Search Results (263)

Intralogistic activities play a critical role in sustaining uninterrupted manufacturing in production systems. With the increased usage of autonomous mobile robots (AMRs) to feed the production systems; a complex problem structure has emerged that includes the simultaneous evaluation of the sizing of the robotic fleet, task assignment and scheduling, as well as feasibility analysis of the investment. In this study, a complete decision-support frame is proposed to decide the minimum number of robots, plan the time index robot-line assignments and calculate the Cost Ratio for multiline manufacturing systems without starvation. In the proposed method, the total robot travel time, plant layout, operation times and safety factors are given as inputs to the time-indexed mixed-integer linear programming (MILP). In the literature, the fleet sizing and the scheduling problems are mostly handled separately. These highly related problems are integrated into one frame in this study. The method is validated by utilizing two worst case scenarios for an uninterrupted operation with changeable batteries and mandatory charging break. The results demonstrate that charging strategies have a huge impact on the number of minimum robots, operational applicability and economic performance. Full article

The rapid development of remote sensing satellite technology has enabled remote sensing images to be widely used in agriculture, meteorology, environmental monitoring and other fields. However, the presence of clouds in these images can lead to blurred and incomplete observations of the Earth’s surface, limiting the quality and applicability of the data. Current cloud detection networks usually adopt a single encoder–decoder structure that uniformly processes all spectral features without distinguishing between various spectral bands. To overcome this limitation, this paper proposes an Optical characteristics-guided Asymmetric Dual Encoder Feature Fusion cloud detection algorithm (OADEF²). The algorithm adopts an asymmetric dual encoder framework to divide the spectral bands of Sentinel-2A into two groups: RGB visible light bands and infrared/atmospheric correction bands, which are subsequently input into two different encoder branches. This method utilizes the unique physical characteristics of different spectral bands to improve the accuracy of cloud detection. In order to direct the focus of the network to cloud-related optical characteristics, an Optical characteristics-guided Multi-Scale cloud feature module (OCGMSCFM) based on Dynamic HOT Index and Full-Band Cloud Index is introduced. This module effectively solves the problem of insufficient representation of cloud features. In order to improve the efficiency of feature fusion, a Feature Aggregation and Filtering module (FAFM) is proposed. This module uses aggregation and techniques to filter basic features, thereby improving the accuracy of cloud detection. In order to overcome the limitations of feature modeling, a dual attention module that fuses Multi-interaction Local Spatial Attention mixed Channel Attention (MILSAMCAM) is added to the decoder. The experimental results validated the effectiveness of this algorithm in cloud detection tasks, achieving an F1-score of 97.30% on the S2-CMC dataset. Full article

Background: Dental caries and poor oral hygiene remain major public health problems among school-aged children, particularly in low- and middle-income countries. Teachers play a strategic role in delivering sustainable school-based oral health education; however, their effectiveness depends on appropriate pedagogical training. Objective: This study aimed to evaluate the effectiveness of a multimedia-assisted microteaching intervention for elementary school teachers in improving students’ oral health knowledge, attitudes, practices, and oral hygiene status. Methods: A mixed-methods sequential explanatory design was employed. Quantitative data were collected from 582 students and their teachers across three groups: multimedia-enhanced microteaching, multimedia-only training, and a control group. Outcomes were assessed using Knowledge–Attitude–Practice (KAP) questionnaires, the Oral Hygiene Index–Simplified (OHI-S), and the Decayed, Missing, and Filled Teeth (DMFT) index before and after a two-month implementation period. Non-parametric statistical tests were applied. Qualitative data were obtained through focus group discussions with teachers and were analyzed thematically. Results: Students in the multimedia-enhanced microteaching group demonstrated greater improvements in KAP scores and OHI-S values compared with the multimedia-only and control groups (p < 0.05). Qualitative findings indicated increased teacher confidence, improved classroom engagement, and better integration of oral health education into daily lessons. Changes in DMFT values were interpreted descriptively due to the short follow-up period. Conclusions: Multimedia-assisted microteaching appears to be a promising approach for strengthening teacher-led oral health education and improving short-term behavioral and hygiene outcomes among elementary school children. Further longitudinal studies are needed to assess long-term clinical effects. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

