Search Results (3,255)

In recent years, agricultural landscapes have increasingly suffered from severe fire incidents, posing significant threats to crop production, economic stability, and environmental sustainability. Timely and precise detection of fires, especially at their incipient stages, remains crucial to mitigate damage and prevent ecological degradation. However, conventional detection methods frequently fall short in accurately identifying small-scale fire outbreaks due to limitations in sensitivity and response speed. Addressing these challenges, this research proposes an advanced fire detection model based on a modified Detection Transformer (DETR) architecture. The proposed framework incorporates an optimized ConvNeXt backbone combined with a novel Feature Enhancement Block (FEB), specifically designed to refine spatial and contextual feature representation for improved detection performance. Extensive evaluations conducted on a carefully curated agricultural fire dataset demonstrate the effectiveness of the proposed model, achieving precision, recall, mean Average Precision (mAP), and F1-score of 89.67%, 86.74%, 85.13%, and 92.43%, respectively, thereby surpassing existing state-of-the-art detection frameworks. These results validate the proposed architecture's capability for reliable, real-time identification, offering substantial potential for enhancing agricultural resilience and sustainability through improved preventive strategies. Full article

To address the challenge of low computational efficiency in nonlinear Economic Model Predictive Control (EMPC) for large-scale systems such as wastewater treatment plants (WWTPs), this paper proposes a Trajectory Piecewise Linearization (TPWL)-based EMPC framework integrated with global maximum error control (GMEC) and Proper Orthogonal Decomposition (POD). The TPWL method constructs a reduced-order model framework, while GMEC iteratively refines the linearization point selection process. A two-stage strategy is employed: first, coarse selection of candidate linearization points along the original nonlinear model’s state trajectory based on Euclidean distance, followed by refinement to determine optimal points that minimize global approximation errors. Simulation results demonstrate that the proposed method reduces computational time by at least 65% under identical weather conditions while maintaining effluent quality and total cost indices within acceptable thresholds. Compared with conventional TPWL-POD approaches, this framework achieves higher model accuracy and superior EMPC control performance. These advancements underscore the method’s potential for real-time implementation in complex industrial systems, balancing computational efficiency with control precision. Additionally, the framework’s modular design enables integration with existing optimization techniques to further reduce computational complexity without compromising effluent quality compliance. Full article

Under the dual pressures of global climate change and rapid urbanization, urban drainage systems (UDS) face severe challenges caused by extreme precipitation events and altered surface hydrological processes. The drainage paradigm is shifting toward resilient systems integrating grey and green infrastructure, necessitating a comprehensive review of the design and operation of grey infrastructure. This study systematically summarizes advances in urban stormwater process-wide regulation, focusing on drainage network design optimization, siting and control strategies for flow control devices (FCDs), and coordinated management of Quasi-Detention Basins (QDBs). Through graph theory-driven topological design, real-time control (RTC) technologies, and multi-objective optimization algorithms (e.g., genetic algorithms, particle swarm optimization), the research demonstrates that decentralized network layouts, dynamic gate regulation, and stormwater resource utilization significantly enhance system resilience and storage redundancy. Additionally, deep learning applications in flow prediction, flood assessment, and intelligent control exhibit potential to overcome limitations of traditional models. Future research should prioritize improving computational efficiency, optimizing hybrid infrastructure synergies, and integrating deep learning with RTC to establish more resilient and adaptive urban stormwater management frameworks. Full article

Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML) have long served as foundational frameworks in instructional design. However, their applicability to contemporary, technologically mediated learning environments remains under-theorized. This review critically examines CLT and CTML, focusing on their assumptions, empirical contributions, and current limitations in addressing the complexities of dynamic, AI-enhanced educational settings. The discussion is further enriched through engagement with complementary perspectives, including self-regulated learning, dual process theory, and connectivism. These frameworks illuminate conceptual convergences but also expose theoretical tensions, particularly regarding unresolved constructs such as germane cognitive load and the methodological challenges associated with real-time cognitive load measurement. In response to these gaps, this paper proposes the Cognitive Load-Aware Modulation (CLAM) strategy—a conceptual model designed to extend cognitive load principles in adaptive, ethically responsive learning environments. Synthesizing insights from cognitive psychology, educational technology, and affective computing, CLAM supports the design of personalized, data-driven instructional systems attuned to learners’ cognitive and emotional states. The model emerges not merely as a theoretical contribution, but as a future-oriented framework rooted in the critical synthesis of the reviewed literature. Its practical applications for real-world educational settings are outlined, and its empirical validation constitutes the next phase of our ongoing research project. Full article

