Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict Quality of Service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Towards this direction, a novel Pareto-based algorithm in fog computing namely Energy effi

Intussusception of the appendix secondary to endometriosis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Intussusception of the appendix is an extremely rare condition that ranges from partial invagination of the appendix to involvement of the entire colon. Endometriosis is an exceptionally rare cause of appendiceal intussusception and only very few cases have been reported in the literature to date.</p> <p>Case presentation</p> <p>A 40 year-old woman presented to clinic with a long history of lower abdominal pain, loose motions and painful, heavy periods. Subsequent colonoscopy revealed submucosal endometriotic nodules in the sigmoid as well as a polyp thought to be arising from the appendix, which had inverted itself. She was referred to a colorectal surgeon because the polyp could not be removed endoscopically despite several attempts. At laparotomy, the appendix had intussuscepted but it was possible to reduce it and therefore a simple appendicectomy was carried out. On histology, there were widespread endometrial deposits within the wall of the appendix and this was thought to be the basis for the intussusception.</p> <p>Conclusion</p> <p>Histological evidence of the lead point is of crucial importance in cases of appendiceal intussusception, in order to exclude an underlying neoplastic process. Consequently, surgical resection is necessary either through an open or a laparoscopic approach. Gastrointestinal endometriosis should be considered as a cause of appendiceal intussusception in post-menarchal women with episodic symptoms and proven disease.</p

Torsion of the gallbladder: a case report

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DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs

Infectious disease-related illness has always posed a concern on a global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help create chances for better care in all circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) tests, Monteux tuberculin skin tests (TST), and ultrasounds, are used to detect and classify these diseases. However, these tests take a lot of time, have a 20% mistake rate, and are 80% sensitive. So, with the aid of a doctor, radiographic tests such as computed tomography (CT) and chest radiograph images (CRIs) are used to detect lung disorders. With CRIs or CT-scan images, there is a danger that the features of various lung diseases’ diagnoses will overlap. The automation of such a method is necessary to correctly classify diseases using CRIs. The key motivation behind the study was that there is no method for identifying and classifying these (LO, pneumonia, VP, BP, TB, COVID-19) lung diseases. In this paper, the DeepLungNet deep learning (DL) model is proposed, which comprises 20 learnable layers, i.e., 18 convolution (ConV) layers and 2 fully connected (FC) layers. The architecture uses the Leaky ReLU (LReLU) activation function, a fire module, a maximum pooling layer, shortcut connections, a batch normalization (BN) operation, and group convolution layers, making it a novel lung diseases classification framework. This is a useful DL-based method for classifying lung disorders, and we tested the effectiveness of the suggested framework on two datasets with a variety of images from different datasets. We have performed two experiments: a five-class classification (TB, pneumonia, COVID-19, LO, and normal) and a six-class classification (VP, BP, COVID-19, normal, TB, and LO). The suggested framework’s average accuracy for classifying lung diseases into TB, pneumonia, COVID-19, LO, and normal using CRIs was an impressive 97.47%. We have verified the performance of our framework on a different publicly accessible database of images from the agriculture sector in order to further assess its performance and validate its generalizability. This study offers an efficient and automated method for classifying lung diseases that aids in the early detection of lung disease. This strategy significantly improves patient survival, possible treatments, and limits the transmission of infectious illnesses throughout society

Efficient Scheduling Strategy for Task Graphs in Heterogeneous Computing Environment

Abstract: Today’s multi-computer systems are heterogeneous in nature, i.e., the machines they are composed of, have varying processing capabilities and are interconnected through high speed networks, thus, making them suitable for performing diverse set of computing-intensive applications. In order to exploit the high performance of such a distributed system, efficient mapping of the tasks on available machines is necessary. This is an active research topic and different strategies have been adopted in literature for the mapping problem. A novel approach has been introduced in the paper for the efficient mapping of the DAG-based applications. The approach that takes into account the lower and upper bounds for the start time of the tasks. The algorithm is based on list scheduling approach and has been compared with the well known list scheduling algorithms existing in the literature. The comparison results for the randomly synthesized graphs as well as the graphs from the real world elucidate that the proposed algorithm significantly outperforms the existing ones on the basis of different cost and performance metrics

Energy-makespan optimization of workflow scheduling in fog-cloud computing

The rapid evolution of smart services and Internet of Things devices accessing cloud data centers can lead to network congestion and increased latency. Fog computing, focusing on ubiquitously connected heterogeneous devices, addresses latency and privacy requirements of workflows executing at the network edge. However, allocating resources in this paradigm is challenging due to the complex and strict Quality of Service constraints. Moreover, simultaneously optimizing conflicting objectives, e.g., energy consumption and workflow makespan increases the complexity of the scheduling process. We investigate workflow scheduling in fog–cloud environments to provide an energy-efficient task schedule within acceptable application completion times. We introduce a scheduling algorithm, Energy Makespan Multi-Objective Optimization, that works in two phases. First, it models the problem as a multi-objective optimization problem and computes a tradeoff between conflicting objectives while allocating fog and cloud resources, and schedules latency-sensitive tasks (with lower computational requirements) to fog resources and computationally complex tasks (with low latency requirements) on cloud resources. We adapt the Deadline-Aware stepwise Frequency Scaling approach to further reduce energy consumption by utilizing unused time slots between two already scheduled tasks on a single node. Our evaluation using synthesized and real-world applications shows that our approach reduces energy consumption, up to 50%, as compared to existing approaches with minimal impact on completion times

Numerical Modeling and Symmetry Analysis of a Pine Wilt Disease Model Using the Mittag–Leffler Kernel

The existence of man is dependent on nature, and this existence can be disturbed by either man-made devastations or by natural disasters. As a universal phenomenon in nature, symmetry has attracted the attention of scholars. The study of symmetry provides insights into physics, chemistry, biology, and mathematics. One of the most important characteristics in the expressive assessment and development of computational design techniques is symmetry. Yet, mathematical models are an important method of studying real-world systems. The symmetry reflected by such a mathematical model reveals the inherent symmetry of real-world systems. This study focuses on the contagious model of pine wilt disease and symmetry, employing the q-HATM (q-Homotopy Analysis Transform Method) to the leading fractional operator Atangana–Baleanu (AB) to arrive at better understanding. The outgrowths are exhibited in the forms of figures and tables. Finally, the paper helps to analyze the practical theory, assisting the prediction of its manner that corresponds to the guidelines when contemplating the replica