T MR images and TSPO-PET, have been examined for the detection of cortical pathology linked to cognitive deficits and thus progression, but are limited in clinical practice due to availability, time constraints, and cost.Electroencephalography (EEG) emerges as a non-invasive, cost-effective tool that reflects cortical activity in MS. Its potential in monitoring cognitive impairment is explored by focusing on nonlinear EEG analysis. The MuST PREDICT^TM project aims to extract linear and nonlinear EEG features, investigating their role in predicting disease progression and optimizing treatment. The study employs retrospective, cross-sectional, and prospective designs, utilizing EEGs from various MS forms, cognitive assessments, serum/cerebrospinal fluid biomarkers and neuroimaging. The methodology involves time-based and spectral feature extraction, employing artificial intelligence classifiers and brain criticality-based approaches.The conceptual framework of an innovative modality is herein presented, that would EEG for early MS progression detection. Standardization of the methodology could lead to the creation of a digital tool for better prognostication and treatment strategy optimization.

Therapies targeting amyloid, such as monoclonal antibodies like Lecanemab, Aducanumab, and Donanemab, have garnered FDA approval but remain controversial due to limited clinical benefits and significant side effects against Alzheimer. The present theorical work utilized Virtual Screening and Docking techniques to explore potential small natural molecules' interactions with the KIBRA protein, a promising target for drug development. Kibra protein is considered a biomarker for synaptic dysfunction and cognitive decline. Employing Autodock Vina and Pyrx software, on several natural compounds and found Hypericin to exhibit the lowest binding energy of approximately -9 kcal/mol. Previous studies have highlighted Hypericin's strong affinity for Alzheimer's Disease-associated targets like ABAD and BACE1. These findings suggest Hypericin could be as a potential therapeutic agent for Alzheimer's Disease. Further research is warranted to validate its efficacy and safety for clinical use.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

In this report we describe the case of a healthy, young, athletic woman who developed acute lymphoblastic leukaemia (ALL)/lymphoblastic lymphoma (LBL) after receiving the second dose of the Pfizer/BioNTech modified mRNA (modRNA) COVID-19 genetic vaccine (marketed as Comirnaty^®). The first dose of the genetic vaccine did not appear to illicit any noticeable side effects, but within 24 hours of the second dose the patient suffered widespread and intensifying bone pain, fever, vomiting, and general malaise. Due to the persistence of the symptoms, the patient underwent a series of tests and examinations including a full laboratory workup, a consult with a clinical immunologist and rheumatologist, a Positron Emission Tomography (PET) imaging, as well as an osteomedullary biopsy. These together led to a definitive diagnosis of ALL. A time interval of 16 weeks from the second vaccination to the diagnosis of cancer was noted. Several similar cases with identical pathology which developed after the modRNA COVID-19 vaccination, are described in case reports in the scientific literature. The massive and indiscriminate use of genetic vaccines to fight COVID-19 is raising serious concerns about their safety and about the technology platform as a whole for this purpose. Growing evidence is accumulating regarding the biodistribution and persistence of the modRNA which can reach, thanks to the lipid nanoparticles, a multitude of tissues and organs of the body, including the bone marrow and other blood-forming organs and tissues. Moreover, there is evidence that the modRNA vaccines display a particular tropism for the bone marrow, influencing the immune system at multiple levels and being able to trigger not only autoimmune-based pathologies, but also neoplastic mechanisms. The aim of this article is to assess, on the basis of the available scientific literature, the risk of developing haematopoietic cancers after modRNA vaccination, and to investigate the potential genetic mechanisms involved in the pathogenesis of disease.

In recent times, the adoption of cloud computing by small and medium-sized 9 enterprises (SMEs) has become a crucial issue. These businesses face substantial difficulties in 10 accessing reliable and affordable IT infrastructure due to economic constraints and technological 11 restrictions. Hybrid cloud computing solutions which incorporate Infrastructure as a Service 12 (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offer a feasible approach to 13overcoming these challenges. This study proposes a systematic literature review of cloud 14 computing's impact on SME performance emphasizing cost-efficiency, reliability, and 15 competitive advantages, using the 2020 Preferred Reporting Items for Systematic Reviews and 16 Meta-Analyses (PRISMA) framework. The inclusion criteria are bounded by (1) publication date 17 between 2014 and 2024, (2) English-language publications, (3) research focused on cloud 18 computing for SMEs, and (4) studies with a clear analytical framework for evaluating SME 19 performance. Following this process, 90 eligible research studies were included. The average 20 total cost for cloud services varies significantly, with operational costs reflecting the scalability 21 of the services and the specific needs of SMEs. This analysis offers valuable guidance for SMEs 22 and stakeholders in optimizing cloud-based strategies for enhanced performance.

Precision farming is transforming how antibiotics get used on farms. It fights the overuse that creates superbugs and harms nature. Farmers now have high-tech tools to use antibiotics ultra-precisely. Drones, AI, and advanced watering equipment help them pinpoint only sick plants and animals needing treatment. This shields crops while avoiding environmental damage. More careful antibiotic use makes livestock healthier and boosts harvests too. And it prevents resistant germs, where antibiotics stop working altogether. The approach helps farms in multiple ways, using fewer resources for even better results. There are some growing pains, like high costs for small farms. And questions around data privacy also cause concern. But overcoming these hurdles could help precision farming spread far and wide. This technology promises safer, greener, and more productive agriculture. Targeted use of antibiotics protects both crops and people from drug-proof germs. And nature benefits when less medicine spreads into soil and water. The future looks bright for farming with precision.

Based on the nonlinear failure criterion and modified tangential technique, the upper bound solutions of supporting pressure on the deep tunnel face were obtained under pore water pressure conditions. The influence of parameters on the supporting pressure and collapse range was investigated according to the unlimited block failure mechanism. It is found that the upper bound solutions of supporting pressure increase with the growth of the nonlinear coefficient and pore water pressure coefficient. The collapse range of the tunnel face scales out with the increase of the nonlinear coefficient and shrinks when increasing the pore water pressure coefficient. Moreover, with the increase of the nonlinear coefficient, the impact strength on supporting pressure and collapse range declines gradually. According to the calculating results, it is manifest that both the pore water pressure and nonlinear criterion factors have negative impacts on the stability of the tunnel face. Thus, more attention should be paid to these parameters to ensure face stability in deep tunnel construction.

The unravelling of the human genome created new perceptions on the origin and evolution of diseases and for cancer in particular, it established the notion that neoplasia has been a companion of life since its ap-pearance on earth. It is not surprising that neoplasms, in various forms, develop in numerous species of animals and even in plants. Unmistakable accounts of cancer with clinical features as are understood today, begin in the 5th c. B.C. The principles and practice of the Hippocratic and Galenic tradition dominated cancer care virtually into the 20th century. Advanced sequencing technologies at the dawn of the 21st century, generate new ther-apeutic opportunities with immunotherapy, oncolytic virotherapy and gene transfer, the latter especially in cases of hereditary cancer.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

The application of distributed fiber optic strain and temperature measurement can be utilized to address a multitude of measurement tasks across a diverse range of fields, particularly in the context of structural health monitoring in the domains of building construction, civil engineering, and special foundation engineering. However, a comprehensive understanding of the influences on the measurement method and the sensors is essential to prevent misinterpretations or measurement deviations. In this context, this study investigated the effects of moisture exposure, including various salt solutions and a high pH value, on a distributed strain measurement using Rayleigh backscattering. Three fiber optic sensors with different coating materials and one uncoated fiber were exposed to five different solutions for 24 hours. The study revealed significant discrepancies in deformation between the three coating types depending on the surrounding solution. Furthermore, in contrast to the prevailing literature, which predominantly describes swelling effects, a negative deformation was observed in a magnesium chloride solution. The findings of this study indicate that corresponding effects can impact the precision of measurement, potentially leading to misinterpretations. Conversely, these effects could be used to conduct large-scale monitoring of chemical components using distributed fiber optic sensing.

This study investigates the role of sustainable inventory management within supply chains, exploring strategies, benefits, challenges, and implications for organizations. Through qualitative research involving semi-structured interviews with supply chain professionals from diverse industries, key themes emerged. Sustainable practices such as green logistics, waste minimization, and ethical sourcing were identified as critical strategies to reduce environmental impact and enhance operational efficiency. Technological advancements, including AI, IoT, and blockchain, facilitate real-time monitoring, predictive analytics, and transparency, supporting data-driven decision-making in inventory management. Benefits include cost reduction, improved brand reputation, regulatory compliance, and risk mitigation, positioning organizations favorably in a competitive landscape shaped by environmental regulations and consumer expectations. Challenges such as cost constraints, regulatory complexities, and organizational inertia necessitate strategic investments, collaborative efforts, and leadership commitment to overcome. The study underscores the importance of integrating environmental, social, and economic considerations into supply chain practices to foster sustainability and resilience. Looking forward, the study advocates for a holistic approach to sustainable supply chain management, emphasizing innovation, stakeholder engagement, and capacity building. By aligning organizational values with societal expectations and regulatory frameworks, businesses can navigate complexities, drive positive environmental and social impacts, and achieve sustainable growth. This research contributes insights into current practices and future directions for sustainable inventory management, informing strategies that balance profitability with environmental stewardship and societal responsibility.

