Arabian Journal for Science and Engineering最新文献

Radioisotopes are one of the essential cornerstones of modern medicine. They serve both diagnostic and therapeutic purposes. These radioisotopes are mainly produced using charged particle accelerators such as cyclotrons. In this paper, we present a description of a compact high-field superconducting magnet as it is considered the most important part of the cyclotron accelerator because it is approximately 60% of the overall TAAC30 cyclotron design. This 30 MeV cyclotron uses the magnet to boost a magnetic field two times higher than the recently developed conventional H-cyclotrons. This magnet will also be a modern, state-of-the-art design not only because of the higher magnetic field, but also smaller size, lower maintenance, lighter weight, and lower power consumption in comparison with any other magnet available. This design also allows both low construction requirements with operation costs for the production of PET isotopes, which require an internal water target. The acceleration frequency is 200 MHz, and an operating power level of 2–3 kW is foreseen for the acceleration, powered by a compact water-cooled solid-state RF amplifier. The cyclotron, as well as the beam, operates as a fully continuous wave with a 100% duty cycle. This design aims to provide a sustainable supply of the critical imaging isotopes F-18 and N13, eliminating the need of supplying from other production facilities for small centers. Additionally, this paper presents simulation results of this magnet using multiple analysis models, which are sufficient and present high capability in accelerator field.

The availability of refined and efficient energy resources has played a decisive role in the advancement of societies, especially since the Industrial Revolution of the mid eighteenth to nineteenth centuries. In the twentieth century, the international energy scenario is experiencing a profound transition in terms of energy resources and their utilization. The energy transition is in response to the challenges the global energy landscape faces such as rapidly growing demand, depleting fossil fuel reserves, surging energy prices, risks associated with the security of supplies, and above all climate change. Nuclear power is an important form of energy making a significant contribution to the electricity mix around the world, especially in developed countries. One of the major advantages of nuclear power is its minimal greenhouse gas emissions as compared to fossil fuels. The paper examines the prospects of nuclear power in the energy transition considering both the trend of phase-out that the technology has experienced primarily in several European countries since the 1980s, as well as the growing interest it has received more recently as a low carbon energy solution toward addressing climate change. It also examines the significance of nuclear power toward energy transition with the help of multi-criteria decision analysis (MCDA) based on expert opinion from developed as well as developing countries.

Al-Hassa, located in eastern Saudi Arabia, hosts the world’s largest oasis and naturally irrigated land. Historically, 280 natural springs irrigated farms, with varying water quality suggesting a complex subsurface regime. To explore this, a multi-geophysical approach was applied in a remote part of the Al-Hassa National Park, where minimum cultural noise from agricultural and industrial activities is present. Five geophysical methods—210 gravity stations, a 3.6 km magnetic profile, 46 magnetotelluric (MT), six audio-magnetotelluric (AMT), and 35 transient electromagnetic (TEM) stations—were acquired to reconstruct a 3D subsurface model. Processing and integration of gravity and electromagnetic data revealed a complex underground structure with lateral resistivity (p_r) discontinuities, a possible salt dome structure, and fracture zones affecting groundwater flow. Key findings include low-resistivity anomalies indicating potential basins filled with low-density (p_d) sediments and high-resistivity zones suggesting basement rocks. The MT model reaches 4.5 km depth (z), while the 2D gravity model extends to 1.8 km. Low-resistivity zones in the MT data correlate with high-potential aquifers. The comparison of the gravity, TEM, and MT data showed good agreement, confirming the subsurface features. These results indicate significant hydrogeological complexity, impacting groundwater management and resource exploration. This comprehensive modeling approach provides insights into the qualitative hydrogeological characteristics and deeper subsurface conditions, potentially impacting the world’s largest conventional oilfield, Ghawar, located in the vicinity of the study area (A).

