Search Results (2,961)

The Moon is an important focal point for expanding space exploration, acting as both a testing ground and connecting network for interplanetary missions. With long-term Moon-based habitation on the horizon, exercise and recreational facilities for space crews are becoming important avenues for maintaining physiological and psychological well-being. This review focuses on the efficacy of virtual reality exergaming interventions for space-related training performance on the Moon and potential long-term celestial habitation. Databases from five different search engines were screened using terms related to exercise and the Moon. The inclusion criteria included populations related to space or terrestrial simulations using virtual reality or exergaming types of training interventions, under different gravitational forces with physiological or psychological exercise outcomes. A total of seven full-length articles were selected for final review. Findings from the review indicate that virtual reality-based exergames can be a lightweight, portable and enjoyable exercise tool for space-related ventures. Although exergames were found to significantly improve psychological parameters such as motivation, perceived exertion, mood, anxiety level, adherence and enjoyment compared to non-virtual reality tools, physiological improvements were not significant. Virtual reality exergames carry multiple potential applications for Moon-based training, operations and habitation owing to their potential to improve physiological and psychological exercise measures. Full article

This study systematically explored immune targets in gastric adenocarcinoma (GAC) suitable for mRNA vaccine development. Based on multi-omics data from public databases, we first screened a set of potential tumor-associated antigen genes. Subsequently, using ten machine learning algorithms, we constructed 101 prognostic models and, through optimization and comparison, selected the Random Survival Forest (RSF) method to establish a clinical prognostic model for GAC consisting of seven genes (TYMP, IFGN, ITGAX, GBP5, GBP4, STAT1, CD84). At both the genetic and protein levels, these genes were closely associated with the antigen presentation process, suggesting the potential functional role of this model in antigen presentation. Further analysis of the immune infiltration characteristics in GAC preliminarily revealed its possible immune evasion mechanisms. Building on this, we designed candidate mRNA vaccine templates for GAC using the mRNAdesigner platform. Additionally, this study investigated the potential roles of the above seven genes in GAC progression and screened small-molecule compounds targeting these genes. Molecular dynamics simulations (MD) were performed to verify the binding stability between these compounds and their corresponding proteins. This study comprehensively simulated the tumor microenvironment (TME) and antigen presentation process in GAC, evaluated the clinical translation potential of the neoantigen prognostic model and its predictive value for immunotherapy, and provided a preliminary design scheme for an mRNA vaccine against GAC. The findings offer new evidence for identifying immune therapy targets in GAC and are expected to advance the development of immunotherapy strategies for GAC. Full article

The main protease (MPro) of coronaviruses (CoVs) is an essential enzyme involved in viral replication and represents an attractive target for antiviral drug discovery. Based on the similar binding pocket residues within the MPro of different CoVs, this study aimed to identify potential inhibitors of SARS-CoV-2 MPro from PDB ID 6M2N using integrated computational approaches. Interaction-based pharmacophore modeling, virtual screening, molecular docking, MM-GBSA binding energy calculation, and molecular dynamics simulation (MDS) were performed using BIOVIA Discovery Studio. The validated pharmacophore model was utilized to screen the ZINC database, followed by docking and 100 ns MDS analyses of the top-ranked compounds. The pharmacophore model 01 demonstrated favorable predictive performance (AUC = 0.781). Virtual screening identified 483 compounds, from which 15 compounds were selected for docking studies. Among them, ZINC95473654 (Lig-1), ZINC95473725 (Lig-2), and ZINC08792368 (Lig-3) exhibited strong binding affinity toward MPro. Lig-1 demonstrated the best docking score and binding free energy, along with stable interactions with key catalytic residues HIS41, CYS145, and GLU166. MDS analyses further confirmed that Lig-1, Lig-2 and Lig-3 maintained stable conformations. The hydrogen bond distance monitoring and post MDS-MM-GBSA results suggest Lig-1 followed by Lig-3 as an inhibitor for MPro and persistent intermolecular interactions throughout the 100 ns simulation period. The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 MPro, representing promising candidates for further experimental validation. Full article

To improve the energy efficiency of hybrid commercial vehicles under varying road gradients, this study develops a road-gradient-aware hierarchical energy management framework. An interactive multiple-model Kalman filter (IMM-KF) is employed to estimate the current road gradient by fusing information from vehicle dynamics and onboard measurements. At the vehicle level, an MPC-based strategy uses future route-gradient information extracted from a pre-reconstructed road profile, rather than predicted by the IMM-KF, to plan an energy-efficient speed trajectory and reduce vehicle energy demand. At the powertrain level, a particle swarm optimization (PSO)-calibrated adaptive equivalent consumption minimization strategy (PA-ECMS) allocates the resulting power demand between the engine and motor, with the SOC-feedback PI parameters calibrated offline. The framework is evaluated in MATLAB R2024a/Simulink using standard driving-cycle and real-road data-based simulation scenarios. The IMM-KF achieves an RMSE of 0.04461° under constant-gradient conditions and 0.47587° under variable-gradient conditions. The upper-layer MPC reduces wheel-end energy consumption by 10.98% compared with PID-based speed control, while PA-ECMS reduces the comprehensive operating cost by 15.3% relative to A-ECMS under the prescribed CHTC-LT cycle. In the integrated real-road simulation, MPC + PA-ECMS reduces fuel consumption and comprehensive operating cost by approximately 4.0% and 4.7%, respectively, compared with PID + A-ECMS. These results demonstrate the effectiveness of coordinating road-gradient estimation, economic speed planning, and hybrid power allocation within the proposed hierarchical framework. Full article

The increasing complexity of automotive production systems, driven by mass customization, the transition from internal combustion engine vehicles to electric vehicles, competitive markets, and cost pressure, has intensified the need for advanced optimization across production and intralogistics planning. Optimization refers to the process of determining the best feasible solution to a decision problem according to a defined objective function, subject to given constraints. Whereas most reviews focus on individual problem classes, this systematic literature review adopts an integrated production and intralogistics perspective. Following the PRISMA statement, four research questions on problem types, their classification, solution methods, and trends are addressed by searching four databases (Web of Science, IEEE Xplore, ACM Digital Library, and Science Direct), yielding 194 publications from 1983 to 2025. The analysis shows that production planning, scheduling, resource management, and uncertainty and robustness are the dominant problem types, while metaheuristics, exact methods, and simulation are the most common, frequently hybridized solution methods. Publication activity has risen sharply, with 73.7% of studies appearing since 2019 and material feeding, sustainability, human–robot collaboration, and machine learning showing increased recent publication activity. A taxonomy classifying optimization problems among five dimensions, problem type, decision level, objectives, solution methodology, and real-data usage is proposed to guide researchers and practitioners towards an integrated, industrially deployed optimization. Full article

Laryngeal pathology requires individualized management because the larynx integrates airway protection, phonation, swallowing, and respiratory function within a compact and highly variable anatomical framework. This narrative review examines how laryngeal anatomy and morphometry support interdisciplinary collaboration and personalized care in oncologic, stenotic, functional, and reconstructive laryngeal disease. Evidence from morphometric studies, CT, MRI, endoscopy, ultrasonography, three-dimensional reconstruction, artificial intelligence, and multidisciplinary clinical workflows was synthesized qualitatively. Key parameters—including vocal fold length, glottic width, thyroid cartilage angle, cricoid diameter, subglottic diameter, anterior commissure thickness, and the status of paraglottic and pre-epiglottic spaces—provide actionable information for diagnosis, T-staging, airway assessment, surgical planning, reconstruction, and functional rehabilitation. Morphometry concretely informs clinical decisions: thyroid cartilage angle guides thyroplasty and phonosurgical planning; subglottic diameter supports stenosis surgery and airway instrumentation; and anterior commissure, conus elasticus, cartilage, and deep-space measurements refine oncologic staging and margin strategy. Technological accelerators, including AI segmentation, radiomics, 3D printing, photogrammetry, and ultrasonography, extend morphometry from static measurement toward predictive modeling and patient-specific simulation. However, implementation remains limited by heterogeneous CT protocols, inconsistent measurement planes, uneven access to advanced technologies, lack of global normative databases, and unresolved ethical issues surrounding AI validation and data governance. This review supports standardizing CT morphometry using parallel vocal fold planes, routinely measuring anterior commissure thickness in T1 glottic cancer, incorporating ultrasonography as a first-line morphometric tool in voice clinics, and validating AI segmentation against population-specific morphometric norms. Laryngeal morphometry should therefore become a routine decision-making framework for precision laryngology. Full article

This study aims to systematically review the current applications and development trends of virtual reality (VR) technology in blue space research. It clarifies the main research methods, technical pathways, application scenarios, and existing knowledge gaps, and integrates the advantages of VR technology with the characteristics of blue spaces to provide references for future urban renewal and urban management research. Following the PRISMA-ScR guidelines, this study searched literature published up to January 2026 in the Web of Science and Scopus databases. A total of 41 original studies were included. A scoping review approach was adopted, employing descriptive statistics, cross-analysis, and classification methods. Existing studies mainly focus on blue space types such as rivers and lakes, with immersive head-mounted displays as the primary method. Most experiments adopt single session, short term exposure designs, suggesting potential positive effects of virtual blue spaces in reducing stress, improving mood, and restoring attention. Other studies demonstrate the application of VR technology in environmental education, design assessment, and urban management within blue spaces. As an indirect exposure and supplementary intervention tool, VR shows significant potential in blue space research, particularly in supporting vulnerable populations, large-scale urban planning and design assessment, and risk management. However, current research still faces several limitations, including insufficient multisensory integration, limited capability in simulating dynamic water environments, lack of methodological standardization, uneven sample distribution, absence of longitudinal studies, and weak interactive and social dimensions. The findings highlight the need for further research on these specific aspects. Full article

Short ordinal self-report scales are widely used to study children’s digital lives, yet their measurement properties can distort conclusions about nonlinear relationships. We introduced an integrated diagnostic workflow for such scales—covering range, structure, reliability, method variance and functional form—and applied it to the TIMSS 2023 Digital Self-Efficacy scale across all 63 Grade 4 and 47 Grade 8 education-system and benchmarking samples (source database N = 719,881 children; 630,461 with complete seven-item measurement data). The scale was endpoint-concentrated, markedly at Grade 8, losing 83% and 95% of its test information between the mean and two standard deviations above it; an exact marginal calculation from a testlet model gave 80% and 89%. The apparent multidimensionality was better represented as localized covariance among three similarly worded items than as a separable second dimension, and omega hierarchical of 0.79 and 0.83 supported using the total score. In a factorial simulation evaluating the population projection coefficient on the analysis scale, endpoint concentration raised rejection of no curvature from 5.4% to 16.7% with raw summed scores, while latent scoring returned it to 5.5% and raised power from 71% to 88%, whether the item parameters were known or, in a smaller supporting condition, estimated in the analysis sample. Applied to cybervictimization, the quadratic association was attenuated but did not reverse sign once covariates were matched, and its prediction interval included zero. The range and reliability diagnostics behaved similarly on a second scale from the same assessment; broader applicability of the full workflow is proposed on theoretical grounds rather than established here. Full article

This paper presents a data-driven run-to-run (R2R) controller for manufacturing processes subject to process drift. The proposed approach combines the exponentially weighted moving average (EWMA) method with the K-nearest neighbors (KNN) algorithm to determine process recipe adjustments. Control actions are derived entirely from historical process output data. In the hybrid controller, the EWMA estimator recursively updates the accumulated process drift using historical process errors and generates the corresponding recipe compensation. The KNN-based controller, in turn, identifies the K nearest neighbors in the historical feature database based on the current process error and determines the compensation action from the associated error–compensation relationships. The proposed controller was evaluated through simulations of a chemical mechanical planarization (CMP) process, with removal rate as the control objective. Under linear process drift with random white-noise disturbances, the proposed controller maintained the removal rate close to the target value, with a maximum overshoot of 4.40%, and satisfied the settling criterion from the beginning of the control process. Its performance was superior to that of the conventional EWMA controller and the standalone KNN controller. The EWMA controller exhibited several oscillations during the initial runs, with a maximum overshoot of 15.17%. Although the KNN controller satisfied the settling criterion from the beginning of the control process and produced a relatively small maximum overshoot of 3.10%, it did not consistently maintain the removal rate near the target value. Under nonlinear process drift with random disturbances, the proposed controller also maintained the process output near the target value with satisfactory stability, provided that the process drift remained within a bounded range. The controller also has a simple and intuitive implementation, which may facilitate practical industrial application. Full article

Simulation of a human neurological disease on an animal model has the advantage of allowing control and manipulation of most of the regulating factors and producing real biological replicas while, beyond several common traits, every human is dynamic and unique. Moreover, one can explore novel therapeutic strategies on animals before asking permission to apply them to humans. Nevertheless, experimental outcomes depend on species, strain, sex, age, hormonal status, diet, exposure to hypoxia, toxins, radiation, external stimuli, stress, and housing conditions. Further complications stem from tissue hetero-cellularity, technological constraints, computational complexity and difficulties integrating the experimental results into a coherent biological picture. Moreover, most diseases are multi-factorial and associated with altered structure and/or expression of several genes. A major problem with genetically engineered animals is that together with the targeted gene(s), numerous other genes are mutated and/or regulated, owing to their interlinkage in functional pathways. This experience-based methodological commentary presents the challenges, relevance and limitations of the mouse, rat and rabbit models we used to decipher the transcriptomic alterations associated with several neurological disorders. Links to publicly accessible databases presenting experimental protocols and expression profiles are provided for readers interested in reanalyzing our data and comparing them with others’ results. Full article

Wood and wood-based materials represent low-carbon sustainable alternatives to petroleum sound absorbers, yet their baseline sound absorption coefficient varies drastically with wood species, anatomical cutting orientation and pore connectivity due to strong structural anisotropy. This review systematically integrates multiscale structural regulation, porous acoustic theories and data-driven optimization into a unified framework, revealing that broadband high sound absorption relies on the synergistic coordination of impedance matching, thermo-viscous dissipation and low-frequency resonant mechanisms, rather than simply maximizing porosity. We quantitatively compare state-of-the-art wood absorbers: directionally frozen wood aerogels achieve near-perfect absorption (α = 0.95–1.00, NRC = 0.82) across 520–6300 Hz, marking the current performance benchmark, while multifunctional superhydrophobic wood aerogels deliver moderate absorption (α ≈ 0.40) but stand out as all-biomass weather-resistant composites. Rigid-frame JCA/JCAL and poroelastic Biot models are clarified for wood’s distinct stiffness characteristics, and existing data-driven approaches are categorized, highlighting that most neural surrogates rely solely on FEM simulation without physical impedance-tube validation. Critical unresolved challenges including poor moisture/fire durability, insufficient industrial scalability and incomplete material databases are summarized, and targeted research priorities covering gradient manufacturing, hybrid physics–machine learning models and lifecycle environmental evaluation are proposed. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional variational autoencoder (cVAE), a class-conditional latent denoising diffusion probabilistic model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits, implemented and evaluated using a classical quantum-circuit simulator, to augment minority arrhythmia classes, and present results based on the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over ten independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification. To our knowledge, the proposed QLR module is the first use of a parameterized quantum circuit as a distributional refiner within a generative augmentation pipeline. While its performance is comparable to that of the classical latent diffusion framework under the present experimental conditions, the proposed approach demonstrates the feasibility of integrating quantum latent operators into generative medical AI pipelines and provides a foundation for future investigations on quantum-enhanced representation learning and data augmentation. Full article

Interleukin-1 receptor-associated kinase 4 (IRAK4) is one of the IRAK family proteins and plays an important role in the regulation of innate and inflammatory responses. In particular, IRAK4 acts as a key regulator of the Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling pathways and has attracted attention as a therapeutic target for immune and inflammatory diseases. In this study, an integrated computational approach combining machine learning, molecular docking, and molecular dynamics simulations was applied to identify putative IRAK4 inhibitor candidates. Bioactivity data of IRAK4 were obtained from the ChEMBL and PubChem databases and evaluated for multiple binary classification models. The optimized XGBoost model based on ECFP4 and PubChem fingerprints achieved an ROC-AUC of 0.996 and an average precision (AP) of 0.991 on the independent test set. After that, 20 candidate compounds with high predictive probability score were finally selected through subsequent screening of the DrugBank database. Among them, DB12168 (MK-0557), DB15040 (TP-271), and DB18152 (Zilurgisertib) exhibited favorable binding free energies and stable complex formation with IRAK4 through molecular dynamics simulations and MM-PBSA calculations. Overall, these results demonstrate that approaches incorporating machine learning and structure-based computational analysis can be useful for discovering and prioritizing potential IRAK4 inhibitor candidates. Full article

Reliability assessments of subsea systems are generally performed at two levels: structural reliability analysis of individual components and functional reliability analysis of the overall system using generic failure-rate databases. This study develops a component-to-system multi-scale framework that integrates these two levels for a subsea separation system operating at 3000 m water depth. At the component level, a Gaussian process regression (GPR) surrogate is developed from 474 finite element simulations of a vertical gravity separator. First-order reliability method (FORM) and Monte Carlo simulation (MCS) are then employed to assess the structural reliability, followed by a time-variant reliability analysis that accounts for corrosion effects. At the system level, the structural reliability model is integrated with functional failure rates through a Bayesian network that considers five equipment items and relevant risk-influencing factors. The surrogate model accurately predicts collapse pressure with an R² value of 0.996. The intact separator achieves a reliability index of 4.55, satisfying the DNV high-safety-class target, with the structural failure mode contributing only 0.0034% of the separator failure rate. Under a corrosion rate of 0.4 mm/year, the reliability index decreases to 3.12 over a 25-year service period. The structural failure rate crosses the DNV medium-safety-class target of 10⁻⁴ per year at year 12, increasing the structural contribution to the overall system failure frequency to 0.33%. Sensitivity analysis indicates that initial ovality and wall thickness are the most influential parameters affecting structural reliability and should therefore be prioritized in design and integrity management strategies. The framework is demonstrated on this physics-consistent dataset; validation against independent nonlinear finite element analyses and experimental collapse data is identified as the necessary next step before the results are used for design. Full article