Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

Abstract: This article reports the results of an ecological study of cancer incidence rates by state in the US for the period 2016-2020. The goals of this study were to determine the extent to which solar UVB doses still reduced cancer risk compared to findings reported in 2006 for cancer mortality rates for the periods 1950–1969 and 1970-1794 as well as cancer incidence rates for the period 1998–2002 and to determine which factors were recently associated with cancer risk. The cancer data for non-Hispanic white (European American) men and women were obtained from the Centers for Disease Control and Prevention. Indices were obtained for solar UVB at the surface for July 1992, and alcohol consumption, and diabetes and obesity prevalence near the 2016–2020 period. Lung cancer incidence rates were also used in the analyses as a surrogate for smoking, diet, and air pollution. The cancers for which solar UVB is significantly associated with reduced incidence are bladder, brain (males), breast, corpus uteri, esophageal, gastric, non-Hodgkin’s lymphoma, pancreatic, and renal cancer. Lung cancer was significantly associated with colorectal, laryngeal, and renal cancer. Diabetes was also significantly associated with breast, liver, and lung cancer. Obesity prevalence was significantly associated with breast, colorectal and renal cancer. Alcohol consumption was associated with bladder and esophageal cancer. Thus diet has become a very important driver of cancer incidence rates. The role of solar UVB in reducing risk of cancer has been reduced due to people spending less time outdoors, wearing sunscreen that blocks UVB but not UVA radiation, and population increases in overweight and obesity, which lowers 25-hydroxyvitamin D concentrations and generates systemic inflammation, a risk factor for cancer. The dietary approach that would reduce the risk of diabetes, obesity, lung cancer, and, therefore cancer, would be one based mostly on whole-plants and restrictions on red and processed meats and ultraprocessed foods. Solar UVB exposure for a few minutes before applying sunscreen and taking vitamin D supplements would also help reduce risk of cancer.Keywords: alcohol consumption; cancer incidence;

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

Background: The pathophysiology of functional dyspepsia (FD) remains insufficiently understood, and the pain and economic constraints associated with endoscopic examinations hinder the precise definition and treatment of this disorder. Exploring the potential of multi-omics biomarkers for FD could yield promising targets for effectively diagnosing and treating this condition. Methods: We recruited 54 FD patients and 29 healthy volunteers and characterized their gut microbiota by shotgun metagenomic sequencing and their serum and urine metabolites by liquid chromatography-mass spectrometry (LC-MS). Results: We identified the changes in circulating metabolites associated with FD and linked to the gut microbial alterations. Among the differential circulating metabolites, we particularly highlighted the strong association of L-glutamate deficiency with the severity of FD symptoms and its strong predictive value for FD. Although no significant changes were noted in the gut microbial community structure of the FD samples, this might be attributed to symptom overlap between epigastric pain syndrome (EPS) and postprandial distress syndrome (PDS). However, significant alterations in the abundance of certain species were observed, including a substantial increase in the abundance of Streptococcus in the digestive tract of patients with FD. Finally, a cross-omics correlation network integrating metabolomic data with microbial community abundance was established, uncovering potential host-microbe-metabolite interactions. Conclusions: This work provides the first comprehensive description of the disrupted state of blood and urine metabolite composition and gut microbiota in patients with FD. These findings potentially offer promising targets for the diagnosis and treatment of this condition through a multi-omics approach, with particular emphasis on serum metabolomics.

This comparative study aims to investigate environmental awareness, knowledge, and safety among students in Montenegro and North Macedonia, considering the unique socio-cultural and environmental contexts of both countries. A mixed-methods approach involving surveys and interviews was employed to gather data from students in educational institutions across Montenegro and North Macedonia. The study assessed various factors, including students' awareness of environmental issues, their perceived level of knowledge, and their attitudes towards safety measures. A comprehensive series of 400 face-to-face interviews was carried out and these interviews encompassed 200 students from Montenegro and an equal number from North Macedonia, taking place at two esteemed academic institutions: The University of Montenegro in Podgorica and The University of St. Clement of Ohrid, Bitola, Republic of North Macedonia. Our hypothetical conceptual framework proposes that a combination of variables, including gender (H1), age (H2), year of study (H3), and rate of study (H4), significantly influences the attitudes of students from Montenegro and North Macedonia towards environmental awareness, safety, and knowledge. Research findings indicate significant variations in environmental awareness and knowledge perception among students in the two countries. While both Montenegro and North Macedonia face environmental challenges, differences in educational systems and socio-economic factors contribute to distinct attitudes and behaviors towards environmental issues and safety practices. This study sheds light on the importance of understanding regional differences in environmental awareness and knowledge perception among students. By identifying areas of strength and areas needing improvement, policymakers and educators can develop targeted interventions to enhance environmental education and foster a culture of sustainability in both Montenegro and North Macedonia.

This paper aims to provide an evaluation of how Value Added Tax (VAT) policy alterations influence market liquidity, trading volume, and stock performance. To fill the existing literature gap on the direct effect of (VAT) adjustments on stock market dynamic, particulate within the banking industry. The study employs an event study methodology to analyse the immediate and medium-term effects on bank stocks listed in markets subject to a (VAT) adjustments. The core of the study analysis is anchored around two pivotal events, the initial application of (VAT) on 1st January 2018, and the subsequent (VAT) increase to 15% on 1st July 2020 using MATLAB R2022b. The study analysis encompasses the period immediately before and after the introduction and subsequent increase (VAT) in kingdom Saudi Ariba (KSA) employing an eleven sample comprises the (KSA) largest banks with a ±7 days window period from the event. The analysis revels that the imposition of 5% (VAT) lead a substation downturn in trading volume and stock returns of 9.5%, and 39.5%, subsequently. While when the (VAT) was increased by 15% the market exhibited a mixed but ultimately positive response of 3.3% trading volumes increases and 128.7% average stock return recovery.

CuAl8Fe5Ni4Zn4Sn1 (OF 2238) is a new lead-free copper alloy for plain bearing applications that was first officially presented in a scientific journal in 2020. Soon after its invention, the use of the alloy for connecting rod bushings in heavy duty internal combustion engines was promoted and validated with customers. The aim of this article is to describe the material properties of the new alloy in more detail than previously and explain how the advantageous properties of CuAl8Fe5Ni4Zn4Sn1 are generated. At the beginning of the article, the general development trends in the field of copper alloys for sliding applications are presented. This is followed by a presentation of the latest research results about CuAl8Fe5Ni4Zn4Sn, before concluding with a brief summary and future outlook, emphasizing on the fact that recent trends in copper-based sliding applications show great additional potential for further material developments.

In recent years there has been a clear increase in bottled water consumption, globally which translates to significant health and environmental concerns. This cross-sectional study aims to understand the attitude and preferences of university students in Qatar towards use of bottled and tap water for drinking purposes using an online survey questionnaire. The number of students who responded to the online survey was 14% of the entire student population within the university (n = 688). Overall, majority of the students reported plastic bottled water as the main source of drinking water they preferred to use on campus (n=468; 68.02%), with a higher proportion of females (72.08%) preferring the use of plastic bottled water. Out of the 468 students who preferred plastic bottled water, safety was the most important factor (43.80%) followed by convenience (16.88%) and taste (15.60%). Cost (15.17%), personal/family habits (5.13%), Environmental concerns (2.14%) and minerals (1.28%) were found to be the least important factors. Among the 45 students who preferred tap water over plastic bottled water, cost (46.67%) was the dominant factor followed by convenience (20.00%) and environmental concerns and safety (13.33% each), taste (4.44%), and personal/family habits (2.22%). This study recommends campaigns to focus on attitude and behavior change and not solely emphasize knowledge; 71.7% believed that plastic water bottles are more harmful to the environment, yet the greater majority still resorted to this source. There is an immediate need to further educate students through environmental and health literacy programs on water consumption and quality. Enabling the population to understand the positive and negative aspects of their choices, may be an effective remedy to ensuring a healthy population and healthy environment.

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.

