Search Results (1,813)

Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research. Full article

Compared with hardware-dependent methods, light field (LF) reconstruction algorithms enable a more economical and convenient acquisition of densely sampled LF (DSLF). Existing learning-based LF reconstruction methods suffer from limited flexibility, as they rely on fixed sampling patterns and predefined angular resolutions. In this paper, we propose a flexible deep learning framework, which can reconstruct DSLF with arbitrary angular resolution from randomly distributed sparse input views of an arbitrary quantity. The proposed framework consists of two core stages, namely the SAI Synthesis and the LF Refinement. The SAI Synthesis adopts Plane Sweep Volume (PSV) to cope with randomly sampled input views, and leverages the Multi-Scale Attention (MSA) module to compute per-view weights for adaptive feature fusion and support arbitrary numbers of input views. The LF Refinement stage integrates intermediate results and fully exploits LF parallax structures to further improve reconstruction quality. Experimental results demonstrate that our method achieves superior flexibility and reconstruction quality, and outperforms most state-of-the-art LF reconstruction methods. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article

Vision Transformer (ViT) has been widely adopted in the computer vision community. However, the standard ViT often contains many parameters, usually performs poorly when trained from scratch on medium-scale datasets, and does not explicitly preserve the local spatial and channel-wise structures within each token. This work proposes a novel model called the Token-Shared Convolutional Projection Vision Transformer (TSCP-ViT). The core idea of TSCP-ViT is to integrate convolutional layers into the multi-head attention mechanism and to apply the same convolutional operation independently to each token, where each token exhibits spatial 2D multi-channel characteristics. In addition, this work introduces a Transformer decoder immediately after each Transformer encoder, enabling the classification tokens to aggregate information from all tokens and be updated using statistical information. Moreover, a trainable Non-Reversing Gate GELU (NRG-GELU) activation is also proposed. Comparative experiments on CIFAR-100, Food-101, and ImageNet100 show that, under comparable parameter counts and without pretraining or knowledge distillation, TSCP-ViT substantially surpasses ViT, outperforms CvT, outperforms ResNet on Food-101, and approaches ResNet on CIFAR-100 and ImageNet100, although with considerably higher FLOPs. Full article

Fire is the core safety threat to the survival and development of timber-framed buildings, and passive fire prevention intervention is the core foundation of fire protection systems for timber-framed buildings. Existing reviews suffer from limitations such as incomplete scenario coverage, insufficient breakdown of intervention mechanisms, and a lack of methodological standardization. This study strictly followed the PRISMA 2020 systematic review guidelines, searching the relevant literature from January 2016 to April 2026 on the Web of Science, Scopus, and Science Direct databases. After standardized screening, 89 valid articles were finally included and a systematic study was conducted through bibliometric analysis, keyword visualization, and multi-dimensional classification coding. The results show that the number of publications in this field has been continuously increasing from 2016 to 2025, with China accounting for 31.46% of the total, ranking first globally. The study constructed a core intervention mechanism system for passive fire prevention in timber-framed buildings, covering four categories: intrinsic flame-retardant modification, isolation protection, structural optimization, and spatial control. The working principles, application effects, advantages and disadvantages, and engineering application scenarios of each mechanism were clarified. This study systematically sorts out the core intervention mechanisms of passive fire prevention in timber-framed buildings, clarifies the research status and development trends in this field, and can provide evidence-based support for the design optimization, technology development, and engineering practice of passive fire protection for timber buildings. Full article

This paper presents an on-load programmable configuration of individual dual active bridge modules on a single-core transformer for wide voltage range and multi-port DC-DC power conversion. The mathematical models of power delivery and control transfer functions are presented for the proposed configurable converter. The universal control structure to implement the programmable configuration, control parameter programming, and closed-loop current regulation is presented. Simulation of the proposed converter and control is implemented in MATLAB/SIMULINK 2026A. A reduced-scale hardware prototype is implemented to validate simulation results. The performance of the converter in terms of feasible on-load switching of configurations and simultaneous regulation of multiple loads are compared to existing topologies, which demonstrated stable operation of proposed converter and control scheme over the investigated voltage range. Full article