In order to harness the abundant solar radiation, huge extent of solar panels have been installed in inhospitable desert terrains in many parts of the world. However, the inevitable accumulation of dust on solar panel naturally deteriorates the photovoltaic performance, by significantly reducing the light transmittance to the device. There are many practical difficulties in employing conventional methods of dust mitigation, as it necessitates huge equipment, a large quantity of water, electricity and manpower to be made available in hostile and remote deserts. In order to circumvent this problem, different variants of self-powered, unmanned, automatic electrodynamic dust repulsion system have been developed and used in the solar panels. The effectiveness of such electrodynamic dust repulsion systems depends on the optimum distribution of electric field on and in between the interdigitated electrodes of the electrodynamic dust repulsion shield (EDS). This work presents the theoretical model to optimize the electric field and electric field distribution in the three-phase AC source-driven EDS system. This model is based on the solution of Laplace equation for the spatially periodic potential present in the electrode system, and it is simulated using COMSOL Multiphysics® software and the Wolfram Mathematica® program for different combinations of electrode voltage in one cycle. Moreover, the parametric dependence of the average electric field on EDS as a function of electrode geometry, dielectric constant, and the thickness of the dielectric coating was also theoretically investigated.

Cancer is a complicated disease that produces deregulatory changes in cellular activities (such as proteins). Data from these levels must be integrated into multi-omics analyses to better understand cancer and its progression. Deep learning approaches have recently helped with multi-omics analysis of cancer data. Breast cancer is a prevalent form of cancer among women, resulting from a multitude of clinical, lifestyle, social, and economic factors. The goal of this study was to predict breast cancer using several machine learning methods. We applied the architecture for mono-omics data analysis of the Cancer Genome Atlas Breast Cancer datasets in our analytical investigation. The following classifiers were used: random forest, partial least squares, Naive Bayes, decision trees, neural networks, and Lasso regularization. They were used and evaluated using the area under the curve metric. The random forest classifier and the Lasso regularization classifier achieved the highest area under the curve values of 0.99 each. These areas under the curve values were obtained using the mono-omics data employed in this investigation. The random forest and Lasso regularization classifiers achieved the maximum prediction accuracy, showing that they are appropriate for this problem. For all mono-omics classification models used in this paper, random forest and Lasso regression offer the best results for all metrics (precision, recall, and F1 score). The integration of various risk factors in breast cancer prediction modeling can aid in early diagnosis and treatment, utilizing data collection, storage, and intelligent systems for disease management. The integration of diverse risk factors in breast cancer prediction modeling holds promise for early diagnosis and treatment. Leveraging data collection, storage, and intelligent systems can further enhance disease management strategies, ultimately contributing to improved patient outcomes.

Ionizing radiation shielding is required to prevent or mitigate the radiological risks resulting therefrom. Low Z materials such as polyethylene are preferable for neutron shielding, while high Z materials such as lead are preferable for photons (gamma and x-rays). Concrete is a conventional shielding material that is used to shield against either photons or neutrons. Although concrete is cheap and can be easily formed, it is responsible for 8% of carbon dioxide emissions. If volcanic silica rocks (VSR) take the role of concrete in radiation shielding, this will help reduce the level of carbon dioxide emission. Monte Carlo code Fluka was used to simulate the experiment setup and calculate the exposure rate on the other side of the shielding samples. The obtained results showed that the linear, mass attenuation, and absorption coefficients of the VSR are almost like those of concrete. These results reveal that the VSR could be used similarly to concrete for the shield against X-rays diagnostic range up to 250 keV.

Fretting wear caused by flow-induced vibration (FIV) is a leading cause of fuel failure in light water nuclear reactors. This study describes a numerical methodology, validated with dedicated experiments, for predicting flow-induced vibrations in cantilever rods exposed to axial water flow, a paradigmatic configuration informative for fuel rods in water-cooled nuclear reactor cores. Utilising strong two-way fluid–structure interaction (FSI) simulations with an efficient computational approach, the study focuses on two key aspects of self-excited FIV: the dominant vibration frequency and the amplitude of the vibration. Correctly reproducing the former depends on optimising the solid domain and FSI coupling, while the latter hinges on the fluid solver’s ability to accurately replicate unsteady flow behaviour, especially in areas of flow separation. Two unsteady Reynolds averaged Navier–Stokes turbulence models, both being high-Reynolds number versions, and several discretisation schemes for the convection transport are evaluated for their capacity to reproduce the correct unsteady flow behaviour. When the axial flow is directed from the free end to the fixed end of the rod, both the Eddy viscosity model k-(omega ) SST and the Reynolds stress model by Launder, Reece, and Rodi reliably predicted the frequency and amplitude of vibrations for a Reynolds number range between 16.4k and 61.7k. When the flow direction is reversed, while vibration frequencies were accurately modelled, replicating precise unsteady flow behaviour proved more challenging. The study underscores the importance of properly resolving the flow in areas of flow separation to achieve accurate simulation of unsteady flow behaviour.

