Search Results (1,717)

Emotion recognition in video (ERV) aims to infer human affect from visual, audio, and contextual signals and is increasingly important for interactive and intelligent systems. Over the past decade, ERV has evolved from handcrafted features and task-specific deep learning models toward transformer-based vision–language models and multimodal large language models (MLLMs). This review surveys this evolution, with an emphasis on engineering considerations relevant to real-world deployment. We analyze multimodal fusion strategies, dataset characteristics, and evaluation protocols, highlighting limitations in robustness, bias, and annotation quality under unconstrained conditions. Emerging MLLM-based approaches are examined in terms of performance, reasoning capability, computational cost, and interaction potential. By comparing task-specific models with foundation model approaches, we clarify their respective strengths for resource-constrained versus context-aware applications. Finally, we outline practical research directions toward building robust, efficient, and deployable ERV systems for applied scenarios such as assistive technologies and human–AI interaction. Full article

This study examines the conceptualization of sustainability in heritage tourism in Saudi Arabia following the introduction of the Saudi Vision 2030 program and the country’s opening to tourism in 2019, both of which aim to diversify the economy and promote cultural heritage. A scoping review methodology based on the Arksey & O’Malley framework has been adopted; data were charted according to the Joanna Briggs Institute (JBI) charting method based on the PRISMA-ScR reporting protocol. Publications from 2019 to 2025 were systematically collected from the database and manual research, resulting in 25 fully accessible studies that met the inclusion criteria. Data were analyzed thematically, revealing six main areas of investigation, encompassing both sustainability outcomes and cross-cutting implementation enablers: heritage conservation and tourism development, architecture and urban planning, policy and governance, community engagement, marketing and technology, and geoheritage and environmental sustainability. The findings indicate that Saudi research in this field is primarily qualitative, focusing on ecological aspects. The studies reveal limited integration of social and technological dimensions, with significant gaps identified in standardized sustainability indicators, longitudinal monitoring, policy implementation, and digital heritage tools. The originality of this study lies in its comprehensive mapping of Saudi heritage tourism sustainability research, highlighting emerging gaps and future agendas. The results also provide a roadmap for policymakers, managers, and scholars to enhance governance policies, community participation, and technological integration, which can contribute to sustainable tourism development in line with Saudi Vision 2030 goals, thereby fostering international competitiveness while preserving cultural and natural heritage. Full article

This research experimentally investigates the association between multidimensional risk factors and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs). This research experimentally investigates the correlation between multidimensional risk variables and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs) in Saudi Arabia, particularly in alignment with the objectives of Saudi Vision 2030. This study utilizes annual data from 1990 to 2024 and employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine the short-run and long-run relationships between economic growth, as measured by GDP, and five key risk dimensions: governance effectiveness, financial development, environmental pressure, human capital, and oil price volatility, which act as proxies for risk dimensions. The main contribution of this study is the integration of these governance, financial, environmental, human capital, and oil price risk factors into a single ARDL framework for Saudi Arabia from 1990 to 2024, using GDP growth as a proxy for progress toward SDGs within the Saudi Vision 2030 context, addressing gaps in prior studies that focus on individual determinants. The empirical evidence indicates a long-term cointegration relationship among the variables. Our findings indicate that government effectiveness and investment in human capital are important positive factors associated with long-term economic growth, thereby validating the importance of institutional improvements and educational expenditures. In contrast, fluctuations in oil prices and environmental pressures are linked to adverse association, highlighting issues related to resource dependency and ecological degradation. Financial development exhibits a negative long-run association, indicating potential inefficiencies or diminishing returns in loan distribution. The study offers essential policy recommendations, such as expediting digital governance reforms, allocating financial resources to non-oil SMEs (SDG 8), aligning educational curricula with labor market demands, and implementing stricter environmental regulations to separate economic growth from emissions. Full article

In health sciences, the population-level burden of dental caries makes oral health education and the integration of theory and practice a priority. This quasi-experimental study examined whether augmented reality (AR) using the Merge Object Viewer improves basic dental knowledge, is associated with visual symptoms, and is acceptable compared with two-dimensional (2D) materials. A total of 321 students enrolled in health-related programmes participated and were assigned to three AR/2D sequences across three blocks (healthy dentition, cariogenesis, and pain management). Outcomes included knowledge (15-item test, pre and post intervention), computer vision syndrome (CVS-Q), acceptance (TAM-AR), and open-ended comments. Knowledge improved in all groups: 2D materials were superior for dentition, AR for cariogenesis, and both were comparable for pain. Two-thirds met criteria for symptoms on the CVS-Q, with a lower prevalence in the AR–2D–AR sequence. Acceptance was high, and comments highlighted usefulness, ease of use, and enjoyment, but also noted language issues and technical overload. Overall, AR appears to be a complementary tool to 2D materials in basic dental education. Full article

Chronic pain is inherently multifactorial, with biological, psychological, and social factors contributing to neuropathic pain (NP) and central sensitization (CS) syndromes. Comorbidity between functional disorders and the lack of clinical biomarkers adds to the challenge of diagnosis and treatment, leading to frustration for healthcare professionals and patients. Available treatments are limited, increasing patient suffering with personal and financial costs. This systematic review examined multisensory processing alterations in chronic pain and reviewed current pharmacological and non-pharmacological interventions. A structured search was conducted on the PubMed database using the keywords Central Sensitization, Fibromyalgia, Complex Regional Pain Syndrome, and Neuropathic Pain, combined with the keywords Vision, Audition, Olfaction, Touch, Taste, and Proprioception. Papers were then filtered to discuss current treatment approaches. Articles within the last five years, from 2018 to 2023, have been included. Papers were excluded if they were animal studies; investigated tissue damage, disease processes, or addiction; or were conference proceedings or non-English. Results were summarized in table form to allow synthesis of evidence. As this study is a systematic review of previously published research rather than a clinical trial or experimental investigation, the risk of bias was assessed independently by at least two reviewers. 138 studies were identified and analyzed. Of these, 96 focused primarily on treatment options for chronic pain and were analyzed for this systematic review. There were a few emerging themes. No one therapy is effective, so a multidisciplinary approach to diagnosis, including pharmacological, somatic, and psychological treatment, is generally predicted to achieve the best outcomes. Cranial neurovascular compromise, especially of the trigeminal, glossopharyngeal, and potentially the vestibulocochlear nerve, is being increasingly revealed with the advancement of neuroimaging. Cortical and deep brain stimulation to evoke neuroplasticity is an emerging and promising therapy and warrants further investigation. Finally, including patients in their treatment plan allows them control and offers the ability to self-manage their pain. Risk of bias limits the ability to judge the quality of evidence. Full article

The environmental and social crises affecting global agri-food systems highlight the need for a profound transformation of production models and their territorial relations. In this context, agroecology, understood as science, practice, and movement, has emerged as a paradigm capable of integrating ecological sustainability, social equity, and community participation. Within this framework, the work carried out by CREA in the AgrEcoMed project (new agroecological approach for soil fertility and biodiversity restoration to improve economic and social resilience of Mediterranean farming systems), funded by the PRIMA programme, investigates agroecology as a social and political process of territorial regeneration. This process is grounded in co-design with local stakeholders, collective learning, and the construction of multi-actor networks for agroecology in the Mediterranean. The Manifesto functions as a tool for participatory governance and value convergence, aiming to consolidate a shared vision for the Mediterranean agroecological transition. The article examines, through an analysis of the existing literature, the role of agroecological networks and empirically examines the function of the collective co-creation of the Manifesto as a tool for social innovation. The methodology is based on a participatory action-research approach that used local focus groups, World Café, and thematic analysis to identify the needs of the companies involved. The results highlight the formation of a multi-actor network currently comprising around 90 members and confirm the effectiveness of the Manifesto as a boundary object for horizontal governance. This demonstrates how sustainability can emerge from dialogue, cooperation, and the co-production of knowledge among local actors. Full article

Interspecies communication is increasingly recognized as an affective–cognitive process co-created between humans and animals rather than a one-directional transmission of signals. This review integrates findings from ethology, neuroscience, welfare science, behavioral studies, and posthumanist ethics to examine how emotional expression, communicative intentionality, and relational engagement shape understanding across species. Research on primates, dogs, elephants, and marine mammals demonstrates that empathy, consolation, cooperative signaling, and multimodal perception rely on evolutionarily conserved mechanisms, including mirror systems, affective contagion, and oxytocin-mediated bonding. These biological insights intersect with ethical considerations concerning animal agency, methodological responsibility, and the interpretation of non-human communication. Emerging technological tools—bioacoustics, machine vision, and AI-assisted modeling—offer new opportunities to analyze complex vocal and behavioral patterns, yet they require careful contextualization to avoid anthropocentric misclassification. Synthesizing these perspectives, the review proposes a relational framework in which meaning arises through shared emotional engagement, embodied interaction, and ethically grounded interpretation. This approach highlights the importance of welfare-oriented, minimally invasive methodologies and supports a broader shift toward recognizing animals as communicative partners whose emotional lives contribute to scientific knowledge. This review primarily synthesizes empirical and theoretical research on primates and dogs, complemented by selected examples from elephants and marine mammals, which provide the most developed evidence base for the affective–cognitive and relational mechanisms discussed. Full article

