Search Results (421)

Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is largely attributed to redundant visual content in sensor-acquired video streams, linguistic ambiguity, and discrepancies in modality-specific representations. Most existing approaches rely on intra-modal feature modeling, processing video and text independently throughout the representation learning stage. However, this isolation undermines semantic alignment by neglecting the potential of cross-modal interactions. In practice, a natural language query typically corresponds to spatiotemporal content in video signals collected through camera-based sensing systems, encompassing a particular sequence of frames and its associated salient subregions. We propose a text-guided visual representation optimization framework tailored to enhance semantic interpretation over video signals captured by visual sensors. This framework leverages textual information to focus on spatiotemporal video content, thereby narrowing the cross-modal gap. Built upon the unified cross-modal embedding space provided by CLIP, our model leverages video data from sensing devices to structure representations and introduces two dedicated modules to semantically refine visual representations across spatial and temporal dimensions. First, we design a Spatial Visual Representation Optimization (SVRO) module to learn spatial information within intra-frames. It selects salient patches related to the text, capturing more fine-grained visual details. Second, we introduce a Temporal Visual Representation Optimization (TVRO) module to learn temporal relations from inter-frames. Temporal triplet loss is employed in TVRO to enhance attention on text-relevant frames and capture clip semantics. Additionally, a self-supervised contrastive loss is introduced at the clip–text level to improve inter-clip discrimination by maximizing semantic variance during training. Experiments on Charades-STA, ActivityNet Captions, and TACoS, widely used benchmark datasets, demonstrate that our method outperforms state-of-the-art methods across multiple metrics. Full article

In the context of the growing emphasis on sustainable development and building safety performance, wooden architecture will attract increasing attention due to its low-carbon characteristics and excellent seismic resistance. In this study, the bibliometric software Citespace is used for data visualization analysis based on the literature related to Chinese wooden architecture in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WOS) databases, aiming to construct an analytical framework that integrates quantitative visualization and qualitative thematic interpretation which could reveal the current status, hotspots, and frontier trends of research in this field. The results show the following: Research on Chinese wooden architecture has shown a steady growth trend, indicating that it has received attention from an increasing number of scholars. Researchers and institutions are mainly concentrated in higher learning and research institutions in economically developed regions. Research hotspots cover subjects such as seismic performance, mortise–tenon structures, imitation wood structures, Dong architecture, Liang Sicheng, and the Society for the Study of Chinese Architecture. The research process of Chinese wooden architecture can be divided into three stages: the macro stage, the specific deepening stage, and the inheritance application and interdisciplinary integration stage. In the future, the focus will be on interdisciplinary research on wooden architecture from ethnic minority cultures and traditional dwellings. Full article

The Western genre has traditionally been associated with American identity and male-dominated narratives. However, recent decades have seen increasing attention to female protagonists, particularly the European woman as a cultural mediator within the frontier context. This study aims to identify the archetypes of the European woman in the Western genre through a diachronic and comparative analysis of the visual language found in European painting from the late 17th to early 19th centuries and in 20th–21st century cinema. The research methodology combines narrative, visual, and semiotic analysis, with a focus on intermedial and intertextual parallels between visual art and film. The study identifies nine archetypal models corresponding to goddesses of the Greek pantheon and traces their transformation across different aesthetic systems. These archetypes, rooted in artistic traditions such as Baroque, Classicism, Romanticism, and others, reappear in Western films through compositional, symbolic, and iconographic strategies, demonstrating their persistence and ability to transcend temporal, medial, and geographical boundaries. The findings suggest that the woman in the Western genre is not merely a central character, but a visual sign that activates cultural memory and engages with deep archetypal structures embedded in the collective unconscious. Full article

