Arabian Journal for Science and Engineering最新文献

Records of bridge fire incidents illustrate that bridge fires can have catastrophic consequences. The severity of these fires can be influenced by various factors such as bridge type, vehicle size, and wind. Contrary to building fires that have been extensively studied, scant attention has been paid to bridge fires and more specifically fire exposure to the suspension bridges. In addition, existing bridge fire literature is mostly concentrated on fire exposure to girders or cables of suspension bridges. Therefore, this study focused on the post-fire condition of a fire-exposed suspension bridge tower using computational fluid dynamics (CFD) modeling techniques and finite element analysis (FEA). The impacts of the main bridge fire parameters including vehicle size, exposure duration, distance between the fire source and tower, and wind effects were also evaluated. Initially, fire dynamic simulator (FDS) was used to simulate 12 different fire scenarios. The time–temperature histories obtained from each scenario were transferred to the ABAQUS finite element (FE) software to conduct transient thermal analysis and obtain the temperature development within the steel tower of the bridge. The post-fire evaluation was performed with respect to the temperature-induced reduction in the yield strength of steel. The results show that fire exposure from a fuel truck in the proximity of a steel tower could significantly reduce the strength of the tower and lead to severe damage. Early control of the fuel truck fire is crucial in reducing the severity of the damage and preventing temperature development in higher areas of the tower. Although a wind toward the tower can significantly increase the fire-induced damage to the bottom parts of the tower, it considerably reduces the temperature exposure to the higher parts of the tower. Fire exposure from a normal vehicle does not put the tower at risk of failure, and an unprotected steel tower can withstand it. However, a bus fire may lead to minor damage. The thermal strengthening of the first 20 m of the tower can help in preventing the potential fire damage.

Physiological signals commonly suffer from contamination by various types of noise, ones of the most foremost are baseline wander (BLW) and power line interference (PLI). The removal of these interferences is a crucial in biomedical signal processing and diseases diagnosis. This paper introduces a digital fractional notch filter derived from the corresponding anti-notch one and specifically designed for the removal of BLW and PLI from physiological signals. The salient feature of the proposed filter is its capability to eliminate any frequency range only by adjusting the single parameter ν which defines the central frequency of the anti-notch filter. The performance of the filter is closely linked to the fractional order α and the number of samples L used in approximating the ideal fractional filter. Genetic algorithms were employed to determine the optimal values for these parameters (α, L). The proposed filter has been implemented on noisy ECG, EEG, and EMG signals, exhibiting its efficiency in removing unwanted noise. Comparative analysis with existing BLW and PLI removal techniques indicates that the proposed filter outperforms them based on the evaluation metrics employed.

Among the common arrays of electrical resistivity tomography, the pole-dipole array is widely used due to its unique advantages such as more apparent resistivity information in the measured profile and the ability to realize advanced detection. However, this array requires an infinite pole, and there is still no uniform standard on the least amount of distance from the survey line that can be regarded as infinity, which is crucial in some space-limited measurement environments. Therefore, in this study, the geometric factor K of the pole-dipole array and quadrupole array is analyzed first, and the root mean square error rate (RMSE%) between different geometric factors is calculated. Then the threshold is set to 5% to determine the theoretical infinite pole minimum multiple ([m]_{t} = 2). With the premise that the survey line length is L and the distance between the infinite pole and the starting electrode of the survey line is m*L, when the survey line length multiple is (m ge [m]_{t} = 2), the influence of the infinite pole on the measured data can be ignored in engineering. Finally, verification experiments are carried out in different environments with anomalous bodies of different properties as test objects, and it is proved that the theoretical value ([m]_{t} = 2) is applicable to various modes of pole-dipole array and tested objects with different properties.

Titanium alloys, especially Ti–6Al–4 V alloy, are significant materials because of their superior mechanical and chemical properties. They are used in most high-strength and elevated temperature applications, such as aircraft, ships, biomedicine, and marine applications. Recently, laser machines were used to cut a variety of sheet metals. It is critical to comprehend how laser cutting parameters influence surface and kerf quality. In this work, a 4 mm-thick Ti–6Al–4 V sheet was cut. Laser beam power, cutting velocity, and assist gas pressure were used as cutting parameters, while surface roughness, kerf width, taper angle, and dross height were measured to determine which parameter has the greatest influence on the cutting quality. It was found that laser power of 3 kW, cutting velocity of 2000 mm/min, and assist gas pressure of 8 bar were the optimized parameters to achieve the minimum surface roughness value of 2.34 ± 0.12 µm and the minimum dross value of 0.270 mm due to the high velocity. The minimum value of kerf width was found to be 0.774 ± 0.016 mm at upper surface and 0.408 ± 0.039 mm at lower surface of cut at cutting conditions of Pu = 2.5 kW, V = 1500 mm/min, and assist gas pressure of 10 bar. The minimum value of kerf taper was found to be 1.89 ± 0.61° at cutting conditions of Pu = 2 kW, V = 1500 mm/min, and assist gas pressure of 8 bar.

The Upper Member of the Upper Cretaceous Thaqab Formation, prominently exposed in Jabal Ruwaydah, Northern Oman, serves as a valuable analogue for subsurface hydrocarbon reservoirs in the Sohar Basin. This study conducts a comprehensive investigation into the lithofacies and microfacies of the Upper Member, integrating field-based sedimentological observations with laboratory-based petrographic analyses to elucidate its geological characteristics. The analysis identifies two distinct lithofacies: grain-dominated and mud-dominated facies. Microfacies analysis further delineates these lithofacies into five types, including polymictic breccia, bioclastic intraclastic grainstone interspersed with rudstone deposits containing rudist debris, quartzose bioclastic packstone transitioning to grainstone, and predominantly calcareous mudstone. Our study presents a novel depositional scenario for the Upper Member of the Thaqab Formation, based on unique lithofacies and microfacies characteristics, platform morphology, and the presence of biota. Our findings suggest a predominantly mud-dominated slope carbonate setting for this unit, with distinct grain-dominated microfacies indicating potential lateral continuity into a deep marine depositional environment to the east, specifically the Sohar Basin. These findings are significant as they provide insights into hydrocarbon-bearing deep marine slope strata within the subsurface Cretaceous succession of the Arabian Peninsula. Additionally, our study extends the application of these findings as an analog for similar facies in the Sohar Basin, thereby, contributing to the global understanding of comparable deep marine systems.

Among the different investigations carried out on polymer additive manufacturing, the influence of the plate material is often neglected. The energy-saving perspective forces us to study the improvement of the 3D printer architecture to optimize the process conditions by minimizing power and energy consumption. This research moves from the intent to preserve the part quality and avoid energy dissipation in the 3D printer plate. This component requires the most energy input to work properly and it is also in direct contact with the workpiece, resulting in paramount for the lower layers. By analyzing a previously assessed set of process parameters, three plate materials were adopted and investigated in this study. Tensile and ultrasonic tests were carried out to evaluate the PLA specimen’s quality and mechanical performance: Both gave good results proving the poor influence of the metallic plate on the workpiece. The results obtained in this experimental study allowed to release of some guidelines for better decision-making in the choice of a material plate for desktop 3D printers to print PLA from a sustainable perspective. The best results have been observed by selecting V = 110 mm/s and T = 215 °C as process conditions by using the aluminum plate. Also, the ultrasound analysis confirms greater uniformity of the material at a printing speed V = 110 mm/s for all the printing plates indicating a low influence of the printing plates used on the material uniformity.

The impact of high-pressure inflow on water pump performance is crucial in determining whether the gravitational potential energy effectively synergizes with the mechanical energy of the pump. Using an independently developed Wankel pump and test platform, we conducted a preliminary demonstration of the gravity-pump pressure collaborative dynamic loading method. The Wankel pump demonstrates excellent performance under self-priming conditions with high-pressure and high-flow rates. The power decreases linearly with increasing inlet pressure during high-pressure inflow, while maintaining acceptable flow fluctuations. This demonstrates the feasibility of the driving method. The trend of total efficiency with inlet pressure is primarily determined by mechanical efficiency, while volatility is influenced by both volumetric efficiency and hydraulic efficiency. The descent stage’s slow fluctuation range plays a critical role in determining the range of utilization of gravitational potential energy, and it is influenced by the target flow rate.

