Search Results (5,829)

In scientific research, the optimization of organic light-emitting diodes (OLEDs) is generally achieved through a lengthy and expensive experimental process as new ideas and configurations are tested on real devices. Electro-optical simulation allows for the rapid evaluation of key performance parameters of device structures, thus reducing manufacturing time and costs. This paper presents an original contribution to the electro-optical modeling and optimization of multilayer OLED devices using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This optimization explicitly incorporates defect states within the ITO/NPB/Alq₃:C545T/Alq₃/LiF-Al structure. The simulated model is calibrated using experimental data by fitting the trap state distribution. The Pareto front resulting from the multi-objective optimization identifies a set of non-dominated configurations, including an optimal intermediate structure defined by an electron transport layer (ETL) thickness of approximately 42 nm and a hole transport layer (HTL) thickness of approximately 53 nm. This configuration leads to a limited reduction of 1.75–2% in current efficiency (${\eta }_c$) while offering a remarkable improvement of 23–30% in power efficiency (${\eta }_p$) compared to the extreme configurations of the optimal Pareto set. Thus, this solution represents an optimal Pareto trade-off between high current efficiency and improved power efficiency. This paper shows that combining defect modeling and thickness optimization provides a reliable framework for the electro-optical optimization of OLED devices. Future work will extend this approach to spectral and colorimetric analysis. Full article

Improving the health supportiveness of the campus built environment is a key strategy for alleviating health pressures in higher education institutions (HEIs). As a complex environmental system, a university campus requires systematic assessment of its environmental health performance to inform science-based design and planning decisions. This study systematically reviews the environmental characteristics of current healthy-campus assessment tools (HCATs) for HEIs and evaluates their compatibility with a Chinese standards context. A three-phase mixed-methods approach identified 12 HCATs, examined their environmental features, and constructed a content framework. Three representative Chinese alternative tools were compared. The results show that: (1) HCATs vary by development context but consistently prioritize physical environmental resources that support health behaviors, such as abundant and reasonable active transportation and fitness facilities and a health-promoting environmental culture, rather than conventional physics performance. (2) Although Chinese tools overlap with HCATs on certain environmental topics, they cannot replace HCATs in terms of environmental integration, coverage, and applicability to HEI settings. (3) Future Chinese HCATs should strengthen environmental support for behavior change and health promotion and improve operability. This study reveals gaps between current Chinese tools and HCATs, underscores the necessity of developing environment-focused HCATs for Chinese HEIs, and provides a foundation for related tools’ development work. Full article

STEM learning unfolds over many years. It is shaped by changing contexts, transitions, and occasional breaks. However, much of the existing work still focuses on single stages or isolated factors. This article introduces the E³ Framework. Its purpose is to provide a language for examining why some STEM trajectories endure, why others fade, and what kinds of ecological alignment allow learning to remain viable in the flow of real life. Based on a systemic approach, we aim to explain how STEM participation is preserved over time. This framework describes stability as the result of interactions among three ecological domains: resources, regulation, and time. We identify five key functions—robustness, regulatory re-alignment, renewal, informational persistence, and environmental fit. These functions show how engagement holds steady or recovers as circumstances shift. The E³ Framework offers a way to analyze how supports, feedback loops, and time-related structures either come together or fall apart. We provide simple design guidelines and matrices to show how educators and policymakers can better support STEM trajectories. Full article

