A number of space groups have been thinking about establishing a permanent, manned base on the moon in recent years. An installation of this type requires an incredibly dependable electrical power system in order to serve scientific equipment and mechanisms for maintaining life while working independently and fully on its own. This study reviews the technology available for space microgrid power generation, distribution, and storage on the moon. By introducing and assessing several parameters that affect the mass and cost of space missions, a thorough analysis of the available possibilities is given. Several lunar sites are also mentioned and addressed in the context of how base placement affects mission costs and how a lunar electrical power system is designed. In order to guarantee that space microgrids on the moon operate independently and dependably, the control system specifications are finally provided. In addition to the analysis, promising future technological solutions that could be put into practice on a lunar microgrid are considered
This research explores the design and performance evaluation of a direct current (DC) nanogrid system powered exclusively by renewable energy sources, with a particular focus on solar energy. The study is conducted in a highly remote rural location, specifically a cirque on Réunion Island, where conventional power infrastructure is limited or unavailable. The nanogrid architecture consists of three main components: a photovoltaic (PV) energy source, a battery storage system, and a consumer load. The system operates entirely in DC, ensuring energy efficiency and minimizing conversion losses. The evaluation is based solely on simulations, without hardware implementation, and considers a network composed of four nanogrids operating under worst-case conditions of variable solar generation and consumption. Several load balancing algorithms, adapted from server management techniques such as Round Robin and Least Connection, are applied to the energy distribution process. A new algorithm, Weighted SOC Round Robin (WSRR), was developed to account for the batteries’ state of charge. Results show that WSRR reduces power outages by up to 45% and lowers energy underload by 25% compared to baseline approaches. The algorithm ensures more stable and efficient energy delivery, highlighting the relevance of simple, adaptive strategies for managing decentralized renewable energy systems in isolated environments. This study contributes to the development of robust control methods for renewable-powered nanogrids, addressing both energy autonomy and system resilience in remote areas.
This work focuses on an efficient load frequency controller (LFC) for an autonomous dual-area hybrid microgrid (HMG). The suggested Two Degree-of-Freedom Proportional Integral Derivative with Filtered Derivative controllers (2DoF-PIDN) are fine-tuned using the Secretary Bird Optimization (SBO) algorithm to generate their optimal gains. Since the hybrid microgrid system combines traditional and renewable energy resources, frequency management becomes more complicated and unreliable. The Integral Time multiplied summation of absolute error (ITAE) is taken as a cost function for defining the controllers’ optimal setting. Simulation findings reveal that the recommended SBO: 2DoF-PIDN controller offers better dynamic performance, with lower frequency deviations, less overshoot, and shorter settling periods compared to other techniques. To validate the suggested approach’s efficacy, it was compared with other techniques such as genetic algorithm and social spider optimizer based PID as well as SBO based PIDN controllers. The effectiveness of the method has been confirmed under load disturbance and renewable fluctuations scenarios.
This study investigates the potential of using high percentages of recycled tire waste as a sustainable solution for soil stabilization, particularly in enhancing the geotechnical properties of clay and sand. The focus is on evaluating the impacts of adding 15%, 30%, and 45% tire shreds by weight to clay soil and sand on various parameters such as California Bearing Ratio (CBR) and direct shear strength. The CBR test results indicate an initial increase in soil strength with 15% tire shreds, followed by a decrease at higher percentages, demonstrating the influence of tire shreds on the mechanical properties of clay. For instance, the CBR value increased from 3.9% with 0% tire shreds to 4.8% with 15% tire shreds, then decreased to 2.35% and 2.1% with 30% and 45% tire shreds, respectively. The direct shear test results for sand show the relationship between shear stress and normal stress for natural sand pure sand mixed with 15%, 30%, and 45% tire shreds. When no tire shred is added, the shear stress is 82.88 kPa. Adding 15% tire shred increases the shear stress to 85.17 kPa. However, as the tire shred content increases to 30% and 45%, the shear stress decreases to 79.17 kPa and 73.72 kPa, respectively. The results indicate that adding tire shreds to the soil significantly reduces its density, making the soil lightweight. This lightweight composite material can be effectively used in engineering projects such as embankments, backfills, and retaining wall reinforcements where reduced weight is advantageous.
The rapid growth of the Internet of Things (IoT) has transformed digital infrastructure into areas such as smart cities, healthcare, and industrial automation. However, this surge in connectivity has also made IoT ecosystems more vulnerable to increasingly sophisticated malware attacks, worsened by limited device resources, diverse protocols, and weak security measures. Traditional security solutions have proven insufficient in these dynamic, resource-limited environments, prompting the development of intelligent, lightweight, and scalable malware detection systems.This survey provides a comprehensive overview of the current research on IoT malware detection, emphasizing advanced machine learning (ML) and deep learning (DL) techniques. It explores a wide range of methods including convolutional neural networks (CNN), long short-term memory networks (LSTM), hybrid CNN-LSTM models, and ensemble classifiers like Random Forests. Focus is given to recent innovations such as image-based malware detection, temporal-spatial behavior modeling, interpretable learning with SHAP and attention mechanisms, and the development of automatic preprocessing pipelines.Furthermore, we critically examine benchmark datasets, especially CIC-IoT2023, IoT-23, and N-BaIoT, which have driven reproducible evaluations and real-world usability. The paper also offers comparative insights into model performance, scalability issues, dataset biases, and constraints on real-time deployment. Lastly, we highlight emerging trends such as federated learning, automated machine learning (AutoML), and transfer learning as key directions for next-generation IoT security frameworks. This survey aims to synthesize existing knowledge and help researchers develop robust, explainable, and deployment-ready malware classification systems to strengthen the rapidly evolving IoT landscape.
In aviation, sustaining certain attitudes during flight such as level, straight and high flying, ascents, and descents requires exact control of the pitch angles of the aircraft. The non-linear and unpredictable dynamics of aircraft cause difficulties for traditional control systems. To address these challenges, we propose a filtered PID (PID-F) controller for airplane pitch-angle control, with parameters tuned using the geometric mean optimizer (GMO). By including a filter parameter to the derivative gain, this combination attempts to efficiently reduce the kick effect and improve the stability and performance of the aircraft pitch control system. The optimization approach is guided by a time-domain-based objective function utilized in the study. The suggested GMO/PID-F approach's consistency and stability are confirmed by the simulation results. The effectiveness of the recommended approach is demonstrated by a comparison analysis of many optimization techniques used in controllers from the literature. In particular, the GMO/PID-F controller demonstrates precision control, no overshoot, quick rise time, and short settling time. The results show that the suggested technique is an effective way to optimize aircraft pitch angle control systems, providing increased reliability as well as effectiveness.
Highly Pathogenic Avian Influenza (HPAI), particularly the hemagglutinin subtype 5 (H5) subtype, poses a critical threat to public and animal health worldwide. Early and accurate outbreak prediction is vital for effective prevention and containment. This study presents a machine learning-based framework for detecting and predicting HPAI outbreaks using real-world surveillance data. Several supervised learning models were evaluated, including XGBoost, CatBoost, LightGBM, and traditional classifiers such as Decision Tree, Random Forest, and Naive Bayes.To enhance model performance, hyperparameter optimization was conducted using Optuna, a state-of-the-art framework that leverages Bayesian optimization. The XGBoost model, in particular, achieved high predictive accuracy, with an F1-score exceeding 0.98 and ROC AUC close to 1.0.Results highlight the potential of integrating ensemble machine learning with automated optimization as a powerful decision-support tool for early epidemic detection. The proposed framework is scalable, interpretable, and adaptable for real-time monitoring applications in veterinary epidemiology and public health systems.
Degradation of dyes which exist in wastewater could be achieved by many ways such as biological oxidation, membrane separation, adsorption, coagulation processes, and irradiation but there are many drawbacks in these methods therefore, many reports began to study more effective ways to overcome these drawbacks such as hydrodynamic cavitation (HC). This review paper focuses on using hydrodynamic cavitation (HC) that is obtained by velocity and pressure variance in the system in dye degradation and offers an overview of previous research which deal with dye removal by HC alone or coupled with advanced oxidation processes (AOPs). The influence of operational parameters such as initial dye concentration, PH, inlet pressure and geometrical parameters including the shape and dimensions on dye degradation efficacy has been investigated. Also, optimal values of these parameters have been illustrated. Different hybrid techniques including HC/photocatalysis, HC/Fenton’s reagent, HC/persulfate, HC/Hydrogen Peroxide, HC/Nanoparticles, and HC/Ozone were also studied and comparative studies between them were implemented. Overall HC offers hopeful treatment because of the easiness of operation, less material and chemical expenses and potential of continuous large-scale operation.
