Search Results (9,667)

Against the backdrop of coordinated development in the Beijing–Tianjin–Hebei region, Hebei Province serves as an ecological safety barrier for the Beijing–Tianjin–Hebei urban agglomeration. Conducting research on land use and land cover change (LUCC) and ecosystem service value (ESV) holds significant theoretical and practical value for elucidating the mechanisms underlying ESV evolution under the combined effects of rapid urbanization and major ecological engineering projects, and for applying these findings to regional land-use planning and ecological conservation and restoration efforts. This research aligns with the United Nations Decade on Ecosystem Restoration (2020–2030). Based on land-use data from 2000, 2010, and 2020, along with 11 categories of natural and socio-economic drivers, this study systematically analyses regional LUCC and calculates ESV using locally adjusted equivalence factors. It examines the spatiotemporal evolution patterns of ESV through the analysis of local spatial autocorrelation indices (LISAs), centroid, and standard deviation ellipses, and employs a GeoDetector to measure ESV drivers. Three scenarios—a natural evolution scenario (NES), economic development scenario (EDS), and ecological protection scenario (EPS)—were established. The patch-generating Land use simulation (PLUS) model was employed to simulate LUCC for 2030 (Kappa = 0.840) and calculate ESV. Results show that from 2000 to 2020, forest land and impervious surfaces in Hebei Province continued to expand, while cropland and grassland decreased. The cumulative ESV increased by 4.85 billion yuan. Slope was the primary driver of spatial variation in ESV, and the interaction between natural and socioeconomic factors demonstrated significantly stronger explanatory power. In 2030, the total ESV under all three scenarios was lower than in 2020. The EPS reached an ESV of 344.72 billion yuan, representing a relatively suitable model that balances development and conservation. Full article

Spatial differentiation of urban natural basement conditions leads to significant differences in urbanization development patterns and land evolution patterns in different regions. Taking Fuzhou, a typical coastal basin city located in the Minjiang River Estuary, as the study area, this paper analyzes the spatiotemporal evolution characteristics of land use/cover change (LUCC) and quantifies its driving mechanism from 1990 to 2020, by using the land use transition matrix (LUTM), the center-of-gravity model (CGM), the standard deviation ellipse (SDE), and the optimal parameters-based geographical detector (OPGD). The results show that (1) the land use structure has undergone drastic restructuring, the built-up land has increased significantly, the grassland has decreased significantly, and the cropland and forest land have shown phased evolution characteristics: a light increase from 1990 to 2000 and a continuous decline from 2000 to 2020. Water exhibited a fluctuating pattern: shrinking from 1990 to 2000, expanding from 2000 to 2010, and shrinking again from 2010 to 2020. (2) Constrained by the terrain of the Minjiang Estuary Basin, the gravity centers of cropland and grassland shifted northwestward, forest land moved southeastward, water shifted northeastward, and built-up land expanded northward. (3) Driving factors exhibited stagewise differences: socioeconomic factors played a dominant role from 1990 to 2000, with population density ($q$ = 0.4029) and nighttime light ($q$ = 0.3639) being significantly higher than other factors. From 2000 to 2010, the terrain constraint effect continued to intensify, with GDP ($q$ = 0.4470), nighttime light ($q$ = 0.3658) and DEM ($q$ = 0.3638) as the dominant factors. From 2010 to 2020, urban land pattern evolution was jointly driven by multiple factors. This study clarifies the land use evolution mechanism of coastal basin cities during urbanization, providing a scientific reference for the sustainable development of similar coastal basin cities. Full article

