Ain Shams Engineering Journal最新文献

Development of advanced materials for drug delivery is of great importance for efficient cancer therapy. Among various materials for drug delivery, boron nitride has attracted much attention due to its unique properties for pharmaceutical applications. The efficiency of pure boron nitride nanosheets (BNNS) and modified BNNS by Ni and Pd atoms in the delivery process of the anticancer medicine 5-fluorouracil (5-FU) is studied here within the framework of density functional theory (DFT) method in different configurations. The computational method was carried out for better understanding the new drug delivery system design and release of drug. Calculation of the adsorption energy revealed that adsorption of drug via the F atom in the perpendicular configuration was more desirable than adsorption in the perpendicular state via the O atom of the drug molecule. On the other hand, Ni and Pd improved the geometry and electronic properties of the adsorption process. The Eads increased from −3.488 for pristine BNNS to −7.365 and −8.287 eV for Ni@BNNS and Pd@BNNS, respectively. The electronic band structures demonstrated the competency of the modified BNNS for adsorption of 5-FU medicine via change in the VBM, CBM, and E_gap values prior and after the molecules are adsorbed onto the surface.

Significance of nanofluids cannot be overlooked because of their enhanced characteristics which play vibrant role in their thermal performance. These make them more effective for practical applications. Addition of multiple types of nanoparticles potentially affect the thermal conductivity of base fluid which directly contribute in the heat transfer mechanism. Hence, the current work deals with the study of tetra nanofluid model including the influence of different parameters. The results obtained through numerical approach and examined that the fluid motion enhanced at variable saddle/nodal regions and reverse variations examined for higher $\lambda$ values. The inclusion of surface convection $B_i=0.1,0.2,0.3,0.4$ particles concentration from $0.04$ to $0.16$, heat generation factor ($Q_1=0.5,1.0,1.5,2.0$) and radiation effects ($R_d=1.0,2.0,3.0,4.0$) are observed reliable physical tools to enhance the heat performance of nanofluids which is advantageous from engineering as well as industrial point of view. Further, thermal boundary layer enlarges for $R_d$ and reduced for $Q_1$ and nanoparticles strength ${\varphi }_i,i=1,2,3$.

The evaluation of X-ray images of hands serves as the basis for Bone Age Assessment (BAA), a critical component in the prediction and analysis of medical disorders. The key areas of interest in this examination are the epiphyseal ossification centres and the carpal bones. Human BAA models, although necessary, are time-consuming and prone to mistakes, emphasising the need for a more efficient computerised BAA model. This study introduces ODL-BAAM, a novel Deep Learning-based Bone Age Assessment Model, aimed at enhancing efficiency and accuracy in medical image analysis. Given the critical role of Bone Age Assessment (BAA) in predicting medical disorders, particularly based on hand X-ray images, there’s a pressing need for more streamlined and reliable computerized BAA models. Leveraging Deep Learning methodologies over classical Machine Learning approaches, ODL-BAAM offers a comprehensive solution. The model begins with preprocessing steps to standardize and normalize X-ray data, crucial for managing the inherent complexities of such images. By integrating Faster RCNN with MobileNet, feature extraction becomes more effective, while the Tunicate Swarm Algorithm optimizes model hyperparameters. Age determination is facilitated through SoftMax layers applied to feature vectors. Through extensive simulation studies, ODL-BAAM demonstrates promising results, showcasing heightened sensitivity, specificity, and overall accuracy compared to existing BAA models. With a remarkable 96.5% accuracy rate, ODL-BAAM represents a significant advancement in the realm of computerized BAA, effectively addressing prior limitations and setting a new standard for medical image analysis.

Electric vehicle charging stations (EVCS) that are based on DC microgrids are presented in this research. The system comprises a solar photovoltaic system (SPVS), storage battery (SB), electric vehicle (EV) and grid. The adaptive interaction artificial neural network (AI-ANN)-based vehicle to grid (V2G) and grid to vehicle (G2V) power management controller (PMC) is suggested for DC microgrid based EVCS. This EVCS is suitable for the residential building and offices where EV may be parked. This EVCS provides the facility to manage the power of the building in addition to charge the EVIt has two different modes of operation. The first mode uses the EV as a power source. In the second mode, the EV functions as a load. This controller is developed to acquire electrical power from the solar photovoltaic system (SPVS), storage battery, EV and grid respectively. If the solar photovoltaic system (SPVS) and storage battery power are insufficient to meet the demand, power is extracted from electric vehicle (V2G). If the solar photovoltaic system (SPVS), storage battery and EV are not sufficient to meet up demand, then deficit power is obtained from the grid (G2V). ANN based power management controller (PMC) Also provides a consistent DC bus voltage and reduces overshoot from 9.6 % to 0 %., settling time from 1.18 sec. to 0.52 sec. and rise time from 0.27 sec. to 0.25 sec. of DC bus voltage compared to conventional controller. The suggested power management controller tested for two different modes i.e., V2G and G2V using MATLAB Simulink software.

Remote sensing (RS) data is an essential tool to quickly detect the effects on the environment of significant forest fires that occur every year. The motivation of this research was to detect the burned areas with RS data quickly after the Rhodes 2023 Forest Fire. For this purpose, optical sensing systems and microwave sensing systems were used. In addition, the Sentinel-5P dataset was preferred to determine the difference due to harmful gases in the atmosphere and to link it with forest fires. Sentinel-2A and Sentinel-2B data enable change detection by creating the Enhanced Vegetation Index (EVI), Normalized Burn Ratio (NBR), and Normalized Difference Water Index (NDWI). As a result, it was determined that approximately 18,900 Ha of forest area was burned or bared. According to the CORINE 2018 burned forest area was approximately one-fifth of the total forest area.

A new paradigm for supporting medical services, especially beneficial for metropolitan regions and individuals experiencing homelessness who use technological communications, is fog-based healthcare service management integrated with the Internet of Things (IoT). This paradigm allows for the flexible transformation of health data into personalized, meaningful health knowledge, potentially having a significant impact on health practices in communities where health departments are not actively engaged. Fog computing and the IoT are crucial components of today’s healthcare system, facilitating the management of vast amounts of big data for disease prediction and diagnosis. However, there is a risk of incorrect diagnosis when a patient has multiple illnesses. This paper aims to develop a model for the diagnosis of cardiovascular diseases and diabetes using a combination of AI and IoT approaches. The proposed model encompasses data collection, preprocessing, classification, and parameter setting. Wearables and sensors, which are part of the IoT, facilitate easy data collection, while artificial intelligence methods use this data for disease detection. As an example of intelligent healthcare systems, the proposed approach employs the Smart Healthcare-Crow Search Optimization (SH-CSO) algorithm to diagnose diseases. By adjusting the “weight” and “bias” parameters of the intelligent healthcare systems model, CSO enhances the classification of medical data. The application of CSO significantly improves the diagnostic outcomes of the intelligent healthcare systems model. The efficacy of the SH-CSO algorithm was validated using medical records. Results demonstrated that the proposed SH-CSO model could diagnose diabetes with a maximum accuracy of 97.26% and heart disease with a maximum accuracy of 96.16%.

The increasing demand for indoor positioning information has led to a growing emphasis on indoor localization. Non-Line-of-Sight (NLOS) conditions diminish the accuracy of Ultra-Wide Band (UWB) system positioning, while over time, Inertial Navigation Systems (INS) suffer from accumulating positioning errors. To address these issues, this paper proposes a method that combines UWB and INS sensors. Compared to individual system positioning methods, this approach effectively enhances localization precision, leveraging the complementary strengths of both systems. The paper utilizes Extended Kalman Filtering (EKF) to fuse residual positioning information, and the obtained residual position results are processed using the Multiple Fading Factor Square Root Kalman Filter technique (MSTSCKF). Moreover, during temporal asynchrony, it updates INS positioning and yaw angle information using EKF output for subsequent INS positioning until the next data correction. To further mitigate NLOS effects, a k-means preprocessing method is applied to UWB data. Root Mean Square Error (RMSE) is used as an evaluation metric. Simulation and experimental results demonstrate that the proposed method effectively accounts for NLOS error influences, thereby enhancing navigation and positioning accuracy.

