Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs

Recent developments have made autonomous vehicles (AVs) closer to hitting our roads. However, their security is still a major concern among drivers as well as manufacturers. Although some work has been done to identify threats and possible solutions, a theoretical framework is needed to measure the security of AVs. In this paper, a simple security model based on defense graphs is proposed to quantitatively assess the likelihood of threats on components of an AV in the presence of available countermeasures. A Bayesian network (BN) analysis is then applied to obtain the associated security risk. In a case study, the model and the analysis are studied for GPS spoofing attacks to demonstrate the effectiveness of the proposed approach for a highly vulnerable component.Comment: IEEE 88th Vehicular Technology Conference: VTC2018-Fal

Mix Design Effects on the Durability of Alkali-Activated Slag Concrete in a Hydrochloric Acid Environment

Because of its high strength, energy reduction, and low environmental impact, researchers have encouraged considering alkali-activated slag concrete (AASC) as a potential alternative to conventional concrete. In this study, the impact of mix design parameters on the durability of AASC, made with ground granulated blast furnace slag and activated with different alkaline solutions (NaOH, KOH, and Na2SiO3 ) immersed up to six months in a hydrochloric acid bath with pH = 3, has been investigated. A total of 13 mix designs were made in a way that, in addition to the type of alkaline solution, considered three other parameters, namely the molarity of alkaline solutions, the weight ratio of alkaline solutions to slag, and the weight ratio of alkaline solutions to sodium silicate. Visual inspections displayed that the AASC samples almost remained intact after exposure to an HCl acid solution with pH = 3 for up to 6 months, while the OPC sample experienced deleterious deterioration. The results clearly show that AASC outperformed OPC concrete when it comes to durability in an HCl acid solution. The strength reduction and weight loss of AASC compared with OPC concrete were approximately one-tenth and one-fifth, respectively. The AASC samples containing potassium hydroxide showed a higher strength reduction and weight loss in the HCl acid solution than the samples made with sodium hydroxide

Effect of Micro-Silica Addition into Electric Arc Furnace Steel Slag Eco-Efficient Concrete

ABSTRACT: Concrete produced from electric arc furnace steel slag aggregates is one of the items that is highly regarded due to its strength, environmental friendliness and cost-effectiveness. Despite the growing interest in using this type of concrete, there are still doubts about the mix proportions and addition effects of electric arc furnace steel slags. In this paper, the performance of replacing natural aggregates by electric arc furnace steel slags aggregate is comprehensively investigated and its effect on mechanical properties is analysed. The relationship between the percentage of replacement of natural aggregate using electric arc furnace steel slags aggregate in two parts of coarse aggregate and fine-grained aggregate and the effect of each of these parts on mechanical properties in concrete is investigated, which may identify the optimal mix proportions of each aggregate that help to improve the strength of the eco efficient concrete using electric arc furnace steel slags

Retro MTA and tricalcium phosphate/retro MTA for guided tissue regeneration of periodontal dehiscence defects in a dog model: a pilot study

Objectives: Retro MTA is a fast setting Calcium silicate cement used in endodontic regeneration procedures in recent years. Beta-tricalcium phosphate (β-TCP) is another common biomaterial used for bone augmentation procedures. The present pilot study was undertaken to evaluate and compare the efficacy of Retro MTA and a mixture of Retro MTA / β-TCP for periodontal tissue regeneration. Materials and methods: In 4 beagle dogs, periodontal dehiscence type defects were created. In each side, one dehiscence defect was left empty as a control site and three treatment modalities were randomly applied for the others: Retro MTA covered with a collagen membrane, Retro MTA + β-TCP covered with a membrane and covering the defect with a membrane without any bone augmentation. After 8 weeks Animals were sacrificed and Histomorphometric and histologic analysis were conducted. Results: Histologic analysis showed more cementum formation for both Retro MTA+ β-TCP (3.74 ± 0.34 mm) and Retro MTA group (3.24 ± 0.56 mm) compared to control group 1 (1. 15 ± 0.45 mm) and control group 2 (0.78 ± 0.65 mm). Formation of newly formed bone and cementum in the experimental groups were significantly higher as compared to the control groups (P < 0.0001). Conclusions: Retro MTA or Retro MTA+ β-TCP covered with a collagen membrane resulted in regeneration of periodontal tissues. However, Retro MTA+ β-TCP showed tendency towards better results than the use of Retro MTA alone. KEYWORDS: Bone regeneration; Calcium silicate cement; Guided tissue regeneration; MT

Microstructural analysis of siderurgical aggregate concrete reinforced with fibers

