Arabian Journal for Science and Engineering最新文献

The effective optimization of radiation shielding materials that protect individuals against potentially harmful radiant hazards is highly desired. Nonetheless, it is still a challenge that should be further investigated. The present work addresses the low-cost development of YBa₂Cu₃O_7-d ceramic doped with TiO₂ as an effective shielding product. The prepared ceramic via solid-state reaction was irradiated with different gamma-ray dosage rates of 0.0, 2.5, and 50 kGy. Then, the phase composition, structure, optical property, and radiation shielding performances were systematically investigated. Examining the structure via XRD disclosed the creation of the desired YBa₂Cu₃O_7-d ceramic with an orthorhombic structure. A Y₂BaCuO₅ secondary phase with low intensities was detected. On the other hand, a decrease in the lattice parameters was observed after irradiation. The optical absorption was found to reduce after irradiation, which may induce bond rearrangements, bond breaking, and changes in the local structure within the ceramic system. On the other hand, the shielding properties of YBa₂Cu₃O_7-d ceramic doped with TiO₂ did not show any variation in competence with changing gamma-ray doses. So, the shielding properties for unexposed samples were assessed, which included the buildup factor, dose rate, a specific absorbed fraction of energy, and a specific dose constant. The dose rate values enhanced with increasing energy, while they showed a reduction with increasing distance. The buildup factor and a specific absorbed fraction of energy values increased with increasing the main free path in all energy ranges. The obtained findings manifest the potential of including the present-developed ceramic in the radiation shielding area.

The earth-to-air heat exchanger (EAHE) is a remarkable passive system that utilizes the earth’s energy for ventilation of air circulating through buried tubes which can lead to a significant reduction in building energy consumption. EAHE’s thermal performance can be affected by the system configuration. The present study optimizes the thermal effectiveness and thermohydraulic performance factor (TPF) of U-type grid-layout EAHEs at constant total heat transfer surface using the Taguchi technique for cooling applications. For this purpose, four important factors were selected including the diameter of branch tube, diameter of main tube, number of branch tubes, and volumetric airflow rate that were taken at four different levels. The best combination for achieving maximum thermal effectiveness and TPF of U-type grid-layout EAHEs was obtained. The research results reveal that the highest effective factor for thermal effectiveness and TPF was the diameter of the branch tube with contributions of 72% and 40.6%. Also, the lowest effective factors were the volumetric airflow rate and diameter of the main tube with contributions of 1.1% and 14.7%, respectively.

Online discussion forums are widely used by students to ask and answer questions related to their learning topics. However, not all questions posted by students receive timely and appropriate feedback from instructors, which can affect the quality and effectiveness of the online learning experience. Therefore, it is important to automatically identify and prioritize student questions from online discussion forums, so that instructors can provide better support and guidance to the students. In this paper, we propose a novel hybrid convolutional neural network (CNN) + bidirectional gated recurrent unit (Bi-GRU) multi-output classification model, which can perform this task with high accuracy and efficiency. Our model consists of two outputs: the first one classifies whether the post is a question or not, and the second one classifies whether the classified question is urgent or not urgent. Our model leverages the advantages of both CNN and Bi-GRU layers to capture both local and global features of the input data, as well as the Bidirectional Encoder Representations from Transformers (BERT) model to provide rich and contextualized word embeddings. The model achieves an F1-weighted score of 94.8% when classifying whether the posts are questions or not, and obtains an 88.5% F1-weighted score while classifying the question into urgent and non-urgent. Distinguishing and classifying urgent student questions with high accuracy and coverage can help instructors provide timely and appropriate feedback, a key factor in reducing dropout rates and improving completion rates.

Kaolin Clay, an economical alternative to nanofillers, is an innovative mineral employed as a concrete additive. Historically, this material was used primarily as a cement substitute in concrete, owing to its inherent cementing properties. This study explores the potential of using Kaolin Clay as a partial replacement for ordinary Portland cement (OPC) and a supplemental cementitious material (SCM), potentially enhancing concrete's affordability, environmental sustainability, and overall mechanical performance and durability. Kaolin Clay exhibits pozzolanic properties, produces a significantly smaller carbon footprint compared to OPC, and is readily available in nature. This research employed thermally and mechanically activated clay, ranging from 0 to 15% (in 2.5% increments), as a partial cement substitute in concrete for performance evaluation. The evaluation encompassed mechanical properties (split tensile strength, compressive strength) and durability performance measures (water absorption, sorptivity coefficient, and acid attack resistance). It was found that concrete with 15% thermally activated (TAK) and mechanically activated (MAK) Kaolin Clay performed admirably compared to the control mix (having no kaolin clay). At 28 days, the strength of the MAK-150 (15% mechanically activated kaolin clay) mix improved by 26.8% and TAK-150 by 30% compared to the control. Likewise, the tensile strength of MAK-150 increased by 25.9% and TAK-150(15% thermally activated kaolin clay) by 33%, revealing the promising potential of Kaolin Clay in enhancing concrete performance. Furthermore, the MAK-150 absorbs 21% less water than the control mix, whereas the TAK-150 absorbs 26% less. The utilization of a composite blend TAK-150, comprising of Kaolin clay, results in a reduction in costs by 8.4% in comparison to the M0 (having no kaolin clay) blend, which includes cement.

Concrete is the most widely used material in the building industry due to its affordability, durability, and strength. However, considering carbon emissions, it is believed that concrete will be replaced by geopolymers in the future. As numerous parameters significantly affect the strength of geopolymers, the performance of potential algorithms for strength prediction needs to be evaluated for different binders to select an appropriate algorithm. This study employs machine learning approaches to provide the best prediction method for the flexural strength and compressive strength of geopolymers. A new dataset containing 533 compressive strength and 533 flexural strength values of geopolymers with different binders such as waste glass (GW), obsidian (OB), and fly ash was created. The best prediction solution, with R² = 0.981 for compressive strength and R² = 0.898 for flexural strength, was obtained from the extreme gradient boosting (XGBoost) algorithm. Additionally, several other machine learning models were employed, including linear regression, k-nearest neighbors, deep neural network, and random forest, with corresponding determination coefficient (R²) values of 0.763, 0.804, 0.93, and 0.96, respectively. These models were trained and evaluated using a dataset encompassing features such as binder types, age, and heat, to forecast the mechanical properties of geopolymers. Among these models, XGBoost demonstrated the highest R² value, indicating superior performance in predicting both compressive and flexural strengths. The findings of this study provide valuable insights into the selection of appropriate machine learning algorithms for predicting mechanical properties in geopolymers, thus contributing to advancements in sustainable construction materials.

A facile method to prepare activated ZnFe₂O₄ nanoparticles through novel metal ion doping using Bi³⁺ is demonstrated. The enhanced activity of the Bi³⁺-doped ZnFe₂O₄ nanoparticle was evaluated through Cr(VI) ions adsorption capability analysis from aqueous samples since industrial effluents have the metal ion as a major source of concern. ZnFe_2-xBi_xO₄ (x = 0.0, 0.05, 0.10, 0.15, 0.2) were synthesized using sol gel method, and a comparative adsorption efficiency study of Bi³⁺-doped and undoped ZnFe₂O₄ was performed. To understand the variation in adsorption efficiency and mechanism of adsorption on the Bi³⁺-doped ZnFe₂O₄, the adsorption study was conducted under varying adsorption conditions including pH, adsorbent dosage, contact time, initial ion concentration, and temperature. All the quantification studies regarding the adsorption were done using atomic adsorption spectroscopy (AAS). The studies suggested enhanced adsorption capability via Bi³⁺ doping as it can enhance the effective surface area and electrostatic charge density on the surface. Adsorption equilibrium information from this study was modeled using prominent adsorption kinetics and isotherm models, for extracting information regarding the performance of the adsorbent, rate of the adsorption and adsorption mechanism, which are imperative in the design and operation of adsorption systems. Scope of regeneration and reusability of the spent adsorbents in water treatment, which is also investigated in this work, addresses the ecological and economic demands for sustainability and also makes the wastewater treatment process cost effective.

The discrimination of rock types within the limestones and dolostones of the Nukhul Formation in the West Younis Field (Gulf of Suez Basin, Egypt) presents significant challenges due to their multi-scale compositional and diagenetic heterogeneity, diverse pore types, complex microstructures, and limited core data. This study aims to characterize the carbonate reservoir of the Early Miocene sediments and establish distinct reservoir rock types by employing textural analysis, geological interpretations (i.e., structural interpretation, fracture analysis, reservoir characteristics) using advanced imaging tools, and petrophysical measurements to model porosity/permeability profiles across the reservoir. A new dataset was obtained from the latest exploratory well in the West Younis Field, incorporating microresistivity and acoustic image logs, well logs, nuclear magnetic resonance (NMR) tools, and drill cutting petrographic analysis. The integration of these datasets provided a comprehensive understanding of the properties of the Early Miocene carbonate reservoir. Based on image logs, the carbonate facies were divided into four reservoir units. Petrographic evaluation further classified two facies (A and B) based on diagenetic factors controlling reservoir quality. The results revealed the occurrence of multiple phases of dolomitization, which influenced the reservoir quality. Early-stage dolomitization enhanced reservoir quality, while late-stage idiotopic dolomite crystal growth diminished it. The study also provided comprehensive information on the original rock fabric/texture, diagenetic processes, porosity types and origins, as well as the spatial distribution of pores (permeability index) within this complex carbonate reservoir. By employing an integrated technique, this study successfully differentiated the carbonate reservoir into distinct rock types, leading to improved reservoir characterization and field development. Additionally, the findings contribute valuable insights for the development and exploration of the Early Miocene carbonate section in the southern Gulf of Suez.

