Arabian Journal for Science and Engineering最新文献

Storage capacity and differential molar enthalpy of adsorption (or isosteric heat of adsorption) are important parameters to understand the characteristic of heterogeneous materials and catalysts for chemical and energy industry applications. Langmuir isotherm and other single site, dual site, and multilayer isotherms are developed for the prediction of adsorption and enthalpy, with a main assumption of constant isosteric heat of adsorption. However, some experimental and simulation data showed some inconsistencies in terms of heat of adsorption. Exploiting molecular simulation, we provide a first principle estimate of gas hosting capacity and associated thermodynamic properties of nanopores as present in type IID kerogen. The adsorption capacity and enthalpy of adsorption of the organic matter was computed using the grand canonical ensemble combined with the fluctuation method. The data obtained were utilized to assess the predictive power of industry standard models such as the Langmuir isotherm and other single site, dual site, and multilayer isotherms with respect to adsorption and enthalpy. The obtained results suggest that the sorption and thermodynamic properties of kerogen nanostructures are best described by monolayer-multisite isotherms rather than multilayer models. In short, for an adsorption theory to be physically consistent, it should capture both adsorption and isosteric heat.

Population growth, increase in energy demand, and environmental problems of fossil fuels have led to the use of renewable energies. One of the applications of solar energy is the solar chimney thermal power plant. Building a solar chimney is not cost-effective due to its low thermal efficiency, so studies have been conducted to increase its efficiency. Among the studies conducted, few have studied the influence of absorber geometry on system performance. In this research, the solar chimney was designed in small dimensions and effect of changing the geometry of the absorbent surface was investigated. The numerical model has been validated with experimental data of Manzanares pilot plant. The geometry was numerically simulated in Ansys Fluent software. Realizable k-ε model for turbulence and DO irradiation model for radiation has been used. The solar radiation 1000 ({W mathord{left/ {vphantom {W {m^{2} }}} right. kern-0pt} {m^{2} }}) is selected. The coupled arithmetic was used as the pressure–velocity coupling scheme. Besides, the discretization method for the pressure term was PRESTO! Algorithm while other terms were second-order. The criterion of convergence in solving all equations is (10^{ - 6}). The results showed that the maximum velocity for height 0.1 and 0.2 m has increased by 6.945% and 8.048%, respectively, compared to the smooth absorber surface. By increasing the height in the center of the solar chimney, the maximum power is obtained at a height of 0.2 m with a value of 2.338 w. Therefore, changing the geometry of the absorber affects the performance of the chimney and can strengthen it.

Abstract

Silicate-based drilling fluid (SDF) has a strong inhibition effect on shale swelling and provides good wellbore stability. SDF has also been widely used in drilling through the reservoir in recent years. However, SDF has certain damage effects on the reservoir, and its damage mechanism is not well understood. In this work, the damage of the tight sandstone formations induced by SDF was assessed by conducting fluid displacement and filtrate imbibition experiments. In addition, the damage mechanisms were further analyzed based on microscopic experiments. The research results mainly included the following four aspects: First, SDF caused significant reservoir damage by solid-phase particles and filtrate intrusion in tight sandstone reservoirs, and the latter was the main reason. Second, the incompatibility between the filtrate of the SDF and formation led to reservoir damage. This was because the SiO₃²⁻, CO₃²⁻, and OH⁻ in the SDF reacted with Ca²⁺, Mg²⁺, and Al³⁺ in the formation, resulting in the generation of new minerals such as kaolinite and gibbsite. Third, the filtrate of the SDF increased the hydrophilicity of the rock surface, which induced the aqueous trapping damage. Finally, SDF was strongly alkaline (pH = 13.08), in which OH⁻ produced by sodium metasilicate hydrolysis had alkaline corrosion effect on minerals, enhancing pore permeability. This work provides experimental evidence for the feasibility discussion of the SDF in tight sandstone reservoirs.

