Active slip control of a vehicle using fuzzy control and active suspension

This paper presents an active slip control system (ASCS) for a four-wheel drive electric vehicle (EV) using an active suspension of the vehicle. The integrated control mechanism is designed using a combination of a fuzzy controller and a nonlinear back-stepping controller to control the slip of the individual wheels with the help of the active suspension of the vehicle. In this research, the presented control mechanism is implemented in two steps. In the first step, based on the friction coefficient calculated from a nonlinear tire model, the fuzzy controller will generate the vehicle roll and pitch angles required to reduce the slipping of the individual wheels by changing the vertical load of the individual wheel. In the second step, a nonlinear back-stepping controller is used to track the required roll and pitch angles using the active suspension of the vehicle. A linear seven degree of freedom (DOF) vertical mathematical model is used for the design of the nonlinear back-stepping controller, while the rules of the fuzzy controller are interpreted from the friction coefficients of the tyre model. The performance of the presented control mechanism is verified using a 14-DOF nonlinear model with nonlinear tyre dynamics. The simulations using a nonlinear vehicle model show that the presented controller can successfully improve vehicle stability by reducing the slipping of the individual wheel

Predicting Revisit Intention of Commuters: A Case Study of Private Bus Company in Pakistan

Present research tends to explore the impact of service quality and relationship switching cost on revisiting intention. This scrutiny is quantitative in nature that has explored new dimensions of service quality designed public transportation industry. Specifically, revisit intention is taken as main contributor in this study based on commuter satisfaction. In Pakistan, intercity bus service has become competitive market after improvement in road structure and economic globalization. This study considered Daewoo Express Bus Service (Pakistan). Data was collected from 167 commuters that travel through private bus services. All proposed hypotheses were supported. Thus, study unraveled several managerial implications such as private bus services should increase service quality by providing them choices about internal environment of buses. Moreover, it’s not a wise decision by removing services to reduce expenses/cost to earn profits. This led intentions for commuters to switch as Daewoo is an educated professional’s choice of traveling, therefore new tools of commuter retentions are required to maintain their brand meaning

Myasthenia Gravis Exacerbation Following COVID-19 Vaccine: A Case Report

Background: As of December 2021, the World Health Organization (WHO) reports that Coronavirus disease 2019 (COVID-19) led to about 5,403,662 deaths. While COVID-19 has resulted in millions of deaths worldwide to date, vaccination remains the mainstay of infection control. AZD1222 (AstraZeneca vaccine) was distributed in Sudan by the COVID-19 Vaccines Global Access facility in March 2021. It was added to the emergency use list by WHO in the middle of February 2021. However, vaccine safety among patients with autoimmune diseases, such as myasthenia gravis (MG), is yet to be established. MG is a relatively rare illness that could result in life-threatening complications. Myasthenic crisis is considered the most serious complication of MG that can lead to death due to aspiration and respiratory failure. Plasma exchange (PLEX), Immunoadsorption (IA), and intravenous Immunoglobulin (IVIG) are the first-line treatment for myasthenic crisis. It is proven that cortisone has a positive effect when used as add-on therapy with PLEX/IA and IVIG.   The case: We report the case of a 37-year-old Sudanese female who presented to the emergency room with an exacerbation of her previously well-controlled MG following her second dose of AZD1222 vaccination. The exacerbation symptoms at time of presentation were severe generalized body weakness that increasing overtime and shortness of breath. Computerized tomography of the chest was performed, and it revealed no evidence of COVID-19. Management at the ER started with rehydration and IV methylprednisolone 1g, followed by IV hydrocortisone 200mg. She continued to deteriorate and was admitted to the intensive care unit where she was intubated and placed on a mechanical ventilator. IVIG was requested but couldn't be obtained due to the low-income setting, and fourteen days after admission patient died due to circulatory collapse. Our study aims to present an MG case with features of MG exacerbation following the administration of the second dose of AZD1222.   Conclusion: Little is known about the effect of different COVID-19 vaccines on subgroups of patients with autoimmune diseases like MG. Although the safety profile of AZD1222 is generally reassuring, people with severe underlying diseases were excluded from trials. Therefore, more efforts and experimental studies may be needed, with closer vigilance in MG patients. It has not been elucidated how the COVID-19 vaccine might provoke autoimmunity, but several theories have been proposed. Molecular mimicry theory can explain how the genetic material of a virus could provoke autoimmunity, it describes the cross-reactivity of antibodies produced against proteins that are encoded by viral genetic material with the proteins located at the post-synaptic membrane. There is a debate about whether vaccine benefit outweighs the risk in MG patients or not. However, we believed that MG patients should be informed about the benefit and risks of COVID-19 vaccination. &nbsp

