Journal of Engineering and Applied Science

Attaining green environment for various machining processes has now caught the attention of many manufacturing industries. The input parameters involved in those machining processes are mainly responsible for achieving the desired performance as they are directly related to the process outputs. Hence, proper selection of the input process parameters becomes vital for having sustainable machining environment. In this paper, an integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods is presented to identify the optimal parametric combinations of two green dry milling processes. In the first example, cutting speed, depth of cut, feed rate and nose radius are treated as the input parameters, while power factor, electric consumption and surface roughness are the responses. On the other hand, in the second example, cutting speed, feed rate, depth of cut and width of cut, and surface roughness, active cutting energy and material removal rate are respectively considered as the input parameters and responses. Instead of considering equal weights, SWARA method assigns relative subjective importance to the responses based on the preference set by the decision-makers, while CoCoSo ranks the experimental trials from the best to the worst. The derived optimal parametric settings are finally analyzed using the developed regression equations. It is observed that SWARA-CoCoSo method outperforms the other popular optimization techniques in identifying the best parametric intermixes for the green dry milling processes for having improved machining performance with minimal environmental effect.

The durability of reinforced concrete (RC) pipes depends upon the corrosion resistance of the reinforcing steel and the resistance of concrete mixes against an aggressive environment. This research paper aims to compare the performance of R.C. pipes made of ordinary Portland cement (OPC) concrete mixtures with others made of two different geopolymer concrete mixes based on different ratios of granulated blast furnace slag (GBFS), fly ash (FA), and pulverized red brick (RB) subjected to three different environments, ambient, tap water (TW), and an aggressive environment, and a solution of 10% magnesium sulfates + 5% chloride (MS-CL). An accelerated corrosion setup has been applied to accelerate the corrosion process in the tested samples. The evaluation of change of compressive strength of concrete and microstructure of different mixes was investigated too. Fourier transform infrared (FTIR) spectroscopy has been studied on all pipes. Geopolymer concrete mixes based on 90% GBFS and 10% RB show better results in all cases. Geopolymer concrete mixes based on 63% GBFS, 27% FA, and 10% RB increase the concrete compressive strength in the magnesium sulfate and chloride environment by 5% compared to tap water. It can be concluded that the geopolymer concrete mixes produced of 90% GBFS and 10% RB perform well under all environments, and its microstructure shows stable behavior in an aggressive environment.

This work introduces an approach for optimization machinability measures of power consumption, machining time, and the surface roughness (PMS). This approach is starting with market customer’s demands, passing by optimizing the machinability measures (PMS), and ending by the optimized cutting conditions. The fuzzy logic was used to define the weights of each of required machinability measurement using method through expert rules depending on factory requirements. Genetic algorithm was formulated for giving optimum output values based on the customer’s demands. A neural network was designed for controlling the input cutting conditions with the PMS output parameters. The proposed soft computing technique creates reasonable results compared to experimental results and gives rich investigations for optimizing the output parameters not only for increasing productivity and quality demands but also for saving power consumed. The variation of consumed power, machining time, and surface roughness was calculated based on different customer demand levels. When the machining time and power consumed importance increased, the proposed technique reduced them by about 20% and 10% for the testes case.

Islamabad, being the capital of Pakistan, is attracting every business. Thus, the city is growing towards traffic congestion as the city’s car ownership rate is rapidly growing. In such a situation, for successful implementation, the policymakers need to understand the public acceptance of carpooling services based on its key motives and constraints. This research explores the key motives and constraints to the introduction scenarios of carpooling service in Islamabad. A stated preference questionnaire survey was conducted via Google Form comprising several parts relating to carpooling. Exploratory and confirmatory factor analyses were processed, and a structural model was developed. Females (both single and married) were less orientated to carpool with males and married males with females. Unknown carpooling partners negatively influenced the factor of intention to shift to carpooling service. Our study provides policymakers and transport planners with an appropriate forecasting model of significant factors. In addition, it provides suggestions to transport planners to design promotional tools to enhance the tendency of carpooling among private car users in favor of reducing traffic congestion and increased car ownership rate in the city.

The idea that computers can build their own programs is extremely significant, and many researchers are working on this challenge. Code generation is described as the process of generating executable code that can be run directly on the computer and fulfills the natural language requirements. It is an intriguing topic that might assist developers to learn a new software technology or programming language, or it could be a simple technique to help in coding through the description of the natural language code developer. In this paper, we present MarianCG, a code generation Transformer model used to tackle the code generation challenge of generating python code from natural language descriptions. Marian neural machine translation (NMT), which is the core model of the Microsoft Translator, is the basis for our NL-to-Code translation engine and is the heart of the teaching model. MarianMT is the teacher language model in our study, and it is one of the most successful machine translation transformers. In our approach, we use a sinusoidal positional embedding technique to represent the position of each token in the text, as well as no layer normalization embedding. Our code generation approach, MarianCG, is based on fine-tuning a machine translation pre-trained language model. This allows us to demonstrate that the pre-trained translation model can also operate and work as a code generation model. The proposed model outperforms recent state-of-the-art models in the problem of code generation when trained on the CoNaLa and DJANGO datasets. MarianCG model scores a BLEU score of 34.43 and an exact match accuracy of 10.2% on the CoNaLa dataset. Also, this model records a BLEU score of 90.41 and an exact match accuracy of 81.83% on the DJANGO dataset. The implementation of MarianCG model and relevant resources are available at https://www.github.com/AhmedSSoliman/MarianCG-NL-to-Code.

NPTs have vast applications because of no tyre puncture, no need for air pressure, low rolling resistance, and also have higher flexibility for design and recyclability. In this research work, different structures of polyurethane (PU) spokes have been designed and analyzed under radial loading conditions which include structures like honeycomb with varying cell angles, simple spoke, and trapezoid type by keeping in view that the cell wall thickness and somehow the mass of the structures remain the same. Based on the Mooney-Rivlin hyper-elastic material model and performing 2D non-linear static structural analysis on different types of NPTs using ANSYS, it has been observed that the simple spoke structure has the lowest spoke stress and deformation values of 2.01 MPa and 11.7 mm, while HC–A1 has the least value of strain energy of 2.58 mJ, at a load of 2500 N. The above results show that the straight spoke structures like simple spoke and trapezoid type have a high load-carrying ability than the honeycomb type NPTs under same boundary conditions. While honeycomb NPTs have higher fatigue life as compared to straight-spoke NPTs.

The debate about polycentricity and subordinacy has always been a critical topic that planners, economists, and socialists argued about for centuries. The idea of concentricity vs decentralization has affected all life metabolic activities. Urban structure has always been declared to be the key factor that affects life metabolism significantly. However, after the pandemic COVID-19, the planning strategies have changed dramatically. The main purpose is to investigate the most appropriate urbanization approach that achieves the best development results. The research methodology is to define and measure the fabric independency as an approach to estimate its self-sufficiency that enables it to stand in front of the pandemic challenges at different circumstances. The paper uses the fabric diversity index as a sensitive indicator of independency and polycentricity of the urban structure. The main conclusion for this paper is that independent polycentric urban agglomerations that are strongly linked achieve much better development results than subordinate cities depending on the main core city. The data used for the analysis are extracted from the Urban Atlas developed by the European Environmental Agency in addition to the UN-Habitat annual report. All calculations, analyses, and deductions are exclusively carried by the author.

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time; (2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises; (3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy; and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

Biophilic design elements are found around us in many landscape elements while we do not perceive them as biophilic design patterns. By developing our understanding of biophilic design as a phenomenon, we could discover simple ways to utilize landscape elements and transform them into a good biophilic design that might have positive impacts on a user’s health and well-being. Activating existing biophilic elements as an approach to a sustainable landscape has not been studied yet. Therefore, we rather analyse some international case studies in order to understand how biophilic design patterns can be implemented and see their different forms. Later, we will also go through an Egyptian biophilic design pattern case study and implement it to reach a sustainable landscape model. To summarize, the purpose of this study is to present a new sustainable landscape approach by activating biophilic design patterns in order to increase landscape efficiency.