Drawing on a comparative qualitative analysis of case studies in reforestation, urban greening, and green mobility—from both the Global North and the Global South—this paper aims to identify recurring critical patterns associated with eco-blind initiatives, in order to uncover the most significant contradictions and risks underlying contemporary green innovation strategies. Eco-blindness occurs when interventions, although genuinely aimed at promoting sustainability, generate negative consequences by prioritizing environmental goals while overlooking the socio-cultural and territorial contexts within which they are embedded. Among the most significant patterns identified are the top-down imposition of sustainability frameworks, the exclusion of local actors from decision-making processes, the commodification of environmental goods, and the symbolic displacement of communities. In response to these challenges, the analysis emphasizes the transformative potential of place-based and participatory approaches, particularly when sustainability initiatives are co-designed with local communities and tailored to the specificities of territories. The paper concludes by reflecting on the potential contributions of social research in fostering more holistic, equitable, and territorially grounded models of environmental governance. Full article

Background/Objectives: Intraoral scanning, a fast-evolving technology, is increasingly integrated into actual dental workflows due to its numerous advantages. Despite its growing adoption, challenges related to the accuracy of digital impressions remain. The existing literature identifies most of the factors influencing intraoral scanning accuracy (defined by precision and trueness), but it is fragmented and lacks a unified synthesis. In response to this gap, the present study aims to consolidate and structure the current evidence on the determinant factors and, based on these findings, to develop a clinically applicable procedural guideline for dental practitioners. Methods: A comprehensive literature review identified 43 distinct factors influencing intraoral scanning. Results: These factors encompass variables such as software versions and updates, implant characteristics (e.g., position, angulation, scan body design), materials, environmental conditions (e.g., lighting), and procedural elements including scanning strategy, pattern, aids, and operator experience. Subsequently, these identified factors were systematically classified into five distinct groups based on inherent similarities and relevance within the scanning workflow: IOS—characteristics and maintenance, intraoral morphology, materials, ambient conditions, and scanning strategy. To translate these findings into a practical framework, a four-step protocol was developed, designed for straightforward application by researchers and clinicians. Conclusions: This protocol—comprising: (1) Maintenance, (2) Evaluation, (3) Establishment and Execution of Scanning Strategy, and (4) Verification—aims to guide users effectively through the intraoral scanning process, mitigate common clinical challenges, and ensure broad applicability across diverse scanner systems, irrespective of the manufacturer or model. Full article

In anomaly detection tasks, labeled defect data are often scarce. Unsupervised learning leverages only normal samples during training, making it particularly suitable for anomaly detection tasks. Among unsupervised methods, normalizing flow models have shown distinct advantages. They allow precise modeling of data distributions and enable direct computation of sample log-likelihoods. Recent work has largely focused on feature fusion strategies. However, most of the flow-based methods emphasize spatial information while neglecting the critical role of channel-wise features. To address this limitation, we propose GCAFlow, a novel flow-based model enhanced with a global context-aware channel attention mechanism. In addition, we design a hierarchical convolutional subnetwork to improve the probabilistic modeling capacity of the flow-based framework. This subnetwork supports more accurate estimation of data likelihoods and enhances anomaly detection performance. We evaluate GCAFlow on three benchmark anomaly detection datasets, and the results demonstrate that it consistently outperforms existing flow-based models in both accuracy and robustness. In particular, on the VisA dataset, GCAFlow achieves an image-level AUROC of 98.2% and a pixel-level AUROC of 99.0%. Full article

This paper argues that investing in artificial intelligence (AI) in developing economies involves significant trade-offs requiring ethical, financial, and geopolitical scrutiny. While AI is increasingly seen as a vehicle for technological leapfrogging, such ambitions often mask structural constraints, including weak infrastructure, limited institutional capacity, and external dependency. Using the economic theory of opportunity cost—extended through the political economy and digital governance perspectives—this study critically examines AI policy strategies in Ghana, Kenya, and Rwanda. A qualitative design grounded in secondary data and a thematic analysis reveal how AI investment may reallocate scarce resources away from essential services, exacerbate inequality, and entrench strategic technological dependency. This paper proposes a public policy framework built on four principles—sequential readiness, strategic alignment, ethical governance, and capacity building—to guide equitable AI deployment. It argues for establishing a digital social compact between states, citizens, and technology actors to safeguard public interest in AI-driven development. Finally, this paper outlines a future research agenda emphasizing the mixed-method evaluation of AI’s long-term social impacts, including employment, inclusion, and public service delivery. Full article

