Arabian Journal for Science and Engineering最新文献

In concentrated photovoltaic (PV) panels, the amount of waste heat generated increases due to the higher incident radiation on the panel surface, leading to a decrease in PV panel efficiency. Therefore, PV-PCM (Phase Change Material) integration is a widely used passive method to reduce and stabilize PV panel temperature. However, particularly in angled PV panels, the movement of the PCM within its container can cause uneven temperature distributions on the PV panel surface. To address this issue, this study employs a trapezoidal geometry to increase the amount of PCM and the surface area exposed to the environment in the regions where the molten PCM accumulates. Furthermore, the effects of PCM area and heat transfer coefficient to the environment on the temperature distribution of the PV panel for different trapezoidal geometries (different tilt angles and the ratio of side surfaces) were investigated. A numerical model was developed for these investigations, and this model was validated with experimental work found in the literature. The results showed that the surface temperature decreased by 5–21 K and the surface temperature uniformity improved between 10 and 44% depending on the parameter change with the use of trapezoidal geometry.

There are many potential uses for wireless rechargeable sensor networks (WRSNs), making them an important and exciting field of research. Extending the network’s lifespan is challenging because of the sensors’ short battery life. However, developing appropriate charging schedules for mobile charging vehicles (MCVs) is a difficult problem. These charging schedule designs can have an influence on WRSNs overall consumption of energy and lifetime. We address the challenge of minimizing travel energy for MCVs in WRSNs. Our proposed solution includes a priority-based charging schedule that balances MCV travel time and charging time effectively. Additionally, we offer a method for selecting charging energy levels to conduct partial charges aiming to prolong the network’s lifespan. We have also incorporated the remaining lifetime of sensor nodes (SNs) as a crucial factor in mitigating the occurrence of dead SNs in the network. In this article, we partition the requested SNs into several partitions and assign an MCV to each region using the Aquila Optimization meta-heuristic approach. A heuristic-based partial charging method is proposed. We compare the outcome of our proposed technique with several other existing algorithms. The outcomes of the simulation indicate that our suggested method performs better than the others. Additionally, an analysis of variance and a post hoc analysis are carried out. We demonstrate, through comprehensive simulations and hypothesis testing, that the proposed scheme increases the number of replenished sensor nodes up to 36.36% and the charging utility up to 97.82% while decreasing the charging time and the number of dead sensor nodes up to 54.16% and 85.86%, respectively.

Dynamic objects in the environment can compromise map quality and, in severe cases, lead to robot localization failures. To address this issue, this paper proposes a simultaneous localization and mapping (SLAM) framework with dynamic point removal capabilities, which incrementally filters out dynamic points during the mapping process to enhance map accuracy and localization reliability. The framework consists of two main modules: the SLAM module and the dynamic point removal module. The SLAM module, based on Fast-LIO, incorporates novel loop detection and filtering algorithms to improve long-term mapping accuracy, while the dynamic point removal module optimizes the map by eliminating dynamic points. The dynamic point removal module employs three key methods. Firstly, to enhance dynamic point identification accuracy and minimize misclassification, a novel multi-resolution height map method is introduced. This method effectively segments static ground points and directly preserves them as static points. Secondly, a visibility-based approach is employed to maximize the removal of suspected dynamic points by comparing range differences between the local map and the current frame. Finally, K-nearest neighbors and principal component analysis methods are utilized to compare feature vectors between clusters, facilitating the recovery of static points that may have been erroneously removed. The proposed method is validated via public datasets and real-world scenarios, demonstrating significant improvements in dynamic point recognition as well as in localization and mapping accuracy compared to other state-of-the-art methods.

Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models (llms) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing (nlp) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.

In this study, we examine the downlink cell-free (CF) multiple aerial relay stations (ARSs) system, where ARSs are outfitted with several antennas, distributed randomly in entire responsible areas, and serve a large number of ground users. The application of the CF model is expected to not only improve the performance of the ARS system but also solve the problem of inconsistent throughput between users and the problem of near-far users in the current advanced networks, such as massive multiple-input multiple-output (mMIMO). Moreover, we propose two ARS selection strategies to cut off poor-quality transmission links in the original CF system, thereby reducing the power consumption of ARSs, reducing interference between users, and improving user throughput. We calculate a closed-form formulation for downlink user throughput under the condition of using conjugate beamforming technology. We optimize the pilot and downlink data transmission coefficients using the successive convex approximation (SCA) method and second-order cone programming (SOCP), respectively. The proposed optimization methods mitigate the problems of "pilot contamination" in channel estimation and inter-user interference in downlink data transmission of systems allowing multiple ARS to access the channel simultaneously. Our findings have assessed the downlink system throughput with pilot and data transmission power optimization for superior results based on a variety of selection strategies as well as the CF model (using all ARSs). In addition, the findings also indicate that the proposed novel ARS selection strategies have better user throughput than the CF technique with a specific number of selected ARS. Our proposed system is a promising technology and opens up various practical application scenarios for 6G networks.

