Sustainable crop production includes methods of growing food in a responsible manner avoiding application and dependence on chemically produced fertilizers and pesticides. The latter means development of approaches that lead to environmentally mild inputs based on the production-consumption-recycling principle. Biofertilizers are an important tool to achieve sustainable crop production. In this work, we report the results of experiments on growth and spore/mycelium production of plant growth promoting A. niger applying standard nutritional medium (potato-dextrose broth, PDB) enriched with 3% insoluble phosphate (20 to 200 mesh hydroxyapatite of animal-bone origin, HABO) and 0 to 80 g/L glycerol (a by-product of biodiesel production. Results showed the ability of A. niger to acidify the medium with the highest titratable acidity of 28.9 mmol/1 (at 5% of glycerol) and solubilize animal bone char under these conditions. As a second stage of the experimental work, the resulting final products were used to formulate gel-based inoculant. Both, the spores and mycelium produced during the fermentation process were further used as a base for formulation to make the biofertilizer production the key in the Sustainable Agriculture. Storage of the resulting products reported here was facilitated by the presence of glycerol in the formulation system.

US life expectancy now lags significantly behind the majority of high-income countries, having grown more slowly since 1980 for reasons that are not evident and have been debated. An exploratory system dynamics model is presented that reproduces the full pattern of US life expectancy from 1960 to the present. Multiple socioeconomic and behavioral factors help to explain the historical pattern, two of them apparently most responsible for the stagnation since 1980: the growth of obesity and the leveling off of growth in social spending. Some of the factors in the model are traced back to earlier causes, and obesity’s growth in particular is traced back to excess growth in private health care spending and its adverse effect on workers’ wages. The model’s base run does a good job of reproducing a variety of historical time series data going back to the 1960s, and counterfactual testing produces plausible results. The model may thus be considered a reasonable starting point for more conclusive future modeling of US life expectancy.

Odor Source Localization (OSL) technology allows autonomous agents like mobile robots to find an unknown odor source in a given environment. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. Compared to traditional olfaction-only OSL method, our proposed method integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by turbulent airflow or vision sensing is impaired by environmental complexities. The model leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the `High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the `Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our method, we implemented the proposed algorithm on a mobile robot in a complex, real-world search environment that presents challenges to both olfaction and vision-sensing modalities. We compared the performance of our proposed algorithm to single sensory modality-based olfaction-only and vision-only navigation algorithms, and a supervised learning-based vision and olfaction fusion navigation algorithm. Experimental results demonstrate that multi-sensory navigation algorithms are statistically superior to single sensory navigation algorithms. The proposed algorithm outperformed the other algorithms in both laminar and turbulent airflow environments. The code for this work can be found at: https://github.com/SunzidHassan/24_Vision-Olfaction-LLM.

Adolescent eating disorders pose a significant public health challenge, deeply affecting both physical and emotional well-being. Early diagnosis is important for improving treatment outcomes, though it remains complex due to multiple influencing factors. The family perspective is essential in this process, as it provides valuable insights into changes in adolescents’ habits and emotional health. Methods: A descriptive qualitative study was conducted in Madrid, Spain, between January and February 2023, using a phenomenological approach to explore the experiences and perspectives of family members of adolescents with eating disorders. Results: Four thematic blocks with their categories were identified: (T1) Diagnosis, (T2) Family (T3) Resources, and (T4) Treatment. Participants emphasized the need for early intervention and a multidisciplinary approach to the treatment of eating disorders. Family therapy was recognized as vital in treatment, and common dissatisfaction in the public health structure underscored the need for greater investment in research and access to specialists. Mental health nurse practitioners have an important role in providing comprehensive care and support as well as mental health literacy. Conclusions: A holistic patient-centered approach, including family involvement and appropriate support systems, is key to improving outcomes and quality of life of adolescents undergoing treatment for eating disorders. Improving healthcare resources and addressing the challenges faced by families is essential. In addition, mental health literacy is critical, as it enables families to understand the disease better, make informed decisions, and actively participate in the recovery process, contributing to improved therapeutic outcomes and quality of life for patients.

Food insecurity (FI) is a persistent issue in Brazil, with significant disparities across the country's macroregions. This study investigated food acquisition locations and types of foods purchased at different levels of FI, utilizing data from 57,920 households in the 2017–2018 Brazilian Household Budget Survey. Food acquisition locations were grouped into supermarkets, small markets, fairs/farmers’ markets/public markets, and others. Foods were categorized into 12 groups: rice, beans, vegetables, fruits, red meats, fish, poultry, eggs, milk and dairy products, bread, cookies, and sugary drinks. Supermarkets are the primary source of food in Brazil. However, in the North and Northeast regions, small markets are more frequently used across all levels of FI. Nationally, most food groups are predominantly purchased at supermarkets. Nevertheless, households experiencing moderate or severe FI rely more on small markets for essential items such as rice, beans, and proteins, as well as ultra-processed foods. These findings highlight the need for public policies that improve food access for vulnerable populations and address regional inequalities. Enhancing access and ensuring food security across different regions is essential to promote equitable and healthy diets throughout the country.

The article presents extremely current issues concerning imaging and measurements carried out using coordinate optical systems in which the result of scanning spatial objects is a point cloud. What is important, the entire accuracy assessment of the conducted fast, effective coordinate measurement nowadays takes place in the computational area, beyond direct sensory percep-tion, and depends increasingly on its power and the computational algorithms used. Further-more, we are getting closer and closer to recognising virtual standards in coordinate metrology as a direction that allows for an increase in measurement accuracy. Especially in recent years, the importance of the problem has been confirmed by the activities of the most significant scientific and research centres. They focus on improving the accuracy of the data obtained in the form of point clouds, using more precise scanning methods for this purpose, and developing filtering algorithms to remove noise, including methods using artificial intelligence. Based on many years of research, the authors identified a problem regarding the difficulty in developing data obtained from optical systems, such as laser triangulation head systems, systems operating on the principle of structural light, photogrammetric systems, as well as computer tomographs, and the impact of these systems on the assessment of measurement accuracy. Therefore, the optical measurement process was analysed in terms of the quality of the acquired point clouds. The authors presented the division of factors, which, in coordinate optical meas-urements, generate errors in data acquisition and development. The authors also indicated which of these factors generate the most significant errors and, above all, emphasised the impact of the software used, the measurement operator, and the methods of cloud filtration applied by the operator. Therefore, the accuracy assessment of the optical measurement has been noted as a critical problem, and so far, it has not been solved effectively. In the article, as a solution to this problem, the authors proposed the OPTI-U method of estimating uncertainty for optical meas-urements. The method, what is very important, was developed and verified under the conditions of an accredited calibration laboratory. The summary also gives directions for further work on developing and disseminating the developed method, particularly introducing virtual standards.

Background: Malnutrition is a widespread issue among old people, significantly impacting health outcomes. Nutritional interventions can improve health, but their success often depends on the attitudes and knowledge of healthcare workers. This study examines the influence of continuing education on healthcare workers' attitudes and practices toward old people's nutrition. Aim: The study assesses healthcare workers' attitudes toward old people's nutrition using the validated Italian version of the SANN-G questionnaire, focusing on staff in nursing homes in Northern Italy. Methods: A cross-sectional study was conducted with 1,789 healthcare workers from 41 facilities. The SANN-G questionnaire measured attitudes across five dimensions: nutritional norms, habits, assessment, intervention, and individualization. Data were collected online and on paper, with descriptive and inferential statistical analyses (chi-square and ANOVA) performed using R software. Results: Most respondents were female (68.59%) and aged 41–50 (33.31%), with nursing assistants comprising 35.83%. Only 23.48% scored positively on the SANN-G scale, with most displaying neutral or negative attitudes. Education on malnutrition significantly improved attitudes, particularly in assessment, intervention, and individualization. Younger respondents and nurses were more likely to have positive attitudes after educational interventions, while older respondents and physicians tended to show neutral or negative attitudes. Conclusion: Continuing education on malnutrition is crucial for fostering positive attitudes among healthcare workers. Specialized education leads to more proactive approaches, highlighting the need for targeted educational programs to improve old people's nutritional care.

This study aimed to determine the prevalence of coinfection with malaria and intestinal parasites and assess its association with anemia in school-aged children from rural and urban settlements in Gabon. This cross-sectional study involved afebrile school children recruited at schools between May and June 2021. Blood and stool samples were collected from participants who provided informed consent to participate in the study. Hemoglobin concentration (Hb) was measured using a HemoCue photometer (HemoCue 201, HemoCue, Angelholm, Sweden). Giemsa-stained blood films were examined to detect malaria parasites and any filarial infections, while the Merthiolate-iodine concentration (MIc) method was used to identify intestinal parasitic infections (IPIs). A total of four hundred and seventy (470) school-aged children were successfully enrolled in this study. The observed prevalence rates were as follows: malaria infection at 69.6%, IPIs at 19.1%, filaria at 5.1%, Schistosoma infection at 15.0%, and anemia at 29.0%. Coinfections of malaria with IPIs, filaria, and Schistosoma were present in 12.3%, 4.7%, and 6.6% of the children, respectively. Malaria and filaria infections were associated with residing in Lastourville (LTV) city and were also correlated with age, whereas IPIs were associated with male gender and living in the city of LTV. Anemia was linked to malaria infection and was more prevalent among children living in rural areas. The findings of this study indicate that malaria, IPIs, and Schistosoma infections continue to pose a significant public health problem in the study area, even though only malaria infection appeared to be associated with anemia. Nevertheless, these results highlight the need for implementing control measures to reduce the rates of malaria, IPIs, filaria, and Schistosoma, particularly in Lastourville.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction, memory decline, and behavioral disturbance, and pathologically characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. Although various hypotheses have been proposed to explain the pathogenesis of AD, including the amyloid beta hypothesis, oxidative stress hypothesis, and abnormal phosphorylation of tau proteins, the exact pathogenic mechanisms underlying AD remain largely undefined. Furthermore, effective curative treatments are very limited. Epidemiologic studies provide convincing evidence for a significant association between type 2 diabetes and AD. Here, we showed energy metabolism using glucose, lactate and ketone bodies and lipids as energy substrates in normal brain, and changes in such energy metabolism due to type 2 diabetes. We also showed the influences of such altered energy metabolism due to type 2 diabetes on the pathology of AD. Furthermore, we comprehensively searched the risk factors related with type 2 diabetes for AD, and showed possible therapeutic interventions based on considering risk factors and altered brain energy metabolism due to type 2 diabetes for the development of AD.

The black soldier fly larvae meal has attracted attention as a sustainable alternative protein ingredient in poultry feed. In the present study, we sought to determine the effect of black soldier fly larvae on the growth performance of broilers as replacement to fishmeal. Forty (40) one day old broiler chicks were randomly selected and distributed in 4 replicates. The Black soldier fly larvae were formulated and added to broiler’s marsh as replacement to fishmeal. Birds were fed on four experimental diets formulated as follows 15% black soldier fly (BSF15), 20% (BSF20) and 25% (BSF25) corresponding to dietary BSF inclusion levels of 150 g/kg, 200 g/kg and 250 g/ kg, respectively and the control diet (BSF0) contained 20% of fishmeal (FM) as protein sources (250 g/kg). Feed and water were provided ad libitum. Proximate analysis showed that BSFLM contained comparable nutrient values to fishmeal. The highest average weight gain was achieved when birds were provided diet with 25% BSFLM. Based on this study, it can be concluded that black soldier fly larvae meal represents an alternative source of animal protein to replace fishmeal in broiler chicken diet in their diet without adverse effect on growth performance and consumer preferance.

The real birth of Brazil can be traced to the remote Jesuit village of São Paulo de Piratininga, founded in 1554, after arduous efforts to ascend the Serra do Mar. This isolated settlement, inhabited by a mixture of Indigenous peoples and Portuguese colonizers, became the pivotal point for the expansion of Portuguese territory in South America. The people of São Paulo, or paulistas, shaped by their harsh environment and violent history, forged a unique identity. Through their relentless expeditions, the bandeirantes ventured deep into the interior, hunting Indigenous peoples for slavery and expanding the Portuguese domain beyond the boundaries defined by European treaties. This essay explores how the humble origins of São Paulo de Piratininga grew into a force that shaped the modern Brazilian state and its borders.

The finite volume method (FVM) is a kind of numerical analysis method that is simple and can take into account the topography as the finite element method (FEM). The adjoint state method (ASM) can easily and efficiently calculate the gradient vector of the objective function for the inversion. We developed a program to apply these methods to 3-D Magnetotelluric (MT) inversion and verified it using test models. In the forward modelling, it was confirmed that the program could perform analysis properly and efficiently considering topography and subsurface resistivity heterogeneity. Furthermore, we conducted the inversion using a synthetic open dataset and could reconstruct the true model of the dataset. The comparison with the previous work revealed that the computation time was comparable in spite of the fact that we calculated without MPI implementation, which the previous work used.

The understanding of properties of multipartite systems is a long-lasting challenge in quantum theory that signals the need for new ideas and alternative frameworks that shed light on the intricacies of the quantum behavior. In this work, we argue that symmetric spaces provide a common language to describe two qubit and two mode Gaussian systems. Our approach relies on the use of equivalence classes that are defined by a subgroup of the maximal symmetry group of the system and involves an involution which enables the Cartan decomposition of the group elements. We work out the symmetric spaces of two qubits and two modes to identify classes which include an equal degree of mixing states, product states, X states, among others. For three-qubits and three-modes, we point out how the framework can be generalized and report partial results about physical interpretations of the symmetric spaces.

Generating coherent and comprehensive responses remains a significant challenge Question-Answering (QA) systems when working with short answers especially for low-resourced languages like Farsi. We present a novel approach to expand these answers into complete, fluent responses, addressing the critical issue of limited Farsi resources and models. Our methodology employs a two-stage process: first, we develop a dataset using rule-based techniques on Farsi text, followed by a BERT-based ranking system to ensure fluency and comprehensibility. The resulting model demonstrates strong compatibility with existing QA systems, particularly those based on knowledge graphs. Notably, our system exhibits enhanced performance when integrated with large language models using Chain-of-Thought (CoT) prompting, leveraging detailed explanations rather than single-word answers. Our approach significantly improves response quality and coherence compared to baseline systems. We release our dataset to support further research in Farsi QA\footnote[1]{\href{https://huggingface.co/datasets/SLPL/syntran-fa}{https://huggingface.co/datasets/SLPL/syntran-fa}}.