Wireless Sensor Networks (WSNs) an energy consumption is a significant problematic due to the limited power resources of sensor nodes. Mobile Sinks (MS) rise the system's flexibility and convenience of data collection. The important role of Load balancing techniques plays in extending network lifetime by uniformly spreading energy usage between sensor nodes. WSN Data Collection for Two-Phase Energy Minimized Load Balancing Scheme (TPEMLB) with Sequential Convex Approximation (SCA), the use of a movable sink, is discovered in this research. The MS collects data from nearby sensors called as sub-sinks along a path as it moves. Data collection throughput is enhanced through efficient data distribution among sub-sinks and a data collection schedule. Geometric programming and SCA methods make an algorithm with guaranteed convergence to meet the problematic task. This research employs a SCA method to discover the best locations for sensor nodes while keeping energy restrictions in mind. Using a series of convex optimization difficulties, this technique repeatedly estimates the optimal sensor node positions that minimize energy consumption while ensuring sufficient coverage of the target region. In the second stage, incorporate a mobile drain that traverses the network intelligently to collect data from sensor nodes. The technique considers sensor node energy levels, data collection rates, and distance from the receptacle to balance network traffic and decrease energy consumption. Subsequently, the proposed model TPEMLB has a higher deployment success rate and efficiency, a more extended network lifetime, lower energy consumption, and better load balancing, and is the preferred solution for the unique challenge.

This research investigates the sustainability potential of traditional architecture, with a specific focus on the application of windcatchers as passive ventilation systems in hot and arid climates. This study fills a significant knowledge gap concerning the integration and performance of such traditional systems in modern architectural designs, particularly within various climates of the UAE. Employing a combination of (CFD) simulation and real-time temperature monitoring in buildings equipped with windcatchers, the research compares these results with those from buildings lacking such systems across different seasons.

Findings indicate that windcatchers significantly outperform conventional cooling systems. They maintained indoor temperatures below 20 °C in January and below 30 °C during the peak heat of April. The most striking results occurred during July and September’s midday heat, where temperatures stayed below 35 °C, significantly cooler compared to nearly 40 °C in non-windcatcher environments. Furthermore, windcatchers achieved these temperature reductions without any energy consumption, leading to considerable savings in operational costs and a reduction in carbon emissions by 74 to 111 kg CO2_e monthly.

The study confirms that integrating windcatchers into modern UAE buildings is not only viable but also enhances environmental sustainability and energy efficiency. This integration supports cultural heritage while addressing modern environmental challenges, marking windcatchers as a crucial element in sustainable building design.

The initial concept of an Unmanned Aerial Vehicle (UAV) design is complicated and unique due to performance parameters like payload capacity, engine power, endurance, service altitude, etc. required to perform a wide range of missions. Empirical correlations between key design parameters can approximate initial characteristics but to explore the entire design space while considering sensitivities of interacting parameters, comprehensive, time consuming and computationally expensive trade-off studies are required to converge the early concept appraisal. The current paper explores the potential of Machine Learning (ML) techniques for rapid and accurate estimation of UAV design parameters in the conceptual phase by extracting knowledge from UAVs already in service. An ML framework based on five different regression models is formulated to estimate the parameters significant to mission profile using database of fixed-wing UAVs key design attributes. The predictive performance of the presented ML approach shows excellent agreement with the actual values during validation and comparatively, turns out to be more accurate than the existing methodology based on empirical correlations. Overall, ML techniques have a great potential for being applied as a surrogate model for evaluating novel UAV design concepts using less computational time and resources.