Search Results (8,462)

Short-term GPU utilization forecasts are useful for scheduling, resource allocation, and capacity planning, but production traces are rarely smooth. They contain spikes, regime changes, idle periods, and incomplete observations. We study this problem on the MIT Supercloud Dataset using a direct, horizon-specific forecasting setup. After resampling the telemetry to 1 min intervals, the neural models are trained on min–max-normalized data and evaluated on the original 0–100% utilization scale after inverse transformation. Persistence and rolling mean predictors are added as non-trainable baselines and are evaluated on the same eligible targets as the neural models. Five sequence models—1D-CNN, GRU, FC-LSTM, Liquid Time-Constant Network (LTC), and Transformer—are compared at 1 min, 10 min, and 1 h horizons. To avoid a gross capacity imbalance, the primary model widths are chosen in a comparable range of approximately 55,000 trainable parameters. This controls the trainable model size only; the architectures still differ in computation, memory access, and optimization behavior. Besides the overall error, the experiments examine high-load periods, abrupt changes, and a 20% random zero-masking condition. Among the five neural models, FC-LSTM gives the lowest MAE and RMSE at the 1 min horizon. The persistence baseline reaches an MAE of 3.92 at this horizon, compared with 3.55 for FC-LSTM, corresponding to a 9.4% lower MAE for FC-LSTM. At 1 h, among the neural models, LTC has the lowest mean RMSE, while FC-LSTM retains the lowest MAE and WAPE. Paired GPU device-level comparisons indicate that the larger improvements over simple baselines are more robust than the small numerical gaps among the strongest neural models. The zero-masking robustness protocol is expanded to five masking seeds and all five primary neural models. Taken together, the results show that the preferred model changes with both the forecast horizon and error criterion. Full article

This study aims to investigate the feasibility of ethanol-digested calcium oxide (CaO-E) as a novel dechlorination sorbent for the efficient removal of hydrogen chloride (HCl) from coal-fired flue gas and further evaluate its influence on mercury speciation and transformation in flue gas, thereby addressing the low efficiency and limited multi-pollutant control capability of conventional dry dechlorination technologies. Based on a laboratory-scale injection reaction system, ethanol-digested calcium-based sorbents were injected into simulated coal-fired flue gas to systematically examine the effects of key factors, including Ca/Cl molar ratio, SO₂, and fly ash, on dechlorination efficiency. Density functional theory (DFT) calculations were further employed to elucidate the reaction mechanisms. Meanwhile, mercury-laden flue gas was introduced to investigate the removal characteristics of elemental mercury (Hg⁰) and oxidized mercury (Hg²⁺) by CaO-E. The experimental results demonstrated that ethanol-digested CaO exhibited significantly superior performance compared with untreated samples, and the formation of a porous calcium hydroxide structure was identified as the key factor responsible for its high dechlorination efficiency. When the Ca/Cl molar ratio reached 4.0, the dechlorination efficiency could be stably maintained above 80%. SO₂ showed a pronounced inhibitory effect on the dechlorination process, whereas fly ash exhibited a slight promoting effect. Mercury removal experiments revealed that CaO-E had limited removal capability toward Hg⁰ but effectively reduced the concentration of Hg²⁺. Specifically, when the Ca/Cl molar ratios were 3 and 5, the Hg²⁺ concentrations decreased to 1.4 and 0.6 μg/m³, respectively. This behavior can be attributed to the fact that Hg²⁺ mainly exists in chlorinated forms such as HgCl₂, which possess strong polarity and can be readily adsorbed by the alkaline active sites on the CaO-E surface. In addition, as the dechlorination process proceeded, chlorine-containing species in the flue gas were gradually consumed, suppressing the oxidation conversion of Hg⁰ to Hg²⁺ and thereby further reducing the Hg²⁺ concentration. Theoretical calculations indicated that both HCl and SO₂ could undergo chemisorption on calcium active sites, while HCl possessed a lower reaction energy barrier and therefore dominated the competitive adsorption process, exhibiting preferential reactivity. Overall, ethanol-digested calcium oxide not only demonstrates excellent HCl removal performance, but also shows the capability to regulate mercury speciation in flue gas to a certain extent, providing both theoretical insights and technical support for the synergistic control of multiple pollutants in coal-fired flue gas. Full article