Is the existence of populist sentiments linked to greater affective polarization? Is there a relationship between these two factors (populist sentiments and affective polarization) and the endorsement of far-right ideologies? Are there differences in the types of media prioritized by individuals with varying levels of affective polarization? This exploratory study, conducted in the context of the early legislative elections in Portugal in April 2024, analyzes data from a sample of 130 first-time young voters. It suggests an association between the defense of far-right arguments, the possession of populist sentiments, and levels of affective polarization. The study also found variations in affective polarization among young people who prioritized different types of social media to inform themselves on political issues.

Software Engineering education has grown during the past couple of decades as a specialized branch of computer science education to address employment demands. Software Engineering education is constantly evolving because of its dynamic nature. However, there are still some gaps between the skills being taught in the universities and skills required for employment. In this paper, we present our experience of designing and delivering a new module to enhance skills for employment. We have taken a structured approach to analyze the gap through literature review and our personal experiences of engaging with employers of our students. Based on the analysis, we arrived at a set of gaps between what we teach and what the employers of our students need. We prioritized the gaps and envisaged a new module ‘Contemporary Software Engineering’ to address the gaps and subsequently help our students for an easy transition to job. As part of our experience, we present the background, design and delivery of the module and reflect on our journey of delivering the module for a few years.

Big data-driven machine learning and artificial intelligence (ML/AI) is not all computational social sciences (CSS) have. Agent-based modeling (ABM) or multi-agent system (MAS) is another fundamentally different but equally useful approach in CSS. In fact, the two approaches start from very different orientations and their differences have deeper root in ontology. ML/AI aims to imitate and then surpass human capacities, from sensing to perceiving, reasoning, calculating, and acting. In contrast, ABM seeks to simulate how social outcomes emerge from the complex interactions of agents’ actions within a specific environment. Yet, precisely because these two technologies are different, they can be complementary to each other. There is a bright future for integrating ABM with ML/AI for tackling real world challenges.

Abstract Introduction: Despite the determination to succeed in eliminating neglected tropical diseases, these have been increasing within certain endemicities, particularly in depriving region. Objective: This study aimed to assess the relative burden of neglected tropical diseases in rural and urban slum settings in Eastern Ethiopia. Methods: A comparative cross-sectional study was conducted from February 25 to April 30, 2024 with a multistage random sampling technique used. A survey tool with the integration of the spectrum was employed. Data were entered using Epi-Data version 3.1 and analyzed using STATA 16. Descriptive statistics and multivariable logistic regression analysis was used to identify the risk factors and the level for statistical significance was declared at p-value < 0.05 with 95 % CI. Result: The response rate was 93 % and the mean age of house heads was 31.6 (Range; 18. 63) with 5.7 average family size. The overall prevalence of NTDs among study participants was 22.6 % (95% CI; 20.2, 25.2); urban slum it was 15 % (95% CI; 12.2, 18.4) while it was 29.8 % (95% CI; 26, 33.9) in rural. Though there is no statistically significant difference, the relative prevalence of NTDs in rural to urban was two. The multivariable analysis provided that the risk factors associated with NTDs were government employment (AOR: 12.5 (1.17, 132.4)), good income (AOR: 0.13 (.03, 0.57)), Child playing with soil (AOR: 5.8 (1.52, 22.0)); child hand washing at critical times ( AOR: 11.2 (1.7, 74.9)), presence of flies in the child faces (AOR: 20.3 (4.7, 88.6)) and type of drinking water sources (AOR: 0.11 (0.02, 0.73)). Conclusion: the burden of NTDs was moderate in the study area dominated by STH, Trachoma, and scabies. Socio economic and WASH factors were found to be associated with NTDs. Collaborated efforts among stakeholders are crucial to prevent and control NTDs.

In this work, we propose an alternative theoretical model to explain the accelerated expansion of the universe, based on the gravitational collapse of a black hole in a higher-dimensional space. We suggest that this collapse causes a rupture in the fabric of spacetime, generating a new universe whose accelerated expansion is a direct consequence of this process. Through detailed calculations, we demonstrate that the current acceleration of the universe is consistent with observational data, such as those from type Ia supernovae and the cosmic microwave background. Additionally, we address the problem of the universe's low initial entropy, proposing that this model provides a natural explanation for the order observed in its early stages. The results suggest that this alternative approach offers a viable explanation for cosmic expansion and raises new questions about the role of gravitational phenomena and the evolution of the universe.

The integration of omics technologies, including genomics, metabolomics, proteomics, and microbiomics, has transformed sports science, particularly in soccer, where these advancements have the potential to enhance player performance, injury prevention, and recovery. This review examines the role of single nucleotide polymorphisms (SNPs) across key genes influencing various physiological traits crucial for elite soccer players. Genes related to the cardiovascular system (ACE, AGT, NOS3, VEGF), muscle structure and function (ACTN3, CKM, MLCK), neurotransmission (ADRA2A, ADRB2, BDNF, DRD1, DBH), connective tissue integrity (COL1A1, COL2A1, COL5A1, ELN, EMILIN1, TNC), energy metabolism (AMPD1, MCT1, UCP1/2/3), oxidative stress response (GSTM1, GSTP, GSTT), hormonal regulation (CYP2D6, HSD17B14), and growth factors (GDF-8, IGF2, HGF) are all highlighted for their roles in influencing soccer-specific traits like endurance, speed, and injury risk. Furthermore, the incorporation of omics data allows for personalized strategies in training, nutrition, and recovery, tailoring interventions to each player's genetic and biochemical profile. For example, genomic markers such as ACE I/D and ACTN3 R577X are linked to key athletic traits like endurance and muscle injury susceptibility, while proteomics sheds light on muscle repair mechanisms, and metabolomics provides real-time insights into energy metabolism and fatigue markers. Microbiomics explores the role of gut health in recovery and overall performance, revealing correlations between microbial diversity and enhanced athletic outcomes. This comprehensive approach, referred to as Socceromics, offers a more holistic understanding of an athlete’s health and performance capabilities. Integrating these findings into real-world applications is essential for optimizing training regimens and reducing injury risk, ultimately pushing the boundaries of what athletes can achieve on the field. Future research should focus on expanding the scope of omics integration and improving the generalizability of findings across diverse populations and genders, thus advancing the field of precision sports medicine.

This paper aims to propose a stochastic linear parameter varying (LPV) model approach to design a state feedback controller for three-phase two-level inverters. To deal with the parameter changes, stochastic noise and delays faced by the inverter, it is modeled as a stochastic LPV system with time delay. The stability analysis and control synthesis are conducted for the LPV system. With parameter-dependent Lyapunov functionals, the sufficient stability condition for asymptotical mean-square stability is obtained. In addition, the slack matrix technique is employed to improve the feasibility and reduce the conservatism of the conditions. The obtained theoretical results are applied to the three-phase two-level inverter, whose currents are treated as state variables and controlled to reach the equilibrium point. The simulation results validate the effectiveness of the proposed theories and demonstrate the advantages of using the slack matrix.

Background and aims: The COVID-19 pandemic significantly impacted the mental health of healthcare professionals, especially those working in Psychosocial Care Centers (CAPS), which are crucial services in the Brazilian mental health system. This study aimed to analyze satisfaction and workload as predictive factors for psychological distress among CAPS professionals during the pandemic. Methods: A cross-sectional study was conducted with 53 professionals from seven CAPS. The Workload Impact Scale (IMPACTO-BR) and Job Satisfaction Scale (SATIS-BR), the General Health Questionnaire (GHQ-12), and a sociodemographic questionnaire were used. Descriptive and analytical statistical analyses were performed. Results: Professionals reported moderate satisfaction (3.67 ± 0.45) and mild workload (1.82 ± 0.63). One-third of the sample showed scores indicative of psychological distress. Logistic regression analysis revealed that workload (OR = 1.39, 95% CI = 0.0025) and low job satisfaction (OR = 1.23, 95% CI = 0.0495) were significant predictors of psychological distress. Conclusions: Low job satisfaction and high professional workload were predictive variables of psychological distress. These findings highlight the need for investments in promoting the quality of life at work for mental health professionals, especially during crises. The implications for human resource management and public policy development emphasize the importance of an integrated approach that considers the well-being of professionals for the effectiveness and sustainability of the psychosocial care model.

Generative adversarial networks consisting of two parts, generator and discriminator, have obtained acceptable results in classification, prediction and generation of new samples in the discussion of 4G, 5G and 6G. In this article, the aim is to review a comprehensive study in relation to these networks. At the beginning of this research, different topics such as network training, network error checking and types of GAN's are investigated. In the following, the applications of GAN will be discussed separately.

Explainability is essential for AI models, especially in clinical settings where understanding the model's decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpretable by humans. On the other hand, using hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model enhances hand-crafted features to maintain human interpretability while achieving performance levels comparable to advanced deep convolutional networks, such as U-Net, for medical image segmentation. ExShall-CNN and its source code are publicly available at: https://github.com/MLBC-lab/ExShall-CNN

Developing cost-efficient systems for transporting CO2 is key to accelerate the deployment of carbon capture and storage. The present work explores the impact of reducing the pressure of tank-based inland and at-sea transport on their techno-economic performance. The study uses established techno-economic models for CO2 transport, adjusted with the most up-to-date knowledge on costs of low-pressure containment and transport. Particularly, the impact of cargo tank material and design on the transport costs shows that low-pressure cargo tank systems can be 50% less expensive than medium-pressure systems if materials with similar price and strength can be used. This results in reductions in transport costs as high as 30% for long distances. This is partly driven by the currently suggested size limitation on medium-pressure shipping which limits its economies of scale. If this limitation is alleviated, the cost advantage of low-pressure shipping compared to medium-pressure is more limited (10-20%) although it remains advantageous. The same scaling effects on capacity were not found for truck and barge inland transport, thus yielding 1-10% cost reductions of low- relative to medium-pressure transport. These results imply that future systems may combine medium-pressure inland and low-pressure at-sea transport, and that efficient solutions connecting the two must be investigated.

There are different audit methods for analysing operational facts and identifying deficiencies in business process management. However, financial data and transactions indirectly relate to physical objects and their attributes, resulting in distinct characteristics for their analysis and auditing methods. It is important to find new methods for finding a process extraction solution based on the financial audit data space to increase the efficiency of the auditors' work by reducing their time costs and increasing the possibility of detecting financial anomalies in large amounts of financial data. The research presents discussed different financial data space (FDS) methods, and process mining (PM) that include related classifications of financial data and external and internal factors that can influence data behavior (value changes) and the peculiarity of FDS is that it includes not only the characteristics of financial data but also other operational characteristics (events, environmental and internal changes, business location) that may be related to the change in financial objects (FO) values. The authors of the paper discuss the prototypes of specific elements in financial data analysis that were developed using the Process Mining software tool Disco and present the conclusions of the effectiveness of the financial processes cube defined to increase the analytical possibilities of the auditor's work with large amounts of data, introducing more flexible functionality for the analysis of financial activities.

Research shows that observers and agents, as key concepts in physics and intelligence science respectively, exhibit profound intrinsic consistency. Based on this understanding, we propose and construct Theory of Universal Intelligent Evolution, known as Omega Theory, which offers an innovative exploration of Theory of Everything (ToE) from the perspective of intelligence science. Within the framework of Omega Theory, this paper unifies the research subjects and dynamic mechanisms of physics and intelligence science, revealing that the intelligence level of the observer is the fundamental source of the differences among classical mechanics, relativity, and quantum mechanics. Furthermore, this paper proposes that core concepts such as subjectivity and objectivity, determinism and uncertainty, as well as time and space, are inherently related to the intelligent state of agents and Universe.Omega Theory consists of three core models: Standard Agent Model, General Model of Agent Evolution, and Intelligent Universe Evolution Model. Standard Agent Model serves as a foundational axiom, defining the unified structure and functional framework of agents, and it is the cornerstone of the theory. Building upon this, we derive General Model of Agent Evolution and Intelligent Universe Evolution Model. Omega Theory posits that the fundamental units of Universe are agents, and that the entire universe is itself an agent. Matter is a special manifestation of agents, and each agent continuously evolves between the states of absolute zero intelligence and omniscience and omnipotence, driven by two intelligent forces that are derived from the theory.To validate this theory, we have designed a thought experiment in the "Experimental Universe 1," where classical mechanics, relativity, and quantum mechanics experiments can be simultaneously conducted. By adjusting the intelligence level of the observer, we observe that different physical theory scenarios emerge in succession as the observer's intelligence level changes.

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

The purpose of this research is to analyze the structural changes in primary energy production across European countries over the period from 1990 to 2022, with a focus on understanding the shifts in energy sources and their implications for energy security and sustainability. By applying clustering methods, the study categorizes European nations into distinct groups based on their primary energy production profiles, revealing significant transitions from fossil fuel-based systems to those increasingly dominated by renewable energy sources. The analysis demonstrates that while some countries have achieved substantial progress in integrating renewable energy, others remain heavily dependent on traditional energy sources such as coal and natural gas. Additionally, the study highlights the growing importance of natural gas as a transitional fuel and the relatively stable role of nuclear energy in certain regions. A key finding of this research is the disparity between energy production and consumption across Europe, with many large economies facing a persistent deficit in domestic energy production, leading to a high reliance on energy imports, particularly of natural gas and oil. This reliance poses significant challenges for energy security, especially in light of recent geopolitical disruptions and fluctuations in global energy markets. The paper also explores the environmental implications of these energy production trends, emphasizing the critical role of renewable energy in meeting the European Union’s decarbonization targets and combating climate change.

