Search Results (1,242)

Public transportation (PT) is key to breaking the vicious cycle of private vehicles, a critical sustainability challenge in developing countries. The increase in population raises the number of private cars, and this trend continues. PT plays a vital role in reducing car use, traffic congestion, and environmental pollution. Fare is crucial to the system’s ability to encourage passengers to use PT. It affects mobility, the quality of life, and the sustainability of the system. This study aims to examine Istanbul’s optimal fare system using the BWM (Best–Worst Method) for PT fare for the first time. Furthermore, it is the first study to compare fare structures and criteria for Istanbul, Europe’s second-largest city, where transportation affects quality of life. The most frequently used fare structures and criteria in the literature and practice were weighted by experts using BWM surveys for the Istanbul Metro. The results show that distance-based fare (DBF) (43.7%) is the best fare structure, while flat fare (FF) (12.2%) is the weakest. For the criteria weightings, benefit received (24.4%) and social equity (22.7%) are seen as superior. Finally, the impact of the criterion on the fare structure was demonstrated through analysis, and its importance for experts in evaluating PT was highlighted. Full article

This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency testing to determine whether machine-learning enhancement is genuinely effective across stations and time windows. The analysis uses hourly records from 528 UHSLC tide gauges, with 31-day short sequences used to reconstruct 180-day sea-level variability. Taking the physical tidal model as the baseline, residuals are corrected using Extremely Randomized Trees, Random Forest, and Gradient Boosting. To avoid false improvement driven solely by error reduction, a hierarchical decision framework is established. Baseline model quality is first screened using NSE and the coefficient of determination, after which mathematical artefacts are identified through diagnostics of peak suppression and variance shrinkage. A five-level classification is then derived from the convergent evidence of twelve performance metrics and four statistical significance tests. The results show a consistent global pattern across all three algorithms. Approximately 57% of stations meet the criterion for genuine improvement, whereas about 42% are associated with an unreliable physical baseline, indicating that the dominant source of failure arises not from the ensemble-learning algorithms themselves, but from spatially varying limitations in the underlying physical baseline. Spatially, the credibility of machine-learning correction is strongly conditioned by baseline quality: stations with effective correction are more continuous along the eastern North Atlantic and European coasts, whereas stations with ineffective correction are more concentrated in the Gulf of Mexico, the Caribbean, and the marginal seas and archipelagic regions of the western Pacific. These results indicate that the observed spatial heterogeneity primarily reflects geographically varying physical and dynamical conditions that control baseline reliability and residual learnability, rather than a standalone difference in the intrinsic capability of ensemble learning itself. Full article

Multilevel threshold image segmentation is a key task in image processing, yet it faces challenges such as low search efficiency in high-dimensional spaces, difficulty in balancing segmentation accuracy and stability, and insufficient adaptability to complex scenes. Existing solutions mainly include traditional thresholding methods and metaheuristic optimization-based schemes, but they still face limitations in high-dimensional and complex segmentation tasks. The standard Seagull Optimization Algorithm (SOA) suffers from shortcomings including a single exploration mechanism, weak local exploitation capability, and a tendency for population diversity to deteriorate, making it difficult to meet the demands of high-dimensional optimization. To address these issues, this paper proposes a multi-strategy fused improved Seagull Optimization Algorithm (MFISOA), which integrates three strategies: adaptive cooperative foraging, differential evolution-driven exploitation, and centroid opposition-based boundary control. These strategies jointly construct a collaborative optimization framework with dynamic resource allocation, fine local search, and population diversity maintenance, thereby improving global exploration efficiency, local exploitation accuracy, and population stability. To evaluate the optimization performance of MFISOA, numerical simulation experiments were conducted on the CEC2017 and CEC2022 benchmark test suites, and comparisons were made with nine other mainstream advanced algorithms. The results show that MFISOA outperforms the competing algorithms in terms of optimization accuracy, convergence speed, and operational stability. Its superiority is further verified by the Wilcoxon rank-sum test and the Friedman test, with statistical significance (p < 0.05). In the multilevel threshold image segmentation task, using the Otsu criterion as the objective function, MFISOA was tested on nine benchmark images under 4-, 6-, 8-, and 10-threshold segmentation scenarios. The results indicate that MFISOA achieves better performance on metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Feature Similarity Index (FSIM), enabling more accurate characterization of image grayscale distribution features and producing higher-quality segmentation results. This study provides an efficient and reliable approach for numerical optimization and multilevel threshold image segmentation. Full article

Cotton planting efficiency is increasingly constrained by narrow planting windows, motivating interest in higher operating speeds if stand establishment and seed placement accuracy can be maintained. Field experiments were conducted in Georgia between 2020 and 2025 to quantify the effects of planter operating parameters and system configurations on cotton planter performance. Trials evaluated combinations of planting speed, row-unit downforce, seed plate type (singulated vs. hill-drop), and seed delivery system using conventional gravity-tube planters and two high-speed planter systems equipped with advanced delivery systems. The achieved population was determined from stand counts, planting quality was assessed using plant position classification relative to theoretical plant spacing, and lint yield was measured at harvest. Across site-years, the achieved population was generally not affected by planting speed or downforce within the tested ranges. With conventional gravity-tube delivery systems, the proportion of perfectly spaced plants declined from 44.0% to 22.1% in 2020 and from 52.8% to 28.4% in 2021 as planting speed increased from 5 to 11 km ${\mathrm{h}}^{-1}$. In contrast, across the advanced planter systems evaluated in 2025, mean perfect spacing remained within a narrow range of 45.8% to 49.5% across 8 to 14 km ${\mathrm{h}}^{-1}$. Hill-drop seed plates increased the achieved population relative to singulated plates in the seed plate × downforce trials, increasing mean achieved population from 79.6 to 87.8 thousand plants h${\mathrm{a}}^{-1}$ at Midville and from 62.2 to 73.1 thousand plants h${\mathrm{a}}^{-1}$ at Plains in 2022, and from 45.4 to 58.1 thousand plants h${\mathrm{a}}^{-1}$ at Midville in 2024, but these increases did not result in consistent lint yield differences. The high-speed hill-drop configuration evaluated in 2025 did not consistently produce plant pairs meeting the hill-drop spacing criterion. These results indicate that current high-speed planter systems can be used for singulated cotton to increase planting productivity while maintaining placement accuracy, although additional research is needed to determine the environmental and management conditions under which spacing improvements translate into yield benefits. Full article

This study develops a reproducible and auditable workflow to prepare regulatory air-quality monitoring time series for subsequent temporal analysis, including observational PRE/POST applications around coal-fired power plant closures in northwestern Spain. The dataset comprises daily concentrations from 28 monitoring stations (2006–2023) for PM₁₀, PM_2.5, NO, NO₂, NO_x, O₃, SO₂, and CO, affected by missingness, structural inconsistencies, and extreme values. The contribution of this study lies in integrating standardized data ingestion and QA/QC chained-equation imputation with Bayesian Ridge regression, hold-out validation, physicochemical consistency checks, and robust extreme-value handling within a traceable processing workflow. Missing values are reconstructed per pollutant using plant-level multi-station pooling to improve stability. Performance is evaluated using a 5% masked hold-out and summarized with MAE, RMSE, R², and bias, complemented by an operational fit-quality label. Post-imputation controls enforce NO–NO₂–NO_x consistency and the physical constraint PM_2.5 ≤ PM₁₀, while extreme values are screened through a hierarchical robustness framework combining a Hampel filter, winsorization, and a Tukey IQR criterion. The workflow outputs documented diagnostics and robust daily series while preserving the traceability of observed values, flags, edits, and final decisions. Full article

