Search Results (1,262)

Active involvement in creative activities, known as creative health, has been shown to enhance wellbeing, with museums serving as unique spaces for health promotion; however, visitors often require guidance to derive significant benefits from these institutions. This study, part of the larger ASBA (Anxiety, Stress, Brain-friendly museum Approach) project, evaluates the first phase of an intervention specifically focused on a Mindfulness protocol adapted to museum contexts. It has employed a single-group pre–post design with 79 healthy adults recruited from the non-clinical population. Participants were involved in a 15 min standardized mindfulness practice adapted from Mindfulness-Based Stress Reduction (MBSR) in either an art or science museum. State anxiety (SAI) and mood (VAS) were assessed at baseline and post-intervention, alongside personality traits (BFI-10) and interest measures to identify individual moderators of treatment response. The practice appeared to reduce state anxiety significantly in both settings, with large effect sizes. Specific moderators emerged: openness to experience predicted anxiety reduction in the art museum, whereas science interest predicted outcomes in the science setting. These findings suggest that brief, standardized mindfulness protocols implemented through the ASBA framework can provide promising immediate benefits for visitor wellbeing across diverse museum environments. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

Training adaptation involves muscular–metabolic remodeling and personality-linked traits such as motivation, self-regulation, and resilience. This narrative review examines how training load oscillation (TLO)—the deliberate variation in exercise intensity, volume, and substrate availability—may function as a systemic epigenetic stimulus capable of shaping both physiological and psychological adaptation. Fluctuating energetic states reconfigure key energy-sensing pathways (AMPK, mTOR, CaMKII, and SIRT1), thereby potentially influencing DNA methylation, histone acetylation, and microRNA programs linked to PGC-1α and BDNF. This review synthesizes converging evidence suggesting links between these molecular responses and behavioral consistency, cognitive control, and stress tolerance. Building on this literature, a systems model of molecular–behavioral coupling is proposed, in which TLO is hypothesized to entrain phase-shifted AMPK/SIRT1 and mTOR windows, alongside CaMKII intensity pulses and a delayed BDNF crest. The model generates testable predictions—such as amplitude-dependent PGC-1α demethylation, BDNF promoter acetylation, and NR3C1 recalibration under recovery-weighted cycles—and highlights practical implications for timing nutritional, cognitive, and recovery inputs to molecular windows. Understanding TLO as an entrainment signal may help integrate physiology and psychology within a coherent, durable performance strategy. This framework is conceptual in scope and intended to generate testable hypotheses rather than assert definitive mechanisms, providing a structured basis for future empirical investigations integrating molecular, physiological, and behavioral outcomes. Full article

Grounding insulation is a key component of electric vehicle drive motors, and cracks may appear during the manufacturing process and assembly. In this paper, the novel method of coupling phase field, mechanic field and electric field is proposed to investigate the coupled propagation characteristics of electromechanical damage in stator end-wingding insulation. The crack propagation model is derived by using the phase field method, where the maximum historical variable is introduced to ensure the forward propagation of the crack damage in insulation. According to the crack evolution states, the electric potential distributions in the insulation domain are determined and the electrical damage variable is defined to quantitatively describe the dynamical evolution mechanism of electric damage with the variation in mechanical damage. The results in this research will contribute to understanding the electrical performance degradation and electromechanical failure of the end-winding insulation in electric vehicle drive motors, which also provides the basis for the mechanism of insulation damage, insulation fault diagnosis and residual life prediction of electrical machines. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

This state-of-the-art review provides a comprehensive, critical synthesis of the rapidly expanding field of HECCs, emphasizing the unique scientific challenges that distinguish these materials from conventional ceramics and high-entropy alloys. Key challenges of HECCs include accurately predicting stable phases and quantifying resultant material properties, optimizing complex fabrication and processing techniques, and establishing a robust correlation between the intricate microstructural characteristics and macroscopic performance. Unlike previous reviews that focus on individual ceramic families, this article integrates the novel features, diverse applications, and recent developmental breakthroughs across carbides, nitrides, borides, and oxides to reveal the unifying principles governing configurational disorder, phase stability, and microstructure property relationships in HECCs. A key novelty of this review work is the systematic mapping of fabrication pathways, including CTR, PAS, SPS, and reactive sintering, against the underlying thermodynamic and kinetic constraints specific to multicomponent ceramic systems. The review introduces emerging ideas such as HEDFT, machine-learning-assisted phase prediction, and entropy–enthalpy competition as foundational tools for next-generation HECC design and performance analysis. Additionally, it uniquely presents densification behavior, diffusion barriers, defect chemistry, and residual stress evolution with mechanical, thermal, and tribological performance across the coating classes. By consolidating theoretical intuitions with experimental developments, this article provides a novel roadmap for predictive compositional design, development, microstructural engineering, and targeted application of HECCs in extreme environments. This work aims to support researchers and coating industries toward the rational development of high-performance HECCs and establish a unified framework for future research in high-entropy ceramic technologies. Full article