The approach to the personal experience and previous ideas about Physical Education of future Primary Education teachers is a starting point of great interest for the teaching of the subject of Didactics of Physical Education. The study aims to investigate these prior beliefs and to verify to what extent this initial perception changes after taking the course on “Didactics of Physical Education”. A concurrent mixed-methods study was conducted, which included two data collection procedures: (1) a pre-experimental design with a single group featuring pre-test and post-test; (2) the analysis of students' autobiographical accounts of their school experience with physical education. The participants were students enrolled in the Bachelor’s degree program in Primary Education at the University of Santiago de Compostela (USC) who undertook the course last year. The results obtained reveal that after taking the subject of Didactics of Physical Education, students give greater value to more positive concepts of learning, socializing, participating, and playing, among others. Similarly, in the post-test, the assessment of concepts such as competitiveness and physical fatigue diminished. In their autobiographical memories, students associate good memories with relationships with classmates and the playful and socializing nature of the subject; and among the bad memories, they highlight the content related to physical performance, competitiveness, and lack of attention to the diversity of students and their individual characteristics.

Melanin is extensively distributed in organisms and believed to be synthetized by tyrosinase (TYR), leading to aberrant coloring and skin conditions when over-produced. Loquat (Eriobotrya japonica (Thunb.) Lindl.) flowers contain large amounts of bioactive substances currently few studied on their suppressive capabilities to melanin synthesis. Its isolate product (LFP) was obtained by ethanol solution extraction and resin purification, of which the inhibitory mechanism on TYR activity was elaborated by enzyme kinetics and multi-spectroscopy determinations and the impact on related proteins’ expression in mouse melanoma B16 cells was analyzed with Western blotting. HPLC-MS analysis indicated that LFP was identified into 137 compounds, with 12 including flavonoids (quercetin, isorhamnoin, p-coumaric acid, etc.), cinnamic acid and its derivatives as well as benzene and its derivatives owning TYR inhibitory activities. LFP inhibited TYR activity in a concentration-dependent manner with its IC50 value being 2.8 mg/mL and an anti-competitive mechanism involved altering the enzyme's conformation. LFP reduced the expression of TYR, TRP1, and TRP2 in melanoma B16 cells, hence inhibiting the synthesis of melanin. Considered collectively, LFP would be a viable option for treating hyperpigmentation caused by tyrosinase, providing a theoretical basis for the value-added utilization of loquat flower resource in beauty and aging related functional products.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

In this paper, a multi-patch weakly singular isogeometric boundary element method (WSIGABEM) for two-dimensional elastostatics is proposed. Since the method based on the weakly singular boundary integral equation, the quadrature techniques, dedicated for the weakly singular and regular integrals, are applied in the method. A new formula for the generation of collocation points is suggested to take the full advantage of the multi-patch technique. The generated collo-cation points are essentially inside the patches without any correction. If the boundary conditions are assumed to be continuous in every patch, no collocation point lies on the discontinuous boundaries, thus simplifying the implementation. The multi-patch WSIGABEM is verified by the simple examples with analytical solutions. The feature of the present multi-patch WSIGABEM is investigated by the comparison with the traditional IGABEM. Furthermore, the combination of the present multi-patch WSIGABEM and the particle swarm optimization algorithm results in a shape optimization method in two-dimensional elastostatics. By changing some specific control points and their weights, the shape optimizations of the fillet corner, the spanner and the arch bridge are verified to be effective.

Hyperuricemia is mainly caused by the overproduction of uric acid (UA) catalyzed from xanthine by xanthine oxidase (XO) in abnormal purine metabolism in human body. There is an increasing demand for developing novel XO inhibitors of plant origins. The inhibition kinetics and type as well as actions of hesperetin against XO were investigated in vitro using multi-spectroscopic methods including UV-Vis absorption, Fourier transform infrared spectroscopy (FT-IR), fluorescence spectroscopy and molecular docking simulation. Results indicated that hesperetin reversibly inhibited XO in a competitive manner with an inhibition constant (Ki) value of (2.15 ± 0.05) × 10-6 mol/L. Hesperetin had a higher scavenging rate against O2- radical generated by the XO reaction system. The binding of hesperetin to the active site of XO altered the protein’s secondary structure as demonstrated by FT-IR and fluorescence spectra. Molecular docking study revealed that hesperetin had highly affinity to XO and the bonding of hesperetin to the molybdopterin (Mo-pt) active center inhibited not only substrate entry, but also prevented oxygen binding to flavin adenine dinucleotide (another active center in XO). Additionally, hydrophobic interactions and hydrogen bonds contributed to the stabilization of the hesperetin-XO complex. The results suggested that hesperetin be a promising natural XO inhibitor to alleviate hyperuricemia and gout.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

The article delves into the crucial realm of compressing hydrogen in its gaseous state, a pivotal process for enhancing its viability in both civil and industrial sectors. The study initiates by providing a concise overview and comparison of diverse hydrogen storage methodologies, laying the groundwork with an in-depth analysis of hydrogen's thermophysical properties. It scrutinizes plausible configurations for hydrogen compression, aiming to strike a delicate balance between energy consumption, predominantly derived from the fuel itself, and the requisite number of compression stages. Notably, to render hydrogen storage competitive in terms of volume, pressures of at least 350 bar are deemed essential, albeit at an energy cost amounting to approximately 10% of the fuel's calorific value. Multi-stage compression emerges as a crucial strategy, not solely for energy efficiency, but also to curtail temperature rise, with an upper limit set at 200°C. This nuanced approach is underlined by the exploration of compression levels commonly cited in the literature, particularly 350 bar and 700 bar. Ultimately, the study advocates for a three-stage compression system as a pragmatic compromise, capable of achieving high-pressure solutions while keeping compression work below 10 MJ/kg, a threshold indicative of sustainable energy utilization.

Soil secondary salinization has seriously affected the greenhouse vegetable production in China. To improve the secondary salinization greenhouse soil and enhance the soil physical-chemical properties in an eco-environmental way, different organic amendments (straw, straw biochar, Trichoderma bio-organic manure, commercial organic manure) were applied using pot experiment for 60 day to comprehensively screen the optimal remediation method. In this study, soil nutrient condition and salt movement were assessed, and predictive models using multi-linear regression (MLR) and Random Forest (RF) were developed to estimate soil salinization parameters. The findings indicated a significant decrease in soil salt content following the application of organic materials compared to the control treatment. Specifically, the addition of straw at a rate of 250g/kg resulted in a 59.38% reduction in soil salt levels after 60 days (P < 0.05). Furthermore, the main salt ions exhibited dynamic changes over the course of the experiment, with reductions observed in Na+, Ca2+, Cl-, and NO3- content under the 250 g/kg straw treatment by 83.63%, 74.67%, 63.26%, 59.07%, respectively, compared to CK (P < 0.05). In addition, the SO42- content under 125 g/kg commercial organic manure amendment reduced by 48.94%, respectively (P < 0.05). Contrary to expectations, the addition of organic materials significantly increased the levels of total nutrients (N/P/K) and available potassium (AK) and phosphorus (AP) in the soil. Specifically, the addition of straw at a rate of 250 g/kg resulted in increases of 18.02%-87.38% in total potassium (TK), 66.67%-200% in total phosphorus (TP), and 142.87%-367.8% in AK after a period of sixty days (P < 0.05). Ultimately, the treatment involving the addition of 250 g/kg of straw demonstrated the most pronounced impact on the physical and chemical properties of the soil. The random forest method shows promise in accurately predicting soil salt and soil sodium adsorption ratio (SAR)indicators, thereby providing a valuable tool for estimating soil properties.