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade. Full article

This study presents an integrated optimization framework that combines the Design of Experiments (DOE) approach with Machine Learning (ML) techniques to minimize defects in the injection molding of Fuel Vapor Charcoal Canisters. The research focuses on five critical process parameters—melt temperature, mold temperature, filling time, pressure holding time, and pure cooling time—whose combined influence on major molding defects (warpage, shrinkage, shear stress, residual stress, and short shots) was systematically investigated. A Taguchi L25 orthogonal array was employed to structure the experiments and identify the optimal parameter levels through signal-to-noise (S/N) ratio analysis using the “smaller-the-better” quality criterion. The Taguchi results revealed that pressure holding time was the most influential factor, followed by mold temperature and melt temperature. Simulation results from SolidWorks Plastics confirmed the reduction in major defects under the optimized settings. To further validate and generalize the DOE findings, a Random Forest regression model was trained on the same dataset to capture nonlinear interactions between parameters. The model achieved an average RMSE of 2.451 ± 0.591 in five-fold cross-validation, demonstrating strong predictive accuracy. Feature importance analysis indicated that pressure holding time accounted for approximately 77.5% of the variance in the defect index, reaffirming its dominant role. A 3D response surface of the global parameter space (mold temperature vs. pressure holding time) revealed a distinct minimum defect region, consistent with the DOE-optimized settings. The Taguchi analysis identified the optimal parameter settings as Melt Temperature at Level 2, Mould Temperature at Level 3, Filling Time at Level 4, Pressure Holding Time at Level 5, and Pure Cooling Time at Level 4, which collectively produced the highest S/N ratios and the lowest overall defect index. The overall discrepancy between DOE and ML predictions was only 12.5%, confirming methodological consistency. The integration of DOE and ML not only enhances parameter interpretability and defect prediction accuracy but also provides a scalable, data-driven approach for intelligent process control and quality assurance in automotive injection molding. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Water is a vital resource that requires long-term legal protection to ensure both ecological values and societal benefits. The European Union’s Water Framework Directive (2000/60/EC) is central to this aim, establishing binding requirements for good ecological and chemical status in all water bodies and legally binding environmental quality standards. Sweden has implemented the Directive into national law; however, its application has been characterized by legal ambiguities, particularly regarding the possibility of considering disproportionate costs in environmental measures. This study examines the scope and application of the disproportionate cost criterion within the context of environmental law and hydropower regulation in Sweden. A comparative overview of the criterion’s application in other EU/EEA countries is also provided. Based on a legal approach, the analysis focuses on how these rules affect hydropower, where the goal of renewable energy production often needs to be weighed against the requirement for ecological recovery. The study concludes that applying the disproportionate costs criterion requires transparency and legal certainty to ensure a fair balance between the social benefits of hydropower and the need for long-term protection of the aquatic environments. To avoid differences in how the criterion is applied in different EU Member States, harmonized guidelines are needed. Full article

This paper presents a novel dual-module Permanent Magnet Linear Synchronous Motor (PMLSM) featuring an unequal tooth-shoe topology, alongside a highly efficient surrogate-assisted framework to maximize average thrust and minimize thrust ripple. To overcome the computational bottleneck of expensive Finite Element Analysis (FEA), we propose a Constraint-Preserving Maximin Latin Hypercube Design (CP-MmLHD) coupled with an ARD-Kriging model and the Expected Hypervolume Improvement (EHVI) criterion. This closed-loop framework expertly handles strict geometric constraints and anisotropic parameter sensitivities. Within a strict budget of only 150 FEA evaluations, the framework successfully identifies a high-quality Pareto front. Notably, a representative optimal design reduces thrust ripple by over 80% without compromising average thrust. Furthermore, comparative experiments demonstrate superior computational efficiency over conventional algorithms, while multi-run statistical benchmarking and stochastic Monte Carlo analysis rigorously confirm the framework’s algorithmic robustness and manufacturing reliability. Full article

Urban mobility is increasingly affected by air pollution and traffic congestion caused by conventional private vehicles, as well as by insufficient flexibility of public transport. Micromobility devices (MMDs) can mitigate these and other negative impacts on quality of life due to their distinctive characteristics, the significance of which is investigated in this research. To address these challenges facing the modern city, a system of 15 hierarchically unstructured criteria influencing the deployment of MMDs in urban areas was established. The relative weights of these criteria were calculated based on the assessments of 16 experts and the criterion weights were determined using four multi-criteria decision-making (MCDM) methods: ARTIW-L (Average Rank Transformation into Weight—Linear), ARTIW-N (Average Rank Transformation into Weight—Non-Linear), DPW (Direct Percentage Weight), and AHP (Analytic Hierarchy Process). The results indicate that the expert judgments are consistent, as Kendall’s coefficient of concordance 0.406 is 3.8 times greater than the minimum value of 0.106 (at a significance level 0.05 and 14 degrees of freedom). In addition, the consistency ratios (C.R.) calculated from the AHP pairwise comparison matrices were below 0.1. The demonstrated consistency of the expert judgements and the compatibility of all matrices justify adopting the average of the relative weights obtained using the four MCDM methods as the final solution. According to the experts, the most important criteria for MMD deployment are travel safety (0.1336), travel duration (0.1302), the influence of infrastructure quality on comfort (0.0841), impact on health (0.0805), and the cost of purchasing an MMD (0.0713), while the remaining criteria are of lower significance. Based on the research results it is expected that the identified micromobility implementation measures will be useful for decision-makers and urban development planners. Full article

Background/Objectives: Prolonged postoperative length of stay (LOS) is associated with increased morbidity and healthcare utilization following surgery for acute cholecystitis. The prognostic value of skeletal muscle density (SMD), a marker of muscle quality, is unclear. We aimed to evaluate the association between SMD and prolonged LOS and to compare the predictive performance of SMD with that of skeletal muscle index (SMI). Methods: A retrospective study of 382 patients who underwent surgery for acute cholecystitis was conducted. LOS was defined using mean- and median-based cut-offs. Multivariate logistic regression was used to identify independent predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and Akaike information criterion (AIC). Robustness was assessed using count-based modeling, spline analysis, and model calibration. Results: Patients with prolonged LOS were older, had lower body mass index and serum albumin levels, higher inflammatory markers, and more comorbidities, and had significantly lower SMD. Higher SMD was independently associated with a reduced risk of prolonged LOS (adjusted OR per 1-HU increase, 0.93; 95% CI, 0.88–0.97; p = 0.002). The SMD-based model showed acceptable discrimination (AUC 0.78) and slightly better model fit than the SMI-based model (AIC 365.1 vs. 371.2). In secondary analyses, patients in the lowest SMD quartile had significantly higher postoperative complication rates than the remaining patients (10.5% vs. 2.8%; p = 0.004). Conclusions: Overall, lower SMD was independently associated with prolonged LOS after surgery for acute cholecystitis and may serve as a readily available imaging biomarker for perioperative risk stratification. Full article

The present study investigates the surface integrity and flank wear of uncoated cermet inserts during dry turning of AISI 304 stainless steel. Three-dimensional metrology techniques were employed to assess both surface roughness and cutting-tool flank wear. Cutting speed and feed rate were the process parameters varied in the experiments. Both parameters exhibited a significant influence on the final surface quality. Specifically, increasing the cutting speed resulted in a deterioration of the surface finish under the evaluated conditions. Considering an average flank wear (VBB) of 0.1 mm as the tool life criterion, tool lives of 15 min and 9 min were achieved at cutting speeds of 120 m/min (lowest level) and 150 m/min (highest level), respectively. At lower cutting speeds, abrasive wear and adhesion were the predominant wear mechanisms, whereas chipping and diffusion became more pronounced at the higher cutting speed. The dry turning of AISI 304 stainless steel with uncoated cermet inserts proved viable in terms of sustainability and surface integrity; however, effective chip evacuation remains a critical concern. The use of compressed air or minimum quantity lubrication (MQL) may help mitigate this issue. Full article

Background/Objectives: Conventional diagnosis of inborn errors of metabolism (IEMs) requires multiple specimen types—urine organic acids, plasma amino acids, and serum acylcarnitines—analyzed on distinct analytical platforms. This multi-assay approach is labor-intensive and limits timely clinical decision making. We aimed to develop a fully automated serum-based LC–MS/MS platform for integrated quantitative metabolite profiling and to establish pediatric reference intervals (RIs) to support diagnostic interpretation. Methods: A fully automated LC–MS/MS system integrated with the CLAM-2030 automated pretreatment module was developed to enable simultaneous quantification of 25 organic acids, 8 amino acids, and 21 acylcarnitines. Analytical performance was assessed for linearity, limits of detection and quantification, precision and accuracy. Serum samples from 296 non-IEM children aged 0–6 years were analyzed to establish pediatric RIs using Box–Cox transformation and Gaussian modeling. Clinical utility was evaluated in sera from 89 patients diagnosed with IEM using z-score-based logistic regression models. Results: The method demonstrated excellent performance, with linearity (r² > 0.99) across calibration ranges, limits of detection and quantification defined by signal-to-noise ratios > 3 and >10, and intra- and inter-assay precision < 15% CV for all 54 analytes. Twenty-one analytes met the acceptance criterion of ±20% accuracy at all quality-control levels. Pediatric RIs provided a quantitative framework for interpreting the metabolic abnormalities. In IEM patients, disease-specific metabolites were consistently outside the established ranges, and z-score-based logistic regression models successfully distinguished major IEM categories, including organic acidemias and long-chain fatty acid oxidation disorders. Conclusions: This fully automated, serum-based LC–MS/MS platform provides a clinically practical and quantitative framework for integrated metabolic profiling using pediatric RIs, supporting diagnosis and monitoring of IEMs in pediatric settings. Full article