Multi-Objective Task Scheduling Approach for Fog Computing

Despite the remarkable work conducted to improve fog computing applications’ efficiency, the task scheduling problem in such an environment is still a big challenge. Optimizing the task scheduling in these applications, i.e. critical healthcare applications, smart cities, and transportation is urgent to save energy, improve the quality of service, reduce the carbon emission rate, and improve the flow time. As proposed in much recent work, dealing with this problem as a single objective problem did not get the desired results. As a result, this paper presents a new multi-objective approach based on integrating the marine predator’s algorithm with the polynomial mutation mechanism (MHMPA) for task scheduling in fog computing environments. In the proposed algorithm, a trade-off between the makespan and the carbon emission ratio based on the Pareto optimality is produced. An external archive is utilized to store the non-dominated solutions generated from the optimization process. Also, another improved version based on the marine predator’s algorithm (MIMPA) by using the Cauchy distribution instead of the Gaussian distribution with the levy Flight to increase the algorithm’s convergence with avoiding stuck into local minima as possible is investigated in this manuscript. The experimental outcomes proved the superiority of the MIMPA over the standard one under various performance metrics. However, the MIMPA couldn’t overcome the MHMPA even after integrating the polynomial mutation strategy with the improved version. Furthermore, several well-known robust multi-objective optimization algorithms are used to test the efficacy of the proposed method. The experiment outcomes show that MHMPA could achieve better outcomes for the various employed performance metrics: Flow time, carbon emission rate, energy, and makespan with an improvement percentage of 414, 27257.46, 64151, and 2 for those metrics, respectively, compared to the second-best compared algorithm

Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched

An Improved Binary Grey-Wolf Optimizer with Simulated Annealing for Feature Selection

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms

Exploring Human Aging Proteins Based on Deep Autoencoders and K-Means Clustering

Aging significantly affects human health and the overall economy, yet understanding of the underlying molecular mechanisms remains limited. Among all human genes, almost three hundred and five have been linked to human aging. While certain subsets of these genes or specific aging-related genes have been extensively studied. There has been a lack of comprehensive examination encompassing the entire set of aging-related genes. Here, the main objective is to overcome understanding based on an innovative approach that combines the capabilities of deep learning. Particularly using One-Dimensional Deep AutoEncoder (1D-DAE). Followed by the K-means clustering technique as a means of unsupervised learning. The proposed technique offers a novel approach for identifying new candidate proteins with compelling computational evidence of their significant roles in aging. The 1D-DAE model was trained on recognized aging proteins and subsequently employed to effectively compress and represent the complete array of features inherent in all human proteins. This transformation reduced the feature space from 21,000 dimensions to a mere 64 dimensions. Leveraging the distinct characteristics of the aging proteins uncovered during the training phase. Subsequently, we applied the K-means algorithm to partition the human proteins into distinct clusters based on their feature similarities. Using these clusters, we made highly accurate predictions about proteins that may play a significant role in the aging process. Among these proteins, some lack previous annotations related to aging, making the results particularly significant in illuminating potential key proteins in the aging process

Role of nanoparticles in diagnosis and management of parasitic diseases: Review article

Background: An extensive class of materials, nanoparticles (NPs) include particulate compounds with a minimum diameter of 100 nanometers (nm). This is because of their tiny size and huge surface area, which allows them to traverse the blood-brain barrier, enter the respiratory system and be adsorbable through endothelial cells. Today, nanoparticles for drug administration are being studied to increase their sustained release, intracellular penetrability as well as bioavailability, due to the constant development and innovation of nanomedicine.Objective: To determine how nanoparticles can help diagnose and treat parasitic diseases.Conclusion: Nanoparticles could be conjugated with proteins and immunoglobulins that could help in specific diagnosis of several parasitic diseases, in addition, improved efficacy and reduced harmful side effects can be achieved by immobilizing antiparasitic medicines on or inside nanomaterials