Thermal energy harvesting and its applications significantly rely on thermal energy storage (TES) materials. Critical factors include the material’s ability to store and release heat with minimal temperature differences, the range of temperatures covered, and repetitive sensitivity. The short duration of heat storage limits the effectiveness of TES. Phase change materials (PCMs) are a current global research focus due to their desirable thermal properties, which improve energy performance and thermal comfort. PCMs require relatively less synthesis effort while maintaining high efficiency and enhancing cost-effectiveness. However, limited temperature range and storage capacity restrict the application of conventional PCMs. Consequently, the demand for high-energy PCM storage with enhanced thermo-physical properties is high. It is essential to explore the potential of new PCMs to improve thermal storage performance and capacity while reducing energy consumption. This review article explores the classifications and applications of PCMs, addresses the challenges in enhancing their thermo-physical properties, and outlines the selection criteria for high-heat storage applications. Additionally, it provides an in-depth analysis of recent research and developments related to PCMs.

This review paper examines the advancements in solid-state power amplifiers (SSPAs) for wireless communication technology. As mobile devices rely on efficient power amplifiers to maintain battery life and ensure clear signal transmission, fabrication technologies like complementary metal–oxide–semiconductor (CMOS) and gallium nitride (GaN) are revolutionizing power amplifier (PA) design. The choice of material depends on the working frequency, with gallium arsenide (GaAs) and GaN suitable for frequencies under 100 GHz, and indium phosphide reaching up to 500 GHz. However, cost is a crucial factor in industrial manufacturing, making CMOS technology advantageous for on-chip system integration. Millimeter-wave chips have different requirements based on their application scenarios. In the Ka-band (26.5–40 GHz), high-power GaN and GaAs chips are preferred for satellite and long-distance communication. In contrast, the 60 GHz band is suited for short-distance high-speed communication and consumer electronics, making lower-cost CMOS and germanium silicon devices the preferred choice. This paper explores critical design considerations for SSPAs, focusing on common structures like envelope tracking, Doherty amplifiers, envelope elimination and restoration, and various linearization methods. We provide a clear comparison of their strengths and weaknesses to empower readers to select the optimal SSPA structure for their needs. Our review aims to facilitate informed decisions in the development of efficient and cost-effective SSPAs for advancing wireless communication technology.

The stagnant Taylor bubble in vertical isothermal turbulent counter-current flow was analyzed using 2D shadowgraphy experiments and two distinct high-fidelity numerical simulations. One simulation employed the geometrical VOF interface tracking method within the OpenFOAM code, while the other utilized the explicit front tracking method of the TrioCFD code. Interface recognition algorithms were applied to the photographs and compared with the results of 3D simulations performed with LES and pseudo-DNS accuracy in OpenFOAM and TrioCFD, respectively. The measured Taylor bubbles exhibited an asymmetric bullet-train shape and a specific speed, which were compared with the predictions of both numerical approaches. Reproducing the experiment proved challenging for both otherwise well-established methods frequently used in interface tracking simulations of two-phase flows. Grid resolution and subgrid turbulent models, known for their success in single-phase turbulence, were less accurate near the water–air interface. Additional experimental parameters compared with simulations were related to the dynamics of tiny disturbance waves with amplitudes ranging from 10 to 100 µm along the interface of the Taylor bubbles. The speed and spectra of the surface disturbance waves were reproduced numerically with moderate success despite detailed grid refinement in the relevant region of the computational domain.