Search Results (27,595)

This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and mitigate theft and vandalism risks posed by a previously installed, highly exposed commercial system. The proposed system employs LoRa technology to transmit water level data from the storage tank to a receiver located 6 km from the water well. When the water level drops below a predefined threshold, the system transmits an activation signal through the LoRa network to start the well pump and trigger tank refilling. In addition, an SMS monitoring module enables users to remotely verify water level and pump operational status at any time. System notifications and operational data are automatically delivered via SMS to predefined phone numbers, enabling continuous supervision without requiring internet connectivity. The implementation of the proposed system thus provides an efficient and reliable solution for water resource management in rural environments, ensuring continuous water availability and preventing supply shortages. LoRa communication enables robust long-range data transmission, while SMS-based monitoring offers real-time operational awareness for end users. The system was validated through field testing in a pilot rural community, demonstrating operational robustness, improved water management efficiency, and measurable positive impacts on residents’ water service continuity. The low-profile physical design significantly reduced theft and vandalism incidents reported by the local water authority. Experimental results showed an average monthly reduction of 41.2% in electrical energy consumption while maintaining high system reliability, physical security, and real-time monitoring capability. Full article

Implantable Cardioverter-Defibrillators (ICDs) are life-sustaining devices used in the prevention of sudden death in patients suffering from advanced cardiac diseases. Although improvements in ICD technology and monitoring capabilities have been made, existing techniques are still not effective in predicting accelerated battery drain, thereby causing unplanned generator replacement and clinically significant device-related events. In this study, machine learning techniques were employed in the assessment of the early detection of ICD battery depletion risk using the collected device interrogation reports. The dataset used consisted of 32 devices in the training set and nine in the testing set. In order to mitigate the problem of a small sample size, a GMM-based data augmentation technique was applied solely to the training data, and actual devices were used for the testing data. Five supervised models, including Logistic Regression, Random Forest, SVM, CatBoost, and a Neural Network (MLP), have been utilized using a repeated stratified cross-validation and a separate held-out test data set. All the models have been tested for their performance using classification metrics. All models demonstrated variable performance with wide confidence intervals due to limited sample size. Support vector machines showed the highest cross-validation discrimination 0.889 ± 0.203, though uncertainty remains substantial given the small datasets (n = 41). From the feature analysis, it was found that atrial sensing amplitude, RV/LV capture threshold, output settings, and implant duration were the most important features for the prediction of high battery depletion risk. These findings suggest that changes in device parameters and implant age are associated with elevated battery depletion risk, supporting the feasibility of telemetry-driven risk stratification for device management. Full article

This review evaluates the emerging role of circulating tumour cells (CTCs) as clinically meaningful, minimally invasive biomarkers for oral squamous cell carcinoma (OSCC). Despite advances in management, OSCC continues to demonstrate high morbidity and mortality, largely due to late diagnosis and the absence of validated biomarkers for early detection or real-time monitoring. Conventional diagnostic tools, tissue biopsy, and imaging provide only static snapshots and fail to capture tumour heterogeneity or evolving biological behaviour. CTCs offer a novel and significant opportunity to address these limitations. Key findings from recent studies highlight that CTC enumeration correlates with tumour burden, nodal metastasis, recurrence, and overall prognosis. Molecular and phenotypic characterisation further reveals dynamic traits such as epithelial–mesenchymal transition, stemness, and therapy resistance, providing insights into metastatic potential and treatment failure. Technological advances, including immunocytochemistry, microfluidic capture platforms, PCR-based assays, and next-generation sequencing, have enhanced the sensitivity and specificity of CTC detection and enabled detailed multi-omic profiling. Collectively, evidence suggests that integrating CTC analysis into OSCC clinical workflows could improve early detection, refine risk stratification, personalise therapeutic strategies, and support longitudinal monitoring of disease dynamics. As research progresses, CTC-based diagnostics represent a promising frontier in shifting OSCC management toward more precise, adaptive, and biologically informed care. Full article

