Search Results (738)

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Global low-carbon innovation networks face increasing vulnerabilities amid growing geopolitical tensions and technological competition. The interdependent structure of low-carbon innovation networks and the risk propagation dynamics within them remain poorly understood. This study investigates vulnerability patterns by constructing a two-layer interdependent network model based on Chinese low-carbon patent data, comprising a low-carbon collaboration network of innovation entities and a low-carbon knowledge network of technological components. Applying dynamic shock propagation modeling, we analyze how risks spread within and between network layers under various shocks. Our findings reveal significant differences in vulnerability distribution: the knowledge network consistently demonstrates greater susceptibility to cascading failures than the collaboration network, reaching complete system failure, while the latter maintains partial resilience, with resilience levels stabilizing at approximately 0.64. Critical node analysis identifies State Grid Corporation as a vulnerability point in the collaboration network, while multiple critical knowledge elements can independently trigger system-wide failures. Cross-network propagation follows distinct patterns, with knowledge-network failures consistently preceding collaboration network disruptions. In addition, propagation from the collaboration network to the knowledge network showed sharp transitions at specific threshold values, while propagation in the reverse direction displayed more gradual responses. These insights suggest tailored resilience strategies, including policy decentralization approaches, ensuring technological redundancy across critical knowledge domains and strengthening cross-network coordination mechanisms to enhance low-carbon innovation system stability. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by dopaminergic neuronal loss, α-synuclein aggregation, and chronic neuroinflammation. Recent evidence suggests that exosomal microRNAs (miRNAs)—small, non-coding RNAs encapsulated in extracellular vesicles—are key regulators of PD pathophysiology and promising candidates for biomarker development and therapeutic intervention. Exosomes facilitate intercellular communication, cross the blood–brain barrier, and protect miRNAs from degradation, rendering them suitable for non-invasive diagnostics and targeted delivery. Specific exosomal miRNAs modulate neuroinflammatory cascades, oxidative stress, and synaptic dysfunction, and their altered expression in cerebrospinal fluid and plasma correlates with disease onset, severity, and progression. Despite their translational promise, challenges persist, including methodological variability in exosome isolation, miRNA profiling, and delivery strategies. This review integrates findings from preclinical models, patient-derived samples, and systems biology to delineate the functional impact of exosomal miRNAs in PD. We propose mechanistic hypotheses linking miRNA dysregulation to molecular pathogenesis and present an interactome model highlighting therapeutic nodes. Advancing exosomal miRNA research may transform the clinical management of PD by enabling earlier diagnosis, molecular stratification, and the development of disease-modifying therapies. Full article

Background: The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is brain age prediction, which estimates biological age to identify ageing patterns that may serve as biomarkers for such disorders. However, a significant problem with most of the GNNs is their depth, which can lead to issues like oversmoothing and diminishing gradients. Methods: In this study, we propose SAGEFusionNet, a GNN architecture specifically designed to enhance brain age prediction and assess PD-related brain ageing patterns using T1-weighted structural MRI (sMRI). SAGEFusionNet learns important ROIs for brain age prediction by incorporating ROI-aware pooling at every layer to overcome the above challenges. Additionally, it incorporates multi-layer feature fusion to capture multi-scale structural information across the network hierarchy and auxiliary supervision to enhance gradient flow and feature learning at multiple depths. The dataset utilised in this study was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. It included a total of 580 T1-weighted sMRI scans from healthy individuals. The brain sMRI scans were parcellated into 56 regions of interest (ROIs) using the LPBA40 brain atlas in CAT12. The anatomical graph was constructed based on grey matter (GM) volume features. This graph served as input to the GNN models, along with GM and white matter (WM) volume as node features. All models were trained using 5-fold cross-validation to predict brain age and subsequently tested for performance evaluation. Results: The proposed framework achieved a mean absolute error (MAE) of $4.24\pm 0.38$ years and a mean Pearson’s Correlation Coefficient (PCC) of $0.72\pm 0.03$ during cross-validation. We also used 215 PD patient scans from the Parkinson’s Progression Markers Initiative (PPMI) database to assess the model’s performance and validate it. The initial findings revealed that out of 215 individuals with Parkinson’s disease, 213 showed higher and 2 showed lower predicted brain ages than their actual ages, with a mean MAE of 13.36 years (95% confidence interval: 12.51–14.28). Conclusions: These results suggest that brain age prediction using the proposed method may provide important insights into neurodegenerative diseases. Full article

With the explosive growth of the Internet of Things (IoT), the traditional single data sharing scheme has difficulty satisfying the data sharing needs of both same-domain and cross-domain IoT devices. In order to realize efficient data sharing of IoT devices in the same domain with data privacy protection and efficient collaboration between IoT devices in different domains, this paper proposes a trusted data sharing scheme in IoT systems based on multi-channel blockchain. The scheme adopts a multi-channel mechanism to isolate the ledger data between IoT devices of different domains; IoT devices of the same domain utilize hybrid encryption to achieve efficient data sharing through smart contracts, and IoT devices of different domains utilize the CKKS algorithm to achieve cross-domain data sharing with privacy protection through proxy nodes (PNs). In addition, this paper adopts decentralized identity (DID) to achieve autonomous identity management to avoid privacy leakage in IoT devices and adopts InterPlanetary File System (IPFS) to store data files to solve the blockchain storage capacity limitation problem. The security analysis proves that this scheme satisfies the IND-CPA security model, and the performance analysis proves that this scheme has good utility in trusted data sharing of IoT devices. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

In the context of global timber supply chains facing policy adjustments, resource fluctuations, and market uncertainties, this study focuses on the resilience of the Sino-Russian timber supply chain. A system dynamics (SD) model is developed to analyze the dynamic evolution of the key segments. By integrating the entropy weight–TOPSIS method, the research quantitatively assesses overall supply chain resilience by synthesizing data from four capability dimensions—Russian logistics and transportation capability, Russian primary wood processing capability, Sino-Russian timber import–export capability, and Heilongjiang furniture sales capability—over the 2017–2033 period. Results indicate a “first decline, then rise” trajectory for resilience, with a minimum normalized resilience index of 0.1549 recorded in 2021, followed by a gradual recovery and sustained strengthening thereafter. Among evaluated segments, Russian logistics demonstrates the strongest short-term shock resistance (36.2% reduction in minimum resilience), while Heilongjiang’s sales segment exhibits optimal long-term recoverability (the normalized resilience index increased by an average of 0.0363 units per year during the recovery phase). Based on these findings, a “short-term logistics enhancement–long-term demand-driven” strategy is proposed to improve resilience, providing actionable insights for the high-quality development of the Sino-Russian timber supply chain. Full article

Identifying when and which adolescents are at increased risk of developing problematic social media use (PSMU) is critical for effective prevention and early intervention. Previous research has examined risk and protective factors using theory-driven (confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously considered personal, peer, and parent characteristics to assess their relative contributions, and none have explored how these factors are structurally interrelated using data-driven (inductive–exploratory) approaches. To address these gaps, this study combines logistic regression and psychological network analysis to examine which personal, parent, and peer factors are most relevant in identifying at-risk/problematic social media use among adolescents. Using three waves of data analyzed cross-sectionally from N = 2441 secondary school students, adolescents were classified as normative (0–1 symptoms) or at-risk/problematic (2+ symptoms) users based on the Social Media Disorder Scale. Logistic regression showed that fear of missing out, impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules uniquely predicted at-risk/problematic use. Psychological network analysis identified self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as central, highly interconnected nodes. These findings show that theory- and data-driven approaches illuminate different aspects of PSMU risk, and that network analysis can generate novel hypotheses about underlying processes. Full article

Geographic routing is an appealing method for wireless sensor networks, as it routes data packets solely based on nodes’ location information rather than global network topology. A fundamental requirement for geographic routing is that source nodes must know the locations of sink nodes to deliver their data. To efficiently provide sink location information, quorum-based sink location service schemes have been introduced, using crossing points between sink location announcement (SLA) and sink location query (SLQ) quorums. However, existing quorum-based schemes typically construct quorums along fixed paths, causing rapid energy depletion of particular sensor nodes and resulting in shorter network lifetimes, especially in irregular sensor fields. To overcome this limitation, we propose an energy-efficient quorum-based sink location service scheme that extends network lifetime by reducing and balancing sensor nodes’ energy consumption. Specifically, our scheme constructs a quadrilateral-shaped SLA quorum using four randomly selected points, and a line-shaped SLQ quorum defined by two randomly chosen points located inside and outside the SLA quorum, respectively. We also address key issues of the proposed scheme, including network holes, irregular boundaries, multiple sources and sinks, and Base Zone sizing, and present methods to handle them. Simulation results demonstrate that the proposed scheme outperforms existing approaches, achieving approximately 29% lower total energy consumption and 27% higher energy balancing across sensor nodes on average. Full article

