Search Results (2,622)

Melon has great economic importance in Brazil, and flower development is the basis for fruit and seed production. The objective of this study was to elucidate the variability of flowering characteristics and to compare qualitative and quantitative reproductive variations in relation to pollen viability and stigmatic receptivity in 21 genotypes, which includes 15 Brazilian accessions. In addition, we evaluated the influence of time on the growth of the pollen tube and its arrival at the ovule in vivo at different intervals (1 h, 2 h, 3 h) after hand pollination in three commercial varieties, one exotic accession, and two intervarietal hybrids, by epifluorescence technique. Three groups were distributed by the clustering method of Scott–Knott at 5% probability; group III included only commercial varieties for the flower width descriptor. C. melo germplasm presented 81% andromonoecious plants and 19% trimonoecious plants. Through the multivariate strategy, these 21 genotypes were distributed into six groups with distinct reproductive characteristics, and male flowering was accelerated compared to female flowering. Regarding pollen viability, it was greater than 95% according to staining methods. Pollen germination rate in vivo was affected by time, with an almost 12.5% increase between 1 h and 3 h after hand pollination, and the in vivo pollen germination in hybrids was lower than in commercial varieties. Brazilian accessions, despite stability in pollen viability and stigma receptivity, have great differences in reproductive terms, such as variations in the quantitative and qualitative characteristics of floral pieces and flowering. This work contributes to the knowledge on varieties, hybrids, exotic accession, and Brazilian melon germplasm by characterizing some of their main agricultural traits, such as reproduction floral biology, and opens up prospects for yield evaluation in plant breeding programs. Full article

A trend analysis mentioned that the global automotive Vehicle-to-Everything—also called V2X—market size will be reached at several billions in the near future. This information clearly highlights the importance of developing V2X communication. Nowadays, automobile manufacturers have introduced vehicles equipped with driver assistance and even conditional autonomous driving features. Light detection and ranging (LiDAR) components are used in sensor networks to detect objects around. Also, vehicles take advantage of LiDAR sensors to discover the neighbor cars in cognitive systems for road safety. Carrying on from our previous works, we found that organizing vehicles into groups can enhance the safety of the vehicle networks by LiDAR assistance. However, the success rate and reliability of grouping vehicles is an important issue. Also, enhancing existing Vehicle-to-Vehicle (V2V) communication mechanisms remains a key factor in ensuring that emergency messages can be transmitted both timely and accurately. To address this, in this research, a method is proposed to make vehicles on the road be self-organized well for Intelligent Transportation Systems (ITS). Also, we found that before data in each car is transmitted, the scenario that data is queued for waiting a random time exponentially distributed outperforms data being sent immediately. Full article

This study investigated the physiological effects of seat vibration during prolonged sitting in a simulated autonomous driving environment. Eleven healthy participants (3 young adults and 8 older adults) viewed a 120-min highway driving video under two conditions: rhythmic seat vibration (2 Hz, mimicking natural respiration) and no vibration. Physiological and behavioral metrics—including Psychomotor Vigilance Task (PVT), seat pressure distribution, heart rate variability (HRV), body acceleration, and skin temperature—were assessed across three phases. Results demonstrated that seat vibration significantly enhanced parasympathetic activity, as evidenced by increased HF power and decreased LF/HF ratio (p < 0.05), suggesting reduced autonomic stress. Additionally, seated posture remained more stable under vibration, with reduced asymmetry and sway, while the no-vibration condition showed time-dependent postural degradation. Interestingly, skin surface temperature was lower in the vibration condition (p < 0.001), indicating a possible thermoregulatory mechanism. In contrast, PVT performance revealed more false starts in the vibration condition, particularly among older adults, suggesting that vibration may not enhance—and could slightly impair—cognitive alertness. These findings suggest that low-frequency seat vibration can support physiological stability and postural control during prolonged sedentary conditions, such as in autonomous vehicles. However, its effects on vigilance appear limited and age-dependent. Overall, rhythmic vibration may contribute to enhancing passenger comfort and reducing fatigue-related risks, particularly in older individuals. Future work should explore adaptive vibration strategies to balance physiological relaxation and cognitive alertness in mobility environments. Full article

Hydrogen has been explored as a greener alternative for greenhouse gas emissions reduction. Sodium borohydride (NaBH₄) is a favorable hydrogen carrier due to its high hydrogen content, safe handling, and rapid hydrogen release. This work presents a novel synthesis of the catalyst CoFe₂O₄/Fe₂O₃ using nanocellulose fibers (TCNF) as reactive templates for metal adsorption and subsequent calcination. The resulting material was tested for H₂ production from basic NaBH₄ aqueous solutions (10–55 °C). The catalyst’s composition is 74.8 wt% CoFe₂O₄, 25 wt% Fe₂O₃, and 0.2 wt% Fe₂(SO₄)₃ with agglomerated spheroidal particles (15–20 nm) and homogeneous Fe and Co distribution. The catalyst produced 1785 mL of H₂ in 15 min at 25 °C (50 mg catalyst, 4.0% NaBH₄, and 2.5 wt% NaOH), close to the stoichiometric maximum (2086 mL). The maximum H₂ generation rate (HGR) reached 3.55 L min⁻¹ g_cat⁻¹ at 40 °C. Activation energies were determined using empirical (38.4 ± 5.3 kJ mol⁻¹) and Langmuir–Hinshelwood (L–H) models (42.2 ± 5.8 kJ mol⁻¹), consistent with values for other Co-ferrite catalysts. Kinetic data fitted better to the L–H model, suggesting that boron complex adsorption precedes H₂ evolution. Full article

Edge intelligence plays an increasingly vital role in ensuring the reliability of distributed microservice-based applications, which are widely used in domains such as e-commerce, industrial IoT, and cloud-edge collaborative platforms. However, anomaly detection in these systems encounters a critical challenge: labeled anomaly data are scarce. This scarcity leads to severe class asymmetry and compromised detection performance, particularly under the resource constraints of edge environments. Recent approaches based on Graph Neural Networks (GNNs)—often integrated with DeepSVDD and regularization techniques—have shown potential, but they rarely address this asymmetry in an adaptive, scenario-specific way. This work proposes Heterogeneous Graph Adaptive Augmentation (HGAA), a framework tailored for edge intelligence scenarios. HGAA dynamically optimizes graph data augmentation by leveraging feedback from online anomaly detection. To enhance detection accuracy while adhering to resource constraints, the framework incorporates a selective bias toward underrepresented anomaly types. It uses knowledge distillation to model dataset-dependent distributions and adaptively adjusts augmentation probabilities, thus avoiding excessive computational overhead in edge environments. Additionally, a dynamic adjustment mechanism evaluates augmentation success rates in real time, refining the selection processes to maintain model robustness. Experiments were conducted on two real-world datasets (TraceLog and FlowGraph) under simulated edge scenarios. Results show that HGAA consistently outperforms competitive baseline methods. Specifically, compared with the best non-adaptive augmentation strategies, HGAA achieves an average improvement of 4.5% in AUC and 4.6% in AP. Even larger gains are observed in challenging cases: for example, when using the HGT model on the TraceLog dataset, AUC improves by 14.6% and AP by 18.1%. Beyond accuracy, HGAA also significantly enhances efficiency: compared with filter-based methods, training time is reduced by up to 71% on TraceLog and 8.6% on FlowGraph, confirming its suitability for resource-constrained edge environments. These results highlight the potential of adaptive, edge-aware augmentation techniques in improving microservice anomaly detection within heterogeneous, resource-limited environments. Full article

Primary Care Pharmacy (PCP) plays a vital role in healthcare systems. This study evaluated the competencies of pharmacy graduates from a community-focused curriculum, emphasizing their skills and personal traits. A structured questionnaire assessed four domains: general characteristics (11 items), PCP skills (16 items: 13 home visit and 3 community engagement skills), PCP personal traits (7 items), and readiness for PCP practice. Two sets of questionnaires were distributed in 2018 to recent pharmacy graduates: one for self-assessment and the other for evaluation by supervisors or co-workers. A 5-point scale (1 = least competent, 5 = most competent) was used. Co-workers gave higher scores than the graduates themselves. In home visit skills, “providing medicine advice” scored highest (4.4 ± 0.6 by graduates; 4.5 ± 0.2 by co-workers), while “performing essential physical exams” scored the lowest (3.5 ± 0.7). For co-workers, the lowest score was “working with a multidisciplinary team” (3.9 ± 0.9). Among community engagement skills, “solving health-related problems” rated highest (3.4 ± 0.7), and “identifying community health needs” rated lowest (3.2 ± 0.7). “Being friendly” and “responsibility” were top-rated personal traits by graduates and co-workers, respectively. The lowest was “coordinating with local organizations.” Graduates showed strong PCP traits and home visit skills but moderate community engagement. Community-based exposure is recommended to enhance these competencies. Full article