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

Viral hemorrhagic fevers (VHFs) comprise a heterogeneous group of severe infectious diseases that continue to represent a major global health concern. Although many VHFs remain endemic to regions of Africa, Asia, and the Americas, their wide geographic distribution, together with increasing international travel and global trade, facilitates the importation of cases into non-endemic areas and raises the risk of secondary transmission under favorable ecological and epidemiological conditions. These infections are frequently associated with high case-fatality rates and impose a substantial social and economic burden, including pressure on healthcare systems, disruption of essential services, and long-term physical and psychological sequelae among survivors. Despite notable advances in recent years, therapeutic options for VHFs remain limited. Supportive care continues to represent the cornerstone of clinical management for most infections, while pathogen-targeted therapies are available only for a restricted number of diseases. Monoclonal antibody-based therapies have achieved the most significant regulatory success to date, particularly for Ebola virus disease. In parallel, several small-molecule antivirals have been investigated in preclinical and clinical settings, including during outbreak responses, although inconsistent efficacy and safety concerns have limited widespread approval. Vaccine development has progressed further, with licensed vaccines available for selected VHFs, including Ebola, yellow fever, and dengue, and multiple candidates based on diverse technological platforms advancing through clinical evaluation. In addition to summarizing current therapeutic and vaccine strategies, this review highlights pharmaceutical development considerations relevant to biologic therapeutics and selected vaccine platforms, including formulation stability, pharmacokinetic behavior, delivery routes, storage requirements, and logistical constraints affecting deployment during outbreak responses. Using a comparative cross-pathogen framework, the review synthesizes recent literature to identify translational gaps, regulatory challenges, and future priorities for the development of safer and more effective medical countermeasures against VHFs. Full article

With the increasing popularity of battery energy storage technology, safety issues have become increasingly important. The battery management system (BMS) is a key device for ensuring the safety of lithium-ion battery systems. While the BMS can effectively prevent faults such as external overheating, overload, or deep discharge, it cannot completely eliminate the possibility of internal short-circuit (ISC) faults—these faults may be caused by multiple factors, such as manufacturing defects. Therefore, reliable ISC detection or mitigation strategies need to be designed within the BMS to reduce the consequences of such faults. This study focuses on the critical role of the BMS in responding to thermal runaway (TR) and thermal propagation (TP) events caused by ISC faults and proposes an active energy-dissipation BMS control strategy. This strategy is compared with existing battery current interrupt device (CID) protection and threshold-type BMS protection schemes. A coupled electro-thermal simulation model was constructed based on thermal runaway test data of 280 Ah lithium iron phosphate batteries, and the proposed strategy was verified within this model. The proposed strategy can effectively suppress thermal propagation and thermal runaway in battery energy storage systems, providing a reference for the safety of battery energy storage systems (BESS). Full article

The use of Large Language Models (LLMs) in organizational contexts has grown rapidly, particularly in project management activities. Despite this expansion, a relevant methodological gap can be observed in the literature: the absence of psychometrically validated instruments capable of measuring the acceptability of these technologies prior to their effective adoption, especially in project-oriented governance contexts. Traditional technology adoption models predominantly focus on a posteriori assessment of individual use, providing limited support for prospective analyses that inform strategic decision-making and organizational coordination mechanisms. In response to this gap, this study aims to develop and validate a psychometric scale to indirectly measure the acceptability, through outcome beliefs and with behavioral predispositions serving as structural proxies of the latent construct of LLM use by project management teams, with a focus on a priori judgments that precede the effective adoption of the technology. The initial scale, composed of 17 items, underwent content validation and was administered to a sample of 154 project management professionals. The latent structure was examined through Exploratory and Confirmatory Factor Analyses, resulting in the refinement of the instrument to 13 items distributed across two correlated factors. The results indicate that LLM acceptability is adequately represented by a bidimensional structure comprising the dimensions Intention/Predisposition and Trust/Perceived Benefit, both demonstrating high internal consistency and good statistical fit, and nomological validity evidenced by significant associations with respondents’ self-reported LLM usage frequency. These findings reinforce the conceptualization of acceptability as a prospective and multidimensional construct, relevant for supporting governance decisions and the adoption of artificial intelligence-based technologies in project-oriented organizational systems. The indirect measurement approach adopted here is theoretically grounded in the premise that a priori acceptability is not directly observable but is constituted by cognitive and dispositional beliefs formed prior to use. Full article

