Search Results (257)

This paper presents a novel large-span ring deployable perimeter truss for the mesh reflector deployable antennas, which is made up of two parts including a single-mobility driving mechanism and a ring deployable metamorphic mechanism. The mechanism design employs polygon approximation, and each side is treated as a basic unit using a modular design approach. By reasonable assembly, a ring deployable metamorphic mechanism with a small folded state and a large deployed state can be formed. Here, multiple singular positions, the axis of its three revolute joints being parallel and coplanar, are used in the fully deployed state, which forms multiple dead-center positions and changes the constraint conditions. The metamorphic motion is thus achieved, and a stable self-locking state is established that greatly enhances the stability. The paper first introduces the mechanism design and evaluation method; the kinematic and dynamic analysis is then conducted, and the simulation validation is also performed. Moreover, a principle design for cable-net structural setting and connection is illustrated. Finally, with the design of a driving system and the fabrication of a physical prototype, the deployable experiments are carried out, and the results show that the perimeter truss can efficiently act as the mesh reflector deployable antennas. Full article

Background/Objectives: This study assessed how to best prepare pediatric and adolescent cancer patients for amputation and support them afterward. Methods: This pilot qualitative study explored pre- and post-amputation experiences from the perspectives of nine pediatric and adolescent survivors who underwent amputation. Hour-long audio-recorded semi-structured interviews were transcribed and analyzed using the Sort and Sift, Think and Shift qualitative approach. Results: Participants described the informational supports they received before surgery, including guidance on what to expect, contact with amputation-related organizations, and exposure to tangible tools, such as a physical model of a knee joint. Emotional support from fellow amputees and healthcare providers, particularly surgeons, was also found to be meaningful. Individuals also identified unmet needs and gaps in emotional care. These included clearer guidance on post-surgical adaptations (e.g., basic self-care and navigating physical limitations) and the need for information tailored to their learning styles. Many emphasized the importance of improved pain management resources, expanded access to mental health services for both them and their families, and support in adjusting to changes in body image and social relationships. Participants also shared advice for future patients, recommending strategies such as personalizing hospital rooms, connecting with other amputees through social media, and using art to process their experience and say goodbye to the lost limb. Conclusions: Interviews with nine cancer survivors provide guidance for improving holistic, patient-centered care throughout the amputation process. Informational and emotional support should be tailored to an individual’s learning style and specific needs, in addition to their age at the time of surgery. Full article

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning. Full article

In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article

With the development of artificial intelligence technology, food image recognition has become an important research direction in the field of computer vision. The region of Northern Shaanxi is famous for its rich food culture. This paper aims to propose a food image recognition method based on an improved ResNet network to enhance the recognition rate of characteristic foods in Northern Shaanxi. Firstly, the principles and structure of basic convolutional neural networks (CNNs) were introduced, with a focus on the application and optimization design of CNNs in food image recognition. This mainly included AC blocks fused with asymmetric convolutions, attention modules based on improving food image recognition performance, and residual structure design for enhancing learning effectiveness. Secondly, the FoodResNet18 model was constructed with a specially designed enhancement block and a deep, shallow shared attention residual module to enhance the feature extraction ability and perception of visual information by the model. To improve the generalization ability of the model, this paper comprehensively preprocessed the self-built Northern Shaanxi Food-300 dataset, covering the sources of data, processing methods, and data augmentation strategies used for training. The model training and comparative analysis show that the food image recognition method based on improved ResNet outperforms traditional CNN models in multiple experiments. In the ablation experiment, the specific contribution of the design module to the final recognition performance was analyzed, and the advantages of the deep shallow shared attention residual module in feature extraction and preservation were verified. Full article

Museums serve as essential cultural centers, yet their mostly visual exhibits restrict access for blind and partially sighted (BPS) individuals. While recent technological advances have started to bridge this gap, many accessibility solutions focus mainly on basic inclusion rather than promoting independent exploration. This research addresses this limitation by creating features that enable visitors’ independence through customizable interaction patterns and self-paced exploration. It improved upon existing interactive tangible user interfaces (ITUIs) by enhancing their audio content and adding more flexible user control options. A mixed-methods approach evaluated the ITUI’s usability, ability to be used independently, and user satisfaction. Quantitative data were gathered using ITUI-specific satisfaction, usability, comparison, and general preference scales, while insights were obtained through notes taken during a think-aloud protocol as participants interacted with the ITUIs, direct observation, and analysis of video recordings of the experiment. The results showed a strong preference for a Pushbutton-based ITUI, which scored highest in usability (M = 87.5), perceived independence (72%), and user control (76%). Participants stressed the importance of tactile interaction, clear feedback, and customizable audio features like volume and playback speed. These findings underscore the vital role of user control and precise feedback in designing accessible museum experiences. Full article

Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements while maintaining competitive accuracy. We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data. Experiments across benchmark datasets demonstrate that: (i) our weakly supervised methods achieve performance comparable to fully supervised approaches while significantly reducing supervision requirements; (ii) the proposed multi-task framework enhances performance through knowledge sharing between related tasks; (iii) our weakly self-supervised approach demonstrates remarkable efficiency with just 10% of labeled data. These results not only highlight the complementary strengths of different learning paradigms, offering insights into tailoring HAR solutions based on the availability of labeled data, but also establish that our novel weakly self-supervised framework offers a promising solution for practical HAR applications where labeled data are limited. Full article

Managers often need to stay motivated and effectively motivate others. Therefore, they should rely on evidence-based interventions to effectively motivate and self-motivate. This research investigated how self-determination theory-based interventions affect employees’ motivation dynamics and motivational consequences within short time frames (i.e., within an hour, within a few weeks or months) in two empirical studies. Study one focused on assessing the effectiveness of a one-day training workshop in helping to improve managers’ work motivation, basic psychological needs satisfaction/frustration, subordinates’ motivation, and perceptions of managers’ needs-supportive/thwarting behaviors within a few weeks. Results support the effectiveness of the training, as managers were rated by their direct subordinates as having fewer needs-thwarting behaviors and reported self-improvement in needs satisfaction and frustration six weeks after completing the training program. Study two used the mean and covariance structure analysis and tested the impact of three types of basic psychological needs-supportive/thwarting and control conditions (3 × 2 × 1 factorial design) on participants’ situational motivation, vitality, and general self-efficacy for playing online word games within 30 min. Multi-group confirmatory factor analysis (CFA) confirmed the scalar measurement invariance, then latent group mean comparison results show consistently lower controlled motivation across the experimental conditions. During a quick online working scenario, the theory-based momentary intervention effectively changed situational extrinsic self-regulation in participants. Supplementary structural equation modeling (SEM; cross-sectional) analyses using experience samples supported the indirect dual-path model from basic needs satisfaction to vitality and general efficacy via situational motivation. We discussed the theoretical implications of the temporal properties of work motivation, the practical implications for employee training, and the limitations. Full article

To enhance the speed control performance of the permanent magnet synchronous motor (PMSM) servo system, an improved sliding mode control method integrating a torque observer is presented. The current loop uses current feedback decoupling PID control, and the speed loop applies sliding mode control. In comparison to previous work in hybrid SMC using fuzzy logic and torque observers, this p proposes a hyperbolic tangent function in replacement of the signum function to solve the conflict between rapidity and chattering in the traditional exponential reaching law, and fuzzy and segmental self-tuning rules adjust relevant switching terms to reduce chattering and improve the sliding mode arrival process. A load torque observer is designed to enhance the system’s anti-interference ability by compensating the observed load torque to the current loop input. Simulation results show that compared with traditional sliding mode control with a load torque observer (SMC + LO), PID control with a load torque observer (PID + LO), and Active Disturbance Rejection Control (ADRC), the proposed strategy can track the desired speed in 0.032 s, has a dynamic deceleration of 2.7 r/min during sudden load increases, and has a recovery time of 0.011 s, while the others have relatively inferior performance. Finally, the model experiment is carried out, and the results of the experiment are basically consistent with the simulation results. Simulation and experimental results confirm the superiority of the proposed control strategy in improving the system’s comprehensive performance. Full article

In practical applications, the fully enclosed structure is always required by self-starting permanent magnet synchronous motors for safety. However, internal heat dissipation can be obstructed as a result, which affects operational reliability. To resolve the issue, this study takes a 3 kW self-starting permanent magnet synchronous motor as the research object. Based on fluid dynamics and fluid solid coupling heat transfer theory, the model is reasonably simplified according to the characteristics of the structure of motor cooling, and basic assumptions and boundary conditions are given to establish a three-dimensional, whole machine solution domain model. The finite element method is used to numerically analyze and calculate under rated conditions. The fluid flow characteristics, heat transfer characteristics, motion trajectories of the cooling medium on the surface of the external casing, fan, and internal stator and rotor domains, and winding ends are analyzed. Therefore, the internal rheological characteristics and temperature rise distribution law of the self-starting permanent magnet synchronous motor can be revealed. Based on the aforementioned research, a novel method to design the wind spur structure on the surface of the rotor end is proposed. By comparing the simulation results of the fluid field and temperature field of the motor under wind spur structures with different lengths and equidistant distributions in the circumferential direction of the rotor end, the influence of the convective heat characteristics can be systematically studied. Lastly, the accuracy of the calculation results and the rationality of the solution method are verified through experiments of temperature rise, and the flow temperature distribution characteristics of the motor can be optimized by the wind spur structure, which can be used in practical applications. Full article

