Search Results (1,458)

With non-rainfall water (NRW), principally dew and fog, serving as an important water source, especially in arid and semiarid regions, factors that may increase the NRW yield may have important hydrological and ecological consequences. On the other hand, dew and fog may also have hazardous effect on inorganic and human-made materials that may undergo corrosion and/or degradation. It has long been noted that dew and fog are affected by neighboring objects, the effect of which was, however, only barely explored. Hypothesizing that it may principally be linked to the sky view factor (SVF) (determining, in turn, substrate temperature and heat flow) and, therefore, to the angle that is formed between the collecting substrate and the height of the neighboring objects, a set of square boxes (30 × 30 or 60 × 60 cm) was constructed. The boxes had variable heights, forming angles of 15°, 30°, 45°, 60°, and 75° between 6 × 6 × 0.1 cm cloth attached to a substratum (10 × 10 × 0.2 cm glass plate overlying 10 × 10 × 0.5 cm plywood) at the center of each box and the top walls of the box. NRW that accumulated at the cloths was compared with cloths placed in the open, serving as control. Another set served to measure the plate temperatures. A clear decrease in NRW, with an angle corresponding to a third-degree polynomial equation, was found (r² = 0.998). Taking 0.1 mm as the threshold for vapor condensation (dew), and taking the average maximal NRW as measured for two years in the Negev (0.20 mm), angles of ≥45° will suffice to impair condensation. However, with the projected decrease in NRW with global warming, even angles of ≥30° may impair condensation in 1–2 decades. While it may decrease the dew amounts and subsequently negatively affect the vegetation in forest clearings and wadis or canyons, it may decrease the exposure of construction materials to corrosion and/or degradation, thus exerting a positive effect on construction materials in urban settings. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Construction projects frequently face risks such as anomalies, delays, and bottlenecks, which can substantially affect timelines and budgets. This study proposes a machine learning (ML)-based framework for early identification of risks in construction projects, enabling pattern understanding and decision-making through clustering, outlier and bottleneck detection, and relevant variables identification. It uses a business process management (BPM) dataset of construction documents and applies clustering techniques to both numerical and mixed datasets to group documents with similar characteristics, enabling the detection of temporal deviations and the patterns behind them. Additionally, an ensemble anomaly detection model based on different algorithms is implemented to identify outliers through key variables, which may indicate hidden risks and planning errors. Explainable artificial intelligence (XAI) techniques are then used to analyse the importance of the variables, supporting the identification and analysis of bottlenecks that may compromise project success. The results reveal an F1 score of 0.73 in bottleneck detection using three understandable decision rules, a 6% rate of anomalies within the dataset, and three distinct project clusters. This approach enables accurate and timely detection of risks while providing valuable insights for decision-making, improving risk management, and optimising project execution in the architecture, engineering and construction (AEC) industry. Full article

To address traditional beer detection limitations, this study proposes a rapid NIRS-based method for detecting key indicators and verifying authenticity. Designing Single-task (STL) and Multi-task learning (MTL) strategies, it employs Variable Importance in Projection for wavelength selection. Deep spectral features were extracted utilizing a Multi-Head Attention (MHA)-fused Convolutional Neural Network (CNN-MHA), Long Short-Term Memory (LSTM-MHA), and hybrid CNN-LSTM-MHA networks. To further enhance model performance, the Bayesian Optimization Algorithm globally optimized network hyperparameters in STL, alongside hyperparameters and multi-task loss weights in MTL. Partial least squares regression, support vector machine regression, and partial least squares discriminant analysis models were established using these features. Results indicate that the MTL-based CNN-LSTM-MHA network effectively learns shared features across multiple tasks, significantly improving model generalization. Specifically, the coefficients of determination ($R^2$) for alcohol content and original wort concentration in the validation set were 0.996 and 0.997, respectively, with relative root mean square errors (rRMSE) of 2.024% and 2.515%. In the independent test set, the $R^2$ were 0.995 and 0.991, with rRMSE of 2.515% and 2.087%, respectively. Furthermore, 100% classification accuracy was achieved across all datasets. This method provides an efficient technical solution for beer market regulation and real-time detection in production processes. Full article

