This study investigates the constructs and related theories that drive social capital in energy sector from the intention perspectives. This research uses theories of 'social support' and 'planned behaviour' alongside satisfaction and perceived value to propose a research model that drives social capital for energy sectors in Malaysia. The model reveals that the Theories of Planned Behaviour (TPB) and Social Support Theory (SST) alongside satisfaction and perceived value factors promote social capital development in energy sectors. Using PLS-SEM to analyse data gathered from energy sector employees in Malaysia, this research demonstrates that social capital is present when there is trust and loyalty among the users and positively effects en

This manuscript presents several applications for solving special kinds of ordinary and partial differential equations using iteration methods such as Adomian decomposition method (ADM), Variation iterative method (VIM) and Taylor series method. These methods can be applied as well as to solve nonperturbed problems and 3rd order parabolic PDEs with variable coefficient. Moreover, we compare the results using ADM, VIM and Taylor series method. These methods are a commination of the two initial conditions.

: In this study, a linear synchronous machine is compared with a linear transverse flux machine. Both machines have been designed and built with the intention of being used as the power take off in a free piston engine. As both topologies are cylindrical, it is not possible to construct either using just flat laminations and so alternative methods are described and demonstrated. Despite the difference in topology and specification, the machines are compared on a common base in terms of rated force and suitability for use as a generator. Experience gained during the manufacture of two prototypes is described.

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic

It is known that life is as series of variety of difficult problems that individual looks
forward to overcome so as to achieve adaptation and to reach the desired aims .The transition
of the students from the school stage to the stage of the university is actually regarded a
dramatic change where students face when they enter university life that differs from what
they lived in secondary school.
The executive functions are considered the main element that participate in solving the
problems of high orders , because it involves the mental abilities that assist individual to
think and initiative as well as solving problems .
These functions include operational planning and the activated memory and inhibition of
q

Background: Ulcerative colitis (UC) is an inflammatory bowel disease restricted to the large intestine, characterized by superficial ulceration. It is a progressive and chronic disease requiring long-term treatment. Although its etiology remains unknown, it is suggested that environmental factors influence genetically susceptible individuals, leading to the onset of the disease. (C-X-C) ligand 9 is a chemokine that belongs to the CXC chemokine family, it plays a role in the differentiation of immune cells such as cytotoxic lymphocytes, natural killer T cells, and macrophages. Its interaction with its corresponding receptor CXCR3 which is expressed by a variety of cells such as effector T cells, CD8+ cytotoxic T cells, and macrophage

The agricultural sector suffers from many risks and natural disasters, such as droughts and heavy rains that cause floods, as well as hail and agricultural pests, etc., that threaten agricultural activity and reduce it, which leads to the failure of farmers and peasants for fear of being subjected to continuous losses. Nevertheless, we notice almost complete reluctance to move towards agricultural insurance, due to the dependence of farmers on the government, which adopts the principle of compensation instead of agricultural insurance when natural disasters happen despite the difficulties and financial hardship as well as the suspicion of corruption that haunt the compensation process and this represents the most important problem for resea

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s