Integrated database system with spatial information for disaster risk management

© 2019 AECE. Despite availability of various image sources for specific areas, a new disaster management system is likely to be implemented by using only one of them. Thus, its applicability and extensibility are severely limited. In addition, real-time update for the disaster area is one of the crucial functions for search and rescue activities. To meet the aforementioned requirements, in this paper, we propose a new spatial data infrastructure by defining the methodological scheme for the raster information. The proposed system has four respective layers to reduce the management cost as well as provide a flexible architecture. In each layer, various open source software or standard technologies are employed to perform the given tasks. The experimental results reveal that the proposed scheme accommodates the requirements for disaster risk management and meets the performance requirements in an efficient way

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively

Entropy based features distribution for anti-ddos model in SDN

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%

The spatiotemporal features of Greenhouse Gases Emissions from Biomass Burning in China from 2000-2012

Greenhouse gases emissions from biomass burning have been given a little attention, especially the spatiotemporal features of biomass burning sources and greenhouse gases emissions have not been comprehensively uncovered. This research undertook IPCC bottom-up inventory guideline to estimate Chinese greenhouse gases emissions from biomass burning and applied geographical information system to reveal biomass burning emissions spatiotemporal features. The purposes were to quantify greenhouse gases emissions from various biomass burning sources and to uncover the spatial and temporal emissions features so to deliver future policy implications in China. The results showed that the average annual biomass burning emissions in China from 2000-2012 were 880.66 Mt for CO2, 96.59 Mt CO2-eq for CH4, and 16.81 Mt CO2-eq for N2O. The spatial pattern of biomass greenhouse gases emissions showed about 50 % of national emission were in the east and south-central regions. The majority of biomass burning emissions were from firewood and crop residues, which accounted for more than 90 % of national biomass burning emissions. All types of biomass burning emissions exhibited similar temporal trends from 2000-2012, with strong inter-annual variability and fluctuant increase. The large grassland and forest fires induced the significant greenhouse gases emissions peaks in the years of 2001, 2003 and 2006. We found that biofuel burning, with low combustion efficiency, is the major emission source. Open burning of biomass was widespread in China, and east and south-central regions were the major distribution of biomass burning greenhouse gases emission. Optimized design for improving the efficiency of biomass utilization and making emission control policy combination with its spatiotemporal features will be the effective way to reduce the biomass burning emissions

Bodacious-instance coverage mechanism for wireless sensor network

Copyright © 2020 Shahzad Ashraf et al. Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA

Lactobacilli: Application in Food Industry

Lactobacillus is an important class of Gram-positive, non-spore-forming bacteria for food industrial applications. The genus Lactobacillus is a potential candidate in fermentation technology for the production of fermented food, feed, and pharmaceutical products. The diverse features of Lactobacilli based on their capability to produce acids, enzymes, bacteriocins by fermenting carbohydrates. Lactobacilli have probiotic potential and therefore applied in dairy [cheese, yoghurt, fermented milk] and nondairy products such as sausages, juices as well as in animal feed in the form of starter culture. Among Lactobacilli, lactic acid-producing bacteria are applied as starter cultures in a variety of fermented foods. Lactobacilli are the natural microflora of the gastrointestinal tract and play a beneficial role against infections. The ability of Lactobacilli to produce bacteriocins and other antifungal compound lead to the development of bioprotective cultures for use in different foods. Bacteriocins has wide applications in food industries for preventing the attack of foodborne pathogens and for manufacturing active packaging materials. This chapter aimed to review significant industrial applications of Lactobacilli with specified strains and also starter cultures with their potential beneficial effects are engrossed. The chapter highlights the commercial applications of Lactobacilli in the food, feed, wine and pharmaceutical industries

Facial expression recognition using lightweight deep learning modeling

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain