Search Results (9,675)

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article

The increasing reliance on digital infrastructures has positioned electronic records management systems (ERMS) as critical mechanisms for supporting organisational governance, accountability, transparency, and effective service delivery. This study presents a structured qualitative literature review examining ERMS implementation across developed and developing institutional contexts to identify key determinants, recurring implementation challenges, and contextual variations in adoption patterns. Drawing on studies published between 2012 and 2026, the review adopts a socio-technical analytical framework that categorises implementation determinants into environmental, technological, and organisational dimensions, specifically: governance and policy alignment; technological infrastructure readiness; interoperability and system integration; and human re-source capacity and organisational culture. The findings indicate that successful ERMS implementation depends on the alignment and interaction of governance frameworks, technological capabilities, and organisational readiness. The analysis further demonstrates that these determinants are highly interdependent and vary according to levels of institutional and digital maturity. In developing contexts, implementation is primarily constrained by inadequate infrastructure, financial limitations, weak policy enforcement, and shortages of skilled personnel. In contrast, digitally mature environments increasingly focus on interoperability, metadata standardisation, usability optimisation, and long-term digital preservation. This study contributes to the literature by synthesising fragmented empirical findings into an integrated socio-technical framework, thereby advancing a more structured understanding of ERMS implementation across diverse governance environments. The review also identifies major methodological limitations within the existing literature, including limited empirical validation, weak longitudinal analysis, language bias, and the predominance of single-institution case study designs. The findings provide practical implications for policymakers, information managers, and institutions seeking to strengthen electronic records management and information governance practices. Future research should prioritise longitudinal, comparative, and cross-national studies to further advance theoretical and empirical understanding of ERMS implementation. Full article

Background/Objectives: Prophylactic antimicrobial use prior to mating in clinically healthy breeding bitches based on vaginal culture results is common despite lacking evidence for a beneficial effect on fertility. Thus, this practice is questionable due to the risk of the development of antimicrobial resistance and dysbiosis. The study aimed to investigate whether vaginal bacteria and antimicrobial treatment influence the pregnancy outcome. Methods: We retrospectively analyzed vaginal swab results from healthy breeding bitches prior to mating. Samples were examined using aerobic culture, and bacterial isolates were identified by MALDI-TOF. The medical records provided data on antimicrobial treatment and pregnancy outcome. Results: Of the 961 available samples, 467 cases had complete information about antimicrobial use and pregnancy outcome. Overall pregnancy rates did not differ significantly between antimicrobial-treated (81.7%) and untreated bitches (79.8%) (p = 0.6922), nor in cases with monocultures (p = 0.4823), high-grade bacterial growth (p = 0.4291), or high-grade growth of Escherichia coli (p > 0.9999) and Streptococcus canis (p = 0.711). Conclusions: In this study population, antimicrobial use did not improve pregnancy rates in healthy bitches, even in cases of opportunistic bacteria. No correlation between vaginal bacteria, antimicrobial use, and pregnancy outcome was identified. Based on these findings, antimicrobial treatment of clinically healthy animals as part of breeding management cannot be recommended and should be disregarded in the context of responsible antimicrobial use. Full article

Background: Health literacy plays an important role in medication understanding, self-management, and engagement with healthcare services among older adults. Limited health literacy may contribute to medication-related problems and reduced utilization of pharmacist-led services in geriatric populations. Methods: A cross-sectional, questionnaire-based survey was conducted among geriatric outpatients (≥60 years) attending a tertiary-care teaching hospital in Saudi Arabia. Health literacy was assessed using a four-domain functional tool—covering prescription label comprehension, understanding of healthcare instructions, confidence in completing medical forms, and comprehension of written health information—developed in alignment with established health literacy frameworks, including the Health Literacy Survey—European Union (HLS-EU) model and Baker’s conceptual framework. Participants were classified as having higher health literacy (0–2 domains impaired) or lower health literacy (3–4 domains impaired). Sociodemographic characteristics, clinical burden, medication self-management behaviors, and awareness of clinical pharmacist services were recorded. Multivariable logistic regression was used to identify factors independently associated with lower health literacy. Results: A total of 200 participants were included. Impairment in three or more domains was observed in 55.5% of participants. Lower health literacy was independently associated with older age, lower educational attainment, lower income, female sex, multimorbidity, and polypharmacy. Participants with lower health literacy reported higher rates of missed or incorrect medication dosing and unreported adverse drug reactions and lower use of medication management aids. Awareness of clinical pharmacist services and prior exposure to pharmacist counseling were significantly lower among participants with lower health literacy. Willingness to receive pharmacist counseling was higher among participants with higher health literacy and greater awareness of pharmacist roles. Conclusions: Health-literacy impairment is common among geriatric outpatients and is associated with medication self-management behaviors and engagement with pharmacist-led services. These findings highlight the relevance of functional health literacy in geriatric medication use and support further research on literacy-sensitive pharmacist-led interventions. Full article