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems. Full article

Antimicrobial resistance represents one of the most formidable global health crises of the 21st century, driven by the diminishing efficacy of conventional antibiotics due to bacterial adaptation and biofilm formation. In response, antimicrobial nanoformulations have emerged as a transformative therapeutic paradigm, offering multifaceted and innovative mechanisms to combat resistant pathogens. This comprehensive review delineates the broad scope and distinct novelty of nano-enabled antimicrobial strategies, moving beyond the single-target limitations of traditional drugs. We systematically explore the diverse architectural classes of nanoformulations—including metallic, polymeric, and self-assembling nanostructures—and elucidate their unique mechanistic actions. These encompass (1) physical disruption of microbial membranes via electrostatic interactions; (2) catalytic generation of reactive oxygen and nitrogen species to induce an ‘oxidative storm’; (3) intracellular sabotage of essential metabolic pathways; (4) the ‘Trojan horse’ strategy for enhanced drug delivery and bioavailability; (5) efflux pump bypass to counteract a major resistance mechanism; (6) penetration and eradication of resilient biofilms; and (7) disarming pathogens through quorum sensing and virulence inhibition. Furthermore, this review highlights the immunomodulatory potential of nanoformulations; their activity beyond bacteria against fungi, viruses, and parasites; and the critical role of the nano-bio interface defined by surface physicochemistry. We also address the translational pathway, considering challenges in nanotoxicology, scalability, and regulatory approval, alongside the ecological impact and economic horizon of these technologies. This sector is projected to reach USD 5.4 to 8.96 billion by 2033 to 2034, with compound annual growth rates of 11 to 21% across antimicrobial nanomaterials, nanocoatings, and nanomedicine applications. By integrating insights from computational modeling and in silico design, this review underscores how nanoformulations leverage synergistic, multi-target approaches to overcome resistance, enhance therapeutic efficacy, and represent a significant leap forward in the future of infectious disease management. The novelty lies in the holistic and mechanistic synthesis of how nanotechnology is redefining antimicrobial warfare, offering a promising arsenal to avert a post-antibiotic era. Full article

Digital transformation in higher education increasingly hinges on the robustness and governability of Information and Communication Technology (ICT) infrastructures, with campus Wi-Fi networks serving as the operational backbone of digital learning, research collaboration, and administrative services. In large universities, these networks typically evolve into heterogeneous, multi-vendor environments, introducing ongoing challenges in monitoring coherence, configuration governance, and cross-platform performance diagnosis. Despite the centrality of these issues, smart campus scholarship has paid limited attention to day-to-day operational management. This study examines the design and operational performance of a dual-platform Wi-Fi network management architecture implemented at Mahasarakham University, Thailand. The architecture strategically integrates SolarWinds and LibreNMS to combine centralized network-wide visibility with fine-grained, device-level diagnostics across a multi-vendor infrastructure. An engineering-oriented mixed-method approach was employed, drawing on production monitoring logs and semi-structured interviews with campus network engineers. Findings indicate that SolarWinds strengthens configuration oversight and campus-level situational awareness, whereas LibreNMS enhances detailed performance analytics and accelerates fault isolation. Their coordinated deployment improves operational stability, diagnostic clarity, and long-term maintainability of campus Wi-Fi systems. The study provides practical architectural guidance for managing heterogeneous ICT infrastructures in smart campus and enterprise-scale environments. Full article

Intelligent vessel navigation increasingly demands high-density bathymetric data. To resolve the limitations of traditional standards and overcome existing management bottlenecks, this study proposes a novel methodology for high-density bathymetric data modeling and system construction integrated with the S-100 framework. Centered on the International Hydrographic Organization (IHO) S-102 standard, this methodology pioneers a strongly correlated management paradigm for datasets, data, and metadata. Leveraging a relational database architecture and a three-level indexing mechanism, it enables the structured organization and efficient retrieval of data throughout its entire life cycle. At the data production stage, geometric feature constraints based on convex hulls are innovatively incorporated to facilitate the interpolation of high-density water depth data and the generation of grid arrays. A data organization and structured storage model based on the three-tier logical architecture of the Hierarchical Data Format version 5 (HDF5) is proposed, which couples the technologies of block-based storage and refined version control to achieve the synergistic optimization of storage costs and access efficiency for high-density water depth data. Validation via field measurements in selected sea areas of the East China Sea demonstrated that the generated S-102 bathymetric data complied with international specifications and achieved excellent terrain restoration accuracy. Meanwhile, the proposed HDF5-based storage strategy achieves a storage space reduction of 83.6%. This research provides authoritative and efficient data support for scenarios such as intelligent navigation and port digitalization, and contributes to the construction of an intelligent shipping ecosystem. Full article

Electricity systems increasingly operate under deep uncertainty driven by geopolitical risk, volatile fuel markets, trade fragmentation, security threats, and technological change. Under such conditions, cost-optimal planning based on assumed trajectories may lead to fragile outcomes, particularly for small and geopolitically exposed systems such as Israel’s. This paper assesses the resilience of alternative electricity transition strategies for Israel using a qualitative robustness framework inspired by Decision Making under Deep Uncertainty and scenario-based energy security analysis. Six policy-relevant strategies are evaluated across structurally distinct stress scenarios. Resilience is assessed along three dimensions: security of supply, dependency exposure, and economic vulnerability, using anchored qualitative scoring and dominance rules. The results indicate that gas-centric strategies exhibit limited robustness, while strategies combining solar deployment with adaptive gas management, smart grids, microgrids, and domestic clean-technology capabilities achieve higher resilience across a wide range of futures. The paper contributes a structured qualitative approach to resilience assessment and offers policy-relevant insights for electricity transitions under deep uncertainty. Full article

