Arabian Journal for Science and Engineering最新文献

Solenoid valves enable flow and motion control functions in the fluid power systems. Even today, on-line diagnosis of fluid power systems still remains a challenging task due to the computational cost and availability of machine operation data sets. For the prior, rapid fault diagnosis of the solenoid fault is of great economic values to the reduction in downtime maintenance. For the latter, currently the data for training networks are the major obstacles, as some of the rare faults are simply unavailable from the usual maintenance data. Facing the challenges, this paper presents a new way of quantifying the spool stiction severeness, a common fault in the solenoid on–off valves, using a proposed coupled physical model, where only temporal features from the solenoid coil driving current were extracted and applied for rapid diagnosis, without the need of spool displacement information. A test system was constructed in laboratory and different settings of valve spool stiction from normal to completely jammed were realized on the hardware. The developed coupled model is validated experimentally and demonstrates the capabilities in capturing the stiction effects. The quantitative diagnosis model based on temporal feature vectors was also tested and compared to the true stiction level, and the proposed sigmoid weightings have shown high prediction accuracy. The initial results have shown that the proposed model can quantify the spool stiction degree with accuracy at least 90% and with computation time less than 500 ms with a CPU at lower than 1.3 GHz.

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.

The purpose of this study was to bioprospect the volatile organic compounds (VOCs) of various Trichoderma harzianum strains to control black rot of postharvest snake fruit, an important fruit commodity in Southeast Asia, caused by the fungus Thielaviopsis paradoxa. Trough an indirect confrontation assay, T. harzianum InaCC F88 was found as the most suppressing strain among others. The strain inhibited T. paradoxa with growth relative to control (GRC) of 71.14%. A volatolomic analysis using Headspace GC–MS of this strain showed the most abundant VOC was isoamyl alcohol (36.06%), followed by 2-methyl-1-propanol (21.92%) and 2-cyclopentenone (10.72%). Isoamyl alcohol as the major compound inhibited T. paradoxa with GRC of 71.44, 28.88, and 2.86% after the addition of 10, 20, and 30 µL of the vapor of pure compound, respectively. Moreover, in a 1.5-L close-container assay, the addition of 300 µL isoamyl alcohol vapor was also able to reduce lesion tissue in the pre-infected fruit up to 29.15% after 7 days of storage in room temperature compared to 58.97% in the absence of the pure compound. In conclusion, T. harzianum InaCC F88 through its VOCs was potential to biocontrol black rot in snake fruit, thus extend its storage time.

In recent years, with the indepth research on driverless technology, model predictive control theory was extensively applied in the field of vehicle control. In order to improve the accurate tracking of reference trajectories by driverless vehicles, a model predictive control trajectory tracking controller for driverless vehicles optimized by an improved sparrow search algorithm is proposed. Firstly, an objective function with constraints is added to the model predictive control trajectory tracking controller by establishing the vehicle dynamics model; Secondly, the improved sparrow search algorithm is enhanced to speed up convergence and expand the program's search capabilities; Then, in order to discover the best value, the model predictive control trajectory tracking controller's prediction time domain and control time domain are optimized using the improved sparrow search algorithm; Finally, to confirm the method's viability, collaborative simulations in Simulink/Carsim were completed. The simulation results show that the lateral errors generated by the improved sparrow search algorithm-based optimized model predictive control trajectory tracking controller are reduced by 53.53% and 65.44%, respectively, when the vehicle speed is 36 km/h, compared with the traditional model predictive control trajectory tracking controller. When the vehicle speed is 54 km/h, the lateral deviations are reduced by 81.08% and 86.76%, respectively. In addition, the optimized model predictive control trajectory tracking controller improves the accuracy and at the same time, the driving stability of the control vehicle is significantly improved.

Turn-over intention recognition of patient is crucial for the advancement of the intelligent nursing field. In this paper, a novel turn-over intention method is proposed based on array air spring mattress. For this method, the turn-over intention of a lying patient can be recognized by identifying the internal pressure distribution of array air springs. To begin with, the samples of turn-over intention are created experimentally, and then input into a model combining Variational Auto-Encoder and Generative Adversarial Network for the sample augmentation to address issues related to low accuracy and poor generalization caused by sample imbalance. Besides, the augmented dataset is conveyed into the Convolutional Neural Network model, for the detection of three states: left/right turn-over intentions and no intention. The research demonstrates that, the similarity of the left and right turn-over intention samples generated by VAE-GAN model is 90.13% and 91.01%, respectively. This increases the diversity of samples and is helpful for intention recognition. The recognition accuracy of the CNN model with sample augmentation is 98.04%, which is 13.4% higher than without sample augmentation. The proposed method is effective to turn-over intention recognition, by identifying the internal pressure distribution of array air spring mattress. The efficiency of intelligent nursing systems can be substantially improved, thus ensuring better patient care and safety.

