Search Results (6,316)

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

Arsenic contamination in groundwater is a significant public health concern, with As(III) posing a greater and more challenging risk than As(V) due to its higher toxicity, mobility, and weaker adsorption affinity. Fe-Mn-based adsorbents offer a promising solution, simultaneously oxidizing As(III) to As(V), enhancing its adsorption. This study evaluates an Fe-Mn nanocomposite across typical batch (20 mg of adsorbent), fixed-bed column (28 g), and pilot-scale (2.5 kg) studies, bridging the gap between laboratory and real-world applications. Batch experiments yielded maximum adsorption capacities of 6.25 mg/g and 4.71 mg/g in a synthetic matrix and real groundwater, respectively, demonstrating the impact of the water matrix on adsorption. Operational constraints and competing anions led to a lower capacity in the pilot (0.551 mg/g). Good agreement was observed between the breakthrough curves in the pilot (breakthrough at 475 bed volumes) and the fixed-bed column studies (365–587 bed volumes) under similar empty bed contact times (EBCTs). The Thomas, Adams–Bohart, and Yoon–Nelson models demonstrated that lower flow rates and extended EBCTs significantly enhance arsenic removal efficiency, prolonging the operational lifespan. Our findings demonstrate the necessity of continuous-flow experiments using real contaminated water sources and the importance of optimizing flow conditions, EBCTs, and pre-treatment in order to successfully scale up Fe-Mn-based adsorbents for sustainable arsenic removal. Full article

Optimizing cable forces in cable-stayed bridges is challenging due to structural nonlinearity and the limitations of traditional methods, which often focus on isolated performance indicators. This study proposes an integrated framework combining Gaussian process regression (GPR) with an enhanced whale optimization algorithm improved by the Salp Swarm Algorithm (EWOSSA). GPR is first used to model the nonlinear relationship between cable forces and structural responses. The EWOSSA then efficiently optimizes the GPR-based model to identify optimal cable forces. A case study on a cable-stayed bridge with a 2 × 145 m main spans demonstrates the effectiveness of the proposed approach. Compared with conventional methods such as the internal-force equilibrium and zero-displacement methods, the EWOSSA-GPR framework achieves superior performance across multiple structural metrics. It ensures a more uniform cable force distribution, reduces girder displacements, and improves bending moment profiles, offering a comprehensive solution for optimal structural performance in cable-stayed bridges. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model’s predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. Full article

As global trade expands, the pressure on container terminals to improve efficiency and capacity grows. Several inspections are performed during the loading and unloading process to minimize delays. In this paper, we explore corrosion as it poses a persistent threat that compromises the durability of containers and leads to costly repairs. However, identifying this threat is no simple task. Corrosion can take many forms, progress unpredictably, and be influenced by various environmental conditions and container types. In collaboration with the Port of Sines, Portugal, this work explores a potential solution for a real-time computer-vision system, with the aim to improve container inspections using deep-learning algorithms. We propose a system based on the semantic segmentation model, DeepLabv3+, for precise corrosion detection using images provided from the terminal. After preparing the data and annotations, we explored two approaches. First, we leveraged a pre-trained model originally designed for bridge corrosion detection. Second, we fine-tuned a version specifically for cargo container assessment. With a corrosion detection performance of 49%, this work showcases the potential of deep learning to automate inspection processes. It also highlights the importance of generalization and training in real-world scenarios and explores innovative solutions for smart gates and terminals. Full article