Effect of nitric oxide donors on uterine and sub-endometrial blood flow in patients with unexplained infertility: a randomized controlled trial

Background: Impaired sub-endometrial perfusion might reduce endometrial receptivity and possibly contribute to unexplained infertility. A favorable effect on sub-endometrial blood flow has been demonstrated with nitric oxide.Methods: This randomized controlled trial evaluated the effect of nitroglycerine on uterine and sub-endometrial blood flow in women with unexplained infertility. Sixty women were randomized into 2 equal groups. The study group received 5mg nitroglycerine patch daily from day 2 of the cycle till the evaluation day and the control group received no treatment. Independent of the study arms, 30 parous women were included as the fertile group. Six to eight days after detecting luteinizing hormone surge, women were assessed for endometrial thickness, uterine artery blood flow with color Doppler and sub-endometrial blood flow with three-dimensional power Doppler.Results: Compared to fertile women, cases with unexplained infertility (control group) had a significantly thinner endometrium, higher uterine artery Doppler indices and lower sub-endometrial blood flow. Women who received nitroglycerin showed a significant improvement in sub-endometrial blood flow while uterine artery blood flow did not show a significant difference; however, the values were also comparable to fertile women. In addition, no effect on endometrial thickness was found with nitroglycerin treatment. Nitroglycerin treatment side effects were headache, blurring of vision and hypotension. These adverse effects were not significant compared to controls.Conclusions: In women with unexplained infertility, nitroglycerin significantly improved the sub-endometrial blood flow but did not affect the endometrial thickness

OPTIMIZATION OF ALOCASIA AMAZONICA PROLIFERATION THROUGH IN-VITRO CULTURE TECHNIQUE

Excised explants were in-vitro cultured on multiplication medium of Murashige and Skoog (MS). This study was carried away inside the tissue culture lab. Horticulture Research Institute, Agricultural Research Center. Giza, Egypt through the period from 2015 to 2017, to research some factors affecting in-vitro propagation of the indoor ornamental plant Alocasia amazonica using benzyladenine amino purine (BAP) and Kinetin at 0, 1, 2, 3, 4 ppm and their interaction. The obtained results indicated that BAP gave the greatest number of shoots, plus the lowest values for shoot length, shoot fresh weight, number regarding roots and total chlorophyll content. Meanwhile, kinetin achieved the highest values for shoot length, shoot fresh weight although it was not necessarily significant. The same was observed in number of leaves, number of roots and total chlorophyll content with no significant difference. MS medium free of hormones demonstrated the greatest number of leaves, number of roots and total chlorophyll content, and the lowest values of number of shoots and shoot length. Using cytokinn at 1 ppm gave the highest shoot length and number of leaves; and the second position for number of shoot and roots. As for 2 ppm of cytokinin application, it gave the greatest values of shoot length, number of leaves and shoot fresh weight, despite the last one was not significant. this concentration got also the other position for number of shoots, 3 ppm had the greatest number of shoots, and the lowest shoot length, number of roots and shoot fresh weight and 4 ppm occupied the second grade concerning number of shoots, and the lowest grades for shoot length, shoot fresh weight, number of leaves, number of roots and total chlorophyll. Regarding the interaction between cytokinin type and concentration found that, the control treatment (Free MS) gave the highest number of leaves. Using BAP at 2 or 3 ppm attained the highest number of shoots. Using Kin at 1 or 2 ppm attained the highest shoots length. Also, Using Kin at 2 ppm attained the highest fresh weight. The application of Kin at 1 ppm was connected with the highest value of number of leaves. The development of roots showed great values on free medium of BAP and Kin as well as medium supplemented with Kin at 1 and 2 ppm. Whereas, root did not demonstrate any presence at higher concentrations of BAP of 2, 3 and 4 ppm. It is usually recommended to use the MS medium supplemented with BAP at 3 ppm which often gave the highest number of shoots. However, the highest values for shoot length, shoot fresh weight and number of roots were recorded on particularly on MS medium supplemented with Kin at 2 ppm