Search Results (4,531)

Background: Online adaptive radiotherapy (oART) can change the dose distribution briefly to match the shape of the targets and organs at risk. Cone-beam computed tomography (CBCT)-guided oART has been introduced in clinical practice and is expected to improve clinical outcomes. This technology allows treatment as an extension of conventional CT-based image-guided radiotherapy technology and has the advantage of relatively short treatment times. However, the clinical and dosimetric benefits of oART have not yet been adequately reported. Methods: We systematically reviewed previous studies evaluating CBCT-guided oART in patients with malignancies. Case reports, conference abstracts, letters, editorials, and reviews were excluded. PubMed was searched from January 2019 to 22 November 2025, supplemented by hand searching. Study design, cancer site, workflow, treatment time, dosimetric outcomes, feasibility, and clinical outcomes were extracted. Owing to clinical and methodological heterogeneity, findings were synthesized narratively. Results: A total of 127 studies were included. Across most studies, adapted plans improved target coverage and reduced or maintained doses to organs at risk compared with scheduled plans. This review provides an overview of the workflows and latest developments in CBCT-guided oART. Furthermore, the clinical and dosimetric benefits at each site, including the chest, abdomen, pelvis, head, and neck, are shown. Conclusions: The development of oART is expected to lead to the introduction of personalized medicine and improve the clinical outcomes of patients with various cancers. Further research and clinical trials are warranted to establish CBCT-guided oART. This review received no external funding and was registered in PROSPERO (CRD420251237632). Full article

Cross-domain distribution shifts severely degrade the diagnostic performance of rolling bearing models under unseen variable operating scenarios. Single-source domain generalization (SDG) builds fault diagnosis models using only single-source vibration data, which fits the practical limitations of industrial data collection. Existing contrastive learning methods adopt uniform spectral perturbations for data augmentation, which easily corrupt fault harmonic characteristics and require massive, labeled training samples. To tackle these drawbacks, this paper proposes an unsupervised contrastive learning framework named FDACL. An adaptive frequency-domain augmentation (AFA) module equipped with learnable weights is designed to separate fault-critical frequency bands from noise components. Differentiated amplitude perturbations are applied to two categories of spectral signals to generate diverse pseudo-samples while retaining intrinsic fault information. A shared encoder is trained with combined InfoNCE contrast loss and classification loss to learn domain-invariant fault representations. Validations are carried out on three datasets, namely Case Western Reserve University (CWRU), Paderborn University (PU), and the industrial CRRC Qingdao Sifang railway wheelset bearing dataset acquired from physical test benches. FDACL achieves average cross-speed diagnostic accuracies of 92.68% and 77.85% on CWRU and PU, respectively, and maintains competitive performance on the Qingdao Sifang industrial dataset. It outperforms state-of-the-art baselines by 4.23–8.71% across all SDG transfer tasks. Ablation experiments and hyperparameter analysis verify the efficacy of the AFA module and contrastive learning scheme, providing an unsupervised diagnostic approach for railway bearings under unknown working conditions. Full article

Knowledge tracing (KT) is a core technology in intelligent tutoring systems that predicts students’ future responses by analyzing their historical interaction sequences. Although existing deep learning-based KT models achieve high predictive accuracy, their “black-box” nature severely hinders practical deployment in educational scenarios. To balance predictive performance and interpretability, this paper proposes AMDKT, an interpretable dual-stream Transformer model grounded in the Student Proficiency–Item Competency Matching (SPIM) mechanism. The model employs two parallel Transformer branches to separately model the temporal evolution of student proficiency and the competency demands of each item and defines the discrepancy between their outputs as “proficiency surplus.” A non-negative regularization loss is further introduced to constrain the proficiency surplus to be non-negative for correctly answered samples, thereby embedding pedagogical rules into the optimization objective and ensuring that the model outputs conform to educational cognitive priors. Experiments on five public datasets show that AMDKT achieves AUC performance comparable to the state-of-the-art AKT model, and obtains statistically superior results against DKT, DKVMN, DEEP-IRT, and DIMKT on most datasets, with relatively weaker performance observed on the KDD Cup 2010 dataset. Ablation studies verify the effectiveness of each component, and visualization results demonstrate that AMDKT produces smooth, coherent, and interpretable student proficiency trajectories, providing a fine-grained tool for quantifying individual learning progress. Therefore, AMDKT offers a feasible solution for applications such as weak knowledge point localization, adaptive exercise recommendation, and learning risk warning. Full article

In April 2024, the Hellenic Cadastre became one of Europe’s first land registries to use a generative AI model (a large language model served through Azure OpenAI) for the legal review of property deeds. Unlike similar European initiatives using classical NLP, Greece applied state-of-the-art generative AI to a massive legacy issue: 390 historical mortgage registries holding an estimated 600 million to one billion paper pages. By April 2026, the system had processed 310,000 acts, reducing the average per-act review time from about thirty minutes to under ten; a very large per-act cost reduction is also reported by the implementation partner, which we treat as a vendor-stated figure. Additionally, the cadastre’s geodetic infrastructure found a second use following the 2023 Tempi rail disaster. In 2026, the Hellenic Positioning System (HEPOS), a 98-station GNSS reference network, began providing corrections for Greece’s real-time train tracking platform. While satellite-based train positioning is not novel in Europe, where consortia such as CLUG have run a decade of research and pilots, this marks its operational deployment in Greece. The Greek case is unique institutionally rather than technically: it repurposed a national CORS network for a citizen-facing train tracking platform as a short-term crisis response, alongside an incomplete ETCS rollout. This paper documents both deployments, measures their impact, maps them onto the nine FELA pathways, and identifies transferable practices. Greece is not presented as a technological frontier, but as an example of how a country can put existing geospatial infrastructure and AI to rapid use in delivering build-back-better policies for the public, in line with the UN 2030 Agenda. Full article

With the wide-scale deployment of battery packs in a variety of applications, some life cycle challenges are coming to light. These challenges include extending their operational life as a pack given their increasing cell-level imbalances and repurposing their cells into new battery packs to give them a second life, e.g., in grid storage. This paper presents a new circuit topology addressing both issues using active (controlled and nondissipative) cell-to-cell balancing for online operation, i.e., while the battery energy storage system is in use. The proposed circuit design is fast and safe for balancing, relying on current control to target each individual cell’s maximum charging and discharging current, while taking into account the pack current. The design has the lowest number of switches and circuit components compared to the state-of-the-art techniques, and is also flexible, allowing for the addition or replacement of cells in series. Its practical hierarchical control system enables speed, reliability, and reconfigurable limits in real-time operation on the individual cells. Experimental validation is carried out on homogeneous and heterogeneous packs in online operation, and the results demonstrate the speed and efficacy of the proposed technique. Full article

This essay examines a long-term collaboration between writer and curator Dr George Vasey and the London-based organisation Intoart, which supports a group of 24 artists with learning disabilities. Throughout the collaboration, a broad array of practice-based curatorial methods have been employed—including field recording, creative writing, and drawing—to develop an interpretative framework for a growing organisational collection of over 6000 artworks. Drawing on John Dewey’s writing on art as experience, the essay expands on Dewey’s concept of thinking through doing in the context of disability arts, arguing for progressive studio provision as a site of exchange and community learning that emphasises an art of lived experience. The essay explores what an artist-led approach might mean in the context of disability, revisiting Dewey’s practice-led approach and foregrounding embodied and experimental forms of engagement. By centring the voice and intention of learning-disabled artists, the project focuses on facilitative strategies that merge the disciplines of art criticism, visual arts, curating, and pedagogy. The essay contributes to ongoing debates in curatorial and disability studies by demonstrating how facilitative and practice-led approaches can reshape interpretative frameworks, artist agency and knowledge production. Full article

Underwater acoustic target recognition (UATR) is challenging due to the complex, multi-scale physical characteristics of marine targets and the strict computational limits of edge platforms like unmanned surface vehicles. To navigate the severe interference of underwater environments, existing methods increasingly rely on heavyweight architectures to achieve high recognition accuracy. However, the massive computational overhead of these models is fundamentally at odds with the restricted power and processing capabilities of practical deployment platforms. To resolve this conflict between performance and deployability, we propose LHK-Net, a lightweight Heterogeneous Kernel Network. By integrating a Heterogeneous Kernel Pyramid with Residual Depthwise Separable Convolutions, LHK-Net dynamically captures multi-scale acoustic features, from macroscopic steady-state harmonics to localized transient impulses, while compressing the model size to merely 0.82 M parameters. Additionally, a dual-domain Time–Frequency Attention module and an Adaptive SK-Fusion mechanism are incorporated for robust noise suppression. Experiments on the DeepShip dataset demonstrate that LHK-Net achieves state-of-the-art accuracy, outperforming heavyweight models at real-time speeds. Extensive visual analyses further validate that the network possesses strong physical interpretability, effectively aligning its internal feature representations with the intrinsic acoustic properties of the targets. Full article

Science, Technology, Engineering, Arts, and Mathematics (STEAM) education has been increasingly recognized for its potential to promote early childhood development, while inclusion has become a fundamental principle of early childhood education. However, research and practice on STEAM in reverse inclusive activities remain limited. This study aimed to explore whether STEAM in reverse inclusive activities in preschools works and how it works. Participants included 6 children with special education needs and 22 typically developing children aged 4–6 years from a public preschool in China. The Assessment, Evaluation, and Programming System Chinese version was used to assess children’s early childhood development in daily routines and during STEAM in reverse inclusive activities. The findings indicated that STEAM in reverse inclusive activities in preschool was effective, with both typically developing children and children with special educational needs demonstrating developmental gains. Children’s early development within the activities was facilitated by the integration of art, embodied learning, effective peer interaction, and flexible teacher support responsive to children’s inquiry problems. Overall, this study provided initial evidence regarding the implementation and effectiveness of STEAM in reverse inclusive activities and offered practical implications for promoting early childhood development. Full article

Multi-server private information retrieval (PIR) based on function secret sharing (FSS) has emerged as a prominent paradigm for achieving sublinear communication. However, standard FSS constructions require full server participation, making them highly vulnerable to single-node fail-stop faults. Existing fault-tolerant schemes mitigate this but inevitably inflate the response overhead to scale with the database size N (e.g., $O\left(\sqrt{N}\right)$). To overcome this limitation, we propose a fault-tolerant PIR (FT-PIR) protocol based on a newly designed $(t,p)$-threshold distributed point function (FT-DPF). By introducing a hierarchical recursive patching mechanism, our scheme transforms rigid all-party evaluations into flexible t-out-of-p reconstructions. This architecture completely decouples the response communication from N and ensures efficient client-side reconstruction via lightweight XOR aggregations. Formal analysis proves that our stateless protocol guarantees $(t-1)$-computational privacy under the semi-honest model. Theoretical analysis demonstrates that the proposed FT-PIR achieves a response complexity bounded by $\mathcal{O}\left({\mathcal{F}}_{\mathrm{maxlevel}}(t,p)\right)$. Comprehensive experimental evaluations confirm that our implementation significantly reduces practical communication and computation overheads, outperforming the state-of-the-art scheme. Full article

Wearable assistive exoskeletons require lightweight, compact, and backdrivable transmission systems with low output impedance to ensure safe and comfortable human–robot interaction. These efficient, modular actuators benefit from reduction mechanisms that minimize axial bulk while providing high motion regularity. While existing transmissions perform well on some of these metrics, their practical implementation is often constrained by geometric complexity, low backdrivability, limited reduction ratios, or standard component sizes. This paper presents a novel combination of a Rolling Element Eccentric (REE) stage and a planetary gearbox, specifically designed for wearable exoskeleton actuation. The proposed architecture integrates a bearing-based REE drive concentrically within the sun gear of a planetary transmission, reducing mechanical complexity and friction and improving regularity. Moreover, the design exploits additively manufactured bearings, enabling substantial weight reduction, reduced encumbrance, and increased design freedom without reliance on standard bearing dimensions. A prototype reducer has been designed and fabricated using additive manufacturing techniques. It was experimentally evaluated and compared with state-of-the-art transmission designs. These investigations demonstrated low friction, minimal backlash, good torsional stiffness, and sufficient backdrivability, despite the high reduction ratio, while maintaining a compact, flat form factor. The experimental results indicate that the proposed rolling element eccentric planetary transmission is a viable and effective solution for lightweight, efficient, axially compact (independently of the implemented reduction ratio), and backdrivable actuators in assistive wearable robotics. Full article

