Articles in This Issue
Abstract
Reflective cracking is a serious issue that Adversely influences the performance and longevity of asphalt overlays over deteriorated pavements. This review Looks for the Technologies which used to reduce the reflection cracks propagation by insert a new Strategies and different design materials. This research dealt with many treatments such as: increasing the layer thickness of Hot Mix Asphalt (HMA), creating modified asphalt by adding polymers to asphalt, rubberizing asphalt, carbon black, sulfur and other different materials. Geosynthetic materials were studied and analyzed to evaluate their ability to increase the layer tensile strength and minimize the effect of reflection cracks such as geotextiles, geogrids, and Stress Absorbing Membrane Interlayers (SAMI). The research shows that the increasing of overlay asphalt layer thickness leads to durability development. On the other hand, using developed materials like Polymer-Modified Asphalt and Stress Absorbing Membrane Interlayers (SAMI) Strategies leads to increasing the service life of the repaired pavement. The conclusion indicated that the development of overlay asphalt layer thickness and layer reinforcement and applying advanced environmental systems can be improving the pavement performance. These Strategies can produce a perfect solution to prevent or reduce the reflection cracks in rigid and flexible pavement.
Abstract
In this work, for ultra-wideband (UWB) applications, a passive filter antenna with edge chamfering is investigated in this paper. The performance of an optimized UWB antenna design that achieves an advanced fractional impedance bandwidth of 102% is confirmed by simulation and experimentation. The performance of the antenna is improved by integrating a lowpass filter (LPF) into the fed line, which suppresses high-frequency radiation with a central frequency of 3.5 GHz (WiMAX), the UWB antenna has been transformed into a narrowband antenna, offering a 43.7% fractional bandwidth that spans the frequency range from 2.7 GHz to 3.9 GHz. A stepped impedance transmission line and an extended fractal H-shaped structure integrated in the microstrip feedline make up the filtering network. Using CST Microwave Studio (CST MWS), key performance parameters such as the radiation patterns, efficiency, gain, and reflection coefficient (S11) were examined. In its prototype, the antenna reduces its size by 5% and is made on a FR4 substrate with a permittivity coefficient of 4.3 and a loss tangent of 0.02. A maximum gain of 1.7 dBi and a peak efficiency of 78% at the center frequency were verified experimentally. The center frequency was verified experimentally. The tiny antenna, which measures 0.30λ₀ × 0.37λ₀ × 0.008λ₀, performs well and is appropriate for UWB applications. The design makes a significant addition to the realm of UWB technology by incorporating elements that improve its ability to adapt.
Abstract
Moisture-induced damage in asphalt pavements, is defined by adhesive failure at the binder-aggregate interface and decreased mechanical integrity, severely reduce pavement durability. The research examines the mechanical properties and moisture sensitivity of hot mix asphalt (HMA) enhanced with styrene-butadiene-styrene (SBS) polymer and including reclaimed asphalt pavement (RAP). Laboratory assessments, including indirect tensile strength (ITS) and tensile strength ratio (TSR) tests, were performed on conventional HMA, SBS-modified HMA (4% SBS), and SBS-modified HMA contained 20% RAP. The results indicated that SBS modification significantly improved mechanical and moisture resistance properties, where unconditioned ITS specimens increased by 37.1% and TSR value enhanced by 13.5%. The incorporation of RAP decreased ITS value by about 21 % relative to pure SBS-modified HMA; nevertheless, the SBS+RAP combination still show higher ITS and TSR values than conventional HMA.
Abstract
This study simulates a free-space optical communication system that uses optical beams with varying responses to atmospheric disturbances to secure transmitted data. Atmospheric turbulence was modeled with high accuracy to replicate real-world conditions closely. Non-diffracting beams were generated and used to represent optical beams and compared in two scenarios, conventional data transmission, and optifusion data protection. This approach facilitated a comprehensive analysis of the transmission environment and the effectiveness of optifusion, identifying the most suitable non-diffracting beam types for secure data propagation. By analyzing the values of key performance metrics of the selected non-diffracting beams across different weather conditions and long propagation distances, the study demonstrated the simulation system's reliability and the optifusion method's effectiveness in enhancing data security. The results showed that non-diffracting beams resist atmospheric turbulences strongly, emphasizing their potential for secure, long-range free-space optical communications.
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
Abstract
This work has studied the size of the mean time between failures (MTBF) because it has a vital role in assessing reliability in manufacturing systems. Previous studies have indicated that the reliability value depends on the size of MTBF, so they indicated only 11 types of time that reliability value depends on, and they used methods of DFR and RCM to enhance the reliability level. To assess and increase reliability value, this work referred to the four main times: mean time between failures (MTBF), mean time to diagnosis (MTTD), mean time to repair (MTTR), and mean time to failure (MTTF) in more detail. Also, it designed a new arrangement of failure notification time, failure diagnosis time, downtime, failure repair, testing time, and recovery periods for ongoing operations in manufacturing systems through a new redistribution of 19 times and time intervals in detail between the four main times, so it revealed and added 8 types of other times and time intervals more than previous studies because they have vital roles in increasing reliability value. Thus, the new arrangement contains two parallel pathways and 19 types of times and time intervals. The first pathway represents 5 positions and 11 types of start and end times; the second pathway represents 4 positions and 8 time intervals. Consequently, MTBF becomes longer because the new arrangement shortens the time distances between the start of failure and repair process end, between diagnosis end and test, and between inspection end and the system's return to normal operating conditions. The motivations are to raise the reliability value, quality level, and effective maintenance and save costs. This work used the data collection and analysis method. The results showed that there is a higher reliability for manufacturing systems when the time arrangement is better, MTBF is longer, MTTD is shorter, MTTR is smaller, MTTF is longer, and the error rate is lower.
Abstract
The security level and robustness of memristive image encryption techniques depend on the order and dynamics complexity of the memristive system. The grid multi-double-scroll (GMDS) chaotic system (CS) offers extremely rich dynamics but the implementation of high-order chaos needs large computation time. To overcome this limitation, researchers have proposed the use of muti-lower-order CSs to assist the encryption process individually. This scenario may reduce the security level since the non-friendly user may attack each involved CS independently. This paper proposes an effective six-dimensional (6D) memristive chaotic system constructed by combining 5D, 5D, and 7D GMDS chaotic systems. Each of the six chaotic sequences is generated from three sequences corresponding to two or three of the basic CSs. The combined CS shares the same total key parameters (initial values and design parameters associated with the three basic CSs) and this leads to a key space of 22392, the highest among the reported image encryption techniques. The combined CS is used to assist the operation of a proposed color image encryption scheme consisting of four sequential stages that perform compressive sensing, scrambling, DNA encoding, and diffusion, respectively. Simulation results validate the feasibility and robust security of the proposed encryption scheme.
Abstract
Whispering Gallery Mode Micro-Resonators (WGMRs) have received significant interest due to their great sensitivity to environmental changes, compact size, and ability to operate over a wide spectral range because their low optical losses produce high-quality factors so that they can be used in various sensing applications. This work investigates the design and implementation of cylindrical WGMRs for Refractive Index (RI) sensing for different delivery fiber diameters. Single Mode Fiber with different waist diameters (80,67.1,18) µm were used as delivery fibers. At the same time, the sensor (resonator) fiber is SMF with a diameter (125 µm). Quality factors and Free Spectral Range (FSR) were calculated and analyzed for each diameter. The quality factor for all diameters was in power of 104, which is considered good. The FSR is inversely proportional to fiber diameter. FSR values were (0.678,1.75,2.03) nm for (80,67.1,18) µm delivery fiber diameters respectively. An analyte prepared by NaCl with different refractive indices is used to investigate the RI sensor performance. Higher sensitivity is obtained from the WGMR with a smaller waist diameter, which is (-)74 nm/RIU. While for the delivery fiber diameters (80,67.1) µm were (-0.28, -9.27) nm/RIU respectively. The submitted sensor will have a good contribution in the field of chemical, biological and medical applications.
Abstract
The paper focuses on the role of participatory design and its various methods—such as awareness methods, social interaction methods, as well as indirect and open methods— that involve all citizens in the process of design, implementation, and future development process. The architect's role in this process is to transform the desires and visions of the participants into a practical reality, ensuring that their needs are met to create vibrant spaces. This involves achieving specific indicators that generate vitality in these spaces, including diversity, communication opportunities, strong identity, concentrated density, accessibility, and safety, all of which enhance social interaction. The paper referred to a number of international examples in Norway and Denmark, and Arab examples in Jordan that proved the effectiveness of the participatory approach in achieving vital environments. Hence, the research problem is represented by the following questions: How does participatory design contribute to enhancing the vitality of the space? To what extent is the participatory design methodology applied to enhance vitality and help achieve a sense of belonging within the space? The paper findings emphasized the importance of participatory design in meeting the needs of the local community and in creating a vital, safe, and inclusive environment characterized by social cohesion, cooperation, ownership, belonging, and equality. This highlights the importance of encouraging the entire community to engage in the design process, which can lead to creative ideas and empower citizens. The paper recommends adopting the participatory design approach to improve the quality of life and enhance the vitality of urban spaces.
Abstract
Photonic crystal fibers (PCFs) are generally divided into two categories: solid-core photonic crystal fibers and hollow-core photonic crystal fibers. In this paper, a long-period fiber Bragg grating (LPFBG) was experimentally fabricated in a hollow-core photonic crystal fiber (HC-PCF) using a CO₂ laser and based on the point-by-point technique. Proper LPFBGs were inscribed using laser powers of 0.9 W and 1.4 W, with grating parameters (grating period, length of each pitch, and depth of each pitch) equal to (136 µm, 48.042 µm, 16 µm) and (142 µm, 74.027 µm, 22.09 µm), respectively, for two samples. The Bragg wavelengths and full-width at half-maximum (FWHM) were (1529.274 nm, 1.34 nm) and (1529.629 nm, 5.11 nm), respectively, for the two samples fabricated using CO₂ laser powers of 0.9 W and 1.4 W. From these results, it was recognized that the optimal LPFBG-HC-PCF was the one fabricated using 0.9 W laser power. The unique structure of hollow-core photonic crystal fibers, which enables light propagation within the air core and provides a large internal surface area, has attracted significant research interest for various sensing و communication applications, Environmental and Biological Monitoring, and medical applications.
Abstract
Hydraulic structures, including cascade aerators, may be acknowledged as important components in improving aeration efficiency because of the intense turbulent mixing combined with large air bubble entrapment at these structures. The main objective of the present study is to achieve maximum aeration efficiency and enhance the concentration of dissolved oxygen in the water since this is an important factor in improving water quality. The present study aims to determine the most proper geometric and dynamic parameters of a typical square-shaped stepped cascade with a total height of 120 cm, and sex steps. A tread of each step is 10 cm and a rise of each step is 20 cm, where aeration efficiency is maximized. The results of the study revealed that the maximum value of water aeration efficiency, meaning an increase in dissolved oxygen in the water using a stepped cascade aerator happened when flow rates of 15 L/min, 25 L/min, and 35 L/min with aeration efficiencies of 22%, 37%, and 42% respectively. Finally, the optimization of flow rates in aeration systems can lead to improved water quality parameters. The most important feature of the present study is the innovation of a natural method of water treatment that relies on the principle of mixing, coagulation, and flocculation by hydraulic methods, which works to reduce the costs of operation.
Abstract
In Urban cities, services are supported by intelligent applications and are connected to each other through ad hoc networks. Any service can be operated using a compatible of an Internet of Things (IoT) technology. This study focuses on the transportation service and finding a non-cost solution to solve the crossroads congestion that affected people time and money. The Wireless Sensor Networks (WSNs) that are planted on the roads can help in monitoring the roads situation by collecting their data and send them through wireless communication to a traffic management center. In this work two phases of time are considered for a crowded area. Low-cost components are suggested to solve the congestion at the cross roads without the need for reconstruct the roads. IoT device such as smart phone can be wirelessly connected to the Traffic Management Center (TMC), which can analyze the incoming data from WSN and send back the calculated time to the police officer to control the green light long and overcome the standard time installed for all directions. The main idea is to solve the congestion problem in real time by extending the time long of the green traffic light for the road direction with the highest vehicle density. The suggested algorithm was operated on a dataset of 6 days and for the time phase from 7:00-10:00am.
Abstract
In this work, a sensor for cooking oils was designed and fabricated for the first time using hollow-core photonic crystal fiber (HC-PCF). This sensor was studied practically, and the results showed a difference in sensitivity depending on the type of oil. The results showed that the wavelength shift occurred with very small changes in the refractive index of the edible oil. The confinement loss was computed. Seven oils with various refractive indices were utilized. Based on our results, the relative sensitivity to various kinds of Canola oil, Sunflower oil, Olive oil, Walnut oil, Sesame oil, Corn oil, and Wheat oil are 79.9321%, 80.1588%, 77.4523%, 77.4889%, and 77.5650%, 77.6652%, 80.5902% respectively. Moreover, the proposed sensor also has low confinement losses of 6.473×10-9dB/m, 1.158×10-9dB/m, 1.2×10-9dB/m, 1.20×10-9dB/m, 1.199×10-9 dB/m, 1.2×10-9dB/m, and 6.347×10-9dB/m respectively. This sensor can be used to measure the quality of oils and distinguish their types, and they can be a practical element in oil detection systems, which will bring about a change in the future in oil detection methods.
Abstract
Road transport undeniably constitutes the predominant mechanism for facilitating the transportation of both goods and individuals on a global scale, serving as an essential backbone for economic and social interactions across diverse regions and cultures. The noticeable decrease in the flow of vehicles, which can be attributed to a plethora of internal and external factors, with a particular emphasis on the phenomenon of congestion, has profound implications that significantly influence fuel consumption rates, contribute to pollution associated with emissions, adversely affect the health and well-being of bystanders, and culminate in a considerable loss of time for individuals navigating these congested environments. In light of their elevated population densities coupled with their classification as emerging economies, South Asian countries find themselves necessitated to implement automated systems for the critical processes of predicting, identifying, and effectively addressing the challenges posed by road traffic congestion in order to enhance urban mobility and overall transport efficiency. This thorough research carefully explores the various techniques that have been utilized to recognize traffic congestion, presenting an extensive assessment of their individual strengths and weaknesses, thus offering insightful observations about the existing situation in this field of study. The examination of the diverse approaches and advanced technologies that have been utilized for the operation of lane-less roadways have been conducted, revealing substantial potential for further innovations that could greatly assist future researchers in their endeavors to enhance traffic management and improve roadway safety and efficiency.
Abstract
This study evaluates the performance and efficiency of four deep learning models—VGG-16, ResNet-50, Inception-V3, and DenseNet-121—in detecting pneumonia from chest X-rays, addressing the critical need for balanced accuracy and computational efficiency in clinical diagnostics. Methods: A dataset of 5,234 chest X-rays (3,875 pneumonia, 1,341 normal) was augmented via rotation, flipping, and zooming to mitigate class imbalance. Models were trained on an RTX 2060 GPU for 40 epochs, with performance assessed using accuracy, F1 score, sensitivity, specificity, precision, and computational metrics (training time, memory usage). Statistical significance was validated via paired t-tests (p < 0.05). Results: DenseNet-121 achieved the highest accuracy (95.2% ± 0.8), F1 score (95.1% ± 0.7), and throughput (400 images/sec) with minimal memory usage (33MB). ResNet-50 and Inception-V3 showed moderate performance, while VGG-16 exhibited overfitting tendencies. In conclusion, DenseNet-121 showed strong performance compared to other models, both in terms of accuracy and processing speed, which is essential for use in real-time clinical settings. However, the small size of the validation set and limited population diversity are important limitations that should be addressed in future studies. Moreover, more testing on larger datasets is needed to confirm the stability of the model and see how the model will work in different settings. Future work should address ethical considerations in AI-driven diagnostics and validate findings across multi-institutional datasets.
Abstract
Maximum Power Point Tracking (MPPT) techniques are essential for maximizing energy extraction from photovoltaic (PV) systems under diverse environmental conditions. This paper reviews three widely used MPPT methods Perturb and Observe (P&O), Fuzzy Logic Control (FLC), and Artificial Neural Networks (ANN) highlighting their effectiveness in addressing challenges such as temperature fluctuations, varying irradiance, and shading. The P&O method is noted for its simplicity and low computational requirements, but it suffers from oscillations around the maximum power point under rapidly changing conditions. FLC offers enhanced adaptability and robustness by mimicking human decision-making, performing well in dynamic environments with moderate complexity. ANN-based methods demonstrate superior tracking efficiency and fast convergence, particularly under complex and highly variable conditions, due to their ability to learn and generalize from data. These findings underscore the importance of continued development of MPPT techniques, especially intelligent and hybrid approaches, to meet the growing demand for sustainable energy. Thus, solar energy remains a highly viable solution for modern energy needs.
Abstract
A dam failure results in losses in terms of economy and infrastructure, in addition to the loss of many lives and assets. Inadequate seepage control procedures are typically the cause of seepage failure in earth-fill dams. For an earthen dam to be waterproof and to minimize seepage, non-homogeneous dams with a clay core are one kind of embankment dam used. As water moves through the dam's core, friction causes it to lose a lot of energy. Both vertical and inclined cores can be used in the design and construction of zoned embankment dams. As a result, choosing the proper materials and dimensions for the earth dam's core is critical. The main objective of this study is to investigate different seepage control strategies for an earth dam (HORAN DAM) using the Finite Element Method (FEM). We modeled and analyzed nine cases of various seepage control techniques that have been modeled and analyzed using SEEP/W, a FEM-based software. The modeling results show using chimney filters reduces pore water pressure more effectively than using toe rock and horizontal filters. Regarding seepage, trapezoidal cores perform better than inclined cores, and the milder slope is preferred over steeper core slopes. The results show when the core permeability decreases, the seepage quantity also decreases. Toe rock decreases seepage more than horizontal filters and chimney filters. Additionally, it has been shown that using a toe rock filter together with a trapezoidal core with a mild slope performs better than using a different filter and a different internal clay core shape.