The rapid growth of computationally intensive tasks in the Internet of Vehicles (IoV) poses a triple challenge to the efficiency, security, and stability of Mobile Edge Computing (MEC). Aiming at the problems that traditional optimization algorithms tend to fall into, where local optimum in task offloading and edge computing nodes are exposed to the risk of data tampering, this paper proposes a secure offloading strategy that integrates the Improved Polar Lights Optimization algorithm (IPLO) and blockchain. First, the truncation operation when a particle crosses the boundary is improved to dynamic rebound by introducing a rebound boundary processing mechanism, which enhances the global search capability of the algorithm; second, the blockchain framework based on the Delegated Byzantine Fault Tolerance (dBFT) consensus is designed to ensure data tampering and cross-node trustworthy sharing in the offloading process. Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle’s power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV. Full article

Prefabricated construction technology (PCT) is a significant driver for promoting high-quality development in the construction industry. Patents, as critical outputs of technological innovation, provide diversified data that can manifest trends in technological innovation systems. However, few studies have comprehensively revealed the dynamics of PCT innovation systems from a systematic view, considering both innovation actors and technologies. Based on 6047 patent data in China, a combination of bibliometric analysis and social network analysis are employed to examine the structure of the PCT innovation system. Subsequently, networks are constructed based on the collaborative relationships between patent applicants and technologies. Through analysis of the metrics of entire networks and nodes, the dynamics of PCT innovation systems is revealed. Generally, China’s PCT innovation system has evolved into a complex network characterized by multi-actor participation and multi-technology collaboration, playing a pivotal role in fostering sustained PCT innovation and generating substantial innovative outcomes. Nevertheless, challenges persist, including insufficient cross-domain collaboration and constrained flows of innovation resources. Moving forward, efforts should prioritize enhancing interdisciplinary cooperation, optimizing the allocation of technological resources, refining policy guidance mechanisms, and strengthening the system’s overall collaborative innovation capacity. Full article

This study proposes a multi-dimensional urban transportation network optimization framework (MTNO-RQDC) to address structural failure risks from aging infrastructure and regional connectivity bottlenecks. Through dual-dataset validation using both the Baltimore road network and PeMS07 traffic flow data, we first develop a traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies for guiding reconstruction planning for the collapsed Francis Scott Key Bridge. Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. The framework introduces three key innovations: (1) a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning; (2) fault-tolerant network topology optimization demonstrated through 1000-epoch Monte Carlo failure simulations; (3) cross-dataset transferability validation showing 15.7% performance variance between Baltimore and PeMS07 environments. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 42,760 to 32,100), 22.4% improvement in public transit path redundancy, and 30.4–44.6% decrease in regional traffic load variance with minimal costs. Hyperparameter analysis reveals two optimal operational modes: rapid cooling (rate = 0.90) achieves 85% improvement within 50 epochs for emergency response, while slow cooling (rate = 0.99) yields 12.7% superior solutions for long-term planning. The framework establishes a new multi-objective paradigm balancing structural resilience, functional connectivity, and computational robustness for sustainable smart city transportation systems. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

This study examines the establishment of a Hub for Circular Economy and Industrial Symbiosis (HUB-CEIS) centred on a forest biomass waste plant in Fundão, Portugal, presenting an innovative model for rural industrial symbiosis, circular economy governance, and sustainable waste management. Designed as a strategic node within a reverse supply chain, the hub facilitates the conversion of solid waste into renewable energy and high-value co-products, including green hydrogen, tailored for industrial and agricultural applications, with an estimated 120 ktCO₂/year reduction and 60 direct jobs. Aligned with the United Nations (UN) Sustainable Development Goals (SDGs) and the Paris Agreement, this initiative addresses global challenges such as decarbonization, resource efficiency, and the energy transition. Employing a mixed research methodology, this study integrates a comprehensive literature review, in-depth stakeholder interviews, and comparative case study analysis to formulate a governance framework fostering regional partnerships between industry, government, and local communities. The findings highlight Fundão’s potential to become a benchmark for rural industrial symbiosis, offering a replicable model for circularity in non-urban contexts, with a projected investment of USD 60 M. Special emphasis is placed on the green hydrogen value chain, positioning it as a key enabler for regional sustainability. This research underscores the importance of cross-sectoral collaboration in achieving scalable and efficient waste recovery processes. By delivering practical insights and a robust governance structure, the study contributes to the circular economy literature, providing actionable strategies for implementing rural reverse supply chains. Beyond validating waste valorization and renewable energy production, the proposed hub establishes a blueprint for sustainable rural industrial development, promoting long-term industrial symbiosis integration. Full article