The Fei Qian (飞箝) Argumentation Scheme, one of the core rhetorical schemes of the School of Diplomacy (Zonghengjia, 纵横家) aimed at controlling others, has yet to receive systematic theoretical analysis, and its significance within the history of Chinese logic remains underexplored. Drawing primarily on the Guiguzi (《鬼谷子》) and related pre-Qin period sources, this article examines the Fei Qian Argumentation Scheme across three dimensions: its structural logic, its intended targets, and its practical application. The analysis reveals that the Fei Qian Argumentation Scheme exhibits a fundamental asymmetry in evaluative standards—it demands factual reliability of the premises while judging the conclusion solely by the criterion of audience acceptability. Operationally, the scheme proceeds by deploying external flattery (fei, 飞) to draw upon captivating words (Gou Qian zhi ci, 钩箝之辞), supplemented where necessary by inward emotional manipulation (Gou Qian, 钩箝). Its application is further stratified by social hierarchy: the scheme is directed upward at rulers (zhi tian xia, 制天下) and laterally at peers (zhi ren, 制人), while subordinates may be directed without recourse to it. In practice, the Fei Qian Scheme presupposes a careful assessment of power and capacity (duo quan liang neng, 度权量能), pursues the goal of winning willing compliance (cong hua, 从化), and resorts to the compound strategy of “encumber responsibilities and reveal weaknesses” (chong lei–zi hui, 重累—訾毁) when the primary scheme fails. The emergence of the Fei Qian scheme form and the broader rise in the School of Diplomacy were inseparable from the specific socio-cultural and historical context of the Spring and Autumn and Warring States periods. To a significant degree, the argumentation theories and practices of the School of Diplomacy advanced the development of bianxue (辩学, the art of disputation) and of Chinese logic as a whole. Full article

This paper focuses on the binary task of parking-slot occupancy detection (vacant vs. occupied) from UAV aerial imagery. Accurate parking-slot detection from UAV imagery is challenged by small target sizes, highly regular rectangular shapes, large-scale variations, complex backgrounds, and perspective distortions. Compared with fixed surveillance cameras, UAV-based detection offers flexible deployment, wide-area coverage, and no requirement for pre-installed infrastructure, making it especially suitable for large open-air parking lots and temporary parking scenarios. To address this, this study proposes a task-specific framework for UAV-based parking-slot detection, with improvements in backbone design, attention modeling, and bounding-box regression. A lightweight MnasNet-inspired backbone is used to improve multi-scale feature extraction at low computational cost. An enhanced EMA module with adaptive grouping, FFT-based frequency enhancement, and gated fusion is introduced to better model the structured patterns of parking lot scenes. In addition, a UIoU+ loss tailored to rectangular geometry is proposed to improve localization quality. Sensitivity analysis and repeated experiments show that the method is stable and statistically reliable. All main metrics are evaluated on an independent held-out test set to ensure generalization. Extensive experiments demonstrate that each component brings consistent performance gains. The proposed model achieves 99.44 ± 0.12% mAP@0.5, 90.31 ± 0.27% mAP@0.5:0.95, 99.27 ± 0.15% precision, and 99.00 ± 0.18% recall on the self-built UAV Parking Lot dataset. Its mAP@0.5:0.95 is 26.01 percentage points higher than the YOLOv11n baseline. Consistent performance improvements are also validated on two additional public benchmarks (CARPK and PKLot), confirming the generalization of the proposed method beyond the self-built dataset. Most importantly, the method supports real-time inference on embedded UAV platforms and achieves state-of-the-art performance among lightweight detectors, making it an ideal solution for practical intelligent parking management. Ablation studies further confirm the complementary synergy between the proposed backbone, attention module, and loss function. Full implementation code, pre-trained weights, and detailed reproduction guidelines are publicly available to ensure research reproducibility. Full article