Gilbert's syndrome (GS) is a common hereditary condition characterized by mild increases in serum bilirubin levels due to inherited defects in bilirubin metabolism. This review, based on data from peer-reviewed articles and journals spanning from 1977 to January 2024, provides a comprehensive overview of over four decades of research on GS. Early studies primarily focused on defining the clinical and genetic characteristics of the syndrome, laying the foundation for subsequent investigations. More recent research has delved into the genetic mechanisms underlying the reduced expression of bilirubin UDP-glucuronosyltransferase, which has significantly enhanced our understanding of the pathogenesis of GS. Furthermore, recent studies have shed light on the clinical implications of GS, including its association with non-alcoholic fatty liver disease and mortality risk, highlighting the complex interplay between genetic factors, bilirubin metabolism, and clinical outcomes. Despite its generally benign nature, thorough research is essential to fully understand the impact of GS on patients' lives. This review emphasizes the importance of ongoing research to deepen our understanding of GS and its broader implications.

With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, K-Nearest Neighbor (KNN), Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

Instagram is a popular social media platform among youth, with over a billion active users worldwide. The platform allows users to share photos, videos, and stories and connect with friends and followers. Instagram has become a significant part of youth culture, with many young people spending a considerable amount of time on the app. According to Enterprise (2012:02), Instagram is a community shared among one member with other members from all over the world. This study examines the impact of Instagram on the physical and mental well-being of Indian youth. With the rapid proliferation of social media platforms in India, Instagram has emerged as a prominent influence on the lives and perceptions of young individuals. Studies have shown that using Instagram among youth can have positive and negative effects. On the positive side, Instagram can be a way for young people to express themselves, connect with others, and discover new interests. However, excessive use of the platform can also lead to negative outcomes, such as social comparison, anxiety, and low self-esteem. Instagram addiction among youth is a growing concern. Some young people may feel compelled to check their accounts frequently and may experience negative emotions if they are unable to do so. In extreme cases, excessive use of the platform can interfere with daily activities and relationships, leading to a negative impact on mental health. Parents and educators can play a role in helping young people manage their Instagram use. This can include setting limits on screen time, encouraging healthy online habits, and promoting offline activities and social interaction. Additionally, mental health professionals can offer support to young people who are struggling with addiction or other negative effects of Instagram use. This study explores the prevalence of Instagram dependency among Indian youth.

Reduction of the density operator for quantum subsystems is derived without the use of the Born Rule.

Orificed Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. From their reliability mainly depends the success of a spacecraft’s mission equipped by electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear system of plasma equations, have been proposed in the open literature. These are solved commonly by means of iterative procedures. This paper investigates the possibility to solve them by means of Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as function of mass flow rate is very narrow (2÷5V), moreover the results match very well the experimental data except at the lowest mass flow rate (0.08mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%; the highest data dispersion regards the plasma density in the emitter region (± 20 % of the average value) and the wall temperatures (± 50K respect to the average values) of orifice and insert; those of the others variables are very tiny.

This work is the first in literature to tackle the difficult open problem of determining the upper bound and threshold theorem for the TDCDP (time-dependent controller parameter) of the (Fokker Planck Kolmogorov) probability density function. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to information theory, graph theory, data science, finance, economics, and beyond. So, this by default adds more taste and credibility to this study. This leads by nature to introducing a different flavor to this ground-breaking research by highlighting the impact of Fokker Planck Kolmogorov(FPK) to revolutionize crypocurrency,which have received its name because it uses encryption to verify transactions, a new debatable digital payment system that doesn't rely on banks to verify transactions. It’s a peer-to-peer system that can enable anyone anywhere to send and receive payments. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Zs’ perceptions to-ward plant-based foods and diets, and explore the relationship between attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet with willingness to adopt green-eating practices. Using cross-sectional data from uni-versity students in Greece, India and UK, various tools were employed to determine the factors influ-encing youths’ consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students’ viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Zs’ willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents toward plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of ani-mal-based foods, dissatisfaction with plant-based foods attributes and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a rather «homogeneity» in the factors influencing youths’ intention to adopt plant-based diets, with attitude components, meal prep-aration indicators, perceived barriers to eating “green” and personal factors, viz. self-assessed knowledge on healthy eating and physical activity, being strongly associated with students’ willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths’ transition to a more sustainable lifestyle.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