Background and objective: Chronic subdural hematoma (cSDH) is one of the most common neurosurgical entities affecting mostly elderly patients. In this study, we aimed to provide detailed characteristics of surgically treated patients with cSDH and evaluate outcomes between the two conventional treatment methods. Materials and methods: Patients with cSDH surgically treated with burr hole craniostomy (BHC) and craniotomy (CO) in tertiary centers in Croatia and Serbia during 2017-2019 were included in the study. Characteristics and outcomes of these patients were prospectively collected. Results: In total, 155 patients (mean age 72.3±13.4) were included in the study. BHC group consisted of 124 (80 %) and CO group of 31 (20 %) patients. 32 (20.6 %) patients presented with bilateral cSDH, head trauma was documented in 92 (59.4 %), and anticoagulant therapy in 37 (23.9 %) individuals. The most common symptom was hemiparesis (n=85, 54.8 %). The two groups did not differ by age. CO group had higher Glasgow Coma Scale (GCS) score at the admission, with a significant decrease at discharge compared to admission (12.6±4.8 vs 14.4±1.7, p

The ground consists of many layers of soil with different properties. The propagation speed and path of seismic waves are affected by different soil layers. It is necessary to understand that layered soil exhibits different dynamic behaviors and responses under the action of seismic waves. This study utilized weathered soil and silica sand as materials to create multi-layered soil conditions with varying degrees of compaction. By conducting 1 g shaking table test on multi-layered soil, the interaction and influence between different soil layers under different earthquake conditions were observed. The approach of numerical analysis aims to validate the experimental results and provide an in-depth understanding of the dynamic behaviors of multi-layered soil grounds during seismic events. The results of this study show that the amplification effect of seismic waves is related to the number of soil layers and the degree of compaction of the soil layers. This indicates that the multi-layered soil ground and the behavior of the soil layers play an important role in the propagation and impact of seismic waves, and this amplification effect is of great significance in actual seismic disaster risk assessment and design.

Previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness of offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the Central Mediterranean Sea. The first part compares the feasibility of offshore-based solar photovoltaic prices with those onshore, taking into consideration Malta’s land prices. The second part considers the use of a novel floating breakwater design that integrates energy storage. The breakwater is used to create a sheltered water area in deep waters for a multi-use marine park. Different revenue streams for the floating break water are considered, including renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, and the provision of energy storage services. It is found that the combined income from the multiple revenue streams is still insufficient to justify the investment and that financially support from Government is essential to render the floating breakwaters viable.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

Predictive factors associated with a decline in swallowing function after 2 years were examined in 3,409 Japanese older people aged ≥ 75 years who had undergone a dental checkup in Gifu Prefecture, Japan. Participants with normal swallowing function in a baseline survey in April 2018 were followed for 2 years. Swallowing function was assessed by a repetitive saliva swallowing test. After 2 years, 429 participants (13%) who could not swallow at least three times in 30 seconds were diagnosed as having a decline in swallowing function. Multivariate logistic regression analyses showed the decline in swallowing function after 2 years was associated with male gender (odds ratio [ORs]: 0.772; 95% confidence interval [CIs]: 0.615–0.969), age ≥ 81 years (presence; ORs: 1.523; 95% CIs: 1.224–1.895), support/care-need certification (presence; ORs: 1.815; 95% CIs: 1.361–2.394), periodontal pocket depth (PPD) ≥ 4 mm (presence; ORs: 1.469; 95% CIs: 1.163–1.856), difficulty in biting hard food (yes; ORs: 1.439; 95% CIs: 1.145–1.808), choking on tea and water (yes; ORs: 2.543; 95% CIs: 2.025–3.193), and dry mouth (yes; ORs: 1.316; 95% CIs: 1.052–1.646) at baseline. Therefore, the dental checkup items associated with a decline in swallowing function after 2 years were a PPD ≥ 4 mm, difficulty in biting hard food, choking on tea and water, and dry mouth. PPD status and confirming to the self-administered questionnaire about biting, choking, and dry mouth may be useful in predicting future decline in swallowing function.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Maternal obesity and over/undernutrition can have a long-lasting impact on offspring health during critical periods in the first 1000 days of life. Children born to mothers with obesity have reduced immune responses to stimuli which increase susceptibility to infections. Recently, maternal western-style diets (WSD), high in fat and simple sugars, have been associated with skewing neonatal immune cell development, and recent evidence suggests that dysregulation of innate immunity in early life has long-term consequences on metabolic diseases and behavioral disorders in later life. Several factors contribute to abnormal innate immune tolerance or trained immunity, including changes in gut microbiota, metabolites, and epigenetic modifications. Critical knowledge gaps remain regarding the mechanisms whereby these factors impact fetal and postnatal immune cell development, especially in precursor stem cells in bone marrow and fetal liver. Components of the maternal microbiota that are transferred from mothers consuming a WSD to their offspring are understudied and identifying cause and effect on neonatal innate and adaptive immune development needs to be refined. Tools including single-cell RNA-sequencing, epigenetic analysis, and spatial location of specific immune cells in liver and bone marrow are critical for understanding immune system programming. Considering the vital role of immune function plays in offspring health, it will be important to understand how maternal diets can control developmental programming of innate and adaptive immunity.

Background: This study addresses the burgeoning impact of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations and seeks to demonstrate latent and advanced LLM reasoning capabilities. The research devises an iterative self-reflective strategy for invoking critical thinking and higher-order reasoning in LLMs based on multimodal exam questions using visuals and text. Methods: The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions, focusing on its response to complex, visual, and textual prompts. Results: The results indicate that latent multi-hop reasoning capabilities can be invoked using the proposed self-reflective strategy, which can effectively steer ChatGPT towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. Conclusions: This work challenges findings from recent studies which claim that existing LLMs are unable to reason on complex multimodal tasks, and therefore this study underscores the need for enhanced online exam security measures like proctoring software, that can mitigate the potential misuse of AI technologies in educational settings.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

The eutrophication of open water bodies, especially in coastal water area, has received intensive public focuses in recent years. Biological treatment of organic pollutants in industrial and domestic wastewater requires microbial flora to participate. The environmental impact of these microbes, particularly in regard to the spread of antibiotic resistance genes and pathogenicity in coastal water, remains largely unknown. We initiate studies to examine antibiotic resistance genes (ARGs) in microbial flora and study the mode of antibiotic spread in the coastal waters of Bohai Bay, North China by using high performance liquid chromatography-mass spectrum analysis and quantitative polymerase chain reaction. The metabolic features of bacteria related to the presence of ARGs in the coastal area showed the enrichment of Pseudomonadale species with triple resistance properties (ampicillin, kanamycin and gentamycin) at the estuary site locating adjacent to a coastal sewage treatment plant where both type I and type II polyketide synthase genes and b-lactam type antibiotics are detected. Pseudomonadale order is phylogenetically belonged to Proteobacteria Phylum, Gamma-proteobacteria Class and contains Pseudomonadaceae family and Pseudomonas genus. QPCR analysis suggests that the percent occupation of Pseudomonadale species to total Proteobacteria in the examined stations is close to several sampling stations close to sewage treatment plants, where consistent release of organic wastes occurred.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

Results from the phase III Keynote-024 clinical trial have established Pembrolizumab monotherapy as the first-line standard of care for patients with metastatic NSCLC who have PD-L1 expression ≥ 50%, EGFR and ALK wild-type. However, given the differences between patients treated in routine clinical practice and within a clinical trial, real-world data are needed to confirm the treatment benefit in standard practice. Given the lack of data on large cohorts of patients with long follow-up, we designed a retrospective observational study of patients with NSCLC who were treated with Pembrolizumab, starting from its reimbursement eligibility until December 2020. The primary endpoints were to assess PFS and OS using the Kaplan-Meier method, treatment response and safety. We followed 880 patients (median follow-up: 35.1 months) until February 2022. Median PFS and OS were 8.6 months (95% CI: 7.6-10.0) and 25.5 months (95% CI: 21.8-31.6), respectively. We also found that ECOG PS, PD-L1 expression and smoking habit were prognostic factors for PFS, while age, sex, ECOG PS, smoking habit and histology had an impact on OS. Multivariable analysis confirms prognostic role of PD-L1 for PFS and ECOG for both PFS and OS. 39.9% of patients reported an adverse event, but only 6.3% of patients discontinued therapy due to toxicity. Our results suggest a long-term benefit of Pembrolizumab in the first-line setting as well as a safety profile consistent with the results of Keynote-042. Many collected variables appear to influence clinical outcome, but results from these exploratory unadjusted analyses should be interpreted with caution.

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rain water. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima (2019). The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increases approximately 17%~18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

This study analyses the changes in land use in the Sado estuary (West-Central Portugal) based on a multitemporal analysis of cartographic data from the 19th century and remote sensing land use maps from the 21st century, updated by field work. A GIS plotting of land-use evolution is summarized in a quantitative table. The comparison shows the changes in land use, with increasing occupation by human economic activities, including extensive agriculture and forestry, as well as localized urbanization and industrialization. The main elements of the landscape impacted by anthropogenic uses were: i) hydrography - river dams affected the flow dynamics and sedimentary processes in the estuary; ii) vegetation – increasing agriculture and forestry reduced the areas of native vegetation, nowadays mostly occupied by vineyards, pine forests and cork oaks. iii) wetlands - tidal and alluvial plains are being occupied by rice cultivation, aquaculture, industries, and ports; iv) coastal dunes – new developments are occupying large areas of Holocene coastal dunes; v) natural environment – mining and dredging affected some habitats and biodiversity. This analysis is intended to help the territorial organization of present and future economic activities, as well as to reduce environmental and social problems, promoting long-term sustainability of this rapidly evolving region.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Especially the ports turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and are considered a reasonable fit. They are suitable for real-time data integration to support decision-making. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore operational pain points, best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal and leave space for optimisation. The experts see great potential in analysing past working shifts data to identify reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of aiding IT solutions.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Retired Electric vehicles (EVs) batteries are reused in second life energy storage applications. However, the overall performance of repurposed energy storage system (ESS) is limited by the unevenness of individual batteries use in it. Therefore, battery grading is required for optimal performance of ESS. Electrochemical impedance spectroscopy (EIS) based evaluation of battery aging is a promising way to grade lithium-ion batteries. However, it is not practical to measure impedance of mass retired batteries due to high complexity and slowness. In this paper, a broadband multi-sine binary signal (MSBS) perturbation integrated with multichannel EIS system is presented to measure impedance spectra for high-speed aging evaluation of lithium-ion batteries or modules. The measurement speed is multi-times higher than conventional EIS with single-channel configuration. The broadband MSBS is validated with reference sinusoidal sweep perturbation and corresponding root-mean-square error (RMSE) analysis is performed. Moreover, the accuracy of the presented multichannel EIS system is validated by impedance spectra measurement of Samsung INR18650-29E batteries and comparing it with those measured with commercial EIS instrument. A chi-squared error under 0.641% is obtained for all 8-channels. Since the non-linearity of battery has significant impact on quality of impedance spectra. Therefore, Kronig-Kramer (KK) transform validation is also performed.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

Pharmacists must acquire the necessary knowledge to provide better drug therapy to patients. In other words, educational programs need to facilitate behavioral changes among pharmacists. Therefore, this study aimed to evaluate how the behavior of pharmacists changed after attending a palliative care educational program. We conducted a web-based questionnaire survey examining the behavior of pharmacists regarding palliative care before participating in the program, two months after participating in the program, and eight months after participating in the program, to determine the behavior and changes over time. For all questions, scores were higher at two and eight months after attending the program than before attending the program (P &lt;0.05). In addition, no significant difference was observed between two and eight months after attending the program for all questions (P =0.504-1.000). The knowledge gained from the educational program was used to repeatedly intervene with patients with cancer, to address the various symptoms they experienced and maintain their behavior. The proven effectiveness of this program serves as a stepping stone for a nationwide rollout across Japan’s 47 prefectures.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

(1) Background: Asthma and Chronic Obstructive Pulmonary Disease (COPD ) are major global health issues, impacting morbidity, mortality, and healthcare costs. Despite the proven benefits of pharmacist involvement in managing these conditions, their potential in hospitals remains underused. This study aimed to identify patients at high risk of rehospitalisation post-exacerbation of asthma or COPD, to target pharmaceutical interventions. (2) Methods: Data from the University Hospital for January-December 2019 on adult admissions for asthma/COPD were analyzed, focusing on demographics, hospitalization, and clinical outcomes. (3) Results: Among the 140 patients enrolled, a significant majority (91%) underwent adjustments in their treatment regimens during their hospital stay, and nearly 59% faced readmission within six months. A survival analysis highlighted a notable disparity in the rates of rehospitalisation-free survival between patients with asthma and those with COPD, identifying COPD patients as having a higher susceptibility to rehospitalisation. (4) Conclusion: The study found COPD pa-tients at greater risk but didn't specify a priority profile, suggesting the necessity for broad, cus-tomized interventions to improve outcomes and reduce healthcare strain.

Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors. Fifty-four healthcare professionals participated in the Delphi study's first round, with 25 in the second. The study identified two primary QoL domains (Physical and Psychological) and 17 subdomains deemed most critical. Additionally, a review of 12 questionnaires revealed two instruments assessing the most important general domains. Findings underscored a misalignment between existing assessment tools and healthcare professionals' clinical priorities in working with colorectal cancer survivors. To enhance support for survivors' QoL, efforts are needed to develop instruments that better align with the demands of routine QoL assessment in clinical practice.

Background: We assessed the pertinence of yearly updating the International Working Group on the Diabetic Foot (IWGDF) risk classification in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot as-sessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data was collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died, and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly. The IWGDF classification can accurately identify those not at risk of DFU.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

ABSTRACT Lubricants play a crucial role in reducing friction and wear between surfaces in relative motion. Engine oil, as a lubricant, is specifically designed to minimize friction and wear between the moving parts of different equipment and machinery. Engine oil blending serves the purpose of adjusting the variety of lubricants available to meet specific performance requirements. Through blending, it is possible to tailor the characteristics of the lubricating oil, such as viscosity, stability, and additive content, to optimize its effectiveness in reducing friction, protecting engine components, and enhancing overall equipment performance. By customizing lubricant formulations through blending, manufacturers can address the diverse lubrication needs of modern engines and machinery, ensuring smooth operation, longevity, and efficiency.To enhance the quality of the lubricant and achieve the desired high-performance multi-grade engine oil, a systematic approach was followed. The process began with testing the specific gravity of the oil using a hydrometer and thermometer to ensure accurate measurements. Subsequently, 5 liters of base oil were carefully measured and poured into a stainless steel reactor pot to serve as the blending vessel. Following this, 1 liter of paraffin oil (100N) was added to the base oil to introduce specific properties to the blend. To further optimize the formulation, 0.42 kg (7% of the total blend) of additives, including a selection of specific performance-enhancing compounds, was incorporated. These additives, such as Aspen, Tackifier, and other essential components like anti-wear, corrosion inhibitors, detergents, dispersants, and antioxidants, were meticulously measured and added to the mix. Through this precise blending process, the goal was to craft a high-quality multi-grade engine oil that meets the stringent performance requirements for various operating conditions and equipment types. After the melted Aspen and dissolved tackifier were added to the mixture, thorough stirring was carried out to ensure uniform distribution until a homogeneous blend was achieved. The mixture was then exposed to heat to facilitate the reaction between the base oil and additives, reaching a temperature of 70°C for optimal blending. Subsequently, 0.5 kg of viscosity index improver was introduced into the mixture and stirred continuously for a period of 5 minutes to ensure proper integration. Following this step, 5 grams of dye were carefully added to the mixture, and stirring was continued to evenly disperse the coloring agent throughout the blend. This meticulous process aimed to ensure that all components were effectively incorporated, facilitating the production of a high-quality, customized multi-grade engine oil with enhanced performance characteristics and optimal lubricating properties. After subjecting the mixture to a temperature of 70°C, the heat source was removed, allowing the product to gradually cool down to room temperature, which was estimated to be around 30°C. Following this cooling process, the product underwent a filtration step to remove any residual impurities present in the mixture. A sample of the purified product was then extracted for further analysis. Quality control tests were meticulously carried out on the sample to ensure that the product met the rigorous standards set by the Standard Organization of Nigeria (SON). The results obtained from the quality control tests were highly promising, indicating that the product indeed complied with the stringent specifications outlined by the regulatory body. This adherence to quality standards is essential in ensuring the product's reliability and performance in various applications. The successful outcome of the quality control test underscores the meticulous attention to detail and commitment to excellence in the production process. The rigorous quality control procedures implemented in the analysis of the product highlight the commitment to upholding high standards of quality and safety in the manufacturing process. By consistently meeting and exceeding industry regulations and standards, the product not only demonstrates its reliability but also reflects a dedication to customer satisfaction and product excellence. The adherence to the SON standards showcases a commitment to producing top-quality products that meet the needs and expectations of consumers and industry stakeholders alike.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

Wind turbines (WTs) are clean a renewable energy source that has been growing in popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses including high loads and harsh environments that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Pow-erful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the aim of maximizing the power density, compactness, performance, and reliability of the WT gearbox. This entails minimizing its weight, volume, and maximum stresses and strains achieved without compromising efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks, the Finite Element Analysis (FEA) to obtain the stresses and strains fields are modelled using Ansys Workbench, while the multibody kinematic and dynamic system is analyzed using MSC Adams. The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. Simulation results can be used to identify potential failure modes and optimize gearbox design to improve performance, extend service life and relia-bility, thereby ensuring proper operation over its lifetime and reducing maintenance costs.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

The purpose of this study was to determine the maximum tolerated dose (MTD) for stereotactic body radiation therapy (SBRT) in the treatment of non-metastatic prostate cancer. This study was a phase 1 dose escalation trial conducted in Japan. Patients with histologically proven prostate cancer without lymph node or distant metastasis were enrolled. The prescribed doses were 42.5, 45, or 47.5 Gy in five fractions. Dose-limiting toxicity (DLT) was defined as grade (G) 3+ gastrointestinal or genitourinary toxicity within 180 days after SBRT completion and a 6 plus 6 design was used as the method of dose escalation. A total of 16 patients were enrolled, with 6 in the 42.5 Gy group and 10 in the 45 Gy group. No DLT was observed in the 42.5 Gy group. In the 45 Gy group, one patient experienced G3 rectal hemorrhage, and another had G4 rectal perforation, leading to the determination of 42.5 Gy as the MTD. None of the patients experienced biochemical recurrence or death during the follow-up period. We concluded that SBRT for non-metastatic prostate cancer at 42.5 Gy in 5 fractions could be safely performed, but a total dose of 45 Gy increased severe toxicity.

Background: Locally advanced rectal cancers (10-20%) can present with infiltration into adjacent viscera. R0 resections remain the key factor for better survival. Traditionally these cases have been managed with open surgery. There are some limitations of laparoscopic surgery in these case, which can potentially be overcome by robotic surgery. Objectives: To determine the feasibility and outcome results of robotic multi-visceral resection of stage T4 rectal carcinoma. Methods: Consecutive patients having locally advanced carcinoma of rectum, who undergone robotic multi-visceral resection as the primary treatment between 2015 -2018 were included, through our database which was maintained prospectively. All perioperative and intraoperative parameters were analyzed. Patient demographics, co-morbidities, preoperative imaging, endoscopic findings, operative data, histopathology results, postoperative hospital course and procedure related postoperative adverse events were noted and analyzed. Results: Twenty-one patients with the diagnosis of nonmetastatic locally advanced rectal cancers were operated with Robotic Si/X platform. The median body mass index was 28.5 kg/m2 (range 21-42). The mean blood loss during operation was 20 ml (range 5-200), and the mean duration of surgery was 280 min (range 240-430). The median hospital stay after operation was 6 days (range 4-25 days). On postoperative histopathology, R0 resection rate was 90.5% (19/21). Conclusions: The robotic multi-visceral en-bloc resection of stage T4 rectal carcinoma is technically feasible and oncologically safe. Advanced technical skills, accurate radiological staging and careful patient selection can allow the extension of minimal access surgery to this challenging group of patients.

Background: Schistosomiasis is the third most important Neglected Tropical Disease, with more than 4 million South Africans being infected. Cases of re-infection with schistosomiasis after being treated using modern medication has opened ways to seeking alternative treatment modalities using medicinal plants. This study aims to explore and understand the way traditional healers diagnose schistosomiasis, the types of traditional medicines they use to treat it in communities with a high prevalence of infection, and to establish whether the unavailability of prescribed anti-schistosomal medicines leads to the use of traditional medicine. Methods: A qualitative exploratory study was conducted using a questionnaire from December 2022 to March 2023 in the iLembe District, KwaZulu-Natal Province, South Africa. In-depth individual interviews were carried out among traditional health practitioners (THPs) involved in managing female urogenital schistosomiasis (FUS). Recorded narrative data was transcribed verbatim, read through thoroughly and coded, and analysed using thematic or content analysis Results: Traditional Health Practitioners provide services to many women who did not want to be treated in the mainstream health care system for their FUS. The disease was perceived and understood the same way by most of the respondents, that being caused by use of fresh water. Various plants species, either alone or in combination, were used to manage bilharzia in women. Senecio serratuloides (Asteraceae) and Hypoxis hemerocallidea (Hypoxidaceae) were prominently used by THPs in FUS management. Conclusion: Traditional Health Practitioners are playing a key role in managing bilharzia in women in rural areas of the iLembe District, this study reported that THPs can diagnose schistosomiasis symptoms based on their knowledge and experience. Respondents indicated that modern conventional treatment was readily available and free in public sector healthcare facilities; however, they consulted patients suffering from FUS. Various traditional herbal medicines are reportedly used by THPs to manage bilharzia in women. Further studies are needed to investigate the views and experiences of clients regarding the success of the treatments used and why they seek care from THPs.

The multi-objective optimization design of hydraulic climbing mechanism (the core mechanism of gantry crane) was realized by using the method of combining central composite design (CCD), Kriging and multi-objective genetic algorithm (MOGA). Firstly, the hydraulic climbing mechanism model was established and the appropriate design variables were selected. The mass, maximum deformation and maximum equivalent stress of the hydraulic climbing mechanism was taken as the optimization objectives. Through the comparative analysis of the parameters of wedge block, anchor block, anchor plate、baffle, the parameters that have the greatest impact on the optimization objectives are selected as geometric constraints. Then, according to the results of experimental design, Kriging model was used to establish the response surface optimization model of objective function. Finally, the optimal results were obtained by using MOGA method. The experimental results show that the optimization strategy is reliable, and the mass of the optimized model is reduced by 10.28%. While meeting the actual production requirements, the lightweight design of the hydraulic climbing mechanism is realized, which saves the design cost and improves the material utilization rate.

Organ-on-a-chip (OOC) is an emerging technology that simulates an artificial organ within a microfluidic cell culture chip. Current cell biology research focuses on in vitro cell culture due to various limitations of in vivo testing. Unfortunately, in vitro cell culture fails to provide an accurate microenvironment, and in vivo cell culture is expensive and has historically been a source of ethical controversy. OOC aims to overcome these shortcomings and provide the best of both in vivo and in vitro cell culture research. The key component of the OOC design is utilizing microfluidics to ensure a stable concentration gradient, dynamic mechanical stress modeling, and accurate reconstruction of a cellular microenvironment. OOC also has the advantage of complete observation and control of the system, which is impossible to recreate in in vivo research. Multiple throughputs, channels, membranes, and chambers are constructed in a polydimethylsiloxane (PDMS) array to simulate various organs on a chip. Various experiments can be performed utilizing OOC technology, including drug delivery research and toxicology. Current technological expansions involve multiple organ microenvironments on a single chip, allowing for studying inter-tissue interactions. Other developments in the OOC technology include finding a more suitable material as a replacement for PDMS and minimizing artefactual error and non-translatable differences.

We aimed to evaluate the association between daily dietary calcium intake and the risk of cardiovascular disease (CVD) in postmenopausal women using data from the Korean National Health and Nutrition Examination Survey (KNHANES). This cross-sectional study included 12,348 women aged 45–70 years who had reached natural menopause. They were classified into three groups according to daily dietary calcium intake: &lt; 400 mg, 400–800 mg, and &gt;800 mg. The risks of CVD, stroke, angina, and myocardial infarction were assessed in each group. Further, we performed subgroup analysis according to the post-menopause duration (≤10 vs. &gt;10 postmenopausal years). We performed logistic regression analysis with adjustment for age, menopausal age, income, urban area, education, insulin use, body mass index, hypertension, diabetes mellitus, dyslipidemia, high alcohol intake, smoking, exercise, oral contraceptive use, and hormonal therapy use. Calcium intake level was not significantly associated with the risk of CVD in the total population and the ≤10 postmenopausal years subgroup. However, in the &gt;10 postmenopausal years subgroup, daily calcium intake &gt;800 mg was associated with significantly decreased risks of all CVD (odds ratio [OR], 0.27; 95% confidence interval [CI], 0.11–0.64), stroke (OR, 0.06; 95% CI, 0.01–0.42), and myocardial infarction (OR, 0.27; 95% CI, 0.11–0.64). Our findings suggest that a dietary calcium intake of &gt;800 mg/day decreases the risk of CVD events in women who have been menopausal for &gt;10 years.

Smart cities are a new approach to facilitating urban development and transformation, driving coordinated development between regions, and achieving sustainable urban development. In the era of smart cities, industrial cloud platforms, as a concrete representation and carrier of “industrial brains”, are gradually becoming one of the core competences of urban manufacturing industries. Case study and qualitative comparative analysis (QCA) of a knitting industrial platform in Yiwu City, China are carried out. The following conclusion can be drawn: the construction mechanism and path of the manufacturing “industrial brain” in the smart city are divided into three stages according to the construction of the industrial platform. These include: functional development (FD), trust-building (TB), and value co-creation (VCC). The research conclusion helps to more deeply understand the effects of industrial platforms on constructing “industrial brains”. It provides effective transformation strategies and guidance for the manufacturing industry in the smart city, and pushes the industry to develop towards an intelligent, efficient, and environmental-friendly one. The research provides useful reference for applying the industrial platform to other industries and fields. Exploring the construction mechanism and path of the manufacturing “industrial brain” in the smart city can also contribute to the establishment of a new industrial ecosystem in the era of smart cities.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

The independence of commercial law and the independence of commercial trial are the key issues in the development of China's market economy, which is of great significance for maintaining the market order and protecting the rights and interests of consumers. With the development of China's market economy, the independence of commercial law and commercial trial has gradually become the focus of social attention. On the one hand, the independence of commercial law refers to the effective distinction between commercial law as a normative system and other laws, which has an independent social and economic basis and a unique role. On the other hand, the independence of commercial trial means that commercial trial has relatively independent space and procedures, which can effectively guarantee the fair settlement of commercial disputes.The independence of commercial law is reflected in many aspects, including commercial law ontology, normative structure and content, external relations and so on. Commercial law has the independent social and economic foundation and the unique role, and is the foundation of the development of market economy. The basic principles, adjustment objects, and system design of commercial law are independent, which makes commercial law play its unique role in social life. There are differences between commercial laws and other laws, such as civil law and economic law, in essence and in expression form, which makes commercial law irreplaceable.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

The structural, elastic, and electronic properties of orthorhombic Cd2SiO4 and Hg2GeO4 were examined under varying pressure conditions using first-principles calculations based on densi-ty-functional theory employing the projector augmented wave method. The obtained cell pa-rameters at 0 GPa were found to align well with existing experimental data. We delved into the pressure-dependence of normalized lattice parameters and elastic constants. In Cd2SiO4, all lat-tice constants decreased as pressure increased, whereas in Hg2GeO4, parameters a and b de-creased while parameter c increased under pressure. Employing the Hill average method, we calculated the elastic moduli and Poisson’s ratio up to 10 GPa, noting an increase with pressure. Evaluation of ductility/brittleness under pressure indicated both compounds remained ductile throughout. We also estimated elastic anisotropy and Debye temperature under varying pres-sures. Cd2SiO4 and Hg2GeO4 were identified as indirect band gap insulators, with estimated band gaps of 3.34 eV and 2.09 eV, respectively. Interestingly, Cd2SiO4 exhibited a significant increase in band gap with increasing pressure, whereas the band gap of Hg2GeO4 decreased under pres-sure, revealing distinct structural and electronic responses despite their similar structures.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

HIV early detection (CD4 counts ≥350 cells/μL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors including physical, cultural, structural, and financial barriers have contributed to HIV late detection. This is the first-of-its-kind study on the regional differences in early detection of HIV within Tajikistan and any country in the Central Asia region. Utilizing Tajikistan Ministry of Health's national HIV data (N=10,700) spanning 2010 to 2023, we developed median regression models with median CD4 cell count as the outcome and as predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old are more likely to be detected early for HIV, whereas those older than 39 years are more prone to late detection. Given their active engagement in age-related and sexual activities, those older than 39 years face a higher risk of HIV transmission to others if diagnosed late. Therefore, they require further attention and targeted interventions. Females are detected earlier compared to their male counterparts, regardless of region of residence. Rural populations are detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. These fluctuations in early detection may signify a shift in attention and resource allocation, potentially affecting efforts to address access gaps during the initial year of the COVID-19 pandemic. There are significant health inequities among different demographic and geographic groups which warrant further attention.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

The aim of the research was to determine the most productive tree cutting methods and the factors influencing them in flat and sloping terrain in tree plantations in Brazil. The study utilized drone-captured video material from eucalyptus and pine plantations harvesting operations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge of the harvesting front or inside. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned methods. In slope terrain, the machines were also equipped with a winch assistance system. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work in sloping terrain. The research results suggest that it would be the most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident if we want to precisely identify the harvesting conditions and work phases in the tree cutting cycle affecting the differences in the productivity of the harvesting chain in tree plantations.

This paper revisits the analytical theory of fractional vibrations with the highlights in five aspects. First, we address the cases of structures with frequency dependent mass or damping or stiffness in Sections 2-4. Second, we introduce the theory based on the general second-order vibration motion equation with frequency dependent elements (mass, damping, stiffness) in Sections 5-7. Third, we present the analytical theory of seven specific classes of second-order vibration systems with frequency dependent mass or damping or stiffness in Sections 8 and 9. Fourth, we bring forward the analytical theory of seven classes of fractional vibration systems in Sections 10-12. Finally, as an application, we give the closed form expression of the forced response to multi-fractional Euler-Bernoulli beam in Section 13. The explanation of the nonlinearity of fractional vibrations is given in Section 14.

Tinnitus is a symptom often associated with hearing loss (HL) and is in many cases more burdening to the individual than the HL itself. Many approaches have been made to explain the development and chronification of the phantom percept as well as different treatment strategies to lower the tinnitus related burden. In many studies, the variance of the HL data is high and therefore the interpretation of specific data might be difficult. With this retrospective study, we attempt to explain a part of this variance by investigating specifically the effects of non-auditory comorbidity categories on pure-tone audiometric data in a tinnitus patient collective that was homogeneous with respect to auditory comorbidities. We found age dependent as well as number of non-auditory comorbidity (e.g. diabetes mellitus) dependent differences in the mean HL of the tinnitus patients as well as differences in the peak HL frequency relative to the tinnitus frequency. The analysis of the age dependent HL within the different non-auditory comorbidities revealed specific – partially opposed – effects of endocrine, circulatory, muscle-skeletal and digestive disease categories on the hearing thresholds of tinnitus patients. Taken together we argue that in future tinnitus (and non-tinnitus) patient studies also non-auditory comorbidities should be taken into account as possible covariables that might explain the variance found in the auditory threshold development of these patients.

The aim of the article is to verify differences in the perception of selected factors of sustainability among small and medium-sized enterprises (SMEs) in Central Europe. The subjects of analysis are factors such as corporate social responsibility (CSR), business risk sources (market, financial, personnel, and operational), reputation and social media, national support, social changes, and crisis events in business. The questionnaire was completed by 1,090 SMEs from the business environments of Visegrad Group countries. Statistical hypotheses were verified using Z-scores for two population properties. The most negatively perceived factors among owners and managers in the SME segment are national support and legislative changes. The most positively perceived factor is reputation and social media. The country of doing business is a significant factor. The perception of crisis events in business is most negative among Czech SMEs. More than 80% of Hungarian SMEs perceive their companies as sustainable. In comparison with Slovak and Polish SMEs, Hungarian perceptions are more positive in the context of sustainability. The country of doing business is the strongest factor in evaluating CSR indicators. The positive perception of CSR in Czech SMEs (43.9%) is lower than in other countries.