Heat stress is a major constraint on poultry productivity in tropical environments, where persistent high temperature and humidity intensify its negative effects on production traits. In this study, we quantified the relationship between thermal load and monthly egg production in black-boned and Thai native chickens and developed a refined temperature–humidity index intended to improve the assessment of heat stress under tropical conditions. A large dataset comprising 136,816 monthly egg production records from 11,530 birds was analyzed using regression models and seven THI equations. The results confirmed that heat stress significantly reduces monthly egg production, while conventional indices showed only moderate explanatory power. In contrast, the refined index consistently improved model performance, providing modest improvements in model fit compared with the original formulation. Notably, genotype-specific responses were identified, with Thai native chickens exhibiting greater tolerance to elevated thermal conditions. Distinct heat stress thresholds were also established, with values of 72 for black-boned and 74 for Thai native chickens. These findings highlight the environmentally sensitive nature of monthly egg production traits and demonstrate that targeted refinement of thermal indices enhances the detection of heat stress effects. This study provides a practical framework for integrating environmental indicators into management and breeding strategies aimed at improving thermal resilience in poultry systems. Full article

Intelligent cost control in engineering, procurement, and construction (EPC) projects depends on the continuous transmission, updating, warning, correction, and reuse of cost data across multiple project stages. To analyse the resilience of this process, this study constructs an EPC project cost-data transmission network using complex network theory and Monte Carlo simulation. Eighteen core nodes and 27 directed weighted edges are identified according to EPC cost-management logic and expert evaluation. Node importance is analysed using weighted degree centrality, betweenness centrality, and PageRank, while network efficiency is used to evaluate cost-data reachability and transmission-path efficiency. Node failure, edge-weight perturbation, random edge failure, random failure and targeted attack, feedback enhancement, critical-node failure–recovery, and robustness checks are then conducted. The results show that Dynamic cost, Cost deviation warning, and Historical cost database are the three most critical nodes. Their failures reduce network efficiency by 44.54%, 37.43%, and 45.27%, respectively. Random edge failure has a stronger effect on network efficiency than edge-weight perturbation; when the edge failure probability increases from 5% to 20%, the average efficiency loss rate rises from 10.54% to 37.30%. Feedback-link enhancement increases network efficiency from 0.1858 to 0.2009 and produces a larger improvement than forward-link enhancement and random seven-edge enhancement. Robustness checks under alternative network assumptions indicate the relative stability of the critical-node identification results within the proposed network structure. The findings provide a scenario-based network perspective for identifying structurally critical nodes, vulnerable transmission links, and feedback-improvement priorities in EPC cost-data transmission. They also offer a methodological basis for future project-level calibration using BIM/5D BIM records, procurement data, cost-management platform logs, and settlement audit data. Full article

Landslide susceptibility assessment is important for disaster prevention and sustainable land-use planning in mountainous regions. However, conventional discretization methods often overlook threshold effects in conditioning factors, and many machine learning models still have limited interpretability. This study develops an integrated framework that combines ChiMerge discretization, Weight of Evidence (WOE) transformation, and tree-based ensemble learning to map landslide susceptibility in Jiuzhaigou County, Sichuan Province, China. A landslide inventory of 164 points was compiled from field investigations and hazard records, and fourteen topographic, geological, and environmental conditioning factors were derived from multi-source spatial datasets. Continuous factors were discretized using ChiMerge, a supervised chi-square-based discretization method that identifies statistically meaningful thresholds according to the distributions of landslide and non-landslide samples. WOE values were then calculated to quantify the association between each factor class and landslide occurrence. Three WOE-based ensemble models, WOE-CatBoost, WOE-LightGBM, and WOE-RF, were constructed and compared. All models showed high predictive performance (AUC > 0.90), with WOE-CatBoost performing best (AUC = 0.9432). Its high and very high susceptibility zones covered 28.59% of the study area but contained 85.96% of observed landslides. High-risk areas were mainly concentrated in steep valleys, fractured lithological zones, erosion belts, and areas affected by engineering activities, such as road construction, slope cutting, tourism infrastructure development, and settlement expansion. The proposed framework improves prediction accuracy and interpretability and provides spatial support for landslide prevention and sustainable land-use management. Full article