The Internet of Things (IoT) is rapidly permeating critical domains, enabling the interconnection and utilization of diverse devices on a large scale while streaming vast volumes of data. In domains like telehealth, intelligent transportation, and autonomous agriculture, ensuring the confidentiality, integrity, and authenticity of collected and exchanged data is paramount. However, the resource limitations and heterogeneous nature of IoT devices often render traditional cryptography-based techniques ineffective or even infeasible for secure data transmission. Consequently, there is an urgent need to develop and implement lightweight, secure, and scalable schemes for data transmission. In this paper, we propose a novel Lightweight Secure and Scalable Scheme (LS3) for data transmission in IoT environments. LS3 is comprised of three phases and utilizes an efficient combination of symmetric keys and Elliptic Curve Menezes–Qu–Vanstone asymmetric key agreement protocol. Through a comprehensive analysis, we demonstrate that LS3 excels in terms of security and scalability and outperforms other existing schemes in terms of computation and communication costs.

Object detection has made significant progress in computer vision. However, challenges remain in detecting small, arbitrarily oriented, and densely distributed objects, especially in aerial remote sensing images. This paper presents MATDet, an end-to-end encoder-decoder detection network based on the Transformer designed for oriented object detection. The network employs multi-layer feature aggregation and rotated anchor matching methods to improve oriented small and densely distributed object detection accuracy. Specifically, the encoder is responsible for encoding labeled image blocks using convolutional neural network (CNN) feature maps. It efficiently fuses these blocks with higher resolution multi-scale features through cross-layer connections, facilitating the extraction of global contextual information. The decoder then performs an upsampling of the encoded features, effectively recovering the full spatial resolution of the feature maps to capture essential local–global semantic features for accurate object localization. In addition, high quality proposed anchor boxes are generated by refined convolution, and the convolved features are adaptively aligned according to the anchor boxes to reduce redundant computation. The proposed MATDet achieves mAPs of 80.35%, 78.83%, 73.60%, and 98.01% on the DOTAv1.0, DOTAv1.5, DIOR, and HRSC2016 datasets, respectively, proving that it outperforms the baseline model for oriented object detection. This validation confirms the feasibility and effectiveness of the proposed methods.

This study investigates a novel class of electrode materials known as mixed transition-metal oxides (MMOs) for their improved electrochemical capabilities. Specifically, a one-pot hydrothermal method is employed to successfully synthesize binary mixed-metal oxides comprising manganese oxide and copper oxide (Mn₂O₃/CuO). The interest in MMOs lies in their potential to enhance multifunctional performance compared to single metal oxides, prompting a comprehensive examination of their structure and properties. Various analytical techniques are utilized to investigate the crystal structures, functional groups and surface morphology of the single and binary MMOs. Moreover, electrochemical experiments are conducted to assess their electrochemical behaviour. The results reveal that the Mn₂O₃ and CuO MMOs’ electrodes exhibit pseudocapacitor-like characteristics, outperforming single metal oxides in terms of specific capacitance. At a current density of 1 A g⁻¹, the Mn₂O₃/CuO MMOs electrodes achieve a specific capacitance of 342.85 F g⁻¹. Notably, the symmetric device demonstrates excellent retentivity (89.97%) even after 5000 repeated cycles, showcasing its exceptional supercapacitive attributes for maximum energy storage capability.

