Search Results (1,525)

This study examines the relationships between intrinsic motivation factors—task autonomy, personal growth and development opportunities, self-actualization, and decision-making participation—and employee retention, as well as the mediating role of perceived organizational pride. Using a quantitative cross-sectional survey, data were collected from 154 academic staff members at Taif University, Saudi Arabia. CFA supported the measurement model, and the hypotheses were tested using Hayes’ PROCESS macro. The findings show that all intrinsic motivation factors are positively associated with employee retention. Perceived organizational pride also mediates these relationships, suggesting that intrinsically motivating work conditions may support retention by strengthening employees’ pride in institutional membership. The results further indicate that developmental and participative factors show stronger associations with retention than task autonomy. This study contributes to employee retention research by integrating intrinsic motivation and identity-based explanations in the context of Saudi higher education. However, given the cross-sectional design and single-university sample, causal interpretation and generalizability should be treated with caution. The findings highlight the importance of growth-oriented, participative, and pride-enhancing work environments for supporting academic staff retention. Full article

Background: Cancer-associated thrombosis (CAT) is a leading cause of morbidity and mortality in cancer patients. Although international guidelines provide comprehensive recommendations for venous thromboembolism (VTE) prevention and treatment, the degree to which clinicians adhere to these guidelines in routine practice remains unclear, particularly in countries with limited national data such as Turkey. Methods: A cross-sectional, descriptive survey was conducted among oncology specialists (medical oncologists, radiation oncologists, and surgical oncologists) and cardiologists practicing across Turkey. A structured, case-based questionnaire comprising 21 multiple-choice questions was distributed electronically via SurveyMonkey. The questionnaire assessed perioperative VTE prophylaxis approaches, VTE risk assessment practices in ambulatory patients, primary and long-term secondary thromboprophylaxis preferences, acute VTE treatment strategies, and management of special clinical scenarios. Responses were analyzed using descriptive statistics and compared between oncologist and cardiologist groups. Results: A total of 84 physicians participated (34 oncologists [40.5%], 50 cardiologists [59.5%]). Perioperative and inpatient VTE prophylaxis practices were largely concordant with guideline recommendations, with 67.9% individualizing prophylaxis decisions and 66.7% initiating prophylaxis in hospitalized immobile patients when not contraindicated. However, only 33.7% routinely performed VTE risk assessment in ambulatory patients, and 64.6% did not use any validated risk scoring system. Low-molecular-weight heparin (LMWH) was the preferred agent for acute VTE treatment (72.6%), while direct oral anticoagulants (DOACs) gained preference in long-term secondary thromboprophylaxis (42.2%). No statistically significant differences were observed between oncologists and cardiologists across all survey items (all p > 0.05). Notably, 94.1% of respondents expressed a need to update their knowledge regarding CAT management. Conclusions: While oncologists and cardiologists in Turkey demonstrate general awareness of CAT guidelines, significant gaps persist in VTE risk stratification and primary prophylaxis for ambulatory cancer patients. The near-universal self-reported need for knowledge updates highlights the urgency for structured multidisciplinary education programs, integration of validated risk scoring tools into clinical workflows, and development of nationally adapted clinical practice guidelines. These findings reflect self-reported practices and may not fully represent actual clinical behavior; future studies incorporating medical record reviews or prescription data are needed to validate these observations. Full article

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application. Full article

The increasing integration of renewable energy introduces uncertainty in microgrid operation, making effective energy management more challenging. Rolling-horizon optimization is used to address this challenge by enabling periodic decision updates; however, most existing approaches rely on fixed optimization horizons and predetermined update frequencies. When prediction accuracy decay (PAD) is considered in adaptive rolling-horizon approaches, it is represented using a fixed decay value, not an online indicator that compares forecasted and actual renewable generation during operation. This leads to suboptimal re-optimization timing, unnecessary computational effort, excessive battery switching, or delayed corrective actions. To address these limitations, this paper proposes a PAD-driven adaptive rolling horizon (ARH) approach, in which re-optimization is triggered using an online PAD indicator computed from the percentage deviation between forecasted and realized renewable generation data. Re-optimization is activated when the PAD indicator exceeds a predefined threshold, enabling adaptive scheduling updates according to real-time forecasting degradation. The problem is formulated as a robust mixed-integer linear programming (MILP) model of a high renewable penetration microgrid, including battery degradation and switching penalties. The energy self-sufficiency ratio (SSR) is used as a key sustainability performance indicator to assess the extent to which local renewable generation and storage satisfy microgrid demand. The proposed approach is first compared with a fixed rolling-horizon approach using a fixed re-optimization interval of 1 h, where the results show a profit improvement of 10.5%. A sensitivity analysis tested the proposed approach under bounded renewable forecast uncertainty levels up to ±15 and different battery capacities. The results indicate that performance is strongly influenced by forecast accuracy and battery capacity, with higher economic gains under low uncertainty and more conservative operation under high uncertainty. Full article

