Arabian Journal for Science and Engineering最新文献

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.

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 escalating demand of drinking water, coupled with increasing agricultural and industrial needs, has imposed a significant strain on groundwater sources worldwide, which is further exacerbated by the implications of climate change. The current research aims to identify the groundwater potential zones (GPZs) and the optimal sites for artificial recharge in the Wadi Waj watershed, Taif, Saudi Arabia. An integrated approach using remote sensing (RS), GIS, multi-criteria decision analysis (MCDA), and the analytic hierarchy process (AHP) was utilized. Seven hydrological and geological factors have been weighted and analyzed using AHP and GIS-based weight overlay approaches. Five GPZs have been mapped with the range from very high to very low where the very high, high, moderate, low, and very low GPZs were 0.046, 33.5, 31.3, 35.1, and 0.023 percent of the basin, respectively. The high GPZs were found in the downstream region, while the low GPZs were located in the upstream region with impermeable rock and steep slopes. A total of 27 locations have been identified as optimal sites for constructing artificial recharge facilities. These sites are positioned strategically to both catch and encourage the infiltration of rainwater into the ground. The findings were verified with the agricultural farms and wells demonstrating the alignment with moderate to high GPZs. The GPZs were also verified with the groundwater depth data obtained from the global model “GLOBGM v1.0,” and the findings showed comparable trends. The findings will support water resources management to enhance regional water security and maintain sustainable groundwater resources.

An essential factor influencing photovoltaic (PV) panel performance is its operating temperature. Various active and passive cooling methods have been explored in the literature to mitigate the effects of high operating temperatures; however, recent research has shown a growing interest in hybrid cooling systems that combine both active and passive approaches. In this context, phase change material (PCM) serves as a passive cooling method, while fluid is employed as an active cooling medium. This study introduces a channel into the PV panel base through which fluid flows. Additionally, a PCM layer is placed at the bottom of the water channel to reduce the average temperature of the fluid, thus extracting more heat compared to direct contact with the PV panel. The proposed model is compared with traditional water-cooled PV panels using a parametric approach, with varying parameters including concentration ratio, environmental temperature, wind speed, mass flow rate of water in the channel, and inlet temperature. The study findings reveal that the proposed model leads to an increase in electricity production within the range of 1.4–7 kW, an improvement in PV efficiency between 1.6 and 3.8%.

The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set of M-matrices defined over the cone of positive definite matrices has emerged as a crucial approach in this field. However, experimentally collected data often deviates from the expected positive semidefinite structure due to introduced noise, posing a challenge in maintaining its physical structure. In this paper, we address this challenge by proposing four methods to reconstruct the initial matrix while maintaining its physical structure. Leveraging advanced techniques, including sequential quadratic programming (SQP), we minimize the impact of noise, ensuring the recovery of the reconstructed matrix. We provide a rigorous proof of convergence for the SQP method, highlighting its effectiveness in achieving reliable reconstructions. Through comparative numerical analyses, we demonstrate the effectiveness of our methods in preserving the original structure of the initial matrix, even in the presence of noise.

This paper explores the innovative application of machine learning and neural network algorithms to predict pore types in carbonate rocks using experimental acoustic properties under ambient pressure conditions. Carbonate reservoirs, crucial for hydrocarbon storage and extraction, present a challenge due to their complex pore structures influenced by diverse depositional environments and diagenetic processes. Traditional petrographic methods for identifying pore types, though accurate, are time-consuming and destructive. Recent approaches leverage log and core-measured compressional wave velocities and porosity, yet variability in data remains an issue. Addressing the challenge, this study distinguishes itself by employing high-resolution physical rock samples from the early Miocene dam formation, eastern province of Saudi Arabia. Through meticulous data preparation, feature engineering, and the evaluation of logistic regression, random forest classifier, gradient boosting classifier, and support vector classifier models, we have developed an advanced model capable of predicting pore types with significant accuracy. Our findings reveal that logistic regression achieves the highest accuracy (71%) among the models, effectively capturing the inherent patterns within our dataset. A detailed analysis using principal component analysis underscored the discriminative power of these models, particularly in identifying interparticle–intraparticle and moldic pore types. This study’s innovative approach, leveraging experimental measurements and machine learning techniques, offers a robust framework for accurately predicting pore types in carbonate rocks. While challenges such as data size and feature limitations persist, the potential implications of our findings for reservoir modeling and efficient hydrocarbon extraction are significant, providing a foundation for future research to build upon.

In this study, we conducted a comparative quantum computational investigation about carbazole/borole derivatives to understand how different configurations like linear and bent, and terminal groups can affect their linear and second hyperpolarizability properties. The goal was to compare the optical and NLO response properties, photovoltaic parameters and charge transfer properties of these linear/bent configurations. Among all the designed compounds the linear compounds exhibited larger linear isotropic and anisotropic polarizability and second hyperpolarizability amplitudes (γ) compared to the bent compounds. The highest values of isotropic polarizability of Py-1L and Py-2L are calculated to be 109.0 × 10^–24 esu and 103.9 × 10^–24 esu, respectively. Notably, linear configurations Py-1L and Py-2L achieved the (leftlangle gamma rightrangle) amplitudes as high as 840.1 × 10⁻³⁶ esu and 776.9 × 10⁻³⁶ esu. When compared to the prototype para-nitroaniline (p-NA) molecule, these amplitudes are found to be ~ 115 times and ~ 113 times larger than p-NA as calculated at the same level of theory. Moreover, TD-DFT calculations also revealed that linear configuration gave better NLO response due to their higher oscillator strengths, dipole moment changes between ground and excited states and lower transition energy values among all the designed compounds. Frontier molecular orbitals, molecular electrostatic potential map, electron density difference and natural bond orbitals analysis indicated that more efficient intramolecular charge transfer in linear configuration, leading to high NLO response than bent configuration. The highest light harvesting efficiencies have been exhibited by Py-1L and Py-2L, with values of 0.928 eV and 0.903 eV, respectively. Overall, the current systematic comparison of NLO polarizabilities and other electronic properties emphasized the importance of configuration-based designs for achieving high performance NLO response properties in the designed compounds.