Many breast cancer patients follow a self-managed treatment pathway, which may lead to gaps in the data available to healthcare professionals, such as information about patients’ everyday symptoms at home. Mobile apps have the potential to bridge this information gap, leading to more effective treatments and interventions, as well as helping breast cancer patients monitor and manage their symptoms. In this paper, we elicit design requirements for breast cancer symptom-management mobile apps using a systematic review following the PRISMA framework. We then evaluate existing cancer symptom-management apps found on the Apple store according to the extent to which they meet these requirements. We find that, whilst some requirements are well supported (such as functionality to record multiple symptoms and provision of information), others are currently not being met, particularly interoperability, functionality related to responses from healthcare professionals, and personalisation. Much work is needed for cancer patients and healthcare professionals to experience the benefits of digital health innovation. The article demonstrates a formal requirements model, in which requirements are categorised as functional and non-functional, and presents a proposal for conceptual design for future mobile apps. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

With growing environmental consciousness and projections that green markets will represent 10% of global market value by 2030, a significant gap persists between consumers’ stated environmental concerns and their actual purchasing behaviour for green products. This study investigates how green brand positioning influences consumer purchase intention for green technology products, examining the mediating roles of self-image congruence and functional congruence, and the moderating effects of product involvement level and product optionality. A quantitative survey was conducted with 354 US participants who possess at least a bachelor’s degree and have experience with technology products, using validated scales through structural equation modelling and mediation analysis. The findings demonstrate a significant positive relationship between green brand positioning and purchase intention. Self-image congruence partially mediated this relationship, while functional congruence also served as a significant mediator. The product involvement level positively moderated the mediation effect of self-image congruence, whereas product optionality negatively moderated the mediation effect of functional congruence. Green brand positioning effectively enhances purchase intention when consumers perceive alignment with their environmental self-image and when products maintain a functional equivalence to non-green alternatives. Companies should focus on building environmental self-congruence while ensuring product quality to maximise green marketing effectiveness and bridge the intention–behaviour gap. Full article

Adaptive reuse of Mediterranean earthen houses offers a unique opportunity to fuse heritage preservation with sustainable development. This study introduces a comprehensive, sustainability-driven framework that reimagines these vernacular structures as culturally rooted and socially inclusive assets for contemporary living. Moving beyond conventional restoration, the proposed framework integrates environmental, socio-cultural, and economic sustainability across six core dimensions: ecological performance and material conservation, respectful functional transformation, structural resilience, cultural continuity and community engagement, adaptive flexibility, and long-term economic viability. Four geographically and culturally diverse case studies—Alhambra in Spain, Ghadames in Libya, the UCCTEA Chamber of Architects Main Building in North Cyprus, and Sheikh Hilal Beehive Houses in Syria—serve as testbeds to examine how earthen heritage can be reactivated in sustainable and context-sensitive ways. Through qualitative analysis, including architectural surveys, visual documentation, and secondary data, the study identifies both embedded sustainable qualities and persistent barriers, such as structural fragility, regulatory constraints, and socio-economic disconnects. By synthesizing theoretical knowledge with real-world applications, the proposed framework offers a replicable model for policymakers, architects, and conservationists aiming to bridge tradition and innovation. This research highlights adaptive reuse as a practical and impactful strategy for extending the life of heritage buildings, enhancing environmental performance, and supporting community-centered cultural regeneration across the Mediterranean region. Full article

Data security is of the utmost importance in the domain of real-time environmental monitoring systems, particularly when employing advanced context-aware intelligent visual analytics. This paper addresses a significant deficiency in the Global System for Mobile Communications (GSM), a widely employed wireless communication system for environmental monitoring. The A5/1 encryption technique, which is extensively employed, ensures the security of user data by utilizing a 64-bit session key that is divided into three linear feedback shift registers (LFSRs). Despite the shown efficacy, the development of a probabilistic model for assessing the vulnerability of breaking or intercepting the session key (Kc) has not yet been achieved. In order to bridge this existing knowledge gap, this study proposes a probabilistic model that aims to evaluate the security of encrypted data within the framework of the Global System for Mobile Communications (GSM). The proposed model implements alterations to the current GSM encryption process by the augmentation of the quantity of Linear Feedback Shift Registers (LFSRs), consequently resulting in an improved level of security. The methodology entails increasing the number of registers while preserving the session key’s length, ensuring that the key length specified by GSM standards remains unaltered. This is especially important for environmental monitoring systems that depend on real-time data analysis and decision-making. In order to elucidate the notion, this analysis considers three distinct scenarios: encryption utilizing a set of five, seven, and nine registers. The majority function is employed to determine the registers that will undergo perturbation, hence increasing the complexity of the bit arrangement and enhancing the security against prospective attackers. This paper provides actual evidence using simulations to illustrate that an increase in the number of registers leads to a decrease in the vulnerability of data interception, hence boosting data security in GSM communication. Simulation results demonstrate that our method substantially reduces the risk of data interception, thereby improving the integrity of context-aware intelligent visual analytics in real-time environmental monitoring systems. Full article