Small Language Models (SLMs) have emerged as a critical area of study within natural language processing, attracting growing attention from both academia and industry. This systematic literature review provides a comprehensive and reproducible analysis of recent developments and advancements in SLMs post-2023. Drawing on 70 English-language studies published between January 2023 and January 2025, identified through Scopus, IEEE Xplore, Web of Science, and ACM Digital Library, and focusing primarily on SLMs (including those with up to 7 billion parameters), this review offers a structured overview of the current state of the art and potential future directions. Designed as a resource for researchers seeking an in-depth global synthesis, the review examines key dimensions such as publication trends, visual data representations, contributing institutions, and the availability of public datasets. It highlights prevailing research challenges and outlines proposed solutions, with a particular focus on widely adopted model architectures, as well as common compression and optimization techniques. This study also evaluates the criteria used to assess the effectiveness of SLMs and discusses emerging de facto standards for industry. The curated data and insights aim to support and inform ongoing and future research in this rapidly evolving field. Full article

In the era of precision medicine, effective data visualisation plays a pivotal role in supporting clinical decision-making by translating complex, multidimensional datasets into intuitive and actionable insights. This paper explores the foundational principles of visual perception, with a specific focus on pre-attentive attributes such as colour, shape, size, orientation, and spatial position, which are processed automatically by the human visual system. Drawing from cognitive psychology and perceptual science, we demonstrate how these attributes can enhance the clarity and usability of medical visualisations, reducing cognitive load and improving interpretive speed in high-stakes clinical environments. Through detailed case studies and visual examples, particularly within the field of oncology, we highlight best practices and common pitfalls in the design of dashboards, nomograms, and interactive platforms. We further examine the integration of advanced tools—such as genomic heatmaps and temporal timelines—into multidisciplinary workflows to support personalised care. Our findings underscore that visually intelligent design is not merely an aesthetic concern but a critical factor in clinical safety, efficiency, and communication, advocating for user-centred and evidence-based approaches in the development of health data interfaces. Full article

When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet (Fusion-Enhanced Network)—that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model’s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively. Full article

Background and Objectives: Recent technological advances have introduced new interventions in the field of stroke rehabilitation. Among them, visual feedback (VF) and non-invasive brain stimulation (NIBS) have gained considerable attention, with growing evidence supporting their efficacy. However, their combined application in lower limb recovery remains to be established. This systematic review aims to evaluate the current evidence on the therapeutic effect of combining VF and NIBS for lower limb motor rehabilitation in stroke patients. Methods: Following PRISMA guidelines, PubMed, Embase, Scopus, and Cochrane databases were searched for randomized controlled trials and observational studies comparing VF and NIBS interventions with either their monotherapy, placebo, or standard treatment. The outcomes evaluated for lower limb function included balance, gait, and motor performance. Results: From 997 studies screened, 5 studies (3 RCTs and 2 cohort studies) were included. Despite heterogeneity in the immersion level, NIBS protocols, and outcome measures, evidence emerged supporting the efficacy of combined VF and NIBS across multiple outcomes. However, the degree to which these interventions outperform standard therapies remains uncertain, primarily due to a limited number of comparator studies and the quality of the existing data. Conclusions: This review provides preliminary insights into the potential of combining VF and NIBS in stroke patients affected by lower limb motor impairments. Future research should focus on standardizing protocols and addressing demographic variability to enhance the reliability and comparability of findings. Full article

The issue of nanoplastics (NPs) in the environment, following that of microplastics (MPs), is receiving increasing attention in the scientific community. Due to their size, these particles require the development and application of new methods for both quantitative and qualitative determination. Consequently, techniques commonly used for analyzing MPs may prove ineffective in the context of NPs. Isolating NPs from samples with complex matrices poses a significant challenge that directly affects analytical outcomes. This paper aims to discuss the main challenges encountered during the analysis of NPs in environmental samples. Various methods for the visualization and identification of NPs are examined, with a focus on microscopic, spectroscopic, and thermal techniques. The advantages and limitations of analytical approaches reported in the literature are summarized, offering guidance for the future development and standardization of methods used to determine NPs in environmental contexts. Full article