Physics-based electrochemical models such as the Doyle–Fuller–Newman (DFN) framework provide high predictive accuracy for lithium-ion batteries but are computationally intensive, limiting their applicability in large-scale and real-time simulations. Reduced-order models such as the Newman–Tiedemann–Gu–Kim (NTGK) model offer improved computational efficiency but typically require experimentally fitted parameters, restricting their scalability across chemistries and operating conditions. This work proposes a physics-informed parameter transfer framework in which NTGK model parameters are derived directly from experimentally validated DFN simulation outputs using a regression-based formulation, thereby reducing dependence on direct experimental parameterization. The approach is applied to LCO–graphite and NMC–graphite cells across multiple discharge rates. The DFN model shows good agreement with experimental data at low to moderate C-rates, with mean absolute errors (MAE) in the range of 20–35 mV at 0.5C. The NTGK model parameterized using DFN-generated synthetic data accurately reproduces the DFN voltage response, with model reduction MAE values as low as 4.5 mV for LCO and 7.17 mV for NMC cells under low-rate operating conditions. Validation against experimental data yields MAE values up to 74 mV for LCO cells and 98 mV for NMC cells at higher C-rates. The proposed framework establishes a direct and physically consistent mapping between high-fidelity electrochemical models and reduced-order representations, enabling scalable and computationally efficient battery simulations while minimizing reliance on extensive experimental parameterization. This approach provides a practical pathway for integrating electrochemical fidelity into system-level and multi-physics battery simulations. Full article

This paper details the implementation of an integrated engineering framework for the real-time assessment of pose and size in fusiform fish, utilizing laser-camera technology. The design, comprising a camera and laser emitter, leverages laser triangulation for accurately measuring distances between key points, providing a reliable baseline for data comparison. Enhanced with the yolov7 model backbone, it includes detection and segmentation features, enabling precise image instance segmentation of fish and laser lines. The system’s dual-network structure, which combines fully connected regression and DSNT-MobileFaceNet networks, efficiently identifies six crucial landmarks on fish—an essential step for detailed pose analysis. This method facilitates the accurate determination of two-dimensional fish posture by analyzing the relative positions of these landmarks. A notable capability of this system is its ability to infer depth information from laser lines on the fish’s body, aiding in the accurate measurement of dimensions such as body length and depth. Empirical results demonstrate the system’s effectiveness, with high mean Average Precision (mAP) values for both object detection (0.9560 for fish, 0.8550 for laser lines) and segmentation (0.9740 for fish, 0.8420 for laser lines). The DSNT-MobileFaceNet network, in particular, shows excellent fitting accuracy with an $R^2$ value of 0.9170. The deep learning model achieves an average error rate of 7.75% in detecting fish data, markedly improving upon the baseline error rate of 14.70%. Overall, this study confirms the proposed system’s capability in accurately assessing fish pose and size. As a rigorous proof of concept validated in a controlled laboratory environment, this work establishes a foundational framework for non-invasive morphological monitoring, suggesting its future applicability in marine biology and aquaculture. Full article

The increasing adoption of alternative fuels such as hydrogen and ammonia in energy systems has created a growing need for advanced diagnostic techniques capable of monitoring combustion products with high specificity and flexibility. In this context, Raman spectroscopy represents a promising optical approach for gas analysis, as it enables the simultaneous detection of multiple species without requiring sample preparation. In this work, the performance of a cost-effective Raman-based system on quantitative detection of nitrogen oxides (NO and NO₂) and nitrous oxide (N₂O) is presented. The experimental setup is based on a multi-pass optical configuration designed to enhance the Raman signal and employs off-the-shelf components, including an uncooled CMOS detector. Calibration measurements were carried out using gas mixtures at known partial pressures, and gas concentrations were retrieved through a nonlinear least-squares fitting procedure applied to the measured spectra. The results show that the system provides linear and repeatable responses for NO and N₂O over the investigated pressure ranges, with low mean errors and limited data dispersion, while NO₂ performance could not be fully quantified in a comparable manner due to the high reactivity of the species under the tested conditions. Overall, the proposed system represents a viable and cost-effective solution for multi-species gas analysis in emerging combustion applications. This work aims to extend the industrial applicability of Raman spectroscopy to NO_x and NO₂ diagnostics. Full article

Background/Objectives: Nursing organizational well-being has important implications for nurses, patients, and healthcare organizations. From a nursing-specific perspective, it arises from the balance between nursing demands and nursing resources in the work environment. However, most available instruments are not grounded in explicit nursing theory and do not allow the identification of well-being profiles through person-centered approaches. This study aimed to develop and evaluate the psychometric properties of the Nursing Organizational Well-being Questionnaire (NOW_Q). Methods: Following COSMIN guidelines, a two-phase design was adopted. Phase 1 involved item generation and expert evaluation, resulting in a 28-item instrument rated on a 5-point frequency scale. Phase 2 consisted of a multicenter cross-sectional study. Construct validity was examined through exploratory and confirmatory factor analyses using cross-validation. Reliability was assessed using ordinal omega coefficients, concurrent validity through associations with a global organizational well-being item, and cluster analysis to explore practical utility. Results: Findings (n = 461 nurses; 7 hospitals) supported an eight-dimension structure: workload, emotional demands, work–family conflict, autonomy, available resources, nurse–nurse relationship, nurse–head nurse relationship, and nurse–physician relationship. The confirmatory model showed good fit (RMSEA = 0.051; CFI = 0.938; TLI = 0.927; SRMR = 0.067), and all dimensions demonstrated satisfactory internal consistency (ordinal omega = 0.75–0.87). Significant associations with global organizational well-being were observed. Three distinct profiles emerged (Nurturing, Observed-Detached, and Withstanding), reflecting different configurations of nursing demands and resources. Conclusions: The NOW_Q is a theory-based, nursing-specific instrument with satisfactory psychometric properties and practical utility for identifying organizational well-being profiles and supporting targeted interventions in clinical settings. Full article

This paper presents a dynamic multi-period mixed-integer programming model for the Disaster Volunteer Task Assignment Problem (DVTAP) that advances the humanitarian logistics literature through an integrated treatment of features that have previously appeared only in isolation. Unlike prior formulations that assume volunteer surplus or steady-state conditions, our model reflects the acute-phase reality where tasks far exceed available volunteers and new task arrivals diminish over time as the disaster stabilizes. We incorporate makespan as an optimization objective alongside deprivation-weighted response time, skill matching, workload balance, and volunteer reliability. Ideal-nadir normalization ensures that all objective components contribute meaningfully regardless of their native units. The approach proceeds in two stages. First, we formulate and solve a single-period baseline MIP under volunteer surplus using the CBC solver at four scales (10 to 500 tasks). All four instances are solved to proven optimality, achieving 80 to 100% task coverage with skill-matching rates of 76.9 to 99.6%. Second, we develop a rolling-horizon algorithm that decomposes the multi-period problem into sequential epoch-level MIPs with state transitions, non-homogeneous Poisson task arrivals, fatigue accumulation, and task surplus conditions where the initial task-to-volunteer ratio exceeds 3:1. Computational experiments on three dynamic scenarios (up to 559 mean cumulative tasks) demonstrate that the algorithm achieves mean task completion rates of 84.21 ± 1.92% (Large-Dynamic), 93.74 ± 2.07% (Small-Dynamic), and 94.59 ± 2.03% (Medium-Dynamic) (mean ± standard deviation across 30 Monte Carlo replications) within a 15 h planning horizon, with per-epoch skill-matching rates of 11 to 20% (substantially lower than the static baseline due to triage-mode epochs that force all-volunteer assignment regardless of skill fit). The results reveal a clear regime transition: early epochs operate under severe task surplus where triage dominates, while later epochs transition to volunteer surplus where optimization of secondary objectives becomes feasible. Comparison against a skill-aware greedy heuristic confirms that the MIP’s advantage lies in global multi-objective coordination. This research contributes both a validated mathematical framework and a practical algorithmic approach for multi-period volunteer assignment under demand decay, extending prior work by Sperling and Schryenthrough explicit Poisson dynamics, fatigue state modeling, and makespan optimization. Full article

