Search Results (2,989)

The Artificial Intelligence of Things (AIoT) is rapidly evolving from basic connectivity to intelligent perception, reasoning, and decision making across domains such as healthcare, manufacturing, transportation, and smart cities. Multimodal generative AI (GAI) and digital twins (DTs) provide complementary solutions. DTs deliver high-fidelity virtual replicas for real-time monitoring, simulation, and optimization with GAI enhancing cognition, cross-modal understanding, and the generation of synthetic data. This survey presents a comprehensive overview of DT–GAI integration in the AIoT. We review the foundations of DTs and multimodal GAI and highlight their complementary roles. We further introduce the Sense–Map–Generate–Act (SMGA) framework, illustrating their interaction through the SMGA loop. We discuss key enabling technologies, including multimodal data fusion, dynamic DT evolution, and cloud–edge–end collaboration. Representative application scenarios, including smart manufacturing, smart cities, autonomous driving, and healthcare, are examined to demonstrate their practical impact. Finally, we outline open challenges, including efficiency, reliability, privacy, and standardization, and we provide directions for future research toward sustainable, trustworthy, and intelligent AIoT systems. Full article

Private fifth generation (5G) networks have emerged as a cornerstone for ultra-reliable, low-latency connectivity across mission-critical domains such as industrial automation, healthcare, and smart cities. Compared to conventional technologies like 4G or Wi-Fi, they provide clear advantages, including enhanced service continuity, higher reliability, and customizable security controls. However, these benefits come with new security challenges, particularly regarding the confidentiality, integrity, and availability of data and services. This article presents a review of security vulnerabilities in private 5G networks. The review pursues four objectives: (i) to identify and categorize key vulnerabilities, (ii) to analyze threats that undermine core security principles, (iii) to evaluate mitigation strategies proposed in the literature, and (iv) to outline gaps that demand further investigation. The findings offer a structured perspective on the evolving threat landscape of private 5G networks, highlighting both well-documented risks and emerging concerns. By mapping vulnerabilities to mitigation approaches and identifying areas where current solutions fall short, this study provides critical insights for researchers, practitioners, and policymakers. Ultimately, the review underscores the urgent need for robust and adaptive security frameworks to ensure the resilience of private 5G deployments in increasingly complex and high-stakes environments. Full article

Preservation design is characterized by high interactions among different skills, including both architectural and engineering field. When the architectural heritage is composed of the ruins of a medieval fortress, the contribution of archeological studies is fundamental to recognize the different construction phases of the building. This work presents the most recent stratigraphic analyses conducted on the fortress of Vogogna, a military masonry castle in Ossola Valley, Piedmont, whose origin is lost in time and provides further support to define the correct interpretation of the architectural artifact. Previous studies showed several shortcomings concerning the historical evolution of the structure and a precise geometrical survey. The authors developed a geometrical model of the archeological site, through advanced survey techniques, and analyzed the historical maps of the cadasters to investigate additions and transformations of the abandoned fortress and its connection with the rural and natural surrounding fields. The updated information provided new indications for the past uses of the building, and the digital model allowed further considerations on the geometrical characteristics of the structures, addressing some choices for the final reuse proposal for the site, today at the center of a wider project to enhance the cultural heritage in the Vogogna area. Full article

The influence of traditional Chinese ritual culture on courtyard spatial sequences is widely acknowledged. However, quantitative analytical methods, such as space syntax, have rarely been applied in studies of ritual–residential space relations. This study uses space syntax, specifically Visibility Graph Analysis (VGA) and axial maps, to conduct a quantitative study of the spatial relationship between ritual and residential areas in Prince Kung’s Mansion. The VGA results indicate a distinct gradient of visual integration, which decreases progressively from the outward-oriented ritual areas, such as the palace gate and halls, through the transitional domestic ritual areas to the inward-oriented residential areas, such as Xijin Zhai and Ledao Tang. This pattern demonstrates a positive correlation between spatial visibility and ritual hierarchy. The axial map results confirm that the central axis and core ritual spaces exhibit the highest spatial connectivity, reflecting their supreme ritual status. More importantly, spatial connectivity is intensified during ritual activities compared to in daily life, indicating that enhanced spatial connectivity is required during rituals. Ritual spaces are characterized by extroversion, high visibility, and connectivity, while residential spaces prioritize introversion and minimal exposure. The deliberately designed ritual–residential architectural spatial sequence of Prince Kung’s Mansion articulates Confucian ideological principles, such as centrality as orthodoxy, gender segregation, and hierarchy. This study visually and quantitatively illustrates the harmony between ritual and residential spaces in Prince Kung’s Mansion. It enhances our understanding of the mechanisms of expression of courtyard ritual cultural spaces, providing evidence-based guidance for functional adaptive transformations in heritage conservation practices. It also offers a fresh perspective on the analysis of courtyard ritual spaces. Full article

The Yellow River Delta (YRD) is a vital agricultural and ecologically fragile zone in China. Understanding the spatial pattern and evolutionary characteristics of Rural Settlements Area (RSA) in this region is crucial for both ecological protection and sustainable development. This study focuses on Dongying, a key YRD city, and compares four advanced deep learning models—U-Net, DeepLabv3+, TransUNet, and TransDeepLab—using fused Sentinel-1 radar and Landsat optical imagery to identify the optimal method for RSA mapping. Results show that TransUNet, integrating polarization and optical features, achieves the highest accuracy, with Precision, Recall, F1 score, and mIoU of 89.27%, 80.70%, 84.77%, and 85.39%, respectively. Accordingly, TransUNet was applied for the spatiotemporal extraction of RSA in 2002, 2008, 2015, 2019, and 2023. The results indicate that medium-sized settlements dominate, showing a “dense in the west/south, sparse in the east/north” pattern with clustered distribution. Settlement patches are generally regular but grow more complex over time while maintaining strong connectivity. In summary, the proposed method offers technical support for RSA identification in the YRD, and the extracted multi-temporal settlement data can serve as a valuable reference for optimizing settlement layout in the region. Full article

Residual Networks (ResNet) address the vanishing gradient problem through skip connections and have become a fundamental architecture for computer vision tasks. However, standard convolutional layers exhibit limited capacity in modeling complex nonlinear relationships. We present EKAResNet, a residual backbone enhanced with a spline-based Kolmogorov–Arnold Network (KAN) head. Specifically, we introduce a KAN-based Feature Classification Module (KAN-FCM) that replaces a portion of the traditional fully connected classifier. This module employs piecewise polynomial (spline) approximation to achieve adaptive nonlinear mapping while maintaining a controlled parameter budget. We evaluate EKAResNet on CIFAR-10 and CIFAR-100, achieving top accuracies of 95.84% and 80.06%, respectively. Importantly, the model maintains a parameter count comparable to strong ResNet and WideResNet baselines. Ablation studies on spline configurations further confirm the contribution of the KAN head. These results demonstrate the effectiveness of integrating KAN structures into ResNet for modeling high-dimensional, complex features. Our work highlights a promising direction for designing deep learning architectures that balance accuracy and computational efficiency. Full article

Escalating human–land conflicts have exacerbated ecosystem degradation, threatening regional sustainable development. As the largest sandy land in China, the Horqin Sandy Land (HSL) in eastern Inner Mongolia exhibits high ecological fragility. Delineating ecological restoration zones (ERZ) is critical to transition from localized restoration to system-wide stability, thereby enhancing regional ecological security, which reflects ecosystem health and integrity. Ecological security patterns (ESP), as spatial configurations that support and maintain ecological security, serve as the foundational framework for ERZ planning. Unlike conventional applications of InVEST and MSPA, this study integrates an ecosystem service assessment with morphological spatial pattern analysis (MSPA) under a “Source–Resistance–Corridor–Note” paradigm to develop a novel “ecological network–zoning” approach. This framework transforms ecological connectivity analysis into actionable restoration zoning, bridging theoretical ESP construction with practical management needs. Key findings include the following: (1) In total, 76 vital ecological source regions were mapped, representing about 10,204.38 km² of ecologically significant land, with primary distribution in the northwestern mountainous regions; (2) A total of 169 ecological corridors were extracted, spanning 4071.94 km in length. Ecological pinch points with 239.91 km² and barrier points with 568.85 km² were systematically identified; (3) A “Five Zones, Three Belts, One Core” spatial strategy was proposed, aligning with regional ecological conditions and development goals. This study provides a transferable methodology for ecosystem restoration in global arid and semi-arid regions, bridging theoretical frameworks with actionable zoning practices. Full article

Rapid population aging calls for a shift from static facility configuration toward understanding how spatial structures coordinate with everyday behavior. This study develops a structure–behavior coordination framework to examine how the spatial embedding of community service centers and surrounding facilities aligns with older adults’ mobility and activity chains. Using Guangzhou as a case, three representative facility aggregation forms—clustered, linear, and patchy—were identified through POI-based spatial analysis. Behavioral mapping supported by Public Participation GIS (PPGIS) and social network analysis captured facility co-use and path continuity, while rank-based measures (Rank-QAP and Rank-Biased Overlap) evaluated correspondence between structural and behavioral centralities. Findings show form-sensitive rather than typological coordination: the clustered case (FY) exhibits compact, mixed-use integration; the linear case (DJ) requires ground-level access along main pedestrian corridors; and the patchy case (LG) relies on a few highly accessible dual-core nodes where improved connectivity strengthens cohesion. Everyday facilities such as markets, parks, and plazas act as behavioral anchors linking routine routes. The framework offers a transferable tool and comparable metrics for diagnosing alignment between built structure and everyday behavior, guiding adaptive, evidence-based planning for age-friendly community systems. Full article

Sustainable urban mobility is gaining importance as cities seek to address congestion and environmental concerns, with cycling infrastructure being an essential component of urban transportation systems. This study proposes a novel integrated, data-driven modeling framework that uniquely combines sustainability and ergonomic design to evaluate and optimize urban cycling networks. A computational model incorporating graph-based analysis, isochrone mapping, and network discontinuity identification was used to assess cycling safety and accessibility within MEL. The findings highlight significant accessibility shortcomings caused by network discontinuities, unsafe segments, and missing links—issues frequently overlooked in conventional cycling network planning. Key employment centers in MEL were found to have limited cycling access, highlighting the need for cross-regional connectivity. The study suggests that targeted micro-interventions and improved connectivity can improve the sustainability and ergonomics of urban cycling networks. The methodological framework developed is scalable and adaptable, making it applicable to other metropolitan regions. This study offers actionable insights for urban planners, advocating for data-driven decision-making and micro-scale network improvements to create a more connected, efficient, and inclusive cycling network. Full article

Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework and N-gram-based textual content analysis to examine job postings related to big data analytics in real-world contexts. A structured analytical process was conducted to derive meaningful insights into workforce trends and skill demands in the big data analytics domain. First, expertise roles and tasks were identified by analyzing job titles and responsibilities. Next, key competencies were categorized into analytical, technical, developer, and soft skills and mapped to corresponding roles. Workforce characteristics such as job types, education levels, and experience requirements were examined to understand hiring patterns. In addition, essential tasks, tools, and frameworks in big data analytics were identified, providing insights into critical technical proficiencies. The findings show that big data analytics requires expertise in data engineering, machine learning, cloud computing, and AI-driven automation. They also emphasize the importance of continuous learning and skill development to sustain a future-ready workforce. By connecting academia and industry, this study provides valuable implications for educators, policymakers, and corporate leaders seeking to strengthen workforce sustainability in the era of big data analytics. Full article