Direct methanol fuel cells (DMFCs) have great potential for use in portable electronics. However, obstacles such as methanol crossover, insufficient proton conductivity, and the high cost of Nafion hinder the broad commercialization of this technology. In line with the prevailing “waste-to-wealth” movement, eggshell powder was chosen as the filler for the Nafion matrix (rN-ES). Nano-calcium carbonate (nano-CaCO₃) was first produced from eggshell waste by a mechanochemical process before inclusion in the Nafion polymer matrix by the solution casting process. Cyclic voltammetry and electrochemical impedance spectroscopy were used to measure methanol permeability and proton conductivity. The composite membrane showed the highest value for ion exchange capacity of 1.25 mmol g⁻¹ and water uptake of 46.54%. Remarkably, the through-plane method showed better proton conductivity (4.87 mS cm⁻¹) compared to N117. The methanol permeability of the rN-ES composite membranes decreased to 3.3 times the permeability of N117. In the passive single-cell test of the DMFC, the use of a composite membrane with 5 wt.% nano-CaCO₃ resulted in a rise in the maximum power density from 9.5 to 12.37 mW cm⁻². These results prove that the incorporation of nano-CaCO₃ as a filler in a Nafion matrix is practicable for DMFC applications.

In two-dimensional axial symmetry finite element analyses, compressible clayey deposits improved by a large group of floating stone columns were performed using the unit cell idealization. The primary focus of this study is to assess the efficiency of floating stone columns in enhancing the consolidation rate of low-permeable soils. Additionally, it aims to evaluate the long-term stability of constructions built along marine coastal areas. To this end, two real case studies were investigated; the Béjaïa and Algiers Mediterranean harbors. Various geometric variables, pertaining to the design of floating stone columns, have been considered to analyze their effect in impacting the consolidation process and the long-term behavior emphasizing their fundamental importance in the design. Besides, a thorough comparison between the design in both short-term and long-term conditions, satisfying the admissible settlement, has been made, ultimately resulting in the optimized design selected. The results also indicate that increasing both the area improvement ratio and the floating column length leads to a speeding up of the consolidation rate. However, in contrast to the area substitution ratio, the column length has comparatively lesser importance in terms of reducing the settlement. Importantly, it is demonstrated that the design of floating stone columns for long-term conditions is significantly distinct from that for short-term conditions, requiring an approximate 40% increase in the area improvement ratio as designs based on the immediate settlement may not align with improved soft soil long-term behavior. Finally, the study reveals that the applied load ultimately governs the design of floating stone columns.

Peak load forecasting is a critical aspect of power system operations and planning. Accurate forecasting of peak loads significantly impacts the overall efficiency and reliability of a power system. Among the numerous load forecasting methods that are used, ensemble learning algorithms have emerged as a popular choice due to their high accuracy. In this research, the author proposes an innovative methodology that integrates the Differencing Operator with the Sliding Window procedure for training and predicting peak loads using commonly employed ensemble learning models such as GBDT, XGBoost, LightGBM, and CatBoost. The performance of the proposed approach was evaluated by analyzing the prediction error and execution time. The results obtained demonstrated improved accuracy in peak load forecasting, with no impact on execution time.

For recognizing small targets, fire-like objects in fire images, and detecting fires across various scenes, we propose a fire detection method based on feature fusion and channel attention. Most existing fire detection methods have specific application scenarios with poor speed or accuracy. To address the issues of poor accuracy when directly applying existing object detection models and the reduced detection speed when improving models for fire targets, our approach aims to balance accurate fire localization with real-time processing. In the backbone of the model, deformable convolution is used to capture rich image information, and channel attention is employed to enhance features. The feature fusion in the neck achieves better localization of small fire targets. The visualized heatmap results indicate the effectiveness of our improved measures. By simultaneously employing multiple improvement measures, our method achieved satisfactory fire detection performance. Experimental results on a self-annotated dataset demonstrate that the best AP@50 of the model can reach 63.9%, the fastest detection speed can reach 114 FPS, and the F1-score is stable at around 63%. Our method strikes a good balance between detection speed and accuracy.

In this paper, a comprehensive risk assessment system is proposed to evaluate the risk of collapse in mountain tunnels. This system integrates risk source identification, dynamic and static risk classification, deep learning prediction, and engineering risk evaluation. Firstly, risk events and sources are identified, and a risk evaluation method combines the fuzzy analytic hierarchy process (FAHP) and interval technique for order preference by similarity to ideal solution (TOPSIS). FAHP is used to calculate weights, and a risk classification table based on five classical values is derived using traditional TOPSIS. The actual project’s risk value is then calculated using Interval TOPSIS to determine the risk level. Secondly, six models (BP, SVM, CNN, LSTM, PSO-SLTM, and EPL) are trained and tested to predict surface settlement at the tunnel portal and using RMSE, MAE, and maximum (minimum and average) error values for comparison; the best model is determined. The study concludes that a two-stage model, which uses ensemble empirical mode decomposition to process raw data and particle swarm optimization to optimize long short-term memory hyperparameters, provides the best predictive results. Finally, static and dynamic risks are combined for a comprehensive risk evaluation. The Aktepe Tunnel Project in Xinjiang, China, serves as a case study to successfully and accurately forecast surface settlement and evaluate the safety of the tunnel portal. This assessment confirms that this section of the tunnel is at average risk and that the current building conditions ensure the safety of the tunnel, the case study validates the rationality of the comprehensive evaluation system, offering a reference for tunnel portal risk evaluation.