Background: HIV-HBV co-infection remains a major public health challenge, particularly in sub-Saharan Africa. HBV co-infection worsens clinical outcomes among people living with HIV by accelerating liver disease and complicating treatment. Although antiretroviral therapy can effectively suppress both viruses, achieving optimal HIV viral suppression remains critical for reducing morbidity and transmission. While several factors influencing viral suppression among PLHIV are well documented, evidence on HIV viral suppression among HIV-HBV co-infected individuals is limited, especially in resource-limited settings like Ethiopia. Furthermore, data on the magnitude of viral suppression and its associated factors in this population are scarce. Therefore, this study aimed to assess the magnitude of HIV viral suppression and identify its associated factors among adult HIV-HBV co-infected patients in Northwest Ethiopia. Objective: This study aimed to assess the magnitude and factors associated with HIV viral suppression among adult people living with HIV-HBV Co-infection in Northwest Ethiopia. Methods: An institution-based cross-sectional study was conducted in Northwest Ethiopia among adults with HIV-HBV co-infection on antiretroviral therapy. A simple random sample of 402 participants was selected. Data were collected using a pretested structured interviewer-administered questionnaire and medical record review, covering sociodemographic, clinical, behavioral, treatment, follow-up, and adherence factors. HIV viral suppression was defined as a plasma viral load < 1000 copies/mL. Data were coded in EpiData 4.6 and analyzed using STATA 18. Descriptive statistics estimated suppression rates. Bivariable and multivariable logistic regression identified factors associated with suppression; variables with p < 0.25 in bivariable analysis were included in the multivariable model. Statistical significance was set at p < 0.05 with adjusted odds ratios and 95% confidence intervals reported. Model fit was assessed using the Hosmer–Lemeshow test, and multicollinearity was checked using variance inflation factors. Results: There were 423 participants in all. Among the 423 HIV-HBV co-infected adults on antiretroviral therapy included in this study, 138 (34, CI, 30–39%) achieved HIV viral suppression, while 264 (66%) had an unsuppressed viral load at the time of assessment. Viral suppression was found to be independently correlated with the ART TDF-3TC-LPV/r regimen, first-line medication adherence, bedridden functional level, missed clinic appointments, and length of therapy. While TDF-3TC-LPV/r usage (AOR 2.34; 95% CI: 1.40–3.90) and longer treatment duration (AOR 2.09; 95% CI: 1.30–3.34) were advantageous, good adherence significantly improved the likelihood of suppression (AOR 5.54; 95% CI: 3.27–9.38). Missed appointments and a bedridden state decreased the likelihood of suppression. Conclusions: HIV viral suppression was achieved in only 34% of participants. Adherence, ART regimen, treatment duration, functional status, and retention in care were significant predictors. Strengthening adherence support, patient retention, optimized ART regimens, routine viral load monitoring, and targeted care for high-risk patients could improve treatment outcomes and help Ethiopia achieve UNAIDS viral suppression targets. Full article

This paper presents a detailed case study of a semi-top-down excavation carried out for the Haowang Tower project in Shaoxing, China, where thick soft clay deposits dominate the subsurface profile. The excavation, covering approximately 10,000 m² in plan area and reaching a depth of 12.35 m, posed significant challenges due to the presence of sensitive adjacent utilities and roads. In response, an integrated design–construction strategy was adopted, combining soldier pile retaining walls with a permanent first-floor slab serving as horizontal bracing. Several innovative structural features—including load-transfer beams, stress-reinforced strips, and soil molds—were introduced to address the specific demands of the semi-top-down method in soft ground. A multi-stage numerical analysis framework was implemented, employing the Hardening-Soil (HS) model within 2D and 3D finite element analyses (PLAXIS), alongside the subgrade reaction method (FRWS2006). Predicted wall deflections, ground settlements, and structural forces were systematically compared with field measurements. The 3D analysis showed good agreement for wall deflections (within 5% of the maximum measured value), validating the approach’s effectiveness. However, the analysis over-predicted ground settlements (e.g., sewage pipe settlement was over-predicted by 60%), indicating a need for more refined settlement prediction models or parameter calibration. Based on this finding, a correction factor of 0.6–0.7 is proposed for settlement prediction when using HS parameters derived from standard drained tests. The results also highlight the importance of spatial effects and the critical role of construction sequencing. This study offers both practical insights and validated numerical tools for similar deep excavations in urban soft clay environments. Full article

Accidents in process industries continue to cause severe casualties, and a small number of events account for a large share of fatalities. This study proposes a Topic–Hierarchy Coincidence Analysis (T-H CNA) framework to identify condition combinations associated with high-fatality outcomes by integrating BERTopic, Human Factors Analysis and Classification System (HFACS), and Coincidence Analysis (CNA). The framework is applied to 121 Chinese investigation reports of serious-or-above chemical accidents from 2015 to 2025. BERTopic is used to extract 25 causal semantic themes from accident-cause texts, which are then mapped by expert classification onto eight second-level HFACS categories (Fleiss’κ = 0.7118). On this basis, CNA identifies minimally sufficient configurations (MSCs) and traces their cross-level transmission pathways. Six three-condition MSCs are obtained, with consistency values ranging from 0.750 to 0.923; the broadest pathway covers 28.3% of high-fatality cases. Deficient organizational climate and failure to correct known problems recur as the main upstream endpoints and remain stable under stricter fatality thresholds. Although operational errors appear in 88.43% of cases, they do not enter any sufficient configuration. The results indicate that high-fatality outcomes are more closely associated with coupled upstream organizational and supervisory failures than with terminal errors alone, supporting upstream-oriented process safety governance. Full article

Cronartium ribicola is a globally distributed fungal pathogen that infects Pinus armandii Franch., causing widespread tree mortality. In this study, we report a chromosomal-level genome assembly of C. ribicola LQ (262.51 Mb, N50 = 15.4 Mb, GC content = 38.4%) using integrated Illumina short-read, PacBio long-read, and Hi-C sequencing technologies. Ultimately, a total of 94.83% of the assembled sequences were anchored onto 17 pseudo-chromosomes. Genome annotation predicted 10,222 protein-coding genes, and repetitive sequences accounted for 91.67% of the genome. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis demonstrated 94.03% genome completeness, with functional annotations covering 88.32% of the genes. A total of 352 carbohydrate-active enzyme (CAZyme) genes, predominantly glycoside hydrolases (45.17%), and 8 secondary metabolite biosynthetic gene clusters were identified, indicating strong host tissue degradation capability and diverse virulence factors. Overall, this study provides valuable genomic resources for dissecting the pathogenicity, host interaction mechanisms, and resistance gene discovery of C. ribicola and establishes a foundation for developing virulence-targeted disease control strategies. 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

Background/Objectives: This study aims to establish a systematic phytochemical characterization and quality evaluation method to systematically evaluate the influence of multiple factors on the chemical composition of Taxillus chinensis, thereby providing a scientific basis for its development, utilization, and quality control standards. Methods: To ensure a targeted and representative metabolic screening, six representative batches covering the major geographical origins and host plants were selected for initial metabolomic profiling. An integrated analytical approach combining UPLC-MS/MS-based widely targeted metabolomics, HPLC fingerprinting, and multi-component quantitative analysis with multivariate statistical analysis was employed. Results: Significant quality variations were identified across the samples. Metabolomics results indicated that while chemical component types were qualitatively consistent across growth conditions, their contents varied significantly. Unique differential metabolites clustered according to specific geographical origins or host plants. KEGG pathway analysis revealed that geographical origin primarily regulated phenylpropanoid biosynthesis, whereas host differences mainly influenced flavonoid and monoterpenoid biosynthesis. Furthermore, HPLC fingerprinting of 20 batches demonstrated similarities greater than 0.9, with 15 common peaks determined. Based on their high relative abundance, differential significance across samples, and documented pharmacological relevance to the herb’s traditional efficacy, six bioactive components—gallic acid, catechin, epicatechin, hyperoside, isoquercitrin, and quercitrin—were identified and quantified. Notably, samples originating from Wuzhou exhibited the highest total content of these components. Consistent with PCA and HCA results, gallic acid, hyperoside, isoquercitrin, and quercitrin were identified as potential markers driving quality differences. Conclusions: This integrated approach allows for a systematic analytical screening of Taxillus chinensis, clarifying chemical variations caused by environmental and biological factors, and supporting the standardization and comprehensive utilization of this medicinal plant. Full article

Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals. 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

Severity factors are widely used to compress torrefaction temperature–time history into a single scalar descriptor. However, whether such scalar representations are structurally sufficient to describe realised conversion across heterogeneous biomass samples remains unclear. In this study, we evaluated the adequacy of single-scalar severity descriptors using a literature-derived dry torrefaction dataset comprising 154 observations from 7 published studies, covering multiple biomass categories and operating conditions. A severity factor, SF(α), was formulated, and its scaling parameter α was optimised through a systematic α-sweep to maximise its relationship with the experimentally determined extent of conversion (EOC). Based on the optimised formulation, EOC was decomposed into severity-implied conversion (SIC) and conversion offset (CO), separating the dominant severity-controlled trajectory from sample-specific deviations. The optimised formulation (α* = 65.1) showed a strong global correlation with EOC (R2 = 0.8593), confirming that severity captures the main average conversion trend. However, nested model comparisons showed that including CO consistently improved explanatory power for both absolute fuel properties and enhancement ratios, with the greatest gains in enhancement space. SIC and CO accounted for 85.9% and 14.1% of the total variance, respectively, indicating that a non-negligible component of conversion variability was not captured by the single severity descriptor. These results show that, although a single severity scalar is useful for describing dataset-level trends, it does not fully resolve sample-level torrefaction behaviour within the analysed dataset. The SIC–CO framework is therefore proposed not as a new severity index or a pre-measurement predictive model, but as a post hoc diagnostic framework for identifying the explanatory limits of scalar severity representations in biomass torrefaction analysis. Full article

This article examines how artificial intelligence (AI), conceptualized as a general-purpose technology (GPT), shapes economic growth and structural transformation through a structured literature review covering the period from 2015 to 2025. The study adopts a structured, mechanism-oriented synthesis approach grounded in transparent search, screening, and thematic classification procedures rather than formal meta-analytic protocols. It develops an integrative innovation ecosystem framework that links three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics. The findings indicate that AI adoption does not generate uniform or automatic growth effects. Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts. In most cases, outcomes depend less on the technology itself and more on complementary conditions—human capital formation, digital and data infrastructure, institutional coordination, and governance capacity—that enable effective diffusion. Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes. Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development. The study contributes by distinguishing foundational theoretical perspectives from the contemporary 2015–2025 evidence base, clarifying the relationship between task transformation and structural transformation, and emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes. Full article

The stroboscopic effect and phantom array effect caused by temporal light modulation (TLM) in light-emitting diode (LED) lighting are important temporal light artifacts (TLAs) that can influence visual perception, task performance, and visual comfort. This review systematically analyzes 40 studies published between 1998 and 2024 to provide a comprehensive overview of the current understanding of both effects. The reviewed literature covers visibility thresholds, influencing parameters, experimental methodologies, and assessment metrics. The analysis shows that reported visibility thresholds for the stroboscopic effect typically range from 550 to 1000 Hz, whereas thresholds for the phantom array effect may extend to 10–15 kHz, suggesting substantial differences in the underlying perceptual mechanisms. In addition to modulation frequency, modulation depth, waveform, duty cycle, luminance, retinal image motion, and observer factors have been identified as important determinants of visibility. The review further highlights significant methodological differences among studies, including variations in experimental design, stimulus generation, participant characteristics, and psychophysical procedures. Although the stroboscopic visibility measure (SVM) provides a standardized framework for evaluating the stroboscopic effect, no comparably validated metric is currently available for the phantom array effect. The review identifies major knowledge gaps regarding the interaction of influencing parameters and the lack of standardized assessment methods. Future research should focus on establishing unified experimental protocols and developing robust metrics for the phantom array effect to support comprehensive lighting standards that protect visual comfort, well-being, and consumer health. Full article

The analysis of specific habitat conditions involves a systematic assessment of environmental variables such as temperature, hydrology, and vegetation, to clarify species’ ecological requirements and develop conservation strategies. Common approaches include statistical modelling, various Habitat Suitability Index (HSI) models, and GIS-based spatial analyses, which quantify factors like topography, land cover and anthropogenic pressures. Today, machine learning (ML) methods are widely applied across engineering disciplines, including water resources management. In this study, ML methods, particularly model trees, are employed to model and predict key abiotic factors relevant to fish communities. The research focuses on the bioindicator species Barbus balcanicus (brook barbel), which inhabits the middle part of the Sutla River (transboundary river basin between Croatia and Slovenia) and serves as an indicator of ecological conditions in this system. Using ML, models for water depth, water velocity, and water temperature were developed and applied together with SWAT (Soil and Water Assessment Tool) data to determine the HSI for future scenarios to support habitat assessment and water management planning. Full article

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article