As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity, while deep learning-based approaches offer superior representational capacity. However, challenges remain in achieving a balance between fine-detail adaptation and computational efficiency. Mamba, a state–space model (SSM)-based architecture, offers linear-time complexity and excels at capturing long-range dependencies in sequences. It has been adopted in remote sensing compression tasks to model long-distance dependencies between pixels. However, despite its effectiveness in global context aggregation, Mamba’s uniform bidirectional scanning is insufficient for capturing high-frequency structures such as edges and textures. Moreover, existing visual state–space (VSS) models built upon Mamba typically treat all channels equally and lack mechanisms to dynamically focus on semantically salient spatial regions. To address these issues, we present an innovative architecture for distant sensing image compression, called the Multi-scale Channel Global Mamba Network (MGMNet). MGMNet integrates a spatial–channel dynamic weighting mechanism into the Mamba architecture, enhancing global semantic modeling while selectively emphasizing informative features. It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. The Channel–Global Information Modeling (CGIM) module enhances conventional VSS by introducing a dual-path attention strategy that reweights spatial and channel information, improving the modeling of long-range dependencies and edge structures. We conducted extensive evaluations on three distinct remote sensing datasets to assess the MGMNet. The results of the investigations revealed that MGMNet outperforms the current SOTA models across various performance metrics. Full article

Microgrids are gaining considerable attention as a promising solution for integrating distributed energy resources and enhancing grid resilience. Model predictive control (MPC) has emerged as a powerful control strategy for microgrids due to its ability to handle complex dynamics and optimization problems. This study aims to conduct a comprehensive assessment of MPC applications and evaluate their overall effectiveness across various microgrid functionalities. Previous studies have not collectively examined MPC and have not explored its advantages and disadvantages in the microgrid. This study systematically categorizes and addresses this gap in the existing literature. An extensive list of suitable research papers was compiled from the Web of Science and analyzed, considering the method of the studies, main focus and objectives, publication year, and findings. Moreover, this research incorporates co-occurrence keyword analysis, covering MPC applications, systematic reviews, microgrids, and review articles. The visualization and analysis of the data obtained from the Web of Science database were conducted using VOS viewer. This discussion includes approaches that help electrical engineers evaluate the benefits and disadvantages of MPC within the microgrid setup. This knowledge enables electrical practitioners to select the appropriate methods for providing a resilient and reliable ecosystem. Full article

Optical coherence tomography (OCT) is a non-invasive imaging technique that provides high-resolution, real-time visualization of soft and hard periodontal tissues. It offers micrometer-level resolution (typically ~10–15 μm) and a scan depth ranging from approximately 0.5 to 2 mm, depending on tissue type and system configuration. The field of view generally spans a few millimeters, which is sufficient for imaging gingiva, sulcus, and superficial bone contours. Over the past two decades, its application in periodontology has gained increasing attention due to its ability to detect structural changes in gingival and alveolar tissues without the need for ionizing radiation. Various OCT modalities, including time-domain, Fourier-domain, and swept-source OCT, have been explored for periodontal assessment, offering valuable insights into tissue morphology, disease progression, and treatment outcomes. Recent innovations include the development of three-dimensional (3D) OCT imaging and OCT angiography (OCTA), enabling the volumetric visualization of periodontal structures and microvascular patterns in vivo. Compared to conventional imaging techniques, such as radiography and cone beam computed tomography (CBCT), OCT offers superior soft tissue contrast and the potential for dynamic in vivo monitoring of periodontal conditions. Recent advancements, including the integration of artificial intelligence (AI) and the development of portable OCT systems, have further expanded its diagnostic capabilities. However, challenges, such as limited penetration depth, high costs, and the need for standardized clinical protocols, must be addressed before widespread clinical implementation. This narrative review provides an updated overview of the principles, applications, and technological advancements of OCT in periodontology. The current limitations and future perspectives of this technology are also discussed, with a focus on its potential role in improving periodontal diagnostics and personalized treatment approaches. Full article

