The transportation industry is one of the greatest contributors to a growing carbon footprint. As the world increasingly prioritizes a greener and cleaner future, the transition from ICEs (Internal Combustion Engine) to EVs (Electric Vehicle) offers significant benefits as EVs are zero-emission vehicles. However, considering the production and disposal stages of EVs, then they cannot be considered entirely emission-free. But in comparison to ICEs, they still have a much smaller carbon footprint. Moreover, studying EV technology is essential to bringing this to the lowest possible extent. However, these advancements come with challenges. In this review article, we have taken an in-depth look into the various challenges, namely the economic challenges, technological challenges, and environmental challenges, faced by EV technology today and the various solutions proposed by researchers, their limitations, and future potential in EV technology advancement Within this context, we discuss the influence of governmental policies, accessibility and affordability of EVs to the masses, natural and man-made disasters, economic instability, technological limitations posed by the vehicle technology, and limitations posed by battery technology with batteries being the central element for EVs, limitations associated with the existing infrastructure and its ability to support widescale use of EVs, and environmental factors like carbon footprint, among other factors, on EVs technology and the opportunities this technology can make use of in the future. This paper discusses these concerns in detail and gives extensive insight into the challenges, solutions, and future trends pertaining to economic impacts, vehicle technology, battery technology, power grids and charging infrastructure, and environmental impact, of EVs and EV technology. The novelty of our paper lies in its comprehensive scrutiny of these topics, while also critically evaluating the perspectives and findings from another research in this domain.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R²), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R² of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R² values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

This study explores the energy demand planning for university loads at the new Sohag University campus in Sohag Al Gadida City, Egypt. It assesses the feasibility of establishing a microgrid on a section of the campus to determine its practicality and potential benefits. The existing power distribution system is analyzed, and the suitability of various distributed generation sources, including photovoltaic, battery, and hydrogen-based microgrid, is evaluated. A techno-economic analysis is conducted to optimize microgrid sizing, using MATLAB R2023a (Optimization Toolbox) to implement a sizing algorithm for a hybrid microgrid system and HOMER Pro 3.14 for component sizing. The optimal microgrid configuration is verified based on the intended power supply potential while minimizing costs. The results demonstrate that the energy costs of the proposed hybrid microgrid system align with previously published values, confirming its feasibility. Additionally, economic analysis reveals that the proposed system not only reduces carbon emissions but also achieves cost savings of 20–30% over 20 years compared to conventional grid supply, with a payback period of 8–10 years.

The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms’ performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R² value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R² values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

This paper focuses on exploring new materials and structure as a means to increase the efficiency of solar cells. Since silicon is widespread on earth, it is desirable to study heterojunction solar cells made mainly of silicon and new materials. Therefore, ZnO/Si and TiO ₂ /Si heterojunction solar cells were studied in this paper. First, the electrical and optical properties of ZnO and TiO ₂ were determined using the Perdew-Burke-Ernzerhof (PBE), PBE functional revised for solids (PBESol) and Perdew-Wang (PW91) functionals of the Generalized gradient approximation (GGA) in Density Functional Theory (DFT). The obtained results in various functionals are assessed and analyzed. It was found that geometric optimized structures of TiO ₂ and ZnO is mechanical stable. Accordingly, in all functionals, the effective mass of the electron in ZnO and TiO ₂ proved to be smaller than that of the hole. The mobility of electrons and holes in ZnO was calculated to be 430.72 cm ² V ^-1 s ^-1 and 5.25 cm ² V ^-1 s ^-1 respectively. In TiO ₂ , it was 355.27 cm ² V ^-1 s ^-1 and 46.38 cm ² V ^-1 s ^-1 . When PW91, PBESol, PBE functionals were used, the dielectric constant was determined to be 11, 11.5, 8.5 for ZnO and 9.5, 10, 9 for TiO ₂ , respectively. According to the DFT results, it was determined that ZnO and TiO ₂ are transparent and mainly n-type direct semiconductors. According to device simulation, the maximum short-circuit current of ZnO/Si and TiO ₂ /Si heterojunction solar cells is 18 mA/cm ² at a thickness of 80 nm and 15.3 mA/cm ² at a thickness of 40 nm. Finally, the average fill factor of ZnO/Si and TiO ₂ /Si solar cells was 0.73 and 0.76 respectively. So TiO ₂ can be used as a transparent contact and ZnO as an emitter layer in a silicon-based solar cell.

Perovskite solar cells (PSCs) are a promising area of research among different new generations of photovoltaic technologies. Their manufacturing costs make them appealing in the PV industry compared to their alternatives. Although PSCs offer high efficiency in thin layers, they are still in the development phase. Hence, optimizing the thickness of each of their layers is a challenging research area. In this paper, we investigate the effect of the thickness of each layer on the photoelectric parameters of n-ZnO/p-CH₃NH₃PbI₃/p-NiO_x solar cell through various simulations. Using the Sol–Gel method, PSC structure can be formed in different thicknesses. Our aim is to identify a functional connection between those thicknesses and the optimum open-circuit voltage and short-circuit current. Simulation results show that the maximum efficiency is obtained using a perovskite layer thickness of 200 nm, an electronic transport layer (ETL) thickness of 60 nm, and a hole transport layer (HTL) thickness of 20 nm. Furthermore, the output power, fill factor, open-circuit voltage, and short-circuit current of this structure are 18.9 mW/cm², 76.94%, 1.188 V, and 20.677 mA/cm², respectively. The maximum open-circuit voltage achieved by a solar cell with perovskite, ETL and HTL layer thicknesses of (200 nm, 60 nm, and 60 nm) is 1.2 V. On the other hand, solar cells with the following thicknesses, 800 nm, 80 nm, and 40 nm, and 600 nm, 80 nm, and 80 nm, achieved a maximum short-circuit current density of 21.46 mA/cm² and a fill factor of 83.35%. As a result, the maximum value of each of the photoelectric parameters is found in structures of different thicknesses. These encouraging results are another step further in the design and manufacturing journey of PSCs as a promising alternative to silicon PV.