Search Results (7,417)

Generative Artificial Intelligence (GenAI) is rapidly reshaping public-sector operations, yet its adoption in developing countries remains poorly understood. Existing research focuses largely on traditional AI in developed contexts, leaving unanswered questions about how GenAI interacts with institutional, organizational, and governance constraints in resource-limited settings. This study examines the organizational factors shaping GenAI adoption in Vietnamese local government using 25 semi-structured interviews analyzed through the Technology–Organization–Environment (TOE) framework. Findings reveal three central dynamics: (1) the emergence of informal, voluntary, and bottom-up experimentation with GenAI among civil servants; (2) significant institutional capacity constraints—including absent strategies, limited budgets, weak integration, and inadequate training—that prevent formal adoption; and (3) an “AI accountability vacuum” characterized by data security concerns, regulatory ambiguity, and unclear responsibility for AI-generated errors. Together, these factors create a state of governance paralysis in which GenAI is simultaneously encouraged and discouraged. The study contributes to theory by extending the TOE framework with an environment-specific construct—the AI accountability vacuum—and by reframing resistance as a rational response to structural gaps rather than technophobia. Practical implications highlight the need for capacity-building, regulatory guidance, accountable governance structures, and leadership-driven institutional support to enable safe and effective GenAI adoption in developing-country public sectors. Full article

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. The kidney is among the organs most susceptible to sepsis-induced injury, and acute kidney injury frequently develops in this context, thereby markedly increasing mortality in affected patients. With continued advances in research, a more comprehensive understanding has been achieved regarding the clinical risk factors, pathophysiological mechanisms, therapeutic responses, and renal recovery processes associated with sepsis-associated acute kidney injury (SA-AKI). These advances have strengthened the capacity for prevention, early detection, and effective management of SA-AKI. Despite this progress, substantial gaps remain in the overall understanding of SA-AKI pathogenesis, including the complex interplay among pathophysiological mechanisms and the extensive cross-regulation of multiple signaling pathways. Consequently, SA-AKI remains a major clinical challenge and imposes a substantial global healthcare burden. There is therefore an urgent need for further research to elucidate the underlying mechanisms of SA-AKI and to identify more effective therapeutic strategies. Unlike previous reviews that primarily focused on individual mechanisms or isolated therapeutic targets, the present review synthesizes the most recent evidence on SA-AKI. Particular emphasis is placed on its pathogenic processes, associated molecular mechanisms and signaling pathways, and emerging therapeutic targets. Special attention is given to the hierarchical relationships among distinct mechanisms during disease progression and their implications for clinical translation. This review aims to inform clinical practice and to identify future research directions, thereby providing valuable insights for both researchers and clinicians in this field. Full article

Accurate runoff forecasting is vital yet challenged by the increasing non-stationarity of hydrological systems, which often exceeds the capacity of traditional single models. Ensemble forecasting, as an effective approach, integrates multiple models’ information to enhance forecasting performance and assess uncertainty. However, existing methods (such as Bayesian Model Averaging and BMA) still have limitations in dealing with complex hydrological scenarios, particularly in the construction and optimization of forecast intervals. This paper proposes a novel hydrological ensemble interval forecasting method based on constrained multi-model weight optimization (Constrained Multi-Model Weight Optimization, CMWO). CMWO utilizes a set of heterogeneous deterministic models to generate members, assigns dynamic optimization weight intervals to enhance flexibility, and employs a multi-objective framework to minimize interval width and errors subject to a ≥95% coverage constraint. Taking the Huangjinxia Reservoir in the upper reaches of the Hanjiang River as a case study, the CMWO method was systematically applied and evaluated for decadal-scale runoff forecasting and comprehensively compared with widely used BMA methods and individual models. The results show that CMWO significantly outperforms in improving point forecast accuracy (measured by RMSE, KGE, etc.) and interval forecast quality (evaluated by PICP, PIAW, CRPS, etc.), especially in generating narrower, more informative prediction intervals while ensuring high reliability. The CMWO method proposed in this study provides a competitive new tool for the effective management of forecasting uncertainty in complex hydrological systems. Full article

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

This paper studies a novel movable antenna (MA)-aided Cell-Free Massive MIMO system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of distributed wireless networks. We aim to maximize the ergodic sum capacity by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To address the non-convex stochastic optimization problem, we propose a novel Constrained Stochastic Successive Convex Approximation (CSSCA) framework, enhanced with a robust slack-variable mechanism to handle non-convex antenna spacing constraints and ensure iterative feasibility. Numerical results show that the considered MA-enhanced system can significantly improve the ergodic capacity compared to fixed-antenna cell-free systems and that the proposed algorithm exhibits robust convergence behavior. Full article

Patients with chronic obstructive pulmonary disease (COPD) commonly experience impaired lung function, reduced exercise tolerance, and respiratory muscle weakness. Owing to the unique properties of the aquatic environment, water-based exercise may provide rehabilitation benefits that differ from those of traditional land-based exercise. Objective: This systematic review and meta-analysis aimed to compare the effects of water-based versus land-based exercise on lung function, exercise capacity, and respiratory muscle function in patients with COPD, thereby providing evidence to inform the optimization of pulmonary rehabilitation exercise modalities. Methods: PubMed, Web of Science, CNKI, and other databases were systematically searched to identify randomized controlled trials comparing water-based and land-based exercise interventions in adults with COPD. Primary outcomes included lung function (FEV₁% predicted and FEV₁/FVC), exercise capacity (six-minute walk distance, 6MWD), respiratory muscle strength (maximal inspiratory pressure (MIP]) and maximal expiratory pressure (MEP). Meta-analyses were performed using Stata 17.0. Results: A total of 14 RCTs were included. Meta-analysis showed that, compared with land-based exercise, water-based exercise significantly improved FEV₁% predicted (WMD = 3.33, 95% CI: 0.02–6.64) and FEV₁/FVC (WMD = 4.00, 95% CI: 1.27–6.73). Regarding exercise capacity, water-based exercise significantly increased 6MWD (WMD = 47.81 m, 95% CI: 20.19–75.44), with more pronounced improvements observed in short-term interventions (≤8 weeks). Respiratory muscle function analyses demonstrated significant improvements in MIP (WMD = 14.22 cmH₂O, 95% CI: 7.75–20.69) and MEP (WMD = 14.40 cmH₂O, 95% CI: 4.92–23.89). Conclusions: Compared with land-based exercise, water-based exercise demonstrates consistent advantages in improving exercise capacity and respiratory muscle function in patients with COPD and shows additional benefits for lung function indices. Therefore, water-based exercise may serve as a valuable adjunct to land-based training within pulmonary rehabilitation programs. Full article

Vascular diseases remain a major global health burden despite remarkable technological advances in vascular surgery and endovascular therapies. Conditions such as peripheral arterial disease, abdominal aortic aneurysm, carotid stenosis, chronic venous disease, diabetic vasculopathies, and vascular chronic ulcers are not only biological entities but are deeply shaped by social structures, cultural norms, and economic inequalities. This article introduces Vascular Sociology as an interdisciplinary field that integrates vascular surgery with medical sociology to provide a more comprehensive understanding of vascular health and disease. Drawing on classical and contemporary sociological theory, including concepts such as social determinants of health, embodiment, illness narratives, and the disease–illness–sickness triad, the article argues that vascular pathology reflects cumulative social exposures across the life course. Socially patterned behaviors, work conditions, food environments, healthcare access, gender norms, and geographic inequalities profoundly influence disease onset, progression, treatment decisions, and outcomes. The paper highlights how surgical success is contingent not only on technical excellence but also on patients’ social contexts, including health literacy, trust in institutions, caregiving resources, and the capacity to adhere to long-term follow-up and rehabilitation. By outlining conceptual foundations, epidemiological evidence, and mixed-methods research strategies, the article positions Vascular Sociology as a framework capable of bridging biomedical knowledge with lived experience. This approach expands the definition of vascular outcomes to include social reintegration, identity transformation, and equity of care, ultimately aiming to improve patient-centered practice, reduce disparities, and inform more socially responsive vascular health policies. Full article

