Search Results (11,215)

Brucellosis remains a major zoonotic disease worldwide, with disproportionate burden in low- and middle-income countries where limited veterinary and healthcare infrastructure constrain effective control measures. However, its pathways of transmission are poorly understood. In pastoralist settings, we hypothesize that camels have a high burden of Brucella spp. and play a key role in spreading it to humans and other livestock. This manuscript presents a study protocol to quantify the relative contribution of various livestock species to brucellosis transmission and identify cost-effective control strategies in Kenya. Using probability-proportional-to-size sampling, we aimed to recruit a longitudinal cohort of 170 households and their herds per site in the Marsabit and Kajiado counties. Households rearing at least one livestock species (cattle, camels, goats, sheep) were eligible. Serum, milk, and vaginal swabs (from livestock), and serum (from humans) were collected for testing using Rose Bengal Test, ELISA, qPCR, and culture methods. Concurrently, surveillance for suspected brucellosis was conducted in study health facilities. A qualitative ethnographic study and livestock movement monitoring using GPS-collared animals were nested within the cohort. These data will be used to parameterize a multi-host, multi-species infectious disease model through Approximate Bayesian Computation. Through this One Health approach, our study will identify and optimize potential interventions and help inform the development of a comprehensive cost-effective national control program for brucellosis. Full article

Improved maternal experiences and outcomes have been widely linked to the presence of birth companions. However, cultural norms, institutional constraints, and privacy concerns frequently restrict women’s choice of birth companions in many Middle Eastern countries, including Jordan. This study investigated Jordanian women’s beliefs and barriers about the presence of companions in the labour room. A qualitative descriptive study design was conducted using Braun and Clarke’s framework for thematic analysis. Thirteen women (ages 21 to 38 years) with prior pregnancy and childbirth experience were chosen from a free health awareness event in Irbid, Northern Jordan in July 2025, to participate in semi-structured interviews. The responses were recorded on audio tapes and subsequently stored in their original format. Data were coded, transcribed, and then thematically analyzed to identify beliefs and perceived barriers. The most significant beliefs were: (i) emotional and psychological support, wherein companionship was thought to alleviate fear and provide reassurance; (ii) strengthening family ties, as women saw shared childbirth experiences as improving family bonds; and (iii) cultural and religious interpretations, wherein female relatives were frequently seen as more acceptable than husbands. Women reported two barriers to allowing companions in the labour room: (i) privacy and modesty issues, where they feared embarrassment, exposure, and judgment, and (ii) institutional and policy restrictions, such as restrictive hospital regulations. Although Jordanian women recognized the emotional and interpersonal benefits of having company during childbirth, they encountered numerous substantial institutional, cultural, and privacy-related barriers. Improving women’s birth experiences and promoting respectful maternity care may be achieved by addressing these issues through culturally sensitive education, privacy-enhancing infrastructure, and regulatory reform. Full article

The Internet of Things (IoT) has transformed industries, healthcare, and smart environments, but introduces severe security threats due to resource constraints, weak protocols, and heterogeneous infrastructures. Traditional Intrusion Detection Systems (IDS) fail to address critical challenges including scalability across billions of devices, interoperability among diverse protocols, real-time responsiveness under strict latency, data privacy in distributed edge networks, and high false positives in imbalanced traffic. This study provides a systematic comparative evaluation of three representative AI models, CNN-BiLSTM, Random Forest, and XGBoost for IoT intrusion detection on the NSL-KDD and UNSW-NB15 datasets. The analysis quantifies the achievable detection performance and inference latency of each approach, revealing a clear accuracy–latency trade-off that can guide practical model selection: CNN-BiLSTM offers the highest detection capability (F1 up to 0.986) at the cost of higher computational overhead, whereas XGBoost and Random Forest deliver competitive accuracy with significantly lower inference latency (sub-millisecond on conventional hardware). These empirical insights support informed deployment decisions in heterogeneous IoT environments where accuracy-critical gateways and latency-critical sensors coexist. Full article

Arc faults in low-voltage three-phase systems are a major hazard for both people and equipment, requiring extremely fast and selective protective measures. This paper presents the concept and simulation analysis of a hybrid arc eliminator that combines multi-section thyristor branches with a fast mechanical short-circuit device. The arc eliminator enables phase-selective arc suppression, ensuring that only the faulted phase is shunted while healthy phases remain in service. Simulations carried out in ATPDraw, supported by experimental reference data, demonstrate effective arc extinction within sub-millisecond to millisecond time scales across the range of inductances typically encountered in real circuits. This study analyzes the influence of circuit inductance on commutation dynamics and arc duration, as well as the distribution of conduction time among thyristor sections to balance thermal stress. A dynamic I²t-based control strategy is proposed to enhance reliability and component utilization, and preliminary perspectives on optimization supported by artificial intelligence are discussed. The results indicate that the arc eliminator can significantly improve personnel safety and equipment resilience, particularly in critical installations such as data centers, mining infrastructure, ships, or photovoltaic and electric vehicle systems. Full article

This study examines two primary research objectives: (1) to investigate the roles of work stress, logistics infrastructure, financial stress, and environmental stress as antecedent factors influencing the wellbeing of truck drivers in Thailand, and (2) to explore the mediating roles of driver fatigue, cognitive impairment, and accident risk in the relationship between antecedent factors and wellbeing. Data were collected from 534 Thai truck drivers through voluntary participation in an online survey utilizing a validated five-point Likert scale instrument with established reliability and validity. The data collected was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings demonstrate that Work Stress, Financial Stress, and Environmental Stress constitute antecedent factors with direct effects on wellbeing. Driver fatigue, cognitive impairment, and accident risk function as complementary partial mediators in the relationships between work stress, environmental stress and wellbeing, while simultaneously serving as competitive partial mediators in the relationship between financial stress and wellbeing. Moreover, these three mediating variables collectively operate as full serial mediators in the relationship between logistic infrastructure and wellbeing. The results show that all antecedent factors significantly affect wellbeing, with financial stress having the strongest impact, followed by environmental stress. Together, these factors explain a substantial portion of wellbeing variance among Thai truck drivers. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Coastal erosion poses a significant risk to the Romanian Black Sea coast, a region characterized by the interaction of natural geomorphological processes and anthropogenic pressures. The research focuses on developing a tool to quantify the cumulative impact of hydro-geo-morphological factors and to assess the vulnerability of the coastal zone to these influences. The approach involves adapting the Coastal Vulnerability Index (CVI)—previously applied in various methodologies—to the specific characteristics of this semi-enclosed basin, which included the exclusion of the tidal range variable due to the Black Sea’s negligible tidal amplitude. The selection of key variables, including coastal geology and geomorphology, shoreline change rates, coastal slope, sea level, and wave regime, was conducted with consideration for the specific characteristics of the Romanian coastal zone. By classifying these variables on a semi-quantitative scale and integrating them into a CVI, the study identifies and maps areas of high vulnerability. The analysis, based on a 1 × 1 km grid resolution, identified sectors of very high vulnerability in the northern Danube Delta unit, particularly along the coastlines of South Sulina–Câşla Vădanei, Sahalin, and Periboina-Edighiol-Vadu. These findings are validated through a comparison with long-term, multidecadal shoreline evolution data, confirming the model’s predictive accuracy. While the 1 × 1 km grid is effective for a macro-scale assessment, the study highlights the need for a finer resolution (e.g., 100 × 100 m) for detailed analysis in the southern region, due to localized geodynamic conditions and the significant influence of coastal infrastructure. Full article

Oil and gas fields in subsidence-prone regions face multiple hazards that threaten the resilience of their infrastructure. This study presents an integrated risk mapping framework for the Yibal field in the Sultanate of Oman, utilizing remote sensing and geophysical data. Multi-temporal PS-InSAR analysis from 2010 to 2023 revealed cumulative surface deformation and tilt anomalies. Micro-seismic and fault proximity data assessed subsurface stress, while a flood risk map-based surface deformation-adjusted elevation captured hydrological susceptibility. All datasets were standardized into five risk zones (ranging from very low to very high) and combined through a weighted overlay analysis, with an emphasis on surface deformation and micro seismic factors. The resulting risk map highlights a central corridor of high vulnerability where subsidence, seismic activity, and drainage pathways converge, overlapping critical infrastructure. The results demonstrate that integrating geomechanical and hydrological factors yields a more accurate assessment of infrastructure risk than single-hazard approaches. This framework is adaptable to other petroleum fields, enhancing infrastructure protection (e.g., pipelines, flowlines, wells, and other oil and gas facilities), and supporting sustainable field management. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

Because of the advancement of IT, cross-domain environments have emerged where independent clouds with different security policies share data. However, sharing data between clouds with heterogeneous security levels is a challenging task, and most existing access control schemes focus on a single cloud domain. Among various access control models, RBAC is suitable for cross-domain data sharing, but existing RBAC schemes cannot provide strong role privacy and do not support freshness in role verification, so they are vulnerable to replay-based misuse of credentials. In this paper, we propose an RBAC scheme for cross-domain cloud environments based on a hash-chain-augmented zk-SNARK and identity-based signatures. The TA issues IBS-based role signing keys to users, and the user proves, through a zk-SNARK circuit, that there exists a valid role signing key satisfying the access policy without revealing the concrete role information to the CDS. In addition, a synchronized hash chain between the user and the CDS is embedded into the proof so that each proof is tied to the current hash-chain state and any previously used proof fails verification when replayed. We formalize role privacy, replay resistance, and MitM resistance in the cross-domain setting and analyze the proposed scheme by comparing it with Saxena and Alam’s I-RBAC, Xu et al.’s RBAC, MO-RBE, and PE-RBAC. The security analysis shows that the proposed scheme achieves robust role privacy against both the CDS and external attackers and prevents replay and man-in-the-middle attacks. Furthermore, the computational cost evaluation based on the number of pairing, exponentiation, point addition, and hash operations confirms that the verifier-side overhead remains comparable to existing schemes, while the additional prover cost is the price for achieving stronger privacy and security. Therefore, the proposed scheme can be applied to cross-domain cloud systems that require secure and privacy-preserving role verification, such as military, healthcare, and government cloud infrastructures. Full article