Natural products from medicinal plants, either as pure compounds or as standardized extracts, provide unlimited opportunities for new drug leads because of the unmatched availability of chemical diversity. The objects of the study are medicinal plant raw materials Saussurea sordida Kar&Kir and Saussurea alpina DC. collected from South Kazakhstan during the flowering period in the spring-summer months of 2021. The present study was aimed to investigate the toxic effects acute administration of ethanol extracts from plants S. sordida and S. alpina in male Wistar rats. The ethanol extract by subjected to Phytochemical Screening, Fourier transform infrared spec-troscopy (FTIR) analysis. Phytochemical screening of the extract revealed the presence of phenolic compounds. FT-IR spectroscopic analysis revealed various characteristic band values with various functional groups in the formulation extract such as amines, alcohol, phenol, alkane, alkene, and conjugated acid groups. FT-IR analysis of peak values with various functional compounds such as alcohols, phenols, carboxylic acids, aldehydes, amides, amino acids, anhydrides, esters, ketones, unsaturated aliphatic, aromatics, unsaturated heterocycles, amines, nitro compound, alkanes, al-kenes and sugars. Histological examination of organ samples after studying the chronic toxicity of ex-tracts of two plant species (S. sordida . and S. alpina) revealed no pathological changes. The acute toxicity study revealed no lethal effects and behavioral signs of toxicity at the tested doses indicating that LD50 is greater than 2000 mg/kg. These results show low toxicity of on short-term use and liver toxicity on long-term use. This study helps to predict the formula and structure of active molecules which can be used as medicinal drugs. This result also enhances the traditional usage of S. sordida and S. alpina , which possesses a number of bioactive compounds.

Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Sexual Violence (SV) can deeply impact victims' physical and psychosocial well-being. Yet many healthcare providers, including registered nurses (RNs), hesitate to screen patients due to a lack of confidence and knowledge. The SATELLITE Sexual Violence Assessment and Care Guide was developed to address this gap; however, the guide’s educational effectiveness remained untested. This pilot study aimed to assess the feasibility, acceptability, and efficacy of an education program based on the SATELLITE guide among RNs in clinical settings, using a pre- and post-test design. Results indicated that the education was not only feasible and acceptable, but also demonstrated the effects as desired with significant increases in RNs' knowledge and confidence in SV screening and care. The program's assessment tool was valid and reliable, and participant recruitment was feasible. Based on these findings, it is recommended that the SATELLITE education program be further tested with a larger RN sample and extended to other healthcare providers. Additionally, exploring different regions, cultural contexts, and healthcare settings would enhance understanding of the program's broader applicability and effectiveness.

Researchers have asserted that, as patients rely on external powerful sources to control their disorders, in addition to their own internal powers and virtues, examining their coping mechanisms can lead to a better understanding of the initiation and progression of chronic diseases and uncover potential opportunities to balance and enhance their psycho-spiritual well-being and possibly their treatment and recovery. This review serves as a user’s manual for investigators who choose to use the CSI-SF to conduct their research on coping behaviors. The CSI-SF that measures four coping strategies based on 16 items was first assessed using the Jackson Heart Study (JHS) cohort in 2007. The reliability and construct validity of the CSI-SF was also later assessed among hemodialysis patients across 13 countries. In this study, the CSI-SF was assessed to be a reliable and valid instrument for measuring coping strategies. The CSI-SF serves the purpose of developing an inner voice that can assist with understanding how people cope with everyday life. The information gathered from administration of the CSI-SF can inform investigators about environmental cues and triggers that can also impact individual health.

Artificial Intelligence (AI) emerges as a powerful catalyst poised to reshape the global sustaina-bility landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile de-viation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts' collective positive perspective regarding AI's pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14 and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10 and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI's impact on peace, justice, and strong institutions as well as on partnerships for the goals respectively. This paper underscores AI as a formidable instrument poised to address humanity's most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future.

The COVID-19-medical emergency has ended worldwide, yet there is still a need to evaluate the psychological impact of these years of unprecedented changes on students’ life. Methods: This study aims to assess and compare COVID-19-related stressors (Relationships-and-Academic-Life; Isolation; Fear-of-Contagion) and psychophysical symptoms reported by university students in April-2020, April 2021, and April 2022. The predictive role of COVID-19-related stressors on psychophysical symptoms within each time was also tested. Data were collected among 637 university students in April-2020 (n = 197), April-2021 (n = 200), April-2022 (n = 240). Results: In April-2022, perceived Isolation and Fear-of-Contagion decreased from the peak registered in April-2021, but stress related to Relationships-and-Academic-Life remained noteworthy high. A sharp and ongoing increase in psychophysical symptoms was found, with more than 50% of students reporting clinical levels of Sleep-Disorders, Depression, Psychoticism, and Interpersonal-Sensitivity. In April-2022, students still perceiving stress related to Relationships-and-Academic-Life and Isolation were at risk for Anxiety, Somatization, and Sleep-Disorders, while those still perceiving stress related to Fear-of-Contagion were also at risk for Depression, Obsessive-Compulsive, and Psychoticism. Conclusion: Findings emphasized the long-lasting detrimental effects of the COVID-19-related stress. Interventions must pinpoint the complex post-pandemic adjustment process and their effects on university students' psychophysical health.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

The main objective of this investigation was to study the effect of organizational competencies development practices on turnover intentions and whether affective commitment explains this relationship. Another of the study's objectives was to test whether these relationships vary ac-cording to the generation to which the participant belongs. The study sample consisted of 2123 participants working in Portuguese organizations. The results indicate that organizational com-petencies development practices (training, individualized support, and functional rotation) nega-tively and significantly affect turnover intentions and that affective commitment mediates this re-lationship. However, these relationships vary according to the participant's generation. For Gen-eration Y and Generation X, this mediating effect is found in all dimensions of organizational competencies development practices. For the baby boomer generation, there is only a mediating effect of affective commitment in the relationship between individualized support and turnover intentions. These results indicate that human resources should consider the generation to which the participant belongs when implementing competencies development practices.

This paper addresses the critical issue of trust in Artificial Intelligence systems, especially when users might find it challenging to comprehend the internal decision-making processes of such systems. A relevant topic of research in this respect is Theory of Mind, which involves attempting to understand these systems as if they possessed beliefs, desires, and intentions. We focus on the latter: intentions, and we examine how producing explanations based on them can improve \emph{understandability} while allowing for a better interpretation of how they align with human values. We also review some existing methods for identifying intentions in AI systems and we conclude with a discussion on possible future directions in this line of research.

Leishmaniasis are neglected diseases that affect regions such as South Asia, South Africa and Latin America, less developed regions. Current treatment, for cutaneous, mucocutaneous and visceral leishmaniosis, which are the main forms of the disease, is based on the administration of pentavalent antimonial and amphotericin B. However, these drugs have low efficacy and high toxicity. For this reason, the search for new treatments for this disease is necessary, based on studies of new molecules and their conformations. The research proposed the conformational study of brominated guanidine compounds with potential antileishmanial activity using Nuclear Magnetic Resonance (NMR) and X-ray diffraction (XRD) techniques. The present study involves the bro-minated molecules, LQOF-G2, LQOF-G30, LQOF-G35 and LQOF-G35-Br. The later was synthesized by the reaction of LQOF-G35 with NBS under IR irradiation at 120 Watts of potency and dichloro-methane as solvent by 12-h of exposition. The obtained results demonstrated the efficiency of the bromination method, since two bromines atoms entered the molecule. Furthermore, NMR analysis showed the occurrence of a conformational change from Z to E when compound LQOF-G35 was brominated to LQOF-G35-Br. This feature was well confirmed by comparative DRX study of the LQOF-G35 and LQOF-G35-Br compounds. The antileishmanial activity of LQOF-G2 e LQOF-G35 motivated the synthesis of new brominated compounds LQOF-G30 e LQOF-G35-Br.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

Despite accumulating evidence that suggests a unique gut microbiota composition in athletes, a comprehensive understanding of this phenomenon is lacking. Furthermore, seasonal variation in the gut microbiota of athletes, particularly during the off-season, remains underexplored. This study aimed to compare the gut microbiotas between athletic subjects (AS) and non-athletic subjects (NS), and to investigate variations between athletic and off-season periods. The data were derived from an observational study involving Japanese male handball players. The results revealed a distinct gut microbiota composition in AS compared with NS, characterized by significantly higher alpha-diversity and a greater relative abundance of Faecalibacterium and Streptococcus. Moreover, a comparative analysis between athletic and off-season periods in AS demonstrated a significant change in alpha-diversity. Notably, AS exhibited significantly higher alpha-diversity than NS during the athletic season, but no significant difference was observed during the off-season. This study demonstrates the characteristics of the gut microbiota of Japanese handball players and highlights the potential for changes in alpha-diversity during the off-season. These findings contribute to our understanding of the dynamic nature of the gut microbiota of athletes throughout the season.

