Search Results (6,312)

Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a systematic review of recent evolutionary and adaptive OCO strategies, offering a detailed taxonomy that classifies algorithms according to their constraint-handling mechanisms and environmental feedback. The analysis first examines Constrained OCO, elucidating the trade-offs between computational efficiency and theoretical guarantees across projection-based methods, projection-free Frank–Wolfe variants, and general convex optimization approaches. It then explores the Unconstrained OCO landscape, emphasizing the shift from parameter-dependent methods to fully adaptive, parameter-free algorithms capable of handling unknown comparator norms and gradient scales. Furthermore, the study synthesizes state-of-the-art applications in power systems, network communication, and quantitative finance, bridging theoretical OCO models with robust engineering solutions. The paper concludes by outlining critical open challenges and future research directions, such as the integration of OCO with deep learning, non-convex optimization, and robustness against adversarial corruptions in data-intensive scenarios. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Urban airborne laser scanning (ALS) point clouds cover extensive geographical areas, rendering dense point-level annotation economically prohibitive and limiting the feasibility of fully supervised learning. In weakly supervised settings for urban ALS data, the natural long-tailed class distribution—where ground and building points dominate and smaller objects are rare—combined with the use of fixed pseudo-label thresholds under sparse annotations exacerbates confirmation bias and increases prediction uncertainty. This ultimately restricts the effective utilization of unlabeled data during training. To overcome these challenges, we propose a pseudo-label confidence-calibrated curriculum learning framework designed for weakly supervised ALS point cloud classification. The framework introduces a confidence-aware self-adaptive soft gating (CSS) mechanism that dynamically adjusts category-specific thresholds online using exponential moving average statistics and scene-aware normalization, eliminating the need for manual scheduling while improving pseudo-label quality. In addition, a reliability-driven soft selection (RSS) constraint is incorporated, in which each point is assigned a comprehensive reliability score that integrates prediction confidence, entropy clarity, and cross-augmentation consistency, enabling adaptive soft weighting to replace hard pseudo-label selection and achieve more balanced sample utilization. These components are further integrated into a unified pseudo-label confidence-calibrated curriculum learning framework (P3CL) that progressively shifts the model’s focus from high-certainty samples to more ambiguous ones, effectively mitigating confirmation bias. Extensive experiments on three public ALS benchmarks demonstrate that the proposed method consistently outperforms existing weakly supervised approaches and achieves competitive performance compared with several fully supervised models. Full article

Debates are taking place in the higher education literature regarding the changing roles of learning technology professionals and their contributions to the scholarship of teaching and learning (SoTL). Whilst much literature discusses motivations and barriers for these professionals in engaging with SoTL, less attention has been directed towards how such engagement might be nurtured and developed. This paper analyses an intervention project designed as a cross-institutional mentoring scheme which aimed to foster SoTL habits and skills in learning technology professionals. The mentor scholar scheme encompassed a series of online group meetings and one-on-one advisor meetings, involving 22 scholars and 18 advisors over a 12-month period. Data was collected using a range of methods including questionnaires and interviews. Our analysis uses Cultural–Historical Activity Theory to grasp the dynamics of the mentor scholar scheme and derive insights into how learning technology professionals attempt to engage with SoTL in their practice. The scheme developed in ways unanticipated by our original design. Key contradictions in the activity were evident through persistent difficulties for learning technology professionals in identifying as a scholar, finding a place within a broader scholarly community, developing a loyalty to scholarship, and positioning it against longstanding professional priorities. Nonetheless, participants viewed the scheme as successful, and we put forward considerable experience of how to mediate and address these issues. The paper contributes new perspectives on catalysing scholarly identity among professional staff in higher education, highlighting the importance of a scholarly community, understanding scholarship as distinct from professionalism, and suggesting that mentoring must be a relational and adaptive process. Full article

The Four-Leg Three-Phase Voltage Source Inverter (4LVSI) is a versatile solution for integrating renewable energy sources (RESs) into distribution networks, as it compensates unbalanced voltages and currents while providing a path for zero-sequence components. Accurate current control is essential to ensure power quality and reliable operation under these conditions. Conventional controllers such as proportional–integral, resonant, or feedback-linearization methods achieve acceptable tracking under static dc-link conditions, but their performance degrades when dc-link voltage dynamics arise due to renewable-source fluctuations. This paper proposes a data-driven model reference neural control (MRNC) strategy for a four-leg inverter connected to RESs, explicitly accounting for dc-link voltage variations. The proposed controller reformulates the classical Model Reference Adaptive Control (MRAC) as a lightweight single-layer neural network whose adaptive weights are updated online using the Recursive Least Squares (RLS) algorithm. In this framework, the dc-link variations are not modeled explicitly but are implicitly learned through the data-driven adaptation process, as their influence is captured in the neural network regressors formed from real-time input–output measurements. This allows the controller to continuously identify the inverter dynamics and compensate the effect of dc-link fluctuations without requiring additional observers or prior modeling. The proposed approach is validated through detailed time-domain simulations and real-time Hardware-in-the-Loop (HIL) experiments implemented at a 10 kHz switching frequency. The results indicated that the RLS-based MRNC controller achieved the lowest steady-state current error, reducing it by approximately 1.85% and 1% compared to the Proportional-Resonant (PR) and One-Step-Ahead (OSAC) controllers, respectively. Moreover, under dc-link voltage variations, the proposed controller significantly reduced the current overshoot, achieving decreases of 5.9 A and 6.36 A relative to the PR controller. Full article

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the word level using only a single RGB camera, under a fixed single-user and single-camera configuration. We propose a word-level contactless typing framework that models each word as a distinctive spatiotemporal finger motion pattern derived from hand joint trajectories. Typing motions are temporally segmented, and direction-aware finger displacements are accumulated to construct compact motion representations that are relatively insensitive to absolute hand position and typing duration within the evaluated setup. Each word is represented by multiple motion prototypes that are incrementally updated through online learning with a trial-delayed adaptation protocol. Experiments with vocabularies of up to 200 words show that the proposed approach progressively learns and recalls word-level motion patterns through repeated interaction, achieving stable recognition performance within the tested configuration at realistic typing speeds. Additional evaluations demonstrate that learned motion representations can transfer from physical keyboards to flat-surface typing within the same experimental setting, even when tactile feedback and visual layout cues are reduced. These results support the feasibility of reframing contactless typing as a word-level motion recall problem, and suggest its potential role as a complementary component to character-centric camera-based input methods under constrained monocular sensing. Full article

Background: Despite clear evidence supporting low-dose computed tomography (LDCT) for lung cancer screening, the participation rate among eligible high-risk individuals remains low. Educational interventions that address gaps in knowledge, attitude, and beliefs may improve screening uptake. Objective: This study describes the systematic use of artificial intelligence to design and develop a series of online educational modules aimed at improving knowledge, attitudes, and beliefs toward lung cancer screening among high-risk individuals. Methods: Guided by the Health Belief Model and principles of digital health education, five interactive online modules were developed by artificial intelligence technology to address key topics: (1) lung cancer epidemiology, etiology, signs, and symptoms; (2) lung cancer treatment and care; (3) lung cancer prevention methods; (4) screening guidelines, benefits, and risks; and (5) screening procedures and results interpretation. The design process included literature review, individual cognitive interviews, expert consultation, and pilot testing among target users. Qualitative individual interviews were conducted with 12 high-risk individuals. Content validity was evaluated by an expert panel (n = 7) using a content validity index (CVI), and pilot usability testing was conducted with 25 high-risk individuals. Results: All five modules achieved high content validity (I-CVI range = 0.90–1.00; S-CVI = 0.96). Usability and satisfaction testing showed that participants rated the modules as clear, engaging, and relevant (mean System Usability Scale score = 88/100, mean satisfaction score = 18.32/20). Participants demonstrated significant improvements in knowledge (p < 0.001), lung cancer stigma (p < 0.001), and health beliefs (p < 0.001) after module completion. Of the 22 participants who completed the 3-month follow-up (88%), 13 (59.1%) reported obtaining LDCT screening. Conclusions: The developed online modules demonstrated strong content validity and usability, indicating their feasibility for use in future intervention studies to promote lung cancer screening knowledge, attitude, beliefs, and participation among high-risk individuals. Full article

Background: The rapid diffusion of generative Artificial Intelligence (AI) in higher education is reshaping students’ learning practices and raising concerns about unequal access and educational equity. In the Italian university context, where institutional guidelines on AI use are still developing, examining how students adopt and perceive tools such as ChatGPT is particularly relevant. Methods: This quantitative study investigated patterns of ChatGPT use and perceptions among Italian university students, with specific attention to its perceived support for learning and the development of transversal skills. Data were collected through an online survey. Differences across socio-demographic and academic characteristics were analysed using Mann–Whitney and Kruskal–Wallis tests, while associations between ChatGPT use, students’ perceptions, and study-related outcomes were examined using Spearman’s rho coefficients. Results: Students perceived ChatGPT as a useful tool, particularly in supporting the development of analytical, writing, and digital skills. Significant differences emerged across student groups. Higher levels of use and more positive perceptions were reported by freshmen, students studying in urban areas, and those with stronger economic resources. Conclusions: ChatGPT adoption and subjectively perceived institutional support and benefits vary by academic experience and socio-economic background. As the findings are based on self-reported perceptions, they reflect perceived rather than measured learning outcomes, highlighting the need for further research using objective indicators. Full article

Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. To bridge this gap, we propose EduSheaf—a unified framework that integrates large language models (LLMs) with a sheaflet-based signed graph neural network. Specifically, LLMs are employed to extract fine-grained semantic embeddings from multiple-choice questions (MCQs), thereby enriching graph representations with contextual knowledge. A signed graph is then constructed to encode student–MCQ interactions, where correct and incorrect responses are represented as positive and negative edges. On top of this, a novel sheaflet-based signed graph neural network performs multi-frequency learning through low-pass and high-pass filters, enabling the joint modeling of global consensus and local variations, while sheaf structures enforce edge-level consistency. Extensive experiments on multiple real-world educational datasets demonstrate that EduSheaf consistently outperforms state-of-the-art baselines, including both semantic-enhanced and signed graph models, in terms of prediction accuracy and robustness. Ablation studies further reveal the complementary roles of semantic embeddings and multi-frequency graph filters. Full article

To understand virtual leaders’ work at the intersection of equity and community, virtual school leadership (VSL) was examined with relevance to preparation and research. Research questions were: How is VSL described in extant literature? How is VSL applicable to leaders’ preparation and development? An integrative review approach was applied to online learning and virtual leadership linked to community and equity concepts. Document analysis was used to qualitatively code 34 (of 132) studies. Despite the demand for cyber schooling, some US preservice programs may lack training on leading equitably and collaboratively in virtual environments. Five findings address what virtual school leaders (aspire to) do in their jobs. Community and equity were leadership orientations as well as concerns discerned from perceptions of virtual schooling. Online public education is ensnared in global democratic backsliding for 82 countries, yet VSL remains underexplored in research. This literature review/conceptual work introduces Equity and Community in K–12 Online Leadership, an original conceptual framework informed by professional standards, virtual learning theories, and factors central to leadership. A critique of findings, along with recommendations for leadership preparation and practice, responds to the call for better preparing preservice leaders for the demands of K–12 online learning. Full article

Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often lead to unstable training and performance degradation on majority classes. To alleviate these challenges, we propose a unified redistribution framework termed as ComReg, which explicitly enforces complementary regularization on feature learning and decision boundary optimization in long-tailed image classification. Specifically, ComReg employs a multi-expert learning framework combined with prior-knowledge-guided online distillation to construct distribution-aware decision boundaries. From the feature space learning perspective, we enhance intra-class compactness and inter-class separability through decoupled-balanced contrastive learning. To further align the distributions in both spaces, we introduce a delay-weighted prototype learning strategy, which incorporates the decision boundary constructed by the head-class expert into the decoupled-balanced contrastive learning process. Extensive experiments on widely used long-tailed benchmarks, including CIFAR10-LT and CIFAR100-LT, as well as the real-world long-tailed datasets such as subsets of MedMNIST v2, demonstrate that our method achieves state-of-the-art performance. Full article

The competency-based medical education (CBME) curriculum was introduced recently for undergraduate courses in medical institutions in India. The program needs a paradigm shift in the teaching and assessment methods. Therefore, challenges at the individual as well as organizational level are expected in the initial years of implementation. We used a mixed-method approach through focus group discussions (FGD) and an online survey to assess the perception and attitude of MBBS phase 1 and 2 teachers towards CBME. Themes were generated from FGD, and quantitative data were collected using a structured questionnaire through an online survey. Nearly 80% of the participating faculty perceived that the CBME curriculum was better than traditional teaching methods. Major challenges were either related to a deficiency of curriculum-optimized learning material (85%), material infrastructure (38%), and manpower (46%), or increased documentation (74%), and time constraints (52%). The faculty felt attitudinal change (63%), better acquaintance with the professional environment (60%), improved participation (58%), and the performance of students (38%) were major commendations of CBME. The CBME curriculum is a welcome change in Indian medical teaching institutes, and faculty intend to improve it through feedback mechanisms. The perceived complexities need to be addressed at different levels through collaborative approaches. Full article

Magnetically coupled resonant wireless power transfer (MC-WPT) systems with multi-relay coupling structures can significantly extend the transmission distance. However, system performance is highly sensitive to the spatial positions and coupling conditions of the relay coils. Any misalignment can alter the energy transfer path, causing shifts in the optimal operating frequency and reductions in efficiency. This makes conventional single-frequency or static-tuning strategies unsuitable for handling complex variations in coupling states. To address this issue, this paper investigates a three-relay MC-WPT system and proposes an adaptive frequency control and energy routing method that combines clustering and Q-learning for scenarios with severe coil misalignment. First, a physical model based on coupled-mode theory is established to describe the relationships among coupling coefficients, operating frequency, and transmission efficiency. High-dimensional coupling state data are then collected under different relay coil misalignment conditions. Next, principal component analysis (PCA) and clustering algorithms are used to extract representative coupling patterns and identify the system’s optimal efficiency points, forming an offline database that includes mappings of optimal frequencies. Furthermore, Q-learning is introduced to enable adaptive frequency control through online state recognition. Finally, under severe coil misalignment, frequency retuning of non-misaligned coils is applied to actively shield misaligned coils and reconstruct the energy transfer path. Simulation and experimental results show that the proposed method can achieve real-time frequency control and dynamic energy routing in multi-relay MC-WPT systems without additional hardware. The system transmission efficiency is significantly improved under all relay misalignment scenarios, effectively addressing the optimal frequency shift problem in multi-relay coupling structures and providing a new approach for intelligent and efficient MC-WPT systems under complex coupling conditions. Full article

Given the burgeoning interest in second language willingness to communicate (L2 WTC) in the context of second language acquisition (SLA) within online learning environments, and the current lack of systematic reviews on the topic, this study employed the PRISMA framework to conduct a comprehensive review of empirical research from January 2010 to March 2025. Through an analysis of 13 examined studies, this review synthesizes key determinants of online L2 WTC into an integrated framework comprising three interrelated dimensions: intrapersonal (e.g., trait-like affective variables and cognitive variables), mediating (state-like affective, cognitive, and interactional variables), and situational (teacher supports and online learning activities). The findings notably highlight the concentrated research attention in Asian contexts and variability in measurement approaches, while underscoring the need for more experimental, idiodynamic, and longitudinal designs to better understand the dynamics of L2 WTC in digital settings. This review identifies critical methodological gaps, offering a clearer foundation for future research in technology-mediated second language acquisition. Full article

Background/Objectives: Healthcare workers have faced increasing emotional strain driven by organizational constraints, rising workload, and accumulated post-pandemic pressure. To support emotional well-being in primary care professionals, the Catalan Health Institute implemented a large-scale psychoeducational group program in its primary care centers. This study explored its feasibility, acceptability, and the factors shaping real-world implementation from the perspectives of participating professionals and community psychologists who taught it. Methods: A qualitative study was conducted involving five online focus groups held with community psychologists (two groups) and primary care professionals who participated in the program (three groups), selected through purposive sampling. Additional qualitative material was obtained from implementation-related field notes. Session transcripts were analyzed using inductive thematic analysis. The study is registered at ClinicalTrials.gov (NCT05720429). Results: Participants described a context of sustained emotional strain that increased motivation to engage with the program. The sessions were perceived as a valuable protected space for emotional expression, interpersonal connection, and learning self-care strategies. Community psychologists were regarded as key facilitators due to their embedded role and contextual knowledge. However, inconsistent managerial engagement, lack of protected time, competing workloads, and inadequate physical spaces were barriers to successful implementation. Participants proposed strengthening institutional support and offering follow-up sessions to consolidate benefits. Conclusions: The program was positively valued and was perceived to provide individual and team-level benefits. Its sustainability requires stronger organizational commitment and integration into routine practice. Findings underscore the need to complement individual-focused interventions with systemic actions addressing workload, staffing, and organizational culture. Full article