Background/Objectives: Histologic remission has emerged as a key treatment target in inflammatory bowel disease (IBD), but routine assessment requires repeated endoscopy and biopsies. Blood-based indices reflecting inflammation, nutritional status and atherogenic risk are inexpensive and widely available, yet their integrated contribution to histologic activity remains unclear. This study addresses this gap by simultaneously analyzing a broad panel of 44 variables—including nutritional status indicators, CBC-derived inflammation indices, and atherogenic lipid indices—in IBD patients. Methods: In this retrospective study, 100 patients with IBD (50 Crohn’s disease [CD], 50 ulcerative colitis [UC]) without additional comorbidities and with concomitant histologic assessment were analyzed. Histologic activity was coded as active vs. remission. At the time of biopsy, the complete blood count, biochemistry and lipid profile were used to calculate immuno-nutritional indices (CONUT score, prognostic nutritional index), inflammatory indices (neutrophil-to-platelet ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio [LMR], systemic immune-inflammation index, systemic immune-inflammation index, systemic inflammation response index [SIRI], aggregate index of systemic inflammation, C-reactive protein to albumin ratio) and atherogenic indices (atherogenic index of plasma [AIP], triglyceride-to-HDL cholesterol ratio). Variable selection was performed separately for CD and UC using least absolute shrinkage and selection operator (LASSO) regression and sparse partial least squares discriminant analysis (sPLS-DA). Independently associated predictors were then entered into multivariable logistic regression models, and their discriminative performance was evaluated using ROC analysis with bootstrap-derived 95% confidence intervals. Results: LASSO analysis identified a broadly similar systemic profile associated with histologic activity in CD and UC, dominated by the CONUT score, SIRI, AIP, LMR and red blood cell parameters, whereas demographic features and most routine biochemical markers were shrunk towards zero. Cross-validated AUCs for the LASSO models were 0.93 in CD and 0.87 in UC. sPLS-DA confirmed this pattern: CONUT, SIRI and AIP consistently showed the highest variable importance in projection scores and loadings on the first latent component. In multivariable regression, the CONUT score, SIRI and AIP remained independent predictors of histologic activity in CD, while hematocrit, CONUT score, SIRI and AIP were independently associated with histologic activity in UC. In ROC analysis, AUCs for CONUT, SIRI and AIP were 0.81, 0.89 and 0.87 in UC, and 0.72, 0.82 and 0.83 in CD, respectively. Conclusions: Histologic activity in IBD is characterized by a coupled systemic profile in which immuno-nutritional compromise (captured by CONUT) forms the core signal, supplemented by systemic inflammation (SIRI) and atherogenic dyslipidemia (AIP). These readily available blood-based indices may help to approximate histologic disease activity in clinical practice. However, considering that comorbid diseases may affect these indices, the strict exclusion criteria applied in this study may limit the generalizability of the findings among patients with IBD. Consequently, further validation in larger prospective cohorts is warranted. Full article

Renewable energy is an important pillar of decarbonization in reducing the impact of climate change. Among the renewable energy sources, solar photovoltaic energy is one of the fastest-growing across West Africa, especially in Côte d’Ivoire. However, its dependence on weather and climate could affect future power system operations. This study aims to quantify how climate change could affect future solar PV potential in Côte d’Ivoire under the RCP8.5 scenario. For this purpose, we used three regional climate model simulations (RCMs) generated by the new high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for the Africa domain (AFR-22). Future changes were computed for two time slices: the near future (2021–2040) and the middle future (2041–2060), relative to the reference period (1986–2005). The performance of the RCMs and their ensemble mean in simulating relevant climate variables was first evaluated with respect to the ERA5 reanalysis and satellite-based (SARAH-2) data during the reference period. Our results indicate that all available RCMs and their ensemble mean reasonably simulate the annual cycle and the spatial patterns features of surface solar radiation, near-air temperature and solar PV potential in Côte d’Ivoire. We also conclude that Côte d’Ivoire is expected to experience a moderate decrease in annual mean solar PV potential during the mid-21st century. The average decrease in solar PV potential over Côte d’Ivoire could range from 0.55% to 2.16% in the near future and from 1.30% to 3.50% during the middle future, according to the considered RCMs. This decline in solar PV potential will be particularly noticeable during the period from June to October in all climatic zones. Overall, these findings provide valuable information for renewable energy planners to ensure the long-term success of solar PV energy projects in the context of climate change in Côte d’Ivoire. Full article

Background: Systemic arterial hypertension (SAH) shows a high prevalence among postmenopausal women and represents an important public health concern. Objective: To evaluate factors associated with SAH in postmenopausal women participating in a resistance training program. Methods: This observational, cross-sectional study included 55 postmenopausal women (66.0 ± 4.9 years) recruited from the “More Active Women” research project, an umbrella experimental and longitudinal study involving resistance training interventions. Cross-sectional data were collected during the baseline assessment (April–May 2025). Sociodemographic variables, nutritional status (body mass index and waist circumference), and behavioral and health-related variables obtained through structured interviews and anthropometric assessments were analyzed. Associations were tested using Pearson’s chi-square test or Fisher’s exact test, with effect size estimated by Phi or Cramer’s V when appropriate, and binary logistic regression was performed for adjusted analyses. Results: Significant associations were observed between SAH and elevated BMI (p = 0.03; φ = 0.30), waist circumference > 88 cm (p = 0.006; φ = 0.40), and lower educational level (p = 0.003; V = 0.47). In the adjusted analysis, waist circumference ≤ 88 cm was associated with a lower likelihood of SAH (OR = 5.54; 95% CI: 0.965–31.872; p = 0.007), whereas lower educational level was associated with a higher likelihood of hypertension (OR = 13.98; 95% CI: 1.505–129.833; p = 0.004). Conclusion: Excess central adiposity and lower educational level are associated with SAH in postmenopausal women, highlighting the importance of integrated health promotion strategies that address both cardiometabolic risk factors and social determinants of health during aging. Full article

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and has a substantial genetic component. Genome-wide association studies (GWASs) have identified more than 100 susceptibility loci; however, replication across populations remains variable, suggesting potential population-specific differences in the genetic determinants of AF. To date, no whole-genome sequencing (WGS)-based study has evaluated AF susceptibility in a Polish population. Methods: We performed WGS (mean coverage 35×) in 233 unrelated individuals recruited within the Thousand Polish Genomes Project, including 56 patients with non-valvular AF and 177 controls without AF. After quality control and linkage disequilibrium pruning within a cardiovascular gene panel, 19,395 variants were analyzed. Association testing was performed using logistic regression adjusted for age and sex, applying both false discovery rate and Bonferroni correction thresholds. Results: No variants reached statistical significance for association with AF after correction for multiple evaluation. Previously reported susceptibility loci were not replicated in this cohort. Age was strongly associated with AF risk, whereas sex showed no significant effect. Given the relatively modest sample size, the study was primarily powered to detect variants with moderate or large effect sizes; smaller genetic effects reported in large GWASs may remain undetected. Conclusions: This pilot WGS-based study provides an initial exploration of AF-associated genetic variation in a Polish population. The absence of significant associations likely reflects the importance of further investigation in larger and well-characterized Central–Eastern European cohorts before genetic risk stratification approaches can be broadly applied across populations. Full article

Understanding the long-term production trends of MDFLAM panels, which are widely used in panel furniture manufacturing, is important for evaluating the sector’s competitiveness and environmental performance. In this study, MDF/HDF production data for Türkiye covering the period 1995–2024 were analyzed. The observations for 1995–2019 were used for model estimation, while the period 2020–2024 was reserved for out-of-sample validation. Production projections for 2025–2030 were generated using the ARIMA time series model. The relationships between fiberboard production and selected socio-economic variables (population, GDP per capita, forest area, and number of enterprises) were evaluated through correlation analysis. While strong correlations were observed in the level data, additional analysis using first-differenced (growth rate) series indicated that these relationships are weak and statistically insignificant in the short term, suggesting that the observed associations are largely influenced by common time trends. Assuming that approximately 60% of total fiberboard production consists of MDFLAM, future GWP values were estimated using verified EPD data. The results indicate that production is expected to continue increasing in the coming years. Although negative GWP values are observed due to biogenic carbon storage during the production stage, this reflects temporary carbon sequestration rather than a permanent reduction in atmospheric emissions. Emissions are expected to increase during end-of-life stages as the stored carbon is released. Overall, the study provides a forward-looking framework by integrating time-series forecasting with EPD-based environmental indicators, offering a useful basis for sustainability assessment and policy-oriented decision-making in the wood-based panel sector. Full article