BACE1 is involved in the production of beta-amyloid peptides, and inhibiting its activity is a potential therapeutic strategy for managing Alzheimer's disease, as excessive beta-amyloid accumulation is a characteristic feature of the disease.Computational results suggest that hypericin has the potential to act as an inhibitor of BACE1. The Molecular Docking results indicate a high binding affinity between hypericin and BACE1, with a notable binding energy score of -10.3 kcal/mol. Additionally, the formation of various chemical bonds, including hydrogen bonds and hydrophobic bonds, further supports the idea that hypericin may effectively interact with the active site of BACE1. It's important to note that while the in silico Molecular Docking results are promising, further in vitro and in vivo studies would be necessary to validate and better understand the inhibitory potential of hypericin against BACE1. Additionally, the practical application of hypericin as a BACE1 inhibitor in a therapeutic context would require careful consideration of factors such as bioavailability, safety, and efficacy.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Human papillomavirus (HPV), a DNA virus, causes one the most common sexually transmitted infections (STI), spreading through direct sexual contact to the anogenital and oropharyngeal regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals HPV causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). More than 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. The HPV driven OPCs present some unique infection driven cellular and molecular features, constituting distinct preventive, therapeutic and prognostic strategies, in contrast to HPV negative (HPV-) OPC. In this manuscript, we drive through the role of HPV, from a sexually transmitted infection to malignancy transformation landscape, while parsing through the significant immune response environment. In this comprehensive review we collate key literature around transmission and epidemiology of HPV, while addressing the current state of clinical management, diagnosis, prevention and screening strategies, that can help shed light on this critical area.

This study investigates the potential of natural compounds against HIV-1 protease, a critical enzyme in the replication cycle of human immunodeficiency virus type 1 (HIV-1). HIV-1 protease is essential for cleaving large precursor proteins into smaller functional proteins necessary for viral maturation and infectivity. Protease inhibitors, specifically targeting HIV-1 protease, effectively suppress viral replication, aiding in the management of HIV infection. Using the SWISS DOCK Server, several natural compounds were investigated for their potential as inhibitors of HIV-1 protease. Docking results revealed that Amentoflavone showed excellent binding affinity with high Estimated ΔG values, suggesting its potential as an inhibitor of HIV-1 protease. Additionally, Amentoflavone exhibited similar promising results when tested against Roussarcoma virus protease. These findings indicate the potential of Amentoflavone as a natural compound against HIV and warrant further investigation into its therapeutic applications.

Einstein was skeptical of part of quantum mechanics, and he believed that Heisenberg’s uncertainty principle did not reflected the full depth of reality. He famously stated, ”God does not play dice with the universe.” However, since quantum mechanics has passed so many experimental tests, most physicists today assume that Einstein was wrong. Nevertheless, we will demonstrate that Heisenberg’s uncertainty principle must be modified to unify gravity with quantum mechanics. This modification leads to the Certainty-Uncertainty principle where the certainty part is strongly linked to gravity. This means Einstein likely was right in his intuition also on this point. The key is to understand that the de Broglie wavelength is not the actual wavelength of matter but merely a derivative of the true matter wavelength, namely the Compton wavelength. Furthermore, one must understand that the gravitational constant, first introduced in 1873, is simply a composite constant needed to rectify the in- complete definition of kilogram mass introduced into Newtonian gravity in 1873.

Sums of M consecutive squared integers (a+i)^2 equaling squared integers (for a<=1, 0<= i<= M-1) yield remarkable regular linear features when plotting values of M in function of a. These features correspond to groupings of pairs of a values for successive same values of M around straight lines of equation mu*M = 2a and are characterized in this paper for rational values of mu.

The detection and recognition of tables in image-based documents is a challenging and essential task for automated data extraction and anal- ysis. This paper introduces a methodology that utilizes Convolutional Neural Networks (CNNs) to address this problem. By harnessing the ro- bust visual recognition abilities of CNNs, the proposed method effectively detects and extracts tables from image-based documents. Additionally, an Encoder-Dual Decoder (EDD) architecture is employed to extract the textual content from table cells, enabling the transformation of visual data into structured, machine-readable formats. This structured data is then further processed using a Vector Space Model (VSM)-based language modeling technique for query-based table retrieval. The methodology’s accuracy is assessed by analyzing the spatial relationships between the extracted cells and comparing them against a pre-defined table structure. The results provide an evidence to the effectiveness of the proposed ap- proach, with an impressive 85% accuracy rate and a corresponding error rate of 15%. This accurate extraction and recognition of tables from image-based documents can significantly enhance applications in data analysis, information retrieval, and knowledge extraction.

