Search Results (25,152)

Carbon pricing has emerged as a central policy tool for addressing climate change and advancing corporate environmental responsibility, yet its impact on firm-level investment and capital allocation remains insufficiently understood, particularly in emerging economies. This study examines whether emissions trading systems (ETS) influence corporate investment behavior and the reallocation of capital, using China as a representative case within the Asia-Pacific region. Employing a panel dataset of 5000 Chinese listed firms (approximately 80,000 firm-year observations) over the 2008–2023 period and a difference-in-differences framework, we identify the causal effect of carbon pricing on corporate investment decisions. The results show that firms exposed to carbon pricing experience a statistically significant reduction in investment following policy implementation. This effect is more pronounced in high-emission industries and operates through declines in profitability and tighter financial conditions. Asset-weighted measures indicate that the adjustment is concentrated among larger firms, and industry-level analysis shows that reductions are disproportionately driven by high-emission sectors. This pattern of heterogeneous investment responses is consistent with carbon pricing influencing investment allocation across firms; however, we do not directly observe the subsequent destination of capital or whether reduced investment in exposed firms is transferred toward cleaner activities. These findings provide new micro-level evidence that carbon markets shape corporate environmental management and real economic decisions by altering firm incentives, cost structures, and expectations. The study contributes to the literature by linking environmental policy tools to firm-level investment behavior and offers practical insights for policymakers and managers navigating the low-carbon transition in emerging economies. Full article

Against the dual background of ICH (intangible cultural heritage) revitalization and urban stock space renewal, the renovation of traditional craft workshops has shifted from limited workshop repair to comprehensive space creation that balances craft protection, cultural dissemination, and sustainable operation. While existing scholarship has explored the functional composition and qualitative design strategies of ICH workshops, there remains a notable research gap in quantitative spatial performance evaluation frameworks tailored to craft production constraints, and the actual contribution of spatial design to living heritage transmission lacks objective measurement tools. This study takes craftsmanship-oriented ICH workshops as the core research object. Based on field investigations, multi-subject questionnaires, and expert consultations, 14 tertiary indicators are selected from three dimensions: production and safeguarding, experience and dissemination, and operation and development, to construct a spatial performance evaluation system for living heritage transmission. The Analytic Hierarchy Process (AHP) is adopted to determine the weight of each indicator. Taking the Yuezhou Fan ICH workshop as an empirical case, this study conducts a quantitative comparison of spatial performance before and after renovation. The results show that the comprehensive performance score of the workshop after renovation has increased by approximately 109.7% compared with that before renovation, among which the production and safeguarding dimension have the most significant improvement, verifying the rationality and practicability of the evaluation system. Theoretically, this study extends the application scope of built environment performance evaluation to the field of craft heritage spaces and establishes a closed-loop logic of “quantitative diagnosis—deficiency identification—targeted optimization” for workshop renovation. Based on the evaluation results, this paper proposes a progressive optimization path of “consolidating production baseline—upgrading experience scenarios—empowering diversified operation”, which provides a generalizable quantitative framework and practical reference for the spatial renovation and performance evaluation of similar craftsmanship-oriented ICH workshops. Full article

Floor plan design normally depends on architectural training or CAD software, a barrier for homeowners, students, and small practices alike. The author asks a narrower question: can a general-purpose, pre-trained text-to-image model draw a usable floor plan straight from a written brief, and can a building code be folded into that process? To find out, four models, Gemini, DALL-E, DeepAI, and Stable Diffusion, were put through tests using ten descriptions of villas and apartment buildings. Relevant Saudi Building Code (SBC) clauses, covering minimum room sizes, corridor widths, accessibility, and fire-safety provisions, were retrieved and written into each prompt before generation, and the outputs were assessed quantitatively by accuracy and latency, with SBC compliance and realism recorded only as qualitative observations. Gemini was the fastest by a wide margin, averaging 8.3 s per plan against 40.5 for Stable Diffusion, the slowest, and it also scored highest for accuracy; that ordering is not established here, however, because the models were not scored by a common judge, and each description was generated only once. DALL-E drew the most realistic images but was slower and looser on detail. One limitation cut across all four: none reported room dimensions reliably, so compliance can be verified only in part from the image. A case is presented in which Gemini printed area labels directly on the image, yet a pixel-level measurement shows the room with the smallest printed area drawn as the largest of the three, an internal inconsistency that needs no external ground truth to demonstrate and that exposes the core limitation of current text-to-image decoders. On the strength of these results, the best model, Gemini, was built into a Django web application that turns a typed description into a viewable plan. The system is offered as an early-stage drafting assistant, not a code-verified architectural design tool. The study sets out plainly what off-the-shelf models and prompt-level guidance deliver without fine-tuning, and where they still fall short of professional, code-verified design. Full article