Expansive soils pose significant challenges to structural stability due to their tendency to swell and shrink with moisture variation. Among the various mitigation strategies, replacing expansive soil with non-expansive material is the most commonly adopted method. However, this approach relies heavily on laboratory testing, which can be inconsistent, time-consuming, and costly, particularly for large-scale applications. Additionally, current guidelines often lack rational methods for determining the optimal thickness of the replacement layer. This study investigates the influence of expansive soil properties and sand replacement thickness on heave beneath square footings. Finite element analysis using ABAQUS was employed to simulate heave of two different expansive. The numerical model was validated using odometer test data and a case study involving a slab-on-grade foundation over expansive clay. Parametric analyses were performed using models of 22 × 22 × 20 m to minimize boundary effects, and a footing size of 2 × 2 × 0.5 m was used. The study evaluated the effects of swelling index, expansive soil depth, and replacement thickness on heave and swelling pressure. Numerical results showed strong agreement, confirming that swelling pressure increases with the swelling index. Sand replacement at depths of 0.5, 1.0, 1.5, and 2.0 m significantly reduced surface heave. However, exceeding 0.5 m in replacement depth may lead to settlement rather than further mitigation of heave.
This paper presents an artificial neural network approach for fault detection and location in a 200-kilometre, 33 kilovolts power distribution system. The research addresses the growing complexity of modern power grids and the increasing need for adaptive, reliable fault detection methods that can swiftly identify and isolate faults. The methodology involves developing a system capable of accurately classifying fault types and determining their precise locations along the distribution line. The system architecture integrates a measurement subsystem with a neural network trained to recognize fault patterns. Various fault scenarios were simulated to evaluate the system's performance. The fault classification model achieved a cross-entropy loss of 0.9849, indicating a need to further refine this part of the system, while the fault location model attained a mean squared error of 2.826 and a regression value of 0.9996, indicating excellent performance in fault location prediction. Test results demonstrated the system's ability to distinguish between different fault types by analyzing current and voltage profiles, though some instances of misclassification were observed, particularly between similar fault types. The mean margin of error for fault location was approximately 5.72%. The research concludes that while artificial neural network approach-based fault detection systems offer significant advantages over traditional methods in handling non-linear fault scenarios, their effectiveness depends largely on the quality of training data, suggesting opportunities for further optimization of the architecture of the artificial neural network.
Soil erosion is a substantial eco-environmental issue often intensified by flooding. For the protection of water and soil, estimating soil erosion is essential. The study utilized the Revised Universal Soil Loss Equation (RUSLE) alongside ArcGIS to analyze soil erosion in Wadi Sudr watershed in Egypt. The research aimed to assess and control erosion in the floodplains The results showed that the RUSLE model is especially influenced by the topographic factor (LS). Soil loss was categorized into five distinct categories. About 68% of the Wadi Sudr area is classified within the Very Low and Low soil erosion categories, primarily located in flat areas with vegetative cover. About 18% lie in moderately sloped terrain, while 14% of Wadi Sudr, characterized by steep slopes and lack of conservation practices, experiences high erosion. Three management scenarios were supposed: contouring, strip cropping, and terracing. The contouring scenario increased the Very Low and Low erosion area to 76%, strip cropping raised it to 88%, and terracing proved the most effective, increasing it to 98%. These findings can support policymakers in implementing management practices to mitigate erosion and safeguard both human life and property.
Large amounts of waste rubber tires are disposed to dumping areas along different cities around the world. They are exposed to burning processes which causing air pollutions. The evaporated Carbon Dioxide (CO2) and others affect the environment and people's health. To overcome these disposals, it can be grinded into powder and re-used in different industries such as roads constructions. Many studies investigated the possibility of using such materials in stabilizing weak materials. Some studies investigated it in treating subgrade soils and others for asphalt layers. This study investigates the effect of using the grinding rubber with Portland cement in improving the soft clay subgrade embankments. Rubber percentages 2.00%, 5.00%, 7.00%, 9.00%, 11.00% and 13.00% and Portland cement percentages 2.00%, 4.00% and 6.00% are used in this study. Soil characteristics; plasticity, strength, and density were investigated through conducting Atterberg Limits, direct shear, California Bearing Ratio and compaction tests to evaluate the behavior of the improved soils. Results indicated that increasing rubber powder in stabilizing the soft clay causes the maximum dry density and the optimum moisture content to be decreased. Using the rubber powder as a stabilizing agent for the soft clay subgrade has low effect on the soil strength and bearing capacity. Using rubber powder and cement together in the stabilization processes lead to moderate improvements in soil strength, bearing capacity and plasticity. The optimum percentages of rubber and cement that lead to significant improvement on soil strength are 4.00% rubber and 6.00% cement.
In recent years, researches on photovoltaic (PV) systems have focused on reducing costs and maximizing conversion efficiency. To achieve maximum efficiency, PV arrays must operate at their maximum power points (MPP), where they generate energy with minimal losses. However, since solar cells exhibit variable current and voltage characteristics depending on irradiation and temperature, an effective maximum power point tracking (MPPT) control strategy is required. This study presents a technical evaluation of a PV system integrated with a brushless DC (BLDC) motor for a water pumping application. The system is modeled in MATLAB/Simulink, incorporating a DC-DC converter and an adaptive neuro-fuzzy inference system (ANFIS) for MPPT control. The ANFIS controller is trained offline to optimize PV output power and regulate water flow rate. A mathematical iteration technique is also introduced for comparative analysis. The proposed system enhances performance by adapting to varying solar insolation and temperature conditions, ensuring maximum power extraction and optimal water flow. Results demonstrate the accuracy, robustness, and effectiveness of the ANFIS-based approach in improving system efficiency, economic feasibility, and fault detection.
This paper focuses on the application of non-uniform arrays (NUAs) to achieve high-level arrival direction estimation (DOAs) and to solve challenges such as target separation and a reduced snapshot. The proposed research includes a theoretical derivative analysis and MATLAB-based simulations, showing better resolution than conventional uniform linear arrays. One of the main concerns of this research is to minimize the number of elements of array antenna, thereby reducing the price and weight of the system while maintaining its performance. Unlike most existing methods, which require complex calculations based on dense arrays or calculations, this method provides a balance between the complexity and error rate of time and space. The results of this research show that NUAs provide good spatial resolution by distinguishing targets even in low S/N and snapshot counts. The results are evaluated by the root average square error and music spectra, as well as an eigenvalue analysis using bar graphs, and demonstrate the effectiveness of the proposed method. The study highlighted NUA's potential to improve DOA estimation capabilities and to provide the best solutions for radar and wireless communication applications at relatively low cost.
The research investigates the role of digital fabrication technologies in the restoration and reconstruction of missing elements in heritage buildings, highlighting the importance of accuracy and adherence to established guidelines, such as those set by ICOMOS. Heritage buildings often suffer from deterioration caused by factors such as material degradation, environmental conditions, and human activities. Restoration efforts, especially those involving the replacement or completion of missing parts, are complex and sensitive, requiring a balance between preserving historical and artistic values while integrating modern techniques.The study will specifically focus on tools like laser scanning, CNC cutting, and 3D printing, examining their applicability in replacing traditional restoration methods that may have been less precise or respectful of the original fabric. Additive Manufacturing (3D printing) is highlighted for its potential to create accurate, cost-effective, and time-efficient replicas that replicate the authentic materials and structures of heritage buildings. This technology offers a promising solution to the challenges of restoration by minimizing human intervention while ensuring highly accurate results.The research will analyze four case studies where both digital fabrication and traditional methods have been employed in heritage restoration, evaluating the outcomes to develop guidelines for future projects. The ultimate goal is to establish a comprehensive framework that outlines best practices for integrating digital fabrication with traditional techniques in heritage building restoration. This approach ensures that these technologies are used effectively and responsibly, preserving the cultural and historical integrity of heritage sites
Currently, in the last decade, a great number of new highways were constructed in Egypt to encourage and facilitate the investment projects. So, a lack of the abundance of quality and quantity of good virgin aggregate is becoming a great problem facing highway construction firms. So, new alternative materials should be discovered to overcome this great problem. The purpose of this study is to make a comparison between utilizing two kinds of reclaimed asphalt pavement materials in hot mix asphalt. To implement this objective, a comprehensive experimental plan was designed. Qualification tests were performed on the study materials. Two kinds of study materials were used in this study; calcareous reclaimed asphalt pavement (RAP) and dolomite reclaimed asphalt pavement. Different percentages of virgin aggregates were used with complementary percentages of RAP in each asphalt mix. Marshal tests were performed to determine Marshal properties and optimum asphalt content in each asphalt mix. Indirect tensile strength test and loss of stability test were conducted on each prepared asphalt mix. After the analysis of study results, it is found that increasing the percentage of reclaimed asphalt pavement will decrease the characteristics of asphalt mixture, at a specific percentage of asphalt content. Also, the ideal percentage of RAP to be utilized in flexible pavement is 25% of calcareous RAP and 30% of dolomite RAP.