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

Agricultural robots operating in greenhouse environments face substantial challenges in detecting tomato stems, including fluctuating lighting, cluttered backgrounds, and the stems’ inherently slender morphology. This study introduces EfficientV1-C2fDWR-IRMB-YOLO (EDI-YOLO), an enhanced model built on YOLOv8n-seg. First, the original backbone is replaced with EfficientNetV1, yielding a 2.3% increase in mAP50 and a 2.6 G reduction in FLOPs. Second, we design a C2f-DWR module that integrates multi-branch dilations with residual connections, enlarging the receptive field and strengthening long-range dependencies; this improves slender-object segmentation by 1.4%. Third, an Inverted Residual Mobile Block (iRMB) is inserted into the neck to apply spatial attention and dual residual paths, boosting key-feature extraction by 1.5% with only +0.7GFLOPs. On a custom tomato-stem dataset, EDI-YOLO achieves 79.3% mAP50 and 33.9% mAP50-95, outperforming the baseline YOLOv8n-seg (75.1%, 31.4%) by 4.2% and 2.6%, and YOLOv5s-seg (66.7%), YOLOv7tiny-seg (75.4%), and YOLOv12s-seg (75.4%) by 12.6%, 3.9%, and 3.9% in mAP50, respectively. Significant improvement is achieved in lateral branch segmentation (60.4% → 65.2%). Running at 86.2 FPS with only 10.4GFLOPs and 8.0 M parameters, EDI-YOLO demonstrates an optimal trade-off between accuracy and efficiency. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

Economic implications of livestock diseases extend far beyond direct treatment costs and affect productivity, trade, and public health. Despite the growing recognition of animal health economics, a comprehensive analysis of its research landscape has been lacking. Therefore, this study employs bibliometric techniques to systematically analyze research on the economics of animal health between 2000 and 2024 using data extracted from the Web of Science Core Collection. A total of 1070 peer-reviewed publications were analyzed to map publication trends, influential authors, research themes, and international collaborations. The results showed that after 2014, the research output increased steadily to a peak in 2018, thus illustrating the increased global interest in economic evaluations of livestock diseases. The USA, UK, and the Netherlands emerged as key contributors, whereas low-income regions showed low research output, indicating an equity gap for animal health economics studies. The most frequently used keywords were “economics”, “cost–benefit analysis”, “economic impact”, “foot-and-mouth disease”, and “vaccination”, with increasing focus on zoonotic diseases. Coauthorship network analysis demonstrated that the institutions are well connected in Europe and North America, but research from developing countries has remained mostly fragmented. However, notable research gaps were discovered: advanced modelling approaches were underutilized, and the translation of economic research into policy was limited. This work highlights the increasing interdisciplinary nature of animal health economics, while emphasizing the need for broader species coverage, stronger international collaboration, and deeper methodological innovation. These insights provide a foundation for guiding future research priorities and shaping evidence-based policies in animal health economics. Full article

Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection model based on the YOLOv11n architecture. First, a Multi-scale Edge-Refinement Context Aggregator (MERCA) module is proposed to replace the original C3k2 block in the backbone. MERCA captures multi-scale contextual features through hierarchical receptive field collaboration and refines edge details, thereby significantly improving the perception of fine structures in tea buds. Furthermore, a Dynamic Hyperbolic Token Statistics Transformer (DHTST) module is developed to replace the original PSA block. This module dynamically adjusts feature responses and statistical measures through attention weighting using learnable threshold parameters, effectively enhancing discriminative features while suppressing background interference. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the original network structure, enabling the adaptive fusion of semantically rich and spatially precise features via bidirectional cross-scale connections while reducing computational complexity. In the self-built tea bud dataset, experimental results demonstrate that compared to the original model, the YO-LO-LMTB model achieves a 2.9% improvement in precision (P), along with increases of 1.6% and 2.0% in mAP50 and mAP50-95, respectively. Simultaneously, the number of parameters decreased by 28.3%, and the model size reduced by 22.6%. To further validate the effectiveness of the improvement scheme, experiments were also conducted using public datasets. The results demonstrate that each enhancement module can boost the model’s detection performance and exhibits strong generalization capabilities. The model not only excels in multi-scale tea bud detection but also offers a valuable reference for reducing computational complexity, thereby providing a technical foundation for the practical application of intelligent tea-picking systems. Full article