Search Results (5,843)

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Pulsator tests are used to characterize the bending fatigue strength of the tooth root. In these tests, the tooth root is loaded not by meshing with another gear but by applying a pulsating load to the tooth flank via a testing machine. This leads to a different S-N curve with respect to the ones obtained through meshing gear tests. This study aims to investigate the impact of cracks in the tooth root on the results of pulsator and meshing tests. Here, we address the issue of load sharing modification during meshing due to the presence of a crack, and its influence on crack propagation. This approach is applied to a real-life example: estimating the finite life of meshing gears based on pulsator tests. This study aims to present an initial procedure for obtaining S-N curves for meshing gears based on those obtained from pulsator tests. The S-N curves obtained from the pulsator test are compensated for by adding the difference in the propagation speed between the two tests calculated by applying the Paris law with parameters extracted from FE simulation; the time spent in propagation is almost doubled in the meshing conditions. Full article

Near-fault pulse-like ground motions (NFPLGMs) impose concentrated energy demands that can severely damage bridges, yet their scarcity and the influence of structural parameter uncertainties are often neglected in seismic fragility assessments. This study proposed a synthesis method for NFPLGMs by superposing low-frequency pulse components (extracted via the Gabor wavelet transform and low-pass filtering) with high-frequency stochastic components based on an evolutionary power spectrum. A three-span reinforced concrete bridge was modeled in OpenSeesPy, and Incremental Dynamic Analysis (IDA), together with a quadratic response surface model, were used to plot seismic fragility curves. The damping ratio (ξ), elastic modulus of steel reinforcement (E_s), yield strength of steel reinforcement (f_y), diameter of longitudinal reinforcement (D), and peak ground acceleration (PGA) were treated as random variables. Sensitivity indices were computed using Monte Carlo sampling (n = 10,000). Results show that ξ most strongly affects the displacement ductility ratio of the bridge pier (u_d) (variation of up to 32.6%), while E_s dominates the shear deformation of the bridge bearing (d) (variation of up to 43.8%). Neglecting structural parameter uncertainties overestimates median PGA thresholds (m_R) for different damage states by 1.5%–36.1%, and replacing NFPLGMs with ordinary ground motions overestimates seismic capacity by 1.7%–36.6%. The bridge bearing is consistently more vulnerable than the pier, with a collapse probability of 0.9566 at PGA = 1.0 g. These findings highlight the necessity of incorporating both NFPLGM characteristics and structural parameter uncertainties into bridge seismic fragility assessment. On the other hand, when seismic retrofitting of bridges is carried out using coating materials, priority should be given to more vulnerable components, such as bridge bearings, to improve the utilization efficiency of limited resources. Full article

Background/Objectives: In gas chromatography–mass spectrometry (GC-MS) library-based compound identification, spectrum preprocessing and associated tuning parameters critically influence identification performance. These parameters are conventionally optimized using grid search, which requires predefined parameter spaces and becomes computationally inefficient as dimensionality increases, often failing to identify optimal values because of discretization. Differential evolution (DE), a population-based metaheuristic optimization algorithm, provides a flexible alternative through efficient global exploration of the parameter space. This study compared the performance of DE and grid search for optimizing compound identification. Methods: Cosine similarity was applied to the NIST GC-MS library. DE was used to maximize either cross-validated accuracy or mean reciprocal rank (MRR). Results were compared with those from a grid search over five equally spaced parameter values. Identification performance was evaluated using accuracy, MRR, and area under the receiver operating characteristic curve (AUC). Results: When all four parameters were optimized simultaneously, DE achieved slightly higher cross-validated accuracy and MRR than grid search, although the absolute differences were modest. More pronounced differences were observed in specific unidimensional tuning scenarios, particularly for the intensity weight factor. Simultaneous multidimensional parameter optimization yielded better performance than isolated parameter tuning. Conclusions: Grid search may be computationally advantageous when the parameter space is known and limited, whereas DE provides a more flexible approach for unknown or high-dimensional search spaces. Overall, DE achieved comparable identification performance to grid search, with modest improvements observed in some optimization settings. A command line Julia-based tool, MSTune, was developed for spectrum preprocessing parameter optimization and is publicly available on GitHub. Full article

