Arabian Journal for Science and Engineering最新文献

This article addresses the challenges encountered in underwater acoustic communication (UWAC) and presents a novel approach for chirp spread spectrum (CSS) communication. CSS is recognized for its ability to adjust to multipath and Doppler dispersion in underwater conditions, despite it usually demands a large bandwidth time product to achieve optimal performance. To address this constraint and improve data rate, the paper proposes a neural network-based receiver for spectral efficient M-ary CSS communication. M-ary communication is accomplished by transmitting chirps with different start and stop frequencies. At the receiver, a multilayer perceptron (MLP) artificial neural network and a one-dimensional convolutional neural network (1D CNN) are used for supervised classification. The neural network is trained offline using a comprehensive dataset developed by the BELLHOP ray tracing algorithm, which simulates various underwater acoustic channels. The application of VTRM pre-processing equalization aims to enhance performance. The simulation results illustrate the superior performance of the proposed receiver when compared to a conventional receiver based on a matched filter. The 16-ary chirp spread spectrum 1D CNN and MLP receivers show a gain of 6 and 4 dB, respectively, in a simulated channel after undergoing VTRM pre-processing. Furthermore, the utilization of a 16-ary 1D CNN receiver results in a noticeable 6 dB enhancement in two recorded channels. However, the MLP receiver outperforms the traditional receiver in terms of bit error rate. The article emphasizes the possibility of higher data rates and enhanced performance in underwater communication systems by employing the proposed M-ary CSS neural network-based method.

This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It  is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.

In the face of the escalating global antibiotic resistance crisis, exploring alternative therapeutic avenues has become imperative. This study investigates the antibacterial potential of Punica granatum and Rhamnus sp. extracts from Dhahran, Saudi Arabia. Antibacterial activities were evaluated against Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, and Enterococcus faecalis using well diffusion, MIC, and MBC assays. The antibacterial tests of both plants revealed that ethanolic extracts demonstrate superior activity compared to chloroform extracts. Also, Enterococcus faecalis proves most susceptible (up to 33 mm inhibition), while Escherichia coli exhibits notable resistance. Although Escherichia coli was the most resistant organism using MIC, Pseudomonas aeruginosa was the most susceptible strain with MIC less than 12.5 mg/mL. The Punica granatum extracts and Rhamnus sp. chloroformed extract display primarily bactericidal effects, whereas the ethanolic extract of Rhamnus sp. has a mainly bacteriostatic effect. Phytochemical analysis reveals a rich composition, including different well-known antimicrobial chemical classes. These findings underscore the potent antibacterial activity of both plants against diverse bacterial strains. The study emphasizes the need for further research into their phytochemical characterization and mechanistic insights.

This work contributes an area-effective low-noise amplifier design with a vast voltage gain range for a wide frequency range. The novel low-noise amplifier has an input stage, a common-gate stage, and another stage of the common-source technique. It is designed using the current mirror, the current bleeding network, and a new active inductor circuit. The noise-canceling network leads to a reduction of noise and power. The current-bleeding network improves the trans-conductance and provides a reduction in overall noise. Active inductors are crucial for achieving maximal gain, extensive bandwidth values, and low power consumption. Body-biasing technique has improved overall performance of the design. The novel low-noise amplifier is simulated and designed at a 0.5 V input voltage cadence virtuoso GPDK 90 nm and GPDK 45 nm complementary metal-oxide semiconductors (CMOS). The power dissipation of the novel active inductor (AI) is 416 µW with an optimized gain value, a small area requirement, and inductance values that varies with different W/L ratios of AI transistors. Power consumption of this low-noise amplifier is 4.85 mW, with optimized S-parameters values. Additionally, a small area and an optimized gain value also adds to the immense potential offered by proposed designs compared to the state-of-the-art low-noise amplifiers.

In a railway traction substation, power electronics devices are used in conjunction with a special transformer of a traction substation to mitigate power quality issues. To study the use of power electronics devices with conventional transformer, this research studies the use of a conventional Delta-star transformer along with power electronics converters. The system forms a co-phase system using single-phase back-to-back converters, which addresses the issue of current unbalance and power factor correction. The control theory derives the value of currents to be drawn from the secondary of traction transformer so as to draw balanced power from the three phases of the grid supply. Accordingly, a control algorithm is developed which calculates the reference converter currents and operates the converters. Validation of control theory is done analytically. Simulation and real-time hardware-in-loop experimentation are performed under different loading conditions. The steady-state and dynamic behavior of the system is studied to validate the proposed control algorithm. The work is presented as a proof of concept.

This study primarily focuses on design, mathematically model and simulate a novel two wheel-legged hybrid robot called W-Leg Jumping robot, which has a unique ability to overcome step-like obstacles efficiently. In general, in transformable wheel-leg robot studies, the leg and wheel structure perform their movements in an interdependent manner. However, in this study, it is aimed to design a robot in which the leg and wheel structure can move independently of each other and to develop a robot that can easily overcome obstacles on flat surfaces with the wheel mode and with the leg mode. The robot can fold its legs hidden within the wheels and deploy its two degree of freedom (DoF) legs when it detects step-like obstacles. This mechanism allows the robot to overcome an obstacle with a height of twice the radius of the robot's open/close mechanism of the legs, along with the two-dimensional kinematic and dynamic analyzes of the legs, are presented in detail within the scope of this study proportional-integral-derivative (PID) controller is designed to control the joint angles of the legs. The reference angle values to be followed according to the height of the obstacle are determined using artificial neural network (ANN). Additionally, motion simulations of the robot are conducted for four different obstacle heights (20, 30, 40, and 50 cm). As a result of the PID controller, when exceeding the highest obstacle of 50 cm, the average absolute joint angular tracking error is max. 1.8829°, average tracking error max. 0.265 s and max.

This paper studies the interference impact on the performance of reconfigurable intelligent surface (RIS)-equipped decode-and-forward (DF) relay networks, where RIS is used as internal part of both the source and relay nodes. For that purpose, approximate closed-form expression is derived for the system outage probability assuming Rayleigh fading channels and opportunistic relaying scheme. Furthermore, to get more insights at the system performance, an approximate but accurate expression is achieved for the outage probability at the high signal-to-noise ratio (SNR) regime, where the system diversity order and coding gain are obtained and analyzed. The findings show that the system can achieve a diversity order of (G_{d}=textrm{min}(N_{1},N_{2})K), where (N_{1}) and (N_{2}) are the numbers of reflecting elements at the source and relays, respectively, and K is the number of relays. Additionally, results illustrate that for the same diversity order, utilizing one relay with multiple reflecting elements gives better performance than utilizing multiple relays each with a single reflecting element. Moreover, findings show that the number of relays not only affects the system diversity order, but also the coding gain of the first hop when it is dominating the system performance. Furthermore, results illustrate that when (N_{1}=N_{2}), the system performance is dominated by the largest interference at either the relay node or the destination. With unequal interference powers, changing number of interferers at the node where interference is larger affects the performance, whereas changing number of interferers at the other node has no effect on the system behavior. On the other hand, when both nodes have the same interference power, changing number of interferers at either node by the same amount results in the same effect on the system performance.

In the realm of additive manufacturing, powder bed fusion (PBF) is recognized as an innovative and highly effective technique for manufacturing titanium-based materials. With an understanding of and regulation for the complex microstructural evolution that occurs during the PBF process, several previous studies have been conducted to enhance the reliability, efficiency, and performance of the PBF through investigation and optimization of microstructure evolution in PBF. These studies have examined various aspects, including feedstock materials, process parameters, and post-processing techniques, in order to gain a comprehensive understanding and control over the progression of microstructure in the PBF. Process parameters are widely acknowledged as critical determinants in the PBF process for titanium-based materials, significantly influencing the quality in 3D printed components. Previous studies have provided an in-depth discussion of the effects of process parameters, such as laser power, scanning speed, and hatching space, on the microstructure evolution of Ti-based materials. The primary objective of this review paper is, therefore, to provide a comprehensive and clear explanation of recent efforts, with a particular focus on investigating the complex evolution of microstructures in Ti-based materials during the PBF process. This thorough discussion is devoted to providing a comprehensive understanding of the effects of process parameters on the evolution of microstructures in Ti-based materials.

The development of novel compounds is essential for the discovery of new therapeutic chemopharmaceuticals. We have synthesized a new Schiff base ligand through condensation of 3,5-dibromo-2-hydroxybenzaldehyde with cyclohexane-1,2-diamine and its relevant transition metal complexes with Mn, Fe, Co, Ni, Cu and Zn metal ions. The synthesized ligand and the metal complexes have been characterized by elemental analysis, UV–Vis, FT-IR, ¹H-NMR, ¹³C-NMR spectroscopy and ESI mass spectrometry; these studies revealed that ligand is bonded to the metal ion via two phenolic oxygen and two azomethine nitrogen. SEM-EDX with mapping analyses of zinc metal complex also demonstrated needle-shaped morphology with uniform distribution of elements. Schiff base metal complexes have been screened for their in vitro antimicrobial activities against Escherichia coli and Staphylococcus aureus bacteria as well as Candida albicans and Aspergillus niger fungi. Also, molecular docking studies inferred the possible interaction of the metal complexes that have been computed using the molecular mechanic calculations using the HyperChem 8.03 molecular modeling program to confirm that there exists a favorable interaction between biomolecules and metal complex. The anticancer tests for MCF-7 breast cancer cell lines conducted with Zn (II) metal complex demonstrated its synergistic effect, exemplifying its involvement in stronger anticancer action, and implementing a higher inhibitory efficiency of the IC₅₀ value of 86.68 ± 0.72 μg/ml for MCF-7 cell. As a result, the current study recognizes the potential of the newly synthesized Schiff base-derived complexes as a preclinical therapeutic agent.

This study presents a hybrid neuro-fuzzy prognostic framework that develops an energy allocation method for current urban power infrastructure. This is accomplished by combining a nature-inspired intelligent optimization method with the learning capabilities, interpretability, and reasoning powers of neural networks, known as an adaptive neuro-fuzzy interface system. To align the problem’s representation with real-world situations, this study includes different charging demand tactics enforced by electric vehicles (EVs) along with the limitations of the power-generating systems at energy facilities to achieve optimal power generation. The implementation of the one-day pricing structure entails a complex optimization process aimed at reducing costs and minimizing the release of detrimental emissions. The proposed model was constructed using an adaptive neuro-fuzzy approach, with the training data derived from this optimization problem as the central component. The proposed approach effectively handled the diverse energy demand patterns demonstrated by EVs, encompassing the Electric Power Research Institute recommendations, stochastic behavior, and both peak and off-peak charging. This cooperation occurs systematically. The Crow Search Algorithm-Adaptive Neural Fuzzy Inference System achieved a reduction in operational expenditures in terms of percentages, such as 2.66%, 3.39%, 3.94%, and 2.63% for all recharging scenarios. These values are lower than those of other advanced approaches. The hybrid strategy that has been established has several benefits, such as the efficient management of various scenarios related to the demand for charging energy for EVs and the development of a predictive cost scheme. This scheme can assist policymakers in formulating cost-effective energy policies and budget plans for future EV loads. Moreover, they offer the advantage of self-reliance, allowing EV owners to charge their cars economically in many accessible situations. Another important component is the ability of urban planners to reduce greenhouse gas emissions from power generation, thereby promoting the development of an ecologically sustainable charging infrastructure. Finally, a benchmark test system was employed to evaluate the efficacy of the proposed approach across different energy consumption patterns.