Search Results (4,896)

This study employs a constructivist grounded theory approach based on 69 in-depth interviews conducted between March 2022 and December 2023 to examine socio-cognitive dynamics in Korea’s bottled water and household water purifier markets. The study addresses a gap in prior research by explaining how product meanings and stakeholder strategies co-evolve across adjacent “safe-water” markets under regulatory and sustainability pressures. Drawing on qualitative data from 69 stakeholders, including producers (n = 30), consumers (n = 19), and institutional experts (n = 20), we analyze how distrust, risk perception, and health consciousness reshape conceptual systems and market strategies. These shifts drive innovation across markets, including new technologies, service models, and branding strategies. The findings show that socio-cognitive stabilization arises through iterative interactions among institutional shocks, producer reinterpretation, and consumer adaptation. In the bottled water market, the meanings of “natural purity” became materially embedded in packaging, mineral labeling, and brand narratives. In the purifier sector, “technological reliability” was institutionalized through service-based maintenance systems and visible quality control technologies. These processes developed within asymmetric communicative environments shaped by corporate branding capacity and media amplification. This study refines socio-cognitive market theory by specifying boundary conditions under institutional distrust in developed economies. Although Republic of Korea possesses advanced drinking water infrastructure comparable to that of other developed economies, public confidence in tap water has periodically weakened following highly salient contamination incidents and regulatory transitions. This paradox provides a theoretically informative context for examining how product meanings and stakeholder behaviors mutually adapt over time. Although environmental impact metrics were not directly measured, the findings suggest that sustainability policies must address socio-cognitive trust dynamics alongside regulatory instruments such as plastic levies, certification schemes, and transparent risk communication. Full article

The discharge of untreated paper mill effluent poses significant ecological risks due to its high organic and nutrient loads. This study aimed to assess the phytoremediation potential of Azolla pinnata for treating paper mill effluent. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling approaches were applied and optimization was used for pollutant removal and plant biomass production. Experiments were designed using a Central Composite Design with two independent variables: effluent concentration (0, 50, and 100%) and plant density (10, 20, and 30 g per container). The responses measured were biochemical oxygen demand (BOD), chemical oxygen demand (COD) removal efficiencies, and final biomass yield after 16 days of exposure. RSM produced statistically significant (p < 0.05) second-order regression models for all three responses (coefficient of determination; R² > 0.98), while ANN showed slightly lower prediction errors within the experimental range studied. Maximum observed removal efficiencies were 91.74% for BOD, 80.91% for COD, and 92.66 g biomass yield under 50% effluent concentration and 30 g plant density. Optimization via both models suggested closely comparable operating conditions (79% effluent concentration and 29 g biomass) for optimal performance. The results indicate that A. pinnata demonstrates potential as a low-cost, nature-based treatment system for industrial effluent remediation under controlled conditions. The integration of data-driven optimization with biological treatment contributes to sustainable effluent management strategies by reducing chemical inputs, minimizing energy demand, and enabling biomass generation with potential downstream valorization. Full article

To satisfy the requirements of voltage support and fault-current limitation for electric-vehicle (EV) charging stations connected to weak distribution networks, an increasing number of charging infrastructures are adopting grid-forming (GFM) converters and vehicle-to-grid (V2G) control strategies. Under AC short-circuit faults and voltage-sag conditions, limiting the fault current injected by the converter is essential to reduce overcurrent risk to power semiconductor devices. For this, an adaptive virtual-impedance-based low-voltage ride-through (LVRT) method is proposed for GFM converters in EV charging stations. First, an overcurrent grading criterion is constructed based on real-time current measurements. In the moderate-overcurrent region, an adaptive virtual reactance is introduced to achieve soft current limiting. In the severe-overcurrent region, hard current limiting is implemented by further increasing the virtual reactance and blocking overcurrent bridge arm. In addition, a virtual-reactance recovery mechanism is designed to ensure smooth post-fault restoration. Based on an RCP + HIL platform, experiments are conducted to validate the proposed fault current-limiting method. Experiment results demonstrate that the proposed method can rapidly suppress fault current, maintain voltage-support capability, and shorten post-fault restoration time. Full article

Patients receiving dialysis treatments suffer from a high rate of systemic comorbid conditions, including cardiovascular disease, mineral and bone disorders, chronic inflammation, amyloidosis, and recurring infections, leading to increased morbidity and mortality rates despite the progress made in the field of renal replacement therapies. The aforementioned conditions result from the continued dysregulation and overproduction of molecular biomarkers, which cannot be adequately monitored by traditional, intermittent laboratory tests. This review critically assesses the newly developed biosensor technologies for the detection of major dialysis biomarkers, including potassium, phosphorus, parathyroid hormone (PTH), β₂-microglobulin, creatinine, and cystatin C, with special emphasis on biosensors based on electrochemistry, optics, impedimetry, nanophotonics, and biological engineering techniques. These recent biosensors have been evaluated based on their analytical performance, the biofluids used in the studies, and their suitability for measuring relevant concentrations of these biomarkers. Special attention is given to biosensors capable of continuous operation or minimally invasive sampling, as well as to newly developed biofluid sampling techniques, including microneedle-, microtube-, and micropillar-based systems, for the long-term monitoring of the biomarkers in the serum of patients receiving dialysis treatments. The biosensing techniques for measuring infection biomarkers have also been discussed, given the high risk of bloodstream and access infections among patients receiving dialysis. The limitations of these biosensors include biofouling, calibration drift, and their integration into the dialysis treatment workflow. Finally, the future prospects of the recent biosensors offer the possibility of the proactive management of the high rate of comorbid conditions in this high-risk population of patients receiving dialysis treatments. Full article

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel–road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3–18 ms for representative 20–50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

Global forced displacement has reached unprecedented levels, with more than 123 million people uprooted by the end of 2024. Although older adults represent a growing proportion of refugee populations, their mental health needs remain overlooked. This scoping review synthesized current evidence on depression among older adult refugees aged 50 years and older. Guided by the Joanna Briggs Institute methodology and reported using PRISMA-ScR standards, searches were conducted in CINAHL, PsycINFO, AgeLine, and Medline for English-language publications from 2015 to 2025. A total of 1971 records were identified, with nine studies (N = 1370 participants) meeting eligibility criteria. Most studies employed cross-sectional designs and were conducted in high-income countries. Depression prevalence was consistently elevated, with rates ranging from 22% to over 70%, depending on population and measurement tools. Risk factors included female sex, widowhood, low socioeconomic status, chronic illness, functional impairment, trauma exposure, language barriers, social isolation, and limited access to care. Protective influences such as family support, higher socioeconomic status, and improved living conditions were identified but inconsistently reported. Findings indicate that older refugees are at high risk of depression, often shaped by intersecting aging- and displacement-related vulnerabilities. Findings highlight the need for culturally specific tools and longitudinal research to inform culturally safe care for older refugees. Full article

Hypoxic pulmonary hypertension (HPH), classified as Group 3 pulmonary hypertension in the current clinical classification system, represents a complex and progressive cardiopulmonary disorder characterized by elevated pulmonary arterial pressure due to chronic alveolar hypoxia. This condition significantly contributes to morbidity and mortality in patients with chronic lung diseases and individuals residing at high altitudes. The pathogenesis of HPH involves a multifactorial interplay between sustained hypoxic pulmonary vasoconstriction, pulmonary vascular remodeling, endothelial dysfunction, and inflammatory responses. This review provides a comprehensive synthesis of recent advances in HPH pathophysiology and their clinical translation, with a focus on integrating molecular mechanisms with emerging therapeutic strategies. The pathogenesis of HPH involves a complex interplay of hypoxia-inducible factor (HIF) signaling, mechanosensitive ion channel dysregulation (particularly TRPC channels), metabolic reprogramming featuring glycolytic shift and mitochondrial dysfunction, immune–inflammatory mechanisms including macrophage-centered immunopathology, and dysregulation of the nitroxidergic system. Recent clinical advances include refined risk stratification using advanced echocardiographic techniques, identification of novel biomarkers such as lactylation-associated proteins, and development of targeted therapies including immunomodulatory approaches, metabolic modulators, and epigenetic interventions. Ongoing clinical trials are investigating innovative strategies ranging from iron supplementation to nanoparticle-based drug delivery systems. Despite these advances, significant translational challenges remain, including limitations of preclinical models, patient heterogeneity, and the need for HPH-specific outcome measures. This review bridges the gap between mechanistic insights and clinical applications, offering an integrated framework that highlights precision medicine approaches, emerging therapeutic targets, and priority research directions for improving outcomes in this challenging condition. Full article

