Search Results (141,291)

Ti–Al alloys have widespread applications as targets in hard coatings by PVD (Physical Vapor Deposition). While the importance of target density is recognized, the densification mechanisms of Ti-67 at% Al targets, particularly during spark plasma sintering (SPS), remain poorly understood, hindering process optimization. This study aims to clarify these mechanisms by fabricating Ti-67 at% Al targets via SPS and examining their densification behavior through a detailed analysis of the creep model based on the stress exponent (n) and apparent activation energy (Q_d). The target’s relative density gradually increased in the temperature range of 370–530 °C, whereas the grain size remained relatively constant, indicating that the densification process dominated during this period. The results reveal that densification is primarily controlled by intergranular diffusion (n ≈ 2, Q_d = 97.29 kJ/mol) and dislocation climbing (n ≈ 3, Q_d = 158.74 kJ/mol). The target’s relative density reached 98.25% at 530 °C, with a corresponding grain size of 10.86 ± 1.08 μm. Additionally, as the temperature increased, the Vickers hardness of the target increased from 61.56 HV to 129.66 HV, and the electrical conductivity rose from 0.23 S/cm to 0.86 S/cm. This work provides a fundamental understanding of the densification kinetics in Ti-67 at% Al alloys during SPS, establishing a crucial guideline for fabricating high-performance PVD targets. Full article

Molecular communication signal detection faces numerous challenges, including complex environments, multi-source noise, and signal drift. Traditional methods rely on precise mathematical models, which are constrained by drift speed and signal-to-noise ratio. To address these issues, this paper proposes an innovative detection model based on the Informer architecture, named ComModel (CModel). This framework integrates probSparse Attention, Cross Attention, and convolutional layers to enhance detection accuracy and adaptability to various environmental conditions. Experimental results demonstrate that CModel consistently outperforms traditional deep neural networks and Transformer-based models, especially in complex scenarios with varying drift speeds and noise levels. As the drift speed increases, CModel maintains superior stability and exhibits lower bit error rates, particularly at medium and high drift speeds. Moreover, CModel shows excellent performance in environments with significant noise. Overall, CModel demonstrates robust and reliable signal detection capabilities in multi-noise environments. Full article

Addressing escalating demands for high-precision compact robotic actuators, this study overcomes persistent challenges from nonlinear transmission dynamics and computational constraints through a co-designed framework integrating three innovations. A real-time second-order characteristic modeling approach enables 10 kHz online parameter identification, reducing computational load by 13.1% versus MPC. Building on this foundation, a hybrid integral sliding-mode controller eliminating modeling errors while maintaining ≤0.25 rad/s tracking error (SRMSE) under variable loads was created. These algorithmic advances are embedded within a miniaturized dual-ARM platform (47 × 47 × 12 mm³) achieving <30-ns overcurrent protection and 36% cost reduction versus DSP/FPGA solutions. Validated via Lyapunov stability proofs and experiments, this framework is particularly effective for high-performance robotic joint control in spatially- and thermally-constrained environments while dynamically compensating for unmodeled nonlinearities. Full article

The success of current entity alignment (EA) tasks largely depends on the supervision information provided by labeled data. Considering the cost of labeled data, most supervised methods are challenging to apply in practical scenarios. Therefore, an increasing number of works based on contrastive learning, active learning, or other deep learning techniques have been developed, to solve the performance bottleneck caused by the lack of labeled data. However, existing unsupervised EA methods still face certain limitations; either their modeling complexity is high or they fail to balance the effectiveness and practicality of alignment. To overcome these issues, we propose a learnable convolutional attention network for unsupervised entity alignment, named LCA-UEA. Specifically, LCA-UEA performs convolution operations before the attention mechanism, ensuring the acquisition of structural information and avoiding the superposition of redundant information. Then, to efficiently filter out invalid neighborhood information of aligned entities, LCA-UEA designs a relation structure reconstruction method based on potential matching relations, thereby enhancing the usability and scalability of the EA method. Notably, a similarity function based on consistency is proposed to better measure the similarity of candidate entity pairs. Finally, we conducted extensive experiments on three datasets of different sizes and types (cross-lingual and monolingual) to verify the superiority of LCA-UEA. Experimental results demonstrate that LCA-UEA significantly improved alignment accuracy, outperforming 25 supervised or unsupervised methods, and improving by 6.4% in Hits@1 over the best baseline in the best case. Full article

Calibration of power equipment has become an essential task in modern power systems. This paper proposes a distributed remote calibration prototype based on a cloud–edge–end architecture by integrating intelligent sensing, Internet of Things (IoT) communication, and edge computing technologies. The prototype employs a high-precision frequency-to-voltage conversion module leveraging satellite signals to address traceability and value transmission challenges in remote calibration, thereby ensuring reliability and stability throughout the process. Additionally, an environmental monitoring module tracks parameters such as temperature, humidity, and electromagnetic interference. Combined with video surveillance and optical character recognition (OCR), this enables intelligent, end-to-end recording and automated data extraction during calibration. Furthermore, a cloud-edge task scheduling algorithm is implemented to offload computational tasks to edge nodes, maximizing resource utilization within the cloud–edge collaborative system and enhancing service quality. The proposed prototype extends existing cloud–edge collaboration frameworks by incorporating calibration instruments and sensing devices into the network, thereby improving the intelligence and accuracy of remote calibration across multiple layers. Furthermore, this approach facilitates synchronized communication and calibration operations across symmetrically deployed remote facilities and reference devices, providing solid technical support to ensure that measurement equipment meets the required precision and performance criteria. Full article

Neuroinflammation plays a central role in the pathogenesis of Alzheimer’s disease (AD), with amyloid-β (Aβ) deposition and neurofibrillary tangles driving both central and peripheral inflammatory responses. This study investigated the neuroprotective and anti-inflammatory effects of Vitex trifolia (VT), Plantago major (PM), Apocyni Veneti Folium (AVF), and Eucommiae folium (EF) using network pharmacology and a co-culture model of PC12 neuronal and Caco-2 intestinal epithelial cells. Bioactive compounds were identified via high-performance liquid chromatography (HPLC) and screened with network pharmacology analysis, yielding 27 for VT, 10 for PM, 6 for AVF, and 3 for EF. Molecular docking confirmed strong binding affinities between the key bioactive compounds and AD-related targets. A co-culture system of PC12 neuronal and Caco-2 intestinal epithelial cells was established to evaluate the effects of VT, PM, AVF, and EF extracts (at concentrations of 10 µg/mL, 20 µg/mL, and 50 µg/mL) and donepezil hydrochloride (positive-control) on Aβ_25–35-induced neurotoxicity and lipopolysaccharide (LPS)-induced intestinal inflammation, to assess cell viability, and effects on oxidative stress, mitochondrial function, and inflammatory markers. The VT, PM, AVF, and EF extracts activated phosphoinositide 3-kinase (PI3K)-Akt-glycogen synthase kinase-3β (GSK-3β) signaling, enhanced phosphorylation of AMP kinase, suggesting inhibition of Aβ accumulation and tau hyperphosphorylation (p < 0.05). However, donepezil hydrochloride only enhanced AMPK phosphorylation. The extracts reduced lipid peroxidation and acetylcholinesterase by about 5-fold. JC-1 staining confirmed preserved mitochondrial membrane potential, while hematoxylin and eosin staining indicated improved intestinal barrier integrity (p < 0.05). PM and AVF reduced the number of mast cells (p < 0.05). In conclusion, these findings highlight the multi-target potential of VT, PM, AVF, and EF in mitigating both neuronal and intestinal inflammation. Their dual regulatory effects on the gut–brain axis suggest promising therapeutic applications in AD through the modulation of central and peripheral immune responses. Full article

We developed a low-cost, high-performance gesture recognition system with a dynamic hand gesture recognition technique based on the Transformer model combined with MediaPipe. The technique accurately extracts hand gesture key points. The system was designed with eight primary gestures: swipe up, swipe down, swipe left, swipe right, thumbs up, OK, click, and enlarge. These gestures serve as alternatives to mouse and keyboard operations, simplifying human–computer interaction interfaces to meet the needs of media system control and presentation switching. The experiment results demonstrated that training deep learning models using the Transformer achieved over 99% accuracy, effectively enhancing recognition performance. Full article

