Search Results (1,789)

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

To investigate concrete drying shrinkage in high-altitude environments, moisture evaporation and shrinkage rates were examined under combined curing regimes of four temperatures (40 °C, 20 °C, 0 °C, −10 °C) and three relative humidities (RH40%, RH60%, RH80%). Curing temperature and humidity primarily regulate shrinkage deformation by altering the internal moisture evaporation rate. Both evaporation and shrinkage rates exhibited a rapid initial increase, followed by deceleration, and finally stabilization with increasing age. A strong positive correlation was observed between these two parameters. The high-temperature and low-humidity condition (40 °C, RH40%) induced the most severe shrinkage. Four machine learning algorithms (XGBoost, RF, ANN, and KNN) were used to construct prediction models. After hyperparameter optimization and cross-validation, the RF models exhibited superior generalization and robustness (test set R² > 0.94). The model accurately captures the complex non-linear relationship between environmental parameters and shrinkage. SHAP analysis on the optimal models identified the moisture evaporation rate as the primary driving factor. The analysis quantified the non-linear contributions of temperature and age, alongside the inhibitory effect of humidity. The study verified the consistency between data-driven models and physical mechanisms. This study elucidates the shrinkage mechanism under extreme conditions. It provides a reliable reference for crack control and life prediction in high-altitude engineering. Full article

Water age reflects water sources, storage, and pathways, and regulates the solute retention and dissolution associated with biogeochemical processes, highlighting its hydrological and ecological importance. However, accurate water age estimation in tracer-aided models depends heavily on the quality and spatio-temporal resolution of precipitation isotopic signals. This study investigates how distributed rainfall δ²H signals affect the simulation of young water fraction (F_yw) via the Storage Age Selection (SAS) model in topographically complex subtropical mountain catchments. Eight precipitation δ²H scenarios were generated using two temporal approaches (stepwise and sinewave) and four spatial interpolation methods: (1) raw data, (2) reversed effective recharge elevation method (rERE), (3) linear regression with elevation (ER), and (4) regression-kriging (RK). Later on, the time-variant SAS model was calibrated against observed stream water δ²H collected from the year 2022 to the year 2024. Results show that the SAS model consistently produced similar F_yw estimates for catchments (8%~40%) across all eight scenarios, demonstrating strong robustness to input uncertainty and validating the dominant role of catchment characteristics in regulating water age. The combined stepwise temporal and rERE spatial approach provided better agreement with observed stream δ²H, particularly in the eastern, steeper catchments, yielding superior model efficiency along with better constrained uncertainty. This study highlights the sensitivity of age-tracking models to precipitation isotopic inputs and provides practical guidance for selecting an interpolation strategy in data-limited mountainous environments. Full article

Background: Surface roughness in turning operations is still verified predominantly after machining, which limits the possibility of timely corrective intervention. Methods: This study examined whether normal-direction peak-to-peak vibration displacement can serve as a practical low-frequency indicator of surface roughness during finish turning of EN AW-2011 aluminum alloy. The analysis was based on 190 synchronized displacement-roughness observation pairs obtained in one controlled experimental campaign on a CQ6230 conventional precision lathe, using a VB-8206SD displacement logger mounted radially on the tool holder and contact profilometry measurements reported as Ra and Rz. The analytical workflow included explicit quality-control safeguards for malformed rows, missing values, and obvious artefacts; in the present dataset, these checks did not indicate a failure state that would invalidate the main calculations. The workflow combined descriptive statistics, moving-average trend inspection, low-frequency FFT and STFT descriptors, Pearson correlation analysis, and ordinary least squares regression. Results: The displacement signal exhibited a mean value of 0.0446 mm with a standard deviation of 0.0256 mm and showed strong within-dataset linear relations with roughness parameters: Ra = 14.204 + 24.191 V (R² = 0.9929, RMSE = 0.052 µm) and Rz = 63.207 + 105.253 V (R² = 0.9905, RMSE = 0.264 µm). Conclusions: The results support setup-specific roughness-related process-state assessment using low-rate normal-direction displacement measurements. However, because the 190 records represent a time-ordered synchronized sequence rather than 190 independent cutting trials, and because no separate validation set was available, the fitted equations should be interpreted as descriptive within-setup calibration rather than as universally validated predictive models. Full article

