Search Results (117)

In high-demand financial environments, employees’ capacity to regulate and sustain personal energy may constitute a critical yet underdeveloped organizational resource. Drawing on the Job Demands–Resources (JD-R) model and Conservation of Resources (COR) theory, this study conceptualizes energy management as a multidimensional personal resource that may support adaptive functioning and innovation under demanding work conditions. Despite increasing conceptual attention to energy-related constructs, systematic scale validation and cross-level performance evidence remain limited. This research adopts a two-study design to develop and validate a multidimensional Energy Management Scale within financial institutions. Study 1 (N = 299 employees from 11 financial institutions) examines the factorial structure, reliability, and nomological validity of the scale. Confirmatory factor analysis is used to examine the proposed four-dimensional configuration of physical, emotional, mental, and spiritual energy. The scale demonstrates acceptable internal consistency reliability and evidence of structural validity, including convergent and discriminant validity. Structural modeling results reveal that overall energy management is positively related to innovative behavior and organizational citizenship behavior. However, perceived workload was significantly associated only with physical energy, suggesting that demand-related mechanisms of energy may not operate uniformly across energy components. Additionally, exploratory institution-level aggregation analyses showed preliminary, counterintuitive negative associations between mean organizational energy levels and return on equity (ROE) in some years. Given the limited number of institutional clusters, these cross-level findings are preliminary and intended to provide initial external criterion evidence rather than confirmatory causal inference. Study 2 (N = 148 employees from two institutions) further examines alternative scale versions and external validity through stress coping capacity, job satisfaction, and life satisfaction. Results were discussed to examine the robustness and predictive validity of the scale across samples. Collectively, this study advances energy management research by providing a psychometrically supported measurement instrument and preliminary multilevel evidence of its organizational relevance. The findings position energy management as a measurable human-capital resource with implications for sustainable workforce innovation and performance in financial institutions. Full article

This study examines how different dimensions of leisure literacy relate to work–leisure boundary management and work performance in digitally connected workplaces, addressing the problem that leisure may function as either a restorative resource or a source of boundary conflict. Drawing on boundary theory, the study adopts an exploratory case-based survey design using data from 75 employees in a Taiwanese fire safety enterprise, combining self-reports, supervisor evaluations, and organizational records, with findings analyzed through correlation, subgroup comparison, and regression-based analyses. The results indicate differentiated pathways: positive leisure attitude is associated with work–leisure balance and higher self-rated performance, whereas excessive leisure involvement is associated with increased boundary conflict. These performance-related patterns were more consistently observed for self-rated than for supervisor-rated performance, so performance implications should be interpreted with appropriate caution. Leisure knowledge shows a regulatory role primarily in reducing conflict rather than directly enhancing balance. The results further suggest that comparative leisure/work importance conditions these relationships: when work and leisure are valued more equally, leisure literacy relates more directly to performance, whereas under value imbalance, boundary management becomes more salient, linking leisure literacy to work outcomes. Family life-cycle differences were also observed, although these are treated as contextual. Overall, the study suggests that leisure literacy may support sustainable work performance by shaping whether leisure functions more as a resource or as a source of friction. By extending boundary theory to the work–leisure interface, the study highlights boundary regulation as a relevant issue for sustainable human resource management in digitally connected environments, particularly under conditions of blurred work–leisure boundaries. Full article

Deploying conditional Generative Adversarial Networks (cGANs) for industrial texture synthesis faces two barriers: the prohibitive cost of manual data annotation and the uncertain alignment between automated evaluation metrics and human perception. This study addresses both challenges for marble texture synthesis using 289 high-resolution industrial scans. We adapt an unsupervised segmentation pipeline combining Simple Linear Iterative Clustering (SLIC) superpixels, Gaussian Mixture Models (GMMs), and graph cut optimization to extract vein structures without manual annotation. Four cGAN architectures—baseline cGAN, Pix2Pix, BicycleGAN, and GauGAN—are benchmarked using a dual-evaluation protocol contrasting ten automated metrics with structured human-centered assessment. The results reveal a significant metric–perception discrepancy. Pix2Pix achieved the best Fréchet Inception Distance (FID = 85.3) yet received the lowest human ratings due to periodic texture artifacts. GauGAN produced textures statistically indistinguishable from real marble, achieving a Visual Turing Pass Rate (VTPR) of 0.533 and a Mean Opinion Score on Marble Authenticity (MOS-MA) of 2.89, despite an inferior FID (87.3). These findings make three contributions: an annotation-free segmentation pipeline, empirical evidence that automated metrics alone are insufficient for architecture selection, and a dual-evaluation framework that establishes human-in-the-loop assessment as essential for quality-critical industrial deployment. Full article

Unmanned aerial vehicles (UAVs) for environmental monitoring typically rely on embedded platforms with limited computational capacity, which constrains the deployment of highly accurate yet computationally demanding object-detection models. To address this challenge and enable real-time image recognition under resource limitations, this study develops three lightweight neural network architectures based on the YOLOv11 framework. The proposed designs aim to significantly reduce computational complexity and parameter count while maintaining stable and reliable detection performance, thereby improving inference efficiency and deployment flexibility on UAV platforms. YOLOv11-M is selected as the baseline model due to its favorable trade-off between detection accuracy and inference speed. Three lightweight strategies are then proposed and evaluated. First, a Ghost Convolution approach replaces portions of standard convolution with low-cost linear operations, effectively reducing both parameter size and computational overhead during feature extraction. Second, MobileNetV4 is employed as the backbone network; its optimized bottleneck structures and attention mechanisms enable substantial model compression without compromising recognition performance. Third, a MobileOne architecture with reparameterization is introduced, in which multi-branch structures enhance feature learning during training and are subsequently merged into a single-path network for inference, thereby significantly reducing computational cost and improving practical deployability. Full article

Thrombosis remains a leading cause of cardiovascular morbidity and mortality. During thrombosis, activated platelets and endothelial cells expose phosphatidylserine (PS) on their outer membranes, creating a surface that accelerates clot formation. Current antithrombotic therapies, such as heparin and warfarin, carry significant bleeding risks, highlighting the need for safer alternatives. In response, we developed a PS-targeting liposomal formulation composed of Zn-dipicolylamine (DPA)-cyanine-3[22,22] and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (molar ratio 3:97). This DPA-harboring liposome (DPAL) binds selectively to PS-rich surfaces such as activated platelets and has demonstrated efficacy in reducing thrombosis in mouse models, with minimal bleeding. In the present study, we examined the interaction of DPAL with albumin, the most abundant plasma protein and a key transporter in the bloodstream, to assess the potential for harmful protein aggregation or structural disruption. Using dynamic light scattering and intrinsic protein fluorescence, we found that, unlike warfarin and heparin, DPAL does not induce any large protein aggregates or cause significant conformational changes near the tryptophan residue when mixed with human serum albumin, suggesting a favorable interaction profile. In addition, we used transwell permeability assays and CyQUANT cell proliferation assays to assess the cytotoxicity of DPAL in cultured human umbilical vein endothelial cells (HUVECs). Our results showed that DPAL does not compromise endothelial barrier integrity in HUVEC monolayers nor the cells’ viability. Our current and previous findings together suggest that DPAL could offer a promising approach to modulate harmful coagulation pathways and provide a new targeted therapeutic strategy for managing thrombotic disorders. Full article

Alfalfa (Medicago sativa L.) is an important perennial leguminous crop whose growth and yield are frequently limited by drought stress because the main planting areas are concentrated in arid and semi-arid regions. Plant growth-promoting rhizobacteria (PGPR) are crucial for enhancing plant stress resistance and constitute an attractive supplementary strategy for alfalfa production, but this has mainly been based on the use of single-strain inoculants in rhizobia. Here, we designed a microbial consortium to alleviate drought stress in alfalfa. Seven PGPR strains isolated from the rhizosphere and five rhizobial strains with in vitro growth-promoting properties obtained from alfalfa nodules were chosen. Based on a comprehensive evaluation of drought tolerance, growth-promoting traits, and metabolite-feeding experiments, we selected Sinorhizobium meliloti GAU-93 and Bacillus mycoides Y1 to construct a drought-resistant microbial consortium (DR-MC). A pot experiment indicated that inoculation with the microbial consortium enhanced drought resistance by increasing osmotic adjustment substance levels and reducing malondialdehyde content, promoting alfalfa growth. Separately, GAU-93 promoted aboveground growth by increasing photosynthetic pigment content under different water potential conditions, whereas Y1 enhanced root development and protected the plant from drought-induced oxidative damage. The DR-MC selected in this study is a valuable tool for further development to improve drought resistance in alfalfa. Full article

Background: The use of augmented reality (AR) in orthopaedics is growing rapidly but is mainly limited to pre-operative planning and teaching. This study is one of the first to describe the intraoperative application within revision arthroplasty for the positioning of customised partial pelvic replacements. Methods: In a proof-of-concept study an AR environment was used during surgery in 11 cases to enhance implant positioning. Postoperatively, a voxel-based CT deviation analysis was carried out to determine the COR deviation and the cup plane deviation angle. Additionally, digital implant superimposition was conducted. Results: Implantation was possible in all cases with a mean COR deviation vector of 4.2 (SD 2.5; 1.2–9.3) mm and a cup plane deviation angle of 4.4 (SD 2.5; 0.7–8.1)°. The implant analysis showed a superimposition of 0.69 (SD 0.15; 0.38–0.88) (Dice-Score calculation). Conclusions: This study is able to report promising results for AR in orthopaedic surgery, showing improved intraoperative feedback in complex operations, resulting in increased accuracy. However, the integration of AR poses a new challenge to the surgical team, especially because the AR users are facing a significantly increased level of intraoperative stress. Further development of this auspicious tool, as well as a conceivable combination with navigation, is necessary to facilitate broader usage. Full article

Background: Heart rate variability (HRV) is a valuable indicator for assessing vascular health, but keeping clinical decision support systems (CDSSs) aligned with the rapidly evolving literature remains challenging. This study aimed to develop an LLM-assisted literature synthesis framework to support transparent HRV-based risk stratification, enabling systematic extraction and organization of HRV evidence from published studies. Methods: An LLM-driven framework was developed to extract HRV parameters from 140 medical abstracts. The system simulated step-by-step human reasoning to identify key HRV indicators and group patient data using predefined statistical thresholds derived from the literature. System performance was evaluated using ECG-derived HRV features as a feasibility evaluation of literature-guided HRV classification. Results: The proposed framework demonstrated an accuracy of 86% in literature-guided HRV classification, with a sensitivity of 81% and a specificity of 87%. Compared with traditional machine learning approaches, the LLM-assisted system provided transparent, literature-grounded reasoning and could be readily updated as new studies became available. Conclusions: Large language models can support evidence-guided parameter selection and feasibility-level HRV-based risk stratification, rather than serving as predictive classifiers. This approach reduces manual effort, enhances transparency, and addresses common “black box” concerns associated with AI-assisted CDSS development in clinical practice. Full article

The current real estate market is in disarray, implying that reforms and incorporation of sustainable and green elements are crucial, especially for younger generations. This becomes more vivid for the case of developing countries and the Middle East as there is a growing interest in green living concepts. This study focuses on Turkish young adults and their purchasing intentions of green real estate options in line with the sustainability agenda for Turkish development goals. In this sense, the indirect impacts of greenwashing and perceived value are examined to address the underlying determinants of purchasing intentions. The theoretical setting of the research combines the stimulus–organism–response model and the theory of planned behavior. Through combined purposive and convenience sampling methods and using partial least squares structural equation modeling (PLS-SEM), a total of 203 surveys were analyzed. The results highlight that a well-established green marketing campaign can uplift perceived value, which in turn enhances purchasing intentions during the evaluation process among potential buyers. Greenwashing is a major diminisher for consumers’ intentions as it creates doubt, distrust, and negative emotions, thus creating a mental barrier for forming intentions towards purchasing green housing options. The findings of this research provide both theoretical and practical implications for improving housing options for young adults through empirical analysis of marketing and consumer behavior mechanisms. Full article

This paper provides geospatial and correlation analysis of heavy metal distribution in the soil cover of the city of Temirtau and its industrial zones. Based on 25 soil samples taken in 2024, concentrations of nine heavy metals (As, Pb, Zn, Cu, Ni, Co, Mn, Cr, Ba) were determined using X-ray fluorescence analysis. Spatial data interpolation was performed using the Kriging method in the ArcGIS Pro environment. The results showed the presence of localized extreme pollution zones, primarily near the Qarmet JSC metallurgical plant. The most significant exceedances of maximum permissible concentrations (MPC), up to 348× MPC for Cr, 160× MPC for Zn, and 72× MPC for As, were recorded at individual locations. Correlation analysis revealed a moderate positive relationship between several elements, particularly Mn and Cu (r = 0.64). Comparison of the spatial distribution of pollution with population data allowed for the assessment of potential environmental risks. This research emphasizes the need to implement systematic monitoring, sustainable land management practices, ecological maps, and preventive measures to reduce the long-term impact of heavy metals on ecosystems and public health, and to promote environmental sustainability in industrial regions. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

While guide dogs remain the primary aid for visually impaired individuals, robotic guides continue to be an important area of research. This study introduces an indoor guide robot designed to physically assist a blind person by holding their hand with a robotic arm and guiding them to a specified destination. To enable hand-holding, we employed a camera combined with object detection to identify the human hand and a closed-loop control system to manage the robotic arm’s movements. For path planning, we implemented a Dueling Double Deep Q Network (D3QN) enhanced with a genetic algorithm. To address dynamic obstacles, the robot utilizes a depth camera alongside fuzzy logic to control its wheels and navigate around them. A 3D point cloud map is generated to determine the start and end points accurately. The D3QN algorithm, supplemented by variables defined using the genetic algorithm, is then used to plan the robot’s path. As a result, the robot can safely guide blind individuals to their destinations without collisions. Full article

With the rise of smart agriculture and the expansion of pig farming, pig aggressive behavior recognition is crucial for maintaining herd health and improving farming efficiency. The differences in background and light variation in different barns can lead to the missed detection and false detection of pig aggressive behaviors. Therefore, we propose a deep learning-based pig aggressive behavior recognition model, in order to improve the adaptability of the model in complex pig environments. This model, combined with MobileNetV2 and Autoformer, can effectively extract local detail features of pig aggression and temporal correlation information of video frame sequences. Both Convolutional Block Attention Module (CBAM) and Advanced Filtering Feature Fusion Pyramid Network (HS-FPN) are integrated into the lightweight convolutional network MobileNetV2, which can more accurately capture key visual features of pig aggression and enhance the ability to detect small targets. We extract temporal correlation information between consecutive frames by the improved Autoformer. The Gate Attention Unit (GAU) is embedded into the Autoformer encoder in order to focus on important features of pig aggression while reducing computational latency. Experimental validation was implemented on public datasets, and the results showed that the classification recall, precision, accuracy, and F1-score of the model proposed in this paper reach 98.08%, 94.44%, 96.23%, and 96.23%, and the parameter quantity is optimized to 10.41 M. Compared with MobileNetV2-LSTM and MobileNetV2-GRU, the accuracy has been improved by 3.5% and 3.0%, respectively. Therefore, this model achieves a balance between recognition accuracy and computational complexity and is more suitable for automatic pig aggression recognition in practical farming scenarios, providing data support for scientific feeding and management strategies in pig farming. Full article

This study applies path planning and obstacle avoidance to drone control for conducting riverbank inspections. Given that the river’s surrounding environments are often windy and filled with overgrown branches and unknown obstacles, this study improves path planning and obstacle avoidance to enable drones to complete inspection tasks using the planned optimal route. Multiple drones are used for larger-scale areas to reduce time consumption and increase efficiency. Regarding path planning, the A* algorithm is improved using a grid-based approach. For obstacle avoidance, depth cameras are installed on the drones, and the obtained images are processed by reinforcement Q-learning with a genetic algorithm to navigate around obstacles. Since maintaining formation is necessary during task execution, the leader–follower method of formation flight ensures that multiple drones can complete inspection tasks while maintaining formation. Full article

In Mediterranean ecosystems, a thorough understanding of seedling regeneration dynamics as well as a good predictive ability of the process is essential for sustainable forest management. Leveraging the predictive capacity of the multilayer perceptron (MLP) as recognized as artificial intelligence methodology, the authors analyzed a real case study with a dataset encompassing environmental, ecological, and forestry variables. The study focused on the cork oak (Quercus suber, L.) seedling regeneration dynamic, which is a critical process for maintaining ecosystem resilience. A set of 10 MLP with a block from 5 to 50 neurons with hyperbolic tangent (TanH), linear (LIN), and Gaussian (GAUS) activation function were tested and their performance for predictive purposes was compared with traditional quantitative approaches. The MLP configured with 40–50 neurons per activation function (TanH, LIN, GAUS) demonstrated outstanding predictive performance, achieving an area under the curve (AUC) of the receiver operating characteristic and precision-recall scores above 0.80. These models made few prediction errors, effectively explaining the majority of the data variance, as indicated by a high generalized R² and a low mislearning ratio. This approach outperformed traditional statistical models in predicting seedling regeneration. Tree density, stand density index, and acorn number played an important role, influencing the cork oak seedling prediction. In conclusion, the results of this research determined the importance of an AI classification modeling technique in the prediction of cork oak regeneration, providing practical references for future forest management strategy decisions. Full article