In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical definition for network lifetime in the IoT is to increase the period of cooperation between objects to carry out all the assigned tasks. The main contribution in this paper is to address the problem of task allocation in the IoT as an optimization problem with a lifetime-aware model. A genetic algorithm is proposed as a task allocation protocol. For the proposed algorithm, a problem-tailored individual representation and a modified uniform crossover are designed. Further, the individual initialization and perturbation operators (crossover and mutation) are designed so as to remedy the infeasibility of any solution located or reached by the proposed genetic algorithm. The results showed reasonable performance for the proposed genetic-based task allocation protocol. Further, the results prove the necessity for designing problem-specific operators instead of adopting the canonical counterparts.
The performance of a diesel engine was tested with diesel oil contaminated with glycol at the engineering workshop/Department of Agricultural Machines and Equipment / College of the Agricultural Engineering Sciences at the University of Baghdad. To investigate the impact of different concentrations of glycol on the performance of a diesel engine, an experimental water-cooled four-stroke motor was utilized, with oil containing 0, 100, and 200 parts per million (ppm). Specific fuel consumption, thermal efficiency, friction power, and exhaust gas temperature were examined as performance indicators. To compare the significance of the treatments, the study employed a full randomization des
The performance of a diesel engine was tested with diesel oil contaminated with glycol at the engineering workshop/Department of Agricultural Machines and Equipment / College of the Agricultural Engineering Sciences at the University of Baghdad. To investigate the impact of different concentrations of glycol on the performance of a diesel engine, an experimental water-cooled four-stroke motor was utilized, with oil containing 0, 100, and 200 parts per million (ppm). Specific fuel consumption, thermal efficiency, friction power, and exhaust gas temperature were examined as performance indicators. To compare the significance of the treatments, the study employed a full randomization des
The performance of a diesel engine was tested with diesel oil contaminated with glycol at the engineering workshop/Department of Agricultural Machines and Equipment / College of the Agricultural Engineering Sciences at the University of Baghdad. To investigate the impact of different concentrations of glycol on the performance of a diesel engine, an experimental water-cooled four-stroke motor was utilized, with oil containing 0, 100, and 200 parts per million (ppm). Specific fuel consumption, thermal efficiency, friction power, and exhaust gas temperature were examined as performance indicators. To compare the significance of the treatments, the study employed a full randomization design (CRD), with three replicates for each treatment at th
... Show MoreThe aim of the current study was to develop a nanostructured double-layer for hydrophobic molecules delivery system. The developed double-layer consisted of polyethylene glycol-based polymeric (PEG) followed by gelatin sub coating of the core hydrophobic molecules containing sodium citrate. The polymeric composition ratio of PEG and the amount of the sub coating gelatin were optimized using the two-level fractional method. The nanoparticles were characterized using AFM and FT-IR techniques. The size of these nano capsules was in the range of 39-76 nm depending on drug loading concentration. The drug was effectively loaded into PEG-Gelatin nanoparticles (≈47%). The hydrophobic molecules-release characteristics in terms of controlled-releas
... Show Moreيهدف البحث الى أعداد بعض تمرينات الاساسية لسلاح الشيش بأستخدام المرايا في تطوير قدرة مستوى تعلم الطالبات في المبارزة ومعرفة الفروق بين المجموعتين التجريبي والضابطة بتأثير استخدام المرايا في مستوى اداء بعض مهارات سلاح الشيش لطالبات المرحلة الثالثة , وقد أستخدمت الباحثتان المنهج التجريبي على عينة من طالبات المرحلة الثالثة , وقد بلغ عددهم (45) طالبة , وقد خرجت الباحثتين بعدة أستنتاجات وهي:- - أن المنهاج التعليمي
... Show MoreIn recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. T
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreIn this paper, we study some cases of a common fixed point theorem for classes of firmly nonexpansive and generalized nonexpansive maps. In addition, we establish that the Picard-Mann iteration is faster than Noor iteration and we used Noor iteration to find the solution of delay differential equation.