Although foamed lightweight soil is widely used for its light weight and high strength, its insufficient dynamic performance under cyclic loading and the poorly understood reinforcement mechanism have become key bottlenecks restricting its optimized application. To investigate the dynamic characteristics and influencing factors of geogrid-reinforced foamed lightweight soil (GRFLS), laboratory dynamic triaxial tests were conducted using a DJSZ-100D dynamic–static triaxial testing system. The effects of the number of geogrid layers and wet density on the dynamic mechanical properties were examined, with analysis focused on failure patterns, backbone curves, dynamic strength, dynamic shear modulus, and damping ratio. The results indicate that the inclusion of geogrids effectively restrained the propagation of longitudinal cracks in the foamed lightweight soil. The hyperbolic backbone curves were well characterized by the Hardin–Drnevich model. An increase in wet density significantly enhanced the dynamic strength, and an optimal number of two reinforcement layers was identified based on the reinforced strength–stress ratio. The dynamic elastic modulus and damping ratio of GRFLS increased with growing dynamic strain. Compared with the unreinforced condition, the initial dynamic elastic modulus of the specimens with two geogrid layers increased by an average of 15.6%, and the maximum damping ratio increased by an average of 12.9%. While both geogrid reinforcement and higher wet density effectively increased the dynamic elastic modulus, only an increase in wet density notably improved the damping ratio. Finally, predictive models for the enhanced dynamic elastic modulus and damping ratio, which incorporate wet density and the number of reinforcement layers, were established. These models indirectly reflect the dynamic deviator stress–strain relationship of GRFLS. This study provides a theoretical basis for engineering construction. Full article

The intricate interaction of a host’s microbiome, the microbiomes of other hosts, and environmental microbial populations significantly impacts host health, given the essential physiological functions the microbiome performs within the organism. The oral microbiome of domesticated animals is also influenced by a variety of host and environmental factors. This study investigated the characteristics of the oral microbiome of dogs and cats under comparable and disparate living conditions, emphasizing the description of diversity patterns, taxonomic composition, and predicted functional profiles. Oral buccal swabs were collected from four groups of companion animals (n = 5 per group): dogs housed alone in single-pet households (Group A), dogs cohabiting with cats in multi-pet households (Group B), cats cohabiting with dogs from the same households (Group C), and cats housed alone in single-pet households (Group D). The cohabiting groups were derived from five multi-pet households, with one dog and one cat sampled from each household. Amplicon sequence variations (ASVs) were used for downstream analysis after 16S rRNA gene sequencing. Rarefaction curve behavior indicated proper sequencing depth. Alpha diversity varied by group (Shannon index, p = 0.045), with Groups C and D having larger diversity. A Beta diversity study revealed community composition differences (Bray–Curtis dissimilarity, R² = 0.257, p = 0.001), with some overlap between groupings. In all samples, Proteobacteria, Firmicutes, Bacteroidota, and Fusobacteriota dominated the microbiome. The relative abundance of Fusobacterium, Porphyromonas, and Pasteurella varied across groups. Core microbiome analysis identified limited overlap of core ASVs between groups, with most taxa being group-specific. Functional prediction using PICRUSt2 suggested differences in predicted metabolic and cellular pathways. Overall, these exploratory findings suggest that the oral microbiome of companion animals may be influenced by host species and cohabitation conditions. Although limited by the small sample size, the study provides preliminary insights into microbial diversity, community structure, and predicted functional profiles that may inform future One Health-oriented investigations. Full article

Geopolymeric concrete beams are gaining increasing attention as sustainable structural members. The paper presents an experimental investigation on the deflection and cracking behavior of geopolymeric recycled aggregate concrete (GRAC) beams, with emphasis on effects of the longitudinal reinforcement ratio and the recycled aggregate (RA) replacement ratio. Using digital image correlation (DIC) technology, the failure modes, load–deflection curves, deflection characteristics, stiffness, and cracking behavior were systematically analyzed. The results indicated that increasing the reinforcement ratio leads to the same trend in GRAC beams as that observed in ordinary reinforced concrete beams. At 50% RA replacement, GRAC beams exhibit improved cracking resistance, 13.41% higher cracking stiffness, 6.93% lower deflection, and enhanced ductility compared to specimens without RA, attributed to the enhanced RA–matrix interface. However, a further increase in the RA replacement ratio leads to poorer flexural performance of the GRAC beams. In addition, predictive models for cracking moment, stiffness, deflection, and maximum crack width of GRAC beams were proposed based on the experimental results, incorporating the plastic influence coefficient, the comprehensive coefficient for the average strain at the extreme compression zone of concrete and the maximum crack width correction factor. The calculated values agreed well with the test data, offering a basis for structural design and engineering application. Full article

An increasing number of higher education institutions in the Republic of Serbia are experiencing a decline in first-year enrollment, posing a significant challenge to their sustainability and effective resource planning. Timely identification of factors influencing candidates’ enrollment decisions, as well as those at risk of not enrolling, is crucial for implementing appropriate institutional measures. This study aims to build and evaluate a machine learning model to predict candidates’ decisions to enroll in a higher education institution based on relevant educational, administrative, demographic, social, and geographic characteristics. Various classification models, including ensemble approaches, were applied and compared in this study. Experimental results indicate that the Stacking Ensemble model achieved slightly higher values of the evaluated imbalance-sensitive metrics compared to the other evaluated models, with an Area Under the ROC Curve (AUC) of 0.756 and a Matthews Correlation Coefficient (MCC) of 0.364, indicating moderately balanced predictive performance in the context of imbalanced data. However, the statistical analysis conducted between the Logistic Regression and Stacking Ensemble models did not indicate a statistically significant difference in performance. The results suggest that ensemble methods may provide certain advantages over individual models, particularly for complex classification problems involving imbalanced data. The application of the proposed model may contribute to improving the decision-making process at higher education institutions, enabling more efficient enrollment policy planning and more optimal resource management. Full article

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma. Despite advances in immunochemotherapy, approximately 30–40% of patients experience relapsed or refractory disease. Nutritional and inflammatory status, reflected by composite indices, may independently influence clinical outcomes. However, the prognostic value of the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), and Hemoglobin-Albumin-Lymphocyte-Platelet (HALP) score has not been well established in DLBCL patients treated with rituximab-based regimens. Methods: We retrospectively analyzed 192 patients with newly diagnosed DLBCL who received at least three cycles of R-CHOP or R-EPOCH at Başakşehir Çam and Sakura City Hospital between January 2020 and January 2026. Receiver operating characteristic (ROC) curve analysis was performed to determine optimal cutoff values. Kaplan–Meier analysis with log-rank testing and univariable/multivariable Cox proportional hazards regression analyses were used to evaluate the prognostic impact of the PNI, GNRI, and HALP on overall survival (OS) and progression-free survival (PFS). Results: Among the six indices evaluated (PNI, GNRI, HALP, SII, ALI, and CAR), the PNI demonstrated the highest discriminatory ability for OS (AUC = 0.734, p = 0.001), followed by the HALP (AUC = 0.671, p = 0.020) and GNRI (AUC = 0.668, p = 0.022). The optimal cutoff values were ≤46.45 for the PNI, ≤46.91 for the GNRI, and ≤223.95 for HALP. Low values of all three indices were significantly associated with elevated LDH levels, advanced Ann Arbor stage, and higher IPI category. Kaplan–Meier analysis demonstrated significantly inferior OS in the low PNI (52.8 ± 2.6 vs. 67.1 ± 1.2 months, p = 0.001), low GNRI (49.5 ± 3.1 vs. 66.0 ± 1.4 months, p = 0.001), and low HALP (58.8 ± 2.8 vs. 64.9 ± 1.2 months, p = 0.005) groups. In separate multivariable Cox models adjusted for sex and IPI, the PNI (HR = 0.216, p = 0.009), HALP (HR = 0.276, p = 0.031), and GNRI (HR = 0.294, p = 0.011) remained independently associated with OS. No significant association was observed between these indices and PFS. Conclusions: The PNI, GNRI, and HALP are independent prognostic markers in patients with DLBCL treated with rituximab-based regimens. These readily available and inexpensive baseline indices may complement the IPI in identifying patients at higher risk of adverse outcomes and support risk stratification at diagnosis. Full article

The South American silver croaker, Plagioscion squamosissimus, holds significant importance for the artisanal fisheries operating in the sub-middle and lower courses of the São Francisco River, located in northeastern Brazil. Its complex horizontal movement patterns and habitat-use preferences are not fully understood in the waters of hydroelectric dam reservoirs, raising important questions for the rational and sustainable management of this species. This study aimed to identify the habitat use of P. squamosissimus individuals captured in three fishers’ associations (Olho D’água do Casado, Petrolândia and Rodelas). Individuals were collected between September 2023 and March 2024. A selection of 25 individuals per location from the same age group (+2 years) was used, following annual age estimation based on existing growth curves. Element-to-calcium (element/Ca) ratios in the otolith cores and edges were determined using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The data were analyzed using univariate and multivariate statistics to assess the degree of separation between individuals in relation to natal origin (otolith cores) and time of capture (otolith edges) from the three sampling sites. Significant differences in element/Ca ratios between core and edges of the otolith were observed for Ba/Ca, Mg/Ca, Mn/Ca and Sr/Ca ratios. These results indicate an ontogenetic change in the habitat use, in which similarity in core signatures suggests a common natal origin, likely influenced by shared environmental conditions of the individuals investigated in this study. Full article

Milk flow dynamics during mechanical milking are strongly influenced by species-specific mammary anatomy, milk partitioning between cisternal and alveolar compartments, and milking management. The present study aimed to compare milkability traits across the main dairy species reared in central Italy using a large dataset collected over 20 years. A total of 7315 animals were included: dairy cows (1103), buffaloes (2870), goats (2399), sheep (754), and donkeys (189). Milk flow curves were recorded using a portable Lactocorder^® device. The following traits were analyzed: milk yield (MY), lag time (LT), milk ejection time (MET), total milking time (TMT), peak flow rate (PFR), average flow rate (AFR), plateau phase (PL), bimodal phase (BM), and bimodality incidence (Bimo). Marked interspecific differences emerged. Dairy cows showed the highest MY and PFR, with bimodality occurring in 23.7% of curves. Buffaloes exhibited lower flow rates, prolonged LT, and extended TMT, reflecting their strong dependence on oxytocin-mediated alveolar milk ejection. Sheep demonstrated short milking times and low bimodality (13.5%), consistent with their large cisternal milk fraction. Goats displayed breed-dependent variability, with specialized dairy breeds showing higher PFR and longer TMT. Donkeys produced low milk volumes but exhibited rapid and efficient milk flow, with the lowest incidence of bimodality (7.4%). Overall, milk flow patterns reflected species-specific udder morphology and physiological mechanisms of milk ejection. Although this field-based study faces inherent limitations in environmental and protocol standardization across farms, the resulting long-term dataset remains highly representative. These findings highlight the importance of tailoring milking machine settings and prestimulation protocols to species and breed characteristics to optimize milking efficiency, labor management, and animal welfare. Full article

The Greater Khingan, a key cold-temperate coniferous forest region in northern China, is frequently affected by forest fires with severe ecological and economic impacts. The study investigates the influence of key environmental and anthropogenic drivers on forest fire susceptibility and evaluates multiple machine-learning approaches for regional fire assessment. Using 2001–2018 fire point data and multi-source remote sensing data, we integrated 13 driving factors across four dimensions: meteorology, topography, vegetation, and human activities. Collinear variables were screened using the Variance Inflation Factor (VIF). Three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—were constructed to assess the long-term potential risk of forest fire occurrence. Driving mechanisms were analyzed using standardized regression coefficients and the SHapley Additive exPlanations (SHAP) interpretable algorithm, and spatial distribution maps of regional forest fire risk were generated based on the optimal model. Among the three models, RF achieved the highest predictive accuracy, with an accuracy of 0.919 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.966, significantly outperforming LR and SVM. SHAP analysis reveals that forest fires are primarily driven by climatic factors (Pres and Prec as core drivers), regulated by topographic factors, and weakly affected by human factors. The proposed framework provides an effective tool for long-term forest fire susceptibility assessment by combining robust predictive performance with interpretable model outputs. The findings provide scientific support for long-term strategic forest fire risk zoning, regional firefighting resource allocation, and the formulation of differentiated prevention and control strategies, and also offer methodological references for forest fire prediction in other cold-temperate forest regions in China. Full article

The Maule Region in Chile possesses a rich cultural heritage associated with petroglyphs created by ancient hunter-gatherer inhabitants. This rock art has suffered damage over time due to natural and anthropic causes. Fostering positive attitudes toward petroglyphs may influence behavioral intentions related to their preservation. This study evaluates an augmented reality card game developed to promote positive attitudes toward the rock art heritage of the Maule Region, examining its usability and the effects of incorporating augmented reality elements. The game achieved a System Usability Scale (SUS) score of 79.7 (SD = 14.2), corresponding to an A-grade on the Sauro-Lewis curved grading scale, indicating good usability.Participants in the game condition showed higher heritage attitudes than controls (M = 6.13, SD = 0.80, $t_{\left(24\right)}$ = −2.33, p = 0.028). Augmented reality enhanced attitudes at moderate levels of usability (B = −1.02, p = 0.043), but produced no detectable main effect in mean comparisons alone. The results indicate that the game constitutes a system with adequate usability, effective in fostering positive attitudes toward cultural heritage, and that augmented reality enhances attitudinal outcomes under conditions of moderate perceived usability. Full article

Investments in urban water supply should be informed by resilience management frameworks that consider traditional reliability requirements, community preparedness during system disruptions, and sustainability goals in long-term planning. Grounded in a framework (WARATA) that integrates these aspects, this paper presents a stepwise, performance-based theoretical approach to resilience quantification, supported by explanations and practical guidance. For instance, in addition to the piped infrastructure components, emergency supply options and human resources should be incorporated within the system boundaries (Step 1), and water supplied to users is recommended as a single performance measure (Step 2). During disruptions, performance at user nodes is influenced by operational rules for resource allocation (Step 3), which must be implemented in the required computer model for simulating performance (Step 4). Equations for computing withstanding, absorptive, restorative, adaptive, and transformative capabilities as time-based metrics are proposed (Step 5), enabling the analysis of results from the bottom up (Step 6) to inform resilience management. Using illustrations of performance curves at individual system nodes, this paper advocates for extended system boundaries that bridge the gap between infrastructure and community resilience; discusses challenges with the modeling of dynamic, adaptive performances; and emphasizes the importance of assessing temporal distances to fail-safe and safe-fail thresholds during disturbances. Pending case study validation and integration into tools for predictive and real-time analyses of options, the quantification approach could support infrastructure and emergency response planning and management, ultimately ensuring sustainable system designs with equitable resilience outcomes. Full article

Numerous factors influence the formation of blasting craters in engineering blasting. Based on the actual parameters of the Daye Iron Mine, this study established six sets of single-hole blasting crater numerical models with different borehole diameters using ANSYS(19.0)/LS-DYNA(R13) software. The variation in blasting crater volume with the scaled depth was analyzed to determine the optimum scaled depth for each borehole diameter, and a functional relationship between the optimum scaled depth and borehole diameter was derived through curve fitting. Furthermore, using a borehole diameter of 0.076 m as a case study, a double-hole blasting crater was developed to investigate the effect of varying hole spacing on blasting crater volume and to determine the optimal hole spacing. The blasting parameters were optimized based on the numerical simulation results. The results show that within the range of borehole diameters considered, the blasting crater volume initially increases and then decreases with increasing scaled depth of the explosive charge. The fitted relationship between the optimum scaled depth and borehole diameter is y = −180.7197x³ + 86.3754x² − 9.5504x + 1.0782. For a borehole diameter of 0.076 m, the optimum scaled depth is 0.7278 m/kg^1/3, and the optimal hole spacing is 0.52 m. Based on blasting similarity theory, the calculated optimum burial depth of the explosive charge is 0.59 m, the critical burial depth is 1.1 m, and the recommended row spacing ranges from 0.95 m to 1.18 m. The findings of this study provide a theoretical basis for optimizing blasting parameters at the Daye Iron Mine and similar mining operations. Full article