Disinformation has become established as a strategic tool in political communication, with the capacity to erode public trust and undermine democratic quality. In an information environment increasingly mediated by artificial intelligence, it is essential to understand how the media articulates disinformation discursively. This study analyses, using a mixed design of quantitative and qualitative content analysis, 178 articles published in the five main Spanish digital newspapers (El País, El Mundo, La Vanguardia, El Español and Eldiario.es), comparing the treatment of two cases of alleged political corruption. The results show significant differences in volume, journalistic genre, tone, framing, and use of disinformation terminology, confirming that the media do not act as neutral transmitters but rather as discursive actors that use disinformation lexicon for the purposes of attack, defence, or ideological legitimisation. There is also a predominance of emotional tones and rhetorical strategies that favour polarisation. Full article

Objective: Dynamic allocation of emergency medical resources is a critical task in the prevention and control of respiratory infectious diseases (RIDs). This study aims to address the challenge of a “run on the healthcare system” by proposing an effective resource allocation strategy to curb the spread of RIDs. Methods: Considering the infection severity of RIDs, the capacity of emergency medical resources (hospitalization rate), and vaccination status, we construct an S_VIⁿR dynamic model of RIDs that considers vaccine failure. Under the constraint of emergency medical resources and with the goal of minimizing the basic reproduction number, we propose a dynamic allocation strategy for distributing emergency medical resources among different types of infected individuals. Results: Simulation results demonstrate that improving the hospitalization efficiency of emergency medical resources significantly contributes to the effective control of RIDs. The model shows that targeted dynamic allocation helps reduce disease transmission. Conclusions: Validation using real-world data confirms that the model is effective and practical. It offers theoretical guidance for dynamically allocating emergency medical resources and supports informed decision-making in response to major emerging RIDs. Full article

Background/Objectives: Colorectal cancer (CRC) is a major public health challenge in Brazil, characterized by marked regional disparities. Although national legislation mandates that treatment begin within 60 days after diagnosis, compliance remains inconsistent, particularly within the Unified Health System (SUS). This study aimed to analyze the time to treatment initiation for colon (C18) and rectal (C20) cancer in Brazil from 2013 to 2024, assessing regional inequalities, temporal trends, and factors associated with treatment delays. Methods: We conducted an ecological study using secondary data from the Ministry of Health’s PAINEL-Oncologia platform, which integrates information from SIA/SUS, SIH/SUS, and SISCAN. Records of patients diagnosed with colon and rectal cancer (ICD-10 C18–C20) were evaluated. Temporal trends were analyzed using Joinpoint regression, and factors associated with delayed treatment initiation (>60 days) were identified through multiple logistic regression models. Results: Persistent discrepancies were observed between diagnostic and treatment trends from 2013 to 2024, with the Annual Percent Change (APC) for diagnosis exceeding that for treatment, particularly among adults aged 55–69 years. The Southeast and South regions accounted for over 70% of all diagnosed cases, starkly contrasting with the less than 25% in the North and Northeast. More than 50% of patients across all clinical stages initiated treatment after the legally mandated 60-day period. Women with rectal cancer had a 28% higher risk (RR = 1.28) of being diagnosed at stage IV. Chemotherapy was the predominant initial therapeutic modality, while the need for combined chemo-radiotherapy was associated with markedly elevated risk ratios for delay (e.g., RR = 26.53 for stage IV rectal cancer). Treatment initiation delays (>60 days) were significantly associated with residence in the North/Northeast regions, female sex (for rectal cancer), advanced-stage disease, and complex therapeutic regimens. Conclusions: The study demonstrates persistent regional inequalities and highlights a substantial mismatch between diagnostic capacity and therapeutic availability in Brazil. These gaps contribute to treatment delays and reinforce the need to strengthen and expand oncological care networks to ensure equitable access and improve outcomes, particularly in underserved regions. Full article

The archaeological terraces of Petra (southern Jordan) have long been recognized for their role in agriculture and flood mitigation. Despite the dominance of fine-grained sediments behind many terrace walls, these systems exhibit high infiltration capacity and remarkable resistance to erosion. This study investigates the hydrological behavior of terrace-trapped sediments through detailed soil texture, aggregate stability, salinity, and chemical analyses across eight representative sites in and around Petra. Grain-size distributions derived from dry and wet sieving, supplemented by laser diffraction, reveal that dry sieving substantially overestimates sand content due to aggregation of fine particles into unstable peds. Wet analyses demonstrate that many terrace soils are clay- or sandy-clay-dominated yet remain highly permeable. Chemical indicators (nitrate, phosphate, potassium, pH, and salinity) further suggest that terracing enhances downward water movement and salt leaching irrespective of clay content. The nature of the terrace settings and their sediment structure (especially the coarse-grained framework) exerts a stronger control on hydrological functioning than texture alone. The results have direct implications for understanding ancient land management in Petra and for informing sustainable terracing practices in modern arid and semi-arid landscapes, as they are effective both in harvesting water and reducing sediment mobilization. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Background: Ongoing healthcare and medical education reforms in Kazakhstan have been accompanied by persistent workforce shortages and reduced inpatient capacity in pediatric care. Therefore, this study aimed to assess and forecast selected healthcare system indicators using acute mastoiditis (AM) as a sentinel condition while also describing its clinical and epidemiological characteristics. Materials and Methods: This study combined an analysis of national healthcare and demographic statistics in Kazakhstan from 1998 to 2024 with a retrospective review of pediatric AM patients treated at a tertiary referral center. Long-term trends in healthcare resources were assessed, and future needs were projected via average annual percentage change (AAPC) and time series forecasting methods. Clinical, laboratory, and radiological data were extracted from medical records. Statistical analyses were performed via SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Results: From 1998 to 2024, the number of pediatricians and ENT hospital beds declined, whereas the density of ENT physicians remained relatively stable, and the proportion of ENT surgical procedures increased. Projections to 2030 suggest continued constraints in pediatric and ENT workforce capacity and further reductions in inpatient beds despite sustained growth in surgical demand. Among 95 pediatric AM cases, complications, most commonly subperiosteal abscess and zygomatic abscess, were identified in 40% of patients. Conclusions: AM may be considered a contextual indicator of pressures within specialized pediatric ENT services rather than a direct measure of healthcare system performance. These findings highlight the need for further studies to validate these observations and better inform healthcare planning. Full article

Disposable absorbent hygiene products (AHPs) contain plastics that are challenging to recycle and not biodegradable, making a significant contribution to landfill. Decreasing the nonbiodegradable mass of products could reduce this burden. Despite this, public data on how AHP design and material selection relate to performance is limited. In this work, fifteen commercial AHPs were characterised using dimensional measurement, infrared spectroscopy, and imaging. Simulated urination, air permeability, and moisture management testing were used to assess expected leakage and user comfort. Sustainable materials currently in use were identified, and their performance compared to typical plastics, informing opportunities to replace or reduce nonbiodegradable materials. Polybutylene adipate terephthalate-based leakproof layers replaced polyolefins. Commercial alternatives to polyacrylate superabsorbent polymers (SAPs), with comparable absorption, were not seen. Although absorbency correlated with the mass of absorbants, SAPs reduced surface moisture after absorption and are known for high absorption capacity under pressure, preventing rewetting. Channels and side guards were observed to prevent side leakage and guide fluid distribution, potentially reducing the need for nonbiodegradable nonwoven and absorbant content by promoting efficient use of the full product mass. While synthetic nonwovens typically outperformed cellulosics, apertured and layered nonwovens were associated with improved moisture transport; polylactic acid rivalled typical thermoplastics as a bio-derived, compostable alternative. Although the need for biopolymer-based SAPs and foams remains, it is hoped that these findings will guide AHP design and promote research in sustainable materials. Full article

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems. Full article