Background/Objectives: Wrist cock-up orthoses are standard for work-related musculoskeletal disorders, yet consensus is lacking on whether commercial orthoses (COs) or custom-made thermoplastic orthoses (THs) better preserve function. While COs offer availability, THs provide a superior anatomical fit. This study evaluated dexterity and satisfaction in healthy female employees to establish a functional baseline for preventive strategies. Methods: Healthy female office workers with no prior musculoskeletal or neurological conditions participated in this randomized cross-over study. Manual dexterity was assessed at baseline and after each of two consecutive workdays, during which participants wore, in a randomized order, either a CO or a TH made by an expert physiotherapist. Outcome measures included the Functional Dexterity Test (FDT), recording time and errors, and the Client Satisfaction with Device (CSD-It) scale. Results: Twenty right-handed women (mean age 45.6 ± 11 years) participated. A significant difference in FDT completion times across conditions (χ² = 12.6, p = 0.002) was found. While both orthoses slowed performance compared to baseline (p < 0.01), the CO allowed for faster dexterity than the TH (p < 0.01). No differences were found in error rates. Regarding satisfaction, the CO achieved significantly better CSD-It scores than the TH (p = 0.0047), despite 60% of users reporting increased skin temperature with the CO. Final preferences were nearly evenly split (55% CO vs. 45% TH). Conclusions: Both orthoses impact manual dexterity without compromising precision. While the CO offered better execution speed and overall satisfaction, the TH version was preferred for prolonged skin tolerability. Selection should be individualized, balancing mechanical efficiency with the superior fit of custom-fabricated solutions in office environments. Full article

Generative artificial intelligence (AI), especially large language models (LLMs) that can write and debug code, is changing how students approach programming work in engineering education. Unlike more open-ended conceptual or modeling tasks, programming fits closely with what these systems do well: generating syntax, fixing errors, building procedural logic, and completing code structures. Hence, programming coursework may be one of the areas in which AI changes performance patterns in a measurable way. This study examines whether that shift appears in actual student outcomes. Using a retrospective pre/post design, it compares results from a pre-AI period (2021–2022) with results from a post-AI period (2023–2025), when generative AI tools became widely available to students. The focal assessment is a comprehensive programming project graded with the same rubric across multiple sections and terms. Performance is evaluated through descriptive statistics, distributional comparisons, and mastery thresholds (≥80%). The post-AI period shows a rise in overall scores, along with strong clustering near the top of the scale. Lower- and middle-range scores become much less common, most students fall in the highest score band, and overall variability declines. These results suggest that generative AI acts as a procedural equalizer in programming contexts, referring to the role of generative AI in reducing performance differences by assisting with rule-based, syntax-driven, and execution-oriented aspects of tasks, thereby raising baseline outcomes while compressing variation among students. It appears to raise lower-end performance and make outcomes more consistent, but it also narrows the spread among stronger students and creates a ceiling effect. That pattern raises questions about assessment validity, skill differentiation, and what “mastery” means when AI can handle much of the procedural work. Using multi-term data from authentic online courses, this study adds empirical evidence to the growing literature on AI in engineering education and identifies programming coursework as a setting where generative AI may have already changed performance dynamics in a structural way. Full article

This study proposes a work-based interpretation procedure, hereafter referred to as the IDEA method, for identifying the failure-related transition load in monotonic static load tests of pre-stressed high-strength concrete pipe piles. The method was examined using nine full-scale axial compression tests from a site in the lower reaches of the Yangtze River, China. Cumulative work was reconstructed from the measured load settlement curves, and an incremental work response indicator was fitted with a one-break continuous segmented-regression model. The breakpoint was taken as the IDEA estimate, while bootstrap confidence intervals and delta BIC were used to evaluate numerical stability and model support. For the present nine piles, IDEA showed close agreement with the code-interpreted reference loads and yielded the lowest MAPE among the five Q-s interpretation methods considered, whereas the Davisson method showed slightly lower COV and RMSE. Additional perturbation analyses indicated low sensitivity to moderate settlement noise but clear sensitivity to sparse loading records and missing pre-failure points. A preliminary external application to 10 published pile cases showed generally favorable agreement with reference loads reinterpreted from digitized external Q-s curves using a uniform abrupt-settlement criterion. Because the original settlement–time records of the external cases were unavailable, the external assessment is treated as a curve-based transferability check rather than a strictly code-certified validation. Full article

Mexico City experiences severe water stress driven by aquifer overexploitation and recurrent droughts. Effective water management requires operational spatial monitoring systems capable of spatially reconstructing meteorological anomalies across multiple temporal scales. In this work we developed an explainable deep learning framework using Long Short-Term Memory (LSTM) networks to spatially reconstruct three drought indices—the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI)—across five accumulation scales (3, 6, 12, 18, and 24 months). To strictly isolate genuine meteorological deviations, we adopted a hybrid statistical approach: SPI was computed following the standard WMO methodology using Gamma distribution fitting, while SPEI and RDI were computed using empirical monthly standardized anomalies to ensure robustness in non-stationary urban climates without forcing distributional assumptions. Model generalization was evaluated using a leave-one-microsite-out validation strategy, training on two stations and testing on a spatially isolated third station, with inter-station distances ranging from 1.8 to 6.7 km, sufficient to capture urban microclimatic heterogeneity while remaining within the same regional climate zone. We quantified feature importance using SHapley Additive exPlanations (SHAP) to provide mathematical transparency. The LSTM achieved predictive performance at long-term scales by effectively capturing deep sequential memory, while short-term reconstructions reflected the inherent noise of urban convective precipitation. The framework demonstrates reliable intra-urban spatial generalization capacity, supporting the development of diagnostic tools for metropolitan water stress assessment. Full article

Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review organizes existing modeling and diagnostic practices into a structured framework for variability-aware NVH assessment, based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative interpretation and future prediction of acoustic scatter in electric drivetrains. Full article

Cultural heritage experiences present unique challenges for user experience (UX) evaluation due to their diversity, contextual variability, and the growing need to balance methodological rigor with low cognitive effort. Traditional UX frameworks often assume a one-size-fits-all approach, which fails to address the complexity of cultural heritage environments. This paper introduces a flexible taxonomy of UX evaluation methodologies designed as a decision-support tool for researchers and practitioners. The taxonomy is built on 11 core dimensions: study type, research phase, research objective, evaluation timing, data nature, facilitation setup, observation setup, research environment, participant profile, cognitive burden, and evaluation standards and instruments. Rather than prescribing a single method, the taxonomy enables the selection and combination of qualitative and quantitative approaches tailored to the context and phase of each cultural heritage project. Representative examples illustrate its application in guiding mixed-methods strategies for measuring cultural resonance. By promoting adaptability and methodological diversity, this work advances human-centered UX evaluation practices for cultural heritage and beyond. Full article

In response to the growing demand for complex thin-walled lightweight alloy components in the automotive and aerospace industries, this study investigates the limitations of traditional gas pressure forming technologies. Using 2A12 aluminum alloy thin sheets as the research material, hot high-speed gas bulging experiments were conducted to study the effects of rapid inflation and rapid deflation processes on the forming accuracy, wall thickness, and strain distribution of bulged components. This aims to provide guidance for theoretical research and validate the superiority of the rapid deflation process. The results show that: (1) When forming cup-shaped components at 400 °C, the die-fitting degree of the component formed by the rapid deflation process reaches 89.5% and the minimum corner radius is 2.5 mm. Overall, the forming accuracy of this process is significantly superior to that of the rapid inflation process. (2) Within the temperature range of 400–450 °C, the rapid deflation process successfully formed a spherical-bottom component with a depth of 30 mm, overcoming the cracking defects induced by localized cooling and non-uniform temperature fields in the rapid inflation process, thereby improving the forming limit. (3) Under consistent conditions, the wall thickness uniformity of the sheet formed by the rapid deflation process is significantly higher than that of the sheet formed by rapid inflation, and the wall thickness uniformity improves with increasing temperature. Future work is expected to further enhance the repeatability and stability of forming accuracy and the forming limits of extreme geometries by further optimizing process parameters and expanding the material applicability range. This will provide practical technical support for the manufacturing of lightweight, high-performance aerospace equipment and automotive components. Full article