As the energy sector transitions toward decarbonisation, low-to-intermediate temperature geothermal resources in sedimentary basins—particularly repurposed oil and gas fields—have emerged as promising candidates for sustainable heat and power generation. Despite their widespread availability, the development of these systems is hindered by gaps in methodology, oversimplified modelling assumptions, and a lack of integrated analyses accounting for long-term reservoir and wellbore dynamics. This study presents a detailed, simulation-based framework to evaluate geothermal energy extraction from depleted petroleum reservoirs, with a focus on low-enthalpy resources (<150 °C). By examining coupling reservoir behaviour, wellbore heat loss, reinjection cooling, and surface energy conversion, the framework provides dynamic insights into system sustainability and net energy output. Through a series of parametric analyses—including production rate, doublet spacing, reservoir temperature, and field configuration—key performance indicators such as gross power, pumping requirements, and thermal breakthrough are quantified. The findings reveal that: (1) net energy output is maximised at optimal flow rate (~70 kg/s for a 90 °C reservoir), beyond which increased pumping offsets thermal gains; (2) doublet spacing has a non-linear impact on reinjection cooling, with larger distances reducing thermal interference and pumping energy; (3) reservoirs with higher temperatures (<120°C) offer significantly better thermodynamic and hydraulic performance, enabling pump-free or low-duty operations at higher flow rates; and (4) wellbore thermal losses and reinjection effects are critical in determining long-term viability, especially in low-permeability or shallow fields. This work demonstrates the importance of a coupled, site-specific modelling in assessing the geothermal viability of petroleum fields and provides a foundation for future techno-economic and sustainability assessments. The results inform optimal design strategies and highlight scenarios where the geothermal development of oil and gas fields can be both technically and energetically viable. Full article

The dissemination of rumors can lead to significant economic damage and pose a grave threat to social harmony and the stability of people’s livelihoods. Consequently, curbing the dissemination of rumors is of paramount importance. The model in the text assumes that the population is homogeneous in terms of transmission behavior. This homogeneity is essentially a manifestation of translational symmetry. This paper undertakes a thorough examination of the impact of time delay on the dissemination of rumors within social networking services. We have developed a model for rumor dissemination, establishing the positivity and boundedness of its solutions, and identified the existence of an equilibrium point. The study further involved determining the critical threshold of the proposed model, accompanied by a comprehensive examination of its Hopf bifurcation characteristics. In the expression of the threshold $R_0$, the parameters appear in a symmetric form, reflecting the balance between dissemination and suppression mechanisms. Furthermore, detailed investigations were carried out to assess both the localized and global stability properties of the system’s equilibrium states. In stability analysis, the symmetry in the distribution of characteristic equation roots determines the system’s dynamic behavior. Through numerical simulations, we analyzed the potential impacts and theoretically examined the factors influencing rumor dissemination, thereby validating our theoretical analysis. An optimal control strategy was formulated, and three control variables were incorporated to describe the strategy. The optimization framework incorporates a specifically designed cost function that simultaneously accounts for infection reduction and resource allocation efficiency in control strategy implementation. The optimal control strategy proposed in the study involves a comparison between symmetric and asymmetric interventions. Symmetric control measures may prove inefficient, whereas asymmetric control demonstrates higher efficacy—highlighting a trade-off in symmetry considerations for optimization problems. Full article

This study proposes a novel virus propagation model designed explicitly for SCADA(supervisory Control and Data Acquisition) industrial networks. It addresses a critical limitation in existing models applied to the Internet and Industrial Internet of Things (IIoT)—their failure to account for inter-node information exchange processes. The model is inspired by the phenomenon that “immune” nodes in real-world and biological systems inhibit the spread of viruses by exchanging information. This model incorporates isolation strategies to curb virus transmission, considering the uncertainty of vulnerable device behavior. Central to this research are the assumptions of a nonlinear infection rate and dual delays, which better mirror the real-world conditions of industrial control networks. This approach diverges significantly from prior studies that relied on bilinear infection rate assumptions. This study constructed an SMIQR model through theoretical derivation and experimental validation. The model enables nodes to autonomously enhance their defenses after receiving risk information while accounting for the impact of inter-node information exchange. Experiments based on real-world data demonstrated the model’s effectiveness in simulating virus propagation and evaluating defense strategies, overcoming the limitations of traditional bilinear infection rate assumptions. Comparative experiments show that the SMIQR model significantly reduces the number of infected nodes in SCADA industrial networks, demonstrating its superior effectiveness in curbing virus spread. Furthermore, the research proposed dynamic isolation tactics that balance industrial operational continuity, providing SCADA industrial networks with a theoretical framework (incorporating nonlinear infection rates and dual delay characteristics) and practical defense solutions to curb malware spread without disrupting production. Full article

Industrial heritage has historical and cultural value and reuse potential. Urban industrialization has a significant social influence on place identity and emotional identity. Shougang Science Fiction Industrial Park (hereinafter referred to as “Shougang Park”) serves as one of the first pilot projects for the transformation of old industrial areas in China. This study examines Shougang Park through a spatial narrative lens, analyzing its industrial heritage via the “author-text-reader” framework. Research reveals the specific implications of the three dimensions and the connections behind them. The findings offer practical strategies for experiential tourism design and adaptive reuse planning, while establishing theoretical models applicable to global post-industrial heritage revitalization. Full article