Objectives: Research on kernel oil content and secondary metabolites in Chionanthus retusus was conducted to evaluate its potential as an oil crop. Methods: Fruits from 30 individual trees were collected to analyze morphological traits, oil content, and the composition of fatty acids, phytosterols, and tocopherols. Correlation, cluster, and principal component analyses were performed on the resulting data. Results: The mean fresh fruit weight, dry fruit weight, dry kernel weight, and kernel percentage were 77.02 g, 24.33 g, 12.22 g, and 51.14%, respectively. Kernel oil content averaged 35.83%, comprising seven fatty acids with oleic acid as the predominant component. Total phytosterol content reached 279.58 mg/100 g oil, with β-sitosterol being the major constituent among seven detected sterols. Total tocopherols were 571.13 μg/g oil, dominated by γ-tocopherol, indicating a potential antioxidant capacity. These components may reduce the demand for synthetic antioxidant food additives. A significant positive correlation was observed between kernel dry weight and oil content (r = 0.760, p < 0.01), supporting kernel dry weight as a key phenotypic indicator for high-oil breeding. Fruit quality traits did not cluster by geographic origin, whereas secondary metabolite profiles showed origin-based clustering. For breeding oil-producing C. retusus, select seeds with superior provenances based on secondary metabolites and cultivate them under optimal conditions to develop varieties with plump fruit, thereby boosting yield. Accessions WS-4 and WS-3 were identified as promising germplasm resources for oil production. Conclusions: The abundant oleic acid, β-sitosterol, and γ-tocopherol in C. retusus kernels highlight its potential as a woody oilseed crop. Full article

Recently, visual odometry and SLAM (Simultaneous Localization and Mapping) have shown tremendous performance improvements compared to LiDAR and 3D sensor techniques. Unfortunately, attempts to achieve these improvements always face numerous challenges due to their complexity and insufficient compatibility for real-time environments. This paper presents an enhanced deep-learning-based SLAM system, primarily for Monocular Visual SLAM, by utilizing a Mini-GRU (gated recurrent unit). The proposed system, MINI-DROID-SLAM, demonstrates significant improvements and robustness through persistent iteration of the camera position. Similar to the original DROID SLAM, the system calculates pixel-wise depth mapping and enhances it using the BA (Bundle Adjustment) technique. The architecture introduced in this research reduces the time used and computation complexity compared to the original DROID-SLAM network. The introduced model is trained locally on a single GPU using monocular camera images from the TartanAir datasets. The training time and reconstruction metric, assessed using ATE (Absolute Trajectory Error), show robustness and high performance compared to the original DROID-SLAM. Full article

Background: Sleep plays a crucial role in the health of older adults, and its quality is influenced by multiple physiological and functional factors. However, the relationship between sleep quality and physical fitness, body composition, and metabolic markers remains unclear. This exploratory study aimed to investigate the associations between sleep quality and physical, metabolic, and body composition variables in older adults, and to evaluate the preliminary performance of a logistic regression model in classifying sleep quality. Methods: A total of 32 subjects participated in this study, with a mean age of 69. The resting arterial pressure (systolic and diastolic), resting heart rate, anthropometrics (high waist girth), body composition (by bioimpedance), and physical fitness (Functional Fitness Test) and sleep quality (Pitsburg sleep-quality index) were evaluated. Group comparisons, associative analysis and logistic regression with 5-fold stratified cross-validation was used to classify sleep quality based on selected non-sleep-related predictors. Results: Individuals with good sleep quality showed significantly better back stretch (t = 2.592; p = 0.015; η² = 0.239), lower limb strength (5TSTS; t = 2.564; p = 0.016; η² = 0.476), and longer total sleep time (t = 6.882; p < 0.001; η² = 0.675). Exploratory correlations showed that poor sleep quality was moderately associated with reduced lower-limb strength and mobility. The logistic regression model including 5TSTS and TUG achieved a mean accuracy of 0.76 ± 0.15, precision of 0.79 ± 0.18, recall of 0.83 ± 0.21, and AUC of 0.74 ± 0.16 across cross-validation folds. Conclusions: These preliminary findings suggest that physical fitness and clinical variables significantly influence sleep quality in older adults. Sleep-quality-dependent patterns suggest that interventions to improve lower limb strength may promote better sleep outcomes. Full article

Background/Objectives: Workplace conflict and aggression pose significant psychosocial risks across diverse professional sectors. This protocol outlines a novel, university-based educational intervention. Developed by a multidisciplinary team from the University Hospital of Pisa, Italy, including occupational physicians and a psychiatrist specializing in work and organizational psychology, its primary purpose is to enhance conflict prevention and crisis management skills. While initially developed and tested within the veterinary sector due to its identified vulnerabilities, the intervention is inherently generalizable to any high-stress professional environment characterized by intense client, customer, or public interactions. Methods: The intervention integrates didactic instruction with active, immersive learning through tailored role-playing scenarios simulating real-world challenging encounters. This study protocol details the structured methodology for evaluating the immediate effectiveness of this training. We are using a specifically developed efficacy scale to assess outcomes. Results: The results demonstrate a significant improvement in all assessed skills from the pre-training to the post-training evaluation. For every item on the scale, the median scores increased, indicating a positive shift in overall group performance. The p-value for each item was <0.001, confirming that the observed improvements were statistically significant. These results demonstrate enhanced conflict resolution skills, improved communication, and an increased sense of self-efficacy among participants. Conclusions: This protocol offers a comprehensive and generalizable approach to addressing workplace psychosocial risks through an innovative educational intervention. A key future goal involves advancing this training methodology by integrating virtual reality (VR) environments with AI-driven avatars for role-playing, aiming to achieve a more realistic and impactful learning experience and sustained behavioral change. Full article

This study, performed in an Iranian context, explores how specific architectural attributes of shopping centers can influence public preferences, with the aim of supporting the development of more sustainable and user-oriented urban environments. A discrete choice experiment involving 260 participants was conducted to assess preferences across seven architectural variables, each presented at varying levels: entrance position, openness (i.e., transparency through windows), architectural style, materials, window shape, scale, and symmetry. Participants evaluated paired facade images and selected their preferred designs, enabling an analysis of how these attributes impact consumer choices. The findings indicate that most variables significantly influenced facade preferences, except for arched windows and low levels of openness. In contrast, high openness emerged as the strongest positive predictor of preference. Participants also showed a marked preference for large-scale (inhumanly scaled) facade attributes, rectangular windows, extruded entrances, asymmetrical compositions, and concrete materials. Moderate preferences were observed for symmetrical designs, mixed window shapes, contemporary and postmodern styles, and brick materials. Conversely, neoclassical style, recessed entrances, stone material, and smaller-scale (humanly scaled) facades received the lowest preference ratings. These results might offer valuable insights for architects and urban planners and guide the creation of more attractive and functional shopping centers, ultimately enhancing the quality of urban life. Full article

This review paper explores the transformative potential of polymer composites and adhesives in reducing the weight of aircraft landing gear, thereby improving fuel efficiency and lowering emissions. The replacement of conventional metallic materials and mechanical fastenings with advanced thermoset/thermoplastic composites and adhesives can significantly enhance durability and performance in demanding operational environments. Unlike traditional fastening methods, the structural adhesives eliminate the weight penalties associated with mechanical fasteners, offering a lighter and more reliable solution that meets the rigorous demands of modern aerospace engineering. Furthermore, the review highlights a variety of manufacturing techniques and innovative materials, including bio-based polymers, self-healing materials, noobed composites, helicoid composites, and hybrid composites. The use of thermosets and vitrimers in adhesive bonding are presented, illustrating their ability to create robust and durable joints that enhance the structural integrity of landing gear systems. The paper also addresses current challenges, including recycling limitations and high material costs. Sustainability considerations, including the integration of self-healing materials, structural health monitoring systems, and circular economy principles, are discussed as essential for aligning the aerospace sector with global climate goals. Full article

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen–Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science. Full article

Objectives: Patients with chronic limb-threatening ischemia (CLTI) represent a high-risk cohort for cardiac surgery due to the systemic atherosclerotic burden and frailty. This study aimed to evaluate the short- and mid-term prognoses of CLTI patients undergoing open cardiac surgery and to assess the prognostic utility of four risk scoring systems: Japan SCORE, SPINACH SCORE, Clinical Frailty Scale (CFS), and Geriatric Nutritional Risk Index (GNRI). Methods: We retrospectively analyzed 44 patients with CLTI who underwent open cardiac surgery between 2014 and 2023. Thirty-day and 1-year mortality were assessed. Patients were stratified using ROC-derived cutoffs for each scoring system. Kaplan–Meier survival curves and time-dependent ROC analyses were used to evaluate predictive performance over time. Results: Thirty-day mortality was significantly associated with a higher Japan SCORE; survivors had significantly lower scores than non-survivors (5.5% vs. 25.8%, p < 0.05). One-year mortality was significantly associated with nutritional status, as survivors showed a significantly higher GNRI than non-survivors (92.0 vs. 86.0, p < 0.05). Time-dependent ROC analysis revealed that the GNRI and SPINACH SCORE’s sustained prognostic accuracy beyond 1 year. Calibration plots showed good agreement between predicted and observed probabilities for the SPINACH SCORE and GNRI, while decision curve analysis (DCA) demonstrated that these two models provided greater net clinical benefit across a range of thresholds, particularly in the 5–20% range. Conclusions: Japan SCORE is effective for short-term risk prediction, while SPINACH SCORE and GNRI offer superior prognostic value for mid-term outcomes. These scoring systems may support preoperative risk stratification and decision-making in CLTI patients undergoing cardiac surgery. Full article