The widespread adoption of Internet of Things (IoT) devices has increased exponentially in recent years. Consequently, the security risks and vulnerabilities related to these unsecured IoT devices are also continuously increasing. Among the significant challenges facing the IoT environment is the threat of Distributed Denial of Service (DDoS) attacks. Several solutions are available in the literature to detect DDoS attacks. However, these detection mechanisms can easily be evaded by attackers using advanced tools and techniques, posing difficulty in detecting such lethal attacks in real time. Therefore, this paper proposes a novel distributed ensemble method for detecting lethal IoT traffic-based DDoS attacks. This method comprises two key stages: first, developing a distributed ensemble method using the breathtaking capabilities of the H2O.ai distributed machine learning platform and the ensemble learning technique. Secondly, this method was deployed on the Apache Storm stream processing framework, to swiftly analyze incoming network streams and categorize them into eleven distinct classes, including benign traffic and ten types of attacks, in near real time. The proposed method accurately identifies specific target categories within a multi-attack classification scenario by utilizing the expertise of various models. Ultimately, the prediction for a target class is determined based on the model with the highest detection rate. The effectiveness of this method has been examined using different configured scenarios. The experimental results show that our method can identify various attack categories more accurately with 99%+ accuracy and 8.45 s quicker than non-ensemble methods.

In this work, a novel approach to the co-hydrothermal carbonization (HTC) process is presented. A laboratory-scale autoclave reactor was used as an open-loop recycling station (OLORS) operating with an unusual blend formed by sewage sludge and an acidic residue from the electroplating industry with a high concentration of iron and zinc chlorides. An in situ impregnated hydrochar was successfully produced and evaluated as adsorbent in H₂S removal from a synthetic biogas achieving performance of up to 65% in reducing H₂S concentration after 260 min of contact in a static system. The standard gaseous mixture containing 3000 ppmv of H₂S was placed into contact with the powdered hydrochar in a batch system, and the decrease in the area ratio between H₂S area peak and total area peak was monitored by gas chromatography as a function of time. The best performance was achieved by the hydrochar HC-5 and may be related to the type of chemical species formed on the surface of the adsorbent. The hydrochars were characterized by XRD, SEM/EDS, N₂ adsorption–desorption, and elemental analysis (CHN). Although the hydrochars did not present a high specific area (3.0–22.0 m² g⁻¹), in general, the massive deposition of iron and zinc chlorides on its surface, reaching up to 13.6 and 6.41 wt.%, respectively, rules the performance of the novel adsorbents. The OLORS approach highlights some aspects of sustainability due to the co-processing of two complex residues to produce a material capable of removing H₂S from a synthetic biogas mixture in a static batch system.

Sulphate attack is a major cause of concrete deterioration in marine environments and its interaction with wave-induced cyclic loading exacerbates the damage. This study has evaluated strengths and fatigue performance (i.e. fatigue life, strain and residual displacement) of sulphate-attacked concrete containing silicomanganese slag, fly ash (FA) and silica fume (SF). Compressive strength, tensile strength and sulphate profile of sulphate-attacked concrete were measured experimentally. Sulphate-induced damage constitutive relations were formulated and used with concrete damaged plasticity (CDP) model to simulate fatigue loading. Experiment showed that incorporating silicomanganese slag lowered sulphate resistance by 4.8–6.6% due to increased sulphate intrusion, but synergy with FA and SF enhanced the resistance by 7.3–13.8% at 365 days. The sulphate penetration depth was 0–20 mm, and the intruded sulphate increased exponentially over time. To evaluate fatigue loading in CDP model, the non-uniform damage was determined as correlation between strength degradation and integral area of sulphate profile. Numerical results were in good agreement with experimental data from literature, with differences of 5.8–26.2% in fatigue life, 9.1–30.1% in fatigue strain and 18.1–41.9% in residual displacement. In long-term deterioration, numerical analysis found that increasing sulphate concentration significantly shortened fatigue life. Despite silicomanganese slag lowered concrete sulphate and fatigue resistance, the inclusion of FA and SF improved the durability and sustainability of concrete for potential marine applications.