In recent years, Cloud computing has been developed and become the foundation of a wide range of applications. It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made. Task scheduling problem is considered one of the most critical cloud computing challenges. The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines. Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction. The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance. In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment. We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application's task. To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT). Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.

In Egypt, pneumococcal vaccines have not yet been introduced as being compulsory. Identification of the circulating serotypes in Egypt is mandatory to determine whether or not the pneumococcal vaccines will be beneficial. The current study aims to identify the serotypes, vaccine coverage, and antimicrobial resistance of Streptococcus pneumoniae colonizing the nasopharynx of Egyptian children younger than 5 years old. The study was conducted in two successive winter seasons (December 2012-February 2013 and December 2013-February 2014). Two hundred children were enrolled, aged from 6 months to 5 years, excluding those with fever, signs of infection, history of antibiotic intake, and hospitalization in the preceding month. Nasopharyngeal (NP) secretions were collected, subjected to culture, and underwent antibiotic susceptibility testing if positive for pneumococci. Real-time polymerase chain reaction (PCR) and serotyping by sequential multiplex PCR for positive cases were included as well. Streptococcus pneumoniae was isolated from 62 subjects. All isolates were sensitive to vancomycin and levofloxacin, but the majority showed resistance to multiple antibiotics. PCR was positive for pneumococci in 113 subjects (56.5%). The most commonly detected serotypes (st) were 6A6B6C (n = 21, 20.8%), 19F (n = 19, 18.8%), 1 (n = 11, 10.9%), 34 (n = 8, 7.9%), and 19A (n = 6, 5.9%). The theoretical coverage of the PCV13 vaccine for the detected serotypes was 72.4%, while that of PCV10 was 65.5%. Based on these percentages, we recommend including pneumococcal conjugate vaccines in the Egyptian national vaccination program.

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It is possible for a natural catastrophe to cause harm to numerous industrial facilities in the same region simultaneously. The natural catastrophe's Natech events may then affect the industrial facilities that are located nearby, so creating a coupling risk. The evaluation of the danger of Natech events coupling is conducted using the technique of multi-criteria decision-making (MCDM) methodology in this investigation. Additionally, the concept of spherical fuzzy is presented as a means of resolving the issue of ambiguity associated with the Natech coupling risk. The Natech Coupling Hazard Index is designed to include both tangible and operational resources in its calculations. The idea of an equal population is being floated as a means of contrasting the dangers presented by physical facilities with those posed by functional amenities. The spherical fuzzy set is an effective method for coping with ambiguity since it presents a broader decision-making region and identifies reluctance. under this paper, a fuzzy MDCM technique using spherical fuzzy AHP is proposed as a solution to the challenge of managing the selection of process mining methods under settings that are unclear and vague. The AHP method is used to compute the weights of criteria and shows the rank and order of alternatives. The application is performed in steps of the spherical fuzzy AHP method.

Gait recognition has gained significant attention in recent years due to its potential applications in various fields, including surveillance, security, and healthcare. Biometric gait identification, which involves recognizing individuals based on their walking patterns, is a challenging task due to the inherent variations in gait caused by factors such as clothing, footwear, and walking speed. In this paper, we propose a computational intelligence approach for biometric gait identification. Specifically, we integrate an intelligent convolutional model to identify human gaits based on the inertial sensory data captured from the body movement during the human walk. Extensive experiments on two datasets demonstrated that the efficiency of the proposed approach outperforms the existing methods. Our approach has the potential to be used in real-world applications such as surveillance systems and healthcare monitoring, where accurate and efficient identification of individuals based on their gait is crucial.