The dynamic landscape of AI adoption and its impact on employment in Malaysia demands a multifaceted approach to navigate the challenges and harness the opportunities presented. While the government's commitment to fostering AI adoption through initiatives like the National AI Framework and the Malaysia Digital Economy Blueprint is commendable, it is imperative to address concerns regarding job displacement and ensure inclusive growth. This study identifies the pressing research problem of workforce readiness for the AI-driven future, emphasizing the need to explore strategies for reskilling and upskilling affected workers. By employing critical lenses such as Marxist, feminist, and postcolonial perspectives, scholars highlight the power dynamics and societal implications inherent in AI deployment, guiding the development of policies and interventions that prioritize workers' interests and address social inequalities. Moving forward, a comprehensive approach combining policy interventions, educational reforms, and inclusive strategies is essential to maximize the benefits of AI adoption while mitigating its potential risks. Longitudinal studies, interdisciplinary research, and comparative analyses will further advance our understanding and inform evidence-based policymaking in this evolving field. Ultimately, by prioritizing workforce preparedness, social equity, and sustainable development, Malaysia can position itself as a leader in the AI-driven global economy, fostering a resilient, inclusive, and prosperous society for all.

In order to search possible residual minor phases in Y-Ba-Cu-O family, powdered mixtures of Y₂O₃ + BaCO₃ + CuO and, independently, superconducting compound YBa₂Cu₃O_7-x have been treated in evacuated cells and elevated temperatures.YBa₂Cu₃O_7-x was reduced to YBa₂Cu₃O₅ by use of the special home designed Taconis-Knudsen vacuum device. A subsequent doping by oxygen converts produced insulator YBa₂Cu₃O₅ to semiconductor or metal YBa₂Cu₃O_5+x (0<x<0.3). In addition to YBa₂Cu₃O₅, 0.05 volume percent of the minor delafossite phase Y₂Cu₂O₄ was being spotted in the powder mixture 1/2 Y₂O₃ + 2BaCO₃+6Cu₂O, heated up to 818 °C in an inert gas atmosphere. An attempt to prepare the insulating bulk delafossite samples was successful, and subsequent doping by oxygen produces novel metallic phases.

Molecular docking, a computational method predicting how molecules bind to target proteins, is employed in this study to explore Polydatin's interactions with Ras-related protein Rab (Rab) proteins using the Mcule Database. This database validates the docking results and offers insights into the pharmacological properties of Polydatin. Notably, specific Rab proteins like Rab-5A, HRas, and Rab-18 exhibit robust interactions with Polydatin, indicated by favorable binding energies.These findings illuminate the potential roles of Polydatin in therapeutic and biological contexts, emphasizing its interaction with Rab proteins. The Mcule Database enhances the credibility of the study, providing additional perspectives on Polydatin's applications. The observed interactions, particularly with key proteins like HRas, present a promising avenue for modulating cellular signaling pathways. This modulation holds therapeutic potential, potentially addressing conditions associated with pathway overactivity, including specific cancer types.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

The lung epithelium regulates gas exchange and is the first line of defense against airborne toxicants and microbial infections. Disruption of the integrity of lung epithelium results in altered permeability and severely impacts normal lung functions. To develop biomarkers of potential harm (BoPH) for assessing adverse health effects of cigarette smoking, we have evaluated lung barrier function. In a single-center, ambulatory, clinical study we measured lung permeability, as the half-life (T1/2) of inhaled 99mTC-DTPA, in 17 subjects consisting of 6 Smokers (SMK), 5 Moist Snuff Consumers (MSC), and 6 Non-Tobacco Consumers (NTC) at three weekly test visits. Smokers, relative to MSC and NTC, exhibited significantly faster clearance of the inhaled probe (shorter T1/2), indicating increased lung permeability. NTC and MSC had comparable clearance times of the inhaled probe (longer T1/2). Least squares regression modeling showed that the variability of the test was minimal and was not significant. Prediction profiling showed that the repeatability of the lung permeability test was robust and was able to differentiate the tobacco product use groups in this study. Thus, altered lung barrier function may serve as a BoPH for assessing the effects of tobacco use.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

Multilingual classrooms are becoming more common worldwide due to increasing mobility. Teaching foreign languages using the mother tongue in multilingual classrooms has become problematic; some of the significant concerns in Japanese language acquisition – such as the high contextuality of the language, vocabulary and kanji learning – make the direct teaching method confusing for many students, especially at the beginner and elementary levels. This article provides the preliminary results of a study carried out as a component of a more extensive empirical research project on instructional languages in classrooms teaching Japanese as a foreign language. It aims to gather, analyse and synthesise data from a survey conducted at a supranational level. The results of the first two sections of a questionnaire containing closed and open-ended questions were used for a quantitative analysis of the profile of Japanese language instructors with a focus on finding possible connections. The wide geographic distribution of the respondents (274 teachers from fifty-seven countries and regions) made it possible to collect data from a more representative sample and draw meaningful conclusions about the spread of multilingual classrooms that would be impossible in smaller-scale research. The study’s primary results show the proportion of multilingual classrooms among countries in different regions and across different educational stages, the distribution of Japanese language instructors in various educational stages depending on the highest level of formal education attained, and the proportion of part-time instructors working in different academic institutions.