This work investigated the effect of cerium (Ce) addition on the wear behavior of commercially pure titanium (CP-Ti) by varying the Ce content to 0.8, 1.4, and 2.0 wt.%. Alloys were fabricated using plasma arc melting, and wear resistance was evaluated under loads of 1 N and 5 N dry sliding condition. Microstructural characterization confirmed the formation of CeO₂ precipitates, whose size and distribution varied with the Ce content. The Ti-0.8Ce alloy exhibited the highest hardness (203 HV), showing a 35% increase compared to CP-Ti, and the lowest wear rate reduced by approximately 47% and 22% under 1 N and 5 N loads, respectively. In contrast, Ti-1.4Ce and Ti-2.0Ce formed coarse CeO₂ precipitates, which acted as third-body abrasives. Although these alloys showed lower average friction coefficients than CP-Ti (up to 22% reduction), the enhanced abrasive interaction promoted material removal and increased wear rates. Notably, Ti-2.0Ce exhibited the most severe degradation in wear resistance, with wear rates increases of 21% and 27% under 1 N and 5 N loads, respectively. These findings demonstrate that while CeO₂ precipitates reduce friction by suppressing direct metal–metal contact, their abrasive nature adversely affects wear resistance when the particle size and volume fraction are excessive. Therefore, 0.8 wt.% Ce was identified as the optimal composition for improving the wear resistance, achieving the best combination of high hardness, low wear rate without excessive third-body abrasion. Full article

The wettability of falling film flow outside multi-row horizontal tubes is a core factor determining the heat and mass transfer performance of falling film heat exchangers, which is critical for their optimized design and stable operation. A visualization experimental platform for falling film flow over ten rows of horizontal tubes was constructed, with water as the working fluid. High-definition imaging technology and image processing methods were employed to systematically investigate the liquid film distribution and wettability under three tube diameters (d = 0.016, 0.019, 0.025 m), four tube spacings (s = 0.75d, 1d, 1.25d, 1.5d), and four inter-tube flow patterns (droplet, columnar, column-sheet, and sheet flow). Two parameters, namely the “total wetting length” and the “total wetting area”, were proposed and defined. The distribution characteristics of the wetting ratio for each row of tubes were analyzed, along with the variation laws of the total wetting area of the ten rows of tubes with respect to tube diameter, tube spacing, and liquid film Reynolds number (Re_l). The following results were indicated: (1) Increasing the fluid flow rate and the tube spacing both promote the growth of the wetting length. When Re_l ≤ 505, with the increase of tube diameter, the percentage of the wetting length of the tenth tube row relative to that of the first tube row decreases under the same fluid flow rate; when Re_l > 505, this percentage first decreases and then increases. (2) The total wetting area exhibits a trend of “first increasing then decreasing” or “continuous increasing” with the tube spacing, and the optimal tube spacing varies by flow pattern: s/d = 1 for droplet flow (d ≤ 0.016 m), s/d = 1.25 for columnar flow, and s/d = 1.25 (0.016 m), 1 (0.019 m), 1.5 (0.025 m) for sheet flow. (3) The effect of tube diameter on the total wetting area is a balance between the inhibitory effect (reduced inter-tube fluid dynamic potential energy) and promotional effect (thinner liquid film spreading). The optimal tube diameter is 0.016 m for droplet flow and 0.025 m for columnar/sheet flow (at s/d = 1.25). (4) The wetting performance follows the order 0.016 m > 0.025 m > 0.019 m when Re_l > 505, and 0.025 m > 0.019 m > 0.016 m when Re_l ≤ 505. Finally, an experimental correlation formula for the wetting ratio considering the Re_l, the tube diameter, and tube spacing was fitted. Comparisons with the present experimental data, the literature simulation results, and the literature experimental data showed average errors of ≤10%, ≤8%, and ≤14%, respectively, indicating high prediction accuracy. This study provides quantitative data and theoretical support for the structural optimization and operation control of multi-row horizontal tube falling film heat exchangers. Full article

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article

Background/Objectives: This study is part of an exploratory mixed-methods project investigating how companies and their employees in Germany dealt with adapted working conditions during the COVID-19 pandemic. Here, we identify predictive factors for employees’ attitudes towards the suitability of work-related technical, organisational, and personal SARS-CoV-2 infection control measures. Methods: In July 2021, when there was little evidence to suggest that the risk of work-related exposure to SARS-CoV-2 differed between occupations and workplaces, a standardised online and an optional paper-and-pencil survey were distributed across seven companies in southern Germany. Multivariate linear regression was used for analysis. Results: A total of 821 employees participated (average response rate: 24.5%). Most of the respondents (93%) worked in large companies, in the production industry (82%), with most of them having office jobs (82%). Around 29% reported doing most of their office work remotely during the pandemic. The perceived suitability of workplace infection control measures was rated quite high, with an overall mean score of 4.11 (SD 0.60) out of a possible 5. Workplace characteristics related to the COVID-19 pandemic as well as individual perception of SARS-CoV2 and COVID-19 in general were the most prominent predictors of attitudes towards the suitability of work-related SARS-CoV-2 infection control. For example, a higher COVID-19-specific reactance was negatively associated with attitudes towards technical (ß = −0.16), organisational (ß = −0.14), and personal (ß = −0.17) infection control measures (all p-values < 0.001). Furthermore, a higher rating of the employer’s commitment to occupational safety and health related to SARS-CoV-2, a higher individual disease perception, and a higher individual COVID-19-specific resilience had a positive association with attitudes towards the suitability of infection control measures. Finally, professional activity as well as company affiliation had statistically significant associations with employees’ attitudes towards the suitability of infection control measures. Conclusions: The results provide insight into factors relevant to pandemic prevention and control. In particular, our findings highlight the potential to implement organisational measures alongside compulsory technical occupational health measures. This could inform the development of pandemic preparedness strategies that prioritise adherence to established occupational infection control measures. Full article

To address the challenge of improving the accuracy and efficiency of automatic transplanting operations in pepper plug seedling transplanters, this study innovatively designed a follow-up seedling picking and depositing mechanism. The core innovation lies in the synchronization of the seedling picking claws with the moving seedling cups, which was achieved by coordinating the motion speeds of the seedling picking and depositing mechanism with the seedling conveying mechanism. This synchronization ensured relative spatial stillness during seedling deposition, significantly enhancing seedling depositing accuracy. To meet the design requirements of this follow-up mechanism, this study presents a comprehensive design of the transplanter, including a three-dimensional model. Key mechanisms, namely the seedling picking and depositing mechanism and the seedling conveying mechanism, were thoroughly analyzed, with detailed explanations of their working principles. The transmission system was designed for reliability and stability, being towed by a tractor with the ground wheel driving the motion of the seedling conveying and distributing mechanisms. The motion mode of the seedling picking and depositing mechanism combined a crank–rocker mechanism and a crank–slider mechanism, utilizing a gear transmission rod for seedling picking and carrying actions, and rail guidance for follow-up seedling depositing. Experimental results validated the effectiveness of this design. In bench tests, the success rates of the seedling picking and depositing mechanism at operating speeds of 100 seedlings/min, 150 seedlings/min, and 200 seedlings/min were 97.4%, 98.44%, and 95.03%, respectively. In field tests, at operating speeds of 90 seedlings/min, 120 seedlings/min, and 150 seedlings/min, the planting success rates were 99.65%, 94.95%, and 89.18%, respectively. These results demonstrated that the follow-up seedling picking and depositing mechanism met the demands of automatic transplanting operations, offering an effective solution to enhance both the operating speed and quality of the transplanter. Full article

Background: Although most amputations caused by diabetes and peripheral artery disease (PAD) are preventable, current limb preservation efforts in the United States remain poorly understood. This study aims to identify key barriers and facilitators to limb preservation from the primary care provider (PCP) perspective. We plan to use the insights from this work to promote targeted intervention strategies. Methods: Using a mixed-methods design, an online 5–10 min survey was distributed to Oklahoma primary care providers who could elect to participate further in a semi-structured, audio-recorded interview. Descriptive analysis was used to summarize survey results. Interviews were transcribed and qualitatively analyzed using grounded theory. Donabedian’s structure, process, and outcome framework was used to categorize how each identified barrier and facilitator increases or reduces the risk of limb loss for at-risk patients at the practice level. Finally, we compared and contrasted survey and interview findings. Results: Thirty surveys were completed (approximately 14% response rate), and seven interviews were conducted with PCPs geographically dispersed across Oklahoma. Most clinicians reported in the survey that they see at-risk limbs at least once every 1–2 months (n = 29, 96.7%). Half of clinicians were satisfied or very satisfied with access to vascular surgery (n = 15, 50.0%), interventional specialists (n = 13, 43.3%), and endocrinologists (n = 12, 40.0%). Finally, survey respondents reported that social needs most often affecting their patients with a limb at risk of amputation include income, health education, transportation, and health insurance. Interviews confirmed PCPs frequently see at-risk limbs. We identified thematic barriers to limb preservation that included limited access to specialty care, limited PCP and patient amputation prevention education, and patient social struggles surrounding transportation, finances, and insurance. Patient advocates (community, clinical, or personal), affordable medications, and more time with patients were reported as facilitators in amputation prevention. Conclusions: Oklahoma PCPs frequently see at-risk feet, realize poor access to care, and desire structural change to support excellent preventive care in diabetes and PAD. Limb preservation in Oklahoma is contingent upon shifting from disempowerment to engagement that requires systemic reform, clinical innovation, and community engagement. We identified several intervention strategies, including increasing education for PCPs to empower them to initiate early prevention, improving early identification and preventive therapy for patients at risk for limb loss, and cultivating specialty care access via networking and policy change. Full article

