Search Results (6,412)

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Background/Objectives: Medication-related harm is a leading global patient safety challenge, yet patients’ lived experiences of medication safety remain underexplored in Central and Eastern European healthcare systems, where structural constraints significantly shape everyday medication use. Methods: This study provides an in-depth qualitative analysis of Hungarian patients’ online narratives, building on a prior netnographic mixed-methods study. Using grounded theory-informed principles and a patient-centred medication safety framework, we inductively analysed 5174 publicly accessible Hungarian-language comments posted on health forums and social media platforms between August 2020 and August 2023. The COM-B model was applied as a secondary lens to map findings onto modifiable behavioural determinants. Results: Access to services and communication emerged as the dominant medication safety concerns. Patients reported long waiting times, limited rural emergency services, and brief consultations leading to delayed or inadequate treatment. Communication gaps included insufficient information on medication duration, side effects, and follow-up, as well as conflicting advice from multiple sources, all of which eroded trust and prompted treatment discontinuation or reliance on informal online communities. Community pharmacists were largely absent from patients’ mental models of care, representing a significant missed opportunity given their accessibility. Less frequently mentioned were medication shortages, healthcare professional workload, and systemic safety culture. Conclusions: Clear, respectful communication and timely access to care are central to medication safety from the patient perspective. Netnography combined with a grounded theory-informed methodology offers a valuable approach for capturing authentic patient perspectives in structurally burdened healthcare systems, with findings relevant beyond the Hungarian context. Full article

Cruise ship fire evacuation is affected not only by fire product spread, but also by how evacuation information is delivered and how passenger flow is organized. However, existing fire evacuation studies have mainly focused on fire products or individual occupant characteristics, while the effects of evacuation organization strategies under dynamic fire conditions, especially in cruise ship environments, remain insufficiently investigated. Therefore, this study designs and compares three evacuation strategies representing different levels of information availability and organizational coordination: a static signage strategy, in which passengers mainly follow predefined evacuation signs; a system warning strategy, in which passengers adjust routes according to threshold-triggered risk information; and a centralized diversion strategy, in which passenger flow is coordinated across zones based on global risk and congestion information. The strategies are evaluated under representative cruise ship fire scenarios. The results show that static signage does not account for the dynamic influence of fire products on the evacuation environment, while system warning strategy provides relatively limited improvement in evacuation performance because of its threshold-triggered mechanism. In contrast, centralized diversion improves evacuation safety by redistributing passenger flow and reducing local congestion, achieving a 98.53% evacuation success rate and reducing the average cumulative congestion time to 4.1159 s in the galley fire scenario. Full article

Small ruminant production in Kosovo is predominantly extensive, and biosecurity practices remain poorly characterized. The emergence of Peste des Petits Ruminants (PPR) in Europe (beginning in 2024) and the first confirmed case in Kosovo (July 2025) highlight the urgent need for baseline biosecurity data to inform disease control. A cross-sectional pilot study was conducted on 63 small ruminant farms (53 meat-producing, 10 dairy-producing) across seven municipalities in Kosovo between September 2025 and February 2026. Biosecurity practices were assessed using the Biocheck.UGent™ questionnaire during direct on-farm visits. External (Ext) biosecurity scores (preventing pathogen introduction) were higher (p < 0.0001) than internal (Int) scores (limiting spread within farms). For external biosecurity, the highest scores were observed for purchase and reproduction (Ext A), intermediate scores existed for feed and water (Ext C) and visitors and farm workers (Ext D), and the lowest scores were found for transport and carcass removal (Ext B) and infrastructure (Ext E). For internal biosecurity, the highest scores were observed for lamb/kid management (Int H) and dairy management (Int I), followed by the management of adult animals (Int J); work organization (Int K) and reproduction management (Int G) formed an intermediate-low cluster, whereas disease management (Int F) scored the lowest. Benchmarking against the Biocheck.UGent™ worldwide database (predominantly intensive systems, thus not directly comparable) indicated that internal biosecurity and overall biosecurity levels were lower than the benchmark, while external biosecurity was comparable for some components. Given the convenience sample (36.4% response rate), findings are exploratory and are not directly generalizable. Larger herd size was positively correlated with external (ρ = 0.54, p < 0.0001), internal (ρ = 0.35, p = 0.005), and overall (ρ = 0.57, p < 0.0001) biosecurity scores. This first empirical biosecurity assessment of small ruminant farms in Kosovo reveals critical gaps in transport hygiene, disease management, and reproductive management pathways that enable PPR spread and perpetuate endemic zoonoses. The positive association between herd size and biosecurity may indicate structural barriers and/or knowledge gaps for small farms. Current biosecurity tools, designed for intensive systems, require adaptation for extensive production systems. These findings provide a baseline for targeted interventions, policy development, and validation of context-appropriate biosecurity instruments in Kosovo and similar extensive systems globally. Full article

Urban water insecurity is an increasingly critical challenge in rapidly urbanizing regions of the Global South, driven by population growth, environmental degradation, infrastructure limitations, and institutional constraints. Kathmandu Valley, Nepal, exemplifies these interconnected pressures. This study presents a systematic review of 45 peer-reviewed and selected grey literature sources published between 2000 and 2025, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included if they examined drinking water contamination, public health risks, household coping practices, wastewater-related exposure, or governance dynamics in Kathmandu Valley, Nepal. Findings were synthesized using a narrative thematic approach. The review identifies widespread contamination across municipal supply systems, groundwater, tanker water, traditional water sources, and household-stored water. Microbial contamination, particularly total coliforms, fecal coliforms, and Escherichia coli, emerged as the most consistently reported and immediate public health concern. Chemical and physicochemical contaminants, including ammonia, iron, arsenic, nitrate, and turbidity, were also widely reported, especially in shallow and deep groundwater systems. Seasonal dynamics further influenced exposure risks, with increased microbial contamination during monsoon periods and greater dependence on alternative and less regulated water sources during dry seasons. The findings further indicate that unsafe water exposure is associated with a substantial burden of waterborne diseases and emerging risks such as antimicrobial resistance. Although household water treatment practices reduced contamination in some cases, risks often persisted due to recontamination during storage and handling. These burdens disproportionately affected marginalized and peri-urban populations with limited access to safe and reliable water infrastructure. The review also highlights persistent governance challenges, including institutional fragmentation, weak regulatory enforcement, inadequate infrastructure investment, and growing dependence on informal water supply systems. Together, these conditions contribute to a hybrid urban water system in which formal and informal sources coexist without consistent quality control. Overall, the evidence demonstrates that water insecurity in Kathmandu Valley is a systemic condition shaped by the interaction of environmental contamination, unequal exposure, household coping limitations, and fragmented governance. By integrating environmental, public health, and governance evidence, this review advances understanding of urban water insecurity in rapidly urbanizing contexts and highlights the need for integrated, equity-oriented, and governance-informed interventions. These findings have broader relevance for cities across the Global South experiencing similar environmental and infrastructural pressures. Full article

Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems’ success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study examines the hypothesised relationships between service quality, system quality, information quality, data quality, and public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality showed the strongest statistical association with public trust, followed by system quality. Service quality was also significantly associated with system, information, and data quality. In addition, system, information, and data quality showed significant indirect statistical relationships in the association between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation. Full article

The construction sector contributes substantially to global greenhouse gas emissions, making material substitutions a key strategy for advancing sustainability transitions. Mass timber has emerged as a low-carbon alternative to mineral-based construction materials, offering biogenic carbon storage and compatibility with prefabricated and industrialized building systems. This study aims to systematically synthesize the economic, cost, and market evidence on mass timber construction by reviewing 143 peer-reviewed publications, with the objective of clarifying what is empirically known and where uncertainties remain. The reviewed literature reveals three core findings. First, economic outcomes are mixed: while several studies report regional value creation, supply-chain upgrading, and alignment with circular-economy principles, others highlight persistent constraints such as limited manufacturing capacity and uneven policy support. Second, construction cost findings vary substantially, ranging from cost parity or modest savings relative to conventional systems to premiums of approximately 10–15%, shaped by regional pricing, labor availability, transportation distance, regulatory conditions, and supply-chain maturity. Third, market-oriented studies consistently identify slow diffusion, limited practitioner experience, and risk-averse investment environments as key barriers to adoption. Overall, the review shows that economic performance is not yet consistently established and underscores the need for more standardized, context-sensitive, and methodologically consistent evaluation frameworks to support informed decision-making and the sustainable scaling of mass timber construction. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a +27.36% reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to 15.2 s, and restricting transient overshoot to just 0.18%. Full article

