Search Results (1,311)

Remote sensing object detection within multi-scale frameworks remains challenging, largely due to structural degradation and semantic misalignment introduced during cross-scale semantic enhancement. As feature hierarchies deepen, high-frequency details for small-object localization decay, while nonlinear transformations and receptive field asymmetry cause cross-scale semantic and spatial offsets. While existing feature pyramid-based approaches improve detection performance through multi-scale fusion or semantic aggregation, they fail to fundamentally address the cumulative information degradation arising from hierarchical feature extraction. To this end, we propose CFBA-FPN, a unified shallow–deep cross-scale feature compensation framework that explicitly models both frequency discrepancies and semantic offsets across scales. Specifically, shallow features are exploited as structural and spatial anchors to inject lost high-frequency information into deeper layers, effectively mitigating structural degradation. Meanwhile, a cross-scale collaborative semantic alignment strategy is introduced to correct semantic inconsistencies and spatial misalignments among multi-scale features. Building upon these designs, a cascaded gated fusion mechanism is developed to adaptively balance shallow structural compensation and deep semantic representation, thereby suppressing background noise and enhancing small-object responses. Extensive experiments on the AI-TOD, VisDrone, and DIOR benchmarks demonstrate that CFBA-FPN consistently improves localization accuracy and recognition capability, validating its effectiveness and generalization ability in remote sensing object detection. Full article

On the 25 September 1987, CBS aired the first episode of Beauty and the Beast. This television fantasy romance centred on the chaste relationship between Catherine Chandler (Linda Hamilton), a New York socialite turned District Attorney investigator, and the beastly Vincent, a man with leonine features who lives in a secret commune of outcasts beneath the city, played by Ron Perlman, but designed by Rick Baker. This article examines Vincent as a core part of Beauty and the Beast’s appeal and as a sight for affective spectacle. It will argue that due to television’s ability to provide audiences with intimacy and proximity, as well as Alexia Smit’s theories of tele-affectivity, Vincent, as a character and as part of the scenography of the television show, allows for “a multisensory, situated experience”. Taking a historical materialist approach, this article will examine the initial reaction to Vincent as a character in the prerelease material and the critical reception upon the release of the first season. It will also explore ideas of responsibility in the creation of Vincent and the tension and collaboration that take place between Perlman and Baker. Full article

This review evaluates the emerging paradigm of multi-agent systems (MASs) for biomedical and clinical data analysis, focusing on their ability to overcome the reasoning and reliability limitations of standalone large language models (LLMs). We synthesize findings from recent architectural frameworks, specifically LangGraph, CrewAI, and the Model Context Protocol (MCP), to examine how specialized agent teams divide labor, utilize precision tools, and cross-verify outputs. We find that MAS architectures yield significant performance gains in various domains: recent implementations improved oncology decision-making accuracy from 30.3% to 87.2% and reached a peak of 93.2% accuracy on USMLE-style benchmarks through simulated clinical evolution. In clinical trial matching, multi-agent frameworks achieved 87.3% accuracy and enhanced clinician screening efficiency by 42.6% (p < 0.001). However, we also highlight critical operational challenges, including an unreliability tax of 15–50× higher token consumption compared to standalone models and the risk of cascading errors where initial hallucinations are amplified across the agent collective. We conclude that while MAS enables a shift toward collaborative intelligence in biomedicine, its clinical and research adoption requires the development of deterministic orchestration and rigorous cost-utility frameworks to ensure safety and expert-centered oversight. Full article

This article explores the incorporation of linguistic landscape (LL) studies into English for General Academic Purposes (EGAP) courses, emphasizing its potential to enhance language learning through real-world engagement. This study highlights the growing interest in LL as a sociolinguistic phenomenon that reflects urban multilingualism and cultural dynamics. The goal of this article is to analyze pedagogical benefits of integrating LL into language education, such as fostering critical thinking, pragmatic competence, intercultural awareness among students, and creating situations in which the target language is used in natural communication. Through a case study conducted at the Guangdong Technion–Israel Institute of Technology, the author presents specific classroom activities and reports on how they can be combined with fieldwork conducted by students. The goal of the tasks was to let students analyze language use in public spaces, classifying the surrounding signs into top-down and bottom-up, and informative and regulatory, and discuss how social prestige of languages is reflected in multilingual signs. In documenting written language in public places, creating their own signs and assessing their peers’ work, students were practicing both receptive and productive skills. Most of the work was done in small groups, which contributed to the students’ ability to collaborate with peers. The findings suggest that LL projects can effectively bridge classroom learning with lived language experiences, although challenges remain in implementation due to time constraints and pedagogical ideologies. Full article