Background/Objectives: Early feeding intolerance (FI) affects 20–50% of critically ill patients receiving enteral nutrition, yet no validated predictive tool exists and diagnosis remains retrospective. Plasma citrulline, mainly synthesized by enterocytes, is considered as the “factor V of the intestine”. The citrulline generation test (CGT) evaluates small bowel mucosal function by measuring the increase in plasma citrulline after glutamine administration. We hypothesized that patients with a lower CGT would be more prone to FI. We aimed to compare CGT results according to the presence or absence of FI and to identify factors associated with CGT. Methods: This prospective study was conducted in the medical intensive care unit (ICU) of Besancon Hospital. Plasma citrulline and glutamine concentrations were measured at admission and after an alanine–glutamine dipeptide bolus. CGT was defined as the slope between basal and 90 min peak plasma citrulline concentrations. Signs of FI were collected during follow-up. Results: Among the sixty-six included patients, nine (14%) developed FI by day three, only characterized by vomiting and gastric residual volumes above 500 mL; abdominal pain or diarrhea were not observed. CGT values did not differ between patients with and without FI. CGT correlated positively with baseline citrulline and peak glutamine concentrations. Conclusions: FI was not associated with CGT, possibly because FI was predominantly gastric rather than intestinal. Low CGT values were associated with low plasma citrulline concentrations, suggesting reduced functional enterocyte mass. The positive correlation between CGT and peak plasma glutamine suggests that CGT not only reflects enterocyte function but also glutamine bioavailability. Full article

Labour markets across Europe increasingly describe workers through skills rather than job titles, and a growing number of large language model (LLM)-based tools now claim to extract these skills automatically from unstructured text at scale. Among these, ESCOX has gained particular traction for its open-source, taxonomy-aligned design, yet like any LLM-based system it remains susceptible to hallucination, prompt sensitivity, and non-deterministic output, risks that are rarely quantified before such tools are deployed in practice. The European Skills, Competences, Qualifications, and Occupations (ESCO) classification provides the standardised reference against which this risk can be measured, but no study has yet benchmarked an ESCO-aligned LLM extractor against an independent, expert-labelled dataset at scale. This study addresses that gap. Candidate skills generated by ESCOX are compared against reference skills already assigned to job vacancies on the EURES portal by national labour-market experts, using job-by-skill matrices to quantify agreement and skill co-occurrence networks to characterise how the two sets diverge structurally. Results reveal the extent to which ESCOX’s automatic output aligns with expert judgement and where systematic divergences occur. These findings offer HR practitioners, policymakers, and labour-market researchers an evidence-based basis for deciding when ESCOX’s output can be trusted directly and when expert oversight remains necessary. Full article