Text-to-motion generation aims to synthesize realistic human motion sequences that accurately reflect natural language descriptions. While recent approaches have improved motion quality, achieving strong semantic alignment between text and motion, especially for fine-grained articulations, remains a significant challenge. In this work, we propose a latent-guided text-to-motion generation framework that strengthens the interaction between textual representations and motion latent sequences. The proposed method integrates a structured motion latent space with a text-conditioned variational generation module, enhanced by a cross-modal attention mechanism. This design enables the model to effectively capture both global motion dynamics and detailed semantic information from text. Extensive experiments on the Motion-X dataset demonstrate that the proposed approach achieves strong semantic alignment, as reflected by improved R-precision and competitive matching performance. In addition, the model improves multi-modality, indicating its ability to generate diverse motion patterns under the same textual condition. Qualitative results further show that the generated motions preserve core action semantics and exhibit coherent temporal dynamics across different motion categories. Overall, the proposed framework provides an effective solution for improving text–motion alignment in high-dimensional motion spaces, highlighting the importance of latent-guided modeling for realistic and semantically consistent motion generation. Full article

Wikidata provides extensive coverage of tourism-related Points of Interest (POIs), yet its heterogeneous type system and uneven metadata limit its direct use in smart tourism applications. This paper presents an end-to-end pipeline that transforms Wikidata POIs into a compact and interpretable tourism-oriented representation supporting multi-category assignments. We collect POIs from six countries—Greece, Italy, Spain, Norway, Sweden, and Denmark—and construct a dataset that integrates core identifiers with textual descriptions, type information, heritage indicators, geographic coordinates, and Wikipedia sitelinks. We introduce an eight-category tourism taxonomy capturing key themes, including cultural venues, archaeological and historic sites, monuments, fortifications, religious sites, protected areas, natural features, and coastal or water locations. As a reproducible baseline, category likelihoods are estimated using sentence embeddings and similarity to category anchor descriptions, producing a probability vector for each POI. Building on this baseline, we propose a fuzzy inference layer that integrates embedding-based probabilities with structured Wikidata signals to generate interpretable membership degrees across categories and enable principled multi-category classification. This fusion is particularly valuable for smart tourism applications, as it supports robust faceted exploration and personalized recommendations (e.g., “historic + coastal”), while providing evidence-based explanations that enhance user trust and facilitate curator oversight when POI metadata is sparse or ambiguous. The resulting pipeline produces ranked POI catalogs by country and category, country-level tourism profiles, and diagnostic views for examining uncertain cases. The approach is fully reproducible and readily adaptable to other geographic regions or domain taxonomies. Full article

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area. Full article

In low-light, occluded, and cluttered environments, single-modality RGB detectors are prone to false positives and missed detections. While infrared (IR) imaging provides relatively stable target visibility under poor illumination, it lacks texture and color information and is susceptible to background thermal noise and imaging variations. To address these limitations, this paper proposes an RGB–IR object detection network, named MCF-YOLO, consisting of three core components. First, the Cross-Modal Hierarchical Fusion (CMHF) module performs stage-wise alignment and fusion on multi-scale features, jointly modeling RGB texture details and IR thermal responses to exploit the structural and semantic complementarity between the two modalities. Second, the Soft Attention Regularization based on Attention Prior (SAR-AP) module derives attention priors from IR features to impose soft constraints on cross-modal attention maps. This mechanism helps the network maintain attention on target-relevant regions, thereby suppressing attention drift caused by low-light noise and complex backgrounds. Third, the Small-Object-Sensitive Detection Head (SOS-Head) processes high-resolution features to strengthen the representation of small targets, improving detection capability in long-range and occluded scenarios. In evaluations on two RGB–IR benchmarks—M3FD and VEDAI—MCF-YOLO achieves improvements of 2.7% in mAP@0.5 and 1.1% in mAP@0.5:0.95 on M3FD, and 5.4% and 4.4%, respectively, on VEDAI. These results suggest that consistency-guided cross-modal fusion and high-resolution small-target modeling are beneficial for RGB–IR detection in low-visibility and cluttered scenes. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