Bangladesh is at the forefront of climate change impacts because of its geographical location, high population density, and constrained socio-economic infrastructure. Our objective is to explore the impacts of climate change on human security components and conflict constellation, and identify adaptation actors through the lens of experts in Bangladesh. We conducted 12 semi-structured qualitative interviews with lead experts using the Problem-centred Interview (PCI) methodology and inductively applied content analysis to analyse the data, complemented with descriptive statistics. Experts see a shift in baseline risk due to the increase in frequency and severity of natural hazards. It exacerbates existing vulnerabilities by declining agricultural productivity, undermining water security and increasing migration. Food, economic, and water security are predominantly impacted, where women and the poor suffer disproportionately. Impacts on urban areas, energy and community security are under-researched. Experts agreed that climate change is a “threat multiplier” and could aggravate political insecurity, leading to conflicts. Individuals and households are primary adaptation actors, followed by governmental and non-governmental organisations. This research contributes to the broader understanding of the complex nexus of climate change impacts, human security, and conflict constellation, complements climate models and provides policy-relevant insights for inclusive, long-term adaptation grounded in local realities in Bangladesh. Full article

This paper presents a PRISMA-guided systematic literature review (2015–2025) of 20 empirical studies on digital transformation in retail banking, examining how artificial intelligence (AI) strengthens cybersecurity, enables FinTech collaboration through interoperable APIs and open-banking infrastructures, and embeds data-driven decision-making across core functions. We searched major databases, applied predefined eligibility criteria, appraised study quality, and coded outcomes related to digital adoption, operational resilience, and customer experience. The synthesis indicates that AI-enabled controls and API-mediated partnerships are consistently associated with higher digital-maturity indicators, conditional on robust model-risk governance and prudent third-party/outsourcing management. Benefits span improved customer experience, efficiency, and inclusion; however, legacy systems, regulatory fragmentation, cyber threats, and organizational resistance remain binding constraints. We propose a unified framework linking technology choices, regulatory design, and organizational outcomes, and distill actionable guidance for policymakers (e.g., interoperable standards, proportional AI governance, sector-wide cyber resilience) and bank managers (sequencing AI use cases, risk controls, and partnership models). Future research should assess emerging technologies—including quantum-safe security and central bank digital currencies (CBDCs)—and their implications for digital-banking stability and trust. Full article

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks. Full article

Sensors in plant and crop monitoring play a key role in improving agricultural efficiency by enabling the collection of data on environmental conditions, soil moisture, temperature, sunlight, and nutrient levels. Traditionally, wide-scale wireless sensor networks (WSNs) gather this information in real-time, supporting the optimization of cultivation processes and plant management. Our paper proposes a novel “plant-to-machine” interface, which uses a plant-based biosensor as a primary data source. This model allows for direct monitoring of the plant’s physiological parameters and environmental interactions via Electrical Impedance Spectroscopy (EIS), aiming to reduce the reliance on extensive sensor networks. We present simple data-gathering hardware, a non-invasive single-wire connection, and a machine learning-based framework that supports the automatic analysis and interpretation of collected data. This approach seeks to simplify monitoring infrastructure and decrease the cost of digitizing crop monitoring. Preliminary results demonstrate the feasibility of the proposed model in monitoring plant responses to sunlight exposure. Full article

Solid waste management remains a critical challenge in rapidly urbanizing regions of the Global South, where limited infrastructure and informal disposal practices compromise environmental and public health. This study addresses the issue of illegal waste dumping in Dakar, Senegal, by integrating remote sensing, geographic information systems, and citizen science into a multi-criteria framework to identify areas most susceptible to dumping. Using Landsat 8 and Sentinel-2 imagery, indicators such as land surface temperature, vegetation, soil, and water indices were combined with demographic and infrastructural data. A citizen survey involving local university students provided social perception scores and criterion weights through the Analytic Hierarchy Process. The resulting susceptibility maps revealed that high and very high dumping probabilities are concentrated around the Mbeubeuss landfill and densely populated areas of Keur Massar, while Malika showed lower susceptibility. Sensitivity analysis confirmed the model’s robustness but highlighted the influence of thermal and social perception variables. The results show that 28–35% of the study area falls under high or very high susceptibility, with hotspots concentrated near wetlands, informal settlements, and poorly serviced road networks. The weighted model demonstrates stronger spatial coherence compared to the unweighted version, offering improved interpretability for waste monitoring. These findings provide actionable insights for the Société Nationale de Gestion Intégrée des Déchets (SONAGED) and for municipal planners to prioritize interventions in high-susceptibility zones. Rather than being entirely novel, this study builds on existing remote sensing, geographic information systems and citizen science approaches by integrating them within a multi-criteria framework specifically adapted to a West African context. Full article