Search Results (58,190)

Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy. Full article

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained $R^2$ 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained $R^2$ 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance. Full article

On 6 February 2023, two devastating earthquakes struck the Kahramanmaraş region in southeastern Türkiye, causing widespread destruction across multiple provinces. Among the most severely affected areas was Hatay, where this study conducted a comprehensive post-earthquake field investigation. The research integrates tectonic, geological, and seismic analyses with structural performance assessments of reinforced concrete and masonry buildings. Particular attention is given to the influence of local soil conditions and geomorphological features on damage distribution. Ground motion records are evaluated alongside observed structural failures to identify key vulnerability factors. The findings highlight critical deficiencies in construction practices and regulatory compliance, and the study concludes with recommendations aimed at enhancing seismic resilience through improved code enforcement, site-specific design strategies, and rigorous quality control during construction to reduce future loss of life and property. Full article

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Background/Objectives: Liver fibrosis is a progressive consequence of chronic liver injury that can evolve into cirrhosis, liver failure, or hepatocellular carcinoma, representing a major global health burden. Fibrogenesis is driven by hepatic stellate cell (HSC) activation, excessive extracellular matrix deposition, and structural disruption of liver tissue, with transforming growth factor-β (TGF-β) signaling and inflammatory mediators as central pathways. Current therapies primarily target the underlying causes, which may halt disease progression but rarely reverse established fibrosis. This review aims to outline current and emerging therapeutic strategies for liver fibrosis, informing both clinical practice and future research directions. Methods: A narrative synthesis of preclinical and clinical evidence was conducted, focusing on pharmacological interventions, microbiota-directed strategies, and innovative modalities under investigation for antifibrotic activity. Results: Bile acids, including ursodeoxycholic acid and derivatives, modulate HSC activity and autophagy. Farnesoid X receptor (FXR) agonists, such as obeticholic acid, reduce fibrosis but are limited by adverse effects. Fatty acid synthase inhibitors, exemplified by denifanstat, show promise in metabolic dysfunction-associated steatohepatitis (MASH). Additional strategies include renin–angiotensin system inhibitors, omega-3 fatty acids, and agents targeting the gut–liver axis. Microbiota-directed interventions—probiotics, prebiotics, symbiotics, antibiotics (e.g., rifaximin), and fecal microbiota transplantation—are emerging as potential modulators of barrier integrity, inflammation, and fibrogenesis, though larger clinical trials are required. Reliable non-invasive biomarkers and innovative trial designs, including adaptive platforms, are essential to improve patient selection and efficiently evaluate multiple agents and combinations. Conclusions: Novel modalities such as immunotherapy, gene editing, and multi-targeted therapies hold additional potential for fibrosis reversal. Continued translational efforts are critical to establish safe, effective, and accessible treatments for patients with liver fibrosis. Full article

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Cente- red on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I${}^2$C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

Healthcare systems worldwide face growing challenges related to staff shortages, excessive workload, and deteriorating working conditions, which compromise both staff well-being and care quality. Despite these issues, there is a lack of validated tools that capture healthcare professionals’ subjective perceptions of resource adequacy. This study presents the development and initial validation of the Staff Resource Adequacy Questionnaire for Healthcare Professionals (SRAQ-HP), a multidimensional tool designed to assess staffing adequacy and workload, quality of care, and working conditions and support. The development process followed a mixed-methods design, incorporating theoretical foundations from Kanter’s empowerment theory, role enactment models, and professional competence frameworks. The initial item pool of 32 statements was reduced to 26 through expert reviews, focus groups, and pilot testing (n = 35). Content validity index (CVI = 0.931) and face validity index (FVI = 0.976) demonstrated high content relevance and clarity. Cronbach’s alpha for the full scale was 0.841, confirming internal consistency. Expert re-review confirmed strong content (S-CVI/Ave = 0.931) and face validity (FVI = 0.976) for the final 26-item version. Three core dimensions were retained: Staffing Adequacy and Workload, Quality of Care, and Working Conditions and Support. The SRAQ-HP provides a novel, evidence-based approach to systematically assess workforce sufficiency and support structures in clinical settings. It can guide decision-making in healthcare institutions and inform national workforce policies. Further research with larger and more diverse samples is needed to confirm its factorial validity and practical applicability. Full article

The extensive deployment of diverse sensors in hospitals has resulted in the collection of various medical time-series data. However, these real-world medical time-series data suffer from limited volume, poor data quality, and privacy concerns, resulting in performance degradation in downstream tasks, such as medical research and clinical decision-making. Existing studies provide generated medical data as a supplement or alternative to real-world data. However, medical time-series data are inherently complex, including temporal data such as laboratory measurements and static event data such as demographics and clinical outcomes, with each patient’s temporal data being influenced by their static event data. This intrinsic complexity makes the generation of high-quality medical time-series data particularly challenging. Traditional methods typically employ Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), but these methods struggle to generate high-quality static event data of medical time-series data and often lack interpretability. Currently, large language models (LLMs) introduce new opportunities for medical data generation, but they face difficulties in generating temporal data and have challenges in specific domain generation tasks. In this study, we are the first to propose an LLM-based framework for modularly generating medical time-series data, QAMT, which generates quality-assured data and ensures the interpretability of the generation process. QAMT constructs a reliable health knowledge graph to provide medical expertise to the LLMs and designs dual modules to simultaneously generate static event data and temporal data, constituting high-quality medical time-series data. Moreover, QAMT introduces a quality assurance module to evaluate the generated data. Unlike existing methods, QAMT preserves the interpretability of the data generation process. Experimental results show that QAMT can generate higher-quality time-series medical data compared with existing methods. Full article

Domestic water heaters traditionally use natural gas or electric resistance to heat stored water. A gas water heater relies on a non-renewable resource, while an electric water heater might rely on electricity generated by a non-renewable resource. This study analyzes the performance of an electric water heater featuring a novel heating element design based on a positive temperature coefficient (PTC) material powered directly by solar photovoltaic (PV) modules in a northern latitude installation. The project analyzes the operation of two different design temperatures of the PTC heating elements (50 ${}^{\circ }$C, and 70 ${}^{\circ }$C) when fed by three solar PV panels during the spring in the high-latitude location of Anchorage, Alaska (61.2${}^{\circ }$ N). Our results show that both design temperatures of the PTC heating elements are able to achieve self-regulation at a sufficient and safe operating temperature for a domestic use case. Analysis of water heater performance directly connected to PV power showed that the PTC-equipped water heater had a limited period of heating when sufficient solar irradiance is available. Because of this, restrictive use of the water heater might be necessary during periods of non-daylight hours to preserve hot water in an insulated tank. However, this PV-to-PTC setup could be effectively used in industrial, commercial, and research settings. Full article

This article presents a comparative evaluation of three established thermal comfort models (ISSO 74, ASHRAE 55, and EN 16798-1) in the context of residential buildings in Algiers, under current and projected Mediterranean climate conditions. By combining field measurements, occupant interviews, and dynamic simulations in DesignBuilder, this research analyzes thermal comfort responses using the RCP 8.5 climate scenario. The analysis demonstrates that ISSO 74 is more suitable for temperature adaptation, while EN 16798-1 offers better humidity tolerance in high-moisture environments. Results reveal that indoor thermal discomfort currently affects more than one-third of the annual hours, with summer discomfort projected to dominate by 2100. Bedrooms are identified as the most thermally vulnerable spaces during peak summer weeks. The article identifies a critical mismatch between existing comfort standards and local climatic realities, calling for the development of an adaptive thermal comfort model tailored to the socio-economic and hygrothermal characteristics of North African cities. Passive strategies and mixed-mode ventilation are recommended as essential for enhancing climate resilience and reducing energy demand. Full article

This study proposes a task planning approach for a distributed constellation dedicated to space target monitoring, grounded in an adaptive genetic algorithm. The approach is designed to address challenges such as the growing number of space targets and the complex constraints inherent in space target monitoring activities. After reviewing the research progress of distributed satellite task planning and adaptive genetic algorithms, a distributed task model featuring master-slave satellites was developed. This model integrates multi-constraint modeling and aims to optimize key performance indicators: task yield rate, task completion rate, resource utilization rate, and load balancing. To enhance the approach, the contract net algorithm is fused with the adaptive genetic algorithm: Firstly, in the tendering phase, centralized tendering is adopted to reduce communication overhead; Secondly, in the bidding phase, improved genetic mechanisms (e.g., dynamic reverse adjustment of crossover and mutation probabilities) and a dynamic population strategy are employed to generate task allocation schemes; Thirdly, in the bid evaluation and winning phase, differentiated strategies are applied to non-repetitive and repetitive tasks. Simulation validation shows that this approach can complete 80% of space target monitoring tasks, balance satellite loads effectively, and manage space target catalogs efficiently. Full article

This research investigates the influence of waste mask fabric scraps (WMFSs) and nano-carbon-modified filler (NCMF) on the mechanical characteristics and durability of hot mix asphalt, aiming to improve pavement performance concerning tensile stress, fatigue, and moisture damage using recycled materials. Asphalt mixtures were created with aggregate and WMFS/NCMF at 0.3% and 0.5% weight percentages (relative to aggregate), with fiber lengths of 8, 12, and 18 mm, utilizing a ‘wet mixing’ method where fibers were incrementally added to aggregates during mixing. The samples underwent indirect tensile strength, moisture susceptibility, and Marshall stability testing. The results demonstrated that incorporating WMFSs and NCMF initially enhanced tensile strength, moisture susceptibility resistance, and Marshall stability, reaching an optimal point; beyond this, further fiber addition diminished these properties. Data analysis identified the sample containing 0.3% fibers at a 12 mm length as the superior performer, showcasing the highest ITS and Marshall stability values. Statistical t-tests revealed significant differences between fiber-containing samples and control groups, verifying the beneficial impact of WMFSs and NCMF. Design-Expert software (Design-Expert 12.0.3) was used to develop functional models predicting asphalt properties based on fiber percentage and length. The optimal combination—12 mm fiber length and 0.3% WMFS/NCMF—demonstrated a 33% increase in tensile strength, a 17% improvement in moisture resistance, and a 70% reduction in fatigue deformation. Safety protocols, including thermal decontamination of WMFSs, were implemented to mitigate potential health risks. Full article

This research seeks to reduce wind-blown sand hazards in saline deserts by introducing hyperbranched PVAc copolymer emulsion as a novel ecological sand-fixing material. The study began with the preparation of the emulsion, then evaluated its fundamental properties and the salt tolerance of latex films through FTIR, SEM, and mechanical strength assessments. The sand-fixing properties (compressive strength, anti-water erosion, anti-wind erosion, thermal aging, freeze–thaw stability, and water retention) were evaluated. In addition, their effects on increasing both the growth of microbes and plants in salty deserts have been evaluated by field experiments to understand their ecological effects. The experimental results showed that the hyperbranched PVAc copolymer emulsion has excellent salt resistance and can be used as an ecological sand-fixing material in salty deserts. The research findings demonstrate that the hyperbranched PVAc copolymer emulsion exhibits superior salt tolerance, rendering it an effective ecological sand-fixing material for saline deserts. Notable attributes encompass its capacity to significantly mitigate NaCl-induced aggregate damage to sand-fixing materials, thereby enhancing sand fixation performance; its robust thermal aging resistance, freeze–thaw stability, and salt tolerance, which enable it to withstand environmental temperature variations; and experimental assessments of sand-based plant and microbial growth confirming favorable ecological impacts. This study presents novel methodologies for designing ecological sand-fixing materials in saline deserts to combat desertification. Full article

Prepartum vaccination of dairy cows against newborn calf diarrhea protects calves during the first weeks of life via the colostrum. Vaccination may also induce non-specific effects (NSEs) beyond antibody production, altering the disease susceptibility and productivity of the vaccinated mother. This retrospective study analyzed herd records and on-site survey data from 73,378 dairy cows on 20 German farms using linear mixed-effects models and random forest algorithms. Management practices and milk yield showed stronger associations with outcomes than vaccination. However, the cows vaccinated with non-live vaccines had increased odds of retained placenta and metritis (OR: 1.5–1.7), as well as endometritis (OR: 3–6), and were 20–24% less likely to conceive than non-vaccinated cows. Among non-live vaccinated cows, those vaccinated 2.5–4 weeks before calving had an 8% higher non-return rate compared to those vaccinated 6–8 weeks prior. Multiparous cows receiving live vaccine components were 1.9 times more likely to conceive, compared to non-live vaccinated multiparous cows. These findings suggest potential NSE of prepartum vaccination on uterine health and fertility. However, this study’s retrospective design limits causal interpretation, and the benefits in calves may outweigh possible adverse effects. Further research should clarify the mechanisms and optimize vaccine timing and composition. Full article

Composite overwrapped pressure vessels (COPVs) have become the core unit for high-pressure hydrogen storage and transportation. However, excessive autofrettage pressure could induce unilateral buckling damage of the metal liner because of large rebound compressive stress induced by large plastic deformation in the depressurization stage. When the liner contains initial defects, its critical unilateral buckling pressure would be further reduced. In this paper, a critical buckling pressure calculation formula was established by finite element analysis and theoretical derivation. Firstly, the classical theoretical calculation models and research methods were analyzed and discussed. Then, by discussing the key influencing parameters, a semi-empirical calculation formula of nonlinear confined buckling pressure of a metal liner with ovality defects was established. Finally, the proposed semi-empirical formula was used to predict the critical internal pressure of a Type-III COPV, and the predicted value was compared with the experimental result. The predicted result was higher than the experimental result and the error range was −2.8%~−23%. The proposed semi-empirical formula of nonlinear confined buckling could provide theoretical support for designing the autofrettage pressure of Type-III COPVs and help to reduce the uncertainty and repeated test cost in the design process. Full article