In recent years, seismic activity in the southern Sichuan region has increased significantly. Frequent moderate-to-strong earthquakes have caused severe building damage, casualties, and substantial economic losses, making regional seismic risk increasingly prominent. Based on historical seismic catalogs, geological settings, macroseismic intensity data, and strong motion records, this study systematically analyzes regional seismicity, spatial distribution, and strong ground motion characteristics, and quantitatively investigates the distribution and variation of seismic intensity. It further explores the impacts of earthquakes on various building structures, geological disaster chains, lifeline engineering, and human safety, as well as the underlying damage mechanisms. Finally, targeting the widely existing brick masonry structures, this paper proposes cost-effective and easy-to-implement seismic reinforcement measures combined with typical failure modes and casualty causes. The results provide a scientific basis for seismic disaster prevention planning, engineering seismic practice, and risk management in southern Sichuan and comparable regions. Full article

Background/Objectives: The management of dyslipidaemia in Slovakia has undergone significant changes in recent years, particularly through the relaxation of prescription restrictions for existing medications and the introduction of new innovative molecules. Achieving target levels of LDL cholesterol (LDL-C) plays a key role in preventing the onset and progression of atherosclerosis-related cardiovascular (CV) diseases. The aim of this study was to analyse how these changes have affected the effectiveness of reaching target LDL-C levels in patients at very high CV risk. Methods: This project was conducted as a retrospective analysis of anonymised LDL-C values from 2020 to 2023 using data from a collaborating nationwide laboratory. Patients included were those diagnosed with acute coronary syndrome (ACS), stroke, and, more generally, those with high and very high CV risk. Target LDL-C values were assessed based on the 2019 ESC/EAS guidelines. Results: A total of 363,020 LDL-C test records from 115,950 patients were evaluated over the four-year study period. Among patients diagnosed with ACS, 2.2–5% achieved target LDL-C levels in the respective years of observation 2020–2023. As many as 6.5–7.4% had LDL-C levels ≥ 4.9 mmol/L. For patients with stroke, only 4–6.6% reached target LDL-C levels, while 5.6–6.7% had levels ≥ 4.9 mmol/L. In the group with very high CV risk, only 1.7–3% achieved target levels, and 7.5–8.7% had extremely high LDL-C levels ≥ 4.9 mmol/L. Despite these modest improvements, over 93.4% of patients in the highest-performing subgroup failed to reach the absolute guideline target threshold in 2023. Conclusions: While the lifting of prescription constraints and the introduction of innovative treatments correlates with a doubling of absolute target attainment and a contraction of extreme hypercholesterolemia, overall control remains critically low in Slovakia. Systematic, protocol-driven combination regimens and intensive follow-up are urgently needed. Full article