National parks serve as critical spatial units for conserving ecological baselines, maintaining genetic diversity, and delivering essential ecosystem services. However, accelerating socio-economic development has increasingly intensified the conflict between ecological protection and transportation infrastructure. Ecologically sustainable transportation planning is, therefore, essential to mitigate habitat fragmentation, facilitate species migration, and conserve biodiversity. This study examines the Giant Panda National Park and its buffer zone, focusing on six mammal species: giant panda, Sichuan snub-nosed monkey, leopard cat, forest musk deer, rock squirrel, and Sichuan takin. By integrating Maxent ecological niche modeling with circuit theory, it identified ecological source areas and potential corridors, and employed a two-step screening approach to design species-specific wildlife crossings. In total, 39 vegetated overpasses were proposed to serve all target species; 34 underpasses were integrated using existing bridge and culvert structures to minimize construction costs; and 27 canopy bridges, incorporating suspension cables and elevated pathways, were designed to connect forest canopies for arboreal species. This study established a multi-species and multi-scale conservation framework, providing both theoretical insights and practical strategies for ecologically integrated transportation planning in national parks, contributing to the synergy between biodiversity conservation and sustainable development goals. Full article

The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This paper proposes a novel framework for embedding security awareness into IoT systems—where security awareness refers to the system’s ability to detect uncertain changes and understand their impact on its security posture. While machine learning and deep learning (ML/DL) models integrated with explainable AI (XAI) methods offer capabilities for threat detection, they often lack contextual interpretation linked to system security. To bridge this gap, our framework maps XAI-generated explanations to a system’s structured security profile, enabling the identification of components affected by detected anomalies or threats. Additionally, we introduce a procedural method to compute an Importance Factor (IF) for each component, reflecting its operational criticality. This framework generates actionable insights by highlighting contextual changes, impacted components, and their respective IFs. We validate the framework using a smart irrigation IoT testbed, demonstrating its capability to enhance security awareness by tracking evolving conditions and providing real-time insights into potential Distributed Denial of Service (DDoS) attacks. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Structural damage in bridges is typically a local phenomenon. When a vehicle passes over the damage location, it induces a local response, which is highly sensitive to the damage. The interaction between the bridge and moving vehicle is a non-stationary time-varying process. The local damage can be accurately identified by analyzing the time-varying characteristics of the bridge response subjected to a moving vehicle. Synchrosqueezing transform, a reassignment method used to sharpen time–frequency representations, offers an effective tool to decompose the non-stationary signal into distinct components. This paper proposes a novel method based on multi-synchrosqueenzing transform to extract the time-varying characteristics of the vehicle–bridge interaction systems for bridge structural health monitoring. A vehicle–bridge interaction model is built to simulate the bridge under moving vehicles. Different damage scenarios of concrete bridges have been simulated. The effect of bridge damage parameters, the vehicle speed, the road surface roughness on the time-varying characteristics of the vehicle–bridge interaction system is studied. Numerical and experimental results demonstrate that the proposed method efficiently and accurately extracts the time-varying features of the vehicle–bridge interaction system, which could serve as potential indicators of structural damage in bridges. Full article