To achieve a green recycled concrete with excellent mechanical properties and workability, this paper utilized recycled concrete powder, fly ash and granulated ground blast furnace slag as primary materials. Recycled concrete aggregates served as coarse aggregates in the formulation of a recycled concrete powder-based geopolymer recycled concrete (RCPGRC). The study investigated the impact of additional water consumption (AWC), recycled fine aggregate content (RFAC) and the mass ratio of solid powder to aggregate (P/A) on both the mechanical property and workability of RCPGRC. Employing variance and range analysis, the research comprehensively assessed the contributing factors to the concrete's performance and identified the optimum mixture ratio. Characterization of the phase composition and micromorphology were characterized through X-ray diffraction and scanning electron microscopy. The results show that: (1) The AWC had the greatest influence on the unconfined compressive strength (UCS), slump, and setting times, while RFAC and P/A were smaller. AWC of 3%, RFAC of 10%, and P/A of 26% were the inflection points of the UCS, slump, and setting times with AWC, RFAC, and P/A, respectively. (2) The production rate and quantity of geopolymer gels production, as well as the cracks and voids, were affected when the mixture ratios deviated from these optimal inflection points. (3) These inflection points can be utilized as the indexes for rapid judge the optimum mixture ratio of RCPGRC.

Electrocatalytic water splitting has been considered as one of the most significant and sustainable approaches for hydrogen production. To make the process more efficient and affordable, there is a need to develop robust, cheap, highly active and stable electrocatalysts. Herein, facile synthesis of copper phosphide nanoparticles (Cu₃P NPs) with size ranging from 30 to 80 nm was carried out by using solvothermal process. Variety of characterization techniques like FTIR, XRD, Raman spectroscopy, dynamic light scattering and SEM–EDX, verified the successful synthesis of Cu₃P NPs with spherical morphology. Three-electrode system containing glassy carbon, platinum mesh and Hg/HgO as working, counter and reference electrode, respectively, was used for the electrochemical characterization. Electrochemical studies, i.e., CV, LSV and chronoamperometric analysis, revealed efficiency and stability of electrocatalyst for electrolysis of water including hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Briefly, the Cu₃P NPs exhibited an excellent OER activity, achieving the current density of 10 mA cm⁻² with an overpotential of 450 mV. Tafel slope value 63 mV dec⁻¹ suggested fast OER reaction kinetics. The Cu₃P catalyst also exhibited significant HER activity, approaching a current density of 10 mA cm⁻² with an overpotential of 447 mV. Fast HER reaction kinetics was observed with a Tafel slope value of 132 mV dec⁻¹. Moreover, the chronoamperometric studies revealed the stability of electrocatalyst providing favorable conditions for sustainable, long-term oxygen and hydrogen production.

High-power nonlinear load characteristics are one of the typical characteristics of multi-electric aircraft power systems. The study provides an improved CNN-LSTM stability analysis method for solving the stability problem of the aircraft power system caused by high-power nonlinear load switching. To address the issue of sample imbalance, this approach creatively incorporates the cost factor into the CNN loss function. In order to handle the issue of computational complexity, the projection layer is added to the LSTM, and a methodology known as CNN-LSTMP is proposed. This algorithm solves the problems of low computational efficiency and huge computational volume. The time series data utilized by the experiment are created by simulating the transient switching process. The data are then labeled, normalized, and model training is carried out. A deep learning algorithm that satisfies the prediction requirements can be created by applying this method to the established simulation model of a multi-electric aircraft power system for stability analysis. According to the results of the experiments, this method’s transient stability analysis accuracy is 93.32%, which has a positive impact on transient analysis and may satisfy application requirements.

The integration of hydrogen into natural gas infrastructure presents a viable strategy for mitigating greenhouse gas emissions and advancing toward carbon neutrality. This study investigates the combustion characteristics and emissions profiles of hydrogen-enriched natural gas mixtures, specifically focusing on the composition of Russian pipeline natural gas. A comprehensive mathematical model was developed to predict emission concentrations and simulate fuel mixture combustion using MATLAB Simulink software. This versatile model facilitates further analysis within the MATLAB ecosystem. The simulation results demonstrate a significant correlation between the hydrogen content in the natural gas mixture and the resulting heat power output. With a constant fuel consumption rate, a notable decrease in heat power was observed as the hydrogen concentration increased, reaching a maximum reduction of 44.9% at a 45% hydrogen content. These findings underscore the feasibility of partially substituting natural gas with hydrogen, while also highlighting the necessity for increased fuel flow rates to maintain equivalent power output levels. This poses additional challenges for natural gas grid operators, necessitating infrastructure adaptations to accommodate higher fuel demands. The insights gained from this research contribute to the growing body of knowledge surrounding hydrogen integration in the energy sector, offering valuable implications for decarbonization strategies and the optimization of natural gas infrastructure.

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.