Search Results (324)

Background/Objectives: Therapeutic drug monitoring (TDM) of antipsychotic medications plays an important role in optimizing treatment efficacy, reducing adverse effects, and supporting adherence. While Ultra-High Performance Liquid Chromatography–Tandem Mass Spectrometry (UHPLC–MS/MS) has long been the gold standard for antipsychotic quantification, recent advances in automated platforms and microsampling raise questions about its current clinical practicality. This systematic review evaluated the clinical applicability and analytical performance of UHPLC-based methods for monitoring antipsychotic drugs, focusing on precision, recovery, matrix effects, and suitability across various biological matrices. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted for studies published between 2013 and 2024 involving UHPLC-based quantification of antipsychotics in clinical samples from adult patients. Data on analytical parameters, sample matrices, and study characteristics were extracted. A custom quality checklist was used to assess methodological rigor. In addition to qualitative synthesis, non-traditional quantitative approaches were applied, including descriptive aggregation of recovery, matrix effects, and precision across studies, as well as correlation analyses to explore relationships among performance parameters. Results: Twelve studies were included, spanning a range of typical and atypical antipsychotics and metabolites. Plasma and serum demonstrated the highest analytical reliability (recovery >90%, minimal matrix effects), while dried blood spots (DBSs), whole blood, and oral fluid showed greater variability. Clinically, UHPLC–MS/MS enabled more accurate dose adjustments and identification of non-adherence, outperforming immunoassays in sensitivity, specificity, and metabolite detection. Microsampling methods showed promise for outpatient and decentralized care but require further clinical validation. Conclusions: UHPLC–MS/MS remains the most robust and reliable method for TDM of antipsychotics, especially when quantification of active metabolites is required. While logistical barriers remain, technological advances may enhance feasibility and support broader integration into routine psychiatric care. Full article

The Dark Web, a hidden segment of the internet, has become a hub for illicit activities, facilitated by various forms of digital identification (IDs) such as email addresses, Telegram accounts, and cryptocurrency wallets. This study conducts a comprehensive analysis of the Dark Web’s identification and communication patterns, focusing on the roles of different ID types and their associated activities. Using a dataset of Dark Web documents, we construct and analyze a bipartite network to model the relationships between IDs and web documents, employing graph–theoretical metrics such as degree centrality, closeness centrality, betweenness centrality, and k-core decomposition, while analyzing subnetworks formed by ID type. Our findings reveal that Telegram forms the backbone of the network, serving as the primary communication tool for hacking-related activities, particularly within Russian-speaking communities. In contrast, email plays a more decentralized role, facilitating finance–crypto and other activities but with a high level of fragmentation and English as the predominant language. XMR (Monero) wallets emerge as a key component in financial transactions, forming a cohesive subnetwork focused on cryptocurrency-related activities. The analysis also highlights the modular and hierarchical nature of the Dark Web, with distinct clusters for hacking, finance–crypto, and drugs–narcotics, often operating independently but with some cross-topic interactions. This study provides a foundation for understanding the Dark Web’s structure and dynamics, offering insights that can inform strategies for monitoring and mitigating its risks. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities. Full article

With the characteristic of “decentralized distribution and local power supply”, small hydropower (SHP) in China has become a core means of solving the problem of insufficient power supply in rural and remote mountainous areas, effectively promoting the improvement of local livelihoods. However, for a long time, SHP has had many problems, such as irrational development, old equipment, and poor economic efficiency, resulting in some rivers with connectivity loss and reduced biodiversity, etc. The Chishui River Watershed is an ecologically valuable river in the upper reaches of the Yangtze River. As an important habitat for rare fish in the upper reaches of the Yangtze River and the only large-scale tributary that maintains a natural flow pattern, the SHP plants’ dismantling and ecological restoration practices in the Chishui River Watershed can set a model for regional sustainable development. This paper adopts the methods of literature review, field research, and case study analysis, combined with the comparison of ecological conditions before and after the dismantling, to systematically analyze the effectiveness and challenges of SHP rectification in the Chishui River Watershed. The study found that after dismantling 88.2% of SHP plants in ecologically sensitive areas, the number of fish species upstream and downstream of the original dam site increased by about 6.67% and 70%, respectively; the natural hydrological connectivity has been restored to the downstream of the Tongzi River, the Gulin River and other rivers, but there are short-term problems such as sediment underflow, increased economic pressure, and the gap of alternative energy sources; the retained power stations have achieved the success and challenges of power generation and ecological management ecological flow control and comprehensive utilization, achieving a balance between power generation and ecological protection. Based on the above findings, the author proposes dynamic monitoring and interdisciplinary tracking research to fill the gap of systematic data support and long-term effect research in the SHP exit mechanism, and the results can provide a reference for the green transition of SHP. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Climate change mitigation for the built environment has become a subject of greatest urgency, as buildings account for nearly 40% of total energy consumption and nearly one-third of total CO₂ emissions. While environmental, social, and governance (ESG) indicators are increasingly used to monitor sustainability performance, their collective role in impacting building-related emissions is yet largely under-investigated. The current research closes that gap through an examination of the ESG dimension–CO₂ emissions intersection of 180 nations from 2000 to 2022, in the hope of illuminating how environmental, social, and governance elements interact to facilitate decarbonization. The research is guided by a multi-method design, including econometric examination, cluster modeling, and machine learning techniques, which provide causal evidence and predictive analysis, respectively. The findings reveal that the deployment of renewable energy significantly reduces emissions, while per capita energy use and PM_2.5 air pollution exacerbate this effect. The social indicators show mixed results: learning, women’s parliamentary representation, and women’s workforce representation reduce emissions, while food production and growth among the lowest-income individuals demonstrate higher emissions. Governance demonstrates mixed results as well, with good regulation reducing emissions under specific conditions yet primarily supporting high-income countries with superior infrastructure. The examination of clusters reveals that ESG-balanced performance is retained by countries in the low-emission clusters, whereas decentralized ESG pillars are associated with higher emissions. Machine learning confirms the existence of non-linear effects and identifies PM_2.5 exposure and renewable energy deployment as the strongest predictors of the relationship. In summary, the findings suggest that successful policies for decarbonizing the built environment are constructed upon the consistency of environmental, social, and governance plans, rather than single steps. Full article

The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article

Background: Glenoid radiolucenct lines (gRLL) and glenoid component subsidence (gSC) after anatomic total shoulder arthroplasty (aTSA) have traditionally been linked to implant loosening and functional decline. However, their impact on long-term clinical outcomes remains unclear. This study aimed to evaluate whether gRLL and gSC are associated with inferior clinical or functional results in patients without revision surgery. Methods: In this retrospective study, 52 aTSA cases (2008–2015) were analyzed with a minimum of five years of clinical and radiographic follow-up. Based on final imaging, patients were categorized according to the presence and extent of gRLL and gSC. Clinical outcomes included the Constant-Murley Score, DASH, VAS for pain, and range of motion (ROM). Radiographic parameters included the critical shoulder angle (CSA), acromiohumeral distance (AHD), lateral offset (LO), humeral head-stem index (HSI), and cranial humeral head decentration (DC). Group comparisons were conducted between: (1) ≤2 vs. 3 gRLL zones, (2) 0 vs. 1 zone, (3) 0 vs. 3 zones, (4) gSC vs. no gSC, and (5) DC vs. no DC. Results: Demographics and baseline characteristics were comparable across groups. Functional scores (Constant, DASH), pain (VAS), and ROM were largely similar. Patients with extensive gRLL showed reduced external rotation (p = 0.01), but the difference remained below the MCID. Similarly, gSC was associated with lower forward elevation (p = 0.04) and external rotation (p = 0.03), both below MCID thresholds. No significant differences were observed for DC. Conclusions: Neither extensive gRLL nor gSC significantly impaired long-term clinical or functional outcomes. As these radiographic changes can occur in the absence of symptoms, regular radiographic monitoring is essential, and revision decisions should be made individually in cases of progressive bone loss. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

We propose an edge-enhanced federated learning framework for real-time itinerary optimization in elderly oriented adventure tourism, addressing the critical need for adaptive scheduling that balances activity intensity with health constraints. The system integrates lightweight convolutional neural networks with a priority-based scheduling algorithm, processing participant profiles and real-time biometric data through a decentralized computation model to enable dynamic adjustments. A modified Hungarian algorithm incorporates physical exertion scores, temporal proximity weights, and health risk factors, then optimizes activity assignments while respecting physiological recovery requirements. The federated learning architecture operates across distributed edge nodes, preserving data privacy through localized model training and periodic global aggregation. Furthermore, the framework interfaces with transportation systems and medical monitoring infrastructure, automatically triggering itinerary modifications when vital sign anomalies exceed adaptive thresholds. Implemented on NVIDIA Jetson AGX Orin modules, the system achieves 300 ms end-to-end latency for real-time schedule updates, meeting stringent safety requirements for elderly participants. The proposed method demonstrates significant improvements over conventional itinerary planners through its edge computing efficiency and personalized adaptation capabilities, particularly in handling the latency-sensitive demands of intensive tourism scenarios. Experimental results show robust performance across diverse participant profiles and activity types, confirming the system’s practical viability for real-world deployment in elderly adventure tourism operations. Full article

