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.

Conventional concrete is a common building material that is often ridden with cracks due to its low tensile strength. Moreover, it has relatively low shear strength and, unless reinforced, undergoes brittle failure under tension and shear. Thus, concrete must be adequately reinforced to prevent brittle tensile and shear failures. Steel fibres are commonly used for this purpose, which can partially or fully replace traditional steel reinforcement. The strength properties and bond characteristics between reinforcing steel fibres and the concrete matrix are crucial in ensuring the effective performance of the composite material. In particular, the quality of the bond has a significant impact on crack development, crack spacing, and crack width, among other parameters. Hence, the proper application of steel fibre-reinforced concrete (SFRC) requires a thorough understanding of the factors influencing its bond behaviour and strength properties. This paper offers a comprehensive review of the main factors controlling the bond behaviour between concrete and steel fibres in SFRC. In particular, we focus on the effects of the physical and mechanical properties of steel fibres (e.g., geometry, inclination angle, embedded length, diameter, and tensile strength) on the bond behaviour. We find that the addition of up to 2% of steel fibres into concrete mixtures can significantly enhance the compressive strength, tensile strength, and flexural strength of concrete components (by about 20%, 143%, and 167%, respectively). Furthermore, a significant enhancement in the pull-out performance of the concrete is observed with the addition of steel fibres at various dosages and geometries.

Interference has been a key roadblock against the effectively deployment of applications for end-users in wireless networks including fifth-generation (5G) and beyond fifth-generation (B5G) networks. Protocols and standards for various communication types have been established and utilised by the community in the last few years. However, interference remains a key challenge, preventing end-users from receiving the quality of service (QoS) expected for many 5G applications. The increased need for better data rates and more exposure to multimedia information lead to a non-orthogonal multiple access (NOMA) scheme that aims to enhance spectral efficiency and link additional applications employing successive interference cancellation and superposition coding mechanisms. Recent work suggests that the NOMA scheme performs better when combined with suitable wireless technologies specifically by incorporating antenna diversity including massive multiple-input multiple-output architecture, data rate fairness, energy efficiency, cooperative relaying, beamforming and equalization, network coding, and space–time coding. In this paper, we discuss several cooperative NOMA systems operating under the decode-and-forward and amplify-and-forward protocols. The paper provides an overview of power-domain NOMA-based cooperative communication, and also provides an outlook of future research directions of this area.

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 transition are detected by the trained SVM model and then refined to eliminate the false-alarm events. 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 F1-score 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.

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance.