Search Results (780)

Background: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21 ± 2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical comparisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, paired t-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimates η²). Results: Immediately after training, significant decreases in SDNN (↓ 35%), RMSSD (↓ 40%), and pNN50 (↓ 55%), accompanied by increases in LF/HF (↑ 32%), were observed. DFA α1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitudinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications. Full article

The deployment of conditionally automated vehicles raises safety concerns, as drivers often engage in non-driving-related tasks (NDRTs), delaying takeover responses. This study investigates driver state monitoring (DSM) using multimodal physiological and ocular signals from the TD2D (Takeover during Distracted L2 Automated Driving) dataset, which includes synchronized electrocardiogram (ECG), photoplethysmography (PPG), electrodermal activity (EDA), and eye-tracking data from 50 participants across ten task conditions. Tasks were reassigned into three workload-based categories informed by NASA-TLX ratings. A unified preprocessing and feature extraction pipeline was applied, and 25 informative features were selected. Random Forest outperformed Support Vector Machine and Multilayer Perceptron models, achieving 0.96 accuracy in within-subject evaluation and 0.69 in cross-subject evaluation with subject-disjoint splits. Sensitivity analysis showed that temporal overlap had a stronger effect than window length, with moderately long windows (5–8 s) and partial overlap providing the most robust generalization. SHAP (Shapley Additive Explanations) analysis confirmed ocular features as the dominant discriminators, while EDA contributed complementary robustness. Additional validation across age strata confirmed stable performance beyond the training cohort. Overall, the results highlight the effectiveness of physiological and ocular measures for distraction detection in automated driving and the need for strategies to further improve cross-driver robustness. Full article

The increasing use of immersive 3-D technologies in education raises critical questions about their cognitive impact on learners. This systematic review evaluates how electroencephalography (EEG) and eye-tracking have been used to objectively measure cognitive load in 3-D learning environments. We conducted a comprehensive literature search (2009–2025) across PubMed, Scopus, Web of Science, PsycInfo, and ERIC, identifying 51 studies that used EEG or eye-tracking in experimental contexts involving stereoscopic or head-mounted 3-D technologies. Our findings suggest that 3-D environments may enhance learning and engagement, particularly in spatial tasks, while affecting cognitive load in complex, task-dependent ways. Studies reported mixed patterns across psychophysiological measures, including spectral features (e.g., frontal theta, parietal alpha), workload indices (e.g., theta/alpha ratio), and gaze-based metrics (e.g., fixation duration, pupil dilation): some studies observed increased load, while others reported reductions or no difference. These discrepancies reflect methodological heterogeneity and underscore the value of time-sensitive assessments. While a moderate cognitive load supports learning, an excessive load may impair performance, and overload thresholds can vary across individuals. EEG and eye-tracking offer scalable methods for monitoring cognitive effort dynamically. Overall, 3-D and XR technologies hold promise but must be aligned with task demands and learner profiles and guided by real-time indicators of cognitive load in immersive environments. Full article

This study provides a comparative analysis of noise exposure and its occupational implications for two types of vessels operating in the Strait of Gibraltar: a conventional steel roll-on/roll-off passenger ferry (Ro-Pax) and an aluminium high-speed catamaran (HSC). A mixed-methods approach was employed, integrating objective acoustic measurements with subjective assessments of fatigue, workload, and circadian typology using validated survey instruments. The comparative framework is based on International Maritime Organization (IMO) Resolution A.468(XII), which establishes design-based noise limits for both vessel types. This framework is supported by the High-Speed Craft (HSC) Code and European Union (EU) Directive 2003/10/EC, both of which address occupational exposure. While both vessels comply with IMO design standards, the HSC consistently exceeds the noise limits set out in the HSC Code and European regulations in the accommodation and bridge areas. These elevated noise levels correlate with higher fatigue and workload scores among HSC crew, particularly in the engine and deck departments. In contrast, the Ro-Pax ferry demonstrates better acoustic insulation due to its steel construction, resulting in lower overall exposure and improved rest conditions. The results highlight the inadequacy of applying uniform noise standards to structurally distinct vessels, emphasising the importance of vessel-specific acoustic management strategies. Crucially, the study reaffirms the importance of maintaining compliance with both IMO design standards and EU occupational health regulations to ensure the comprehensive protection of seafarers’ well-being and safety. Full article

Healthcare professionals (namely, occupational therapists) face ergonomic risk factors that may lead to work-related musculoskeletal disorders (WRMSD). Ergonomic assessments are crucial to mitigate this occupational issue. Wearable devices are a potential solution for such assessments, providing continuous measurement of biomechanical and physiological parameters. Ergo4workers (E4W) is a mobile application designed to integrate data from independent wearable sensors—motion capture system, surface electromyography, force platform, and smartwatch—to provide an overview of the posture and workload of occupational therapists. It can help identify poor work practices and raise awareness about ergonomic risk factors. This paper describes the development of E4W by following a User-Centred Design (UCD) approach. The initial stage focused on specifying the context of use in collaboration with six occupational therapists. Then the app was implemented using WordPress. Three iterations of the UCD cycle were performed. The usability test of prototype 1 was carried out in a laboratory environment, while the others were tested in a real healthcare work environment. The Cognitive Walkthrough was applied in the usability tests of prototypes 1 and 2. The System Usability Scale evaluated prototype 3. Results evidenced positive feedback, reflecting an easy-to-use and intuitive smartphone app that does not interfere with daily work activities. Full article

This study examined how perceptions of artificial intelligence (AI) relate to technostress in healthcare professionals, testing whether AI acceptance mediates this relationship and whether self-efficacy moderates the formation of acceptance. Seventy-one participants completed measures of Perceptions of AI (Shinners), AI Acceptance (UTAUT), Self-Efficacy, and four technostress outcomes: Technostress Overall, Techno-Overload, Techno-Complexity/Insecurity, and Techno-Uncertainty. Conditional process analyses (PROCESS Model 7; 5000 bootstrap samples) were performed controlling for sex, age (years), and professional role (radiology residents, attending radiologists, PhD researchers). Perceptions of AI were directly and positively associated with Technostress Overall (b = 0.57, p = 0.003), Techno-Overload (b = 0.58, p = 0.008), and Techno-Complexity/Insecurity (b = 0.83, p < 0.001), but not with Techno-Uncertainty (b = −0.02, p = 0.930). AI Acceptance negatively predicted the same three outcomes (e.g., Technostress Overall b = −0.55, p = 0.004), and conditional indirect effects indicated significant negative mediation at low, mean, and high self-efficacy for these three outcomes. Self-efficacy moderated the Perceptions → Acceptance path (interaction b = −0.165, p = 0.028), with a stronger X→M effect at lower self-efficacy, but indices of moderated mediation were not significant for any outcome. The results suggest that perceptions of AI exert both demand-like direct effects and buffering indirect effects via acceptance; implementation should therefore foster acceptance, build competence, and address workload and organizational clarity. Full article

Cloud computing brings numerous advantages that can be leveraged through containerized workloads to deliver agile, dependable, and cost-effective microservices. However, the security of such cloud-based services depends on the assumption of trusting potentially vulnerable components, such as code installed on the host. The addition of confidential computing technology to the cloud computing landscape brings the possibility of stronger security guarantees by removing such assumptions. Nevertheless, the merger of containerization and confidential computing technologies creates a complex ecosystem. In this work, we show how Kubernetes workloads can be secured despite these challenges. In addition, we design, analyze, and evaluate five different Kubernetes deployment models using the infrastructure of three of the most popular cloud providers with CPUs from two major vendors. Our evaluation shows that performance can vary significantly across the possible deployment models while remaining similar across CPU vendors and cloud providers. Our security analysis highlights the trade-offs between different workload isolation levels, trusted computing base size, and measurement reproducibility. Through a comprehensive performance, security, and financial analysis, we identify the deployment models best suited to different scenarios. Full article

Latvia faces one of the lowest nurse-to-population ratios in the EU, resulting in critical staff shortages in intensive care units (ICUs). Nurses frequently care for more patients than recommended, which not only compromises patient safety but also places heavy psycho-emotional burdens on staff. The aim of this study was to examine organizational-level relationships between objectively measured ICU nursing workload and subjectively reported psycho-emotional outcomes, including moral distress, burnout, and intention to leave one’s job. A secondary analysis combined data from two cross-sectional studies conducted in 2025. Workload was measured using 3420 Nursing Activities Score (NAS) protocols from three hospitals, while 155 ICU nurses from 16 units completed validated instruments assessing moral distress, burnout, and turnover intentions. The findings revealed persistent nurse shortages, with one ICU showing deficits exceeding 70% and mean NASs above 100 points per nurse per shift. Nurses reported moderate moral distress, particularly in situations of unsafe patient ratios and aggressive treatment, while burnout levels were moderate to high, especially in personal and work-related dimensions. About one-quarter of respondents were actively considering leaving their jobs. Moral distress significantly correlated with burnout (r = 0.357, p < 0.001), and organizational-level comparison indicated that higher workload was associated with greater emotional strain. These results not only highlight urgent national challenges but also resonate with international evidence on the link between unsafe staffing, moral distress, and workforce sustainability. Implementing systematic workload monitoring, safe staffing ratios, and structured support mechanisms is essential to safeguard ICU nurses’ well-being, reduce turnover, and protect patient safety in both Latvian and global contexts. Full article

Background/Objectives: Mental fatigue during driving, whether passive (arising from monotony) or active (caused by cognitive overload), is a critical factor for road safety. Despite the growing interest in monitoring techniques based on neurophysiological signals, current biomarkers are primarily validated only for detecting passive mental fatigue under monotonous conditions. The objective of this study is to evaluate the sensitivity of the MDrow index, which is based on EEG Alpha band activity, previously validated for detecting passive mental fatigue, with respect to active mental fatigue, i.e., the mental fatigue occurring in cognitively demanding driving scenarios. Methods: A simulated experimental protocol was developed featuring three driving scenarios with increasing complexity: monotonous, urban, and urban with dual tasks. Nineteen participants took part in the experiment, during which electroencephalogram (EEG), photoplethysmogram (PPG), and electrodermal activity (EDA) data were collected in addition to subjective assessments, namely the Karolinska Sleepiness Scale (KSS) and the Driving Activity Load Index (DALI) questionnaires. Results:The findings indicate that MDrow shows sensitivity to both passive and active mental fatigue (p < 0.001), thereby demonstrating stability even in the presence of additional cognitive demands. Furthermore, Heart Rate (HR) and Heart Rate Variability (HRV) increased significantly during the execution of more complex tasks, thereby suggesting a heightened response to mental workload in comparison to mental fatigue alone. Conversely, electrodermal measures evidenced no sensitivity to mental fatigue-related changes. Conclusions: These findings confirm the MDrow index’s validity as an objective and continuous marker of mental fatigue, even under cognitively demanding conditions. Full article

This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was adopted, combining adapted self-report questionnaires, high-frequency eye-tracking data collected with Tobii Pro Glasses 3, and lux-level measurements. Key eye-movement metrics—including fixation duration, visit patterns, and pupil diameter—were analyzed within defined work zones (Areas of Interest). To reduce data complexity and uncover latent patterns of visual behavior, Principal Component Analysis was applied. Results revealed a progressive increase in visual fatigue across the workweek and throughout shifts, particularly during night work, and showed a strong association with inadequate lighting. Tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings provide a practical framework for improving operator well-being and inspection performance through sensor-based monitoring and environment-specific design enhancements, in alignment with the goals of Industry 5.0. Full article

The exponential growth of AI applications has intensified the demand for efficient inference hardware capable of delivering low-latency, high-throughput, and energy-efficient performance. This study presents a systematic, empirical comparison of GPU- and NPU-based server platforms across key AI inference domains: text-to-text, text-to-image, multimodal understanding, and object detection. We configure representative models—LLama-family for text generation, Stable Diffusion variants for image synthesis, LLaVA-NeXT for multimodal tasks, and YOLO11 series for object detection—on a dual NVIDIA A100 GPU server and an eight-chip RBLN-CA12 NPU server. Performance metrics including latency, throughput, power consumption, and energy efficiency are measured under realistic workloads. Results demonstrate that NPUs match or exceed GPU throughput in many inference scenarios while consuming 35–70% less power. Moreover, optimization with the vLLM library on NPUs nearly doubles the tokens-per-second and yields a 92% increase in power efficiency. Our findings validate the potential of NPU-based inference architectures to reduce operational costs and energy footprints, offering a viable alternative to the prevailing GPU-dominated paradigm. Full article

The digital transformation of the construction industry is still lagging due to its incomplete implementation throughout the entire building lifecycle. One stakeholder in particular has been largely overlooked thus far: public administration. This study explores the potential integration of augmented reality (AR) into the processes of building authorities, with a particular focus on the review part of the permissions process, taking the City of Vienna as an example. As part of the EU-funded BRISE-Vienna project, an AR platform was developed and tested and an AR application was designed to enhance the transparency, stakeholder communication, and efficiency throughout the process. This study compares the proposed AR-based review process with the traditional plan-based approach, assessing both hard and soft factors. To this end, the durations of the individual process steps were measured, with a particular focus on the time spent by the officers (as a hard factor). In addition, qualitative surveys were conducted to gather the subjective impressions of the test participants (as soft factors). The key findings were a reduction in the officers’ workloads and an improvement in spatial understanding. While the overall review time remained similar, the use of AR reduced officers’ workload by over 40%. Additionally, the test participants stated that AR improved their spatial understanding and alleviated the time pressure within the process. This case study demonstrates the potential of AR in the permissions process and could serve as a model for other cities and countries. Full article

Background: The Nursing Work Environment (NWE) plays a critical role in determining the quality of care, staff well-being, and organizational performance, particularly in oncology settings. Despite increasing attention, a comprehensive synthesis of organizational factors shaping oncology NWEs has been lacking. This scoping review aimed to describe the key features of oncology NWEs and to explore the outcomes associated with these characteristics. Methods: A scoping review was conducted following the Joanna Briggs Institute guidelines. Peer-reviewed studies published in English or Italian were included without time restrictions. Literature searches were performed in MEDLINE via PubMed, CINAHL, and Scopus between January and April 2025. Results: Twenty studies met the inclusion criteria. Key organizational characteristics of oncology NWEs were grouped into the following four domains: leadership and organizational support; workload and resource availability; ethical climate and collegial relationships; and physical and structural conditions of care settings. Across the studies, a positive NWE was frequently reported to be associated with improved nurse-related outcomes and, to a lesser extent, with patient-related outcomes. However, these associations should be interpreted with caution due to the heterogeneity of contexts and the predominance of cross-sectional designs. Conclusions: The NWE is a strategic element in delivering effective, safe, and sustainable oncology care. Practical actions for nurse managers and healthcare leaders include implementing leadership training programs, ensuring adequate staffing and resource allocation, fostering open communication, and promoting interdisciplinary collaboration. These measures are essential to protect staff well-being and guarantee high-quality, patient-centered care. Full article

At present, it is a real challenge to measure brain signals outside of the lab with portable systems that are robust, comfortable and easy to use. We propose in this article a bimodal electroencephalography–functional near-infrared spectroscopy (EEG-fNIRS) sensor whose spatial geometry allows the robust estimation of colocated electrical and hemodynamic brain activity. The geometry allows for the correction of extra-cerebral activity (short-channel distance) as well as the computation of the spatial gradient of absorbance required in the spatially resolved spectroscopy (SRS) method. The complete system is described, detailing the technical solutions implemented to provide signals at 250 Hz for both synchronized modalities and without crosstalk. The system performances are validated during an N-Back mental workload protocol. Full article

In view of the problems that the track mode accuracy of the automatic steering gear on survey ships cannot meet the requirements of hydrographic survey accuracy and the workload of manual steering is large, an intelligent auxiliary decision-making algorithm based on LSTM and multiple linear regression is proposed. By learning historical track information, marine environment information, historical steering data, hull state data, etc., it provides the helm with auxiliary operation prompt information, such as the command course and its adjustment timing (time range, area), so as to reduce the number of times the helm steers. The effectiveness of the algorithm is verified through sea trials. The results show that the number of steering times is reduced by 45.5% and the number of effective measuring points is increased by 1.5% through the algorithm in this paper. This result confirms that the algorithm can improve the operational efficiency of offshore survey tasks by optimizing human–computer interaction. Full article