Urbanization influences food culture, particularly in low- and middle-income countries, where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labeling standards are crucial for regulating manufacturing practices and aiding consumers in making healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labeling standards in Dar es Salaam, Tanzania. A cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labeling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size. The majority of the snacks demonstrated partial adherence to labeling standards. Imported products showed significantly better full adherence to both Tanzanian and International standards. Notably, over half of the products used English for labeling, and infrequently used the recommended World Health Organization Front-of-Pack nutrition Labeling. Product category, origin, and package size were significantly associated with higher levels of International standard adherence (p<0.05). The inadequate adherence to mandatory labeling standards, scarce use of Swahili and FoPL highlight the need to strengthen labeling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labeling practices is of urgency as we seek to address diet-related Non-communicable diseases.

Analysis of complex DNA mixtures is limited by the selected genetic markers characterized and assay technology. Small panels of short tandem repeats (STRs) are the current standards in most countries. Extension of STRs with hundreds of single nucleotide polymorphisms (SNPs) is now commercially available. The current set of extension SNPs focus on phenotypic and biogeographic ancestry informative SNPs. Larger panels with thousands of SNPs have been developed that can identify up to 10 contributors to forensic DNA samples. Herein, multiple sets of SNPs were selected with the focus of increasing the upper limit of detectable contributors. Larger SNP panel sets with lower minor allele frequency (MAF) alleles can detect large numbers of contributors to in silico mixtures of randomly selected profiles from the 1,000 Genomes project. These results provide a path to enable characterization of complex DNA mixtures that are currently impossible to analyse.

Advanced Persistent Threats (APTs) are sophisticated cyberattacks aimed at stealing sensitive information or causing damage over an extended period. Detecting APTs is crucial for maintaining cybersecurity, and machine learning (ML) has emerged as a powerful tool in this domain. This paper explores the role of ML algorithms in detecting APTs, comparing their accuracy and effectiveness. We evaluate various algorithms, discuss the challenges in achieving superior accuracy, and suggest strategies for improvement. Our findings highlight the potential and limitations of ML in APT detection, emphasizing the need for continuous advancements in this field.

AbstractBackground: Empowering Communities, Combating HIV/AIDS: The Indispensable Role of Community-Based Strategies in Awareness and PreventionObjective: Highlight the significance of community-based approaches in addressing the ongoing HIV/AIDS pandemic through enhanced awareness, reduced stigma, and effective prevention measures.Method: This article provides a theoretical analysis of community-based HIV/AIDS interventions, drawing upon key strategies and successful examples from diverse settings.Results: Community-based initiatives emerge as critical factors in empowering individuals and communities to take ownership of their health. Through culturally-sensitive education, accessible testing, and comprehensive support services, these initiatives effectively diminish the social stigma surrounding HIV/AIDS and promote preventative behaviors. Examples of successful programs illustrate the potential of community-driven approaches to achieve measurable reductions in transmission rates and improved health outcomes.Conclusion: The fight against HIV/AIDS necessitates robust community engagement. Community-based interventions, grounded in local knowledge and addressing specific needs, offer a promising pathway towards fostering health equity, reducing stigma, and ultimately curtailing the spread of the virus. Further research and investment in these initiatives are crucial to ensuring their scalability and sustained impact on global HIV/AIDS prevention efforts.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

Models of quantum measurements are presented, including observers subject to the laws of quantum mechanics. Observers perform multiple measurements in an attempt to disprove the collapse hypothesis. It is shown that under reasonable assumptions the effort cannot succeed. Under a unitary evolution the end state is a correlation between the result of the first measurement and the result(s) of the others, consistent with the observer's collapse hypothesis. The observer may conclude that the laws of nature include a non-unitary collapse mechanism, even if one does not actually exist. Discard of information is the crucial factor which distinguishes entanglement from measurement.

Mutations in p53 and KRAS are seen in most cases of colon cancer. The impact of these mutations on signaling pathways related to cancer growth has been studied in depth, but relatively less is known on their effects on amino acid transporters in cancer cells. This represents a significant knowledge gap because amino acid nutrition in cancer cells profoundly influences macropinocytosis and ferroptosis, two processes with opposing effects on tumor growth. Here we used isogenic colon cancer cell lines to investigate the effects of p53 loss and KRAS activation on two amino acid transporters relevant to macropinocytosis (SLC38A5) and ferroptosis (SLC7A11). Our studies show that the predominant effect of p53 loss is to induce SLC7A11 with resultant potentiation of antioxidant machinery and protection of cancer cells from ferroptosis whereas KRAS activation induces not only SLC7A11 but also SLC38A5, thus offering protection from ferroptosis as well as improving amino acid nutrition in cancer cells via accelerated macropinocytosis. Niclosamide, an FDA-approved anti-helminthic, blocks the functions of SLC7A11 and SLC38A5, thus inducing ferroptosis and suppressing macropinocytosis with resultant effective reversal of tumor-promoting actions of oncogenic mutations in p53 and KRAS. These findings underscore the potential of this drug in colon cancer treatment.

Purpose: Arab Americans are one of the largest ethnic groups in the United States. The mental health of Arab Americans and opinions on mental health, however, remain understudied despite higher rates of mental illness and lower rates of treatment. Unique cultural conceptualizations of mental illness may generate stigma, contributing to disparities in treatment. This study aimed to assess the attitudes and perspectives of Arab Americans regarding mental illness in order to address barriers to treatment. Methods:Using an anonymous bilingual Qualtrics survey, we assessed Arab and non-Arab participants (n = 294; ages 18+) on their conceptualizations of the causes of mental illness and on their stigma towards mental illness and mental health treatments. Comparisons were performed between Arabs and non-Arabs, as well as among Arabs. Results:Compared to non-Arabs, Arabs reported higher average mental illness stigma and were more likely to rate psychiatric medications as addictive and unhelpful. Among Arabs, being Christian, male, and older (>29 years old) were each associated with higher stigma. Arabs were less likely than non-Arabs to believe in a biological explanation for mental illness. Conclusion: Our results demonstrate higher levels of stigma towards mental illness among Arab Americans, especially Christian, male, and older Arabs, potentially related to a weaker belief in a biomedical model for mental illness. These are among the first findings describing mental illness stigma and beliefs about mental health in an Arab American community and may inform future interventions providing culturally competent care to Arab Americans.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

Background. Azithromycin (AZM) is widely used in cystic fibrosis (CF) treatment following clinical trials demonstrating improved lung function and fewer pulmonary exacerbations. While the precise mechanisms remain elusive, immunomodulatory actions are thought to be involved. We previously reported impaired phagocytosis and defective anti-inflammatory M2 macrophage polarization in CF macrophages. This study systematically analyzed the effect of AZM on the functions of unpolarized and M1/M2 polarized macrophages in CF. Methods: Monocytes, isolated from the venous blood of patients with CF (pwCF) and healthy controls (HCs) were differentiated into monocyte-derived macrophages (MDMs) and infected with P. aeruginosa. P. aeruginosa uptake and killing by MDMs in the presence or absence of AZM was studied. M1 and M2 macrophage polarizations were induced and their functions and cytokine release were analyzed. Results: Following AZM treatment, both HC and CF MDMs exhibited a significant increase in P. aeruginosa uptake and killing, however, lysosomal acidification remained unchanged. AZM treatment led to higher activation of ERK1/2 in both HC and CF MDMs. Pharmacological inhibition of ERK1/2 using U0126 significantly reduced P. aeruginosa uptake in HC MDMs. M1 macrophage polarization remained unaffected, however, AZM treatment led to increased IL-6 and IL-10 release in both HC and CF M1 macrophages. AZM also significantly increased the phagocytic index for both pHrodo E. coli and S. aureus in CF M1 macrophages. In CF, AZM treatment promoted anti-inflammatory M2 macrophage polarization, with an increased percentage of CD209+ M2 macrophages, induction of the M2 gene CCL18, along with its secretion in the culture supernatant. However, AZM did not restore endocytosis in CF, another essential feature of M2 macrophages. Conclusions: This study highlights the cellular functions and molecular targets of AZM which may involve improved uptake of both gram-positive and gram-negative bacteria, restored anti-inflammatory macrophage polarization in CF. This may in turn shape the reduced lung inflammation observed in clinical trials. In addition, we confirmed the role of ERK1/2 activation for bacterial uptake.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

Metallothioneins are multifunctional proteins implicated in various cellular processes. They have been used as biomarkers of heavy metal exposure and contamination due to their intrinsic ability to bind heavy metals and their transcriptional response to both physiological and noxious metal ions such as cadmium (Cd) and mercury (Hg). In this study, we aimed to clarify the role of iron and reactive oxygen species (ROSs) in the induction of the metallothionein system (Mtt) in the ciliate protozoan Tetrahymena thermophila. We investigated the relative mRNA abundances of the metallothionein genes Mtt1, Mtt2/4, and Mtt5, revealing for the first time their responsiveness to iron exposure. Furthermore, by using inhibitors of superoxide dismutase (SOD) and catalase (CAT), alone or in combination with iron, we highlighted the roles of superoxide ion and endogenous hydrogen peroxide, as well as the complex interplay between the metal and ROSs. These results enhance our understanding of the metallothionein system in ciliates and suggest that ROSs may be a primary evolutionary driver for the selection of these proteins in nature.