Different applications heavily benefit from automatic deep learning including image classification, segmentation, and analysis. It significantly adds value to clinical systems through computer-aided detection, curing planning, diagnosis, and therapy through the acquisition of the most informative images. However, this deep learning approach faces one of the main hurdles in image processing: the necessity of a large, labeled dataset. Actually, such requirements in medical image analysis applications are considered excessively costly to acquire. Active learning methods can mitigate such issues by reducing the number of annotated images while raising the model’s performance. This paper introduces an active learning framework based on a novel sampling technique, where it queries the unannotated samples that behave differently from current training set samples. The adaptive sampling method is optimized by stochastic gradient descent approximation. This optimization leads to the construction of an adaptable and robust system that meets the needs of medical control systems. Moreover, such novelties contribute to a respectful enhancement of the model’s deep network performance when training over a few numbers of annotated images to reach underlying accuracy. The proposed structure outperforms other AL methods, as proved by the experimental results using stochastic gradient descent optimization technique over Skin Cancer, Pediatric Pneumonia, and COVID-19 datasets, which achieved an accuracy of 72.5%, 90%, and 90.5 using only 42.8%, 8%, and 5% human-labeled training data, respectively.

Nature-inspired metaheuristic algorithm plays an autocratic role in optimization (OP). The dominance of metaheuristic algorithms has managed to solicit the focus upon themselves and has overshadowed other types of OP algorithms. Random optimization (RO) is one such type of underrepresented OP algorithm, which commanded significant interest in the mid-’60 s but eventually lost its glitter due to its lackluster OP performance. Pure random orthogonal search (PROS) is a recently published RO algorithm that has revived interest in RO. PROS is a simple, hyperparameter-free OP algorithm capable of dissipating performance better than some established metaheuristic algorithms. Unlike pure random search (PRS), where the optimizer is free to move anywhere within the feasible region, PROS effectively restricts the explorable feasible region to the region strictly orthogonal to the current location, and this restriction immensely boosts its OP performance. Between the two extremes of PRS and PROS, a spectrum of possible movement patterns merits our attention. In this paper, we perform several numerical experiments to study how the freedom to move in different dimensions (Degrees of Freedom) influences the performance of the PRS & PROS algorithm. Further, the notion of an ‘Active Feasible Region’ is introduced to analyze PROS and other related RO algorithms. We propose two simple modifications to the PROS algorithm based on the experiments. The modifications yield marginal performance gains over PROS. Nevertheless, valuable insights are revealed upon the effect of different degrees of freedom and orthogonality constraint and how they could be leveraged to our advantage. The python code is publicly available at: https://github.com/Shahul-Rahman/Less-is-more.

Accurate and precise segmentation of the left atrium (LA) is crucial in the early diagnosis and treatment of atrial fibrillation (AF), which is the most common heart rhythm disease in cases. The size of fibrotic tissue in patients with AF is based on manual examination of images obtained from the gadolinium-enhanced cardiac magnetic resonance imaging (MRI) technique. However, manual examination of the acquired images is time-consuming and has many difficulties, such as LA thickness between observers and resolution according to MR devices. To overcome the challenges of manual segmentation of images obtained from MRI devices, end-to-end, fully automated deep learning-based segmentation architectures have become extremely important today. In this study, an encoder–decoder-based V-shaped deep learning architecture is proposed for precise segmentation of LA. In the proposed architecture, standard convolution and depthwise separable convolution are used together. Thus, sparsely connected blocks with fewer parameters and deeply separable convolutions learn the feature representations better, increasing the robustness of the model. In addition, the bottleneck attention module has been added to each encoder layer, allowing the network to learn which features to focus on and which features to suppress in images by attention mapping channel and spatially. The proposed architecture obtained 0.915 dice and 0.844 Jaccard scores in the STACOM 2018 challenge dataset. The obtained results draw attention to the robustness of the model.