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition. Full article

Background: The therapeutic potential of natural products in cognitive disabilities has drawn growing attention, yet a comprehensive analysis of trends and key contributors is lacking. This study provides a bibliometric overview highlighting growth patterns, themes, and future directions. Methods: A comprehensive Scopus search with multistep filtering was conducted by applying keywords related to natural products and cognitive disabilities to titles, abstracts, and keywords, initially retrieving 10,011 documents. Filters for original articles and English language reduced the results to 5688. Data extracted in October 2024 were analyzed using Excel and the R-package, yielding performance and citation indices. Differential proliferation was visualized using a Sankey diagram, while thematic maps highlighted key research themes, geographic trends, and subject clusters. Results: The field exhibited an annual growth rate of 12.36% from 1971 to 2024, with 2021 being the most productive year (497 articles). In recent decades, citation metrics have highlighted significant impacts. Thematic maps and Sankey diagrams revealed the research focus, geographic trends, and collaboration. Alzheimer’s disease dominates the field, alongside topics such as oxidative stress, neuroprotection, and molecular docking. Emerging trends include ferroptosis, UPLC-Q-TOF-MS, and network pharmacology, which have marked advancements in therapeutic and computational approaches. Conclusions: This analysis underscores the dynamic and interdisciplinary nature of this field, highlighting areas for future exploration, particularly underrepresented cognitive disorders and novel therapeutic approaches. Full article

Background: In today’s rapidly evolving business environment, organizational agility (OA) has become increasingly critical for companies to maintain competitiveness and sustainability. Business intelligence (BI) is pivotal in enabling organizational agility by providing the necessary tools and insights to navigate uncertainties and capitalize on opportunities. This study aimed to investigate the relationship between BI and organizational agility, particularly within the pharmaceutical manufacturing sector in the Middle East and North Africa (MENA) region. The systematic literature review followed Kitchenham’s guidelines, which was supplemented with a VOS analysis to visualize the interconnectedness of BI and organizational agility. The analysis revealed a direct relationship between BI and organizational agility, with the critical variables of innovation, competitive advantage, firm performance, and dynamic capabilities influencing this connection. The MENA region shows promise in contributing to this field, but further research is needed. Leveraging BI capabilities can enhance organizational agility, positioning companies for sustained success amidst uncertainty. Addressing challenges and fostering a supportive organizational culture is essential for realizing the full potential of BI-driven agility. This study makes an original and timely contribution by examining the relationship between business intelligence (BI) and organizational agility (OA) through a systematic literature review across multiple countries. The study focuses specifically on the Middle East and North Africa (MENA) region, which has received insufficient attention in previous research. Unlike previous studies that focus on isolated cases, this work combines bibliometric analysis with a structured review methodology. It provides a clear summary of how BI supports key outcomes such as innovation, dynamic capabilities, and competitive advantage Full article

Professional reflection is key to the professionalization of pedagogical professionals. Using a mixed-methods design that combines eye-tracking methodology with retrospective written reflections, this study investigates the visual and reflective processes of early childhood professionals when interpreting video-recorded pedagogical situations. A remote eye-tracking device (Tobii Pro Fusion) was used to capture eye movements. Sixteen participants watched videos of pedagogical situations in a kindergarten while their eye movements were recorded to investigate their visual focus, followed by open-ended written reflections to investigate their reflective focus. Eye-tracking data revealed that participants focused predominantly on situational features and children’s actions, whereas written reflections mainly addressed the actions of both the children and the professionals. The triangulated data indicated partial congruence between visual and reflective focus, particularly regarding child-related aspects. These findings suggest that although situational features attract visual attention, reflective processes prioritize behavioral actions over environmental context. Eye-tracking and reflective data provide insights, emphasizing the importance of triangulating methods to gain a holistic understanding of professional reflection in early childhood education. This methodological approach holds promise for professional development and training in early childhood education, aiming to foster reflective practice and enhance professional vision. Full article