Search Results (3,254)

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support. Full article

As an important ecological security barrier on the Tibetan Plateau, southeastern Tibet is crucial to maintaining regional carbon balance under climate change. This study innovatively integrates multi-source remote sensing data (Landsat 8, Sentinel-1, and GEDI) on the Google Earth Engine (GEE) platform, and uses machine learning to model forest carbon storage dynamics from 2019 to 2023. The fusion of multi-source data improves forest vertical structure characterization and makes up for the shortage of single optical data. By comparing machine learning algorithms, the Gradient Boosting model performs excellently (validation set R² = 0.909, RMSE = 26.608 Mg/Ha), achieving high-resolution spatiotemporal mapping. The results show significant spatial heterogeneity; the increase in carbon storage in the central and southern regions is mainly in contrast to the scattered decreases in the eastern and western regions, reflecting vegetation restoration and topographic influence. High-altitude areas are subject to climate restrictions and small changes, while low-altitude areas show significant fluctuations due to human activities. Key drivers were elevation (importance score 22.06), slope (17.00), and temperature (22.04). Land use transformation (such as forest expansion) promotes net carbon accumulation and highlights the effectiveness of regional protection policies. This study provides a scientific basis for targeted ecological management of high-altitude ecosystems. Full article

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven human identification system that leverages WiFi-based wireless sensing and contrastive learning techniques. IdentiFi utilizes self-supervised and semi-supervised learning to extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even in dynamic, multi-occupant settings. The system’s temporal and contextual contrasting modules enhance its ability to model human motion and reduce multi-user interference, while class-aware contrastive learning minimizes the need for extensive labeled datasets. Extensive evaluations demonstrate that IdentiFi outperforms existing methods in terms of scalability, adaptability, and privacy preservation, making it highly suitable for AI agents in smart homes, healthcare facilities, security systems, and personalized services. Full article

Existing smart contract honeypot detection approaches exhibit high false negatives and positives due to (i) their inability to generate transaction sequences triggering order-dependent traps and (ii) their limited code coverage from traditional fuzzing’s random mutations. In this paper, we propose a hybrid fuzzing framework for smart contract honeypot detection based on taint analysis, SCH-Hunter. SCH-Hunter conducts source-code-level feature analysis of smart contracts and extracts data dependency relationships between variables from the generated Control Flow Graph to construct specific transaction sequences for fuzzing. A symbolic execution module is also introduced to resolve complex conditional branches that fuzzing alone fails to penetrate, enabling constraint solving. Furthermore, real-time dynamic taint propagation monitoring is implemented using taint analysis techniques, leveraging taint flow information to optimize seed mutation processes, thereby directing mutation resources toward high-value code regions. Finally, by integrating EVM (Ethereum Virtual Machine) code instrumentation with taint information flow analysis, the framework effectively identifies and detects security-sensitive operations, ultimately generating a comprehensive detection report. Empirical results are as follows. (i) For code coverage, SCH-Hunter performs better than the state-of-art tool, HoneyBadger, achieving higher average code coverage rates on both datasets, surpassing it by 4.79% and 17.41%, respectively. (ii) For detection capabilities, SCH-Hunter is not only roughly on par with HoneyBadger in terms of precision and recall rate but also capable of detecting a wider variety of smart contract honeypot techniques. (iii) For the evaluation of components, we conducted three ablation studies to demonstrate that the proposed modules in SCH-Hunter significantly improve the framework’s detection capability, code coverage, and detection efficiency, respectively. Full article

With the explosion of vehicle-to-infrastructure (V2I) communications in the internet of vehicles (IoV), it is still very important to ensure secure authentication and efficient key agreement because of the vulnerabilities in the existing protocols such as physical capture attacks, privacy leakage, and low computational efficiency. This paper proposes a physical unclonable function (PUF)-based multi-factor authentication and key agreement protocol tailored for V2I environments, named as PMAKA-IoV. The protocol integrates hardware-based PUFs with biometric features, utilizing fuzzy extractors to mitigate biometric template risks, while employing dynamic pseudonyms and lightweight cryptographic operations to enhance anonymity and reduce overhead. Security analysis demonstrates its resilience against physical capture attacks, replay attacks, man-in-the-middle attacks, and desynchronization attacks, and it is verified by formal verification using the strand space model and the automated Scyther tool. Performance analysis demonstrates that, compared to other related schemes, the PMAKA-IoV protocol maintains lower communication and storage overhead. Full article

Since 2019, China has been promoting the renovation of old urban residential areas built in 2000 or earlier. However, older communities surrounding large urban hospitals face unique challenges, including deteriorating infrastructure, complex social dynamics, and conflicts between tenants and residents. This study focuses on old communities near Xiangya Hospital in Changsha, Hunan Province, employing questionnaire surveys to analyze residential satisfaction and demands across three dimensions: housing spaces, community public spaces, and social relations. Using multilevel linear regression, structural equation modeling, and moderation effect analysis, this research systematically investigates influencing factors and group heterogeneity. The findings reveal that community greening, recreational facilities, and property management are core drivers of residential satisfaction, while social relationships and public spaces play critical mediating roles. Distinct group-specific needs emerged: elderly residents prioritized greening, security, and property management responsiveness; medical students emphasized sound insulation and tenant management; and patients and their families heavily emphasized ventilation and lighting, hygienic conditions, and infrastructure. To address these issues, the study proposes an integrated renewal strategy emphasizing the integration of physical upgrades and soft governance. The findings provide theoretical and practical insights for the systematic renewal of similar older hospital-adjacent communities. Full article

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions. Full article

The symmetry of renewable generations (RGs) and synchronous generations (SGs) is jeopardized by the increase in the penetration RGs, which threatens the secure operation of power systems. Moreover, the control mode transition of RGs during the frequency regulation (FR) process complicates system frequency behaviors. Hence, it is supposed to design a frequency stability constrained unit commitment (FSCUC) model to satisfy the inertia requirements. First, dynamic frequency behaviors are characterized while considering the control mode transition of RGs. Subsequently, the frequency predictive model is developed through a Zero-Order Hold (ZOH) discretization technique. Next, the frequency predictive model is embedded into a stochastic unit commitment (UC). Moreover, a progressive inertia increment (PII)-based solution algorithm is designed to reduce the computational burden. Finally, numerical experiments are conducted in IEEE 24-bus and 118-bus systems to validate the effectiveness of the proposed method. The simulation results show that the frequency stability indices can be improved by 30% by increasing the system inertia by 43% at least with the additional costs of only 0.66%, when compared with existing methods. Full article

Collaborative filtering, as a widely used recommendation method, is widely applied but susceptible to data poisoning attacks, where malicious actors inject synthetic user interaction data to manipulate recommendation results and secure illicit benefits. Traditional poisoning attack methods require in-depth understanding of the recommendation system. However, they fail to address its dynamic nature and algorithmic complexity, thereby hindering effective breaches of the system’s defensive mechanisms. In this paper, we propose DRLAttack, a deep reinforcement learning-based framework for data poisoning attacks. DRLAttack can launch both white-box and black-box data poisoning attacks. In the white-box setting, DRLAttack dynamically tailors attack strategies to recommendation context changes, generating more potent and stealthy fake user interactions for the precise targeting of data poisoning. Furthermore, we extend DRLAttack to black-box settings. By introducing spy users to simulate the behavior of active and inactive users into the training dataset, we indirectly obtain the promotion status of target items and adjust the attack strategy in response. Experimental results on real-world recommendation system datasets demonstrate that DRLAttack can effectively manipulate recommendation results. Full article

Traditional intellectual property transaction traceability has problems such as information asymmetry, traceability information storage methods relying on centralized databases, and easy tampering of transaction information, etc. A blockchain-based information security mechanism for intellectual property transaction traceability is proposed. Firstly, through the analysis of massive intellectual property transaction case information, the commonality and individuality data are studied, and the structure and scope of data collection requirements for traceability information are established; secondly, the traceability information structure is constructed based on the smart contract and PROV data origin model, the signature verification of traceability information is completed based on the BLS threshold signature of the Dynamic DKG protocol, and the signature process integrates the PROV model and constructs a chained signature structure. The multi-level traceability information verification strategy and process are developed to achieve the security protection of traceability information throughout the entire life cycle of intellectual property transactions. Full article

Cybersecurity researchers and security analysts rely heavily on data to train and test network threat detection models, and to conduct post-breach forensic analyses. Comprehensive data-including network traces, host telemetry, and contextual information-are crucial for these tasks. However, widely used public datasets often suffer from outdated network traffic and features, statistical anomalies, and simulation artifacts. Furthermore, existing data collection systems frequently face architectural and computational limitations, necessitating workarounds that result in incomplete or disconnected data. Currently, no framework provides comprehensive data collection from all network segments without requiring specialized or proprietary hardware or software agents. This paper introduces InDepth, a scalable system employing a distributed, data-link layer architecture that enables comprehensive data acquisition across entire networks. We also present a model cyber range capable of dynamically generating datasets for evaluation. We demonstrate the effectiveness of InDepth using real-world network data. Full article

One of the main reasons for migration is the search for a better quality of life. The concept of quality of life is very broad, encompassing economic, social, political, and cultural factors. According to the World Migration Report 2022, 3.6 percent of the world’s population are migrants. This number is growing due to geopolitical reasons. Increasing migration flows affect the growth of the part of the population living in urban areas, that is, urbanisation. The scale of migration is growing along with the search for a better life. In Lithuania, according to the Department of Statistics, as well as throughout the world, the number of people living in cities is constantly growing; for comparison, a 3% growth has been observed over 4 years, in the European Union, according to the World Bank, 1%, and in the world—1%. The term urbanisation also describes social changes that are determined by the concentration of the population. To ensure quality of life, cities face challenges such as ensuring security, integration of migrants into society and the labour market, the functioning of the health and education system, and sustainable development of cities. Despite growing interest, the impact of migrant flows on the quality of life in cities has not been sufficiently studied in the world scientific literature. Most research is focused on the causes of migration, migrant integration, demographic changes, or labour market interactions. However, less attention is paid to how the dynamics of migrant flows affect the quality of life in cities. Comprehensive assessment is lacking. The goal is to assess the link between quality of life and the dynamics of migration flows in urbanised areas. The article, which conducted a systematic and comparative analysis of concepts published in the scientific literature, formed the concept of quality of life in urban areas, identified the factors that determine quality of life, and studied the link between the quality of life in the city and the dynamics of migration flows. This assessment will allow us to combine the factors that determine quality of life in terms of changes in migrant flows into a common system. To achieve this goal, statistical processing, correlation analysis, and CRITIC methods will be applied. Full article

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article

Opacity, an important property of the information flow in discrete-event systems (DESs), characterizes whether the secret information in a system is ambiguous to a passive observer (called an intruder). Observation models play a critical role in the analysis of opacity. In this paper, instead of adopting a fully static observation model or a fully dynamic observation model, we use a novel Orwellian-type observation model to study the verification of the current-state opacity (CSO), where the observability of an unobservable event can be re-interpreted once certain/several specific conditions are met. First, a K-delay Orwellian observation mechanism (KOOM) is proposed as a novel Orwellian-type observation mechanism for extending the existing Orwellian projection. The main characteristics of the KOOM are delaying the inevitable information release and narrowing the release range for historical information to protect the secrets in a system to a greater extent than with the existing Orwellian projection. Second, we formulate the definitions of standard and strong CSO under the KOOM. Finally, we address the verification problem for these two types of opacity by constructing two novel information structures called a standard K-delay verifier and a strong K-delay verifier, respectively. An analysis of the computational complexity and illustrative examples are also presented for the proposed results. Overall, the proposed notions of standard and strong CSO under the KOOM capture the security privacy requirements regarding a delayed release in applications, such as intelligent transportation systems, etc. Full article

This paper presents a hybrid path planning method that integrates an enhanced A-Star algorithm with the Dynamic Window Approach (DWA). The proposed approach addresses the limitations of conventional A-Star algorithms in global path planning, particularly their inability to adaptively avoid obstacles in real-time. To improve navigation safety, the A-Star search strategy is enhanced by avoiding paths that intersect with obstacle vertices or pass through narrow channels. Additionally, a node optimization technique is introduced to remove redundant nodes by checking for collinearity in consecutive nodes. This optimization reduces the path length and ensures that the path maintains a safe distance from obstacles using parallel lines. An advanced Bézier curve smoothing method is also proposed, which adaptively selects control points to improve path smoothness and driving stability. By incorporating these improvements, the enhanced A-Star algorithm is combined with DWA to facilitate dynamic obstacle avoidance while generating global paths. The method accounts for the kinematic characteristics of the USV, as well as physical constraints such as linear and angular velocities, enabling effective handling of obstacles in dynamic environments and ensuring safe navigation. Simulation results demonstrate that the proposed algorithm generates secure global paths, significantly optimizing node count, path length, and smoothness, while effectively avoiding dynamic obstacles, thus ensuring safe navigation of the USV. Full article