Wound healing is a complex, multi-phase process requiring coordinated interactions among diverse cell types and molecular pathways to restore tissue integrity. Dysregulation can lead to chronic non-healing wounds or excessive scarring, posing major clinical and economic burdens. Single-omics interrogate individual molecular layers, such as the genome, transcriptome, proteome, metabolome, or epigenome, and have revealed key cellular players, but provide a limited view of dynamic wound repair. Single-cell technologies provide higher resolution to single-omic data by resolving cell-type and state-specific heterogeneity, enabling precise characterization of cellular populations. Multi-omics integrates multiple molecular layers at single-cell resolution, reconstructing regulatory networks, epigenetic landscapes, and cell–cell interactions underlying healing outcomes. Recent advances in single-cell and spatial multi-omics have revealed fibroblast subpopulations with distinct fibrotic or regenerative roles and immune–epithelial interactions critical for re-epithelialization. Integration with computational tools and artificial intelligence (AI) continues to reveal cellular interactions, predict healing outcomes, and guide development of personalized therapies. Despite technical and translational challenges, including data integration and cost, multi-omics are increasingly shaping the future of precision wound care. This review highlights how multi-omics is redefining understanding of wound biology and fibrosis and explores emerging applications such as smart biosensors and predictive models with potential to transform wound care. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

Model predictive control (MPC) ensures stable motor operation provided that accurate motor parameters and state information are available. However, in certain environments, direct sensor measurement of rotor position and speed is infeasible, and sensorless methods are required to estimate the rotor position and speed. Sensorless methods utilizing a sliding mode observer (SMO) and a quadrature phase-locked loop (QPLL) are widely adopted, but it may encounter issues such as inaccurate motor parameters and delayed measurement results. To address these challenges, this paper proposes an integrated method that employs a nonlinear extended state observer (NLESO) to reduce observation delays in rotor position estimation. Additionally, a model reference adaptive system (MRAS)-based inductance observer is utilized to correct parameter inaccuracies. This combined approach achieves robust motor control with low delay. Simulation results validate the effectiveness and robustness of the proposed method. Full article

Addressing the complexity of transient evolution and the difficulty of rapid risk quantification in high-penetration renewable energy receiving-end grids under short-circuit faults, this paper proposes a rapid risk assessment method based on the fault transient evolution process. The method first constructs a directed weighted graph model to characterize the fault transient evolution process. It then integrates mechanism analysis with data-driven approaches to establish state transition models and temporal feature models, which are used to generate the fault evolution path. Based on the transient evolution path, this paper defines the equivalent active power loss as the risk index and rapidly quantifies it through a phased simplified calculation approach. Finally, validation using a provincial power grid case study confirms the efficacy of the method and successfully achieves reliable predictions of fault evolution scenarios, as well as rapid and effective assessment of power loss during the transient process. Full article

Lithium batteries have emerged as the mainstream technology in the current energy storage field due to their advantages, such as high energy density and long cycle life. However, from a multi-physics coupling perspective, research remains relatively scarce regarding the analysis of dendrite nucleation and growth, as well as their influence on lithium dendrite growth. Based on the phase field theory, this study develops a mechanical-thermal-electrochemical coupling model to systematically investigate the evolution mechanisms and suppression strategies of lithium dendrites induced by relevant physical quantities through the coupled effects of mechanical, thermal, and electrochemical fields. The dynamic behavior of the solid-solid interface is characterized by introducing order parameters. The governing nonlinear partial differential equations are formulated by combining the Cahn-Hilliard and Ginzburg-Landau equations. The present numerical results and the previous results are compared to validate the present model in properly predicting lithium dendrite growth. Numerical simulations are performed to analyze the influence of various physical parameters, such as electric potential, anisotropic intensity and anisotropic modulus, on the morphological evolution of lithium dendrites. These findings provide critical insights for advancing strategies to suppress lithium dendrite growth and enhance battery performance in solid-state lithium batteries under multi-field coupling conditions. Full article

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy. Full article

This study numerically investigates pavement damage caused by explosions in buried leaking natural gas pipelines using a coupled Lagrangian–Eulerian (CLE) framework in LS-DYNA. The gas phase is described by a Jones–Wilkins–Lee-based equation of state, while soil and pavement are modeled using a pressure-dependent soil model and the Riedel–Hiermaier–Thoma concrete model with strain-based erosion, respectively. The approach is validated against benchmark underground explosion tests in sand and blast tests on reinforced concrete slabs, demonstrating accurate prediction of pressure histories, ejecta evolution, and crater or damage patterns. Parametric analyses are then conducted for different leaked gas masses and pipeline burial depths to quantify shock transmission, soil heave, pavement deflection, and damage evolution. The results indicate that the dynamic response of the pavement structure is most pronounced directly above the detonation point and intensifies significantly with increasing total leaked gas mass. For a total leaked gas mass of 36 kg, the maximum vertical deflection, the peak kinetic energy, and the peak pressure at the bottom interface at this location reach 148.46 mm, 14.64 kJ, and 10.82 MPa, respectively. Moreover, a deflection-based index is introduced to classify pavement response into slight (<20 mm), moderate (20–40 mm), severe (40–80 mm), and collapse (>80 mm) states, and empirical curves are derived to predict damage level from leakage mass and burial depth. Finally, the effectiveness of carbon fiber reinforced polymer (CFRP) strengthening schemes is assessed, showing that top and bottom surface reinforcement with a total CFRP thickness of 2.67 mm could reduce vertical deflection by up to 37.93% and significantly mitigates longitudinal cracking. The results provide a rational basis for safety assessment and blast resistant design of pavement structures above buried gas pipelines. Full article