Abstract: This study aimed to evaluate the long-term impact of paediatric dentistry programmes on children and adolescents to reduce oral health inequalities. It measures and assesses the improvement effects of paediatric dentistry programmes on the oral health of children and adolescents as part of the efforts to decrease oral health disparities in this age group. It included 406 individuals who participated in student and paediatric dentistry programmes between 2013 and 2019 at screening facilities in Gwangjin-gu, Seoul. Frequency analysis was conducted for demographic characteristics, and binary logistic regression analysis was performed to identify factors influencing the prevalence of dental caries as the dependent variable. Data were analysed using PASW Statistics with the statistical significance level set at α=0.05. Regarding oral health indicators based on the frequency of participation in paediatric dentistry programmes for children and adolescents, participants with seven or more sessions had lower prevalence rates of dental caries, malocclusion, and periodontal disease than those with only one session. Second, when comparing oral health indicators in paediatric dentistry programmes between primary and adolescent age groups, individuals under continuous management showed a decrease in permanent teeth affected by dental caries, dental caries prevalence, and malocclusion prevalence (excluding primary school age). Third, binary logistic regression analysis revealed significant influences (p

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

In recent years, humanity has faced a growing challenge posed by the rise of antibiotic-resistant bacteria, resulting in an alarming surge in fatalities and the emergence of increasingly hard-to-manage diseases. Among these resilient pathogens is Acinetobacter baumannii, due to its escalating prevalence in hospitals, elevated mortality rates, and resistance to treatment. Despite its clinical significance, our understanding of this pathogen in non-hospital settings and its virulence factors remains limited. Therefore, this review aims to shed light on the latest research regarding its ecological niche, microbiological traits, and antibiotic resistance profiles. Recent investigations have expanded its detection to various environmental niches, including rivers, treatment plants, and soils. Moreover, this bacterium has been discovered in diverse food sources such as meat and vegetables, as well as in farm animals and household pets like dogs and cats. This broader presence of Acinetobacter baumannii outside of hospital environments underscores a significant risk of environmental contamination, necessitating heightened awareness and preventive measures to address potential public health implications.

To better understand anogenital warts (AGW) natural history and dynamic of HPV6/11 infection in regional hairs, 32 newly diagnosed AGW male patients and 32 age-matched healthy controls were closely followed. During enrollment and six follow-up visits (every 2.6 months) 43 AGW tissue and 1,232 anogenital and eyebrow hair samples were collected. This represents the closest longitudinal monitoring of AGW patients to date. Patients were treated according to standard-of-care. HPV6/11 prevalence was 19.9% in patients’ hair samples (HPV6 B1 in 87.5%) and 0% in controls. The highest HPV6/11 prevalence was found in pubic hairs (29.0%) and lowest in eyebrows (7.1%). Odds for HPV6/11-positive hairs increased with smoking, shaving anogenital region and age. A close association between HPV6/11 presence in hairs and clinically visible AGW was observed. The proportion of patients with visible AGW and HPV6/11-positive hairs declined during follow-up with similar trends. No particular HPV6/11 variant was linked with increased AGW recurrence, but sublineage HPV6 B1 showed significantly higher clearance from hairs. Despite treatment, 78.1% and 62.5% of AGW patients experienced one and two/more post-initial AGW episodes, respectively. Patients having HPV6/11-positive hairs or visible AGW at preceding visit demonstrated substantially higher odds for presenting with visible AGW in subsequent visit.

Extra virgin olive oil (EVOO) is recognized for its numerous health benefits attributed to its rich phenolic components. NMR has emerged as a prevalent technique for precisely identifying these compounds. Among Mediterranean countries, Greece stands as the third-largest producer of olives, with the Epirus region notably advancing in olive cultivation, contributing significantly to the dynamic growth of the region. In this study, an NMR method was employed based on the acquisition of an 1H NMR spectrum along with the multiple resonant suppression in order to increase the sensitivity. Using the above method, 198 samples of extra virgin olive oil, primarily sourced from the Epirus region were analyzed and both the qualitative and quantitative aspects of phenolic compounds were acquired. In addition, we examined the effect of various factors, such as, variety, harvest month and region origin on the phenolic compounds’ concentration. The results revealed an average total phenolic content of 246 mg/kg, closely approaching the EU health claim limit of 250 mg/kg. Approximately 15% of the samples were confidently characterized as high-phenolic olive oil. The highest concentrations were observed in Thesprotia samples, with several Lianolia variety exceeding the total phenolic content of 400 mg/kg. Statistical tests demonstrated a significant influence of the olive variety and the month of fruit harvest on phenolic component concentration, followed by the region of origin. A very strong correlation was noted between the total phenolics content and the levels of oleocanthal and oleacein, with a correlation coefficient (r) of 0.924. Upon optimization of all factors affecting olive oil quality, the majority of EVOO from the Epirus region have the potential to be characterized as high in phenolic content.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

The worldwide competition to vaccinate against COVID-19 has highlighted the importance of immunization measures in containing the virus. Saudi Arabia, like the rest of the globe, must assess socio-demographic subtleties, chronic illnesses, vaccination effects, and public health policy implications as it navigates this complicated environment. This cross-sectional study examined Saudi COVID-19 vaccine in many ways. It examined the socio-demographic makeup of vaccine recipients, the prevalence of chronic conditions in this population, vaccine administration patterns, post-vaccination side effects, and vaccine-infection rates. A questionnaire-based online survey of 401 participants ensured gender, nationality, age, weight, and educational diversity. We analyzed vaccine manufacturer choice for each dosage, side effects after the first and second doses, and chronic diseases in the sample. Pfizer/BioNTech vaccines are preferred for all three doses, reflecting worldwide vaccination acceptance patterns. Side effects were not significantly different between vaccination kinds. Interestingly, immunization reduced infection rates, demonstrating the efficacy of COVID-19 vaccines. The data fills important information gaps and illuminates Saudi COVID-19 vaccination kinetics. These data support the global preference for mRNA vaccines, demonstrating the faith in this breakthrough technology. They also sponsor COVID-19 vaccine safety and effectiveness studies. The dramatic drop in infection rates among vaccinated people shows how vaccinations suppress the pandemic. These findings inform Saudi Arabia's immunization efforts. As booster doses become more common, long-term vaccination research is crucial. Vaccine reluctance, especially in certain demographics, must be addressed. Due to vaccines' protective potential, equitable vaccine distribution and large immunization programs are essential for herd immunity and pandemic management. This thorough cross-sectional study helps policymakers and healthcare practitioners build successful and inclusive COVID-19 immunization regimens in Saudi Arabia.

Cholinesterase (ChE) and secretase (BACE) inhibitors, and fibril- and β-amyloid-suppressing medicines are used to treat Alzheimer's disease (AD) symptomatically. The prevalence and complex nature of AD have increased the urgent need for multi-targeted directed ligands (MTDLs). This is because MTDLs can prevent potential drug-drug interactions during poly-therapy and have a better therapeutic profile than single targeted agents. Using piperazine linker or spacer and two primary scaffolds, benzofuran and 8-hydroxyquinoline, a unique class of multi-targeted medicines was recently reported by our group. These compounds showed re-markable effectiveness in inhibiting Aβ1-42 aggregation and acting as iron chelators.The results prompted us to synthesize an additional series of compounds as multimodal anti-AD agents and investigate its inhibitor activities to ChE (AChE/BChE) and BACE1 enzymes. The resulting in-hibitory effects suggested that the compounds under study might be used to improve cognitive function. The docking analysis' findings revealed that the compounds bind to AChE and BChE by forming H-bond interactions with amino acid residues at binding sites and µ-stacking inter-actions with aromatic residues, whereas the binding to BACE1 only revealed H-bond interac-tions with amino acid residues at binding sites.