High-entropy alloys (HEAs), composed of five or more principal elements in near-equimolar ratios, have emerged as a groundbreaking class of materials for next-generation flexible electronics. This review systematically examines the unique potential of HEAs as sensing materials, moving beyond their traditional role as structural components. We first elucidate the fundamental mechanisms—core effects including lattice distortion, sluggish diffusion, and the cocktail effect—that endow HEAs with an exceptional synergy of high strength, good ductility, tunable electrical resistivity, and superior electrocatalytic activity. Subsequently, we critically analyze the state-of-the-art strategies for processing HEA-based micro/nano structures, including mechanical alloying, wet-chemical synthesis, and non-equilibrium deposition techniques, with an emphasis on their compatibility with flexible substrates. The core of the review categorizes and discusses the latest advances in HEA-based flexible sensors for strain/stress, gas, and electrochemical (e.g., glucose, biomarkers, heavy metals) detection, highlighting the structure–property–performance relationships. Representative studies have demonstrated that HEA flexible strain sensors achieve a temperature coefficient of resistance as low as 45.59 ppm/K with no signal drift over 6000 stretching cycles; room-temperature hydrogen sensors reach a detection limit down to 31 ppb with a response time of 19 s; and non-enzymatic glucose sensors deliver a sensitivity up to 3043 μA·mM⁻¹·cm⁻². Finally, we summarize the key challenges—such as manufacturing scalability, long-term stability under dynamic deformation, and cost-effectiveness—and provide a forward-looking perspective on promising research directions, including high-throughput compositional screening, multi-functional sensor arrays, and the integration of machine learning for rational material design. Full article

Background: Obesity is a multifactorial metabolic disorder characterized by chronic low-grade inflammation, oxidative stress, mitochondrial dysfunction, lipotoxicity, dysregulated adipogenesis, and alterations in the gut microbiota, which collectively contribute to insulin resistance and cardiometabolic complications. In this context, dietary patterns rich in bioactive compounds have gained relevance as potential strategies to modulate these interconnected pathways. Objective: To assess the potential of the Milpa Diet (a sustainable, plant-dominant Mesoamerican eating pattern centered on the ancient three sisters’ polyculture of maize, beans, and squash, along with chili) as a culturally relevant, multi-axis functional dietary pattern, and to evaluate the molecular mechanisms underlying obesity-associated with metabolic dysfunction. Methods: A scoping review of preclinical and clinical studies was conducted using Medline via PubMed, Scopus, and Web of Science databases. The ChEMBL database was also used to identify chemical structures. The search focused on evidence related to inflammation, oxidative stress, adipogenesis, lipotoxicity, mitochondrial function, and gut microbiota modulation in the context of the main foods of the Milpa Diet, including maize, legumes, chili peppers, nopal, and quelites. Studies were selected based on peer-review status and their relevance to molecular, metabolic, and functional outcomes. Results: The current evidence shows that the core components of the Milpa Diet provide dietary fiber and a broad range of bioactive compounds, such as flavonoids, carotenoids, capsaicinoids, phenolic acids, pigments, and vitamins, which exhibit antioxidant and anti-inflammatory effects. These compounds have been associated with modulation of adipogenesis and lipotoxicity, preservation of mitochondrial function, and favorable regulation of gut microbiota composition and activity, collectively influencing metabolic pathways relevant to obesity. Conclusions: Overall, mechanistic and emerging clinical evidence suggests that the Milpa Diet represents a multi-axis nutritional strategy with potential to mitigate obesity-related metabolic dysfunction through coordinated effects on inflammation, oxidative stress, adipogenesis, lipotoxicity, mitochondrial function, and gut microbiota regulation. Although comprehensive clinical trials evaluating this dietary pattern as an integrated intervention remain limited, current evidence supports its relevance for future translational research, public health strategies, and the development of sustainable dietary models aimed at improving metabolic health. Full article

Synthetic aperture radar (SAR) imagery provides all-weather, day-and-night observation capabilities that complement electro-optical (EO) imaging; however, the limited number of operational SAR satellites and the difficulty of acquiring expert-annotated SAR datasets constrain deep-learning-based SAR image analysis. In this paper, we propose EO2SAR-Diff, a conditional latent diffusion framework that translates EO aerial images into realistic synthetic SAR images. The framework comprises three core components: (1) domain-adaptive LoRA pre-training that anchors the Stable Diffusion backbone in the remote sensing domain, (2) a style extraction and injection network that captures SAR-specific visual characteristics via multi-scale feature encoding and parallel cross-attention, and (3) a multi-branch ControlNet with three parallel branches for complementary structural guidance. These components are coordinated by a dual-axis feature injection strategy that modulates conditioning strength along both spatial (per-block) and temporal (per-timestep) dimensions. Experiments on the DOTA 1.0 and SARDet-100K datasets demonstrate that EO2SAR-Diff ranks in the top tier among all compared methods in distributional alignment with real SAR imagery, in terms of FID and KID computed with two SAR-domain-adapted feature extractors. Augmenting the SAR training set with our synthetic images yields consistent improvements in downstream object detection performance, confirming the practical utility of the proposed framework. Full article