Porcine reproductive and respiratory syndrome (PRRS) represents a critical infectious disease caused by the PRRS virus (PRRSV), posing a substantial threat to the global swine industry. In China, there is currently an epidemic trend characterized by the coexistence of multiple evolving genotypes. Effective prevention and control measures are contingent upon the availability of rapid, precise, and sensitive pathogen detection technologies. Addressing the need for swift differentiation of the predominant circulating strains, including the classical strains (PRRSV-C), the highly pathogenic strains (PRRSV-HP), and NADC30-like strains (PRRSV-NA), this study focuses on the NSP2 region of each lineage. It establishes a triple TaqMan-qPCR method capable of simultaneously genotyping these three lineages. The method demonstrated no cross-reactivity with other viruses, including porcine parvovirus (PPV), porcine transmissible gastroenteritis virus (TGEV), porcine pseudorabies virus (PRV), classical swine fever virus (CFSV), African swine fever virus (ASFV), porcine epidemic diarrhea virus (PEDV), porcine rotavirus (RV), and porcine circovirus (PCV2), thereby fully affirming its specificity. The sensitivity analysis demonstrated that the limit of detection (LOD) for the NSP2 gene in each lineage was 1 copy/μL based on the purified plasmids. Both inter-group and intra-group coefficients of variation (CV) were less than 4%, indicating high reproducibility. Comparative studies with commercial kits revealed that the developed TaqMan-qPCR method exhibited 100% relative sensitivity and a relative conformity rate exceeding 98%, suggesting its potential as a viable alternative to commercial kits. Furthermore, the analysis of 1049 clinical samples using the qPCR method indicated that the PRRSV-NADC30-like strains are currently the predominant circulating strain in clinical settings in Hebei Province. In conclusion, this study developed a triple TaqMan-qPCR method capable of simultaneously identifying PRRSV-C, PRRSV-HP and PRRSV-NA, enabling rapid and accurate identification of the PRRSV genotypes prevalent in pig populations. This provides a robust technical tool for the development of targeted immunization and prevention strategies. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Accurate parameterization of the photovoltaic single-diode model is usually obtained from complete current–voltage (I-V) measurements. However, full I-V curve tracing is not always available in real monitoring environments, where the most accessible variables are irradiance and module temperature. This paper proposes a hybrid methodology for reconstructing the five parameters of the single-diode model from irradiance and temperature data. The method first estimates the maximum-power point and the remaining remarkable points of the I-V curve as well as the photocurrent ($I_{ph}$) through regression models calibrated on measured data. These predicted points are sufficient to solve the SDM equation. A numerical approach is then used to identify the five SDM parameters while enforcing physical admissibility constraints. The method is validated using NREL outdoor datasets from three locations and several photovoltaic technologies. The results show that the maximum-power current is estimated with very high reliability, with $R^2$ values close to unity in almost all cases. Voltage estimation is less stable and depends more strongly on technology and temperature sensor location. The reconstructed I-V curves are physically admissible for most crystalline silicon, HIT, and CdTe modules, whereas CIGS and amorphous silicon modules exhibit lower admissibility. The proposed method should therefore be understood as an environmental-data-driven reconstruction tool when complete I-V curves are unavailable, rather than as a replacement for direct full-curve fitting techniques such as TSLLS or Reduced Form. Full article

Background: The radial forearm free flap (RFFF) remains one of the most reliable and widely adopted reconstructive options for head and neck defects because of its consistent vascular anatomy, long pedicle, and excellent tissue pliability. Nevertheless, donor-site morbidity continues to represent its principal drawback and may negatively affect postoperative recovery, upper-limb function, and patient satisfaction. Although numerous surgical modifications and donor-site reconstruction techniques have been proposed to reduce morbidity, the available evidence remains fragmented and heterogeneous. This systematic review and meta-analysis aimed to quantify the incidence of the principal donor-site complications following RFFF harvest and critically evaluate the current evidence regarding strategies to minimize donor-site morbidity. Methods: This systematic review and meta-analysis was conducted according to the PRISMA 2020 statement and prospectively registered in PROSPERO (CRD420261423543). PubMed/MEDLINE, Embase, and Scopus were systematically searched from inception to June 2026. Clinical studies reporting donor-site outcomes after RFFF harvest for head and neck reconstruction were eligible. Primary outcomes were tendon exposure and skin graft loss/failure. Random-effects meta-analyses were performed to estimate pooled incidence rates. Secondary complications, functional outcomes, and patient-reported outcome measures were synthesized qualitatively. Methodological quality and certainty of evidence were assessed using the Joanna Briggs Institute critical appraisal tools and the GRADE framework. Results: Twenty-two studies met the inclusion criteria for qualitative synthesis. Fourteen studies were included in the meta-analysis of tendon exposure and twelve in the meta-analysis of graft loss/failure. The pooled incidence of tendon exposure was 8% (95% CI, 7–11%; I² = 0%), whereas the pooled incidence of graft loss/failure was 11% (95% CI, 7–16%; I² = 67.1%). Delayed wound healing, infection, sensory disturbances, scar-related morbidity, and functional impairment were reported inconsistently across studies, precluding quantitative synthesis. Available studies generally reported limited long-term functional impairment, although substantial heterogeneity in assessment methods and follow-up precluded quantitative synthesis. Overall certainty of evidence was rated as low. Conclusions: Donor-site morbidity following RFFF harvest remains clinically relevant despite the excellent reconstructive reliability of the flap. Approximately one in twelve patients experiences tendon exposure and one in ten experiences graft loss or failure. Current evidence does not support the superiority of any specific donor-site reconstruction technique. Future high-quality prospective comparative studies adopting standardized outcome definitions and validated patient-reported measures are required to optimize donor-site management and improve reconstructive decision-making. Full article