Abstract

This study aimed to develop a layered circular metal composite that would combine high strength, low density, and developable surface properties. The outer part of this composite material called the sheath was made of AISI 4140 steel, and the inner part, as the core, was composed of Al/B4C (boron carbide) mixed metal matrix composite. Al/B4C powder mixing ratios were determined by volume rate as 5, 15, and 25% B4C. Al2024 powder with an average particle size of 40 µm and B4C with particle sizes of 5, 17, and 58 µm were used. Composite materials were produced by forming the pre-products obtained by compressing Al/B4C powder mixtures into steel tubes using the drawing method. The drawing process was carried out at room temperature, 250 °C, and 400 °C, and with three different deformation extents (16, 30, and 37%). In the composite materials produced under all temperature conditions, increasing of the deformation extent increased the compression strength of the materials. Compression strength also increased with B4C reinforcement at all temperature conditions, but it decreased when the ratio of reinforcement passed over 15%. The gas nitriding process was applied to the produced composites to improve their surface properties. Strength values showed improvement after the nitriding process, and a thicker nitride layer was obtained on the steel sheath in highly deformed materials. As a result, the study presented the production of a composite with different sheath-core materials by rod drawing method and the effect of production variables on the material's mechanical properties. In addition, it was shown that the desired surface quality can be obtained by the gas nitriding process at low temperatures.

To avoid irreversible vision loss, early detection and diagnosis of Diabetic Retinopathy (DR) severity is critical. The percentage of people undertaking eye examinations has risen in recent years, increasing the burden on Ophthalmologists. Various Artificial Intelligence (AI) screening systems have recently been deployed to improve the accuracy of DR diagnosis. However, owing to their black-box nature, most successful AI screening systems are still held back in reality for medical decision aid. The need for an Explainable Artificial Intelligence (XAI) screening system to help Ophthalmologists in DR diagnosis is inevitable. The proposed work is divided into three phases: (i) pre-processing, (ii) optic disk localization, and (iii) DR severity classification. On top of the deep learning model, the proposed work implements a Local Interpretable Model-agnostic Explanations (LIME) explainer to describe what features of the retinal image took part in justifying the predictions. The proposed framework outputs a pixel-value tensor, explaining the possible pixel values contributing to the model’s prediction. MESSIDOR data collection is used for experimental analysis. When compared with other deep learning models, the proposed framework achieved a better accuracy of 98.04%, sensitivity of 99.69%, specificity of 96.37%, f1-score of 96.99% and error rate of 3.60%. Incorporating explainable deep learning models for diabetic retinopathy severity grading improves diagnostic accuracy and provides clinicians with clear insights, enabling trust and informed decision-making in DR diagnosis. This proposed technique enormously advances more effective and responsible healthcare procedures.

Batteries for electric vehicles (EVs) have a capacity decay issue as they age. As a result, the use of lithium-ion is becoming more popular with super-capacitors (SCs), particularly in EVs. Over the decrease of carbon dioxide emissions, SC batteries offer a substantial benefit. In EVs, a dependable mechanism that guarantees the SC batteries’ capacity for charging and discharging is crucial. The main obstacle for EVs is the long life of ultra-capacitor battery’s because SCs have a deterioration effect over multiple cycles. Therefore, accurate early prediction of these SC batteries is crucial. The data-based model is more accurate than mechanism-based and model-based methods created for this purpose. The proposed data-driven models, such as machine learning (ML), estimate the electrical parameters for the smooth functioning and working of SCs in addition to considering their operating status. The main factor determining whether electric vehicles can be sustained is an increase in battery cycle life. With a lowest root mean square error of 0.04614 and a mean squared error of 0.002 and an accuracy of 89.6%, ML-based models with various architectures and topologies have been created in this study to reliably estimate the deterioration of SCs capacitance.

To understand the information processing mechanism of the brain, it is important to decode the bidirectional communication between the brain and organs. For this purpose, computational models were proposed to simulate brain–organ interfaces at different levels of abstraction. Conventional computational models can be modified to understand the bidirectional interactions for further clarification and treatment of morbidity. In this work, a unified model of excitable cells (brain, heart, and pancreatic cells) is proposed that can predict the electrical response with adrenergic features. This enables us to activate the sparsely coupled cardio-neural network to estimate the heart rate variability, one of the key features to identify a healthy heart. The recent advancements in nano- and bioelectronics will make it possible to build and deploy the brain–heart interface as a nanochip in the body to monitor and control the electrophysiological abnormality of the brain and heart by integrating nano-regulators with ion channels for stimulation.