Employing commercial off-the-shelf coherent optical transmission components and methods to design a continuous variable quantum key distribution (CVQKD) system is a promising trend of achieving QKD with high security key rate (SKR) and cost-effectiveness. In this paper, we explore a CVQKD system based on the widely used polarization division multiplexed (PDM) coherent optical transmission structure and pilot-aided digital signal processing methods. A simplified pilot-aided phase noise compensation scheme based on frequency division multiplexing (FDM) is proposed, which introduces less total excess noise than classical pilot-aided schemes based on time division multiplexing (TDM). In addition, the two schemes of training symbol (TS)-aided equalization are compared to find the optimal strategy for TS insertion, where the scheme based on block insertion strategy can provide the SKR gain of around 29%, 22%, and 15% compared with the scheme based on fine-grained insertion strategy at the transmission distance of 5 km, 25 km, and 50 km, respectively. The joint optimization of pilot-aided and TS-aided methods in this work can provide a reference for achieving a CVQKD system with a high SKR and low complexity in metropolitan-scale applications. Full article

The growing integration of renewable-based distributed energy resources within local energy communities is significantly reshaping the operational dynamics of medium voltage distribution networks, particularly affecting their reliability and protection schemes. This work investigates the technical impacts of the high penetration of distributed generation within sustainable local energy communities on the effectiveness of fault detection, location, isolation, and service restoration processes, from the point of view of Distribution System Operators. From a supply continuity perspective, the methodology of the present work comprises a comprehensive, quantitative, system-level assessment based on probabilistic, scenario-based simulations of fault events on a CIGRE benchmark distribution network. The models incorporate component fault rates and repair times derived from EPRI databases and compute standard IEEE indices over a one-year horizon, considering manual, hybrid, and fully automated operation scenarios. The results highlight the significant potential of automation to enhance supply continuity. However, the qualitative assessment carried out through laboratory-based Hardware-in-the-Loop tests reveals critical vulnerabilities in fault-detection devices, particularly when inverter-based distributed generation units contribute to fault currents. Consequently, quantitative evaluations based on a sensitivity analysis incorporating these findings, varying the reliability of fault-detection systems, indicate that the reliability improvements expected from increased automation levels are significantly deteriorated if protection malfunctions occur due to fault current contributions from distributed generation. These results underscore the need for the evolution of protection technologies in medium voltage networks to ensure reliability under future scenarios characterised by high shares of distributed energy resources and local energy communities. Full article

Inefficient drying of alfalfa round bales causes significant nutrient loss (up to 50%) and quality degradation due primarily to uneven drying in existing processing methods. To address this challenge requiring dedicated equipment and optimized processes, this study developed a specialized hot-air drying test bench (CGT-1). A coupled heat and mass transfer model was established, and 3D dynamic simulations of temperature, humidity, and wind speed distributions within bales were performed using COMSOL multiphysics to evaluate drying inhomogeneity. Single-factor experiments and multi-factor response surface methodology (RSM) based on Box–Behnken design were employed to investigate the effects of hot air temperature (50–65 °C), wind speed (2–5 m/s), and air duct opening diameter (400–600 mm) on moisture content, drying rate, and energy consumption. Results demonstrated that larger duct diameters (600 mm) and higher wind speeds (5 m/s) significantly enhanced field uniformity. RSM optimization identified optimal parameters: temperature at 65 °C, wind speed of 5 m/s, and duct diameter of 600 mm, achieving a drying time of 119.2 min and a drying rate of 0.62 kg/(kg·min). Validation experiments confirmed the model’s accuracy. These findings provide a solid theoretical foundation and technical support for designing and optimizing alfalfa round-bale drying equipment. Future work should explore segmented drying strategies to enhance energy efficiency. Full article