Search Results (11,565)

Background and Objectives: Regular exercise improves pain, fatigue, and overall function in individuals with fibromyalgia (FM), yet adherence remains low, especially among employed adults. This study examined symptom and workplace-related factors associated with exercise participation among employees with FM. Materials and Methods: A cross-sectional workplace survey was conducted across nine large employers (>100 employees) representing diverse occupational roles. Participants (n = 1044) reported FM diagnosis, exercise participation (≥3 sessions/week), perceived exercise barriers, sleep duration, and job-related stress. Comparisons were conducted between employees with and without FM, and within-group analyses explored exercise-related patterns among those with FM. Results: Forty-two participants (4.0%) reported a formal FM diagnosis. Compared with employees without FM, those with FM were older and reported significantly greater pain, fatigue, emotional stress and poorer sleep (all p < 0.01). Despite this increased symptom burden, rates of regular exercise did not differ between FM and non-FM employees (40.5% vs. 36.8%, p = 0.38). Within the FM group, exercisers and non-exercisers showed minimal observable differences in symptom severity. However, employees with FM reported shorter exercise session durations and identified sleep disruption, fatigue, and work-related demands as prominent barriers. Conclusions: Among employed adults with FM, symptom severity alone did not appear to distinguish those who engaged in regular exercise from those who did not. Instead, modifiable workplace and environmental barriers were indicated as key factors influencing exercise participation, emphasizing the need for occupationally informed strategies to support sustained physical activity in this population. Full article

Apparent heat stress is usually expressed as Heat Index (HI), which reflects the combined impact of both temperatures and relative humidity upon human thermal tolerance. In the present study, the objectives were mainly to map the seasonal variations in HI across Oman and to investigate the environmental factors affecting their distribution. Seasonal HI calculations were applied using empirical equations, employing skin temperatures and relative humidity reanalysis data for Oman. These climatic datasets were acquired from the fifth-generation atmospheric reanalysis of global climate and weather (ERA5) produced by the European Copernicus Climate Change Services. Seasonal HI maps were produced using spatial interpolation techniques. Results showed that significant parts of the country fall into the high HI category, particularly in summer, where outdoor work is particularly vulnerable due to prevailing severe thermal stress. During fall and spring, considerable regions exert high HI values, while winter exhibits the lowest HI values throughout the country. Particularly, solar radiation was found to positively correlate with the HI for all of seasons, which eventually amplifies thermal stress, while the wind speed and topography exhibit negative and reducing influences upon HI. Climate change could exacerbate the severity of heat stress, particularly during spring, when the frequency of abnormal heatwaves is maximum. Maps of the seasonal pattern of heat stress could be beneficial in urban planning and sustainable development in this region. Full article

In white-collar workplaces, individuals with autism spectrum disorder (ASD) traits may experience psychological strain and reduced productivity. This study examined structural relationships among ASD traits, social camouflaging, psychological distress, and work functioning impairment, with a focus on gender differences using a secondary analysis of data from an online survey of 543 Japanese white-collar workers (284 men, 259 women). Validated instruments were used to assess ASD traits, camouflaging, psychological distress, and work functioning impairment. Multi-group structural equation modeling by gender was applied using a NIOSH-inspired model. Men scored higher on the Imagination subscale of ASD traits, whereas women scored higher on Attention Switching and Assimilation. ASD traits were indirectly associated with work impairment through psychological distress, while the direct path between ASD traits and work impairment became negative when distress was controlled, indicating a statistical suppression pattern that was more pronounced among women. Assimilation was significantly associated with psychological distress in women but not in men, although the gender difference was at the trend level. The findings indicate a cross-sectional, context-dependent association between ASD traits and work functioning and highlight the importance of considering both gender and workplace context in non-clinical working populations. Full article

Reservoir fluid identification is a critical aspect of oil and gas geophysical exploration. Accurate fluid identification directly impacts the interpretation of subsurface geological conditions, reduces exploration risks, and provides essential guidance for formulating oil and gas development strategies. Therefore, reliable and precise fluid identification is indispensable across different stages of oil and gas exploration and production. This study proposes a hierarchical classification method based on conditional Variational Autoencoder (cVAE) and time series forest (TSF) algorithms to address reservoir fluid identification under complex geological conditions. The main contributions of this work are as follows: (i) the cVAE is used to pre-process the logging data to suppress local high-frequency disturbances and isolated anomalies that may exist in the logging curves, thereby improving the quality of the input data; and (ii) hierarchical classification strategy is utilized to perform the fluid identification task in two steps. The first step involves a top-level classification to distinguish the gas bearing layer from the non-gas layer. The second step refines this classification into subcategories, including the gas layer (GL), gas–water layer (GW), gas-bearing water layer (GBW), water layer (WL), and non-reservoir layer (DW). This can fully address the challenges of imbalanced datasets and improve the recognition accuracy of minority classes. Additionally, integrating the TSF algorithm within the hierarchical classification framework effectively captures the sequential characteristics of well logging data, improving the model’s ability to recognize complex geological patterns. A real-world application in a block of the Yinggehai Basin in the South China Sea demonstrated the superior performance of the proposed model. Experimental results show that the method achieves an accuracy of over 84% in all four wells, enabling accurate and reliable reservoir fluid classification. Full article

Future lunar missions require robust 5G communication links, and their design depends partly on path loss characterization, link budget planning inputs, and path prediction loss models tailored to the Moon’s environmental conditions. This work develops a site-specific 5G large-scale path loss model for Shoemaker Rim F at 5.855 GHz using a high-resolution lunar digital elevation map and 3D ray tracing in Wireless Insite. Two link configurations were studied—dipole transmitter to dipole receiver (DD) and omni transmitter to dipole receiver (OD)—under five path loss cases: measured path loss, free space path loss (FSPL) with and without antenna patterns, and excess path loss with and without antenna patterns. The close-in (CI) and floating intercept (FI) model parameters are derived to develop a mathematical model for path loss prediction for the Shoemaker RIF’s terrain on the lunar south pole. The CI and FI for the DD configuration revealed a path loss exponent of 2.5378 and RMSE values of 45.15 dB and 43.898 dB, while the CI and FI for the OD configuration yielded a path loss exponent of 4.3280 and RMSE values of 6.301 dB and 66.739 dB, indicating strong sensitivity to the transmitter radiation pattern. Full article

Human pose estimation is a core computer vision task with broad applications, yet its performance degrades significantly in crowded scenes and under heavy occlusion due to missing or unreliable visual evidence. To address this limitation, this work reformulates occluded pose estimation as a structured pattern restoration problem and proposes a graph-based framework that models the human body as a relational skeletal graph. Starting from noisy or incomplete keypoint detections, the proposed method employs a graph neural network to propagate contextual information from visible joints to occluded ones through iterative message passing. Geometry-aware constraints on bone lengths and joint angles are integrated to enforce anatomical plausibility, while an occlusion-aware prediction mechanism distinguishes visible from missing joints during inference. Experiments on COCO-Keypoints, CrowdPose, and OCHuman demonstrate consistent improvements over strong baselines, particularly under moderate and severe occlusion, confirming the effectiveness of structural reasoning for robust pose estimation in real-world environments. These results confirm that explicit structural reasoning enables more accurate, stable, and reliable human pose estimation in real-world, occlusion-heavy environments. Full article

Oxalis tuberosa (oca) is a tuber crop native to the Andes, valued for its nutrition but understudied genetically. Its strict short-day (SD) tuberization suggests a photoperiodic control mechanism similar to that of potato, where an FT-like protein acts as a mobile “tuberigen” signal. To identify this key regulator, we generated a de novo genome assembly for oca using long- and short-read sequencing. Integrated transcriptomic analysis of leaves under long-day (LD) and SD conditions, along with stems, roots, and tubers, enabled gene annotation and expression analysis. Our study focused on the Phosphatidylethanolamine-Binding Protein (PEBP) gene family, the source of florigen and tuberigen signals. We identified 23 OtPEBP genes and characterized their expression patterns. Among these, we discovered three FT-like homologs that are specifically and strongly upregulated in leaves under SD conditions. We therefore propose these genes as the prime candidates for the mobile tuberigen signal in oca. This work provides the foundational genomic resource for O. tuberosa and advances our understanding of the conserved photoperiodic network controlling storage organ formation beyond the Solanaceae family. Full article

In most AFM nanoindentation experiments on soft biological samples, classical contact mechanics models, such as Hertz or Sneddon’s equations, are commonly employed to determine the Young’s modulus. However, biological materials are inherently heterogeneous, and their mechanical properties often depend on the indentation depth. In this work, we present a novel and simple approach to quantify how the apparent modulus varies with increasing indentation depth. The method is based on the general indentation equation for axisymmetric indenters combined with a straightforward polynomial fitting of the force–indentation data. The proposed approach offers significant advantages, as it greatly simplifies the fitting process without requiring any advanced algorithms, while maintaining high accuracy. In addition, it is shown that the depth-dependent mechanical properties of cells can be described by a simple law, $E\left(h\right)=C_d/h+E_l$, where $E_l$ is the limiting value of the apparent modulus at large indentations, and $C_d/h$ represents the depth-dependent contribution dominant at the initial stages of the indentation process. Here, $C_d$ is a positive stiffness coefficient, and $h$ is the indentation depth. This is a very important result, indicating that by using the pair of coefficients $C_d$ and $E_l$, we can fully describe the mechanical properties of cells, capturing their depth-dependent mechanical behavior. Experiments on fibroblasts and H4 human glioma cells confirm the accuracy of this equation. The proposed methods provide an accessible and reliable framework for nanoscale mechanical characterization, offering insights into the depth-dependent elasticity of heterogeneous soft materials and revealing mechanical patterns in biological samples. Full article

The career anchor (CA) is a metaphor created by Edgar Schein to illustrate the role of patterns of self-perceived talents, motives, and values in guiding, stabilizing (i.e., anchoring), and integrating a person’s work career. With the early years of work experience, this pattern tends to stabilize into one of the possible CAs and plays two main roles: guiding the selection of specific occupations and work environments; shaping individual reactions to the actual occupation and work environment. Since Schein’s initial conceptualization, theoretical refinements have been proposed, suggesting that CAs can change over time and that multiple CAs can coexist. Although substantial evidence supports the theory’s key predictions, the available literature appears fragmented, with a primary focus on descriptive concerns. Actual measurement issues also limit the development of theoretical knowledge. This entry provides an updated overview of the central predictions related to CAs, aiming at promoting greater integration and coherence in research and practice. Full article

As generative AI (GAI) becomes increasingly embedded in higher education teaching, its influence on teachers’ instructional behaviors has shown complex and even contradictory patterns. Moving beyond the dominant single-path perspective that emphasizes technological empowerment, this study integrates Conservation of Resources theory and Social Exchange Theory to develop a dual-path framework explaining how GAI simultaneously enables and depletes teachers’ psychological resources. Using survey data from 436 university design teachers in mainland China, structural equation modeling and conditional process analysis were employed. The results indicate that GAI use enhances teaching self-efficacy and teaching-related well-being, thereby promoting innovative work behavior and reducing work withdrawal through a resource-enabling pathway. Conversely, GAI use also increases AI-related anxiety and teaching-related occupational stress, forming a resource-depleting pathway that suppresses innovation and intensifies withdrawal tendencies. Further analyses show that perceived organizational support strengthens the positive effects of GAI, whereas psychological contract breach amplifies its negative impacts. These findings extend research on teacher behavior in educational technology contexts and offer practical insights for fostering supportive environments and mitigating psychological costs during GAI integration. Full article

PACS1 Syndrome is an ultra-rare neurodevelopmental disorder characterized by intellectual disability, behavioral disturbances, and multisystem involvement. While clinical knowledge is growing, its impact on quality of life (QoL) has not been systematically evaluated, and it is critical to understand the lived experience and psychosocial well-being of these individuals beyond strictly medical outcomes. This study aimed to assess QoL in individuals aged 4–21 years with PACS1 Syndrome using the validated KidsLife scale, proxy-reported by primary caregivers, given the intellectual disabilities and communicative limitations of this population. Twenty-one participants from Spain and other countries were recruited through the Spanish PACS1 Association, and 39 questionnaires from mothers and fathers were analyzed. The KidsLife scale provides standardized scores across eight QoL domains and a global QoL index (QoLI). The mean QoLI was 48.1 ± 28.3, slightly below the median for individuals with intellectual disability, but higher than other neurodevelopmental disorders such as Cornelia de Lange Syndrome. The findings revealed a pattern: while domains related to social inclusion, rights, and physical and material well-being were relatively preserved, reflecting adequate care and access to resources, the most significant compromises were observed in autonomy-related domains, specifically self-determination, interpersonal relationships, and personal development. Most individuals showed a high degree of dependency, and those with greater dependency exhibited lower QoL scores. This situation led more than half of families to reduce their working hours, with caregiving responsibilities disproportionately falling on mothers. Although no statistically significant differences were found between parental ratings, mothers tended to report higher QoL. These findings reflect the substantial functional impact of PACS1 Syndrome and emphasize the need for multidisciplinary support to improve autonomy, social participation, and overall well-being. Full article

The information symmetry between humans and machines can enhance mutual perception and understanding, leading to more robust cooperation. Intention recognition is a key technology in natural human–machine collaboration (HMC). However, as the complexity of the system increases, the amount of information and the types of tasks become numerous, which leads to continuous dynamic changes in interaction intentions. New sensing technologies such as electroencephalogram (EEG) have provided a continuous and unobtrusive monitoring approach to accurately and effectively identify intentions. But the complexity of physiological responses and the uncertain nature of intention make cross-subject recognition difficult, resulting in poor generalization performance and coarse-grained recognition patterns. To address these limitations, we proposed a framework for modeling tasks in complex systems and applied it to model tasks in an industrial system. Then, we use operators’ EEG data to effectively recognize the fine-grained intention patterns within different typical task scenarios, such as monitoring production and communication tasks. By inputting the improved multi-channel phase synchronization features into a machine learning classifier, cross-subject accuracy rates of 99.42% and 99.91% were achieved. This work furnishes systematic, field-tested cases for task modeling in the industrial field, demonstrates high-performance implicit intention recognition with a single EEG modality, and refines the granularity of implicit intention recognition. It provides theoretical underpinnings and technical support for both human–machine information symmetry and the advancement of HMC hybrid intelligence. Full article

Background: Agricultural logistics in arid, geographically dispersed areas require complex trade-offs among efficiency, equity, and robustness under uncertainty. Standard multi-objective vehicle routing problem (VRP) formulations, which primarily focus on cost or environmental parameters, do not explicitly account for social equity or transparency in decision-making. However, existing work seldom combines the objective of social equity as an endogenous optimization objective with robustness and interpretability within a unified mathematical framework. Methods: In this paper, we present a systems engineering decision-support framework informed by a multi-objective mixed-integer linear programming formulation for agricultural logistics planning. Economic, environmental, operational, and social equity goals are combined through ε-constraint to create trade-offs that can be interpreted at the policy level. We assess robustness against demand and travel-time uncertainty using the Bertsimas–Sim framework. A staged activation strategy separates conceptual model completeness from numerical implementation, and sensitivity analyses are conducted by perturbing vital operational parameters. Results: An illustrative situation in Northern Chile shows that this framework produces stable decision regimes and clear trade-offs in practice. The results show that meaningful improvements in workload balance and service equity can be achieved with negligible changes in operational efficiency. As we have learned in sensitivity experiments, assignment structures and qualitative trade-off patterns are robust under realistic parameter variations, and structural changes occur only beyond known threshold regimes. Conclusions: The major contribution of this work is the formulation of a systems engineering framework that extends traditional multi-objective VRP formulations and integrates social equity, robustness, and decision transparency as core design principles. Instead of focusing only on numerical optimization performance, the framework encourages auditable planning decisions in the face of uncertainty. The numerical analysis results are for a proof-of-concept scale only; however, the framework can be extended to larger agricultural networks using decomposition and/or hybrid solutions. Full article

Impaired glucose metabolism, a known precursor to type 2 diabetes, is associated with dysregulation of the autonomic nervous system. To assess such autonomic states, consumer wearable devices provide continuous, non-invasive physiological monitoring and may capture autonomic signatures related to metabolic status. This exploratory study examined whether dynamic features of heart rate (HR) and heart rate variability (HRV) during sleep—derived from a consumer wrist-worn device (Fitbit)—are associated with glucose metabolism status in free-living adults. We analyzed 189 nights from 18 participants (7 participants in the higher-glycemic-risk group, estimated glycated hemoglobin (HbA1c) ≥ 5.5%; 11 participants in the lower-glycemic-risk group, estimated HbA1c < 5.5%). From 28 candidate HR/HRV variables, Elastic Net regression ($\alpha =0.5$) was applied to identify features associated with nocturnal mean glucose. Fourteen features retained non-zero coefficients; notably, dynamic features capturing overnight trends and variability patterns showed stronger associations than conventional static mean values. The nocturnal trends of within-window standard deviation and variance of $ln\left(\mathrm{RMSSD}\right)$ (root mean square of successive differences between consecutive RR intervals, estimated here from PPG-derived inter-beat intervals; RMSSD) emerged as prominent candidates, alongside HR variability indices. Independent between-group comparisons further confirmed that two dynamic HRV features differed significantly between the lower- and higher-glycemic-risk groups (both $\mathit{p}<0.05$; Cohen’s $\left|d\right|>1.1$). Specifically, the lower-glycemic-risk group exhibited decreasing overnight trends in HRV variability, consistent with progressive autonomic stabilization during sleep. In contrast, the higher-glycemic-risk group showed increasing variability trends, suggestive of persistent autonomic instability. These directional patterns are consistent with prior evidence linking autonomic dysfunction to impaired glucose metabolism. We characterize these findings as hypothesis-generating. The identified dynamic HR/HRV features represent physiologically plausible candidate correlates of glycemic status and warrant confirmatory investigation in larger, independent cohorts with laboratory-measured HbA1c. More broadly, this work highlights the potential of widely available, consumer-grade wearable devices to move beyond activity tracking and support continuous, real-world assessment of cardiometabolic health, thereby expanding their utility in everyday health monitoring and preventive medicine. Full article

Condition monitoring of railway track surfaces is crucial for ensuring the safety, operational efficiency, and effective maintenance of railway systems. This work presents a data-driven modelling and an experimental methodology for identifying and classifying contaminants on railway tracks using vibration analysis and artificial intelligence techniques. In this study, the railway dynamics were physically simulated using a 1:20 scaled test rig, where the rails were treated with various contaminants (oil, water, and sand), and the resulting vehicle vibrations were recorded by on-board accelerometers and gyroscopes. To construct the predictive model, a hybrid architecture was designed integrating Short-Time Fourier Transform (STFT) for time-frequency feature extraction and a multi-channel Convolutional Neural Network (CNN) for pattern recognition. Initial results indicate that accelerometer data, particularly from longitudinal and lateral vibrations, are more effective than gyroscope data for classifying certain contaminants. To enhance classification robustness, this work introduces a multi-channel CNN that simultaneously processes the most informative signals, leading to a significant improvement in detection accuracy across all tested contaminants. This study validates the effectiveness of the proposed methodology as a robust and reliable solution for contaminant detection, while also confirming the utility of the scaled testbed as a valuable platform for future research in railway dynamics. Full article