The Grey Wolf Optimizer (GWO) is a popular metaheuristic, yet it often suffers from premature convergence and rapid diversity loss in complex, high-dimensional, or highly constrained optimization problems. This paper introduces HAGWO, a novel Hierarchical Adversarial Grey Wolf Optimizer that addresses these limitations through three synergistic enhancements: dynamic hierarchical population stratification, adaptive Levy flight perturbation, and hierarchical adversarial-like position updating. These mechanisms enable adaptive balancing of global exploration and local exploitation while preserving population diversity throughout the search process. Extensive experiments on the CEC 2017 bound-constrained benchmark suite across 30D, 50D, and 100D dimensions demonstrate that HAGWO achieves superior overall performance among eight state-of-the-art algorithms, with statistically significant advantages confirmed by Friedman mean ranks and Wilcoxon signed-rank tests. When adapted to the strongly NP-hard three-dimensional bin packing problem with identical bins (3D-SBSBPP), HAGWO delivers highly competitive results, outperforming the well-established BRKGA and most other metaheuristics while closely approaching the original GWO in solution quality and exhibiting exceptional run-to-run stability. By rigorously evaluating HAGWO across both high-dimensional continuous benchmarks and a practical constrained combinatorial application, this study validates the effectiveness of its hierarchical adversarial-like framework and provides valuable insights into algorithm design and transferability across different problem domains. Full article

This study examines the mediumistic paintings of Guo Fengyi (郭鳳怡 1942–2010) through the lens of Daoist inner contemplation (neiguan 內觀) and traditional Chinese medicine. Guo developed distinctive self-cultivation techniques, including Ziran Chaoneng Gong (自然超能功 Natural Superpower Qigong), Qi’e Gong (企鵝功 Penguin Qigong 氣功), and Liu Ling Shuzi Gong (劉陵數字功 Liu Ling Numerology Qigong), which resonate with ancient Chinese practices of Zhouyi (周易) divination, shamanic ritual, and Daoist visualization. Through sustained spiritual practice, she experienced what she understood as mystical revelations transmitted by the High Masters. Vivid visionary images repeatedly emerged in her consciousness, accompanied by an overwhelming sense of being guided by an unseen spiritual agency to depict an unknown transcendent realm. Following these experiences, she created nearly one thousand mediumistic paintings. Her work delves into inner landscapes of consciousness and energetic experience, expanding visual art research into the domain of “inner vision”. This study argues that Guo’s art challenges ocular-centric creative paradigms: her mode of “seeing” operates through the supersensory “eye of spirit” rather than physical sight, and artistic creation becomes a process of receiving higher-dimensional messages rather than singular self-expression. Her practice offers a critical reference for recovering the spiritual dimension in contemporary art and provides theoretical perspectives for comparative studies of international mediumistic artists such as Hilma af Klint (1862–1944) and Georgiana Houghton (1814–1884). Full article

Power Flow (PF) analysis is a fundamental tool in distribution systems since it determines the steady-state operating point for specified input conditions. Modern distribution networks face high distributed energy resource (DER) penetration and variable operating conditions, requiring repeated PF evaluations in time-series and scenario-based studies. Their unbalanced operation and high $R/X$ ratios can challenge conventional PF solvers, thereby requiring accurate, robust, and scalable methods. Prior studies have examined nonlinear distribution PF solvers and uncertainty-based formulations, but the review literature remains limited to specific categories and lacks a unified discussion of linearized models, numerical robustness, and acceleration techniques. Therefore, this paper presents a state-of-the-art review of PF methods for modern distribution networks, covering 205 studies published between 2000 and 2026. It summarizes conventional nonlinear PF formulations, reviews linearized models with their assumptions and applicability, and surveys numerical robustness strategies for improved convergence. The methods are compared according to their applicability to radial, weakly meshed, and unbalanced networks, while practical selection criteria are provided based on accuracy, convergence reliability, and computational requirements. Probabilistic, interval, and fuzzy approaches are also reviewed under renewable and load uncertainty. Finally, acceleration strategies for repeated PF evaluation are discussed, emphasizing sparse numerical implementations, topology-based schemes, and physics-informed surrogate models. Full article