Plant-parasiticnematodes represent a significant biosecurity threat in cross-border plant quarantine, necessitating precise identification for effective border control. While DL models have demonstrated success in nematode image classification based on morphological features, the limited availability of high-quality samples and the species-specific nature of nematodes result in insufficient training data, which constrains model performance. Although generative models have shown promise in data augmentation, they often struggle to balance morphological fidelity and phenotypic diversity. This paper proposes a novel few-shot data augmentation framework based on a morphology-constrained latent diffusion model, which, for the first time, integrates morphological constraints into the latent diffusion process. By geometrically parameterizing nematode morphology, the proposed approach enhances topological fidelity in the generated images and addresses key limitations of traditional generative models in controlling biological shapes. This framework is designed to augment nematode image datasets and improve classification performance under limited data conditions. The framework consists of three key components: First, we incorporate a fine-tuning strategy that preserves the generalization capability of model in few-shot settings. Second, we extract morphological constraints from nematode images using edge detection and a moving least squares method, capturing key structural details. Finally, we embed these constraints into the latent space of the diffusion model, ensuring generated images maintain both fidelity and diversity. Experimental results demonstrate that our approach significantly enhances classification accuracy. For imbalanced datasets, the Top-1 accuracy of multiple classification models improved by 7.34–14.66% compared to models trained without augmentation, and by 2.0–5.67% compared to models using traditional data augmentation. Additionally, when replacing up to 25% of real images with generated ones in a balanced dataset, model performance remained nearly unchanged, indicating the robustness and effectiveness of the method. Ablation experiments demonstrate that the morphology-guided strategy achieves superior image quality compared to both unconstrained and edge-based constraint methods, with a Fréchet Inception Distance of 12.95 and an Inception Score of 1.21 ± 0.057. These results indicate that the proposed method effectively balances morphological fidelity and phenotypic diversity in image generation. Full article

This study developed an integrated framework to enhance agility, resilience, sustainability, and inclusiveness in Emirati public transport organizations. Using a mixed-methods approach, the research combined semi-structured interviews with 19 experts and a structured questionnaire administered to 38 specialists. The DEMATEL method was applied to analyze and visualize the interdependencies among key factors influencing transport system performance. Results indicate that operational efficiency, demand–supply forecasting, and ridership estimation are central to agility; green migration strategies, governance, and service design drive resilience; service diversity, technology, and infrastructure adequacy underpin sustainability; and service level types and seamless transfers are critical to inclusiveness. These dimensions were synthesized into a cohesive model that captures both strategic alignment and system adaptability. The study contributes a validated, multi-dimensional decision-making tool for policymakers and transport authorities, offering practical guidance for aligning transport strategies with national goals and the UN Sustainable Development Goals. While tailored to the UAE context, the framework is adaptable to other urban environments undergoing rapid transformation. While tailored to the UAE context, the framework is adaptable to other urban environments undergoing rapid transformation. The study’s empirical rigor is established through a validated questionnaire and expert-based DEMATEL analysis, ensuring theoretical robustness and real-world applicability. Full article

By combining density functional theory with the non-equilibrium Green’s function method, we conducted a first-principles investigation of spin-dependent transport properties in a molecular device featuring a dynamic covalent chemical bridge connected to zigzag graphene nanoribbon electrodes. The effects of spin-filtering and spin-rectifying on the I–V characteristics are revealed and explained for the proposed molecular device. Interestingly, our results demonstrate that all three devices exhibit significant single-spin-filtering behavior in parallel (P) magnetization and dual-spin-filtering effects in antiparallel (AP) configurations, achieving nearly 100% spin-filtering efficiency. At the same time, from the I–V curves, we find that there is a weak negative differential resistance effect. Moreover, a high rectifying ratio is found for spin-up electron transport in AP magnetization, which is explained by the transmission spectrum and local density of state. The fundamental mechanisms governing these phenomena have been elucidated through a systematic analysis of spin-resolved transmission spectra and spin-polarized electron transport pathways. These results extend the design principles of spin-controlled molecular electronics beyond graphene-based systems, offering a universal strategy for manipulating spin-polarized currents through dynamic covalent interfaces. The nearly ideal spin-filtering efficiency and tunable rectification suggest potential applications in energy-efficient spintronic logic gates and non-volatile memory devices, while the methodology provides a framework for optimizing spin-dependent transport in hybrid organic–inorganic nanoarchitectures. Our findings suggest that such systems are promising candidates for future spintronic applications. Full article