(1) Introduction: Early identification of patients at high risk of recurrent events after hospitalization for acute decompensated heart failure (ADHF) remains challenging. The Larissa Heart Failure Risk Score (LHFRS) is a simple prognostic tool based on hypertension, coronary artery disease, and red blood cell distribution width. Urinary sodium has recently emerged as an objective marker of natriuretic response and decongestion. We prospectively evaluated whether incorporation of urinary sodium improves the prognostic performance of the LHFRS. (2) Methods: This prospective single-center observational study enrolled 130 consecutive adults hospitalized with ADHF. Clinical, laboratory, electrocardiographic, and echocardiographic data were collected at admission. Spot urinary sodium and chloride were measured at admission and 2 h after intravenous loop diuretic administration according to a standardized decongestion protocol. The primary endpoint was heart failure rehospitalization within 3 months. Secondary endpoints included all-cause mortality and the composite of death or heart failure rehospitalization. Multivariable logistic regression with bootstrap internal validation (1000 resamples) was used to identify independent predictors of outcomes. (3) Results: The study population included patients across the spectrum of heart failure phenotypes (HFrEF 58%, HFmrEF 7%, HFpEF 35%). During follow-up, 48 patients (36.9%) experienced heart failure rehospitalization and 23 (17.7%) died. The LHFRS independently predicted 3-month rehospitalization (B = 0.490, p = 0.041). Admission urinary sodium and 2-h urinary sodium provided incremental prognostic information beyond the LHFRS and remained independently associated with rehospitalization after multivariable adjustment (p = 0.008 and p = 0.001, respectively). Urinary chloride demonstrated similar prognostic associations, whereas conventional renal biomarkers, including serum creatinine, urea, estimated glomerular filtration rate, serum sodium, and NT-proBNP, did not consistently retain independent prognostic significance. The LHFRS was also significantly associated with the composite endpoint of death or rehospitalization (B = 1.173, p = 0.002), while its association with mortality alone was not statistically significant (B = −5.551, p = 0.256). (4) Conclusions: Lower admission and 2-h urinary sodium concentrations were associated with 3-month HF rehospitalization after adjustment for the LHFRS. These findings are hypothesis-generating and require confirmation in larger, externally validated multicenter cohorts. Full article

Research on AI-mediated vocabulary learning has expanded rapidly, yet the delayed effects of GPT-4-based ChatGPT model via a web interface on lexical retention remain underexplored, particularly in English as a Foreign Language (EFL) contexts such as Saudi Arabia. This study investigates whether ChatGPT is associated with differences in sustained vocabulary learning compared with traditional instruction, and the short- and delayed effects of ChatGPT on vocabulary learning and retention among Saudi EFL learners using an explanatory sequential mixed-methods design. Forty undergraduate learners were assigned to experimental and control groups and studied the target vocabulary sets during a four-week intervention, followed by a four-week retention interval. Vocabulary development was measured at three intervals with a Vocabulary Knowledge Scale, which was administered at the pre-test, post-test and delayed post-test stages. Results showed higher VKS scores at the immediate and delayed post-tests in the ChatGPT group compared with traditional instruction. Qualitative findings indicated that learners perceived ChatGPT as supporting motivation, confidence, and personalised engagement, while also identifying challenges related to interpreting complex AI feedback. These findings highlight the pedagogical promise of integrating AI tools into vocabulary instruction while also indicating the need for guided teacher mediation. Implications for EFL pedagogy and recommendations for future research on AI-supported language learning, such as comprehending the role of AI in developing language learning by investigating other language skills, engaging wider samples, and applying longer interventions and retention periods are also discussed. Full article

Hydrogenation reactors are critical equipment in the petrochemical industry, yet their material degradation is governed by coupled multi-mechanism damage. Current assessment practices largely neglect this complexity, remaining single-factor oriented and overlooking synergistic interactions and temporal evolution. This paper proposes a regionally differentiated, multi-level framework integrating 5 primary and 17 secondary indicators with a hybrid AHP-EWM weighting strategy that synthesizes expert knowledge and measured data. A multi-factor coupling correction coefficient is introduced to provide a preliminary estimate of the synergistic acceleration effect among damage mechanisms, while a GM(1,1) gray model enables dynamic trend prediction. Applied to a 25-year 2.25Cr-1Mo steel reactor, the method produces regional degradation values of 0.378, 0.607, and 0.533 for the base metal, welds, and cladding layer, respectively, with an overall baseline of 0.453 rising by 11% to 0.503 after coupling correction. Compared with exponential regression, ARIMA, and BP neural networks, GM(1,1) is selected for its balanced performance in small-sample fitting, extrapolation stability, and physical interpretability. Sensitivity analysis confirms stable degradation grading even with ±50% coupling coefficient variations. The proposed approach mitigates the underestimation inherent in conventional single-mechanism assessments and offers a quantitative tool for full-lifecycle risk management and predictive maintenance of hydrogenation reactors. Full article

To overcome the dual bottlenecks of scarce actual strong earthquake records and high computational costs of nonlinear time history analysis, this study proposes a fast prediction method for structural nonlinear response that integrates systematic seismic sample expansion and machine learning technology by studying mature methods in the industry. A total of 500 ground motion records were created through the application of the amplitude scaling approach. Subsequently, the development of the steel frame structure was carried out through the application of the Abaqus software(Abaqus 2021 Edition) for the purpose of carrying out the nonlinear time history analysis to obtain the maximum interstory drift ratio (IDR) as the target response parameter. The XGBoost model was optimized to obtain improved results through the application of various evaluation criteria. Subsequently, the Shapley Additive exPlanations (SHAP) tool was applied to “open the black box” model to obtain the coupled effect of the various parameters, including displacement-related intensity measures such as RMSD and PGD on the maximum IDR during significant structure deformations. The method developed within this research has the potential to be a powerful tool for the prediction of the seismic responses. The method can be used in many areas, including probabilistic seismic demand, fragility assessment, and rapid evaluation of earthquake damage. Full article

The effects of biostimulants on raspberry remain insufficiently understood, particularly in relation to cultivar-specific responses and the potential of remote sensing for treatment evaluation. The aim of this study was to assess the impact of foliar-applied biostimulants on yield and physiological responses of two floricane raspberry cultivars, Glen Ample and Przehyba, using UAV-based multispectral imaging under field conditions. Five treatment variants were tested, including a control and four biostimulant formulations based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant application significantly affected yield and fruit number per plant, whereas fruit weight remained unchanged. The highest yield values per plant regardless of cultivar were associated with treatments based on plant-derived amino acids (2578.78 g) and seaweed-containing (2483.36–2544.49 g) formulations, while the lowest values were recorded in the control (2335.66 g) and after the application of animal-derived amino acids (2351.80 g). Multispectral analysis revealed treatment-dependent temporal changes in vegetation indices, with clearer trends emerging after aggregation of relative percentage changes across measurement intervals. These results indicate that biostimulant effectiveness in floricane raspberry is strongly dependent on cultivar, formulation type, and temporal context. UAV-based multispectral imaging proved to be a promising non-destructive tool for tracking physiological responses to biostimulation under field conditions. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article