The development of cracks in concrete structures is one of the significant issues with maintaining high strength after hardening. One way to prevent and control this problem is to use fibers. This paper investigates concrete containing electric arc furnace slag aggregates reinforced with fibers. The fibers used in this study are steel fibers and three kinds of polypropylene fibers; polyolefin fibers (modified polypropylene), polypropylene homopolymer, and high-toughness polypropylene. By checking the compressive and flexural strength of concretes made with fibers, it can be seen that the best results at 28 days are found for concrete with steel fibers, namely 62 MPa with 0.9% of fibers. On the contrary, the lowest values are for concrete containing polyolefin fibers, 51 MPa, and the same percentage of fibers. Additionally, under flexural strength testing, at the age of 28 days, the strength of these samples with 0.9% of fibers was 9.54 MPa, a value that is comparable to test concrete with the same percentage of steel fibers, 10.67 MPa, despite the low workability of concrete containing polyolefin fibers with a slump of 25 mm. Moreover, the boundary transition area analysis shows that the excellent connection between the fibers and cement paste near the siderurgical aggregate has caused no cracks in this area. In contrast, cracks can be observed in critical areas near the natural aggregates

The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete

The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application of the aforementioned models is developed based on statistical tools such as coefficient of determination, mean absolute error, root mean squared error, and some others, and the application potential of each of these models is investigated. To study the effects of RBAC factors on the performance of representative data-driven models, the Sensitivity Analysis (SA) method is used. The findings revealed that ANN with R2 value of 0.9102 has a great application potential in predicting the compressive strength of concrete. In the absence of ANN, ANFIS with R2 value of 0.8538 is also an excellent substitute for predictions. MLR was shown to be less effective than the preceding models and is only recommended for preliminary estimations. In addition, Subsequent sensitivity analysis on the database indicates the reliability of the prediction models have a strong correlation to the number of input parameters. The application of ANN and ANFIS as a precursor to traditional methods can eliminate the need for old-style tests, thus, constituting a significant reduction in time and expense needed for design and/or repairs

Effect of Duration of Use of a Toothbrush on its Filament's Tapering and Plaque Removal Efficacy

Background and Objectives: Dental professionals often recommend changing toothbrushes every three months due to their wear and decreased plaque removal efficacy. This study aimed to assess the correlation of duration of use of a toothbrush and its wear and then the relationship of wear of toothbrush and Plaque Index (PI) of users and tapering of toothbrush filaments after three months of use was evaluated as well. Materials and Methods: In this study, 60 female students were enrolled according to the eligibility criteria and received a new set of toothbrush and toothpaste. They were requested to brush their teeth using the Bass technique twice a day for 2 minutes and the Ramfjord PI was measured at baseline and after 3 months. The wear of 47 toothbrushes after three months of use was evaluated using the Rawls index. Of collected 47 toothbrushes, 30 were randomly chosen and inspected under an electron microscope to determine the degree of tapering of bristles. Data were analyzed using SPSS version 20 via Spearman's correlation coefficient, paired t-test and Pearson's correlation coefficient (alpha=0.05). Results: After 3 months, the mean PI score significantly decreased (paired t-test, P=0.034). The PI at 3 months after use had no significant correlation with the toothbrush wear score (Spearman's correlation coefficient, P=0.61) but the toothbrush wear score had a significant correlation with tapering of bristles (Spearman's correlation coefficient, P=0.04). No significant association existed between PI at 3 months after use and tapering of bristles (Pearson's correlation coefficient, P=0.69). Conclusion: The duration of use and wear of toothbrush alone do not affect the quality of plaque removal. Practicing Oral hygiene can affect the quality of plaque removal

Application of probabilistic inference to resiliency and security analysis of cyber-physical systems

Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering & Computer ScienceThis dissertation studies two important topics regarding the resiliency and the security of cyber-physical systems (CPSs). In the first work, a self-healing graphical representation is proposed to study the contagion of failures in self-healing interdependent networks. To this end, a graphical model representation of an interdependent cyber-physical system is proposed, in which nodes denote various cyber or physical functionalities, and edges capture the interactions between nodes. Then, a message-passing (belief propagation) algorithm is applied to this representation in order to analyze network reactions to initial disruptions. The framework is then extended to cases where the propagation of failures in the physical network is faster than the healing responses of the cyber network. Such scenarios are of interest in many real-life applications, such as the smart grid. As a result, it is proven that as the number of message-passing iterations increases, the network reaches a steady-state condition that would be either a complete healing or a complete collapse. The findings from this analysis help network designers have a better understanding of the resiliency of CPSs. In the second work, security measurement and the malicious node detection of autonomous vehicles in intelligent transportation systems are studied. First, a simple security model based on Bayesian defense graphs is proposed to quantitatively assess the likelihood of threats against autonomous vehicles (AVs) in the presence of available countermeasures. Then, a game-theoretic model is represented using a local voting-based game to detect misbehaving neighboring vehicles in places where centrally managed stations are absent. In order to capture the inherent uncertainty of vehicles in ephemeral vehicular networks, a Bayesian game is used in which malicious nodes can potentially impact the result of the game. Then, equilibria of this game are obtained to study the strategies of malicious and benign nodes in networks. Using the analysis, the game parameters can be designed to achieve the maximum performance of misbehavior detection in vehicular networks