Mobile Wireless Sensor Networks (MWSN) are the overgrowth and emerging technology. Routing process in MWSN is more complicated than static one. Therefore, many routing protocols have been implemented recently for MWSN to accomplish progress in energy consumption field. This paper presents a Mobility based Genetic Algorithm Hierarchical routing Protocol (MGAHP) to achieve maximum lifetime of the network and improve the stable period of MWSN. The basic idea of the proposed MGAHP protocol is using Genetic Algorithm (GA) to find the optimum number of Cluster Heads (CHs) and their locations depending on minimizing the energy consumption of the sensor nodes. Simulation results exhibited that the proposed MGAHP protocol gives better improvement in energy efficient than LEACH-M, CBR-Mobile, and MACRO protocols.

The recent energy crisis is encouraging researches to utilize renewable energies in new cities and to activate their applications. One of the most important energies is generating electricity from solar energy directly via Photovoltaics (PV), PV systems have varied and widened in many desert areas in Egypt in the current century. The current electricity supply has many defects and disadvantages, and this leads necessarily to another alternative such as PV systems, as PV efficiency has increased and their costs have been reduced, also many new visions of applying PV systems on building envelope have emerged, and many simulation software are currently used to study and evaluate the quantitative performance and the economic impact of such systems. The paper aims to evaluate applying current PV systems on the existing models of residential buildings in new cities in Egypt, and accurately the study is focusing on the youth housing models in new Assiut city, furthermore, a simulation software is used for more accuracy to calculate the quantitative adequacy and the economic impact of PV application. The paper starts with the research motivations and the need to PV systems in Egypt, and a theoretical background of current PV modules that can be used in such case studies. Then, the PV application cases on each model are specified from varying application methods, PV modules distribution, reinforcing techniques and others. Furthermore, the quantitative and economic impact is illustrated for each case by using a simulation tool. Then, analyzing characteristics, advantages and defects of each case is articled to extract the main results. Finally, the paper ends with specifying the appropriateness of applying PV systems on the case study, and the main features of extending such systems on new cities in Egypt and treating the disadvantages was discussed.

Building integrated photovoltaics (BIPV) receives growing attentions due to both architectural and engineering favorability. Large commercial building envelopes present a great potential of utilizing solar radiation, especially in climate zones with rich solar resources. Most current studies have been focused on predicting and optimizing power generation of BIPV on designed envelope systems, which leaves limited room for performance improvement of BIPV. This study introduces a framework of an optimization method that formulates the best building envelope shapes and the most matching BIPV systems. A set of criteria are established to determine the best alternatives of envelope variations, upon which the power generation and economic impact of different BIPV systems are evaluated and compared. The proposed optimization process was demonstrated using a general commercial building design application in Egypt. The developed tool can help designers in achieving an optimized building envelope that is most suitable for PV integration.

A growing attention has been paid to building integrated photovoltaics (BIPV) from both architectural and engineering favorability. There are various computational tools developed to provide computations to optimize BIPVs and often simulations for predicting their performance. This provides a great potential for designers to have different helpful tools to be utilized. This paper introduces a comparative analysis of the most common computational tools that compute or simulate main parameters of BIPVs: building energy consumption, solar exposure radiation flux and PV system performance. These computational tools are classified based the method of processing the inputs and compared using evaluation criteria. Also, optimization algorithms that can be used in optimizing BIPVs have been compared. This comparative analysis helps designers to determine better tool/s and algorithm/s for their design cases and required optimization for BIPV. The main findings of this study are the capabilities, limitations, advantages and disadvantages of each computational tool and optimization algorithm presented, in addition to the best selections among them via a comparative analysis to be used for different design cases.

A growing attention has been paid to building integrated photovoltaics (BIPV) when designing net-zero energy buildings. Envelope features of large commercial buildings can be properly designed to both enhance PV integration and reduce building energy use. Many studies have been focused on predicting PV performance of designed systems or optimizing building envelope properties to reduce energy consumption. This study introduces an optimization framework using genetic algorithm (GA) via the GenOpt program to determine the best options for building envelope designs to reduce net building energy cost and increase PV utilization capacity/efficiency. A set of envelope design features were tested in this study, such as, building dimensions, window-to-wall-ratio (WWR), orientation, and PV integration placement, upon which the associated PV and building energy cost are evaluated and compared. Cubic commercial buildings commonly found in Egypt were used to demonstrate the application of the proposed optimization process. The developed tool can help designers to determine the optimal envelopes with appropriate BIPV options from both energy and economic perspectives.

Building integrated Photovoltaics (BIPV) receives growing attentions from both architectural and energy saving perspectives. Large commercial building envelopes can be utilized due to their great potential of reducing building energy consumption and increasing PV integration impact, especially in climate zones with rich solar resources. Most current studies have been focused on predicting electricity generation of BIPV systems with existing envelope geometries, while few studies have discussed the generation of proper envelope shapes for PV integration due to the challenge of integrating architecture and engineering. This paper introduces a novel optimization method for BIPV shape development based on the shape grammar theory. The method reforms given building shapes/envelopes to produce a set of better BIPV shape alternatives, as well as determines the best placement and matching BIPV systems for the optimized envelopes. The main set of criteria considered during the generation and optimization process include PV power generation, PV economic impact and building energy consumption. Architectural preferences are included in generating preferred design alternatives, such as view consideration and shape direction. Commercial buildings in Egypt are used to demonstrate and validate the applications of the developed method and tool. The method and tool can help designers in achieving an optimal design of building envelope that is most suitable for maximizing PV integration.

Robotic surgery is one of the most recent technologies in healthcare building field. Due to the design complexity of Robotic surgery wards, computational implementations are being developed to either measure the effect of inserting advanced technologies as Electronic medical recorders and tele surgery, or evaluate design alternatives on healthcare building. This paper presents a design framework that responds to the need for coordinating design phases for Robotic Surgery Wards (RSWs) computationally. This proposed design framework for RSWs can generate functional RSW alternatives and more than one solution for each alternative. The framework has been structured based on the main architectural considerations of RSWs which are geometric and topological, the economic considerations, specific developed pools for shape and corridor patterns, and the theory of “Shape Grammars" has been utilized to compute the framework to generate a vast number of design alternatives. Accordingly, a computational implementation has been established to assist designers in early design stages. Numerical validation for the applications of the developed framework and implementation has been conducted by using reference examples of RSWs. The main finding in this paper is providing healthcare building designers with a computational implementation that generates RSW alternative computationally based on specific shape and cost levels.

A growing attention has been paid to building envelope features for achieving lower energy consumption especially in large office buildings and hot climate zones, since these features and their variables are affecting energy consumption widely and with different sensitivity. Therefore, this paper conducts simulation-based comparative analyses between main envelope features with their internal variables; the selected features for this study are building geometry ratios, orientations and common envelope finishing materials (FMs). Two applications have been conducted (comparing cases with either a same or different building volumes), and more than 500 cases/simulations have been conducted and studied in total. Accordingly, sensitive features and variables have been determined to enrich design decisions for different cases, along with best variables' integrations that achieve best energy consumption through the proposed applications and cases. Cubic office buildings in Egypt have been used to demonstrate the study, and energy simulations have been achieved using eQuest (DOE-2). Results show that lower height with wider roof achieves best energy consumption if building volume is fixed via comparisons, and vice versa. Gravel and galvanized steel represent best studied roof and walls' FMs, while roofing shingles is the worst one. If building volume is varied via comparisons, horizontal dimensions are the most sensitive feature that affects energy consumption per m², while FMs and height represent lowest sensitivity among studied features. Ranking of cases, features, variables along with sensitive features in details have been analyzed and discussed through the paper.

Utilizing polymers for asphalt concrete (AC) mixture modification has many drawbacks that hinder its wide implementations for roadway construction. Recently, research on employing complementary materials, such as nanomaterials, to balance negative impacts of polymers while enhancing the AC mixture’s performance has received great attention. This study aimed to investigate the effect of incorporating nanoclay (NC) particles on the performance of a high-density polyethylene (HDPE)-modified AC mixture. A 60/70 asphalt binder was first modified with HDPE, and then NC particles were gradually added at a concentration of 1–4% by weight of the asphalt binder. The binders’ physical characteristics, storage stability, and chemical change were scrutinized. AC mixture performance, including pseudo-stiffness, moisture damage resistance, stripping susceptibility, and rutting tendency, was investigated. A statistical analysis on the experimental results was conducted using Kruskal–Wallis and Dunn tests. Test results showed that employing NC/HDPE significantly increased penetration index and thereby enhanced binder temperature sensitivity. Moreover, it prevented oxidation action and separation and, therefore, enhanced binder storage stability. Furthermore, incorporating NC amplified pseudo-stiffness and significantly improved resistance against moisture damage and stripping of HDPE-modified mixtures. Moreover, it improved both elastic (recoverable) and plastic (unrecoverable) deformations of mixtures. The most satisfactory results were attained when incorporating 3% of NC.

The dynamic modulus (E*) of hot-mix asphalt mixtures is one of the most tedious and time-consuming laboratory testing material properties. It requires costly, advanced equipment and skills that are not easily accessible in the majority of laboratories yet. Thus, many studies have been dedicated to developing E* predictive models. Unfortunately, it is a complex task due to the many input variables and their non-linear effect on the E*. This study applies a deep residual neural networks (DRNNs) technique for the first time to the problem to enhance the E* prediction capabilities. The proposed DRNNs architecture utilizes residual connections (i.e., shortcuts) that bypass some layers in the deep network structure in order to alleviate the problem of training with high accuracy. An intensive laboratory database is employed in the DRNNs model development considering all influential input parameters such as; mixture gradation, volumetric properties, binder characteristics, and testing conditions parameters. Moreover, a brute force enumeration is integrated in the model to reduce the number of needed input variables and identify the best combinations of them. Then, the proposed DRNNs performance, with the best combination of inputs, is evaluated using representative performance indicators and compared with the well-known E* predictive models, namely; Witczak 1-37A, Witczak 1-40D, and Hirsch models. Finally, a variance-based global sensitivity (VB-GS) analysis is conducted with the Monte Carlo simulation aid to highlight each input variable effect on the E* magnitude in real practice while removing the potential distortion of results due to the input variables correlations. Performance evaluation indicators reveal that the DRNNs model outperforms other E* prediction ones. Furthermore, VB-GS analysis shows that, among all feasible inputs, binder stiffness characteristics and testing temperature are the most significant ones.