Organic–inorganic hybrid crystalline materials formed in multicomponent aqueous systems are of interest because their phase behavior and physicochemical properties can be controlled by composition and crystallization conditions. In this study, the phase equilibria and crystallization behavior of the ternary aqueous Ca(ClO₃)₂·2CO(NH₂)₂–CH₂ClCOOH·(C₂H₄OH)₃N–H₂O system were investigated over the temperature range of −24 to 60 °C using the visual-polythermal method. Experimental data obtained for the two boundary binary subsystems and eight internal sections were used to construct the polythermal phase diagram. The diagram revealed distinct crystallization fields corresponding to ice, Ca(ClO₃)₂·2CO(NH₂)₂·2H₂O, CH₂ClCOOH·(C₂H₄OH)₃N, and a separate crystallization region associated with a previously unreported crystalline phase with the proposed composition ClCH₂COOH·Ca(ClO₃)₂·(C₂H₄OH)₃N. The solid phase was isolated from its crystallization region, washed with cold distilled water, dried to constant mass, and characterized by complementary Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS), thermogravimetric analysis, derivative thermogravimetry, and differential scanning calorimetry (TG–DTG–DSC), and powder X-ray diffraction (PXRD). The experimentally determined Ca²⁺ and ClO₃⁻ contents were reasonably consistent with the proposed composition, while FT-IR spectroscopy revealed characteristic chlorate vibrations and changes in the vibrational environment of the organic component. SEM showed predominantly prismatic and plate-like crystalline morphologies, and EDS confirmed the presence of Ca, Cl, O, C, and N. Thermal analysis demonstrated multistage decomposition, with comparatively good thermal stability below approximately 150 °C. PXRD revealed a diffraction fingerprint distinct from those of the starting components and the corresponding physical mixture. Preliminary indexing of 19 principal reflections was consistent with a tetragonal candidate lattice with a = b = 7.7411(5) Å, c = 24.7182(10) Å, V = 1481.2(5) ų, and M₂₀ ≈ 23.0. The crystallographic analysis is considered preliminary because the diffraction profile was reconstructed from the available pattern and was not subjected to complete structure refinement. Overall, the combined phase-equilibrium, compositional, spectroscopic, morphological, thermal, and diffraction data support the isolation of a distinct organic–inorganic crystalline phase with the proposed composition. Full article

Digital transformation has reshaped work processes across the tourism industry, increasing the need to understand how employees perceive organizational digital systems and how these perceptions are associated with workplace behaviors. This study examined the relationships among perceived digital system support (PDSS), psychological empowerment, and voice behavior among tourism employees from an organizational behavior perspective grounded in the Technology Acceptance Model. Data were collected through an online survey of employees working in travel agencies, airlines, hotels, and foodservice organizations in South Korea, and 314 valid responses were analyzed using structural equation modeling. The results indicated that PDSS was positively associated with psychological empowerment, and psychological empowerment was positively associated with voice behavior. By contrast, the direct relationship between PDSS and voice behavior was not statistically significant, whereas the indirect association via psychological empowerment was significant, indicating a pattern consistent with full mediation. These findings suggest that employees’ positive perceptions of organizational digital systems show an indirect association with constructive voice behavior via psychological empowerment, particularly competence, self-determination, and impact. The study extends the application of the Technology Acceptance Model from technology acceptance to employee organizational behavior in digital work environments and highlights the importance of fostering psychologically empowering workplaces during digital transformation. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article

Learning from feedback depends not only on the feedback provided but also on how learners interpret, evaluate, and respond to it. This descriptive phenomenological study explored how preclinical medical students experienced receiving peer evaluation in a team-based learning (TBL) course. Forty-one reflective journals written by first-year medical students (27 men, 14 women) after receiving anonymous peer feedback were analyzed using Colaizzi’s seven-step descriptive phenomenological method. Five interrelated themes emerged: preparing to contribute, encountering oneself through peers’ eyes, making sense of emotional responses, transforming feedback into future practice, and becoming a responsible evaluator. Students described peer feedback as a social mirror that revealed differences between their self-perceptions and those of their peers. They reported a range of emotional responses and also reflected on the credibility and relevance of the feedback they received. Many students expressed intentions for future participation, and some reflected on their own responsibilities as evaluators. Taken together, the accounts described self-reflection, emotional appraisal, and intentions for future participation after peer evaluation. The findings suggest that peer evaluation accompanied by open-ended reflection may provide a useful context for examining how students experience and make sense of feedback. Whether such reflection contributes to subsequent changes in behavior, feedback literacy, self-regulated learning, or professionalism requires further study. Full article

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, whether their internal dynamics are consistent with irreversible degradation mechanisms remains largely unexplored. This study revisits GRU-based prognostics from a physics-consistency perspective and analyzes the potential mismatch between gating mechanisms and degradation evolution. A full-life benchmarking framework is developed based on the XJTU-SY bearing run-to-failure dataset. A training-based health indicator (HI) is constructed through multi-domain vibration feature extraction and principal component analysis, where the degradation-state representation and RUL prediction objective are explicitly distinguished to avoid physically inconsistent supervision. Several representative approaches, including statistical models and deep learning architectures (LSTM, GRU, TCN, and Transformer), are evaluated using both prediction accuracy metrics (RMSE, MAE, and R²) and physical consistency criteria (monotonicity index, monotonicity violation index, and degradation trend consistency). Experimental results demonstrate that superior prediction accuracy does not necessarily guarantee physically consistent degradation modeling. Although GRU provides competitive RUL prediction performance, its hidden-state evolution and gating responses exhibit noticeable non-monotonic behaviors during degradation progression. These findings reveal a potential discrepancy between prediction-oriented recurrent learning mechanisms and irreversible degradation dynamics, highlighting the importance of incorporating physics-consistency evaluation into reliable data-driven prognostic models. Full article

Archaeological sites are fragile, non-renewable heritage environments where visitors’ inappropriate behavior may cause irreversible damage. This study designed and evaluated a VR serious game, Guarding the Terracotta Warriors, to promote visitors’ responsible visiting behavior intention at archaeological sites. Examining the Terracotta Warriors Museum as a case study, this study identified conservation needs through literature and case analysis, expert interviews, and preliminary visitor interviews, and translated them into four design requirements: path guidance for visiting boundaries, reflection on visiting order, contextualized risk decisions, and concrete feedback on behavioral consequences. Based on the MDA framework, these requirements were further transformed into interactive game mechanisms. A within-subject experiment with 46 participants compared the VR serious game with conservation-content-matched video instruction, followed by semi-structured interviews with 20 participants. Quantitative results showed that the VR serious game produced significantly higher scores than video instruction in presence, experience satisfaction, conservation-related attitude, awareness of consequences, personal norms, and responsible visiting behavior intention. Task load was slightly higher in the VR condition, but its mean score remained below the midpoint of the seven-point scale, indicating a relatively modest level of perceived task load. Qualitative findings further indicated that embodied exploration, safe-to-fail risk decisions, visitor-order feedback, and irreversible restoration feedback helped participants understand conservation boundaries and behavioral consequences. This study provides a design framework and empirical evidence for using VR serious games to support responsible visitor education at archaeological sites. Full article

The digital transformation of cultural tourism has increased interest in understanding how technological innovation and sustainability influence destination choice among younger travellers. Grounded in the Theory of Planned Behavior, this study examines the relationship between Smart Technologies, Sustainability, and Generation Z’s intention to visit digitally enhanced cultural destinations. Data were collected from 354 Generation Z respondents and analysed using SmartPLS4, complemented by mediation analysis, Importance–Performance Map Analysis, PLSpredict, and the Cross-Validated Predictive Ability Test. The findings reveal that Smart Technologies exert a strong positive effect on Visit Intention (β = 0.575, p < 0.001) and Sustainability (β = 0.437, p < 0.001), while Sustainability also positively influences Visit Intention (β = 0.194, p < 0.001). Sustainability partially mediates the relationship between Smart Technologies and Visit Intention (β = 0.085, p < 0.001). The model explains 46.6% of the variance in Visit Intention, while IPMA identifies Smart Technologies as the most influential managerial priority. The study contributes to smart tourism literature by providing an integrated TPB-based framework linking technology, sustainability, and destination intention. The findings suggest that technological innovation is the primary driver of Visit Intention, while Sustainability plays a complementary role among Generation Z travellers. Full article

Coarse-grain starches are promising raw materials for developing diversified functional food ingredients, particularly low-glycemic products. However, the systematic structure–function relationships among multiple coarse-grain varieties remain poorly understood. This study aimed to comprehensively characterize the structural, processing, and digestive properties of ten coarse-grain starches to provide a fundamental basis for their targeted industrial utilization. Multiple analytical techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), rapid visco analysis (RVA), and in vitro simulated digestion, were employed to characterize the structural, thermal, rheological, pasting, and digestive properties of the starches. The tested starches were classified into two crystalline types, and significant differences were observed among samples in short-range molecular order, gelatinization behavior, gel rheological properties, pasting characteristics, and the distribution of three digestion fractions. Pearson correlation analysis revealed structure–function correlations, showing that crystalline ordering plays an important role in starch gelatinization behavior, whereas molecular packing is closely associated with pasting performance and in vitro digestibility. The distinct differences in starch properties, together with the identified structure–function relationships, provide a basis for targeted raw material selection in food processing and the development of functional foods with tailored digestibility characteristics. Full article

Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty. First, the best–worst method is extended by using linguistic intuitionistic fuzzy numbers to represent pairwise preference information with linguistic membership, non-membership, and indeterminacy degrees. A utility transformation is then introduced to convert linguistic intuitionistic fuzzy comparisons into numerical preference values, enabling criteria weights to be derived through linear programming models. Second, a two-stage group decision-support framework is developed for superior–committee decision structures. In the first stage, committee expert influence is calculated by integrating prior expert weights obtained from the superior expert’s evaluation with judgment-derived expert weights based on certainty and agreement. In the second stage, the final criteria weights are obtained by combining the superior expert’s judgments with the weighted committee judgments. A constructed UAV criteria-weighting case and complementary numerical analyses are presented to illustrate the calculation process and examine the behavior of the proposed method. The results show that the framework provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems. Full article

Femtosecond laser processing enables spatially controlled modification of the structure, composition, and functionality of advanced materials through highly localized energy deposition and laser–matter interaction mechanisms. This review discusses the role of ultrafast laser irradiation in the engineering of oxide-based functional systems, including functional oxides, oxide-containing layers, interfaces, and heterogeneous structures whose properties are substantially determined by an oxide component. The mechanisms governing laser-induced oxidation, phase transformation, elemental redistribution, defect generation, and hierarchical micro-/nanostructure formation are considered. Particular attention is given to the ability of femtosecond laser processing to create surfaces with tailored interactions with light, liquids, biological environments, and external stimuli, enabling responsive devices and advanced manufacturing strategies. Laser-modified oxide layers and nanostructured interfaces are analyzed as pathways for controlling surface energy, optical properties, chemical activity, and functional response. The relationship between laser-generated architectures and their applications in sensing, actuation, wetting control, and multifunctional systems is discussed. By connecting ultrafast laser surface engineering with emerging 4D manufacturing concepts, this review highlights femtosecond laser technologies as a versatile platform for designing systems with spatially programmed functionality and, where stimulus-dependent behavior is demonstrated, time-dependent performance. Such approaches provide opportunities for integrating adaptive oxide-based functional systems into advanced manufacturing. Full article

Modern intrusion detection systems (IDSs) are often evaluated under matched training and test conditions, whereas deployment environments involve changing traffic distributions, heterogeneous feature-generation pipelines, and shifting attack prevalence. This study investigates benchmark-shift-aware intrusion detection through harmonized cross-dataset evaluation of HIKARI-2021, CICIDS2017, and a CICIoT2023 sample subset. Two payload-free feature spaces are constructed: Rich-64 for detailed HIKARI-2021/CICIDS2017 analysis and Minimal-13 for three-way comparison. Using XGBoost, a supervised Transformer, and a masked-feature self-supervised Transformer, we evaluate discrimination, calibration, threshold transfer, alert-budget behavior, chronological robustness, and score-orientation stability. Across five in-domain XGBoost settings, observed false-positive rates were 4.94–5.40%, and F1-scores ranged from 0.507 to 0.995. Under strict Rich-64 HIKARI-2021-to-CICIDS2017 transfer, all models had zero recall at source-derived thresholds, with two showing inverted score orientation. In the reverse direction, XGBoost reached an 18.9% target false-positive rate, while a nominal 5% target-side alert budget yielded F1 = 0.112. Chronological evaluation further showed that improved ranking metrics did not guarantee stable validation-derived operating behavior. The study provides a reproducible diagnostic framework for evaluating IDS robustness under evolving benchmark conditions. Full article

A prescribed time-adaptive event-triggered sliding mode control method is proposed for a second-order aircraft antiskid braking system with unmeasurable longitudinal velocity, subject to unknown actuator faults and external disturbances. Based on the time scale transformation technique, a prescribed-time observer is constructed to estimate the unmeasurable longitudinal velocity. A practical prescribed-time super-twisting observer with a saturated gain is designed to estimate the disturbance. Within the prescribed time convergence framework, an adaptive update law and a nonsingular integral sliding surface are developed to compensate for actuator faults. Building on this, a time-varying dynamic threshold event-triggering mechanism is incorporated into the prescribed time-sliding mode control process, while excluding Zeno behavior and reducing the control update frequency. The aforementioned prescribed-time observers and the event-triggered adaptive sliding mode controller form a strict temporal hierarchical architecture. Based on Lyapunov stability theory, it is proved that the closed-loop system is practically prescribed-time stable and that all closed-loop signals are uniformly ultimately bounded. Comparative simulation results verify the effectiveness of the proposed method. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article