Cyber-Physical System Checkpointing and Recovery

Transitioning to more open architectures has been making Cyber-Physical Systems (CPS) vulnerable to malicious attacks that are beyond the conventional cyber attacks. This paper studies attack-resilience enhancement for a system under emerging attacks in the environment of the controller. An effective way to address this problem is to make system state estimation accurate enough for control regardless of the compromised components. This work follows this way and develops a procedure named CPS checkpointing and recovery, which leverages historical data to recover failed system states. Specially, we first propose a new concept of physical-state recovery. The essential operation is defined as rolling the system forward starting from a consistent historical system state. Second, we design a checkpointing protocol that defines how to record system states for the recovery. The protocol introduces a sliding window that accommodates attack-detection delay to improve the correctness of stored states. Third, we present a use case of CPS checkpointing and recovery that deals with compromised sensor measurements. At last, we evaluate our design through conducting simulator-based experiments and illustrating the use of our design with an unmanned vehicle case study

GAN-based Image Compression with Improved RDO Process

GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image perceptual degeneration in color, texture, and structure as well as 2) the inaccurate entropy model. In this paper, we present a novel GAN-based image compression approach with improved rate-distortion optimization (RDO) process. To achieve this, we utilize the DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture, and structure. Besides, we absorb the discretized gaussian-laplacian-logistic mixture model (GLLMM) for entropy modeling to improve the accuracy in estimating the probability distributions of the latent representation. During the evaluation process, instead of evaluating the perceptual quality of the reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score (MOS) experiment among different codecs, which fully reflects the actual perceptual results of humans. Experimental results demonstrate that the proposed method outperforms the existing GAN-based methods and the state-of-the-art hybrid codec (i.e., VVC)

Assessment of Urban Transportation Metabolism from Life Cycle Perspective: A Multi-method Study

Abstract The goal of this study is to provide a multi-method based on the eco-thermodynamic framework to examine the environmental sustainability of urban public transportation systems. Urban transportation metabolism (UTM), as a metaphor of urban systematic research methodology for transportation system, has been proposed and combined with life cycle assessment (LCA). Results show that the most important factors in assessing the acceptability of a transportation system are not only the direct fuel consumption, and the energy and material costs of the vehicles, but also the energy and materials costs for the upstream and downstream side of the infrastructure construction and vehicle fuel

The antioxidant activity of polysaccharides from Armillaria gallica

The purpose of this study was to investigate the antioxidant activity of Armillaria gallica polysaccharides. It explored whether Armillaria gallica polysaccharides (AgP) could prevent HepG2 cells from H2O2-induced oxidative damage. The results demonstrated that HepG2 cells were significantly protected by AgP, and efficiently suppressed the production of reactive oxygen species (ROS) in HepG2 cells. Additionally, AgP significantly decreased the abnormal leakage of alanine aminotransferase (ALT) and lactate dehydrogenase (LDH) caused by H2O2, protecting cell membrane integrity. It was discovered that AgP was also found to regulate the activities of antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-PX), while reducing malondialdehyde (MDA), thus protecting cells from oxidative damage. According to the flow cytometry analysis and measurement of caspase-3, caspase-8, and caspase-9 activities, AgP could modulate apoptosis-related proteins and attenuate ROS-mediated cell apoptosis

Top-gate zinc-tin-oxide thin-film transistors based on organic gate insulator

The thin-film transistors (TFTs) using the pulsed-plasma-deposition–prepared amorphous Zn-Sn-O (a-ZTO) as active layer and the dip-coated polymethylmethacrylate (PMMA) as gate insulator were fabricated. The results display that the PMMA film shows anti-reflection phenomenon when the PMMA layer combines with the ZTO layer to form a double-layer configuration. Moreover, the oxygen vacancy existing in ZTO decreased with the increase of the substrate heating. Compared to the reference a-ZTO TFTs with a channel prepared at RT and subsequently annealing at $150\ ^{\circ}\text{C}$ , a markedly improved performance was evidenced for TFT via treatment of heating in the process of preparation ($150\ ^{\circ}\text{C}$ in O2 ambient). The optimum a-ZTO TFTs, operating in an enhancement mode, showed a high mobility of $6.25\ \text{cm}^{2}\text{/Vs}$ and an on/off ratio over 105

Life cycle assessment of atmospheric environmental impact on the large-scale promotion of electric vehicles in China

Decarbonizing the transportation sector emerges as a pivotal step in addressing climate change. In recent years, rapid growth in China’s new energy automotive industry has significantly contributed to transportation decarbonization. However, environmental challenges in producing and recycling electric vehicles (EVs) may limit emission reduction benefits. In this study, we establish a comprehensive life cycle assessment model for vehicles to analyze the gap in air pollutant and greenhouse gas emissions between electric vehicles and internal combustion engine vehicles (ICEVs). Based on this model, the environmental benefits of further promoting electric vehicles in China are evaluated. Results reveal that, compared to ICEVs, EVs reduce life cycle emissions of CO2 by 12%, NOx by 69%, and VOCs by 9%. Primary constraints on EVs in emission reduction are traced to raw material and component production, notably lithium batteries. By 2025, under the low carbon EVs policy scenario, widespread EV production and sales could cut lifecycle emissions by 3.55 million tons of CO2, 3,6289 tons of NOx, and 4315 tons of VOCs. During the driving stage, these indicators contribute 495%, 124%, and 253%, respectively, to total emission reduction throughout the lifecycle. This study conducts a comprehensive lifecycle analysis of greenhouse gases and various air pollutants for Chinese EVs. It integrates the latest market trends, application progress, and policy guidelines into scenario design, identifying key sources and indicators of atmospheric pollution in the EV production chain. The findings offer valuable policy insights into China’s role in the global emission reduction process

Influence of the Design Parameters of a Fuel Thermal Management System on Its Thermal Endurance

The aerodynamic heating of a high-speed vehicle is destined to lead to a continuous fuselage temperature rise. However, its airborne thermal load rises exponentially. This will severely limit the thermal endurance of the high-speed vehicle and the working time of the electronic equipment. A jet-propelled high-speed vehicle usually uses fuel to generate thrust, so fuel thermal management technology has had much attention paid to it. During the vehicle design, its total amount of fuel should match its flight envelope. However, determining the amount of carried fuel is very difficult because it is affected by many factors. In order to analyze the relationship between the above influence factors and the flight envelope, a typical fuel thermal management system is set up for high-speed vehicles. Its dynamic characteristic equations are built correspondingly. A conception of thermal endurance is further presented to reveal the maximum flight time. Some flight conditions are used to analyze the influence of the main design parameters on the thermal endurance of high-speed vehicles. The results can help to design the parameters of fuel thermal management systems for high-speed vehicles

Performance modeling based on artificial neural network in virtualized environments

Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability and high availability. Although virtualization technology has the advantages such as fault isolation, environmental isolation and security isolation, current virtualization techniques do not have effective performance isolation among virtual machines. The hidden resource competition does exist which is especially severe for applications running on the same physical machine. It is essential to build models to accurately predict application performance interference among virtual machines to mitigate performance interference effect. In this paper, we mainly focus on performance modeling in virtualized environments. We explore modeling techniques of artificial neural network and regression models and evaluate their effectiveness in modeling application performance in virtualized environments. Based on the performance prediction model, we propose the resource management architecture. Experimental evaluations show that our performance model has good prediction performance over regression models. © 2013 IFSA.Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability and high availability. Although virtualization technology has the advantages such as fault isolation, environmental isolation and security isolation, current virtualization techniques do not have effective performance isolation among virtual machines. The hidden resource competition does exist which is especially severe for applications running on the same physical machine. It is essential to build models to accurately predict application performance interference among virtual machines to mitigate performance interference effect. In this paper, we mainly focus on performance modeling in virtualized environments. We explore modeling techniques of artificial neural network and regression models and evaluate their effectiveness in modeling application performance in virtualized environments. Based on the performance prediction model, we propose the resource management architecture. Experimental evaluations show that our performance model has good prediction performance over regression models. © 2013 IFSA

Study on the anticancer biological mechanism of Resveratrol

Trans 3, 4 ′, 5-3 hydroxy 2 styrene (resveratrol) is a kind of naturally occurring polyphenols phytoalexin. Resveratrol has significant anti-cancer activity, mainly exists in grapes, berries, and peanuts, has anti-aging, protect the heart, antioxidant, antiproliferation, promote apoptosis and immune regulation. Resveratrol has been widely concerned in the treatment of cancer and autoimmune diseases resveratrol. Resveratrol synthase (RS) is a key enzyme in the synthesis of resveratrol synthase. In this study, RS containing genes were placed under the control of fruit-specific promoter RJ39 to transform tomatoes by transgenic method. The extraction of fruits containing RS genes showed an obvious absorption peak on the HPLC chromatographic map, and it also had an obvious inhibitory effect on the growth of Hela cells

Performance modeling on the basis of application type in virtualized environments

Virtualization technology plays an essential role in resource in modern large data centers while it also causes interference among virtual machines which co-located in common physical machine contending for the shared physical resources. In this paper, we study the performance prediction models in virtualized environment. Unclassified model developed from all types of applications is quite inaccuracy to predict performance of test applications as it is too general. We respectively develop models for each type of applications classified by the resources that they use. See5/C5 technology is used to determine the type of test application before executing its corresponding performance prediction model and linear regression technique is adopt to develop performance prediction models. Finally, comparing classified models with the unclassified one, the former get 0.038 average prediction errors for test applications while unclassified model arrives 0.609 average prediction errors. © 2013 ACADEMY PUBLISHER.Virtualization technology plays an essential role in resource in modern large data centers while it also causes interference among virtual machines which co-located in common physical machine contending for the shared physical resources. In this paper, we study the performance prediction models in virtualized environment. Unclassified model developed from all types of applications is quite inaccuracy to predict performance of test applications as it is too general. We respectively develop models for each type of applications classified by the resources that they use. See5/C5 technology is used to determine the type of test application before executing its corresponding performance prediction model and linear regression technique is adopt to develop performance prediction models. Finally, comparing classified models with the unclassified one, the former get 0.038 average prediction errors for test applications while unclassified model arrives 0.609 average prediction errors. © 2013 ACADEMY PUBLISHER