Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio

Global analysis of land-use changes in karst areas and the implications for water resources [Globale Analyse von Landnutzungsänderungen in Karstgebieten und deren Auswirkungen auf die Wasserressourcen]

Karstgebiete weisen wertvolle Grundwasserressourcen und eine hohe Artenvielfalt auf, sind jedoch besonders anfällig für den Klimawandel und menschliche Einflüsse. Landnutzungswandel ist Ursache und Folge von globalen Umweltveränderungen. Die Veröffentlichung von Daten wie denen des Land-Cover-Projektes der Climate Change Initiative (CCI-LC) der europäischen Weltraumbehörde ESA und der World Karst Aquifer Map (WOKAM) hat es ermöglicht, weltweite Landnutzungsveränderungen in Karstgebieten zu untersuchen. In dieser Arbeit wird zunächst die globale Verteilung der Nutzung von Karstlandschaften im Jahr 2020 untersucht, sowie die Charakteristika von Landnutzungsübergängen zwischen 1992 und 2020 analysiert. Dann werden zwei Indikatoren vorgeschlagen, um die räumlichen Merkmale von Landnutzungsveränderungen in globalen Karstgebieten zu identifizieren: Der Anteil von Landnutzungsveränderungen und der dominante Typ von Landnutzungsveränderungen. Schließlich werden drei Beispiele für Landnutzungsveränderungen in Karstgebieten im Detail analysiert. Die Landnutzungstypen und ihre Anteile der globalen Karstgebiete (in abnehmender Größe) sind: Wald (31.78%), kahle Flächen (27.58%), Ackerland (19.02%), Grasland (10.87%), Strauchland (7.21%), Feuchtgebiete (1.67%), Eis und Schnee (1.16%) und urbanes Gebiet (0.71%). Die Gesamtfläche der globalen Landnutzungsveränderungen in Karstgebieten beträgt 1.3 Millionen km², etwa 4.85% der globalen Karstfläche. Der Trend des Landnutzungswandels in den weltweiten Karstgebieten wird von Aufforstung dominiert, ergänzt durch vereinzelte Urbanisierung und landwirtschaftliche Rekultivierung. Die tropischen Klimazonen weisen eine höhere Intensität von Landnutzungsveränderungen auf. Regionen mit landwirtschaftlicher Rekultivierung stimmen weitgehend mit der Bevölkerungsdichte überein. Diese Ergebnisse spiegeln den Einfluss menschlicher Aktivitäten und des Klimawandels auf Landnutzungsveränderungen in globalen Karstgebieten wider und dienen als Grundlage für weitere Forschung und Planung im Bereich des Landressourcenmanagements

Effect of hip fracture on prognosis of acute cerebral infarction

OBJECTIVES: Hip fractures are a worldwide public health problem. The incidence of hip fracture is high among the elderly, and it is an important cause of death and disability in this population. This observational study aimed to investigate the effect of acute hip fracture on the recovery of neurological function and the prognosis of patients with acute cerebral infarction, as well as whether surgical treatment of combined acute fracture can improve the prognosis of patients. METHODS: Thirty patients with acute cerebral infarction combined with acute hip fracture, who were hospitalized in two hospitals between January 1, 2013 and December 31, 2019, were included. The patients did not undergo surgical treatment. The control group included patients with common acute cerebral infarction without hip fracture admitted in the same period. The neurological function recovery, hospitalization period, half a year recovery rate, incidence of complications, and one-year mortality rate between the two groups were compared. Eleven patients with acute cerebral infarction combined with hip fracture, who underwent surgical treatment, were selected and compared with those in the non-surgery group. RESULTS: Compared with patients with common acute cerebral infarction, the National Institutes of Health Stroke Scale score of those with acute cerebral infarction combined with hip fracture was higher (7.2±5.4 vs. 5.6%±4.3, p=0.034), the hospitalization period was prolonged (16.1±8.9% vs. 12.2±5.3, p=0.041), and the half a year recovery rate was lower (26.7% vs. 53.3%, p=0.016). Additionally, the incidence of pulmonary infection and lower extremity deep vein thrombosis was increased (30% vs. 11.7%, p=0.03; 6.7% vs. 0, p=0.043). The one-year mortality rate of patients with hip fracture was higher than that of patients with common cerebral infarction (23.3% vs. 6.7%, p=0.027). Compared with the non-surgical group, the good recovery rate after half a year of surgical treatment of the group with cerebral infarction and acute hip fracture had an increasing trend, while the hospitalization cycle, incidence of complications, and one-year mortality rate were all decreased, although this was not statistically significant. CONCLUSIONS: Acute cerebral infarction combined with hip fracture leads to worse neurological recovery, prolonged hospitalization period, increased complications, decreased patient prognosis, and increased one-year mortality. Surgical treatment improves the prognosis of patients with acute cerebral infarction. These findings may provide insights into the management of acute cerebral infarction

Vibration analysis of a cylinder with slight diameter and thickness variations

The cross section of circular cylinder in their dynamic model is always considered as a perfect circle, which means radii at every point on the circle are the same. In real engineering structure, there are slight fluctuations in shape of the circular cylinder which is different from those in ideal model. Meanwhile, effects of structural fluctuations on its dynamic characteristic are rarely analyzed before. To study problem mentioned above, the geometric shape of a typical, apparently symmetrical cylinder is examined experimentally to demonstrate that a small variation in diameter and thickness indeed exists in practice firstly. Because fluctuations in diameter and thickness of the cylinder are related to each other, we need to separate effects of a slight variation in its diameter and thickness on structural dynamic characteristics to search the key factor of influence. Then, two simplified modes, which are modeled by finite element method, are used to study the effects of diameter or thickness variation alone on the natural frequencies and modal shapes of the free cylinder. It is revealed that the diameter variation described by the simplified model captures the key influencing elements which affect the modal characteristics of the free cylinder. Finally, a free cylinder with both variations is analyzed numerically and the results are verified experimentally. This work illustrates that significant discrepancies inevitably exist between the measured results of an actual free cylinder and an assumed symmetrical model even if there is only a very slight variation in its geometric shape

3D printing high interfacial bonding polyether ether ketone components via pyrolysis reactions

Recently, 3D-printed polyether-ether-ketone (PEEK) components have been shown to offer many applications in state-of-the-art electronics, 5G wireless communications, medical implantations, and aerospace components. Nevertheless, a critical barrier that limits the application of 3D printed PEEK components is their weak interfacial bonding strength. Herein, we propose a novel method to improve this unsatisfied situation via the interface plasticizing effect of benzene derivatives obtained from the thermal pyrolysis of trisilanolphenyl polyhedral oligomeric silsequioxane (POSS). Based on this method, the bonding strength of the filaments and interlayers of 3D-printed POSS/PEEK components can reach 82.9 MPa and 59.8 MPa, respectively. Moreover, the enhancing mechanism of the pyrolysis products derived from the POSS is characterized using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), Fourier transform infrared spectroscopy (FTIR), and X-ray computed tomography (X-CT). Our proposed strategy broadens the novel design space for developing additional 3D-printed materials with satisfactory interfacial bonding strength

Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer

With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the above-mentioned severe challenges, this paper customizes a weakly-supervised edge computing anomaly detection framework, i.e., Federated Learning-based Transformer framework (\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the Transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and Transformer. Furthermore, extensive case studies on four benchmark data sets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and Transformer to deal with anomaly detection problems in cloud manufacturing

Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development

Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.Comment: 7 pages, 6 figure

Phototriggered Complex Motion by Programmable Construction of Light-Driven Molecular Motors in Liquid Crystal Networks

Recent developments in artificial molecular machines have enabled precisely controlled molecular motion, which allows several distinct mechanical operations at the nanoscale. However, harnessing and amplifying molecular motion along multiple length scales to induce macroscopic motion are still major challenges and comprise an important next step toward future actuators and soft robotics. The key to addressing this challenge relies on effective integration of synthetic molecular machines in a hierarchically aligned structure so numerous individual molecular motions can be collected in a cooperative way and amplified to higher length scales and eventually lead to macroscopic motion. Here, we report the complex motion of liquid crystal networks embedded with molecular motors triggered by single-wavelength illumination. By design, both racemic and enantiomerically pure molecular motors are programmably integrated into liquid crystal networks with a defined orientation. The motors have multiple functions acting as cross-linkers, actuators, and chiral dopants inside the network. The collective rotary motion of motors resulted in multiple types of motion of the polymeric film, including bending, wavy motion, fast unidirectional movement on surfaces, and synchronized helical motion with different handedness, paving the way for the future design of responsive materials with enhanced complex functions