The objective of this scientific study is to examine whether the climate change attention of the chief executive officer promotes corporate social responsibility. To perform the extensive calculations required for this analysis, the study utilizes comprehensive panel data sourced from Carbon Disclosure Project, KLD, and financial databases. The scientific research methods used include two-stage instrumental variable estimation and difference-in-differences approaches to rigorously establish a causal relationship. The results identify a significant positive correlation between chief executive officer climate change attention and overall corporate social responsibility. Specifically, this executive focus significantly improves external and internal corporate social responsibility while reducing socially irresponsible performance; however, it does not enhance material corporate social responsibility. Furthermore, the findings indicate that this positive effect is significantly amplified when chief executive officers are in the early stages of their careers or receive high compensation, particularly equity-based compensation. Additionally, the implementation of a corporate low-carbon strategy serves as an important mediating channel for improving social performance. In conclusion, executive cognitive attention is a fundamental determinant of a firm’s strategic behaviors. It is recommended that corporate boards structure equity-based compensation to align with sustainability goals and actively support low-carbon strategies to maximize the positive impact of executive attention on sustainable development. Full article

External defects considerably reduce the quality, consumer acceptance, and market value of citrus fruits. Therefore, a rapid and reliable, non-destructive inspection method is necessary for postharvest processing. In this study, a non-destructive approach for external defect classification of citrus fruits is developed by combining visible–near infrared hyperspectral imaging (HSI) with effective wavelength selection (EWS) algorithms. First, 1702 spectral samples of normal and defective regions on citrus fruit surfaces were collected. A partial least squares discriminant analysis (PLS-DA) model was developed using the full wavelength range (400–1000 nm), which achieved 99.02% prediction accuracy. Four EWS algorithms—weighted regression coefficients, variable importance in projection, sequential forward selection (SFS(5, 10, 15)), and random frog—were evaluated for optimal spectral dimensionality and computational efficiency. The SFS15-PLS-DA model, which selected 15 optimal variables out of the initial 300 and used maximum normalization preprocessing, achieved the highest prediction accuracy of 99.80%. This model demonstrated near-perfect classification while reducing the total number of wavelengths by 95.0% (from 300 to 15 wavelengths). Further, a pixel-wise image classification algorithm was implemented using the optimal model, which effectively detected physical damage, pest infestation, and fungal decay. These results demonstrate that combining HSI with EWS enables compact, interpretable, and high-performance models suitable for real-time postharvest sorting. This approach has strong potential to enhance automation, speed, and reliability in commercial citrus quality assessment. Full article

Variations in terrestrial carbon flux influence atmospheric CO₂ exchange and related climate feedback, with Net ecosystem productivity (NEP) serving as a key metric for assessing ecosystem carbon source–sink dynamics. Given the vital ecological barrier function of the Tibetan Plateau (TP), understanding the spatiotemporal variability of NEP and its climatic controls is essential for elucidating carbon sink and climate interactions under ongoing climate change. The spatiotemporal dynamics of NEP across the TP from 1979 to 2018 are investigated using the process-based Community Land Model version 5.0 (CLM5.0). And climate sensitivity experiments are conducted to quantify the relative contributions of different climate factors to NEP variability. Furthermore, future changes in NEP for the period 2025–2100 under multiple Shared Socioeconomic Pathway (SSP) scenarios are projected. The results indicate that the TP functioned predominantly as a net carbon sink during the historical period, with a multi-year mean NEP of 23.96 g C m² yr⁻¹. Spatially, NEP showed a significantly increasing gradient from the northwest to the southeast. During 1979–2018, NEP exhibited an overall decreasing trend across most regions of the TP. Air temperature was identified as the dominant controlling factor, accounting for approximately 68% of the interannual NEP variability, followed by solar radiation (21%) and precipitation (11%). The dominant climatic drivers of NEP variation differ among regions: air temperature predominates in the southwestern and southeastern regions, radiation dominates in the northwestern and central areas, and precipitation exerts a controlling effect in the northern and western regions. Future projections suggest that NEP remains positive under all SSP scenarios, indicating that the TP is likely to persist as a carbon sink throughout the 21st century. This study provides important reference for the development of ecological protection, restoration planning, and regional carbon neutrality strategies. Full article

Background: Encouraging fruit and vegetable (FV) consumption early in childhood is important for long-term healthy eating. Though parents play an important role in shaping children’s FV-related taste preferences and consumption, validated instruments assessing the range of parenting practices that specifically support young children’s FV intake are scarce. Furthermore, little attention has been given to low-income families, cultural inclusivity, and FV practices across different settings. The current study sought to conduct an initial examination and explore the measurement equivalency of a new FV parenting practices questionnaire (FVPPQ) across racially/ethnically diverse groups that address these gaps. Methods: Data for this paper came from a large project focused on parents’ FV parenting practices with young children enrolled in Head Start programs in the southern part of the U.S. Inclusion criteria were (a) parent/legal guardian being eighteen or older, (b) being the primary person responsible for child feeding, and (c) the child not requiring a special diet (e.g., diabetic). Using a multi-phases project approach, we (1) developed a preliminary conceptual map of parenting practice domains by reviewing existing measures on FV parenting practices; (2) completed and content-analyzed data from 18 focus groups (n = 62) to identify and further revise the preliminary conceptual map of domains, (3) administered a questionnaire with 11 domains of FV parenting practices, and then (4) empirically explored and reduced the measure while evaluating its content, construct, and criterion validity, and cultural equivalency across Non-Hispanic White, Hispanic White, and Black parents (n = 281). Results: Findings from Phases 1 and 2 generated a 107-item questionnaire that was reduced during phase 3 through a series of principal component and confirmatory factor analyses to the final FVPPQ with 21 items in four unique domains, showing good variability and inter-item consistency reliability: (1) Availability (5 items); (2) modeling (5 items); child-focused (5 items); and pressure (6 items). Three of the four domains evidenced cultural equivalency. Conclusions: The FVPPQ with four unique subscales demonstrated good content, construct validity, and partial measurement equivalency across racially/ethnically diverse groups of parents. Further confirmatory validation is warranted in larger samples, but the FVPPQ might be a promising and easily administered measure for research and applied interventions in nutrition, health behavior, and parenting contexts. Full article

Satellite remote sensing increasingly supports water quality monitoring, yet the temporal transferability of machine learning (ML) models remains insufficiently tested, particularly in coastal shallow lakes subject to hydrological variability. This study evaluates the predictive robustness of satellite-based ML models for electrical conductivity (EC), turbidity (TUR), water temperature (WT), and dissolved oxygen (DO) in Vrana Lake, Croatia. A total of 409 in situ measurements collected during 2023–2024 and 2025 were paired with Sentinel-2 and Landsat 8–9 imagery. Pearson, Spearman, and Kendall correlation analyses were applied for parameter-specific band selection using original, inverse, quadratic, and logarithmic feature transformations. Seventeen regression algorithms were evaluated under six training–testing split strategies, including strict temporal projection. WT exhibited high robustness (R² ≈ 0.90 under temporal projection) due to its strong dependence on thermal bands, while DO achieved moderate temporal stability (R² = 0.51) using log-transformed predictors. EC and TUR demonstrated substantial performance degradation under temporal separation (R² = 0.14 and −4.62, respectively), reflecting sensitivity to distribution shifts. For parameters showing sufficient stability, interpretable band-based retrieval equations were derived using the most strongly correlated spectral predictors. These findings highlight the importance of temporally structured validation and demonstrate that model complexity does not guarantee operational robustness in shallow, dynamically evolving lake systems. Full article

Komagataeibacter strains are important bacterial cellulose producers, yet closely related isolates can differ in cellulose yield, pellicle properties, and genetic stability during propagation. Such variability suggests that lineage structure and mobile genetic elements both contribute to strain-level genomic divergence. Here, complete genome comparisons were used to integrate vertical relatedness, gene-content structure, cellulose-associated signatures, and mobilome heterogeneity across 22 closed Komagataeibacter assemblies. A maximum likelihood phylogeny inferred from 642 single copy core genes provided the lineage scaffold. An anvi’o pangenome analysis defined a constant core gene cluster component across genomes and a noncore fraction that accounted for most of the genome differences in gene content. Targeted features linked to cellulose biosynthesis and local c-di-GMP-associated context were extracted from each genome. These features captured differences in bcs neighborhood composition and the presence of nearby GGDEF and EAL domain signals. The resulting feature matrix was projected by principal component analysis to summarize between-genome variation. Mobilome profiles were strongly strain dependent. Plasmid homology clustering identified 12 clusters comprising 36 plasmids from 13 genomes, including two dominant clusters of seven and six plasmids. Mash-based distance summaries further distinguished clusters consistent with conserved backbones from clusters consistent with heterogeneous, module-driven relationships. Prophage sequences, assessed as VIBRANT-predicted regions, were widespread but sparse per genome and dominated by medium length fragments. Insertion sequence burden ranged from 50 to 181 elements per genome, indicating substantial differences in transposition-associated sequence content. Pairwise association tests did not support robust cross module covariation beyond expected relationships among pangenome composition metrics at the current sampling depth. Overall, these results provide a complete genome reference framework linking lineage structure and mobilome heterogeneity, and they define reusable resources for comparative studies in bacterial cellulose biotechnology. Full article