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability. Full article

Widening participation policy in England is increasingly collaborative. Since 2018, higher education (HE) institutions charging above the basic tuition fee limit are required to set out strategies to mitigate ‘risks to equality of opportunity’ for people from more disadvantaged backgrounds and their ability to access and progress through and from higher education’. Universities are encouraged to work with schools to implement outreach initiatives such as supporting raising attainment—stimulating prospects for strategic collaboration and leadership across organisational boundaries. While the majority of leadership studies in the educational research literature showcase individual settings or sectors, our study of a schools–university partnership investigates collaborative leadership practices across institutional and sector borders. Drawing ethnomethodological insights from rich qualitative data compiled 15 months into the partnership—comprising semi-structured interviews with school leaders and teachers, meeting observations, and researcher field notes—we present a unique school stakeholders’ perspective of a boundary-spanning partnership focused on university outreach and educational improvement. Venturing across institutional borders revealed pathways to develop more diffuse forms of coordinated action around a common goal—activating increased leadership-based collaboration and creativity among school stakeholders alongside a need for greater shared understanding to avoid potential misalignments. Facilitated by ‘knowledge brokering’ between school and university stakeholders, features of collaborative leadership manifested as a blended phenomenon—with teachers and leaders signalling pragmatic shifts in attainment-raising framing and practice. Implications for both schools and HE sectors are offered, distinctively at the intersection of school leadership and widening participation. Full article

This study presents the development, validation, and standardization of the QVA-I, a brief instrument designed to assess teachers’ perceived self-efficacy across four interrelated dimensions: the perceived effectiveness of their educational institution, the ability to design and implement inclusive teaching strategies, the quality of classroom relationships (particularly in the context of students with special educational needs), and the perceived relevance and application of their academic and professional training. Rooted in an ecological and community-oriented framework, the QVA-I conceptualizes teachers as active agents of transformation within their institutions and the broader community. A total of 718 teachers from preschool, primary, and lower secondary schools in Italy participated in the study. The psychometric analyses (including EFA and CFA) confirmed the instrument’s structural validity and internal consistency (α = 0.91), supporting a four-factor model aligned with theoretical expectations. The QVA-I offers a reliable and concise tool for research and intervention, particularly in contexts aiming to promote inclusive education, systemic collaboration, and school–community partnerships Full article

Semantic interoperability remains a critical challenge in Spatial Data Infrastructures (SDIs), particularly when aligning authoritative taxonomies with collaborative folksonomies. Traditional alignment tools often fail to bridge the semantic and structural asymmetry between these schemas. This paper evaluates the capability of Large Language Models (LLMs), specifically distinguishing between traditional architectures and emerging Large Reasoning Models (LRMs), to perform semantic alignment between the Brazilian national topographic data model standard (EDGV) and OpenStreetMap (OSM). Using a formal ontology as a prompting scaffold, we tested seven model versions (including ChatGPT 5, DeepSeek R1, and Gemini 2.5) on their ability to bridge the gap between rigid hierarchical classes and the dynamic, ‘long-tail’ vocabulary of the folksonomy. Results reveal a distinct trade-off: while traditional LLMs exhibited ‘lexical rigidity’ and popularity bias—failing to map low-frequency tags—Reasoning Models demonstrated significantly improved capacity for semantic expansion, correctly identifying complex many-to-one (n:1) relationships across linguistic barriers. However, this reasoning depth often came at the cost of ‘hallucination by over-specification’ and syntactic instability in generating OWL code. We conclude that a neuro-symbolic approach, positioning LRMs as ‘Semantic Catalysts’ within a Human-in-the-Loop (HITL) workflow, provides a viable pathway for interoperability, balancing generative power with the need for logical rigor and spatial validation. Full article

Tire blowouts represent one of the most hazardous fault scenarios for electric vehicles (EVs). While collaborative active steering control (ASC) and direct yaw moment control (DYC) can theoretically maintain stability during these events, the strong coupling effects between them make controller design challenging. To address this, an adaptive control algorithm based on broad-extreme reinforcement learning (RL), named broad critic extreme actor (BCEA), is proposed. Compared to traditional controllers, the proposed BCEA architecture is simpler to design and demonstrates enhanced robustness. Crucially, it achieves significantly faster training speed than traditional RL methods such as deep deterministic policy gradient (DDPG). Both simulation and scaled prototype tests verify the ability of the BCEA-based controller to maintain vehicle stability during different types of tire blowout scenarios. Furthermore, compared to traditional RL methods, the training efficiency is improved by more than 80%. These results indicate that the proposed BCEA controller is a promising advancement for vehicle stability control under critical failure conditions. Full article

This paper introduces a deep learning-based framework for video watermarking that achieves robust, imperceptible, and fast embedding under a wide range of visual and temporal conditions. The proposed method is organized into seven modules that collaboratively perform frame encoding, semantic region analysis, block selection, watermark transformation, and spatiotemporal injection, followed by decoding and multi-objective optimization. A key component of the framework is its ability to learn a visual importance map, which guides a saliency-based block selection strategy. This allows the model to embed the watermark in perceptually redundant regions while minimizing distortion. To enhance resilience, the watermark is distributed across multiple frames, leveraging temporal redundancy to improve recovery under frame loss, insertion, and reordering. Experimental evaluations conducted on a large-scale video dataset demonstrate that the proposed method achieves high fidelity, while preserving low decoding error rates under compression, noise, and temporal distortions. The proposed method operates processing 38 video frames per second on a standard GPU. Additional ablation studies confirm the contribution of each module to the system’s robustness. This framework offers a promising solution for watermarking in streaming, surveillance, and content verification applications. Full article

The present article explores how the development of inner resources can serve as a decisive lever to initiate and sustain individual, organizational, and societal transformations. (1) We first examine the concept of emotional capital, understood as the ability to mobilize emotional competencies defined by models of emotional intelligence, a capital that boosts other forms of capital and enables transformation. (2) We then link this to a capacitating approach, grounded in the work of Sen, which focuses on valuing and expanding human potential. (3) We will introduce the paradigm of bienvivance as an economic and social perspective that ensures a better way of co-vivance, a bienvivance economy; a societal model which proposes to reorient our systems toward a collective dynamic of vitality and meaning, shared living, sustainability, and regeneration. Taken together, these three dimensions pave the way for transformations that connect inner growth with outer change, across educational, organizational, and societal practices. In this article, (4) we will illustrate such a bienvivance approach focused on capacitating pedagogy and emotional capital development via collaborative learning and co-construction of competencies’ student portfolio exercises, as an intrinsic part of development of learners’ lifelong competencies and a lever of potentials’ unlocking, and recognition’s decolonization. Full article

The rapid progress of remote sensing (RS) and computer vision has greatly advanced change detection (CD), and hybrid architectures combining Transformers and convolutional neural networks (CNNs) have shown strong potential in recent years. Nevertheless, reliable CD for very high-resolution (VHR) imagery remains challenging due to large appearance variations across acquisition times, complex background clutter, and target structural diversity. These factors often hinder the modeling of fine edge textures, the maintenance of feature continuity, and the suppression of false changes caused by illumination fluctuations. To address these issues, this paper proposes a Cross-layer Feature Fusion Framework (CLFF) that achieves more accurate and stable change detection by explicitly enhancing the collaborative fusion capability of multi-layer features. The core component of this framework is the Multi-level Interaction Perception Block (MP-Block), which organizes effective interactions among features of different semantic levels. Based on the embedded Multi-branch Interaction Fusion Mechanism (MIFM), the MP-Block accomplishes collaborative refinement and reorganization of cross-layer features through two parallel paths for feature reconstruction and recalibration: the Response-aware Feature Reconstruction Branch (RFRB) and Adaptive Channel Group Fusion Branch (ACGF). Additionally, a lightweight position-aware attention module is introduced to adaptively modulate spatial responses, further suppressing background interference and highlighting key information related to changes. This method effectively mitigates the limitations of traditional CNNs, such as limited receptive fields and insufficient multi-layer feature interaction, while significantly enhancing the ability to collaboratively model multi-layer contextual information. To verify its effectiveness, systematic experiments were conducted on four widely used change detection benchmark datasets: LEVIR, WHU, SYSU and HRCUS. The results show that, compared to corresponding baseline models, CLFF achieves performance improvements of 1.35%, 2.78%, 3.54% and 4.85% in the IoU metric, respectively. Full article

With the continuously increasing penetration of renewable energy and the growing structural complexity of modern energy systems, the efficient and reliable solution of large-scale, high-dimensional, nonlinear, and strongly constrained optimization problems has become a critical research topic in the field of intelligent optimization. The Secretary Bird Optimization Algorithm (SBOA), a recently proposed swarm intelligence method, achieves a global search by simulating the hunting and defense behaviors of secretary birds and has demonstrated a certain potential in continuous optimization problems. However, when applied to high-dimensional, multimodal, and complex engineering optimization problems, the standard SBOA still suffers from limitations in collaborative search capabilities, late-stage convergence accuracy, and boundary-handling mechanisms. To address these shortcomings, this paper proposes an Adaptive Collaborative Secretary Bird Optimization Algorithm (ACSBOA). From a multi-strategy collaborative perspective, three key mechanisms are incorporated into the original SBOA: (1) an adaptive collaborative search strategy, which integrates positional information from the best, suboptimal, worst, and randomly selected individuals to guide the population toward more directional and efficient exploration; (2) a quadratic interpolation-based local exploitation strategy, designed to enhance fine-grained search capability during the later stages of optimization; and (3) a soft boundary pullback mechanism, which preserves solution feasibility while effectively maintaining population diversity. Through the synergistic interaction of these strategies, ACSBOA achieves a better balance of exploration ability, convergence speed, and algorithmic stability. The optimization performance of ACSBOA is systematically evaluated on the CEC2017 and CEC2022 benchmark suites across different problem dimensions and function categories. The experimental results demonstrate that ACSBOA significantly outperforms several state-of-the-art comparison algorithms in terms of solution accuracy, convergence speed, and robustness. Furthermore, ACSBOA is successfully applied to a 24 h optimal scheduling problem of a grid-connected microgrid. The simulation results indicate that the proposed algorithm can substantially reduce operational costs while satisfying all system operating constraints, thereby validating its effectiveness and practical applicability in real-world engineering optimization problems. Full article

This paper provides insight into the development of Earth Observation (EO) research within geographic and environmental sciences from 1978 to 2024, using a spatially explicit bibliometric approach. The research is based on 28,871 publications indexed in the Web of Science database, which includes four EO-related subject categories: remote sensing, environmental science, geography physical, and geography. Two main phases of the de velopment of EO research are identified. The first period (1978–2011) is marked by fundamental research on early satellite imagery, while the second period (2012–2024) represents a strong growth spurred by open data policies, the Sentinel missions and the development of cloud computing platforms. The results indicate marked geographical asymmetries. Research activities are concentrated in the United States, China, Canada and Western Europe, while many countries of the Global South remain underrepresented and rely more heavily on international collaboration. These spatial disparities reflect the uneven global distribution of scientific and technological capacity. Thematic and network analyses show a shift in focus from sensor- and data-driven research towards the application of machine learning, time-series analysis, land use and land cover change studies and Sentinel-based applications. The results provide a contextual framework for understanding how the development of environmental observation research capacity and technological change are shaping contemporary environmental research and its ability to respond to global environmental change. Full article

In order to elucidate the trait structure of yield formation and optimize the selection strategy for breeding high-yield spring rapeseed, this study systematically evaluated the genetic variation, interrelationship, and contribution to yield of 10 key agronomic traits. A comprehensive assessment of 26 varieties across five test environments was conducted using the coefficient of variation, phenotypic correlation, path analysis, principal component analysis, and grey relational analysis. The results showed that the variations in plant height, branch position, and the number of primary effective branches were the most abundant (CV > 0.20), indicating high genetic improvement potential. Among the yield components, a significant positive correlation was observed between the number of effective pods per plant and the number of seeds per pod. The direct positive effect of pod length on yield per plant was the strongest (path coefficient = 0.467), indicating that yield formation was more dependent on pod structure and grain filling ability. Principal component analysis showed that PC1 had a contribution rate of 94.2%, driven mainly by the effective pod number of the whole plant. This could be used as a comprehensive index to distinguish between different ecological groups and evaluate the overall growth potential. Grey correlation analysis further clarified that the effective length of the main inflorescence was most closely related to yield per plant (correlation degree = 0.847). In summary, this study proposes a high-yield breeding strategy of ‘quality first, collaborative improvement’, whereby pod length, 1000-grain weight, and effective length of the main inflorescence are used as core selection traits. This novel study involves coordinating and optimizing the number of effective branches and inflorescence structure, as well as screening stable genotypes through multi-environment identification, in order to achieve the efficient integration of yield components. Full article