The purpose of this study is to develop and validate a System Dynamics (SD) model that illustrates how Artificial Intelligence (AI), including generative AI, alters project lifecycle behavior under a hybrid agile–predictive governance approach. The study method uses SD model to operationalize the PMBOK performance domains as an interconnected system of stocks, flows, and feedback loops. These constructs and their interaction represent delivery progress, stakeholder engagement, team capacity, measurement accuracy, governance alignment, and uncertainty exposure. Planning effectiveness is treated as an emergent performance indicator arising from the interaction of the planning-related feedback structures. The proposed model embeds AI levers for planning, risk, measurement, stakeholder sensing, and team support. A calibrated baseline model representing conventional project dynamics was validated in two ways. First it was validated structurally against PMBOK guidance and the SD literature. Secondly, it was validated behaviorally against stylized project trajectories. The AI-augmented variant was then simulated under identical initial conditions to assess marginal effects. Across multiple scenarios, AI integration reduced peak uncertainty exposure by up to 33%. Also, the AI-augmented system showed reduced planning effort by 15%, and improved monitoring and risk sensing by accelerating feedback and reducing delays by 25%. AI also improved measurement accuracy trajectories and accelerated cumulative delivery while lowering volatility in work completion rates. Governance coherence and development approach alignment improved, while stakeholder engagement and team capacity showed smaller changes. The results demonstrate that AI primarily acts as an enabler that strengthens high-impact feedback loops in planning, monitoring, and risk sensing within a hybrid methodology. AI also delineates boundaries where managerial judgment and cultural change remain critical for effective framework validation. Full article

To address the lag and limited sensitivity of conventional shipping freight indicators, this study develops a news-based sentiment measure for the shipping market and examines its association with BDI dynamics. Using shipping-related news headlines from 2019 to 2025, a RoBERTa classifier fine-tuned on manually annotated data is used to quantify headline sentiment, and a daily Cumulative Sentiment Index (CSI) is constructed using an event-smoothing window with exponential decay. A higher CSI indicates more positive market sentiment, while a lower CSI reflects more negative sentiment. Empirical evidence shows that CSI exhibits pronounced responses around major market events and is closely linked to BDI behavior in event windows. In addition, an EGARCH specification augmented with CSI indicates that sentiment is significantly associated with conditional volatility, suggesting that news-based sentiment contains incremental information relevant to BDI risk dynamics. Overall, the proposed CSI provides a quantitative approach to tracking shipping market sentiment using publicly available news headlines and offers a complementary perspective to transaction-based freight indices. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

An integrated control strategy is proposed for direct-drive wave energy conversion (DDWEC) systems to address displacement safety constraints and improve the robustness of sensorless position estimation. Under strong wave excitation, buoy displacement may exceed its stroke limit due to conventional amplitude control, leading to mechanical risks. To mitigate this, a displacement-constrained damping regulation law is introduced, incorporating a displacement-dependent correction factor that retains optimal damping within a safe region and increases additional damping smoothly as the displacement approaches its limit. For sensorless operation, a dual-time-scale adaptive amplitude modulation strategy is developed, based on high-frequency square-wave voltage injection. By decoupling the fast position-estimation loop from the slow injection-amplitude adjustment, the demodulated high-frequency current remains within an optimal band, ensuring a high signal-to-noise ratio (SNR) under disturbances and parameter variations. Simulation results show that displacement boundary violations are eliminated, with a 25.7% reduction in peak displacement and only a 7.65% reduction in average captured power. The injection amplitude is adaptively regulated to maintain the demodulated current within the measurement band, enhancing position-estimation stability and accuracy. A fail-safe boundary for extreme sea states (H_s ≈ 2.2 m) is also identified, ensuring robust operation under varying conditions. Full article

Aiming at the poor robustness of maintenance schemes in industrial equipment maintenance projects, which arises from uncertain factors including fault degree, maintenance time, and resource availability, this paper proposes a synergistic cost-risk optimization method that integrates Distributionally Robust Optimization (DRO) and Conditional Value-at-Risk (CVaR). First, the paper analyzes the uncertainty characteristics of such projects and constructs a distribution ambiguity set based on the Wasserstein distance to depict unknown probability distributions. Second, a two-stage DRO-CVaR optimization model is established: the first stage formulates a pre-optimization scheme to minimize maintenance costs, and the second stage introduces CVaR for extreme risk measurement, thus achieving optimal decision-making under the worst-case scenario. Finally, a nested Column-and-Constraint Generation (C&CG) algorithm is designed to solve the proposed model. A numerical example is conducted for verification, and results show that compared with traditional stochastic programming and pure DRO methods, the proposed method reduces the total cost by 10.4%, the worst-case scenario loss by 28.9%, and the CVaR value by 32.0%. It thus exhibits superior economic efficiency and risk resistance in uncertain environments. Full article

Introduction: Firefighters are exposed to toxic smoke and hazardous environmental conditions that place them at risk for pulmonary disorders. This study aimed to determine the prevalence of pulmonary symptoms and disorders among full-time firefighters in the City of Cape Town Fire and Rescue Service and to explore associations with sociodemographic, physical and behavioural risk factors. Methods: A cross-sectional, quantitative study was conducted among 278 full-time firefighters using convenience sampling. Data were collected using a researcher-generated questionnaire and spirometry measurements. Physical characteristics were assessed using bioelectrical impedance analysis. Descriptive and inferential statistics were applied (Kruskal–Wallis H, Chi-squared test, Pearson’s correlation), with p < 0.05 denoting statistical significance. Results: More than half (53.60%) of firefighters presented with at least one pulmonary symptom. Coughing (54.36%) and nasal congestion (40.94%) were the most common symptoms. Pulmonary symptoms were more frequent among firefighters aged 30–49 years. Significant differences were found in pulmonary function between age categories (p < 0.01) and obesity in firefighters (p < 0.01). Negative correlations were found between FVC and BMI (r =−0.35), BG% (r = −0.47) and years of experience (r =−0.21). Conclusions: Findings highlight the occupational burden of pulmonary health risks and the need for regular screening and preventive strategies within firefighting populations. Full article

Background: Despite improvements in life expectancy, people living with HIV (PWH) continue displaying immune activation and high rates of comorbid conditions. No comparative studies concerning activation markers exist between simplification strategies to either oral or long-acting (LA) dual ART. Methods: We prospectively collected plasma samples from PWH on successful ART, simplifying treatment from triple oral to either oral or LA dual ART based on integrase inhibitors. We measured changes in soluble CD14 (sCD14), soluble CD163 (sCD163), monocyte chemoattractant protein-1, and interleukin-6. Background measurements and markers of microbial translocation and gut integrity (I-FABP, LBP) were also collected. Results: From 2019 to 2023, 38 PWH were analyzed (mean age 52, 87% male, 21 years HIV diagnosis, CD4 730 cells/mm³, nadir CD4 317 cells/mm³, AIDS 13%). After 7.2 months, sCD14 trajectories differed according to regimen (+0.43 ng/mL, p = 0.033 for LA ART, −0.62 ng/mL, p < 0.001 for oral ART) but were not related to I-FABP or to LBP values. In case of CD4 nadir < 200 cc/mm³, AIDS, or very-low-level viremia, sCD163 values significantly increased when switching to oral but not to LA dual ART. Conclusion: We found different trends in immune activation markers and risk factors associated with PWH switching to either oral or LA ART, requiring larger studies. Full article