Foreign fiber (FF, plural: FFs) contamination has been demonstrated to have a substantial impact on the quality and profitability of cotton textiles. Machine vision technology, characterized by its non-contact approach and high efficiency, has emerged as the primary solution for detecting FFs in cotton. This paper commences with a precise definition and classification of FF and a concomitant analysis of the mechanisms of contamination. Subsequently, a systematic review of global research advancements in imaging technologies and the evolution of algorithms is conducted. This paper emphasizes the use of X-ray, ultraviolet fluorescence, line laser, polarized light, infrared imaging, and hyperspectral imaging techniques for FF detection. Through a comparative analysis, it reveals the applicable scope and effectiveness of various imaging schemes. Regarding the evolution of algorithms, this paper expounds on the technical development process from traditional image processing to machine learning (ML) and deep learning (DL). The study meticulously examines the strengths and weaknesses of each algorithmic stage. In conclusion, this paper synthesizes the prevailing technical challenges confronting machine vision detection of FFs in cotton and proffers recommendations for future research directions in this domain, emphasizing multi-technology integration, algorithm optimization, and hardware innovations. Full article

Background: Diabetic retinopathy is a leading cause of vision impairment and a significant complication of diabetes mellitus, especially in low- and middle-income countries. This study aimed to identify the barriers affecting diabetic retinopathy screening among people with diabetes mellitus. Methods: This cross-sectional study was conducted between April and October 2024 at the National Center for Diabetes, Endocrinology and Genetics. Data collection was performed using a structured, validated electronic questionnaire adapted from previous studies. Sample size calculation was carried out before data collection. Data were collected using a structured electronic questionnaire. A total of 998 responses were included in the study. The collected data incorporated sociodemographic characteristics, diabetes history, screening practices, and reported barriers. Descriptive and categorical data analyses were performed. Results: Of 998 participants, 82% were over 50 years old, 79% had type 2 diabetes mellitus, and 30% had never had an eye examination. Diabetic retinopathy was diagnosed in 12%. The main barriers to regular attendance among those previously screened (699) were as follows: lack of awareness of its importance (11%), believing that being asymptomatic made screening unnecessary (19%), and transportation difficulties (14%). Among those never screened (299), 56% lacked awareness, 62% believed being asymptomatic negates the need for screening, and 13% faced transportation difficulties. Age > 50 years, higher educational level, availability of health insurance, longer duration of diagnosis of diabetes mellitus, and HbA_1c > 7% were significantly associated with prior screening (p < 0.05). Conclusions: Public health strategies should enhance the education provided to people and physician–person communication and remove logistical obstacles to improve screening compliance. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant interobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep-learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine-learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears. Full article

This article analyses the iconographic cycle of the Sacristy of the Virgin in the Basílica del Pilar, with the aim of unveiling the complex system of visual symbols present in its bas-reliefs. Through a typological and exegetical approach, the study examines the various Marian representations, highlighting their connection with Old Testament, Patristic, Scholastic, and Baroque traditions. The research demonstrates how these visual emblems embody the principles of Divine Motherhood, purity, Co-Redemptrix, and the Virgin’s spiritual superiority. Furthermore, it explores the relationship between the carvings and hermeneutic and emblematic literature, revealing how tradition and devotion intertwine to configure a genuine Baroque iconographic breviary. The findings allow for an interpretation of Mary not only as an object of worship but also as a theological paradigm and aesthetic model of divine perfection, thus offering a comprehensive vision of Baroque Mariology and of the didactic and devotional function of the Sacristy of the Virgin. Full article

This mixed-methods study investigates the strategic management of digital transformation in Bulgarian schools by analysing principals’ self-reported leadership practices and styles. Using data from a nationally representative sample (N = 349) gathered through the SELFIE tool, complemented by 30 in-depth interviews, the research examines how school leaders understand and enact their roles as digital leaders within a context of fragmented policies and uneven digital capacity. Quantitative results reveal a central paradox: although 89.7% of principals claim to actively support teachers’ digital innovation, only about half report having a formalised digital strategy. This imbalance between strong operational support and weak institutionalisation reflects the dominant approach to school digitalisation in Bulgaria. Qualitative cluster analysis identifies three leadership profiles: (1) a strategic–collaborative profile, characterised by long-term planning, partnerships, and data-driven decisions; (2) a supportive–collaborative profile focused on teacher communities and context-specific professional development but lacking strategic vision; and (3) a balanced–pragmatic profile oriented toward measurable improvements and adaptive responses. Triangulation with national assessment data shows that leadership styles align with institutional contexts: high-performing schools tend to apply strategic–collaborative leadership, while lower-performing schools adopt pragmatic, adaptive approaches. The study argues that digital transformation requires context-sensitive frameworks recognising multiple developmental trajectories, highlighting the need for differentiated policies that support strategic institutionalisation of existing digital innovations while addressing structural inequalities. Full article

Solar energy is a widely available renewable source suitable for diverse applications, including residential, industrial and aerospace sectors. To maximize energy capture, solar tracking systems adjust panels to maintain perpendicular alignment with sunlight. Various tracking techniques are employed to adjust these trackers, such as sensors, predefined algorithms, deep learning, and image-processing techniques. Image processing-based trackers have gained prominence for their precision and accuracy. This approach uses cameras as sensors to capture real-time sky images and analyze them to detect the sun and its coordinates, orienting solar panels toward its center. This technology can be integrated with other techniques to enhance energy output with high accuracy, minimal tracking error, and low maintenance requirements. This review examines computer vision methods used in solar tracking systems, synthesizing findings from 26 studies published between 2009 and 2024. The paper discusses main system components, methods utilized, and results obtained. Findings demonstrate that the robustness and accuracy of these tracking systems have increased compared to other tracking systems, while tracking error has decreased. Full article

Background/Objectives: Neovascularization, defined as the sprouting of new blood vessels from pre-existing vasculature, is a critical pathological feature in ocular diseases such as pathological myopia and represents a leading cause of corneal vision loss. Vascular endothelial growth factor A (VEGFA) plays a pivotal role in endothelial cell proliferation, migration, survival by anti-apoptotic signaling, and vascular permeability. Dysregulation of VEGFA is closely linked to pathological neovascularization. Exosomes, nanosized phospholipid bilayer vesicles ranging from 30 to 150 nm, have emerged as promising gene delivery vehicles due to their intrinsic low immunogenicity, superior cellular uptake, and enhanced in vivo stability. This study aimed to investigate whether highly purified mesenchymal stem cell (MSC)-derived exosomes loaded with VEGFA siRNA labeled with FAM can effectively suppress pathological corneal neovascularization (CNV) via targeeted cellular transduction and VEGFA inhibition. Furthermore, we examined whether the therapeutic effect involves the modulation of the PI3K–Akt–Caspase-3 signaling axis. Methods: Exosomes purified by chromatography were characterized by electronmicroscopy, standard marker immunoblotting, and nanoparticle tracking analysis. In vitro, we assessed exosome uptake and cytoplasmic release, suppression of VEGFA mRNA/protein, cell viability, and apoptosis. In a mouse CNV model, we evaluated tissue reach and stromal retention after repeated intrastromal injections; anterior segment angiogenic indices; CD31/VEGFA immunofluorescence/immunoblotting; phosphorylated PI3K and Akt; cleaved caspase-3; histology (H&E); and systemic safety (liver, kidney, and spleen). Results: Exosomes were of high quality and showed peak efficacy at 48 h, with decreased VEGFA mRNA/protein, reduced viability, and increased apoptosis in vitro. In vivo, efficient delivery and stromal retention were observed, with accelerated inhibition of neovascularization after Day 14 and maximal effect on Days 17–19. Treatment reduced CD31 and VEGFA, decreased p-PI3K and p-Akt, and increased cleaved caspase-3. Histologically, concurrent reductions in neovascularization, inflammatory cell infiltration, and inflammatory epithelial thickening were observed, alongside a favorable systemic safety profile. Conclusions:VEGFA siRNA-loaded exosomes effectively reduce pathological CNV via a causal sequence of intracellular uptake, cytoplasmic release, targeted inhibition, and phenotypic suppression. Supported by consistent PI3K–Akt inhibition and caspase-3–mediated apoptosis induction, these exosomes represent a promising local gene therapy that can complement existing antibody-based treatments. Full article