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The production process of conventional building materials consumes a high amount of energy which has a negative impact on the environment. The use of locally available materials and upgradation of traditional techniques can be a good option for sustainable development. Consequently, earth has attracted the attention of the researchers as a building construction material for its availability and lower environmental impact. On the other hand, in developing countries waste disposal from the agricultural and industrial sectors raises another serious concern. The scientists have introduced such waste additives into the earth matrix to improve its performance. Therefore, the present paper reviews the state-of-the-art of research on the effects of these various agro and non-agro wastes in the production of unfired earth blocks. This study is divided into three sections: The first section outlines the different types of waste materials and earth blocks considered in the selected papers. The second part deals in depth with the test results of the different properties (density, water absorption, compressive strength, flexural strength and thermal conductivity) of unfired earth blocks containing waste materials. The last section analyses and compares the results with the current earth-building construction standards. The literature survey presents that the waste materials have a clear potential to partly replace earth by complying with certain requirements. Moreover, the application of such wastes for the development of building construction materials provides a solution that decreases energy usage as well as contributes to effective waste management. Future research on establishing guidelines and standards for the development and production of these sustainable unfired earth building materials is recommended.

With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1score are within the ranges (1.31 -2.58), (1.53 -4.28), and (1.41 -3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time.

Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using ''buffer management'' and ''elasticity management''. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user's request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that the ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches.

Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method for computing discrete Krawtchouk polynomial coefficients swiftly and efficiently. The presented method proposes a new initial value that does not tend to be zero as the polynomial size increases. In addition, a combination of the existing recurrence relations is presented which are in the n- and x-directions. The utilized recurrence relations are developed to reduce the computational cost. The proposed method computes approximately 12.5% of the polynomial coefficients, and then symmetry relations are employed to compute the rest of the polynomial coefficients. The proposed method is evaluated against existing methods in terms of computational cost and maximum size can be generated. In addition, a reconstruction error analysis for image is performed using the proposed method for large signal sizes. The evaluation shows that the proposed method outperforms other existing methods.

The building walls which form the major part of the building envelope thermally interact with the changing surrounding environment throughout the day influencing the indoor thermal comfort of the space. This paper aims at assessing in detail the different aspects (thermophysical properties, thickness, exposure to solar heat gain, etc.) of opaque building wall materials affecting the indoor thermal environment and energy efficiency of the buildings in tropical climate (in the summer and winter days) by conducting simplified simulation analysis using the Integrated Environmental Solutions Virtual Environment (IES-VE) program. Besides, the thermal efficiency of a number of selected wall materials with different thermal properties and wall configurations was analysed to determine the most optimal option for the studied climate. This study first developed the conditions for parametric simulation analysis and then addressed selected findings by comparing the thermal responses of the materials to moderate outdoor temperature and energy-saving potential. While energy consumption estimation for a complete operational building is a complex method by which the performance of the wall materials cannot be properly defined, as a result, this simplistic simulation approach can guide the designers to preliminary analyse the different building wall materials in order to select the best thermal efficiency solution.

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