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The development of information technology has brought great convenience to our lives, but at the same time, the unfairness and privacy issues brought about by traditional centralized systems cannot be ignored. Blockchain is a peer-to-peer and decentralized ledger technology that has the characteristics of transparency, consistency, traceability and fairness, but it reveals private information in some scenarios. Secure multi-party computation (MPC) guarantees enhanced privacy and correctness, so many researchers have been trying to combine secure MPC with blockchain to deal with privacy and trust issues. In this paper, we used homomorphic encryption, secret sharing and zero-knowledge proofs to construct a publicly verifiable secure MPC protocol consisting of two parts—an on-chain computation phase and an off-chain preprocessing phase—and we integrated the protocol as part of the chaincode in Hyperledger Fabric to protect the privacy of transaction data. Experiments showed that our solution performed well on a permissioned blockchain. Most of the time taken to complete the protocol was spent on communication, so the performance has a great deal of room to grow.

Global Navigation Satellite Systems (GNSS) support numerous applications, including mission-critical ones that require a high level of integrity for safe operations, such as air, maritime, and land-based navigation. Fault Detection and Exclusion (FDE) is crucial for mission-critical applications, as faulty measurements significantly impact system integrity. FDE can be applied within the positioning algorithm in the measurement’s domain and the integrity monitoring domain. Previous research has utilized a limited number of Machine Learning (ML) models and Quality Indicators (QIs) for the FDE process in the measurement domain. It has not evaluated the pseudorange measurement fault thresholds that need to be detected. In addition, ML models were mainly evaluated based on accuracy, which alone does not provide a comprehensive evaluation. This paper introduces a comprehensive framework for traditional ML-based FDE prediction models in the measurement domain for pseudorange in complex environments. For the first time, this study evaluates the fault detection thresholds across 40 values, ranging from 1 to 40 m, using six ML models for FDE. These models include Decision Tree, K-Nearest Neighbors (KNN), Discriminant, Logistic, Neural Network, and Trees (Boosted, Bagged, and Rusboosted). The models are comprehensively assessed based on four key aspects: accuracy, probability of misdetection, probability of fault detection, and the percentage of excluded data. The results show that ML models can provide a high level of performance in the FDE process, exceeding 95% accuracy when the fault threshold is equal to or greater than 4 m, with KNN providing the highest FDE performance.

In order to explore more effective methods of landslide disaster monitoring and controlling, NPR anchor cable and optical fiber grating strain sensor are physically combined to form a slope reinforcement-monitoring integration system, with PVC pipe an intermediary. Physical model test is carried out according to timely warning of landslide disasters of the Newtonian force monitoring system. At the same time, the optical fiber sensing technology has the condition of continuous perception of time and space. The feasibility of monitoring the whole life cycle of slope instability and positioning the potential sliding surface is discussed. Through the analysis of the test results, it is concluded that the fiber grating strain sensor can effectively monitor the deep displacement of the slope. PVC pipe and the soil have deformation coordination, which is able to effectively reinforce the slope body together with the combined anchor cable sensing device, and can continuously sense the potential sliding surface in the whole life cycle of the slope. By comparing the results of Newton force monitoring curve with the fiber grating strain monitoring, the mechanical law of “Newton force sudden drops, immediately catastrophe happens” is further verified. The feasibility of the combined sensing and early warning method of optical fiber sensing technology and Newton force monitoring is verified. This method realizes the further optimization of Newton force monitoring system. Article highlights This article is to study the joint detection method of Newton force monitoring and optical fiber monitoring. The main research method is indoor test. The feasibility of combined detection of Newton force monitoring and optical fiber monitoring is discussed through slope test.

The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor‐critic (a2c) algorithm‐based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real‐world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method’s efficiency on the traffic flow rate and road structure.

Oxide-ion conducting materials are gaining considerable attention in various applications ranging from oxide fuel cells to oxygen permeation membranes. The oxide ion migration mechanisms are the basis for designing oxide-ion conducting materials. Here, enlightened by proton diffusion in hydrogen-bond networks, we report the coordination polyhedra cooperative mechanism with similar Grotthuss process of oxide ion migration in tellurites. Bi 2 Te 2 O 7 and Bi 2 Te 4 O 11 were selected due to their abundance of secondary bonds similar to hydrogen bonds and show high oxide ionic conductivity as mixed electronic and ionic conductors. Neutron total scattering experiments with reverse Monte Carlo simulations indicated that the oxide ion migration in those two tellurites is a synergetic effect of mutual transition between Te-O secondary bonds and covalent bonds assisted by Te-O polyhedra rotation. This detailed investigation of the cooperative mechanism with similar Grotthuss process at the atomic scale provides a direction for optimization and discovering oxide ion conducting materials. Oxide-ion conductors are important energy materials. Here, the authors found a tellurite-based oxygen ion conductor highlighting the interplay of secondary bond interactions and Grotthuss-like processes.

The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes.

The impact of rhenium doping on the transport properties and electron localization in monolayer graphene was experimentally investigated. In this study, we report the emergence of unsaturated negative magnetoresistance in Re-doped graphene devices, which is observed exclusively at low temperatures. Moreover, angle-dependent measurements reveal a pronounced anisotropy in the negative magnetoresistance. This phenomenon is attributed to the disorder and localized magnetic moments introduced by Re doping, which lead to charge carrier localization and are accompanied by substantial magnetocrystalline anisotropy energy.

Rheumatoid arthritis (RA) is an autoimmune disease mainly characterized by chronic polyarthritis. Many types of cells play pivotal roles in the pathogenicity of RA, such as T cells, B cells, macrophages, dendritic cells (DCs), osteoclasts (OCs), and fibroblast-like synoviocytes (FLS). Tripterygium wilfordii Hook f. (TwHf) and its ingredients are able to control disease activity by regulating the functions of cells mentioned above, and the clinical studies have highlighted the importance of TwHf ingredients in RA treatment. They have been demonstrated to improve the RA symptoms of animal models and patients. In this review, we discussed the effect of TwHf ingredients on pathogenicity cells, including disease/cell phenotypes and molecular mechanisms. Here, we constructed a cell-cell interaction network to visualize the effect of TwHf ingredients. We found that TwHf ingredients could inhibit the differentiation and proliferation of the pathogenicity cells. Besides, the components could decrease the levels of pathogenicity cytokines [i.e., interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α)]. Many signaling pathways are involved in the underlying mechanisms, such as PI3K, NF-κB, and MAPK signaling pathways.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.