Urban agriculture in space-constrained cities requires compact, reproducible propagation systems. Therefore, the aim of this Technical Note is to design, implement, and functionally validate a low-cost, modular hydroponic chamber (SSHG) for early-stage vegetative propagation. This system couples DHT11-based temperature/RH monitoring with rule-based actuation—irrigation 4×/day and temperature-triggered ventilation—under the control of an Arduino Uno microcontroller; LED lighting was not controlled nor analyzed. Two 15-day trials with basil (Ocimum basilicum L.) yielded rooting rates of 61.7% (37/60) and 43.3% (26/60) under a deliberate minimal-input configuration without nutrient solutions or rooting hormones. Environmental summaries and spatial survival maps revealed edge-effect patterns and RH variability that inform irrigation layout improvements. The chamber, bill of materials, and protocol are documented to support replication and iteration. Thus, the SSHG provides a transferable baseline for educators and researchers to audit, reproduce, and improve small-footprint, controlled-environment propagation. Beyond its technical feasibility, the SSHG contributes to sustainability by leveraging low-cost, readily available components, enabling decentralized seedling production in space-constrained settings, and operating under a minimal-input configuration. In line with widely reported hydroponic efficiencies (e.g., lower water use relative to soil-based propagation), this open and replicable platform aligns with SDGs 2, 11, 12, and 13. Full article

Bridge health monitoring is critical for infrastructure safety, especially with the growing deployment of IoT sensors. This work addresses the challenge of securely storing large volumes of sensor data and extracting actionable insights for timely damage detection. We propose BIONIB, a novel framework that combines an unsupervised machine learning approach called the Novelty Index (NI) with a scalable blockchain platform (EOSIO) for secure, real-time monitoring of bridges. BIONIB leverages EOSIO’s smart contracts for efficient, programmable, and secure data management across distributed sensor nodes. Experiments on real-world bridge sensor data under varying loads, climatic conditions, and health states demonstrate BIONIB’s practical effectiveness. Key findings include CPU utilization below 40% across scenarios, a twofold increase in storage efficiency, and acceptable latency degradation, which is not critical in this domain. Our comparative analysis suggests that BIONIB fills a unique niche by coupling NI-based detection with a decentralized architecture, offering real-time alerts and transparent, verifiable records across sensor nodes. Full article

This article proposes an Energy Management System (EMS) for smart microgrids with a decentralized multi-agent system (MAS) based on a bio-inspired T-Cell optimization algorithm. The proposed system allows real-time control and dynamic balancing of loads while addressing the challenges of intermittent renewable energy sources like solar and wind. The system operates within the tertiary control layer; the optimal set points are computed by the T-Cell algorithm across energy sources and storage units. The set points are implemented and validated in real-time by the OPAL-RT simulation platform. The system contains a real-time feedback loop, which continuously monitors voltage levels and system performance, allowing the system to readjust in case of anomalies or power imbalances. Contrary to classical methods like Model Predictive Control (MPC) or Particle Swarm Optimization (PSO), the T-Cell algorithm demonstrates greater robustness to uncertainty and better adaptability to dynamic operating conditions. The MAS is implemented over the JADE platform, enabling decentralized coordination, autonomous response to disturbances, and continuous system optimization to ensure stability and reduce reliance on the main grid. The results demonstrate the system’s effectiveness in maintaining the voltages within acceptable limits of regulation (±5%), reducing reliance on the main grid, and optimizing the integration of renewable sources. The real-time closed-loop solution provides a scalable and reliable microgrid energy management solution under real-world constraints. Full article

The storage and monitoring of nuclear fuel, whether spent or fresh, are key components of the nuclear energy life cycle, with significant implications for safety and sustainability. With the global focus on carbon neutrality, interest in advanced management solutions is rising. This paper provides a comprehensive analysis of modern technologies for the design, storage, and monitoring of nuclear fuel, highlighting current trends and future challenges. The study encompasses both spent and fresh nuclear fuel, with a focus on radiological safety, structural integrity, and digital monitoring. Data were organized into the following categories: storage types (wet/dry), monitored parameters, surveillance technologies (sensors, AI, IoT, and Digital Twin), simulation models, and emerging directions. A comparison between fresh and spent fuel shows a clear shift toward intelligent systems using non-invasive sensors, deep-learning algorithms, and decentralized architectures (e.g., blockchain-IoT). Despite progress, challenges remain, such as limited interoperability across system generations and insufficient experimental validation. This paper provides a solid foundation for researchers, suggesting future directions that include the full integration of AI in monitoring, broader numerical simulations for reliability, and the standardization of digital interfaces. These measures could significantly enhance the safety and efficiency of nuclear fuel storage systems. Full article