This study proposes a human-centered multimodal framework for characterizing safety-relevant driver functional domains in professional bus drivers. Unlike conventional approaches that rely on isolated psychological or physical assessments, the proposed framework integrates self-perception, psychomotor performance, and cognitive–perceptual assessment to provide an exploratory, structured characterization of driver-related functional capacities. Eighteen professional bus drivers participated in this study. Self-perception data were obtained from all 18 participants, whereas psychomotor and cognitive–perceptual assessments were completed by 16 participants. These measurements were used to examine multiple domains relevant to driving safety, including behavioral awareness, motor coordination, attention, visual tracking, and hazard-perception-related processing. Given the modest sample size, the study should be regarded as an exploratory pilot investigation. Data were analyzed using a laboratory-based cross-sectional between-subjects design to examine age- and gender-related differences across the assessed domains. The findings suggested that selected age- and gender-related differences and descriptive tendencies were observable across multiple domains. Male drivers descriptively showed higher self-rating scores, female drivers showed different performance tendencies in selected psychomotor tasks, and male drivers demonstrated substantially greater grip strength. Older drivers showed slower and less efficient performance in several cognitive–perceptual measures, with the clearest age-related effect observed in the tachistoscopic traffic test, where older participants showed a higher error tendency under time-constrained traffic-scene processing conditions. The constructs and measures proposed in this study are intended as general laboratory-based assessments of driver-related capabilities rather than direct measures of actual driving performance, real-time driver-state indicators, or validated sensor-based monitoring indicators. As candidate human-factor constructs, they may inform future driver monitoring research by helping clarify how driver-related signals or behaviors could eventually be linked to underlying functional and safety-related meaning in intelligent transportation environments. Full article

Outdoor thermal comfort in hot, arid regions critically influences campus open-space use and the sustainability of university campuses, including reduced cooling energy demand and enhanced livability, yet validated integrated assessments remain scarce. This study aims to explore the relationship among microclimate conditions, thermal comfort, and the sustainable use of campus open spaces in a hot, arid region, with the goal of identifying design strategies that enhance both user comfort and environmental sustainability. The study incorporated: (1) a site audit; (2) exploratory RayMan simulations (n = 180, unvalidated) calculating Physiological Equivalent Temperature (PET) across five zones; and (3) a June survey (n = 156, 52% response rate). Physical analysis revealed height-to-width ratios of 0.13–0.30, representing an 80–91% deficit below the 1.5 minimum commonly recommended benchmark for effective shading in the literature. Unvalidated simulations estimated a mean annual PET of 31.2 °C (SD = 4.8 °C), with 17.6% of annual PET values within the comfort range and 65.2% within the hot range. For June, unvalidated simulations estimated 4% of PET values within the comfort range, while 35.5% of respondents reported thermal comfort (mean ASHRAE 1.66, warm range)—a descriptive discrepancy of 31.5 percentage points. Self-reported social factors (friends: 79.8%) ranked higher than shading space selection responses; behavioral observations are required to confirm actual use patterns. Priority interventions from physical analysis and user reports include optimized shade, cool materials (albedo ≥ 0.60), and intentional greening—subject to validation with calibrated measurements. By linking microclimate modification to increased open-space usability and reduced cooling energy demand, this research contributes to sustainable campus planning frameworks. Pending field validation and seasonal surveys, the quantitative thermal comfort estimates should be considered exploratory rather than conclusive. Full article

Accurate liquid level regulation is critical for ensuring printing quality and process stability in stereolithography (SLA) 3D printing. However, traditional liquid level control methods often suffer from insufficient prediction accuracy, poor disturbance rejection capability, and limited adaptability under dynamic printing conditions. To address these challenges, this paper proposes an enhanced Transformer-based time series prediction model integrated with an xLSTM module for SLA liquid level prediction and adaptive control. By embedding the xLSTM structure into the Transformer encoder, the proposed model combines the global dependency modeling capability of self-attention mechanisms with the local temporal feature extraction capability of recurrent memory units, thereby improving the prediction accuracy and robustness of liquid level sequences. Experimental datasets were collected from an actual SLA printing platform, including multiple process-related features such as layer height, laser power, platform position, and vacuum pressure. Comparative experiments were conducted against conventional Transformer, LSTM, xLSTM, GRU, TCN, and PID-based methods. The results demonstrate that the proposed model achieves the best prediction performance, with an MAE of 0.174, RMSE of 0.222, and R² of 0.9903. Compared with the original Transformer model, the proposed approach significantly reduces prediction error and improves fitting stability. In disturbance rejection experiments, the proposed strategy effectively suppresses liquid level fluctuations under sudden pulse interference conditions, exhibiting superior robustness and dynamic response capability compared with traditional PID control. Furthermore, physical printing experiments verify that the proposed method can improve surface quality, contour accuracy, and structural stability of printed parts. Overall, the proposed Transformer–xLSTM framework provides an effective intelligent prediction and control solution for SLA liquid level regulation, offering significant potential for high-precision and intelligent additive manufacturing applications. Full article

The southern pine beetle (SPB) is a serious pest of pine forests from Central America to the eastern United States, with a recent range expansion into the northeastern United States. Efforts to detect and monitor SPB activity began in 1960 as part of an overall integrated pest management system to limit its impact to southern pine forests. The ubiquity of SPB’s pine hosts in the southern United States, in the form of plantations and natural mixed stands, along with the regular occurrence of SPB outbreaks over a vast region, makes SPB a leading driver of overall forest health across this region. We review the past and current methodology for collecting SPB-related pine mortality and outbreak data using aerial and ground survey techniques and remote sensing via satellite imagery. We show how historical and ongoing measurements of SPB abundance, from pheromone-baited traps and aerial surveys, are used to forecast near-term probabilities of outbreaks with a statistical model (actualized through a public URL) that captures the natural tendency of SPB populations to be very high or very low. Insect forecasts can also be combined with maps of the host distributions to generate predictions of short-term regional risks and longer-term tree mortality forecasts via the US Forest Service’ National Insect and Disease Risk Map (NIDRM). Because the measurements of insect abundance and impact outcomes have become part of continuing forest management operations, statistical models can continue to be improved and there is self-reinforcing feedback between models and management. Improved understanding and monitoring of prominent insect pests that impact abundant tree species is a pathway to managing forest health more broadly. Full article

The study examines judicial practice regarding claims for damages and non-pecuniary damages (hereinafter: NPDs) arising from violations of the duty to inform in healthcare by comparing two periods (2008–2010 and 2018–2020) in the context of patient self-determination and European trends in patient rights. The 193 final judgments selected from the Wolters Kluwer Law Database based on keyword searches underwent qualitative content analysis and quantitative processing using SPSS (Statistical Package for the Social Sciences, SPSS version 25.0). A selection criterion was that the judgment should assess on its merits whether the duty to inform had been fulfilled or violated. The real value of the adjudged compensation was compared and normalized in relation to the minimum wage (multiplied by the minimum wage) in order to reveal the actual socio-economic weight of the compensation. The results show that while in 2008–2010, the lack of information was mostly considered an additional element of professional negligence, by 2018–2020, it was recognized as a separate violation of personality rights that infringed on the right to self-determination, and the rate of complete rejection of claims for NPDs decreased. However, the increase in nominal amounts was accompanied only to a limited extent by an increase in the real value of compensation. The findings suggest that Hungarian judicial practice is moving closer to the autonomy-centred European approach, while strengthening the reparative function of NPDs—ensuring compensation that is perceptible in real terms—remains an open task. Full article

This research aims to improve real-time decision-making, process-state reconstruction, and multi-objective operational optimization in intelligent composting systems through an integrated framework based on hierarchical digital twin and Edge–Cloud architecture. Unlike previous studies that mainly focused on data monitoring or static optimization, the proposed framework enables dynamic reconstruction of process state, predictive decision-making, and intelligent assignment of tasks between Edge and Cloud layers simultaneously. The main innovation of the research lies in the combination of multilayer digital twin, dynamic decision rule for Edge–Cloud orchestration, and fuzzy multi-objective optimization in an integrated structure. In the proposed model, biological and operational uncertainties are modeled using triangular fuzzy numbers and control decisions are updated in real-time based on the actual system state. The results, compared to the baseline system without hierarchical digital twin and without Edge–Cloud orchestration, showed that the proposed framework was able to reduce the composting process time by about 28%, significantly reduce energy consumption, increase the compost quality index by 0.91, and effectively control the emission of undesirable compounds. The results also showed that the hierarchical Edge–Cloud architecture, by transferring time-sensitive decisions to the Edge layer and performing complex analyses in the Cloud, simultaneously improved the response time, process state reconstruction accuracy, and decision-making stability under dynamic and uncertain conditions. This research is an effective step in the development of intelligent, predictive, and self-adaptive systems for biological processes and sustainable waste management. Full article