In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convolution networks, this paper innovatively proposes an adaptive dynamic micro-expression recognition algorithm based on self-supervised learning, namely MADV-Net. Firstly, a basic model is pre-trained with accurate tag data, and then an efficient facial motion encoder is used to embed facial coding unit tags. Finally, a cascaded pyramid structure is constructed by the multi-level adaptive dynamic encoder, and the multi-level head perceptron is used as the input into the classification loss function to calculate facial micro-motion features in the dynamic video stream. In this study, a large number of experiments were carried out on the open-source datasets SMIC, CASME-II, CAS(ME)^2, and SAMM. Compared with the 13 mainstream SOTA methods, the average recognition accuracy of MADV-Net is 72.87%, 89.94%, 83.32% and 89.53%, respectively. The stable generalization ability of this method is proven, providing a new research paradigm for automatic emotion recognition. Full article

Understanding the evolution of societal values is crucial amidst globalization and migration. This study aimed to (1) map Portugal’s basic human values (BHVs) profile in 2020 through comparison with six European countries (Bulgaria, France, United Kingdom, Hungary, Italy, and Norway) and (2) analyze Portugal’s BHVs trajectory from 2002 to 2020. Drawing on Schwartz’s theory and European Social Survey (ESS) data, we applied descriptive statistics, similarity indices, post-stratification weighting, and trend analysis after extensive data cleaning. Results indicate that in 2020, Portugal displayed high self-transcendence and relatively high openness to change, aligning most closely with the United Kingdom and Hungary. Longitudinal analysis revealed a shift after 2012, marked by rising hedonism, stimulation, and self-direction, and declining conformity. These value dynamics offer insights into future societal demands and potential tensions. Moreover, Portugal’s emerging value configuration—balancing self-transcendence with growing openness—may foster economic opportunities by enhancing attractiveness for innovation ecosystems (linked to self-direction and stimulation), experience-driven tourism (hedonism), and investments aligned with sustainable and social goals (universalism and benevolence). Monitoring value changes remains essential to anticipate societal transformations and inform policy design. Full article

Teaching self-efficacy refers to a teacher’s confidence in their ability to engage students and foster learning, directly influencing their instructional planning, strategies, and student assessment practices. Neuroscience education for teachers has been shown to increase enthusiasm and support professional growth by introducing essential brain-related principles. This study investigated whether prior exposure to neuroscience predicts teaching self-efficacy among Brazilian basic education teachers. A total of 1120 teachers completed online surveys, providing sociodemographic information, educational background, teaching experience, and data regarding their previous neuroscience exposure. Participants’ neuroscience knowledge was assessed through a questionnaire designed to measure familiarity with fundamental neuroscience concepts, and teaching self-efficacy was evaluated using the Teacher Sense of Efficacy Scale (TSES). The results indicated that teachers with prior exposure to extracurricular neuroscience courses demonstrated significantly higher neuroscience knowledge. Additionally, those with previous neuroscience exposure exhibited a marginally significant increase in self-efficacy for instructional strategies and a significant increase in classroom management, while no significant differences were observed in student engagement. Regression analyses confirmed that neuroscience exposure significantly predicted self-efficacy in instructional strategies and classroom management. These findings reinforce the connection between neuroscience education and enhanced teaching self-efficacy, underscoring the importance of neuroeducation programs as valuable tools for supporting teachers’ professional development and well-being. Full article

“Moral shocks” are unexpected events or pieces of information that so deeply challenge one’s basic values and sense of the world that they profoundly reorient a person’s understanding of life and even self. Yet those who experience significant moral shocks rarely participate in related activism and instead experience grief as highly privatized and apolitical, a reality that serves the status quo and most powerful. This article considers how religious resources can help metabolize private grief into public lament and catalyze political grievance. Analyzing the rise of gun control activism after an elementary school mass shooting in Nashville, Tennessee, I argue religious resources help metabolize moral shocks into social change in five significant ways: (1) cultivating practiced, purposeful pathos, (2) offering collective lament, (3) building networked resiliency materially and theologically, (4) risking new alliances of accompaniment, and (5) storying hope. This case analysis contributes to a broader claim for political theology: Christianity can be understood as a movement based on a moral shock. This framing then animates practices of care to accompany those in moral distress and help disciple grief into a movement of faith that resists death-dealing political and social policy. Full article

Human action recognition has become a key direction in computer vision. Deep learning models, particularly when combined with sensor data fusion, can significantly enhance various applications by learning complex patterns and relationships from diverse data streams. Thus, this study proposes a new model, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN), in order to enhance performance in the classification of the main tennis strokes: forehand, backhand, volley forehand, and volley backhand. The proposed network incorporates seven basic blocks that are combined with two types of module: an Adaptive Consecutive Attention Module, and Graph Self-Attention module. They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. Moreover, the FFGCAN utilizes a fusion of motion capture data that generates a channel-specific topology map for each output channel, reflecting how joints are connected when the tennis player is moving. The proposed solution was verified utilizing three well-known motion capture datasets, THETIS, Tennis-Mocap, and 3DTennisDS, each containing tennis movements in various formats. A series of experiments were performed, including data division into training (70%), validating (15%), and testing (15%) subsets. The testing utilized five trials. The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. The proposed model demonstrated excellent tennis movement prediction ability. Full article