Tube-in-tube structures have become a preferred solution for high-rise buildings in seismically active regions due to their efficient lateral load resistance and structural redundancy. However, their complex behavior under earthquake loading, especially in irregular configurations, requires seismic evaluation. This review paper presents a comprehensive analysis of previous research on the seismic performance of tube-in-tube systems, with a focus on nonlinear static pushover analysis as the primary evaluation method. The tube-in-tube system consists of a central shear core and perimeter moment-resisting frames, providing high lateral stiffness and energy dissipation capacity. Both regular and irregular configurations are evaluated, focusing on how geometric and structural irregularities influence seismic behavior and failure mechanisms. Key parameters discussed include the core-to-frame stiffness ratio, plan and vertical irregularities, mass eccentricities, and plastic hinge distribution. These factors strongly influence lateral load resistance, ductility, and overall structural performance. Research indicates that regular tube-in-tube systems generally exhibit stable and ductile responses with limited stiffness degradation. In contrast, irregular configurations are more vulnerable to early stiffness loss and localized failures, emphasizing the importance of advanced seismic design strategies to enhance structural resilience.
The objective of this research is to conduct a parametric study for unstiffened and stiffened steel beam columns with openings and develop their design equations. Since the presence of openings in the beam columns is inevitable, maintaining strength is crucial. Steel beam columns with openings are stiffened with two different stiffening schemes around the opening: horizontally and vertically. Unfortunately, there are no available design equations for steel beam columns with openings except Darwin’s’ which are suitable for compact beams’ web. The parameters studied are the opening shape, web slenderness (non-compact and slender), the effect of stiffening, and opening location with respect to the support. Interaction curves are plotted according to analytical finite element models. Both lateral-torsional buckling and local buckling are observed. Using a square opening instead of a rectangular opening result in a rise in the moment’s capacity of up to 18%. Moment resistance can be increased by up to 19% and 20%, respectively, when beam columns are stiffened with vertical and horizontal stiffeners around the opening. The normal and moment resistances decreased by up to 2% and 10%, respectively, when the opening was moved from support to near mid-span. Finally, the proposed design equations for beam columns with openings give agreeable results in the moment and normal strengths.
The current study conducts an experimental investigation of material selection for specimens prepared from various percentages of weight of sisal fibers (SFs) as natural fibers and glass fibers (GFs) as synthetic fibers with different stacking sequences and fiber directions to select the optimum material with the lowest density and water absorption content and the highest damping, flexural strength, and interlaminar shear strength (ILSS). Eight laminated composite materials comprised of natural fibers such as sisal fibers and synthetic fibers such as glass fibers are created using the hand layup technique. The material section problem is solved using complex proportional assessment (COPRAS) based on measuring multiple criteria weights using the CRITIC approach. Five competing criteria, including density, water absorption content, damping, flexural strength, and ILSS, are measured to select the optimal laminated composite material. The findings demonstrated that material No. 6 of stacking sequences [RGF/ RGF/BGF/ RGF/BGF/RGF/ RGF]is the best option, whereas material No. 1 of stacking sequences [SF0/SF90/RGF/BGF/RGF/SF90/ SF0]is the worst.
Pump sumps often face issues like water stagnation and vortex formation due to inadequate consideration of hydraulic conditions during design. This research employs Computational Fluid Dynamics (CFD) simulations to explore how different sump shapes and configurations impact flow patterns. Key factors examined include sump geometry, suction direction, inlet level, and the positional relationship between inlet and outlet pipes.The results demonstrate that circular sumps, high-level inlets, and horizontal suction pipes significantly reduce water stagnation and vortex intensity. Placing inlet and suction pipes on opposite sides further improves flow patterns. These findings provide practical guidelines for designing more efficient pump sumps, enhancing hydraulic performance, and reducing maintenance needs.Additionally, the study underscores the importance of optimizing sump configurations for various operational scenarios. By integrating advanced computational techniques with robust design principles, the research offers actionable recommendations that can be directly applied to improve the performance and reliability of pump sumps. The insights gained from CFD simulations pave the way for more efficient water management practices, ultimately contributing to reduced operational costs and enhanced system reliability in hydraulic engineering applications.This study highlights the critical role of detailed hydraulic analysis in pump sump design and demonstrates the value of CFD simulations in developing effective solutions for minimizing common hydraulic issues.