The main theme of this work is to present the results and conclusions of a study conducted to explore the feasibility and efficiency of the crop area and crop production estimations in the context of small farming systems for food security, assisted by Remote Sensing (RS) technology. The study carried out in the three selected Greek prefectures of Ileia, Larisa and Imathia (NUTS-3) and focused on the development and testing of RS methods and classification techniques used in the production of land and crop cover maps. The purpose was to unveil the role of small farming plots (less than 5 ha) in a food security context and determine their contribution in estimating the crop area, the production, and the spatial distribution, factors which remain unclear, mainly because the official statistical offices rarely include them in the surveys, particularly in the non-developed countries. The efficiency of using and combining Sentinel satellite images acquired during the spring-summer season of 2017 with field surveys implemented on stratified samples of square segments for crop area estimations was assessed. The produced results show good classification accuracies for several key-crops under small scale farming systems with various environmental and territorial conditions. Noteworthy, the satellite data and derived products can be effectively used for stratification purposes and a posteriori correction of crop area estimates from ground observations. The knowledge of the unbiased crop area estimation is a key element for the estimation of the total crop production and, therefore, the management of crop products. The unbiased crop area computation and the crop production estimates was performed only for the highly accurate key-crop products (FScore > 75\%). Then, the key-crop production in each region was determined by using the estimated self-reported crop yields multiplied by the corresponding key-crop area of the small farming plots. The derived results indicate that small farming plots make an important contribution in the integration of the key-crop production of the selected crops with the official statistical data. Finally, potential changes occurred in the cultivation of small plots from the previous cultivation year in the key-crop areas of the same key-crop products of the three regions considered were also estimated (and mapped) by the Land Parcel Identification System (LPIS) of the Greek Integrated Administration and Control System (IACS) and agree with those reported by the official statistics.

Background: There is paucity of evidence on the association of genetic propensity for hippocampal atrophy with cognitive outcomes. Therefore, we examined the relationship of a Polygenic Risk Score for hippocampal atrophy (PRShp) with the incidence of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) as well as the rates of cognitive decline. Methods: Participants were drawn from the population based HELIAD cohort. Comprehensive neuropsychological assessments were performed at baseline and follow-up. PRShp was derived from the summary statistics of a large GWAS for hippocampal volume. Cox proportional hazards models as well as generalized estimating equations (GEEs) were used to evaluate the association of PRShp with the combined incidence of aMCI/AD and cognitive changes over time, respectively. All models were adjusted for age, sex, education, and Apolipoprotein E (APOE) genotype. Results: Our analysis included 618 older adults, among whom 73 developed aMCI/AD after an average follow-up of 2.96±0.8 years. Each additional SD of PRShp elevated the relative hazard for incident aMCI/AD by 46%. Participants at the top quartile of PRShp had almost three times higher risk of converting to aMCI/AD compared to the lowest quartile group. Higher PRShp scores were also linked to steeper global cognitive and memory decline. The impact of PRShp was greater among women and younger adults. Conclusions: Our findings support the association of PRShp with aMCI/AD incidence and with global cognitive and memory decline over time. PRS association was sex and age dependent, suggesting that these factors should be considered in genetic modelling for AD.

A common technique for producing large-scale hollow plastic items is rotomoulding. Rotation speed, mould design, and temperature management significantly affect the quality of finished products. The growing demand may cause the rotomoulding industry to reach a peak soon. Few studies have been applied in the industrial sector. Meanwhile, the lack of sufficient research on the phase transition from the heat source for the rotomoulding procedure led to a great research gap in this field. Although, their conclusions contribute significantly to the topic but cannot be considered accurate in the analysis. Proper temperature variations in both the heating and cooling phases impact the final product quality. Moreover, if the required temperature is not achieved it may result in raw material ( HDPE, LLDPE) loss. To produce the necessary amount of hot air by convection, this review article focuses on the use of ducts as the medium for hot air circulation to the mould, ducting model analysis, and mould model adjustments. Additionally, insulation has been discussed concerning sustainable heat, which could help the industry to better comprehend the procedure.

Biomedical large language models (LLMs) have made significant strides, but their reliance on external retrieval mechanisms presents challenges in accuracy and computational efficiency. To address these issues, we propose MedRAG-Refine, a generative LLM designed specifically for the biomedical domain. Our model integrates a two-stage fine-tuning process, incorporating a self-reflection mechanism to improve reasoning quality. We evaluate our model on MedQA, MedMCQA, and MMLU datasets, demonstrating superior performance over state-of-the-art methods. Additionally, human evaluations confirm the enhanced accuracy and reasoning quality of our model in real-world medical tasks.

This study investigates the influence of four soil improvement methods—microbially induced carbonate precipitation (MICP), electrokinetics (EK), chemical additives, and a combination of EK and chemical additives—on the dispersivity, mechanical properties, and microstructure of dispersive soil. A series of tests was designed to evaluate the effectiveness of these methods on dispersive soil. Both the original and treated soil samples were tested to assess changes in soil properties, including dispersivity, plasticity, pH, unconfined compressive strength (UCS), shear strength, and microstructure. Dispersivity was assessed using pinhole tests, crumb tests, double hydrometer tests, and exchangeable sodium percentage tests. The experimental results indicate that the combined EK and chemical additives method significantly reduces the dispersivity and plasticity of the dispersive soil compared to the other methods, leading to improved UCS. The EK and chemical additive methods individually demonstrate effective modification under a voltage of 48V and an additive content of 4%, respectively, enhancing the shear strength of the dispersive soil. MICP does not significantly improve the dispersivity of dispersive soil, it does enhance the shear strength of the treated soil, with a particularly notable increase in the internal friction angle. Overall, the combined method shows more remarkable improvements in the dispersive soil than any single method. In summary, the combination of EK and chemical additives has significant potential for improving the dispersivity and mechanical properties of dispersive soil.

Global food insecurity and environmental degradation creates the need to develop more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology promises enhanced crop traits, it also harbors risks of extensive environmental damage. Here, we highlight the complexities and risks associated with plant synthetic biology, while presenting the potential of multilevel mathematical modeling to assess and mitigate those risks effectively. Despite its potential, the application of multiscale mathematical models in plants remains underutilized. We advocate for the integration of technological advancements in agricultural data analysis to foster a comprehensive understanding of crops across scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper sets the stage for the creation and utilization of integrated multiscale mathematical models. Leveraging modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, enhancing the safety of genetically engineered species. To illustrate the potential of the combination, we outline ongoing efforts to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga and drought.

3D printing technology has made significant strides in smart textiles—fabrics embedded with electronics like conductive fibers and sensors—now widely applied in areas such as health con-dition monitoring, wearable energy-harvesting devices, and interactive textiles that respond to environmental conditions and color changes. Different 3D printing methods, such as Fused Deposition Modeling (FDM), Selective Laser Sintering (SLS), Direct Ink Writing (DIW), and PolyJet printing, are used in the fabrication of smart textiles. This study provides a detailed, applica-tion-specific overview of 3D printing technologies—such as FDM, SLS, DIW, and PolyJet print-ing—and their use in smart textiles across various sectors, including wearable technology, medical textiles, and smart fashion design. We gathered articles and reports from Scopus and Google Scholar, providing a concise assessment of 3D printing materials for smart textiles to give readers a quick understanding of the field. The timeline of 3D printing’s use in smart textiles, from 2016 to 2023, highlights significant advancements in various additive manufacturing techniques applied to smart textiles. The review emphasizes the importance of understanding 3D printing techniques such as FDM, SLS, DIW, and PolyJet, as their applicability for selecting the best approach to in-corporating advanced functionalities into smart textiles.

Background: Cardiovascular diseases (CVDs) and their risk factors are increasing with associated disability and mortality burden globally, especially in low- and middle-income countries, including Cameroon. Physiotherapist-led health promotion (PLHP) interventions provide opportunities to improve health and reduce this burden. Understanding physiotherapists' practice, confidence, and perception is crucial for designing effective, context-specific PLHP interventions. Methods: This qualitative study explored physiotherapists' practice, perceptions, and confidence in delivering PLHP to pwCVDs in Cameroon. Results: Sixteen participants completed the interviews and analyses of the transcripts generated three main themes which included 1) Perception of Physiotherapists’ role in health promotion (HP), 2) Current practice of PLHP 3) Competence in delivery of PLHP. Physiotherapists believe that delivering HP interventions in practice is within their professional role. Current HP practice was limited to exercise, physical activity and dietary/nutritional interventions. Participants reported a lack of knowledge and formal training in PLHP delivery. Conclusion: Despite strong interest in HP, HP practice among physiotherapists is limited in scope, under-resourced and limited by a lack of confidence in delivering behavioural change interventions. These findings are relevant for the design of appropriate clinical training and policies for the care of pwCVDs.

This international multicenter study is aimed to assess the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying anterior cruciate ligament (ACL) injuries, with emphasis in the need for standardization in ACL injury assessment to improve diagnostic precision and enhance treatment outcomes. Methods: The study included over 600 ACL evaluations from five Latin American countries (Bolivia, Chile, Colombia, Ecuador, and Mexico) performed by eight experienced orthopedic surgeons. The PSM app, using smartphone gyroscopes and accelerometers, was employed to standardize the pivot-shift test, and the data was analyzed using MATLAB software. Tests were classified into eight classes, and a neural network model was developed to categorize the injury grades. Results: Statistical analysis demonstrated significant differences between standardized and control tests, supporting the standardization's effectiveness. The neural network model exhibited high classification accuracy (94.7%), with precision, recall, and F1 scores exceeding 90%. Additionally, the model demonstrated an ability to reliably grade ACL injuries with an area under the curve (AUC) of 0.80, indicating high diagnostic accuracy. Conclusions: The PSM mobile application, combined with standardized pivot-shift techniques, proved to be a reliable tool for diagnosing and classifying ACL injuries. The neural network model showed high performance in predicting injury grades, making this method a valuable tool in clinical practice for improving diagnostic precision and treatment planning.

Contemporary analysis of the chemical composition of medicinal plants, and their biological activities, is important to find new potent therapeutic products. This study deciphers the chemical profiles of three essential oils (EOs) obtained from different flowers of Lantana camara, and evaluates their antioxidant and anticancer activities. The three flowers (white, pink, and orange) were collected, dried, ground, and further extracted by hydrodistillation to yield EO1, EO2 and EO3 respectively. Gas chromatography-mass spectroscopy was employed to identify existing chemical compounds in all samples. Their antioxidant activities were screened through DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging assay in addition to the FRAP (ferric reducing antioxidant power) assay. The anti-proliferative effects were evaluated on two different breast cancer cell lines, MCF-7 and MDA-MB-231, and compared to a normal human breast cell line MCF-10A, using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium) assay. The three EOs showed notable antioxidant potential attributed to the active phytochemical compounds. Results were supported by positive correlation between the antioxidant activity and the total phenolic and flavonoid content. Most notably, EO1 (from white flowers) exhibited a remarkable dose-dependent anti-proliferative effect in both breast cancer cell lines. Marked by a sharp reduction in cell proliferation after only 24 hours of treatment, EO1 reflects its potent role as anti-cancer agent. We suggest that this highly selective activity is associated with the presence of bicyclogermacrene and epi-bicyclosesquiphellandrene in its chemical composition.

Gastrointestinal (GI) cancers are the most common causes of global cancer burden and cancer-related deaths. Chronic inflammation is a hallmark feature of GI tumors and plays an important role in tumor initiation, progression, and therapeutic response. The genetic makeup of cancer cell, as well as epigenetic alterations, and biological processes constitute an inflammatory tumor microenvironment (TME) that provides an essential nich for cancer initiation and progression. Cancer-associated inflammation, a key component of TME, displays protumoral or antitumoral activity depending on the composition, spatial distribution, and functional status of the immune cells involved, thus adversely affecting response to cancer treatment and clinical outcomes, whereas acute inflammation supports T-cell priming, activation, and infiltration into malignant tissues, thereby conferring anti-tumoral immunity. Inflammatory cells and proinflammatory molecules within the TME act as a bridge between inflammation and cancer. The majority of cells involved in the inflammatory process in the TME are innate immune cells, which play a critical role in shapping both the cellular composition of the TME and the plasticity of cancer cells and stromal cells. Activation of innate immune cells in the TME initiates the production of inflammatory, regenerative, and anti-inflammatory soluble molecules, resulting in adaptive immune response to the cancer. Additionally, aberrant activation of intracellular signaling pathways, inflammatory molecules, and metabolites have critical roles in the development and healing of inflammation during carcinogenesis. Cancer-associated inflammation may inhibit the effectiveness of anti-cancer agents by causing enzymatic degradation of drugs and modulating the immun activity, thus affecting survival of patient. In this review article, we highlight the genetic and cellular mechanisms involved in the formation of the inflammatory component of the TME in GI cancers, the soluble molecules, the cancer cell-immune cell interactions, and the implications of all these for clinical outcomes. We also comprehensively address the mechanisms linking chronic inflammation to cancer, with a focus on the most frequent GI cancers.

Background: Traditional root canal therapy (RCT) has been the primary method for treating necrotic pulp, but it presents limitations such as loss of vitality and postoperative fractures. Recent advancements in regenerative endodontics offer alternative strategies to restore pulp tissue vitality and function. These approaches involve stem cell transplantation, cell homing, and the use of organic scaffolds to promote pulp regeneration. Highlight: This article reviews various regenerative endodontic techniques, including autogenic and allogenic dental pulp transplantation, the use of amniotic membrane scaffolds, platelet-rich fibrin therapy, specialized pro-resolving mediators, nanofibrous and bioceramic-based scaffolds, injectable scaffolds, and dentin matrix proteins. These strategies leverage stem cells, growth factors, and biomaterials to create conducive microenvironments for cell attachment, proliferation, and differentiation, thereby facilitating pulp tissue regeneration. Methods: The literature search was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords like "regenerative endodontics," "pulpitis," "pulp regeneration," and "platelet-rich fibrin." Conclusion: The reviewed approaches in regenerative endodontics show promising potential for overcoming the limitations of traditional RCT and promoting pulp tissue regeneration. However, further research is needed to optimize protocols, address challenges, and translate these advancements into routine clinical practice. Ultimately, these innovations have the potential to enhance patient outcomes in endodontic treatment by restoring pulp vitality and function effectively.

Overtourism in national parks and protected areas can cause irreversible damage to the environment, compromising the quality of soil, water, wildlife, and vegetation. Thus, it is critical for park managers to have detailed information on visitor activities and spatial dynamics in order to prioritize actions capable of mitigating undesirable impacts in the most frequently visited areas. In this article, we use georeferenced trails and photographs from Wikiloc and Flickr web platforms to determine the spatial visitation patterns in the Atlantic Islands of Galicia National Park (AINP) from 2008 to 2023. Maps showing trail usage intensity and the distribution of photographs according to land use allowed us to identify the most frequented land uses by visitors and the areas of highest tourist pressure within the AINP. The results show that distribution patterns vary between platforms. Shrubland (37%) and marine cliffs (27%) were the most photographed land uses by visitors, while artificial areas (14%) were the most frequented by Wikiloc users. Cíes island emerges as the most popular tourist destination, as evidenced by the greater number of trails and photographs compared to Ons, Sálvora, and Cortegada. This study shows how social media data, specifically trails and geotagged photographs from Wikiloc and Flickr, can support and complement the monitoring of visitor use and impact in protected areas.

The mammalian or mechanistic target of rapamycin complex 1 (mTORC1) is activated on the surface of lysosomes and phosphorylates substrates at various subcellular locations, including the lysosome, cytosol, and nucleus. However, the signaling and biological functions of nuclear mTORC1 (nmTORC1) are not well understood, primarily due to limited tools for monitoring mTORC1 activity in the nucleus. In this study, we developed a genetically encoded nmTORC1 sensor, termed nTORSEL, based on the phosphorylation of eukaryotic initiation factor 4E (eIF4E) binding protein 1 (4EBP1) by mTORC1 within the nucleus. nTORSEL, like its predecessor TORSEL, exhibits a fluorescent punctate pattern in the nucleus through multivalent protein-protein interactions between oligomerized 4EBP1 and eIF4E when nmTORC1 activity is low. We validated nTORSEL using biochemical analyses and imaging techniques across representative cell lines with varying levels of nmTORC1 activity. Notably, nTORSEL specifically detects physiological, pharmacological, and genetic inhibition of nmTORC1 in mouse embryonic fibroblast (MEF) cells but not in HEK293T cells. Therefore, nTORSEL is an effective tool for investigating nuclear mTORC1 signaling in cell lines.

This paper presents a comprehensive approach to enhancing submersible safety through mathematical modeling and decision-making frameworks. We develop models for submersible location prediction, emergency preparedness, and scenario extrapolation, including a Seawater Density Model, Submersible Mechanical Model, Bayesian Searching Model, and Extended Kalman Filter. Sensitivity analysis confirms the robustness of these models under varying conditions, making them adaptable for different marine environments.In the first section, we focus on accurately locating a submersible after communication loss. The Seawater Density Model employs a hyperbolic tangent function to model depth-dependent density in the Ionian Sea, while the Submersible Mechanical Model simulates underwater dynamics using the 4th order Runge-Kutta method. Uncertainties are addressed using an Extended Kalman Filter, enhancing accuracy, as shown by trajectory and Mean Squared Error (MSE) comparisons.In the second section, we develop a Bayesian Searching Model to efficiently locate a missing submersible. This model iteratively updates location probabilities using a bimodal Gaussian distribution. The search zone is discretized into grids, and simulations demonstrate the model's ability to effectively narrow down the search area and improve detection success.In the third section, we adapt the models to different environments, such as the Caribbean Sea. A warning and obstacle avoidance system is introduced to manage multiple submersibles in close proximity, dynamically adjusting their paths to avoid collisions.Reliability analysis demonstrates the robustness of the models against changes in density and sudden environmental shifts, showing minimal deviation from neutral buoyancy. These results confirm the reliability and adaptability of the models for enhancing submersible safety across various marine settings.

Since the early 2000s, high-risk E. coli clones that presented a highly virulent and multidrug resistance profile have been identified, and their genome is completely sequenced. Molecular techniques make it possible to follow the evolution and spread of those clones in different parts of the world. The results of several studies monitoring the emergence of new high-risk E. coli clones showed an increase in the number of clones and the virulence strategies and in the emergence of clones with the ability to cause intestinal and urinary infections combined with a multidrug resistance profile. We certainly can't stop the emergence of new high-risk clones. The remaining questions are: How can we use the knowledge about E. coli high-risk clones to our advantage in the war against multidrug antibiotic resistance? How can we stop the spread of those clones and improve diagnostic and treatment stewardship? This is a challenge that requires collaboration and shared knowledge.

In this paper, we examine the interplay between the structural and spectral properties of the unit graph G(Zn) for n=p1r1p2r2...pkrk, where p1,p2,...,pk are distinct primes and k,r1,r2,...,rk are positive integers such that at least one of the ri must be greater than 1. We first analyze the structure of the unit graph of Zn as a generalized join graph under these conditions. We then determine the Laplacian spectrum of G(Zn) and deduce that it is integral for all n. Consequently, we obtain Laplacian spectral radius and algebraic connectivity of G(Zn). We also prove that the vertex connectivity of G(Zpq) is (p−2)q, where 2≠p&lt;q. We deduce the vertex connectivity of G(Zn) when n=prqs, where 2≠p&lt;q are primes and r,s are positive integers. Finally, we present conjectures about the vertex connectivity of G(Zn) when n=p1p2...pk and n=p1r1p2r2...pkrk, where pi are distinct primes, ri are positive integers, and 1≤i≤k.

The rapid advancement of Earth observation systems and satellite networks driven by commercial space has led to a significant increase in the number of remote sensing satellites, thereby establishing the groundwork for all-weather, continuous cluster collaborative observation. Following anti-irradiation hardening measures, low-cost commercial off-the-shelf (COTS) devices are effectively operated within intricate space environments, leading to substantial enhancements in on-orbit computing capabilities of individual satellites. Leveraging the on-board distributed processing framework, it fully utilizes wide-area scattered space computing resources and automatically establishes a network of neighboring satellites through inter-satellite communication links to construct a spatially distributed and computationally cooperative space cloud computing environment. This enables the sharing of space computing resources and facilitates satellite-based mission coordination, reducing reliance on ground-based remote sensing satellite services while enhancing the response efficiency of remote sensing application services. The present paper pro-vides a comprehensive review of the current development status, existing challenges, and problems faced by space-based remote sensing on-orbit processing platforms and on-board intelligent processing algorithms, starting from the immediate application requirements of remote sensing satellite in-orbit processing. In view of the anticipated surge in large-scale computing resources in space, a distributed remote sensing satellite data processing architecture based on large-scale on-board computing power is constructed, and the architecture is designed in detail from the aspects of physical architecture, software architecture, workflow and key technologies. Finally, three real-time service scenarios that align with the construction content of an ultra-low orbit satellite constellation and 6G technology have been designed to provide solutions and references for subsequent development of space-based intelligent remote sensing constellations.

This study explores the intricate relationship between spectral irradiance variations and polar cap mean vertical total electron content (MVTEC) climatology, using ground-based GNSS measure-ments from the Thule station in the Arctic. The analysis focuses on understanding how different solar and magnetospheric processes drive changes in MVTEC patterns over a 2-year period. Three primary factors are identified as key drivers of MVTEC variations: (1) Russell-McPherron Effect: During equinoxes, enhanced energy transfer from the solar wind to the magnetosphere, governed by the Russell-McPherron effect, leads to increased MVTEC variability. This phenomenon arises due to the changing orientation of the solar magnetospheric coordinate system relative to the solar equatorial system, which affects the efficiency of energy deposition in the magnetosphere. As a result, higher ionospheric disturbances are observed during these periods, highlighting the sea-sonal influence of geomagnetic activity on polar cap TEC patterns. (2) Solar Irradiance Variations: The study identifies a strong correlation between fluctuations in solar EUV and F10.7, both proxies for solar irradiance, and the 27-day oscillations in MVTEC, especially during the summer months. These periodic variations are closely tied to the rotational behavior of the sun, suggesting a direct link between solar activity and ionospheric dynamics. The findings emphasize how solar spectral irradiance influences the ionization levels in the polar cap region, with implications for under-standing seasonal and short-term changes in the high-latitude ionosphere. (3) E-Layer Conductivity: Seasonal changes in the E-layer's conductivity also play a crucial role in modulating MVTEC variability. During summer, the presence of a conductive E-layer enhances cross-field plasma diffusion, leading to faster plasma decay and reduced MVTEC fluctuations. In contrast, the winter months are characterized by an insulating E-layer, which slows down plasma decay and allows F-layer structures to persist longer, resulting in increased MVTEC variability. This seasonal dis-parity underscores the importance of the E-layer's physical properties in shaping high-latitude ionospheric behavior. The findings of this study underscore the complex interplay between solar wind activity, solar irradiance, and ionospheric dynamics in shaping the observed patterns of polar cap MVTEC. By revealing the combined effects of solar and geomagnetic processes, this research contributes to a more comprehensive understanding of high-latitude ionospheric variability. Further investigation is needed to fully elucidate the mechanisms behind these interactions, particularly in terms of their implications for space weather forecasting and the operation of navigation systems in polar regions. Enhanced models that incorporate these insights can improve the prediction and mitigation of space weather effects on satellite-based technologies and communication systems.

Background/Objectives: Emerging data support an essential role of glucagon for lipid metabolism. However, data on the role of dietary fat intake for glucagon secretion is limited. This analysis investigated whether altering nutritional fat intake affects glucagon levels in healthy subjects. Methods: 92 twins (age: 31±14 years, BMI: 23±3 kg/m2) consumed two 6-week diets: first a low fat, high carbohydrate diet (LFD) followed by an isocaloric high fat, low carbohydrate diet (HFD). 24 twins (age: 39±15 years, BMI: 24±2 kg/m2) continued with a high protein diet (HPD). Clinical investigation days were performed after 6 weeks of LFD, after 1 and 6 weeks of HFD and after 6 weeks of HPD. Results: The LFD caused a significant decrease of fasting glucagon (-27 %, p&lt;0.001) compared to baseline. After 6 weeks of HFD glucagon increased (117 %, p&lt;0.001 vs. LFD) while free fatty acids decreased. 6 weeks of HPD further increased glucagon levels (72 %, p=0.502 vs. HFD) although fasting amino acid levels remained constant. Fasting insulin and HOMA-IR moderately increased after one week of HFD, while 6 weeks of HPD significantly decreased both. The fasting glucagon-to-insulin ratio decreased through LFD (p&lt;0.001) but increased after HFD (p&lt;0.001) and even further after HPD (p=0.018). Liver fat, triglycerides and blood glucose did not increase during the HFD. The heritability of glucagon levels was 45% with the LFD. Conclusions: A HFD increases glucagon levels and the glucagon-to-insulin ratio under isocaloric conditions compared to a LFD in healthy lean subjects. This rise in glucagon may represent a favorable metabolic response as neither glucose levels nor insulin resistance or liver fat showed clinically relevant increases.

As the performance of large language models is proven in general domains, we can consider applying them to specific tasks to improve the system’s specialization and responsiveness. In this paper, we will develop a lightweight software system based on RAG (Retrieval-Augmented Generation) to test whether this approach can provide effective advice and protection for patients after discharge. All patient data in this study comes from the neurology department. We will select three diseases — Acute Cerebral Infarction, Atherosclerosis, and Hypertension to test our system, and the generated results will be evaluated by professional doctors. Additionally, we will introduce a memory mechanism to enhance the system’s performance and responsiveness. Finally, we will include suggestions for system improvements and extensions. The experimental results indicate that both the foundational capabilities of large language models (such as Llama 3 and GPT-4) and the system architecture (e.g., the integration of RAG and memory mechanisms) significantly influence overall performance. By comparing different models and architectures, it is observed that the GPT-4 system combined with RAG and memory mechanisms outperforms the baseline in terms of total score and improvements, demonstrating the effectiveness of system architecture optimization in enhancing the models’ ability to handle complex tasks. Since the data still contains sensitive information of patients and hospitals after being desensitized, we cannot show all the test data. However, after obtaining the hospital’s permission, through data desensitization, we showed some of the test data. You can click here to get the prototype we developed and some test data.

China's "Going Out" strategy and the "Belt and Road" initiative have significantly propelled the expansion of overseas contractual engagements, with a pronounced impact in the Engineering, Procurement, and Construction (EPC) sector. EPC contracting is increasingly recognized for its superior resource allocation and integration efficiency. Within the EPC framework, procurement is pivotal, exerting a direct influence on project cost, schedule, and quality parameters. This paper argues that the incorporation of supply chain management into the procurement processes of international EPC projects can optimize efficiency and mitigate costs. Through forging collaborative partnerships with suppliers and stakeholders, EPC contractors may realize enhanced project outcomes. While the integration of supply chains has been efficaciously realized in the manufacturing domain, its transposition to the construction industry is nascent and predominantly resides within the ambit of scholarly inquiry. The present discourse endeavors to elucidate the imperatives and methodologies for supply chain integration within the procurement management of international EPC ventures, thereby furnishing both theoretical and pragmatic guidance for principal contractors. An extensive literature review and bibliometric analysis conducted herein delineate extant research lacunae and articulate prospective trajectories for scholarly exploration in the procurement management sphere of international EPC projects. The advocated integrative modality seeks to deliver a systemic and holistic solution framework conducive to the triumphant execution of EPC projects, engendering viable avenues and opportunities for the advancement and symbiosis within the pertinent industrial sectors. The integration of supply chain management in EPC projects is a burgeoning field that holds significant potential for advancing sustainability in construction. As the industry moves toward greener practices, the role of EPC contractors in driving sustainable procurement and project management becomes increasingly vital.

In this paper a Luenberger-type state observer for a class of nonlinear systems with multiple delays is presented. Sufficient conditions are provided to guarantee practical stability of the error dynamics. An exponential decay of the observation error dynamics is assured using Lyapunov-Krasovskii functionals and the feasibility of Linear Matrix Inequalities. Also, a time-delay SIR compartmental epidemiological model is presented. Time delay corresponds to the transition rates between compartments. The model considers that a part of the recovered population becomes susceptible again after a period following recovery. Three time-delays are incorporated due to the exchange of individuals between the population compartments: $\tau_{1,2,3}$, for the dead-times of recovery, immunity loss and incubation, respectively. It is shown that the effective reproduction number of the delay model depends on the rate of the susceptible population which became infected but after a period starts to be infectious and the fraction of the infectious recovered after a time-delay. For the resulting delay model, the problem of state estimation is addressed. To illustrate the efficiency and performance of the proposed observer, we apply the developed results to the COVID-19 pandemic. The observer can estimate the compartmental populations of Susceptible $S(t)$ and Recovered $R(t)$ from only the availability of real data of the Infectious compartmental population $I_r(t)$. The $I_r(t)$ confirmed data used for the state estimation are from a 55-day window with the highest impact on the Mexican population, as reported by the World Health Organization (WHO).

In this paper, we introduce generalized entropy, the acceleration of its entropy and its the partial entropy. We assume that generalized entropy can be expressed as a second-order polynomial by applying the idea of logistics function to its entropy. In other words, we assume that the acceleration of generalized entropy is a constant. Besides, we show that the negative inverse of the partial entropy can express Newton's gravity, which is an inverse square law. By applying these concepts, we attempt to explain that 1) Gravity is constant within small distances with some conditions. It is possible that gravity have 5-states within small enough distance. There is possible anti-force, which is the opposite of Newtonian gravity Among the 5-states. Furthermore, within small distance, we show the possibility that the gravitational potential and the Coulomb potential can be treated in the same way, that 2) The rotation speed of a galaxy does not depend on its radius if the radius is within the size level of the universe. (The galaxy rotation curve problem), and that 3) The gravitational acceleration toward the center changes at long distances compared to the Newton's gravity. We show that it is an extension of Newton's gravity. Furthermore, we show that the possibility of the existence of some constants which controls gravity and the speed of galaxies, and that gravity may relate on entropy.

This article investigates the impact of minimalism, collectivist culture, and environmental concern support on green purchase intention through the mediating role of pro-environmental behaviours. We found the following key insights based on our analysis of data from 385 participants in collectivist cultures at the emerging economy countries: (1) pro-environmental behaviours have a positive mediating relationship between minimalism and green purchase intention; (2) pro-environmental behaviours have a positive mediating relationship between collectivist culture and green purchase intention; and (3) pro-environmental behaviours have a positive mediating relationship between environmental concern and green purchase intention. Additionally, the findings show that people who embrace minimalism in collectivist cultures are more inclined to take part in activities that benefit the environment. Similarly, the findings will have an influence on policy-making and practical implementation in similar socioeconomic conditions. Implications relating to theory and practice are discussed.

The complex network relationship between leaf traits and their multiple traits is closely related to the environment, and is the result of interactions between various environmental factors such as climate and soil. Based on previous work, we selected 20 P. euphratica trees along the desert riparian forest of the main stream of the Tarim River for leaf sample collection and habitat survey. We used 27 leaf traits of P. euphratica to visualize the leaf trait network (LTN). Through network structure parameters, such as edge density, diameter, average path length, and average clustering coefficient, the spatial pattern of the LTN and its relationships with 11 soil factors and 19 climatic factors were discussed using principal component analysis and correlation analysis. The results showed that: (1) there were significant differences in the parameters of the leaf trait network of P. euphratica along the main stream of the Tarim River. The variation coefficients of the diameter and average path length were the largest, respectively, whereas that of the average clustering coefficient was the smallest. (2) There were both positive and negative correlations between each parameter and soil factors, such as soil water content, total potassium, and organic matter, and climatic factors, such as isothermality, minimum temperature of the coldest month, and precipitation of the wettest month. (3) The explanatory power of climate alone on the leaf traits of P. euphratica was generally higher than that of soil factors, indicating that climatic conditions play a more decisive role in shaping the network structure of leaf traits of P. euphratica. However, regarding the influence of network modularity, the contribution of soil factors was significant, indicating that the influence of soil conditions on some LTN parameters cannot be ignored.

Digital weight-loss services (DWLSs) that supplement continuous lifestyle coaching with Semaglutide therapy have shown promise in delivering continuous and effective obesity care. However, the extent to which lifestyle coaching approach and Semaglutide brand influences patient engagement and program effectiveness is unknown. This study retrospectively analyzed several engagement markers and weight loss percentage over 16 weeks in a large Semaglutide-supported DWLS in the UK. The comparative analysis found that patients who received lifestyle coaching that was proactive and personalized sent a statistically higher number of messages to their health coach (M=19.37 vs M=8.55) and opened the program app more frequently (M = 49.31 days vs M = 40.06 days) than patients whose coaching was reactive and standardized. Mean 16-week weight-loss was 10.1% in the proactive group compared to 8.9% in the reactive group, but the difference was not statistically significant. A two-sample t-test found that female patients (M = 9.76) tended to lose more weight than male patients (M = 6.88), (t(152) = 1.89 , p = 0.04). The findings add vital layers of nuance to the emerging literature on Semaglutide-supported DWLSs, indicating that a proactive, personalized coaching approach leads to better patient engagement with DWLS health coaches and that Wegovy has a comparable effect to Ozempic as a supplement to DWLS coaching. Future research should seek to conduct comparable investigations of other obesity services and over a longer period.

Background Small intestinal neuroendocrine tumors (SI-NETs) are characterized by carcinoid syndrome and carcinoid heart disease (CHD). The aim of study was to identify early risk markers for carcinoid heart disease and survival in a prospective median-term follow-up setting. Methods We measured 5-HIAA, cumulative 5-HIAA exposure (Cum-5-HIAA) based on repeat measurements, proBNP, vascular function, hepatic tumor load and transthoracic echocardiography (TTE) were assessed at baseline and during median 5-year follow-up. Of 65 patients with SI-NETs; 54 patients underwent prospective follow-up. In addition, survival was evaluated during median follow-up of 6 years. Results At baseline three patients had CHD. During median follow-up of 5 years, two patients (4%) developed CHD. Cum-5-HIAA and proBNP correlated with CHD (Westberg Score, Spearman’s ρ = 0.32 and 0.31, respectively). Cum-5-HIAA had a superior diagnostic capability, predicting CHD in receiver operator characteristics analysis with AUC of 0.98 (95% CI: 0.94–1.00) and outperformed proBNP, chromogranin A (CgA), as well as individual serum 5-HIAA measurements (AUC = 0.75, 0.85, and 0.91, respectively). Minor changes in valve regurgitation were frequently detected but did not correlate with vascular function. In 29% of tricuspid and 30% of pulmonic valves regurgitation increased or decreased. CHD, hepatic tumor load, serum 5-HIAA, and elevated aortic pulse wave velocity (PWV) associated with increased mortality in SI-NET patients. Conclusions Cum-5-HIAA is a promising biomarker for CHD risk and outperformed other biomarkers. CHD and hepatic tumor load are strongest predictors of mortality. PWV is a novel predictor of survival. The incidence of CHD was small among the SI-NET patients, probably reflecting successful treatment regimens.

In this study, the expression and localization of gonadotropin-releasing hormone (GnRH1) and kisspeptin (KISS1) and their specific receptors in canine ovarian and uterine tissues were investigated after the application of deslorelin acetate (Suprelorin^®, 4.7 mg, Virbac, France) in the late pre-pubertal period. We hypothesized that chronic treatment of prepubertal dogs with deslorelin would alter the expression of GnRH and kisspeptin genes in the uterus and ovaries. Ovarian and uterine samples of 25 dogs with an average age of 7.8±0.2 months and from mixed breeds were used. Following implant insertion, dogs entered estrus (EST; n=6); dogs without estrus (N-EST; n=10) comprised the experimental groups. Nine dogs with placebo implants served as a control (CONT). Ovarian and uterine tissues were investigated for expression of GnRH1, GnRHR, KISS1 and KISS1R/GPR54 mRNA and protein by using IHC and RT-qPCR. In uterus, expression of GnRH1significantly decreased in response to deslorelin treatment in the N-EST, compared with the control group. Compared with CONT, KISS1R expression in ovarian samples was significantly lower in EST group. Uterine protein expression of GnRH1 appeared weaker in N-EST than CONT. While GnRH1-system members and KISS1 protein were localized in the follicles at various stages and stroma, no or weak signals were detected for KISS1R in the ovarian samples. Deslorelin-mediated induction of puberty by changing the expression of some of the GnRH and KISS1-system members seems to have an effect on ovarian and uterine functionality. Deslorelin implants can therefore be not considered as a valuable alternative to induce fertile estrus in late-prepubertal bitches. However, further studies with a large number of animals are needed to clarify the effect of deslorelin-mediated induction of puberty.

Background: a supplement containing Saccharomyces cerevisiae DSM 34246 (Canobios-BL) var. boulardii was tested to demonstrate its efficacy as a gut flora stabilizer in two breed of dogs, West Highland White Terrier (WT) and German Shepherd (GS). Methods: a total of 53 healthy adult dogs, 28 WT and 25 GS, were included in the study. The animals were randomly assigned to a control group (CTR: WT 14/28, GS 12/25) and to a treated group (SACC; WT 14/28, GS 13/25). The CTR Group was fed with dry commercial food, while the SACC one was fed with the same food but supplemented with the feed additive S. boulardii at the concentration of 5 X 10⁹ CFU/kg. The study lasted 35 days and included six evaluation time points (T₀-T₅). Body Weight (BW), Body Condition Score (BCS), Fecal Score (FS), FS measured with a penetrometer (FSp), Fecal Dry matter (DM), Fecal Humidity (UM) and Fecal IgA (IgA) were measured at each time point. Results: a significant improvement (p<0.05) in BCS, FS, FSp, MCS, DM, UM and IgA of the treated group with respect to the control one for both WT and GS were reported. Conclusions: the positive effect of S. boulardii supplementation on the improvement of the fecal parameters and on the maintenance of good physiological as well as biological conditions in healthy adult dogs in breeding conditions.

The lightweight and displacement-stable design of the mechanical support structure within the APTS (Acquisition, Pointing, and Tracking System) is crucial for enhancing the payload capacity of remote sensing, satellite communication, and laser systems, while still meeting specified functional requirements. This paper adopts the Solid Isotropic Material with Penalization (SIMP) method to investigate the structural topology optimization of the L-shaped bracket in the APTS, aiming to minimize structural compliance while using volume, key point displacement, and maximum stress as constraints. In the optimization model, differences in the topology of the L-shaped bracket structure are explored to minimize structural compliance, which is done under volume, key point displacement, and stress constraints, the results are compared with the initial reinforced structure. The innovative L-shaped bracket structure obtained through topology optimization uses significantly less material than the initial reinforced design, while still meeting the displacement and stress constraints. After smoothing, rounding, and finite element analysis, the displacement and stress performance of the optimized L-shaped bracket structure satisfies the set constraints. The method proposed in this paper offers an innovative solution for the lightweight design of mechanical support structures in APTS, with significant engineering application potential.

In this paper I dig deep down along the slope of what touches me deeply – a democratic decline in climate politics with increasing use of hateful and threatening rhetoric – nasty rhetoric. I follow the course of critical researchers in management and organization studies, striving to move away from traditional, horizontal norms of academic writing that elision the author from the text, to-wards a more vertical writing incorporating the voice of the author. Embodying and resonating with my own emotional experiences of far-right hate and threat campaigns, I qualitatively analyze written, spoken and visual material from newspapers, magazines, blogs, podcasts, photos, vid-eos, television, radio and social media, to understand the nature of nasty rhetoric in Swedish climate politics, and the implications thereof for democracy. A far-right populist nativist party is currently holding tangible powers, dictating the ambitions, content and process of Swedish cli-mate politics. In less than two years, Swedish climate politics have turned into an antidemocratic divisive politics portraying climate science as “a point of view”, female climate journalists as “moron hags” that should be “fired” and “raped”, and the climate justice movement as “terror-ists” and “a threat to Swedish democracy” that should be “sent to prison” and “executed”. Nasty rhetoric is used not only by anonymous trolls in social media, but openly by the prime minister, cabinet ministers and parliamentarians. Their use of nasty rhetoric aims to silence the opponents to the current paradigm shift in Swedish climate policy, breaching democratic norms such as le-gitimacy, accountability and justice, but also to mobilize followers and expand they hate and threats. The opposition is also using nasty rhetoric, but of a less aggressive nature, to reveal far-right populist climate policy and politicians as a naked emperor. Nasty rhetoric is a powerful tactic to mobilize more offenders, while leaving its targets with fear and anxiety and a need for disappearance from public debate. Many targets resign or stay silent, negatively affecting plu-ralistic debates and the scrutiny of power in democracies. I chose to break the silence and write differently to increase our knowledge of the phenomenon of nasty rhetoric.

Islatravir (ISL) is the first-in-class nucleoside reverse transcriptase translocation inhibitor (NRTtI) with novel modes of action. Data on ISL resistance is currently limited, particularly to HIV-1 non-B subtypes. This study aimed to assess prevalent nucleos(t)ide reverse transcriptase inhibitor (NRTI) resistance mutations in HIV-1 subtype C for phenotypic resistance to ISL. Prevalent single and combinations of NRTI resistance mutations were selected from a routine HIV-1 genotypic drug resistance testing database and introduced into HIV-1 subtype C-like pseudoviruses, which were then tested for ISL susceptibility. Single NRTI resistance mutations were susceptible or showed only a low level of resistance to ISL. This included thymidine analogue mutations (TAMs, i.e., M41L, D67N, K70R, T215FY, K219EQ) and non-TAMs (i.e., A62V, K65R, K70ET, L74IV, A114S, Y115F, M184V). Combinations of M184V with one or more additional NRTI resistance mutations generally displayed reduced ISL susceptibilities. This was more prominent for combinations that included M184V+TAMs, and particularly M184V+TAM-2 mutations. Combinations that included M184V+K65R did not impact significantly on ISL susceptibility. Our study suggests that ISL would be effective in treating people living with HIV (PLWH) failing tenofovir disoproxil fumarate (TDF)/lamivudine (3TC) or TDF/emtricitabine (FTC)-containing regimens but would be less effective in PLH failing zidovudine (AZT) with 3TC or FTC-containing regimens.

In order to accurately assess the temperature action and effect of steel-concrete composite girder bridges in plateau and cold area, this paper investigated nearly 50 years of historical meteorological data from 26 meteorological observation stations in the Tibet region of China, and numerical simulation of the temperature field of the composite girder was carried out by using the most unfavorable extreme meteorological conditions of each meteorological station as the boundary, respectively. The most unfavorable values of effective temperature and positive and negative vertical temperature gradients were obtained. The geographical variability of the temperature actions at different meteorological stations was analyzed, and the contour maps of the extreme values of the effective temperature and temperature gradient actions were further obtained by using the spatial interpolation method. The study shows that the effective temperature of the composite girder is significantly affected by the climatic environment, and the isotherm map can visualize the geographical distribution law of the effective temperature extremes of the concrete box girder. The maximum and minimum effective temperatures in Tibet range from 18.28 ℃ to 42.27 ℃ and from -41.07 ℃ to 4.71 ℃ respectively, with a geographic variability of 23.99 ℃ and 45.78 ℃ respectively. The maximum regional difference of positive and negative temperature gradient reaches 11.32 ℃ and 7.69 ℃ respectively. Referring to the Chinese specification and the European specification of the composite girder's temperature role in the value, the highest effective temperature and positive temperature gradient in most of the areas in Tibet is too conservative, and the lowest effective temperature and negative temperature gradient is not safe enough, which may lead to the composite girder bridges with cracking of the concrete deck slabs and the disease of the bearings and expansion joints.

This paper explores the effects of internal marketing practices and transformational leadership on employee customer-oriented behaviour. It uses social exchange and broadened-and-build theories to extend the motivation-opportunity-ability framework to the internal marketing and customer-oriented behaviour literature. Basic psychological needs theory is applied to refine the conceptualisation of internal marketing practices for a more holistic view of employee needs by including spiritual needs. Survey data from 174 frontline employees from the service sector in Pakistan is analysed using PLS-SEM. Results reveal a positive influence of internal marketing practices and transformational leadership on employee customer-oriented behaviour. Specific internal marketing practices and strategies are offered.

This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. While traditional centralized data processing methods limit effective collaboration across agencies due to data privacy and compliance concerns, federated learning avoids the risk of privacy breaches through a distributed architecture that allows participants to train artificial intelligence (AI) models together without sharing raw data. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy. In addition, this study explores the scalability and generality of federated learning in the medical field, and points out that the technology is not only suitable for drug development, but also has broad cross-industry application potential, especially in areas such as finance and insurance, where data privacy is critical.

This study characterized groundwater resources for the Nakivale sub-catchment of the transboundary Victoria basin in Uganda using classical hydrochemical and stable isotopic approaches. Groundwater in the study area is essential for domestic, agricultural, and industrial uses. As a sub-domain of the larger Victoria Basin, it also plays a crucial role in shaping the hydrological characteristics of this vital transboundary basin, both in terms of quality and quantity fronts. This makes its sustainable management and development vital. The predominant groundwater type is Ca-SO4, with other types including Ca-HCO3, Na-Cl, Na-HCO3, and Ca-Mg-SO4-Cl. Hydrochemical facies analysis highlights the importance of rock-water interactions in controlling groundwater chemistry, mainly through incongruent chemical weathering of Ca-rich plagioclase feldspars and the oxidation of sulfide minerals, such as pyrite which are prevalent in the study area. Ground-water recharge is primarily influenced by the area’s topography, with recharge zones characterized by lineament networks, located in elevated areas. Stable isotope analyses indicate that groundwater mainly originates from local precipitation, while tritium data suggests the presence of both recent and older groundwater (likely over 20 years old). The study’s comprehensive approach and findings contribute significantly to the understanding of the region's groundwater systems, thus providing valuable insights for policymakers and stakeholders involved in water resource management and development strategies.

Nanothermodynamics provides the theoretical foundation for understanding stable distributions of statistically independent subsystems inside larger systems. In this review it is emphasized that adapting ideas from nanothermodynamics to simplistic models improves agreement with the measured properties of many materials. Examples include non-classical critical scaling near ferromagnetic transitions, thermal and dynamic behavior near liquid-glass transitions, and the 1/f-like noise in metal films and qubits. A key feature in several models is to allow separate time steps for distinct conservation laws: one type of step conserves energy and the other conserves momentum (e.g. dipole alignment). This “orthogonal dynamics” explains how the relaxation of a single parameter can exhibit multiple responses such as primary, secondary, and microscopic peaks in the dielectric loss of supercooled liquids, and the crossover in thermal fluctuations from Johnson-Nyquist (white) noise at high frequencies to 1/f-like noise at low frequencies. Nanothermodynamics also provides new insight into three basic questions. First, it gives a novel solution to Gibbs’ paradox for the entropy of the semi-classical ideal gas. Second, it yields the stable equilibrium of Ising’s original model for finite-sized chains of interacting binary degrees of freedom (“spins”). Third, it confronts Loschmidt’s paradox for the arrow of time, showing that an intrinsically irreversible step is required for maximum entropy and the second law of thermodynamics, not only in the thermodynamic limit but also in systems as small as N=2 particles.

Background The prevalence of autism has increased substantially among Latine children, however, few service systems are prepared to provide culturally relevant services. Parents Taking Action (PTA), is a culturally informed parent-mediated psychoeducation program designed to meet the informational needs of Latine families of children with autism. The purpose of this study was to pilot a hybrid model of intervention by including direct parent coaching through pivotal response training (PRT) along with PTA among Latina mothers of children with autism. Methods Ten Latina mothers of children 8 and under with autism participated in this study. Mothers received PTA from two bilingual trained promotoras (community health workers). PTA included 14 2-hour psychoeducation sessions. Mothers also received 4 one-hour sessions of PRT from bilingual/bicultural coaches. Results Paired samples t-tests indicated significant increases in family outcomes, self-efficacy in using the intervention strategies, and frequency of using strategies, from pre to post-test. Conclusions This study suggests that culturally informed parent-mediated autism intervention coupled with parent coaching positively affects family outcomes among Latine families of children with autism.

Background: Nearly half of congenital heart defects (CHDs) related mortality occur during infancy although advancement in treatments have increased the survival rates. This study comprehensively examines overall and surgical mortality of CHD infants with the highest mortality in an effort to improve our understanding of CHD epidemiology. Methods: Participants were drawn from a dataset of 1,964,691 infants born between 2014 and 2018 in Korea. Target patients defined as those with crucial CHDs including classical critical CHDs and diverse categorical defects but excluding simple shunt defects. Overall mortality (procedural and natural mortality) and procedural mortality (interventional and surgical mortality) for infants were analyzed. Results: Performance rate of multiple procedures in infants with crucial CHDs was 16%. Overall and surgical mortalities of crucial CHDs were 8% and 7%. Mortalities of palliative procedures were relatively high. Procedural mortalities for infants were significantly decreased in tetralogy of Fallot (TOF), atrioventricular septal defects, and total anomalous pulmonary venous return (TAPVR) compared with overall mortalities for infants. Surgical mortalities for infants involving TOF and TAPVR were significantly lower, but those for infants involving hypoplastic left heart syndrome (HLHS) was higher than the those for all ages. Conclusions: Palliative procedural technique in infants must be improved to obtain better outcome particularly in palliative surgery of HLHS. The infantile procedural outcomes for TOF and TAPVR are excellent and important to overcome disastrous circumstances during infancy. This comprehensive study of overall and procedural mortalities of CHDs may lay a cornerstone for CHD epidemiology in Korean infants.

CXC chemokine receptor 1 (CXCR1) is an important regulator for neutrophil granulocyte activation through binding to the ligand interleukin-8 (IL-8). Upon binding to IL-8, CXCR1 activates downstream signaling, critical for innate and adaptive immune responses. The IL-8-CXCR1 axis also plays an important role in tumor progression, especially in the tumor microenvironment. CXCR1 antagonists or anti-IL-8 monoclonal antibodies (mAbs) have been developed and evaluated in clinical trials for inflammatory diseases and tumors. In this study, we developed novel mAbs for mouse CXCR1 (mCXCR1) using the N-terminal peptide immunization. Among the established anti-mCXCR1 mAbs, Cx₁Mab-8 (rat IgG_2b, kappa) recognized mCXCR1-overexpressed Chinese hamster ovary-K1 (CHO/mCXCR1) and mCXCR1-overexpressed LN229 (LN229/mCXCR1) by flow cytometry. The dissociation constant (K_D) values of Cx₁Mab-8 for CHO/mCXCR1 and LN229/mCXCR1 were determined as 5.1 × 10⁻¹⁰ M and 1.3 × 10⁻⁹ M, respectively. These results indicated that Cx₁Mab-8 is useful for detecting mCXCR1 by flow cytometry with high affinity and can obtain the proof of concept in preclinical studies.

Digital terrestrial television is now implemented in many world wide countries and is now mature. Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) is the European standard adopted or deployed by European and African countries which uses Orthogonal Frequency Division Multiplexing (OFDM) modulation to achieve a good throughput performance. However, its main particularity is the number of subcarriers operated for OFDM modulation which is high (up to 32768). Also, mobile reception is planned in DVB-T2 in addition to rooftop antenna and portable receptions planned in DVB-T. But, the main challenge of DVB-T2 for mobile reception is the presence of Carrier Frequency Offset (CFO) which degrades the system performance by inducing an InterCarrier Interference (ICI) on DVB-T2 signal. This paper evaluates the system performance in the presence of the CFO when Gaussian noise and TU6 channel are applied. Universal Filtered Multicarrier (UFMC) and Non Uniform Constellations (NUCs) have previously demonstrated good performances in comparison with OFDM and Quadrature Amplitude Modulation (QAM) in DVB-T2. The impact of CFO on the UFMC and NUCs based DVB-T2 system is additionally investigated in this work. The results demonstrate that the penalties induced by CFO insertion in UFMC and NUCs based DVB-T2 are highly reduced in comparison to those for the native DVB-T2. At a BER of 10−3, the CFO penalties induced by the native DVB-T2 are respectively 0.96 dB and 4 dB when only AWGN is used and when TU6 is additionally considered. The penalties are equals to 0.84 dB and 0.2 dB for UFMC/NUCs based DVB-T2.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are known for efficacy in managing blood sugar levels in type 2 diabetes and also improving cardiovascular and renal outcomes. However, increased risk of urogenital infections (UGIs) remains a significant concern in their safety profile. This study aimed to investigate the association between SGLT2i therapy and UGIs in a real-world clinical set up of a tertiary care centre in India. The key hypothesis was SGLT2i use is associated with incidence of UGIs while the secondary hypothesis included the relationship of the incidence of risk with the dose. Methods: This was a six-month prospective observational study, that enrolled 309 adults ≥18 years of age, who were newly prescribed SGLT2 inhibitors, particularly Dapagliflozin 5 mg and 10 mg from October 2023. The incidence of UGIs was assessed through patient interviews and medical records during the follow up from 2 weeks of initiation. UGIs were defined as clinical manifestations that resolved on SGLT2i discontinuation or appropriate treatment. Results: Of 309 initiators, 300 patients were followed up. 9 patients didn’t follow up in OPD. From these, 58 (19.3%)patients developed UGIs, comprising of 47 (15.6%) bacterial infections and 11 (3.6%) UGIs of fungal origin. The association was performed on basis of logistic regression analysis which revealed a statistically significant association of UGIs with the prescription of SGLT2i. Higher doses of SGLT2 inhibitors was observed to be associated with significantly increasing the risk of UGIs (p < 0.05). No significant risk factors such as body mass index (BMI), age, sex, HbA1c, or educational status were identified to have significant associations with UGI occurrence. Conclusion: This study highlights a clinically relevant association between SGLT2i use and urogenital infections.

With increasing global attention on carbon emission control, the carbon market has become an important tool for carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon market. Given the relative maturity of the EU carbon market and electricity market, this paper identifies key points of market volatility through the Coupling coordination model analysis, and uses the GPT large model analysis to analyze policy factors that have a greater impact on carbon market and electricity market transactions. The research results show that market-regulating policies and expectation management policies have the greatest impact on the degree of coupling between the carbon and electricity markets. The impact of secondary policies such as international cooperation policies and structural adjustment policies is relatively insignificant. Although the development of China's carbon market faces many challenges, reasonable policy interventions are expected to achieve significant results in the domestic carbon market. Finally, this paper also draws on analogy analysis to draw corresponding implications for China from the EU's carbon-electricity coupling policy promulgation.

Residual gas in exetainer vials leads to nitrous oxide (N2O) gas underestimation. This study compared four evacuation methods such as gas exchange by displacement (GED), evacuation with a vacuum pump (VP), evacuation with a syringe (VS) and pre-evacuated Exetainer (PEE) to assess the residual gas effect on gas sample concentration. PEE and VP methods showed superior performance, displaying lower deviations from actual gas concentrations with RMSE values of 0.239 and 0.179, respectively, at 12.5 ppm standard gas levels. Conversely, GED and VS methods exhibited higher deviations with RMSE values of 0.740 and 0.448, respectively, at 12.5 ppm. Errors were more pronounced at lower gas concentrations. To address residual gas effects in GED and VS methods, standard gases were stored for calibration in evacuated vials (EVM) matching the sample collection method, rather than in syringes (SM). This EVM approach enhanced precision by 14.43% and 6.24% for GED and VS methods, respectively, at 0.781 ppm standard gas levels. The study concludes that PEE and VP methods are more reliable for vial evacuation. Using vials prepared using the same method as sample collection vials can mitigate residual gas effects effectively. This study holds substantial importance in enhancing the accurate measurement of greenhouse gas emissions, particularly focusing on nitrous oxide.

Vopson and Lepadatu recently proposed the Second Law of Infodynamics. The law states that while total entropy increases, information entropy declines over time. They state that the law has applications over a wide range of disciplines, but they leave many key questions unanswered. This article analyzes and reformulates the law based on the thermocontextual interpretation (TCI). The TCI generalizes Hamiltonian mechanics by defining states and transitions thermocontextually, with respect to an ambient-temperature reference state. The TCI partitions energy into ambient heat, which is thermally random and unknowable, and exergy, which is knowable and usable. The TCI is further generalized here to account for a reference observer’s actual knowledge. This enables partitioning exergy into accessible exergy, which is known and accessible for use, and configurational energy, which is knowable but unknown and inaccessible. The TCI is firmly based on empirically validated postulates. The Second Law of thermodynamics and its information-based analogue, MaxEnt are logically derived corollaries. Another corollary is a reformulated Second Law of Infodynamics. It states that an external agent seeks to increase its access to exergy by narrowing its information gap with a potential exergy source. The principle is key to the origin of self-replicating chemicals and life.

Respiratory diseases are common in rabbits, but subclinical conditions can be challenging to diagnose and may cause respiratory problems during anesthesia. CT is the preferred method for diagnosing lung diseases, but anesthesia can alter lung volume and cause lung lobe collapse. In this study, seventeen healthy 5-month-old male New Zealand white rabbits underwent thoracic CT scans under different conditions. Rabbits were sedated with midazolam and butorphanol and scanned in a sphinx position they were then anesthetized with dexmedetomidine and ketamine and scanned again in sternal recumbency during spontaneous breathing. Lastly, apnea was induced using intermittent positive pressure ventilation (IPPV) for a final scan. Lung volume and density were measured using the 3D Slicer software, with thresholds set between -1050 and -100 Hounsfield Units (HU). Sedated animals had significantly higher total lung volume (69.39±10.04 cm³) than anesthetized (47.10±9.28 cm³) and anesthetized in apnea rabbits (48.60±7.40cm³). Mean lung attenuation during sedation was -611.26 HU (right) and -636.00 HU (left). After anesthesia induction, values increased to -552.75 HU (right) and -561.90 HU (left). Following apnea induction, attenuation slightly decreased to -569.40 HU (right) and -579.94 HU (left). The results indicate that sedation may be preferable for rabbit lung CT to minimize anesthesia-related changes.

A Berry geometrical phase is identified in a strongly metastable system containing dynamically responsive clathrate hydrate structures within a crystal-fluid material. High energy degeneracy in the associated chemistry produces local stability and false vacuum conditions that lead to non-extensive and non-additive contributions in the fundamental thermodynamic relation. Application of Ginzburg-Landau theory and the scaling laws reveals a coherence length and a penetration depth for a macro-scale dual superconductor. The coherence length determines non-extensive volume changes whilst its inverse gives the Higgs mass. The penetration depth determines the extent of QCD vacuum suppression whilst its inverse gives the vector boson mass together with indirect manifestation as non-additive hyperbolic curvature. Simultaneous emergence of the Ginzburg-Landau superconducting phase transition is consistent with gauge-invariant coupling of the scalar field to the Yang-Mills action in QCD. The discovery of an energy gap in the gradient energy term of the system Lagrangian is associated with a critical correlation length revealed in the transition from a gapped to a gapless superconducting state. Together with the emergence and absorption of the Higgs-like scalar field, a mechanism for describing a renormalized QCD mass gap arises.

This study investigates the time-resolved aerodynamics in the cavity regions of a full-scale, high-speed low-pressure turbine stage representative of geared turbofan engines. The turbine stage is tested in the von Karman Institute’s short-duration rotating facility at different purge rates (PR) injected through the upstream hub cavity. Spectra from the shroud and downstream hub cavity show perturbations linked to blade passing frequency and rotor speed. Asynchronous flow structures associated with ingress/egress mechanisms are observed in the rim seal of the purged cavity. At 0% PR, the ingress region extends to the entire rim seal, and pressure fluctuations are maximized. At 1% PR, the instability is suppressed and the cavity sealed. At 0.5%, the rim-seal instability exhibits multiple peaks in the spectra, each corresponding to a state of the instability. Kelvin-Helmholtz instabilities are identified as trigger mechanisms. A novel technique, based on the properties of the cross-power spectral density, is developed to determine speed and wavelength of the rotating structures, achieving higher precision than the commonly used cross-correlation method. Moreover, unlike the standard methodology, this approach allows to calculate the structure characteristics for all the instability states. Spectral analysis at the turbine outlet shows that rim-seal-induced instabilities propagate into regions occupied by secondary flows. A methodology is proposed to quantify the magnitude of the induced fluctuations, showing that the interaction with secondary flows amplifies the instability at the stage outlet.

Enterprise Systems (ES), including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM), play a critical role in streamlining processes, integrating data, and enhancing decision-making across executive, managerial, and operational levels in organizations. These systems facilitate real-time information flow, improve coordination, and increase efficiency. This systematic review aims to identify the success factors and reasons for failure in the implementation of Enterprise Systems across various organizational levels, drawing from empirical research and case studies. A systematic search of databases such as Google Scholar, Web of Science, and Scopus was conducted. The review examined key factors influencing ES success, including top management support, user involvement, clear objectives, and change management. Failures were linked to misalignment with business goals, insufficient training, and poor communication. Studies were selected based on eligibility criteria, including publication date (within the last 20 years) and relevance to ES implementation. Out of the 64 studies reviewed, 76% focused on Small and Medium-sized Enterprises (SMEs), while 12% specifically addressed small businesses. The analysis revealed that 46% of the studies emphasized the role of ES in strategic planning at the executive level, 34% examined ES's impact on managerial performance in SMEs, and 20% explored the alignment of ES with business strategy. The review also found that on-premises solutions dominated ES implementation (67%), followed by cloud-based solutions (33%). Key success factors identified were organizational readiness, strategic alignment with business goals, and user engagement. Common failure causes included insufficient training (35%), lack of organizational readiness (28%), and poor change management (22%). This review provides insights into the critical success factors for ES implementation and common pitfalls. Findings emphasize the need for strategic alignment, comprehensive training, and stakeholder involvement at all organizational levels. These results offer a roadmap for organizations to optimize future ES implementations and ensure long-term sustainability through effective planning and change management.

Drought stress is a major issue in agriculture, as it is becoming more prevalent due to climate change and can reduce crop yields significantly. Low water amounts, particularly at the flowering stage, limit plants from undergoing typical growth and reproductive development, including gametogenesis, fertilization, and embryogenesis. Therefore, it is important that rice growers have a method of quickly determining whether or not their crops are undergoing drought stress to enact mitigation strategies. This study aims to use machine learning computer vision to accurately detect drought stress in plants. Before I developed models, I conducted data pre-processing on images of Setaria plants with varying water contents. Then, I built several machine learning models, including K-Nearest-Neighbors, Decision Tree, Logistic Regression, and ResNet 18. I then assessed the effectiveness of these models by calculating their accuracy scores. I found that K-Nearest-Neighbors, Decision Tree, and Logistic Regression models obtained 80.2%, 70.7%, and 75.9% accuracy scores, respectively, while ResNet18 obtained a very high accuracy of 97.3%. Therefore, these models can serve as a promising future solution for non-invasively detecting drought stress in rice plants, potentially saving crop producers millions of dollars in yield.

This article is dedicated to critically appraising the current knowledge and advancements in rediscovering a thematic religious and cultural route that traverses European continental territories, from the Baltic regions to Rome, known as Romea Strata. This spiritual, diverse, and interreligious project combines two approaches: the historical and heritage fashioners. The article assesses secondary sources that clarify a scientific background, discussing the historicity of Romea Strata’s direction from Tallinn to Rome, through European capitals and minor localities. Mobility in space motivated by culture and faith is a human phenomenon. Historical knowledge of this phenomenon constitutes an important theme for understanding the different potentials of intercultural dialogue, as a strong awareness of the heritage capabilities motivates communities to collaborate on a sustainable development project.

In this study, the asymmetric effect of oil and natural gas price shocks on the trade balance of 11 CESEE countries was estimated. These countries are net importers of oil and natural gas, and price shocks are often the cause of imbalances in trade. Quarterly data for the period 1994Q1 to 2023Q4 and nonlinear ARDL model were used. In the long run, a significant asymmetric effect due to the oil price shock on the trade balance of Bulgaria, Czechia, Latvia and Slovenia was found, as well as a significant asymmetric effect due to the natural gas price shock on the trade balance of Croatia. Also in the long run, a significant asymmetric effect due to wholesale prices on the trade balance of Romania, Bulgaria and Croatia was found as well as a significant asymmetric effect due to industrial production on the trade balance of Croatia, Czechia and Latvia was found, in addition to a significant asymmetric effect due to the exchange rate on the trade balance of Czechia, Poland and Romania.

Objective : Children < 15 years old are thought to be the most vulnerable group to contracting the infection than adults due to their immature or developing immunity. We investigated whether index patient child leprosy are associated with parental education, parental unemployment, parental income, household crowding, inadequate ventilation household contacts, neighbours contact and nutritional deficiency in Dili, Liquica, Manatuto, Baucau, RAEOA. Methods: We designed matched case-control study. Cases were fathers of children with leprosy under 15 years old while controls were non random sample of children without leprosy who live in the same household and neighbourhood with child leprosy < 15 years who have the same age and gender with child leprosy. The case-to-control ratio was 1:2. We identified the factors that significantly differed between the cases and controls in a multivariable binary logistic regression analysis and reported the odds ratio (OR) and 95% confidence interval (CI)Results: We found similar demographic among the 40 cases and 80 controls. The research results indicated that out of the 8 variables examined, 4 variables household contacts, household crowding, inadequate ventilation, and nutritional deficiency were significant risk factors (P value < 0.005). In a multivariate analysis, household contact was identified as a significant factor in the occurrence of leprosy in children under 15 years old (P value = 0.003, AOR = 3.788, 95% CI: 1.593–9.003)Conclusion: Considering that household contacts consist of a recognizable group of individuals under 15 years old with a high risk of disease, as they live in close proximity to a source of infection, it is essential to focus on education, routine screening, and the application of chemoprophylactic protocols towards leprosy prevention among household contacts, especially blood relatives.

Interest in leadership skills and leadership development is burgeoning both in academic circles and business organizations. Leadership research has produced a large number of leadership models. The distinctions between leadership and management, skills required for leadership, and methods of leadership development are ongoing areas of study. An attempt is made to provide an overarching theory that showcases some crucial distinctions. The proposed theory holds that ‘managing’ is about improving a situation, and ‘leading’ is about improving a situation by stretching oneself either by exploiting unique personal characteristics or by exploring underdeveloped skills and/ or enabling others and organizations to do the same. Thus, leadership is an act of self-expansion, and everyone can lead, including followers. Leadership development requires a series of acts of leading and learning, leading to leadership positions in a Darwinian process. Thus, at the heart of leadership is experimentation, and hence, it can be rightly called the art of management. What leaders do successfully today becomes management science tomorrow. The ever-growing list of leadership skills and leadership theories with more prosocial views is evidence of leadership in theory and practice. We also propose holistic leadership as a new type of leadership.

Methylene blue is a cationic organic dye found in wastewater, groundwater, and surface water, due to industrial release in the environment. This emerging pollutant is very persistent and, depending on the doses, can harm human and environmental health. In this work, a Surface Plasmon Resonance Biosensor using a BK7 prism with a coating of 3 nm chromium and 50 nm of gold in the Kretschmann configuration was developed for the detection of methylene blue, using for the first time, laccases immobilized on a gold surface as bio-receptors for this organic dye. The enzyme immobilization through carbodiimide bonds, using EDC/NHS crosslinkers, and allowed to analyze samples with minimal preparation. The method was validated, achieving a LOD of 4.61 mg L-1 and LOQ of 15.37 mg L-1, a working range from 0-100 mg L-1, and a R2=0.9614 in a real-time analysis. A sample of rainwater spiked with methylene blue was analyzed, and a recovery of 122.46 ± 4.41% was obtained.

In the rapidly evolving field of natural language processing (NLP), the processing of the Chinese language, with its unique complexities, presents significant challenges, especially in the context of Large Language Models (LLMs) like LLaMA2. These challenges are further exacerbated by the presence of non-standardized text prevalent across digital Chinese content. To address these challenges, this paper proposes a novel hybrid approach that seamlessly integrates deep contextual embeddings with Convolutional Neural Networks (CNNs) to enhance the processing of standardized Chinese text. Our approach involves a multi-stage process wherein deep contextual embeddings are first utilized to capture the nuanced semantic relationships within text. Following this, CNNs are employed to identify and exploit structural and syntactic patterns, facilitating a comprehensive understanding of the text. This hybrid model significantly improves LLaMA2’s efficiency and accuracy across various Chinese text processing tasks by ensuring that both semantic depth and structural nuances are accurately captured. The effectiveness of our model is demonstrated through rigorous testing across several benchmarks, showcasing its superiority in processing Chinese text with enhanced accuracy and speed. This research not only contributes to the advancement of text processing capabilities of LLMs but also opens new avenues for their application in tasks such as automated translation and sentiment analysis.

Wasp venom allergy can trigger severe allergic reactions, and predicting these acute responses remains challenging. This study evaluates the utility of immune system indexes, particularly the eosinophil-basophil/lymphocyte (EBLR) and eosinophil-basophil-platelet/lymphocyte (EBP/L) ratios, in assessing the severity of allergic reactions in patients with wasp venom allergy. A total of 61 patients with confirmed wasp venom allergy were categorized according to the Mueller scale, which classifies the severity of allergic reactions. Blood samples were analyzed for total and specific IgE levels, alongside a range of hematological and biochemical parameters. The study found significant differences in the EBLR and EBP/L indexes between patients with mild (Mueller I-II) and severe (Mueller III-IV) allergic reactions, with higher values indicating more severe responses. However, no significant differences were observed in other immune indexes, such as PLR, NLR, SII, and SIRI, or in additional blood parameters. These findings suggest that the EBLR and EBP/L ratios may serve as useful markers for predicting the severity of allergic re-actions in patients with wasp venom allergy. This is the first study to establish such a link, alt-hough further research with larger cohorts is necessary to confirm these results and their poten-tial application in clinical settings.

Reviewed the research progress of Newton’s iterative methods for nonlinear equation. Convergence with second order, third order, fourth order, fifth order, sixth order, seventh order, eighth order and ninth order Newton’s iterative methods are given in turn, and the efficiency index of these iterative methods are analyzed. Numerical experiments show that the convergence process of the three Newton iterative methods, and the results indicate that the convergence of the higher-order Newton’s method can be well demonstrated only when the initial point is close to the root.

For the first time porous tungsten coatings were obtained by vacuum annealing of coatings consisting of both lead solution in β-W and a mixture of Pb solution in β-W and pure lead. Electron-microscopic studies of the obtained coatings before and after vacuum annealing at 800 оС for 1 hour and 6 hours were carried out. The formation of a new phase of tungsten with an octa-lattice and parameter a = 0.4878±0.0005 nm was recorded.

Background: This study evaluates Endoscopic Sinusectomy procedures performed by young and experienced surgeons to compare outcomes achieved by young surgeons to those obtained by experienced surgeons. Methods: From a prospectively maintained database, patients with chronic non recurrent pilonidal disease who underwent Endoscopic Sinusectomy since 1 September 2011 to 31 December 2021, were enrolled. After the propensity score analysis, 200 patients were involved and divided in the group of procedures performed by young surgeons and the group performed by experienced surgeons. Results: About recurrence rate at 1-year follow up (7% vs. 10%, p=0.45) and 5-years follow-up (12.5% vs. 17.5%, p=0.53) and about time off from work (3.54±1.65 vs. 3.61±1.61, p=0.78) no significant results were found. About operative time (30.44±5.57 vs. 43.43±3.83, p=0.004), infection rate (4% vs. 14%, p = 0.013), time to healing of wound (27.89±11.06 vs. 42.22±13.63, p=0.002) and incomplete healing of wound rate (3% vs. 11%, p=0.027) statistically significant differences were found. The multivariate analysis demonstrated that being young surgeons influences the above-mentioned significant results and that the mean distance between lateral orifice and midline impacts on the recurrence rate at 1- and 5-years follow-up for both experienced surgeons’ (p=0.001 and p=0.009) and young surgeons’ procedures (p=0.001 and p=0.003). Conclusions: The recurrence rate doesn’t increase during the training but an adequate training path to perform endoscopic technique is need. A call to perform high-quality randomized controlled studies for establishing how many surgeries are required to be proficient and to outline the ideal endoscopic technique training path.

Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated pa-tients and may develop into life-threatening conditions. Yet existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administrated prophylactically. This study aims to evaluate the suitability of volatile bacterial me-tabolites for breath-based test diagnosing Ventilation-Associated Pneumonia (VAP). The in vitro experiments with pathogenic microorganisms most prevalent in VAP patients (i.e., Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa) were performed to identify bacteria-derived metabolites using specially designed cultivation system enabling headspace sam-pling for GC-MS analysis. Thirty-nine compounds were found to be significantly metabolized by tested species and, therefore, selected for monitoring in the exhaled breath of critically ill, me-chanically ventilated (MV) patients. The emission of volatiles from medical respiratory devices was investigated to estimate the risk of spoiling breath results with exogenous pollutants. Bacterial metabolites were then evaluated to differentiate VAP patients from non-infected MV controls using receiver operating characteristic (ROC) analysis, with AUC, sensitivity, and specificity calculated. Nine bacterial metabolites that passed verification through a non-parametric ANOVA test for significance and LASSO penalization were identified as key discriminators between VAP and non-VAP patients. The diagnostic model achieved an AUC of 0.893, with sensitivity and specificity values of 87% and 82.4%, respectively, being competitive with traditional methods. Further vali-dation could solidify its clinical utility in critical care settings.

Object: Due to the fact that glioma surgery requires manipulation of the brain parenchyma and the expression of inflammatory mediators, there is often a possibility of central nervous system (CNS) infection occurring after surgery. Previous studies on CNS infections related to neurosurgery have uniformly explored the risk factors for CNS infections in all neurosurgery surgeries that require craniotomy, but have not explored the risk factors and clinical characteristics of CNS infections after glioma resection. Methods: We included patients who underwent glioma resection from January 2015 to March 2023, systematically recorded population characteristics, clinical, surgical, laboratory and survival data, analyzed high-risk factors for postoperative CNS infections, summarized cerebrospinal fluid characteristics, antibiotic use, and explored whether CNS infections affects the long-term prognosis of glioma patients after surgery. Results:All 337 patients underwent glioma resection, and 61 patients (18.1%) developed CNS infections after surgery. Ventricle Opening, post-op other systemic infections, post-op maximum cavity diameter, and peripheral blood Monocyte% are independent risk factors for postoperative CNS infections. The median protein content in cerebrospinal fluid of patients with CNS infections is 1.73g/L ([IQR] 1.03-3.06), the median glucose content is 2.7mmol/L ([IQR] 1.90-3.70), and the median WBC is 1478×106cells/L ([IQR] 467-4204.25×106), with a median percentage of multinucleated cells of 84.1% ([IQR] 74.55% -90.83%). Meropenem and Vancomycin are the most commonly used antibiotics, with an average usage time of 7.79 days and 8.41 days, respectively. Survival analysis suggests that the occurrence of CNS infections does not affect the long-term prognosis of glioma patients after surgery. Conclusions: This study separately explored the risk factors for CNS infections in patients with glioma after surgery, instead of all diseases which need craniotomy，and summarized their cerebrospinal fluid characteristics and antibiotic use. Although CNS infection is only one of the possible complications during the treatment of gliomas, we believe that this will make a certain contribution to neurosurgeons' individualized prevention, diagnosis, and treatment of patients in the diagnosis and treatment of gliomas.

Purpose: Report of a novel genetic variant in a family with an unusual clinical presentation of posterior polymorphous corneal dystrophy (PPCD). Methods: Documentation of clinical findings of the anterior and posterior segments using slit lamp analysis and imaging tools (Scheimpflug imaging, optical coherence tomography), along with documentation of best spectacle corrected visual acuity (BSCVA; LogMAR). Next-generation sequencing (NGS) of the genes of interest was performed by the IDTxGen® sytem (Inherited Disease Panel v1.0 and sequencing on an Illumina NextSeq® 500) based on DNA extracted from peripheral blood lymphocytes. Data analysis was performed using the SeqNext module of the SEQUENCE Pilot software (version 5.1.0, JSI Medical Systems). A histological examination of four corneas in both patients (father and son) was carried out by hematoxylin-eosin (HE) staining. Results: A novel heterozygous variant in the ZEB1 gene was identified in a 41-year-old man and his 13-year-old son. The 2-bp duplication in exon 8 results in a frameshift and a premature stop codon (p.(Asp883Argfs*39)), predicted to result in nonsense-mediated decay (NMD) and to lead to functional impairment of the ZEB1 protein. The variant was classified as likely pathogenic applying the American College of Medical Genetics and Genomics (ACMG) criteria. The variant is located in the penultimate 3´base pairs of the ZEB1 gene. The C-terminus of the ZEB1 gene encodes the C2H2-type protein domains of the ZEB1 zinc finger transcription factor protein which are relevant in epithelial-mesenchymal transition (EMT) by mediating the transition between cell lineages. The patients presented with an unusual symmetrical crescent-shaped bullous edema with a temporal focus with retrocorneal snail-track-like changes. Histological examination of all hematoxylin-eosin (HE) stained samples revealed a peripheral multilayer of endothelial cells adjacent to the trabecular meshwork (TMW), along with a central reduction of corneal endothelial cells and a pathological thickening of Descemet's membrane of up to 18.7 m. Conclusions: We report a novel variant of dominantly inherited PPCD3. Pathological production of a protein with an effect on the transition between cell lineages could explain the pathogenic epithelial transformation of the corneal endothelium, characterized by snail-track-like retrocorneal changes with a symmetric temporal crescent-shaped corneal edema.

We propose the Ramsey approach for the analysis of behavior of isolated mechanical systems containing interacting particles. The total momentum of the system in the frame of the center of masses is zero. The mechanical system is described by the Ramsey, bi-colored, complete graph. Vectors of momenta of the particles pi serve as the vertices of the graph. We start from the graph representing the system in the frame of the center of masses, the momenta of the particles in this system are pcmi. If (pcmi(t)∙pcmj(t))≥0 is true, vectors of momenta of the particles numbered i and j are connected with the red link; if (pcmi(t)∙pcmj(t))&lt;0 takes place, the vectors of momenta are connected with the green link. Thus, the complete, bi-colored graph emerges. Consider the isolated system built of six interacting particles. According to the Ramsey Theorem, the graph inevitably comprises at least one monochromatic triangle. The coloring procedure is invariant relatively rotations/translations of frames; thus, the graph representing the system contains at least one monochromatic triangle in any of frames emerging by rotation/translation of the original frame. This gives rise to the novel kind of a mechanical invariant. Similar coloring is introduced for the angular momenta of the particles. However, the coloring procedure is sensitive to Galilean/Lorenz transformations. Extensions of the suggested approach are discussed.

The chemical profiles of young and mature wines produced from three grape varieties Merlot, Mavrud and Sauvignon blanc were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy and advanced statistical methods. Moreover, grape ales, a hybrid of beer and wine, were subjected to analysis in order to facilitate a comparison of their composition with that of traditional wines. A total of 37 compounds were identified and quantified, and orthogonal partial least squares discriminant analysis (OPLS-DA) models were employed to distinguish the chemical profiles of young and mature wines, as well as those of grape ales. The findings demonstrate that the fermentation and aging processes result in the formation of distinctive chemical signatures in wines, with key compounds such as shikimic acid and fructose contributing to this differentiation. The identified compounds comprise seven alcohols (2,3-butanediol, glycerol, isobutanol, isopentanol, myo-inositol, 1-propanol, 2-phenylethanol), six organic acids (galacturonic, citric, lactic, malic, shikimic, succinic), three amino acids (alanine, proline, tyrosine), four sugars (arabinose, fructose, galactose), coutaric acid and acetoin. The levels of these 22 components enabled the successful differentiation of young and mature wines among the three grape varieties. These results highlight significant chemical differences between grape ales and wines, thereby underscoring the potential of grape ales as an innovative fermented beverage.

One widely used method to predict concrete strength development based on temperature variations during curing is the equivalent maturity time (te) method. This method uses the activation energy (Ea) as its key parameter, which reflects the cement’s sensitivity to temperature. However, research shows that the Ea value varies depending on factors such as cement type, water-cement ratio, temperature, and additives. The permanent subject of discussion is the question of what value of the Ea parameter should be assumed. In this paper, a new approach is proposed by using a neural network analysis method to develop a strength-temperature-time history model. It was assumed that the ANN-fc% model would have 4 inputs including hydration heat (Q), activation energy (Ea), temperature (T) and time (t). A data set was collected and research was carried out on mortars using: 6 cements, curing temperatures 5-35°C, strength within 90 days. The results showed that the ANN analysis method allows for estimating the relative compressive strength with sufficient accuracy. Input nodes analysis showed that Q influences early strength gain, while Ea affects later strength development. The method of using the ANN model to calculate strength depending on the temperature change during maturation was shown.

The effects of neighboring transitions (ENT) on electromagnetically induced transparency (EIT), electromagnetically induced absorption (EIA), and the conversion between EIA and EIT in a degenerate multi-level system of 87Rb atoms were studied in terms of the angle (θ) between the polarization axes of the coupling and probe beams. The predicted critical values of θ, in which EIT transitioned to EIA, were consistent with the experimental values. In this work these results were systematically confirmed using the calculated spectra by varying the frequency spacings in the excited state of 87Rb via a factor called the ratio. We observed that when the ratio was less than 0.1, the critical angle θc was inverted. This may be attributed to the interplay between the strengths of the EIA and EIT as the ENT varied. We also discovered that by modifying the frequency spacings in the excited state of 87Rb, it becomes feasible to predict ENT and the interplay between EIT and EIA in alkali-metal atoms.

We study whether the center of relativistic energy (CRE) is always constant in an isolated frame of reference (IFR). First, we assume that two objects in a moving isolated frame of reference (MIFR) move parallel to and very close to a coordinate axis from opposite directions and with equal speed. Here each length in the direction of travel of the objects is much longer than that perpendicular to the direction of travel of them, and moreover the latter is negligibly extremely short. Hence, the location of the CRE (LCRE) of the objects is very close to the coordinate axis in initial condition. When they become perfectly symmetric with respect to the coordinate axis, the forces perpendicular to the direction of travel of the objects are applied to each CRE and thereby a perfectly inelastic collision between them occurs on the coordinate axis. The combined object (CO) resulting from the perfectly inelastic collision begins to rotate because each momentum of the objects before the combination between them acts like the moment of force on the CO. For simplicity, we examine the energy distribution of the CO when it becomes perpendicular to the coordinate axis due rotation. The magnitude of velocity of each minute portion (MP) symmetrical with respect to the coordinate axis are different depending on whether the direction of each rotational velocity is the same or opposite to the direction of travel of the MIFR. Then the energy of each MP is not the same. As a result, the LCRE of the CO moves to the direction perpendicular to the coordinate axis, and its LCRE becomes far from the coordinate axis since the length in such direction is long enough. Therefore, we conclude that the LCRE in the MIFR is not necessarily invariant. Second, we suppose a process in which a force causes a negative acceleration on a moving object (MO) and thereafter the MO eventually comes to rest. What is considered here is the LCRE when the MO comes to rest. We pay attention to the position of the force applied to the MO because the transmission time of force (TTF) inside the MO may vary depending on the position of the MO where the force is applied. As a result, we find that the position at which the MO comes to rest differs depending on the TTF inside the MO. Therefore, we conclude that the LCRE in an IFR differs depending on the position of the MO where the force is applied, in other words, it in an IFR is not necessarily uniquely determined.

After the launch of a high-resolution remote sensing satellite, representative spatial quality estimators (RER, FWHM, MTF50, MTFA) are measured from images taken of ground Edge targets. In this work, the best spatial quality estimator is proposed by quantitatively comparing and analyzing the precision between Relative Edge Response (RER), Full Width at Half Maximum (FWHM), MTF value at Nyquist frequency (MTF50), and MTF Area between 0 and Nyquist frequency (MTFA). While the basic method for the measurement of spatial quality estimators on Edge targets is already well established, this work summarizes and explains the uncertain factors and problems in the measurement procedure that affect the accuracy and precision of spatial quality estimators. It also considers how to improve the precision of spatial quality estimators during the measurement procedure. The contents and results of this work were discussed by various satellite development organizations in the Geo-Spatial Working Group within CEOS WGCV IVOS from 2012 to 2019, and the Edge target Spatial quality Measurement Python code (ESMP) was developed in 2019 to reflect the findings of this workshop. Using 483 Edge targets from worldwide images taken by KOMPSAT-3A, which has been in operation since 2017, the results obtained via ESMP show that the precision of RER, FWHM, and MTFA are approximately three to four times higher than that of MTF50 when comparing the Coefficient of Variance (CV) statistics.