Oral mucosal melanoma (OMM) is a rare and aggressive malignancy that differs markedly from cutaneous melanoma in terms of epidemiology, genetic characteristics, clinical presentation, and treatment response. Despite advances in understanding OMM pathogenesis and the development of novel therapeutic strategies, early diagnosis remains challenging due to its low prevalence, anatomically concealed locations, and frequent multifocality. This review emphasizes the importance of the early detection of OMM using non-invasive imaging methods—specifically dermoscopy and reflectance confocal microscopy (RCM)—and explores their potential role in guiding treatment decisions, preventing disease progression, and improving prognosis. A narrative review of the PubMed database was conducted using the terms “oral melanoma,” “oral melanoma dermoscopy,” and “oral melanoma reflectance confocal microscopy.” Seventy-two relevant review articles were included. In addition, two illustrative clinical cases from our practice are presented to demonstrate the diagnostic value of non-invasive imaging techniques. Although biopsy and histopathology remain the diagnostic gold standards, they are invasive, time-consuming, and may be poorly tolerated, particularly in patients with multifocal lesions. Dermoscopy and RCM provide real-time, high-resolution imaging that enables the detection of early tissue abnormalities not visible to the naked eye. These techniques show good correlation with clinical and histopathological findings, thereby enhancing diagnostic accuracy and facilitating follow-up without the need for repeated biopsies. In our cases, they were instrumental in identifying recurrence and guiding clinical management. However, several limitations should be considered, including restricted accessibility, anatomical constraints, and the requirement for specialized training and expertise. Non-invasive imaging techniques may support clinicians in the early recognition and evaluation of suspicious oral lesions; however, histopathologic examination remains essential for definitive diagnosis. Wider implementation and further technological refinement are needed to optimize their integration into clinical practice. Full article

The rapid digitalization of cities is reshaping urban planning practices; however, significant gaps persist between technological investments and institutional governance capacity, particularly in transition economies. This study investigates how digital tools can be systematically embedded within planning processes to improve decision-making quality, coordination, and administrative efficiency. Drawing on urban governance theory and an empirical implementation study conducted in Yerevan, Armenia (population 1.1 million) between 2019 and 2023, the paper develops and operationalizes a multi-layer governance framework that aligns digital instruments—including geospatial information systems, performance dashboards, and decision-support platforms—with strategic, tactical, and operational levels of city management. The framework is evaluated through institutional analysis of municipal policy documents, planning databases, and semi-structured interviews with planning officials. The results reveal substantial governance barriers, including data fragmentation, organizational silos, and limited digital capacity. Framework-based implementation produced measurable improvements: planning decision cycles shortened by 43%, GIS utilization increased from 18% to 68% of eligible projects, inter-agency data sharing rose sixfold, and annual cost savings of approximately $1.2 million were achieved through reduced duplication and faster approvals. By combining conceptual design with empirical validation, the study advances digital urban governance research and offers a transferable, evidence-based model for implementing resilient and efficient data-driven planning systems in resource-constrained contexts. Full article

For effective winter road management in snow-prone regions, timely snow removal that reflects weather and traffic conditions is required. In Akita City, Japan, city hall staff measure snow depth and dispatch contracted snow removal crews only when a predefined threshold is exceeded. Consequently, dispatch decisions depend heavily on staff experience. This study demonstrates objective, experience-independent dispatching based on predicting the number of snowplow dozers in operation, thereby reducing the municipal decision burden and improving contractor efficiency. The target variable is highly imbalanced, with long non-operational periods and wide variations in the number of deployed units during snowfall events. When trained directly on such data, models tend to regress toward near-median values and face difficulty capturing operational dynamics. To address this issue, we propose a two-stage framework: firstly, a classifier predicts whether snow removal operations will occur; secondly, a regressor estimates the number of operating dozers based on the operation. We further integrate multi-year datasets to enhance generalization across diverse snow conditions. Experiments showed that the proposed approach achieved an AUPRC of 0.84 for operation occurrence and an RMSE of 1.85 for dozer-count estimation, outperforming models trained on a single year. Full article