Search Results (138)

Empirical research on organizational behavior contributes significantly to the examination of workplace impacts and to a deeper understanding of the underlying drivers of employee behavior. The long-term sustainability of organizations operating in VUCA environments depends largely on flexible adaptation and effective change management. The aim of this research is to examine the associations between workplace factors related to organizational change and employees’ intention to quit and perceived job insecurity during a crisis. Special emphasis is placed on stress caused by organizational change as an appraisal-based construct that may statistically mediate the association between employees’ psychological and physical strain and intention to quit. The research was conducted on a Hungarian sample, using a questionnaire survey among employees working in an SME environment. Based on the TSM (Transactional Stress Model), JD-R (Job Demands–Resources), and COR (Conservation of Resources) frameworks, hypotheses were formulated regarding the associations among workplace strain, stress caused by organizational change, intention to quit, and job insecurity. The hypotheses were tested using mediation models estimated within a structural equation modeling (SEM) framework. The results indicate a significant positive association between psychological strain and intention to quit, together with a significant indirect effect through stress caused by organizational change, a pattern consistent with partial mediation. Physical strain was also significantly and positively associated with intention to quit, but its indirect effect through stress caused by organizational change was not statistically significant. Indifferent leadership was significantly and positively associated with job insecurity, while the hypothesized indirect effect through stress caused by organizational change was not statistically significant. Full article

We propose a novel hybrid architecture that integrates a continuous-time Liquid Neural Network with an optimized Long Short-Term Memory network for seismic vulnerability state assessment of ancient halls. The proposed system processes multi-source structural monitoring data, including acceleration, strain, displacement, temperature, and humidity signals. A central challenge in this domain is the long-term state drift that occurs when continuous-time models are exposed to prolonged seismic events or sequences of aftershocks. To address this issue, we introduce a learnable event-triggered discrete reset mechanism that monitors a prediction error signal and an auxiliary drift accumulation variable. When a trigger condition is met, the Liquid Neural Network hidden state is reinitialized to a learned baseline state, thereby preventing divergence from physically plausible structural dynamics. This reset mechanism is inspired by hybrid small-gain and impulsive control frameworks and provides rigorous stability guarantees. The continuous-time dynamics are modeled as a neural ordinary differential equation with a sinusoidal activation function, solved via a fixed-step Runge–Kutta integrator. The discrete-time pathway employs a two-layer LSTM with Bayesian-optimized hyperparameters. A learned attention mechanism fuses the hidden states from both pathways, and the combined representation is passed through a feedforward network to produce a four-class seismic vulnerability index. The entire system is trained end-to-end with an adaptive loss function that balances reconstruction accuracy, drift penalization, and classification performance. We first pre-train the model on synthetic data generated from a calibrated finite element model of a representative ancient hall in Rucheng, Hunan. Transfer learning then fine-tunes the model on real monitoring data. Our approach replaces conventional fragility-curve-based estimates with a data-driven, adaptive vulnerability assessment that is site-specific and robust to non-stationary excitation. The hybrid switched-system framework therefore offers a principled solution to the state drift problem while maintaining the expressive power of continuous-time neural dynamics. Full article

Uhthoff’s phenomenon (UP) is a transient, fully reversible exacerbation of pre-existing neurological deficits in multiple sclerosis (MS) patients, triggered by minor elevations in core body temperature. In clinical practice, UP is a frequent source of distress, often misidentified as an acute inflammatory disease relapse. This narrative review provides a critical analysis of UP, addressing methodological heterogeneity in its epidemiological estimates, its clinical presentation, and its differential diagnosis from true relapses. Mechanistically, we synthesize traditional concepts of temperature-dependent conduction block with modern insights into neuroenergetic failure, mitochondrial dysfunction, inflammatory mediators, and autonomic dysregulation. Furthermore, this work delineates current management strategies, establishing a clear distinction between robust evidence-based interventions and expert-informed practical guidance for patient education, physical rehabilitation planning, targeted active/passive cooling, and pharmacological approaches. Characterized as a pseudo-relapse, UP occurs independently of novel focal neuroinflammation. In conclusion, an isolated episode of Uhthoff’s phenomenon (UP) represents a transient, functional conduction block and does not inflict acute, permanent structural damage to the axon. However, emerging frameworks suggest that frequent, repeated episodes subject the already demyelinated axon to recurrent metabolic strain. Over time, these successive neuroenergetic crises do not directly destroy the fiber, but they may gradually exhaust the cell’s metabolic reserve. This cumulative stress potentially increases long-term secondary axonal vulnerability, making the axon more susceptible to the progressive neurodegenerative processes inherent to multiple sclerosis. Full article