Vision Transformers (ViTs) have achieved remarkable success; however, their internal decision-making mechanisms remain largely opaque. The Dynamic Accumulated Attention Map (DAAM) has emerged as a prominent interpretability tool that visualizes model focus by aggregating attention weights across successive blocks. Nevertheless, the fidelity of these maps, specifically the extent to which they accurately reflect the underlying pixels that drive the final classification, is often taken for granted. In this study, we evaluate the fidelity of DAAM using both supervised Data-efficient Image Transformers (DeiT) and self-supervised Self-distillation with no labels (DINO) models under strategic spatial perturbations. Our analysis reveals a critical failure mode in DINO under severe background noise at a Signal-to-Noise Ratio ($\mathrm{SNR}=-20$ dB): while the DAAM visualization paradoxically maintains a sharp and “accurate” focus on the target object, the model’s actual output suffers from total class flipping, shifting from the correct label to an entirely unrelated category. Statistical results on 1000 ImageNet samples confirm this decoupling of visualization and decision influence; DINO’s robustness accuracy plummets to 2.70%, yet its attention maps remain anchored to the salient object. These findings demonstrate that the accumulated saliency provided by DAAM can be a misleading indicator of model logic, as it may highlight regions that the model “observes” but no longer correctly “interprets.” We conclude that interpretability frameworks must transcend spatial heatmaps and incorporate causal fidelity metrics to avoid the pitfalls of deceptive visualization. Full article

Background: Adopting a healthy lifestyle through a balanced diet and regular physical activity is essential for chronic disease prevention, but healthcare workers face occupational constraints that may limit such behaviors. This study assessed perceived capability for healthy diet and physical activity among healthcare workers in the Jazan region of Saudi Arabia using the Capability Assessment for Diet and Activity (CADA) instrument and examined associated factors. Methods: A cross-sectional analytical study was conducted in 2026 in governmental healthcare facilities in the Jazan Health Cluster. A structured electronic questionnaire collected sociodemographic, occupational, and health-related data alongside the 34-item CADA. Total, Diet and Physical Activity CADA scores (1–5) were analyzed using descriptive statistics and multivariable ordinary least squares regression adjusted for sex, education, profession, and workplace; standardized coefficients and Cohen’s f² were reported. Results: A total of 601 healthcare workers participated. Internal consistency was good (Cronbach’s α = 0.84 for the full scale). Mean Total CADA was 3.28 ± 0.80 (scale midpoint 3.0); perceived Diet capability (3.45 ± 0.85) was higher than perceived Physical Activity capability (3.11 ± 0.85). Female sex was independently associated with lower Physical Activity CADA (β = −0.16; 95% CI −0.32 to −0.01; p = 0.042). Bachelor’s and board/doctoral qualifications were associated with higher Total CADA (β = 0.20; 95% CI 0.02 to 0.38; p = 0.026 and β = 0.33; 95% CI 0.07 to 0.58; p = 0.013, respectively). Compared with hospital-based participants, those in primary healthcare centers had lower Total (β = −0.19; 95% CI −0.32 to −0.05; p = 0.007), Diet (β = −0.17; 95% CI −0.31 to −0.02; p = 0.024) and Physical Activity (β = −0.21; 95% CI −0.35 to −0.06; p = 0.006) CADA scores. Effect sizes were small (|β*| ≤ 0.16; R² = 0.076–0.082; Cohen’s f² = 0.08–0.09). Conclusions: As CADA captures perceived capability, these findings reflect self-perception rather than objectively measured behavior; longitudinal studies combining CADA with validated behavioral instruments are warranted to clarify whether perceived capability translates into actual dietary and physical-activity behaviors in healthcare workers, and to evaluate whether workplace-based interventions targeting time pressure and access to supportive environments improve both perceived capability and measured behavior. Full article

UAV-based wildlife monitoring encounters tremendous challenges posed by complex environments, such as the extremely low proportion of effective targets in aerial images and variations in remote sensing scales. This paper presents a novel fusion framework named YOLIP, which integrates a detection head with semantic perception capabilities and an implicit feature adjustment module to boost detection accuracy and feature representation ability. Specifically, this paper redesigns the detection head to enable it to simultaneously learn spatial positioning and semantic features, thereby achieving more reliable extraction of regional features. The implicit feature modulation module introduces a dual-path fusion mechanism, which elevates the feature quality through geometric–semantic fusion, thereby improving the consistency and robustness of the detection. Furthermore, this paper also develops an asynchronous scheduling strategy, which can selectively execute computationally intensive operations to achieve computational optimization, enabling this framework to adapt to actual detection scenarios based on unmanned aerial vehicles. In this study, we conducted numerous experiments on the self-built drone wildlife dataset as well as the publicly available aerial wildlife dataset. Theresults demonstrate that compared with existing detection models, YOLIP improves mAP@0.5 by 11.6% while maintaining an efficient inference speed, achieving an improvement in detection performance. In addition, cross-dataset evaluation verifies the stable performance and generalization capability of the proposed method across multiple real-world scenarios. Full article

The steady advancement of online education has not automatically translated into improved educational quality. Teacher training often continues to focus on the technical use of digital tools, while the pedagogical processes through which teachers report supporting students’ digital competence remain insufficiently understood. The objective of this study was to examine the sequential and predictive structure of teachers’ digital competence using the DigCompEdu framework as a reference. A quantitative cross-sectional study was conducted with a sample of 136 university teachers involved in online education. Data were collected through a self-reported questionnaire based on DigCompEdu and analyzed in two phases: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANNs). The PLS-SEM results suggested a sequential pattern of associations among teacher-reported constructs: Professional Commitment (PC) was positively associated with Digital Resource Management (DR), which in turn was positively associated with Digital Pedagogy (DP) and Assessment and Feedback (AF). These dimensions were associated with Student Empowerment (SE), which showed the strongest positive relationship with teachers’ reported practices for Facilitating Students’ Digital Competence (FS). The ANN sensitivity analysis showed adequate predictive performance in the testing phase (RMSE = 0.155) and identified Student Empowerment as the predictor with the highest normalized importance within the specified model. These findings suggest that faculty development in online higher education may benefit from moving beyond basic digital literacy and platform management toward pedagogical design, formative assessment, inclusive participation, and learner agency. However, the results should be interpreted as evidence of teacher-reported facilitation practices within the analyzed sample, rather than as direct evidence of students’ actual digital competence development. Full article

In cold-region open-pit mine slopes, damage accumulation and mechanical deterioration induced by in situ stress and seasonal freeze–thaw alternation can easily trigger sudden instability. To investigate the effects of temperature difference under coupled constant loading and freeze–thaw action on the mechanical response and failure precursors of rock, based on the self-developed TCDR-I temperature–stress coupled testing system, uniaxial compression tests and real-time acoustic emission monitoring were conducted on water-saturated sandstone under a constant load of 1.4 MPa and multiple freeze–thaw temperature gradients. The mechanical behavior of freeze–thawed water-saturated sandstone and the acoustic emission characteristics during failure were analyzed. Combined with critical slowing down theory, the failure precursor characteristics of water-saturated sandstone under freeze–thaw action were investigated, and the internal mechanism of damage accumulation and defect evolution under the coupled effects of constant load and freeze–thaw temperature difference was revealed. The results show that, with increasing freeze–thaw temperature difference, the number of cracks and crack ratio in the loaded water-saturated sandstone gradually increased, whereas the compressive strength, elastic modulus, and total strain energy gradually decreased. After freeze–thaw treatment at −40 to 20 °C, the compressive strength, elastic modulus, and total strain energy decreased by 19.24%, 13.72%, and 44.77%, respectively, compared with those of the unfrozen–thawed specimens. During specimen failure, the dominant crack type gradually shifted from shear cracking to tensile cracking. The acoustic emission b-value and precursor points identified from multiparameter variance can both be used as criteria for predicting specimen failure. The warning lead time increased with increasing freeze–thaw temperature difference. After freeze–thaw treatment at −40 to 20 °C, the predicted failure times based on these two indicators preceded the actual failure time by 11.05 s and 16.19 s, respectively. The findings provide a theoretical basis for the early warning of sudden disasters in rock masses in cold-region engineering. Full article