Investigation of vibration’s effect on driver in optimal motion cueing algorithm

The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver’s body model incorporates the effects of vibrations on the driver’s health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver’s body model in conjunction with the driver’s perception model to minimize the sensation error. Moreover, this article employs H1 control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively)

The Spectrum of Oesophagal Varices, Portal Hypertensive Gastropathy and Child Pugh’s Class in Cirrhotic Patients at Tertiary CareHospital

Objective: To find the association of Child-Pugh’s Class with oesophagal varices and portal hypertensive gastropathy in cirrhotic patients at CMH Lahore. Study Design: Cross sectional study Place and Duration of Study: Department of Gastroenterology and Department of Pathology, Combined Military Hospital Lahore Pakistan, from Feb to May 2021. Methodology: All patients with cirrhosis of the liver, irrespective of aetiology, who underwent upper gastrointestinal endoscopy, were included in the study. Lab data was retrieved from the Pathology Department to calculate Child Pugh’s score. Endoscopic findings of oesophagal varices and portal gastropathy were recorded and their correlation/association with Child-Pugh’s Class was calculated separately by using Pearson’s coefficient. Results: A total of 148 patients were included in the study. Male patients were 90(60.8%) and female were 58(39.2%). The age range was 27-85 years, with the mean of patients being 55.93±13.19 years. Association of Child Pugh’s Class with oesophagal varices and portal hypertensive gastropathy revealed that higher grades of oesophagal varices (Grade-lll) and severe portal hypertensive gastropathy were found in Child Pugh’s Class-B (13.51%, 14.18%) and C (15.54%, 16.2%) as compared to Class- A (4.72 %, 1.35%). Child-Pugh’s Class positively correlates with both oesophagal varices and portal hypertensive gastropathy by Pearson’s coefficient r=0.594 and 0.035, respectively; both have significant p values (p <0.05). Conclusion: Child-Pugh’s Class has a positive correlation with both oesophagal varices and portal hypertensive gastropathy in patients with cirrhosi

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Robust Sliding Mode Control of a Unipolar Power Inverter

The key issue in the practical implementation of the sliding mode (SM) control–based power inverter is the variable switching frequency. This variable switching frequency not only induces electromagnetic interference (EMI) noise, but also reduces the efficiency of the inverter, as the size of the inductor and capacitor does not alter in tandem with this variable frequency. In this context, fixed switching frequency–based SM control techniques are proposed; however, some of them are too complex, while others compromise the inherent properties of SM control. In this research, a fixed frequency SM controller is proposed, which is based on the novel low-pass filter extraction of the discontinuous control signal. This allows the technique to be implemented with fewer hardware components, thus reducing the complications of implementation, while maintaining the robustness and parametric invariance of SM control. A simulation-based comparison with an existing pulse width modulated (PWM) SM controller is presented as the benchmark. In comparison with the sigmoid function SM controller, an improvement of 50% in the settling time along with zero steady-state errors and a further 37% and 42% improvement in the undershoot and overshoot, respectively, is reported in the simulation. A hardware setup is established to validate the proposed technique, which substantiates the simulation results and its disturbance rejection properties

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.</jats:p

Fake News Data Exploration and Analytics

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications

Real-Time DDoS Attack Detection System Using Big Data Approach

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds