Automatic modulation classification (AMC) is vital in cognitive radio, spectrum management, and military applications. The rapid evolution of wireless networks demands reliable and effective techniques. Traditional AMC methods, like likelihood-based and feature-based algorithms, face limits in computational complexity and generalization across different channel conditions. This paper combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN) to form a novel hybrid deep learning model. The model exploits each branch’s strengths for temporal and spatial feature extraction. The Bi-LSTM branch identifies long-term temporal relationships, such as phase transitions, in signal sequences. In contrast, the CNN branch captures local spatial patterns like constellation structures. The proposed model outperforms baseline models—CLDNN, CNN-GRU, ResNet-LSTM, and VTCNN2—while maintaining lower computational complexity (410,762 parameters) on the RML2016.10b dataset. It achieves a classification accuracy of 92.32% at 18 dB SNR and performs robustly across low-to-high SNR regimes. These results reveal its potential for deployment in real-time wireless scenarios that are both noisy and dynamic.

The increasing reliance on renewable energy sources has intensified the need for efficient energy management systems (EMS) in DC microgrids. However, existing testbeds for EMS development suffer from limitations in modularity, scalability, and cost-effectiveness, restricting their practical applicability in research and education. This study proposes a novel, scalable, and cost-effective testbed for distributed EMS in DC microgrids, integrating photovoltaic (PV) panels, wind turbines (WT), and a battery energy storage system (BESS). The testbed incorporates a bidirectional controller for optimized power flow management and physically distributed controllers for real-time energy coordination. A co-simulation framework using MATLAB/Simulink validates the system’s performance under varying operational conditions. Simulation and experimental results demonstrate an improvement in power regulation efficiency by 90%, a 80% reduction in conversion losses, and enhanced voltage stability compared to conventional models. The proposed testbed provides a versatile platform for rapid EMS prototyping, bridging the gap between theoretical research and real-world implementation. Future work will focus on expanding smart grid functionalities and incorporating machine learning-based optimization techniques.

The concept of Gated Communities emerged globally in the 1960s. In Egypt, these communities appeared as complete residential suburbs for the wealthy classes on the outskirts of Cairo, such as Maadi, Garden City, and New Cairo, serving as communities for the upper classes. The rapid growth of these communities has been accompanied by the emergence of some drawbacks that negatively impact the social sustainability of these gated urban communities.Many studies have addressed this phenomenon and why individuals reside in such communities, but few have evaluated their social sustainability. This research paper focuses on assessing the social sustainability of gated communities within the context of the characteristics of closed urban complexes using an Analytic Network Process (ANP) model .Through this analysis, researchers could assign relative weights to the impact of closed community features on achieving social sustainability and analyze the dynamics of social behavior and its correlation with urban characteristics. The model was applied to a community called "Madinaty" assessing its social sustainability, with the results showing an achievement of 67% of the social sustainability requirements. The findings highlighted the robust communal features that contribute to enhancement, along with weak aspects that require support and improvement of their efficiency

This study presents an innovative approach to addressing the persistent issue of voltage minimization in dynamic load feeding within real-world grids, particularly in Egypt. The proposed method leverages a hybrid multi-stage renewable energy system, designed to enhance voltage stability and efficiency in isolated or utility grid-connected scenarios via a DC bus. The hybrid system integrates multi-stage hydro-matrix power water wheels and a multi-stage PV solar system, creating a sustainable energy solution tailored for regions reliant on the River Nile's channels. The hydro-matrix component comprises three undershot water wheels coupled with an induction generator, collectively producing approximately 20 kW of output power. Simultaneously, the PV solar system consists of eight parallel strings, each with fourteen series modules, delivering a total output of 28 kW. The energy outputs from these two subsystems are converted into DC voltage and integrated via the DC bus. To further enhance system reliability, a controlled battery system is incorporated, effectively mitigating performance fluctuations caused by variations in water speed, PV cell temperature, and solar irradiance. This hybrid multi-stage renewable energy system demonstrates its potential to feed diverse load types in rural and semi-urban areas near the Nile, addressing challenges such as low power factors and voltage minimization. The findings underscore the system's significance in advancing renewable energy utilization and improving grid stability in regions with fluctuating renewable energy resources.

Mosques, being public spaces, should be designed with the needs and preferences of the wider community in mind in order to facilitate spiritual practice and convenience. Through face-to-face interviews, mosque users have expressed a desire for a range of specific characteristics in their places of worship, further highlighting the importance of incorporating user input into the design process. The study aims to examine the design features of various mosques and assess their compliance with user preferences, with the broad goal of ensuring that mosques are capable of addressing their functional role. A field survey of six mosques was carried out. The findings show that mosque designers have not fully considered these preferences, resulting in a relative discrepancy between user preferences and actual mosque designs, which introduces concerns about the effectiveness and functionality of mosque spaces. Accordingly, the study recommends active community engagement in the design process from the early stages and emphasizes the need for retrofitting current mosques to address design deficiencies. In this context, the findings are useful for identifying areas for improvement for current and future mosques. They can also be utilized as a practical guideline for architects, designers, and other stakeholders involved in the mosque building process.

Traffic accidents threaten human's safety and properties as well as restrict the management of transportation systems. Therefore, traffic accidents prediction models could help stakeholders to understand accident causes toward controlling their effects. This paper aims at developing a prediction model for a rural road in Egypt using actual accident data with survey data for pavement conditions, traffic flow presented as average hourly traffic per lane, speed, minor access, traffic signs conditions and road width. The relationship between accident number and the influencing factors is modeled using fuzzy logic algorithm which deals with nonlinear relationships between parameters. The results show that the developed model produced accurate and stable traffic accident predictions (R 2 =0.79). The results indicated that average accident rate decreases as road width increases where, a reduction in accident rate of about 5.26% is reached due to road width widening by 3.5 m. The results indicate that road accidents on the studied road can be reduced by 7.38% if the speed is limited to 75 km per hour. About 17.90% reduction is achieved by improving road surface condition. Also results indicate a positive relationship between traffic volume and accidents rate.

This study examines the influence of cement type, water/cement ratio, and curing process on the chloride ion permeability of concrete using ordinary Portland cement (OPC) and sulfate-resistant cement (SRC). Three concrete mixtures containing both types of cement and water/cement ratios of 0.4, 0.5, and 0.6 were investigated with two different curing methods. The cement content was 400 kg/m 3 , and the ratio of fines to aggregates was 0.5. In the first curing method, concrete specimens were cured underwater, while in the second method, specimens were cured at 22 o C and 80% relative humidity (RH) and sprayed with water twice daily for seven days. The slump, air content, and unit weight were measured as fresh concrete properties. After 28 days of curing, the compression, tension, flexure, pulse velocity, and dynamic modulus of elasticity tests for hardened concrete were carried out. In addition, titration analysis was used to determine the chloride ion permeability of concrete made with water/cement ratio of 0.4. The results reveal that cement type, water/cement ratio, and curing process greatly influence concrete's mechanical characteristics and chloride resistance.