Educators’ epistemological experience of facilitating medical students’ active learning is under-researched, especially concerning non-biomedical learning in integrated curricula. Longitudinal, qualitative research on problem-based learning (PBL) tutors’ long-term insights is rare. Therefore, this study explores the following question: How do tutors conceptualise knowledge and knowing, particularly non-biomedical, after substantial experience in an integrated, problem-based medical curriculum and how does that relate to the student perspective? In a mixed-method study (pragmatism paradigm), sixteen years after semi-structured interviews with inaugural PBL tutors, follow-up interviews with the remaining ten revisited their replies about the population health knowledge theme. Via e-questionnaire, two years later, 9/10 tutors discussed student comments about their own knowledge base from four historical surveys (two student-cohorts, Years 1 and 5). Those surveys also provided a backdrop of comments on the public health knowledge theme, including threshold concepts and reducing health inequalities, plus Moore’s Cognitive Complexity Index (CCI). Each survey found mean CCI in Perry position 3–4 transition (multiplicity-to-relativism). Uncertainty or concern, especially about feared basic science gaps, prevailed across CCI scores. Public health knowledge appeared ‘worthy’ but unappealing for students’ professional identity, but tutors now appreciated its ‘ways of knowing’ and were more reflective, flexible, and accommodating about their own and students’ knowledge. Persistent challenges were student uncertainty or concern about knowledge gaps, particularly basic science, and conflict between knowledge types, for which staff and student epistemological support should be explicitly anticipated. Further research should explore staff–student epistemologies about other types of knowledge. Full article

Background/Objectives: Digestive problems are common in the general population and may be influenced by lifestyle, emotional status and diet. This study aimed to estimate the prevalence of digestive problems in Spanish adults and examined associated factors. Methods: Descriptive cross-sectional analysis of anonymized adult microdata from the 2023 Spanish Health Survey was performed. Data were collected using a mixed-mode design (self-administered web questionnaire with interviewer-administered follow-up). Digestive problems were recoded by combining gastric ulcer, constipation, and prescribed use of laxatives, astringent drugs, and stomach medication. Therefore, digestive problems are primarily defined as the presence of gastric ulcers, diarrhea, and/or constipation. Variables included sociodemographic, Body Mass Index (BMI), smoking, alcohol intake, physical activity, Personal Health Questionnaire Depression Scale (PHQ-8), World Health Organization Well Being Index (WHO-5), and macronutrient intake estimated from a Food-Frequency Questionnaire using the Spanish Food Composition Database (BEDCA). Group comparisons and multivariable logistic regression were conducted (95% CI; significance level set at p < 0.05). Results: Of 34,148 participants, 13,518 provided information on digestive problems; among these respondents, 3860 (28.6%) reported having digestive issues. Prevalence ranged from 5.2% to 36.5% among national territories. Higher odds (OR) of digestive problems were associated with age (OR 1.026, 95% CI 1.023–1.029), female sex (OR 1.168, 1.070–1.276), non-smoking (OR 1.240, 1.005–1.531) and ex-smoking (OR 1.447, 1.272–1.647) compared to current smokers, higher PHQ-8 scores (OR 1.040, 1.029–1.051), greater protein intake (OR 1.016, 1.009–1.023), consumption of sweet pastries (OR 1.058, 1.039–1.077), and dairy products (OR 1.027, 1.002–1.053); in contrast, lower odds were associated with higher WHO-5 scores (OR 0.985, 0.982–0.987), total fiber intake (OR 0.968, 0.949–0.987), and legume consumption (OR 0.894, 0.856–0.933). Conclusions: Digestive problems show considerable variability in prevalence among survey-based Spanish sample. Digestive problems were associated with older age, female sex, depressive symptoms, high-protein intake, and higher consumption of sweet pastries and dairy products, whereas higher well-being scores, higher fiber intake and legume consumption were associated with lower odds of digestive problems. Full article

In selective laser melting (SLM), real-time visual inspection of powder spreading quality is essential for maintaining dimensional accuracy and mechanical performance. However, reflections from metallic chamber walls introduce non-uniform illumination and reduce local contrast, hindering reliable defect detection. To overcome this problem, a chamber-reflection-aware image enhancement method is proposed, integrating a physical reflection model with a dual-channel deep network. A Gaussian-based curved-surface reflection model is first developed to describe the spatial distribution of reflective interference. The enhancement network then processes the input through two complementary channels: a Retinex-based branch to extract illumination-invariant reflectance components and a principal components analysis (PCA)-based branch to preserve structural information. Furthermore, a noise-aware loss function is designed to suppress the mixed Gaussian–Poisson noise that is inherent in SLM imaging. Experiments conducted on real SLM monitoring data demonstrate that the proposed method significantly improves contrast and defect visibility, outperforming existing enhancement algorithms in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). The approach provides a physically interpretable and robust preprocessing framework for online SLM quality monitoring. Full article

Hybrid AC/DC transmission expansion planning must balance investment cost, supply reliability and AC/DC stability, which challenges conventional mathematical programming and heuristic methods. This paper proposes a multi-objective planning framework based on an improved multi-step backtracking α-Q(λ) reinforcement learning algorithm with eligibility traces and an adaptive learning factor. A tri-objective model minimises annual economic cost, expected power shortage and a comprehensive electrical index that combines electrical betweenness, commutation-failure margin and effective short-circuit ratio. The mixed-integer planning problem is reformulated as an interactive learning process, where the state encodes candidate line construction decisions, the action builds or cancels lines, and the eligibility-trace matrix is used to quantify line importance. Case studies on the Garver-6 system, the IEEE 24-bus reliability test system and a 500 kV regional hybrid AC/DC grid show that, compared with classical Q-learning, the proposed method yields lower annual cost, reduced expected power shortage and improved AC/DC stability; in the 500 kV system, the expected annual power shortage is reduced from 70,810 MWh to 28,320 MWh. Full article

Background: The rapid rise in antimicrobial resistance has become one of the 10 most pressing health problems worldwide in recent years. Antibiotic stewardship offers hope in the fight against antibiotic resistance, but it is currently still falling short of expectations. A better understanding of the dynamics of the interaction between antibiotic consumption and the emergence and spread of resistance is urgently needed. Methods: We discuss a simple dynamic model based on a differential equation to describe the increase in the proportion of a pathogen’s antimicrobial resistance to an antibiotic as a function of the time-dependent consumption of that antibiotic. Furthermore, we investigate the association of heterogeneity in the consumption of antibiotics with the rate of resistant pathogens. Data basis is the hospital information system and the patient data-management system of a German hospital, restricted to the intensive care unit. To quantify heterogeneity, we discuss and compare different entropy measures. Results: For some pathogen–antibiotic pairs, the consumption-dependent dynamic model for the growth in the proportion of antimicrobial resistance provides acceptable predictions, while for others, the model is less suitable. Cross-resistance and complex interactions with other pathogens and antibiotics may be responsible for this, suggesting that the observed dynamic behavior should be complementary, described using heterogeneity models. Time courses of Shannon entropy, the Antibiotic Heterogeneity Index, and the negative Gini Index correlate positively with the time series of the resistance rate. Thus, an increase in heterogeneity correlates with a decreasing resistance rate. However, a time-delayed cross-correlation of a differential entropy measure with resistance share suggests a functional dependence that can be utilized for antibiotic stewardship. Conclusions: Evidence is provided that the amount of consumption of certain antibiotics drives the corresponding proportions of pathogens’ resistance to these antibiotics; however, the model predictions of these univariable models are generally not sufficiently good, pointing to a more complex interaction dynamics. Therefore, we switch to the level of structural features and show that the degree of constantly mixing of the shares of antibiotic consumption has a control function regarding the incidence of resistance. Controlling differential consumption heterogeneity, therefore, appears to be a feasible operational basis for antibiotic stewardship. Experimental studies are demanded to identify functional dependencies; however, the integration of clinical expertise with model-based prediction appears to be a feasible antibiotic stewardship strategy. Full article

Background: Childhood obesity and mental health problems are major public health concerns worldwide. Early-life exposure to green spaces has been shown to promote physical activity, reduce obesity risk, and improve cognitive and emotional development. Schoolyards offer a unique opportunity to promote health, as children spend a large proportion of their time at school. Methods: This quasi-experimental protocol study investigates the effects of transforming gray schoolyards into biodiverse green spaces on children’s health and well-being. Four primary schools in Limburg (Belgium and The Netherlands) were recruited: two intervention schools and two control schools. Children aged 7–12 years were enrolled, with baseline data collected in November 2021 and follow-up measurements scheduled every six months until November 2023. Outcomes include body mass index (BMI) z-score (primary outcome), waist circumference, diet, cognitive performance, psychological well-being, biodiversity knowledge, and physical activity. Data will be analyzed using linear mixed models, and cost-effectiveness analyses will be performed. Expected Results: Improvements in BMI z-scores, cognitive functioning, dietary behavior, and psychological well-being are expected among children in green schoolyards compared to those in control schools. Increased biodiversity awareness and reduced exposure to black carbon are also anticipated. Conclusions: This study is designed to provide evidence on the health impacts of greener schoolyards and contribute to strategies for promoting child development through environmental interventions. Full article

This paper addresses the retail pricing problem for electricity retailers who also act as virtual power plant (VPP) operators, aggregating distributed energy resources (DERs). In future power markets where multiple such retailers compete for customers, a key challenge is to design pricing strategies that balance economic profitability with low-carbon objectives. Existing research often overlooks the impact of retailers’ heterogeneous resource portfolios, particularly the share of low-carbon resources like photovoltaics (PVs), on their competitive advantage and pricing decisions. To bridge this gap, we propose a novel retail pricing model that integrates a non-cooperative game framework with Markov Decision Processes (MDPs). The model enables each retailer to formulate optimal real-time pricing strategies by anticipating competitors’ actions and customer responses, ultimately reaching a Nash equilibrium. A distinctive feature of our approach is the incorporation of spatially differentiated carbon emission factors, which are adjusted based on each retailer’s share of PV generation. This creates a tangible low-carbon incentive, allowing retailers with greener resource mixes to leverage their environmental advantage. The proposed framework is validated on a modified IEEE 30-bus system with six competing retailers. Simulation results demonstrate that our method effectively incentivizes optimal load distribution, alleviates network congestion, and improves branch loading indices. Critically, retailers with a higher share of PV resources achieved significantly higher profits, directly translating their low-carbon advantage into economic value. Notably, the Branch Load Index (BLI) was reduced by 12% and node voltage deviations were improved by 1.32% at Bus 12, demonstrating the model’s effectiveness in integrating economic and low-carbon objectives. Full article

To address the edge blurring issue of drone inspection images of mountainous transmission lines caused by non-uniform haze interference, as well as the low operational efficiency of traditional dehazing algorithms due to increased network complexity, this paper proposes a high-frequency guided dual-branch attention multi-scale hierarchical dehazing network for transmission line scenarios. The network adopts a core architecture of multi-block hierarchical processing combined with a multi-scale integration scheme, with each layer based on an asymmetric encoder–decoder with residual channels as the basic framework. A Mix structure module is embedded in the encoder to construct a dual-branch attention mechanism: the low-frequency global perception branch cascades channel attention and pixel attention to model global features; the high-frequency local enhancement branch adopts a multi-directional edge feature extraction method to capture edge information, which is well-adapted to the structural characteristics of transmission line conductors and towers. Additionally, a fog density estimation branch based on the dark channel mean is added to dynamically adjust the weights of the dual branches according to haze concentration, solving the problem of attention failure caused by attenuation of high-frequency signals in dense haze regions. At the decoder end, depthwise separable convolution is used to construct lightweight residual modules, which reduce running time while maintaining feature expression capability. At the output stage, an inter-block feature fusion module is introduced to eliminate cross-block artifacts caused by multi-block processing through multi-strategy collaborative optimization. Experimental results on the public datasets NH-HAZE20, NH-HAZE21, O-HAZE, and the self-built foggy transmission line dataset show that, compared with classic and cutting-edge algorithms, the proposed algorithm significantly outperforms others in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM); its running time is 19% shorter than that of DMPHN. Subjectively, the restored images have continuous and complete edges and high color fidelity, which can meet the practical needs of subsequent fault detection in transmission line inspection. Full article

The Vehicle Routing Problem (VRP) is central to last-mile logistics, yet a gap remains when products have late release times and vehicles can be reloaded en route via mobile satellites that rendezvous with reloading vehicles at customer locations. We propose the VRP with Release Times and Reloading at Mobile Satellites (VRP-RT-RMS) and develop two mixed-integer linear programming formulations: a three-index (MILP-3) and a two-index (MILP-2). The objective minimizes total distance subject to capacity, route duration, synchronization, and time constraints. We generated 40 instances from real data (10 per size $N\in \{10,15,20,25\}$). En-route reloads simultaneously reduce distance and fleet size and can restore feasibility when the classical VRP is infeasible. To contrast the classical VRP with our VRP-RT-RMS, we analyzed a particular instance with $N=10$ customers: total distance decreased by 7.26% and the number of vehicles fell from 5 to 3. As instance size grows, MILP-2 shows superior scalability and efficiency compared with MILP-3. Beyond the technical scope, coordinating reloads is pertinent to urban operations with late product releases, lowering kilometers traveled and delivery times. Full article

Metal components such as rolling bearings are prone to wear, cracks, and defects in harsh environments and long-term use, leading to performance degradation and potential equipment failures. Therefore, detecting surface cracks and other defects in rolling bearings is of great significance for ensuring equipment reliability and safety. However, traditional signal decomposition methods like EEMD and FEEMD suffer from residual noise and mode mixing issues, while deconvolution algorithms such as CYCBD are sensitive to parameter settings and struggle in high-noise environments. To mitigate the susceptibility of fault signals to background noise interference, this paper proposes a fault feature extraction method based on fast complementary ensemble empirical mode decomposition (FCEEMD) and adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). Firstly, we propose FCEEMD, which effectively eliminates the residual noise of ensemble empirical mode decomposition (EEMD) and fast ensemble empirical mode decomposition (FEEMD) by introducing paired white noise with opposite signs, solving the problems of traditional decomposition methods that are greatly affected by noise, having large reconstruction errors, and being high time-consuming. Subsequently, a new intrinsic mode function (IMF) screening index based on correlation coefficients and energy kurtosis is developed to effectively mitigate noise influence and enhance the quality of signal reconstruction. Secondly, the ACYCBD model is constructed, and the hidden periodic frequency is detected by the enhanced Hilbert phase synchronization (EHPS) estimator, which significantly enhances the extraction effect of the real periodic fault features in the noise. Finally, instantaneous energy tracking of bearing fault characteristic frequency is achieved through Teager energy operator demodulation, thereby accurately extracting fault state features. The experiment shows that the proposed method accurately extracts the fault characteristic frequencies of 164.062 Hz for inner ring faults and 105.469 Hz for outer ring faults, confirming its superior accuracy and efficiency in rolling bearing fault diagnosis. Full article

Background and objectives: Evaluating Large Language Models (LLMs) presents two interrelated challenges: the general problem of assessing model performance across diverse tasks and the specific problem of using LLMs themselves as evaluators in pedagogical and educational contexts. Existing approaches often rely on single metrics or opaque preference-based methods, which fail to capture critical dimensions such as explanation quality, robustness, and argumentative diversity—attributes essential in instructional settings. This paper introduces PEARL, a novel framework conceived, operationalized, and evaluated in the present work using LLM-based scorers, designed to provide interpretable, reproducible, and pedagogically meaningful assessments across multiple performance dimensions. Methods: PEARL integrates three specialized rubrics—Technical, Argumentative, And Explanation-focused—covering aspects such as factual accuracy, clarity, completeness, originality, dialecticality, and explanatory usefulness. The framework defines seven complementary metrics: Rubric Win Count (RWC), Global Win Rate (GWR), Rubric Mean Advantage (RMA), Consistency Spread (CS), Win Confidence Score (WCS), Explanation Quality Index (EQI), and Dialectical Presence Rate (DPR). We evaluated PEARL by evaluating eight open-weight instruction-tuned LLMs across 51 prompts, with outputs scored independently by GPT-4 and LLaMA 3:instruct. This constitutes LLM-based evaluation, and observed alignment with the GPT-4 proxy is mixed across metrics. Results: Preference-based metrics (RMA, RWC, and GWR) show evidence of group separation, reported with bootstrap confidence intervals and interpreted as exploratory due to small samples, while robustness-oriented (CS and WCS) and reasoning-diversity (DPR) metrics capture complementary aspects of performance not reflected in global win rate. RMA and RWC exhibit statistically significant, FDR-controlled correlations with the GPT-4 proxy, and correlation mapping highlights the complementary and partially orthogonal nature of PEARL’s evaluation dimensions. Originality: PEARL is the first LLM evaluation framework to combine multi-rubric scoring, explanation-aware metrics, robustness analysis, and multi-LLM-evaluator analysis into a single, extensible system. Its multidimensional design supports both high-level benchmarking and targeted diagnostic assessment, offering a rigorous, transparent, and versatile methodology for researchers, developers, and educators working with LLMs in high-stakes and instructional contexts. Full article