Introduction: Ado-trastuzumab emtansine (T-DM1) is a targeted agent for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, which combines the anti-tumor activity of trastuzumab with the cytotoxic effect of DM1, a microtubule inhibitor. Although T-DM1 has improved outcomes in patients with HER2-positive breast cancer, portal hypertension may occur during treatment in the absence of overt cirrhosis on liver biopsy. These clinical and pathological features are consistent with porto-sinusoidal vascular disorder (PSVD). This study aimed to summarize the reported clinical, biochemical, imaging, histological, therapeutic, and prognostic features of T-DM1-associated PSVD. Methods: PubMed and Web of Science were searched for published cases of T-DM1-associated PSVD. Given the evolving terminology of PSVD, related terms, including non-cirrhotic portal hypertension and nodular regenerative hyperplasia, were also included in the search strategy. If the patient has a recorded history of T-DM1 exposure and the liver biopsy results meet PSVD criteria, the case is included regardless of whether there is clinical, endoscopic, or imaging evidence of portal hypertension. Cases without liver biopsy or with features suggestive of overt cirrhosis were excluded. Patient-level data were extracted and descriptively summarized, including demographic characteristics, clinical manifestations, biochemical indicators, imaging examination results, liver biopsy results, treatment methods, and prognosis. Unreported data were considered missing values and were not imputed. Results: Seven eligible articles comprising eight patients were identified. All patients were female, with a mean age of 60.38 years and a median age of 62.50 years. The interval from T-DM1 initiation to PSVD diagnosis ranged from 6 to 30 months. When reported, the mean interval from treatment initiation to symptom onset was 18.3 months. Thrombocytopenia and splenomegaly were observed in 7 of 8 patients. Mild elevations in alanine aminotransferase and aspartate aminotransferase were observed in all patients. Liver biopsy showed thinned and disorganized hepatic plates accompanied by nodular regeneration of hepatocytes in six patients. Clinical improvement was observed after discontinuation or modification of T-DM1 in most cases. Conclusions: T-DM1-associated PSVD is a rare but clinically significant complication that may develop months after treatment initiation. It commonly presents with thrombocytopenia, splenomegaly, gastrointestinal bleeding, or mild liver biochemical abnormalities in the absence of overt cirrhosis. Early recognition of unexplained platelet decline, splenic enlargement, or portal hypertension-related findings during T-DM1 therapy may facilitate timely diagnosis and individualized management. Withdrawal or modification of the suspected drug may contribute to clinical improvement, although further studies are needed to clarify the mechanism and optimal management strategy. Full article

Electric vehicle (EV) transport by ship is expanding beyond industrial logistics centred on automobile production, trade, and pure car and truck carriers (PCTCs) into daily transportation for island tourism, commuting, and essential mobility. According to Korea Maritime Transportation Safety Authority (KOMSA) vessel status data as of March 2026, 104 of 146 domestic passenger ships were car-ferry passenger ships, accounting for 71.2% of the fleet and operating on 75 of 99 designated routes nationwide. Korea Shipping Association (KSA) operational records show that the EV transport rate on these routes increased from 0.76% in 2024 to 1.21% in 2025, with some routes exceeding 2.0–4.7%. Unlike enclosed multi-deck PCTC vehicle spaces, Korean coastal car-ferry passenger ships generally have single-tier open vehicle decks and bow ramp gates. Crosswinds on open decks may reduce smoke detector activation probability by 60–75%. Although Article 97 of the Standard for Ship Fire-Fighting Appliance newly requires dedicated EV fire-fighting equipment for car-ferry ships, it remains primarily equipment-prescriptive and does not yet provide open-deck-specific performance requirements for wind-resistant detection, fixed EV-zone cooling, EV-designated stowage arrangements, or passenger–operator safety management obligations. This study applies the five-step International Maritime Organization (IMO) Formal Safety Assessment (FSA) procedure to support improvements to EV fire-fighting equipment standards for coastal car-ferry passenger ships. Hazard identification (HAZID) was conducted with a 15-member advisory panel, and probability elicitation was performed through a Delphi survey with 10 core experts, showing strong consensus (Kendall’s W = 0.74, p < 0.01). Fault tree analysis (FTA) and event tree analysis (ETA) probabilities were derived from the Delphi results and the international literature. H-07, representing wind-induced smoke dilution, was identified as the dominant single-point vulnerability within the detection-failure branch. Monte Carlo-based FTA–ETA analysis (n = 10,000) estimated annual fire frequencies of 5.9 × 10⁻², 1.8 × 10⁻¹, and 2.9 × 10⁻¹ yr⁻¹ at EV loading ratios of 10%, 30%, and 50%, respectively, with 2.47 expected fatalities per fire. Risk entered the IMO ALARP band above a 30% EV loading ratio and exceeded the maximum tolerable crew risk above 50%. The combined application of risk control options (RCOs) 2, 3, and 4 reduced annual expected fatalities by 85.6%. Based on these results, six RCOs and institutional recommendations are proposed, including strengthened safety management obligations for passenger ship operators. Full article

Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability. Full article

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability. Full article

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi-arid regions, yet long-term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi-source precipitation datasets—CHIRPS, IMERG, and ERA5-Land—against long-term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single-peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year-to-year variability. CHIRPS performs best overall, with the strongest monthly efficiency scores of any product and a consistent, operationally correctable dry bias of 5–13%. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals. ERA5-Land performs worst across nearly all metrics, with monthly NSE near or below zero, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro-climatic monitoring in White Nile State, while IMERG and ERA5-Land may still be useful in more specialized or time-specific applications. Full article