The COVID-19 pandemic has revealed significant disparities in disease severity and mortality across various population groups and regions. These disparities cannot be attributed solely to healthcare systems, socioeconomic factors, living conditions, or individual immune landscapes; environmental factors also play a crucial role. This work explores how variations in nasal morphology related to age, gender, and race can affect the respiratory system's ability to filter, warm, and humidify inhaled air. These variations may contribute to differences in how cold, dry, polluted, and infected air impact the respiratory system, thus influencing the observed patterns of disparities in COVID-19 outcomes. The analysis suggests that individuals with genetically determined wider nasal cavities, often found in populations originating from warmer climates, have decreased filtration and air conditioning efficiency, leading to increased susceptibility to respiratory infections and disproportionately high mortality rates from COVID-19. Moreover, age-related changes in nasal morphology, such as increased nasal volume and involution atrophy of the mucosa, contribute to impaired air conditioning and filtration, thereby increasing the risk of severe outcomes in the elderly. Gender differences in nasal morphology, with men typically having larger nasal passages compared to women, are linked to poorer air conditioning and filtration, potentially contributing to the higher risk of severe COVID-19 in men. Preliminary statistical analyses of the U.S. population suggest that men with favorable nasal structures may have a longer life expectancy than those with less optimal nasal anatomy; however, this should be regarded as a hypothesis. The findings underscore significant gaps in our understanding and highlight the urgent need for further research to unravel the complex interplay between nasal morphology, respiratory system dynamics, and environmental factors in shaping disease susceptibility and outcomes.

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.

We will apply Pascal’s hydrostatic pressure law to black holes and also to the Hubble sphere. Haug has recently demonstrated how hydrostatic pressure in water can be utilized to determine the Planck length. One can conceptualize the energy in the Hubble sphere as a type of superfluid, and if this is correct, then Blaise Pascal’s hydrostatic pressure formula may also be applicable to such a superfluid, even within the Hubble sphere. By employing the same method on the Hubble sphere and considering it as a Schwarzschild black hole with the critical Friedmann mass, we can estimate the Planck length based on its hypothetical hydrostatic pressure.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

: Microplastics and nanoplastics (MNPs) are becoming an increasingly severe global problem due to their widespread distribution and complex impact on living organisms. Apart from their environmental impact, the effects of MNPs on living organisms have also continued to attract attention. The harmful impact of MNPs has been extensively documented in marine invertebrates and larger marine vertebrates like fish. However, the research on the toxicity of these particles on mammals is still limited, and their possible effects on humans are poorly understood. Considering that MNPs are commonly found in food or food packaging, humans are primarily exposed to them through ingestion. It would be valuable to investigate the potential harmful effects of these particles on gut health. This review focuses on recent research exploring the toxicological impacts of micro- and nanoplastics on the gut, as observed in human cell lines and mammalian models. Available data from various studies indicate that the accumulation of MNPs in mammalian models and human cells may result in adverse consequences, in terms of epithelial toxicity, immune toxicity, and disruption of gut microbiota. The paper also discusses the current research limitations and prospects in this field, aiming to provide a scientific basis and reference for further studies on the toxic mechanisms of micro- and nanoplastics.

SDG target 12.3 calls for halving food waste at retail, food service, and household by 2030. The food waste index for this purpose includes parts attached to food that are usually removed intentionally ("unavoidable food waste") and allows counting the conversion to animal feed and utilisation as biomaterial as means of reduction. In Japan, the "Food Waste Recycling Law" which has been in effect since 2001, promotes the recycling of food waste generated from businesses, designating feed conversion as top priority. On the other hand, based on the more recent "Food Loss Reduction Promotion Act" of 2019, the government is currently promoting the reduction of avoidable food waste, as to meet the SDG target. Based on statistical sources and interviews with Ministry officers, this paper explains the history and achievements of Japan's 23-year policy based on the Food Recycling Law, as well as the tension between the two legislations.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of AN- TXR cell adhesion molecule 2 (ANTXR2)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ANTXR2-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning be- tween the ANTXR2 and X. In this research work, we cover combinations of ANTXR2 with Collagen (COL), Integrin (ITG), Matrix metalloproteinases (MMP), WNT, TNF and Interleukin (IL) .