Environmental contamination by rare earth elements (REEs) is increasing globally due to their extensive use in modern technologies, medicine, agriculture, and aquaculture. Their release into aquatic systems via wastewater discharge, industrial emissions, surface runoff, and atmospheric deposition has raised concerns regarding their environmental fate and potential ecotoxicological effects. Despite this, information on REE accumulation in marine predators remains limited. This study provides a multi-species assessment of REE bioaccumulation in elasmobranchs. Concentrations of 14 REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, and Yb) were quantified in liver and muscle tissues of six elasmobranch species collected from demersal and deep-sea habitats along the Portuguese continental shelf. Generalized linear models (GLMs) were used to evaluate differences in REE concentrations among species and tissues, and to explore potential patterns associated with ecological traits. Results indicated that REE concentrations varied significantly across tissues and species, with muscle generally exhibiting higher accumulation than liver. Overall, this study provides the first comprehensive baseline of REE bioaccumulation in elasmobranchs from the Portuguese coast, contributing to a better understanding of emerging contaminants in marine food webs. These findings have important implications for environmental biomonitoring and highlight potential risks associated with seafood consumption. Full article

The global transition toward green energy has increased demand for metals and intensified the need for sustainable supply sources. Tellurium (Te), an essential metal for photovoltaics technology, is produced primarily as a by-product of copper refinery slimes treatment. This study conducts a life-cycle assessment (LCA) study of Te production to investigate the effect of environmental impact allocation choices on LCA results in multi-product metal systems. A cradle-to-gate LCA model of the Te product system was developed in SimaPro v9.5.0.1 software by combining industrial data, Ecoinvent v3.7.1 datasets, and literature information. Environmental impacts were quantified using the ReCiPe v1.04 Midpoint method for a functional unit of 1 kg of refined Te. The product system’s multi-functionality was investigated using mass and economic allocation and a system sub-division method. Sensitivity analyses examined the effects of the Te concentration in anode slimes and their recovery efficiency on impact estimates. The results show that mass allocation assigns higher burdens to Te than economic allocation does. System sub-division yields significantly lower impacts than allocation procedures by attributing burdens only to Te-specific recovery processes. Higher Te grades and improved recovery efficiencies markedly reduced impact estimates. These findings demonstrate the importance of allocation choices on the LCA of by-product metals. Full article

The development of rapid and reproducible image analysis approaches that support genus-level pre-classification of macrofungi is important for taxonomic pre-evaluation and controlled microscopic data analysis. In this study, an advanced deep learning-based approach, namely the Attention-Enhanced Hybrid CNN–ViT Framework, was rigorously evaluated for genus-level classification, using basidiospore micrographs of five carefully selected macrofungal genera. The proposed approach integrates the ability of convolutional neural networks to identify local texture and contour patterns with the global context-modelling capability of Vision Transformer structures. The objective is to enhance the extraction of distinctive representations from microscopic spore images through feature fusion and attention mechanisms. A series of experiments was conducted on a curated dataset consisting of light microscopy images of the genera Agaricus, Hebeloma, Inocybe, Amanita, and Russula. The models were compared using a range of evaluation metrics, including accuracy, F1-score, MCC, ROC-AUC, and PR-AUC. The results showed that the InceptionV3 + ViT-B16 + Fusion configuration was the most successful hybrid model, achieving an accuracy of 0.9213 ± 0.0182, an F1-score of 0.9212 ± 0.0179, a Matthews correlation coefficient (MCC) of 0.9040 ± 0.0222, a receiver operating characteristic (ROC)-area under the curve (AUC) of 0.9896 ± 0.0069, and a precision-recall (PR)-AUC of 0.9684 ± 0.0192, respectively. The present findings demonstrate that basidiospore images can carry distinctive visual information for genus-level automated classification under controlled conditions. However, it is important to note that these results should not be interpreted as claims of species-level identification or field generalisability. This is due to the use of a single microscope-camera system, a single preparation protocol, and the absence of an independent external test set. The present study demonstrates that deep learning-based microscopic image analysis can be evaluated as a preliminary classification tool in macrofungal taxonomy. It also shows that such tools can provide a foundation for future work supported by specimen-level validation, external test sets, and different imaging protocols. Full article

In the context of global population growth and intensifying climate change, ensuring food security remains a critical challenge. Orchards are more productive than arable crops, contributing significantly to the nutrition of a growing population. Ecologically, due to the absence of frequent soil tillage, orchards resemble natural forest ecosystems more closely than other agricultural systems. Irrigated orchards are particularly productive and enhance biodiversity in territories where water scarcity is the limiting factor for ecosystems. This review, the result of extensive reflection and a comprehensive analysis of the literature on orchard sustainability, synthesizes evidence on the diverse ecosystem services provided by these perennial systems. Due to their structural complexity, well-managed orchards contribute significantly to climate regulation through carbon sequestration, microclimate cooling, and soil erosion prevention. Furthermore, they support nutrient cycling and provide cultural value. This paper establishes an integrated scientific framework to inform evidence-based policies and reshape societal perceptions. It argues that recognizing orchards as multifunctional landscapes, rather than mere resource consumers, is critical for environmental resilience, supporting their fair valuation as essential components of a sustainable bioeconomy. Full article

Global shifts in energy policy have contributed to an increase in electricity generation from renewable sources, which introduces unique issues with volatility and grid reliability. Robust grid-scale energy storage methods must fill the gap between generation and consumption. Flywheel energy storage (FES) is a mechanical technology that utilizes the stored kinetic energy of a rotating body, but is typically only suited for shorter-term frequency regulation due to significant windage losses. In this work, a novel Python 3.13-based simulation and optimization tool is presented and used to optimize geometric design parameters for efficiency, energy density, and other metrics. The simulation utilizes a 1 degree-of-freedom, multi-regime fluid friction model with a time-marching algorithm. The optimization functionality utilizes pyswarms, a particle swarm optimization package, with adjustable search parameters and cost functions to evaluate simulation results. Optimization parameters include geometric parameters of rotor radius, shaft radius, airgap width, and airgap height; material properties of mass and moment of inertia; and initial angular velocity. An optimal initial angular velocity is found for a particular geometry, lasting 30 times longer until self-discharge versus the worst values. This work can inform the design of flywheel systems to minimize windage losses and promote the technology’s utility for longer-term energy storage. Full article

Amid the global transition toward sustainable energy systems, the hybrid energy storage system (HESS) plays a vital role due to its combined advantages of high energy density and high power density. However, distributed HESSs in islanded microgrids still lack effective management strategies for handling complex and abnormal operating conditions, which may compromise system stability. Therefore, this paper proposes a coordinated control and management strategy for distributed HESSs based on grid-forming (GFM) converters. First, a dynamic following decoupling algorithm based on actual power anchoring is proposed to eliminate the reverse active power regulation phenomenon during the initial transient period while enabling the frequency restoration process and the power transfer process to be completed independently. Second, to address communication interruptions in the multi-agent system, a communication weight update mechanism and a local degraded control strategy are designed to ensure that the system can still operate stably when communication is disconnected. Furthermore, through an information relay mechanism, a faulty converter is redefined as an information relay node to maintain the global communication topology of the multi-agent system under converter fault conditions. Finally, hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed control strategy, demonstrating its ability to enhance microgrid resilience and sustainability. Full article