Detecting changepoints in time series is a fundamental task in statistical modeling and data-driven decision-making. We introduce a novel set partitioning-based model for changepoint detection that leverages combinatorial optimization to identify an optimal set of segments explaining the observed data. Unlike conventional dynamic programming approaches, which rely on restrictive structural assumptions on the cost function to ensure tractability, our formulation is based on Integer Linear Programming. While the standard additivity assumption on segment-wise costs is retained, the proposed framework departs from existing methods in its ability to incorporate both local and global structural constraints directly within the optimization model. In particular, it supports a broad class of constraints, ranging from simple segment-level restrictions to complex global conditions coupling multiple segments, without requiring modifications to the underlying solution scheme. This enhanced modeling capability constitutes the main contribution of the work, significantly increasing the expressiveness of the framework while preserving the tractability of additive cost structures. The model’s design enables high adaptability to different application domains, including finance, bioinformatics, and industrial monitoring. The efficiency of modern MILP solvers, combined with tailored dominance rules, enables the solution of instances with several hundreds of observations in practical time. Computational results indicate that the approach extends tractability beyond previously studied settings, effectively handling classes of instances whose structural constraints could not be accommodated by existing methods, while retaining robustness and interpretability. Full article

Background: Accurate estimation of water and energy requirements is fundamental for establishing dietary reference values in young adults. However, evidence integrating objectively measured energy expenditure with detailed water turnover components remains limited in Chinese populations. Objectives: To quantify water intake, water loss, and energy expenditure in healthy young college students, and to examine how energy metabolism is associated with specific components of water turnover under free-living conditions. Methods: Twenty-one healthy adults aged 18–25 years participated in a 14-day observational study conducted in Beijing, China. Total energy expenditure (TEE) was measured over 14 days using the doubly labeled water (DLW) method. Physical activity was monitored over 7 consecutive days using a triaxial accelerometer. Water intake was assessed using multiple methods: water from beverages (including plain drinking water and other beverages) was recorded over 7 days using 24 h fluid intake records, while water from food was measured during days 5–7 using weighed food records combined with duplicate portion and direct drying methods. Urinary and fecal water loss were quantified using 24 h collections conducted during days 5–7. Metabolic water production and insensible water losses were estimated using established physiological equations. Multivariable linear regression analyses were conducted to examine associations between energy-related variables and components of water turnover. Results: Mean total daily water intake was 3023 mL, with water from beverages accounting for 54.1%, water from food for 36.7%, and metabolic water for 9.1%. Mean total daily water loss was 1931 mL, predominantly from urinary excretion (81.0%). DLW-measured TEE averaged 2018.6 kcal/day and was higher in males than in females. Most regression models examining total water intake and beverage-derived water were not statistically significant, and no consistent associations were observed between these variables and total energy intake, TEE, or PAEE. In contrast, TEE was positively associated with metabolic water production and respiratory water loss (both p < 0.001). Significant associations with total energy intake were observed for water from food and fecal water loss (both p < 0.01), whereas other water intake components showed no significant associations. Conclusions: In young adults, energy metabolism appears to be more closely associated with physiologically regulated components of water turnover than with voluntary water intake. These findings suggest a divergence between endogenous and behaviorally regulated pathways of water turnover and highlight the importance of considering component-specific water dynamics when examining hydration and energy balance, although confirmation in larger studies is warranted. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) of immunosuppressants is essential in treating pediatric kidney diseases; however, repeated venipuncture is burdensome in children. We evaluated whether minimally invasive fingerstick capillary sampling combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS) provides results analytically comparable to those of conventional venous sampling. Methods: Capillary whole blood (2.8 µL) was collected via fingersticks from pediatric patients receiving mycophenolate mofetil, with or without tacrolimus (TAC) or cyclosporine A (CsA). Drug concentrations were quantified using a previously validated simultaneous LC-MS/MS method and compared with conventional venous sampling using linear regression and Bland–Altman analyses. Results: Seventy-four paired samples from 21 patients were analyzed. Strong correlations were observed between capillary and venous samples for mycophenolic acid (MPA), TAC, and CsA (R² > 0.90). Hematocrit correction improved agreement for MPA. Bland–Altman analyses demonstrated acceptable bias across analytes. Conclusions: Fingerstick-based microvolume sampling combined with LC-MS/MS provides analytically reliable immunosuppressant quantification in pediatric patients. Although larger clinical validation is required, this minimally invasive approach may reduce procedural burden and may support future outpatient or home-based TDM strategies. Full article

Sitka spruce (Picea sitchensis (Bong.) Carr.) is among the most commercially important tree species in European and North American forestry, and genetic improvement programmes are therefore essential for promoting its productivity and sustainability. This research emphasises the significance of the breeding programmes. The primary objective of this study was to provide more reliable information on family selection for the improvement programme of Sitka spruce by accounting for competition and environmental heterogeneity effects. Analyses in the present study were carried out on historical inventory data of height (HT) and diameter at breast height (DBH) from a half-sib progeny trial of Sitka spruce in Ireland. Tree measurement data were collected at ages 6, 12, 15 and 20 years. A mixed linear model incorporating spatial and competition terms was applied to estimate genetic parameters of the Sitka spruce population. The direct genetic effects of each family on its own phenotypes and the competition effect on its neighbour’s phenotype were examined over time. The study demonstrated an analytical approach for assessing both genetic as well as environmental aspects of competition in a Sitka spruce progeny trial. The combined model integrating competition and spatial terms (model CS) improved model fit compared with the basic model, which only included the random effects of genetic and experimental design factors (model B), with an AIC difference of up to 3609 between them. Residual error obtained from model CS was usually smaller than from model B, with the greatest reduction of 85%. Furthermore, model CS generally improved the estimation of heritability for growth traits, by up to 241, when compared with model B. In addition, genetic differences in competitive ability among families were also observed. Families with favourable combinations of direct genetic and competitive breeding values were suggested for selection in subsequent cycles of the breeding programme, i.e., families with relatively high direct genetic breeding value but low and consistent competitive breeding value over time. This work develops a practical framework to inform future family selection for Sitka spruce improvement programmes. Full article

Background: The clinical course of COVID-19 is highly variable, ranging from asymptomatic infection to critical illness with hyperinflammation and multiorgan failure. Oxidative stress plays a central role in COVID-19 pathogenesis, and genetic polymorphisms in glutathione S-transferase (GST) enzymes, particularly GSTM1 and GSTT1 null genotypes, may impair antioxidant defense and exacerbate inflammatory responses. This study aimed to investigate the association of GSTM1 and GSTT1 null genotypes with both disease severity and serum cytokine levels in hospitalized COVID-19 patients. Methods: This cross-sectional study enrolled 137 COVID-19 patients hospitalized during the second pandemic wave (July–September 2020). Patients were stratified into mild (n = 67) and severe (n = 70) groups based on clinical criteria. GSTM1 and GSTT1 polymorphisms were determined by multiplex polymerase chain reaction. Serum levels of 13 cytokines were measured using flow cytometry. Logistic regression analyzed genotype associations with disease severity, and multivariate linear regression assessed relationships between null genotypes and pro-inflammatory cytokine levels (IL-6, TNF-α, IL-17A, IFN-γ), adjusted for age, sex, hypertension, and diabetes. Results: The GSTT1 null genotype was significantly associated with severe COVID-19 (adjusted OR = 2.56, 95% CI: 1.08–6.07, p = 0.032). Severe patients exhibited significantly elevated levels of IL-6 (75.6% increase, p = 0.008), TNF-α (69.4% increase, p = 0.005), IL-17A (54.4% increase, p = 0.016), and IFN-γ (10.1% increase, p = 0.021). Both GSTM1 and GSTT1 null genotypes were associated with higher levels of these cytokines, with stronger effects observed for GSTT1 null. In multivariate analysis, GSTT1 null independently predicted elevated IL-6 (β = 52.6, p = 0.003), TNF-α (β = 13.8, p = 0.002), IL-17A (β = 2.4, p = 0.001), and IFN-γ (β = 56.4, p = 0.012). The combined both null genotype showed the strongest associations but was limited by small sample size (n = 10) and should be interpreted with caution. Conclusions: The GSTT1 null genotype is associated with severe COVID-19 and appears to be associated with heightened pro-inflammatory cytokine responses, particularly IL-6, TNF-α, IL-17A, and IFN-γ. These findings suggest a potential role for genetic impairment of antioxidant defense may contribute to hyperinflammation in COVID-19 hyperinflammation, although validation in larger cohorts is needed. Full article

Optimization has played a fundamental role in the evolution of chemical engineering, enabling systematic decision-making under technical, economic, and environmental constraints. This review presents a structured and comparative analysis of the historical development and current state of optimization methodologies applied to chemical engineering, covering the transition from early linear and nonlinear programming approaches to advanced data-driven and artificial intelligence-based frameworks. A systematic literature review was conducted following the PRISMA guidelines, through which a total of 101 articles were retained for analysis. The results indicate that mixed-integer programming and decomposition-based methods remain widely adopted for structured industrial problems, while metaheuristic and hybrid data-driven approaches have experienced significant growth in recent years. In particular, a clear trend toward the integration of machine learning and surrogate modeling techniques is observed, driven by the need to address large-scale, non-convex, and highly nonlinear systems. The analysis reveals a clear methodological shift from classical linear optimization frameworks toward hybrid optimization strategies capable of addressing large-scale, non-convex, and highly nonlinear problems. Finally, current challenges and future research directions are identified, emphasizing the need for robust hybrid approaches that combine mathematical programming and intelligent algorithms to effectively manage complexity in next-generation chemical systems. Full article

This comparative cross-sectional study simultaneously investigated the dual health burden of body mass index (BMI) and common mental disorders (CMDs) driven by occupational stressors in two stepwise regression models. By classifying stress exposure into three clinically relevant tiers (low, moderate, and severe) in two distinctive populations—a hospital and a manufacturing company—we used the validated SDS-30 and SRQ-20 instruments. The robust multiple regression models uncovered a highly nuanced landscape of employee well-being that highlights the context-dependent nature of psychosocial hazards. The most compelling findings emerged from the interaction analyses, which demonstrated that the physical and mental consequences of severe stress do not impact the workforce uniformly. Regarding mental health, severe occupational stress proved to be a potent catalyst for CMD symptoms, but this psychological toll was significantly magnified within the hospital sector relative to the manufacturing environment. An opposite, yet equally context-dependent, pattern emerged regarding physical health. In the main-effects-adjusted model, the severity of occupational stressors did not demonstrate a statistically significant linear association with an overall increase in BMI. However, the interaction model revealed a hidden vulnerability: employees in operational field roles who report severe stress are highly susceptible to severe BMI increases compared with admin personnel. While administrative staff may face sedentary risks, field workers under severe stress likely endure higher physiological allostatic load, erratic shift patterns that disrupt circadian metabolic rhythms, and potentially poorer dietary coping mechanisms during active labor. This combination of physical exhaustion and severe psychological tension severely disrupts metabolic homeostasis, forcing the redistribution of adipose tissue and driving the observed BMI spike. Full article

Electron correlation and quantum interference are pivotal in mesoscopic transport. We theoretically study the nonequilibrium transport dynamics of a triangular triple-quantum-dot (TTQD) molecule connected to fermionic reservoirs using the exact hierarchical equations of motion (HEOM) formalism. We demonstrate a counterintuitive transport signature in which the stationary current is significantly enhanced by increasing U, a behavior distinct from the suppression typically observed in linear quantum dot arrays. By analyzing the evolution of spectral functions, we attribute this enhancement to the interplay between Coulomb-interaction-induced energy shifts and quantum interference effects specific to the triangular topology. We also explore how the circulation of chiral currents and electrode coupling strength modulate these interaction effects. Finally, we present a three-dimensional map of the transport current as a function of inter-dot tunneling (t) and Coulomb interaction (U), illustrating their combined effect on the current magnitude and its applications. Full article

Rapid urban expansion and the growing spatial requirements of utility-scale photovoltaic (PV) deployment compete for the same category of land—flat, accessible, and high-insolation terrain—yet the scale, trajectory, and planning-sensitivity of this conflict remain poorly characterised at the regional level. This study quantifies the spatiotemporal competition between urban construction land and PV-suitable land in the Yangtze River Delta (YRD) from 2000 to 2020 and projects its evolution to 2030 under three development scenarios. Built-up areas were extracted for three epochs using a Random Forest (RF) classifier on the Google Earth Engine (GEE) platform, achieving overall accuracies of 87.7–94.5% and Kappa coefficients of 0.718–0.739. PV site suitability was evaluated through a hybrid Multi-Criteria Decision Analysis (MCDA) framework combining Boolean exclusion constraints with an Analytic Hierarchy Process (AHP)-based Weighted Linear Combination model; the weight structure was validated by a Consistency Ratio of 0.006, and a One-At-a-Time sensitivity analysis confirmed spatial robustness across threshold scenarios. Spatial overlay analysis reveals that the cumulative area of PV-suitable land occupied by urban built-up uses grew from 15,862 km² in 2000 to 23,872 km² in 2020, representing an incremental loss of 8010 km² over two decades. Future conflict was projected using the PLUS model, calibrated on 2010–2020 observed expansion and validated against the 2020 classified map (OA = 93.99%, Kappa = 0.91). Under the Business-as-Usual (BAU) scenario, 33,368 km² of currently open PV-suitable land faces urban encroachment by 2030; the Ecological Conservation Priority (ECP) scenario reduces this figure to approximately 30,767 km², while the Economic Development (ED) scenario yields a near-identical outcome to BAU, indicating that development velocity alone does not determine the spatial extent of conflict—the allocation of growth does. These findings provide a quantitative basis for designating energy-strategic reserve zones within national spatial planning frameworks and demonstrate that targeted spatial governance, applied at high-pressure locations, can substantially slow the erosion of the region’s solar energy land base. Full article

Understanding the toxicity of micro- and nanoplastics (MNPs) in aquatic systems, combined with temperature, is essential in order to assess ecological hazard in a multi-stressor environment. This study investigated the biological responses of marine and freshwater organisms of different trophic levels (including primary producers, decomposers, and consumers) exposed to polystyrene (PS) MNPs, tested at varying concentrations and particle sizes under two temperature conditions (control and +2 °C). Overall, differences were observed between trophic levels: Paracentrotus lividus larvae were more sensitive to higher temperatures, Daphnia magna exhibited a non-linear pattern, and microalgae have generally shown low sensitivity to both MNPs and high temperatures. However, the MNPs’ responses were not generally concentration-dependent, with the exception of Dunaliella tertiolecta. The effects recorded at increased temperature generally varied among species, indicating that even a moderate increase in temperature can modulate responses in different organisms. In the marine system, hazard levels increased with temperature, whereas in freshwater, they were higher but temperature-independent. These results highlight the importance of integrated assessment approaches to accurately evaluate the ecological hazard associated with MNPs pollution in the context of climate change. Full article

High energy consumption in winter greenhouses poses a challenge to agricultural sustainability in Northern China, where heating costs typically account for 40–60% of total operating expenses. This study integrated a non-linear cost–volume–profit (CVP) analysis and data envelopment analysis (DEA) to balance cucumber yields with escalating energy costs. A single-season, single-factor experiment was conducted using insulated greenhouse compartments to evaluate four night temperature gradients (10 °C, 13 °C, 16 °C, and 19 °C). Results showed that although the 19 °C treatment (T3) achieved the highest marketable yield, it was associated with lower economic return because heating costs increased disproportionately. Among the four tested nighttime temperatures, the 16 °C treatment (T2) showed the most favorable observed combination of yield, net profit, and DEA-based efficiency indicators under the present experimental conditions. However, because the experiment was conducted in a single season within a compartment-based greenhouse system and the CVP relationship was fitted using treatment-level means, this result should be interpreted as a preliminary and condition-specific finding rather than as definitive evidence of a universal optimum temperature. Accordingly, the integrated bio-economic framework presented here is best viewed as an analytical prototype that merits further validation across multiple seasons, cultivars, and greenhouse systems. Full article