Breast cancer disproportionately affects young women raising minor children. Unresolved parenting concerns can impair patients mental health, reduce treatment adherence, and compromise long-term oncological outcomes. However, previous studies have paid limited attention to the heterogeneity of parenting concerns in this population and lack a theory-grounded classification framework for stratified supportive care. This descriptive qualitative study, framed by social role theory, recruited 17 Chinese breast cancer patients through maximum variation sampling and conducted semi-structured in-depth interviews. From four dimensions—role expectation, role load, role resources, and role adaptation—the study extracted four distinct parenting concern personas: role overload, role strain, role resilience, and role disablement. The four subgroups differed significantly in maternal role expectations, treatment-related physical burden, family support resources, and coping strategies. These findings can assist clinicians in identifying the specific types of distress faced by different patient subgroups, thereby providing a basis for developing stratified and individualized precision support strategies. Full article

Plastic production and consumption have increased substantially due to the favorable properties of these materials, raising significant environmental concerns because most plastics do not fully decompose. A substantial portion of ocean plastic waste comes from ghost gear, including abandoned fishing nets. In this context, mechanical recycling offers a promising route for recovering these materials and reducing their environmental impact. This study evaluates the feasibility of using recycled fishing nets made of polyamide 6 (PA6) to manufacture injection-molded parts. Injection-molded specimens produced from recycled fishing nets were descriptively compared with specimens obtained from commercially available extruded PA6 plates, used as a commercial processing reference. Both materials were also subjected to an additional post-injection mechanical recycling cycle to evaluate short-term property retention after reprocessing. The feasibility of producing cardholders by injection molding was also examined as a proof of concept. Physical and mechanical characterization showed that the recycled fishing-net PA6 exhibited higher average yield stress and Young’s modulus, but lower stress and strain at break and lower impact toughness than the commercial PA6 reference. The second recycling cycle did not produce a consistent deterioration across the measured properties, although statistical equivalence between the two recycling conditions was not established. The successful production and assembly of cardholders demonstrated the laboratory-scale processability of recovered fishing-net PA6, while further characterization, additional recycling cycles, and process optimization are required to assess long-term recyclability and industrial viability. Full article

Conductive hydrogels combine tissue conformability, conductivity, and biocompatibility, making them an emerging class of materials for soft and wearable bioelectronics applications. This review comprehensively presents the latest developments in studies of conductive hydrogels for wearable sensing applications, including materials chemistry, mass and charge transport physics, and device engineering. Starting with an examination of the main materials used, such as conducting-polymer networks, nanocomposites, and ionic hydrogels, we analyze how molecular network design, hydration state, and conducting-phase organization jointly determine the electromechanical performance and durability of devices. We discuss how microscopic mechanisms related to mechanical behavior, transport phenomena under deformation, and the influence of viscoelasticity on conductivity as a function of hydration degree are linked to the macroscopic response of devices. The review discusses how strain, pressure, temperature, and multimodal sensing are detected as a function of the device architecture and its transduction mechanisms. Aspects related to interface engineering, skin adhesion, long-term reliability, and the development of metrological frameworks necessary for comparison between different studies in the literature are explored. The latest developments in sustainability, biodegradability, and AI-assisted materials design are also presented. By integrating these analyses, the review suggests design principles and performance maps that can be used for the development of next-generation CH-based wearable devices that optimize mechanical softness, multimodal sensing capability, and environmental durability. Full article

Most studies on circular mineral-based materials report short-term mechanical properties without providing predictive frameworks that link chemical degradation to long-term mechanical performance. This study develops and validates a leaching-index-driven chemo-mechanical framework for predicting the degradation of a natural hydraulic lime (NHL)–pozzolan mortar exposed to sulfuric acid. A normalized ionic-release index was used to drive all parameters of a trilinear continuum damage mechanics (CDM) model, enabling the prediction of complete stress–strain responses from leachate chemistry alone. The framework was calibrated using an extensive experimental dataset comprising accelerated acidic exposure at pH 1.5, 2.0, and 3.0 for durations up to 6000 h. Iron release was identified as the most suitable degradation indicator based on its monotonic evolution and strong correlation with mechanical deterioration. Power-law relationships linking the normalized leaching index to elastic modulus, peak strength, transition strain, and post-peak energy were established and validated against independent exposure groups, yielding prediction errors generally below 20%. Validation was performed against three independent blind exposure groups withheld from calibration, yielding mean deviations of approximately 18% in the elastic modulus and 13% in the peak strength, so that the quantitative validation rests on this limited three-group set, whereas the extension to circular mineral binders is presented only on a prospective, non-validated basis. A kinetic sub-model was further introduced to relate exposure conditions to the leaching index, enabling a complete predictive chain from environmental exposure to mechanical response and service-life estimation. Sensitivity analysis showed that post-peak energy dissipation degrades approximately 1.3–1.5 times faster than stiffness and strength, indicating that ductility-related parameters govern long-term reliability. The methodological architecture is further discussed, on a prospective and non-validated basis, in relation to circular mineral binders relevant to Baltic industrial and municipal by-product streams, including municipal-waste bottom ash, slag, fly ash, and recycled glass. Although experimental validation is limited to the NHL–pozzolan system, the proposed framework provides a physically grounded and data-efficient pathway for durability assessment and future durability-oriented design of circular mineral binders in the Baltic region. Full article

This article discusses the relevant problem of ensuring transport connectivity under the conditions of temporal restrictions of the road network, which arise during repair, communal and emergency operations. It is established that the existing organizational and intellectual methods of traffic management do not eliminate physical decrease in road capacity, while construction of stationary structures with different levels is limited by high costs and long terms of implementation. The above substantiates the need for the development of mobile overpasses as adaptive engineering solutions ensuring continuity of the traffic flows. The purpose of the research is to develop a scientifically substantiated theoretical and experimental methodology for designing a mobile overpass as an integrated system “structure-moving load”, taking into account its dynamic behavior. The paper proposes an integrated approach based on the use of physical similarity theory and dimensionless analysis. A differential equation of dynamic bending of a beam on an elastic foundation is formulated taking into account inertia, damping, base reaction and the effect of a moving mass, and then its nondimensionalization is performed to obtain a similarity criteria system. The scientific novelty of the research consists in developing a system of dimensionless criteria to describe the relationship between the structural, dynamic and operational parameters of a mobile overpass, as well as in the formation of a criterion base for large-scale modeling and transfer of the results to full-scale structures. The proposed methodology describes the mobile overpass as an integrated transport-engineering system accounting for the coupled interaction between the deformable structure, moving traffic load, elastic foundation, and damping effects. Experimental verification was performed on a specially designed stand in the scale 1:4. The results obtained showed the quasi-static nature of the structure performance with moderate damping and rigid base. It is established that the distribution of engineering stresses along the span length has a regular character and retains its shape when the load level changes, which confirms fulfillment of similarity conditions. Regression analysis revealed a close to linear dependence of stresses on the load mass with a high degree of confidence $(R^2\approx 0.995)$. The practical significance of the research consists in creating an engineering method for express design of mobile overpasses, which allows for assessing their stress–strain state, stability and serviceability without expensive full-scale tests. The proposed approach can be used in designing temporary transportation structures under the conditions of urban area, and in operation in areas of road operations and emergency situations. Full article

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

Designing an optimal digital pathology workspace is essential to ensure diagnostic accuracy and safeguard the long-term well-being of pathologists. While digital pathology improves reproducibility, facilitates multidisciplinary collaboration, and supports data-driven precision medicine, its clinical effectiveness depends not only on computational performance but also on the physical and ergonomic environment in which pathologists operate. Inadequate workstation design may impair visual perception, increase cognitive and musculoskeletal strain, and potentially affect diagnostic consistency. Moreover, the progressive integration of artificial intelligence (AI) into routine diagnostics introduces additional requirements related to display performance, visualization interfaces, and human–machine interaction. Despite the rapid global adoption of digital pathology systems, standardized recommendations addressing ergonomic, environmental, and technological aspects of the digital workspace remain limited. In this work, we propose a clinically oriented framework for the design of digital pathology workspaces suitable for AI-assisted diagnostics. Key elements include the selection and calibration of medical-grade displays, ergonomic furniture and input devices, optimized ambient lighting conditions, and institutional quality assurance procedures. Emerging developments, such as intelligent ergonomic monitoring, advanced visualization interfaces, and adaptive AI-assisted workflows, may further support safe, sustainable, and high-performance digital diagnostic environments. Full article

The long-term stability of submerged sandbars and protected shorelines in large alluvial rivers depends on the serviceability of flexible mattresses installed on the riverbed. Distributed fiber optic sensing is one of the few practical methods for monitoring deformation along these underwater systems over engineering-scale distances. Yet BOTDR-derived strain-difference profiles are often heavily contaminated by noise and rarely have reliable clean references. To address this issue, this study develops TrendBlend-BSFormer, a blind self-supervised denoising framework for in situ BOTDR strain data from underwater flexible mattresses. The framework combines four key features: blind-spot masking, a one-dimensional encoder decoder backbone, a Transformer bottleneck for long-range spatial dependence, and a multi-scale trend-detail blending branch with dual signal-noise heads. The framework was validated using annual and daily BOTDR field data from the Yudaizhou shoreline protection project in the Yangtze River, containing 9343 and 9875 valid measurement points, respectively. TrendBlend-BSFormer achieved pseudo-SNR/RMSE/MAE values of 14.22 dB, 15.03 με and 12.05 με for the annual data set and 5.32 dB, 8.02 με and 6.45 με for the daily data set, improving the pseudo-SNR by 1.45 dB and 2.95 dB relative to the published BiLSTM-CNN benchmark. It also reduced the high-frequency energy ratio from 0.172 to 0.011 for the annual data and from 0.424 to 0.112 for the daily data. The denoised profiles suppress isolated spikes while preserving mechanically plausible peaks, valleys, and short-range fluctuations, indicating that blind self-supervised denoising can provide a more physically credible strategy for BOTDR-based monitoring in complex underwater environments. Full article

Healthcare organizations face emerging challenges that threaten the safety of professionals and patients, as well as the performance and long-term sustainability of healthcare systems. Health problems such as work-related musculoskeletal disorders are highly prevalent among nurses, not only due to the physical demands but also because of significant psychosocial stressors and mental health challenges inherent in healthcare environments. This study investigates the influence of psychosocial risks at work (PSRs) on the occurrence of work-related musculoskeletal disorders (WRMSDs) in nurses. A cross-sectional study was conducted, using a snowball recruitment method, from October 2025 to March 2026, among 266 nurses. Data were collected using the Psychosocial Risk Factors scale (INSAT_ERPS) and The Depression, Anxiety and Stress Scale-21 Items (DASS-21), to examine relationships among PSRs, mental health and WRMSDs using descriptive and inferential statistics. Key psychosocial determinants of WRMSDs include high psychological strain—manifesting as anxiety—compounded by psychosocial stressors such as work intensity, employment relations, and emotional demands. The results highlight the importance of addressing PSR and mental health, to reduce the incidence of WRMSDs among nurses. Interventions focused on improving working conditions and promoting mental health may be effective in preventing WRMSDs. Full article

Structural health monitoring of buried pipelines is essential due to their exposure to corrosion, impact loads, and geotechnical disturbances, which may induce abnormal vibration responses. Acceleration signals provide direct and sensitive measurements of buried pipeline structural dynamic behavior, and are therefore suitable for early anomaly identification. An acceleration-based intelligent framework integrating Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and a Long Short-Term Memory (LSTM) network is proposed for buried pipeline condition recognition. First, the raw acceleration signals are decomposed into a set of intrinsic mode functions (IMFs) using ICEEMDAN to enhance time–frequency resolution and isolate weak transient impact components associated with buried pipeline structural anomalies. Subsequently, multi-scale features extracted from the IMFs are fused and fed into an LSTM network to capture temporal dependencies and perform supervised health state classification. Experimental results demonstrate that the proposed framework achieves an F1-score of 0.70 and a Precision–Recall AUC of 0.72 for identifying anomalies. Furthermore, cross-validation utilizing multi-source field data (dynamic acceleration and quasi-static strain) confirms the model’s physical interpretability and its stable performance under severe noise interference. The results validate the feasibility of combining advanced signal decomposition with deep learning techniques for buried pipeline anomaly pre-warning, providing a rigorous methodological basis for the safe operation of critical energy infrastructures. Full article

To address the durability limitations of conventional crack sealants under coupled extreme temperatures and traffic loads in long-life pavements, a bio-oil composite-modified patching material was developed using 90# base asphalt as the matrix, synergistically modified with crumb rubber (CR) and epoxidized soybean oil (ESO). To resolve the contradictory requirements for high elasticity and thermal expansion/contraction coordination in sealants, ESO was introduced; its polar epoxy groups optimize phase compatibility and promote low-temperature stress relaxation without restricting thermal deformability. Rheological evaluations revealed that the optimal system (OPT) successfully extended the service temperature window from PG 76–−24 °C (baseline) to PG 82–−24 °C, significantly enhancing its adaptability to extreme climatic fluctuations. At −24 °C, OPT exhibited a reduced creep stiffness (S) of 164 MPa and an increased creep rate (m) of 0.312, with a cracking resistance ratio (k) as low as 525.6; the quantitative significance of these metrics lies in granting the sealant superior stress relaxation capacity, enabling it to accommodate dynamic crack widening without interfacial debonding or brittle fracture. Fatigue testing via time sweeps demonstrated that N_f50 reached 2890 cycles, highlighting robust long-term resistance against high-frequency shear strains induced by tire edges. Micro-mechanistic analyses (FTIR, TG/DTG, and DSC) confirmed that the modification is primarily driven by physical blending. The elevation of the thermal decomposition threshold (T5%) to 302.4 °C and the residue at 600 °C to 44.8% provide a critical safety margin for high-temperature construction heating, preventing thermal degradation. Furthermore, the glass transition temperature (T_g) decreased to approximately −35.2 °C. These findings establish a rigorous quantitative and mechanistic framework for designing sustainable, high-performance patching materials for resilient pavement maintenance. Full article

Silicone rubber foam is widely used in multi-field engineering protection due to its excellent cushioning and thermal insulation properties. However, its performance degradation caused by long-term service aging seriously affects equipment reliability. Establishing a constitutive model that can accurately characterize the mechanical response during aging is crucial for studying performance degradation and finite element simulation. Traditional multi-parameter aging constitutive models suffer from problems such as easy convergence to local optimal solutions and poor physical interpretability of parameters. To address these issues, this study systematically characterizes the evolution laws of the stress–strain response, compression set, and stress relaxation of silicone rubber foam over an aging period of 0–768 h through accelerated thermal aging and uniaxial compression tests and proposes a second-order Ogden aging constitutive model with a single time-varying parameter. This model fixes α₁, α₂, and μ₂ as constants and only sets μ₁ as the time-varying parameter, reducing the number of parameters to be fitted from four to one. The coefficient of determination (R²) of the full-cycle stress–strain curve fitting is ≥0.9966. Meanwhile, a quantitative physical correlation between μ₁ and macroscopic aging performance indicators is established, enabling the direct prediction of the mechanical response of aged materials using measurable macroscopic indicators. This work provides an efficient and reliable modeling method for the aging performance evaluation and structural simulation of silicone rubber foam. Full article