alexandria engineering journal最新文献

This paper addresses a significant research gap in the study of surface waves propagation in a nonhomogeneous, within a magneto-thermoviscoelastic material of higher order, initial stress, rotation, gravity effects and voids. This study provides analytical solutions for surface waves propagating through a medium consisting of a magneto-thermoelastic material with voids under the rotation, electro-magnetic field, gravity field and initial stress. The analytical solutions are derived for the displacement components, volume fraction, temperature to Stoneley and Rayleigh waves are computed numerically and presented graphically considering the external parameters impact. Furthermore, this investigates how magnetic field, voids, gravity, initial stress and fiber-reinforced parameters influence these wave phenomena. This investigation provides valuable insights into the synergistic dynamics among electric constituents, voids, Stoneley and Rayleigh waves propagation, enabling advancements in sensor technology, augmented energy harvesting methodologies, and pioneering seismic monitoring approaches. For certain materials, numerical simulations are provided and graphically displayed. The results of this study reveal several unique cases that significantly contribute to the understanding of Rayleigh and Stoneley waves propagation within this intricate material system, particularly in the presence of voids.

The disturbance of the coal caving mechanism is the main method to solve the problem of coal falling into an arch during the top coal caving process, and the clearance in the caving mechanism is an important factor affecting its disturbance. To study the influence of clearance on the hydraulic supports, the dynamic analysis of hydraulic supports with two-level and three-level caving mechanisms is conducted considering the clearance. Based on Lagrange's equations of first kind, the mathematical model is constructed, and the equivalent spring stiffness of the oil cylinder is solved. The dynamic response of hydraulic support is studied by solving the mathematical model, and the relative position changes of the journal and bearing are compared. The research results prove that clearance has a better disturbance effect on the three-level caving mechanism. At the same time, the influence of clearance size and motion process on the motion error of the three-level caving mechanism is analyzed. Using the Kriging surrogate model and polynomial interpolation, the motion prediction model for the secondary tail beam with different clearance sizes is proposed, and the predicted values of the test samples are compared with the numerical solution values to prove the feasibility.

This study proposed a novel electrochemical sensor for the sensitive measurement of the eye medication ofloxacin (OFX), which is based on a NiO@C-dot nanocomposite. The NiO@C-dot nanocomposite was synthesized using the sol-gel method, which was then applied to alter a glassy carbon electrode (GCE). With regard to OFX, the modified GCE shown outstanding electrochemical activity along with good sensitivity, selectivity, and stability. With OFX concentrations ranging from 5 µM to 975 µM, the NiO@C-dot/GCE showed a linear relationship with a sensitivity of 0.63994 µA/µM. The limit of detection (LOD) was 0.023 µM. The response to 50 µM OFX was measured, and the electrode was stored at 4°C after each test in order to evaluate the sensor's long-term stability and repeatability. After 20 days, the peak current response was still 97.89 % of its initial value. After 40 days of prolonged storage, 93.78 % of the electrode's original responsiveness was retained. The sensor showed a recovery rate of 97.37–99.70 % in all sample types when it was used to detect OFX in human urine, tap water, and food samples. These findings highlight the NiO@C-dot/GCE's outstanding selectivity, dependability, and longevity as solid platforms for OFX detection in complicated samples for a range of applications.

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field.

In this research, experimental studies and numerical analyses were carried out to investigate how the usage of geotextile and geopolymer coated geotextile as a hybrid method changes the uplift behavior of the screw piles in cohesionless soil. In this context, traditional pile behavior, the effect of different number of helixes and embedment depths on screw piles, the mechanism of geotextile and the effects of geopolymer coating process were investigated. In addition, experimental studies were modeled by using Plaxis 3D and parametric studies were carried out after verification between the results of experimental study and numerical analysis. In the numerical analysis, a segmented helix model consisting of four 90-degree slices was developed instead of the planar helixes commonly used in the literature. For further investigation of the effectiveness of hybrid method, parameters such as improvement ratios and breakout factors were calculated. When the results obtained within the scope of the study were evaluated, the geopolymer coating process increased the bearing capacity of the geotextile by 24 % at 27 % less elongation. It was also seen that uncoated and geopolymer coated geotextile increased the screw pile performance in terms of improvement ratios by 294 % and 364 %, respectively.

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the “whole-electric-bicycle annotation” method, which could help with the development of electric bicycle detection applications in lifts.

Cancer represents a prominent health concern on a global scale and stands as a significant contributor to mortality. The accurate quantification of anti-cancer drugs such as paclitaxel in human biofluids is critical for effective treatment and monitoring. In this study, a straightforward one-step hydrothermal method was presented for synthesizing carbon dots (CDs), eliminating the need for any additional decoration or modification, by utilizing combretum micranthum extract as a natural carbon source. A paclitaxel sensor was fabricated by modifying a glassy carbon electrode (GCE) with the green synthesized CDs. At optimal conditions, the CDs-GCE exhibited a linear response for paclitaxel analysis in a range of 0.07 µM to 35 µM, with a low detection limit of 2.1 nM. The suggested sensor indicates acceptable reproducibility for paclitaxel detection ((RSD=2.6 %). In addition, the CDs-GCE depicts a good resistant versus common interfering species including flutamide, dopamine, glucose, nilutamide, lactose, tinidazole, ascorbic acid, and L-cysteine. The applicability of the sensor for paclitaxel detection in human serum and human urine samples was effectively assessed. The presented electrochemical sensing protocol for paclitaxel detection offers several merits, including a low limit of detection, fast response time, resistance to interference, and ease of use.

Construction projects are prone to accidents and injuries, necessitating a focus on implementing safety programs. However, the implementation of such programs is influenced by various factors. Developing countries often have poor safety performance in their building sectors, with limited research in this area. This study aimed to identify essential safety program activities (SPAs) specific to the building sector. Through a literature review and survey, 25 SPAs were identified and validated via a pilot survey involving building sector experts. A questionnaire survey was conducted with 105 participants from the construction industry and academia. They were then categorized into four interconnected measurements using Exploratory Factor Analysis (EFA): Safety Program Management and Development (SPMD), Safety Culture Development (SCD), Safety Risk and Hazard Management (SRHM), and Safety Leadership, Responsibility, and Commitment (SLRC). The impact of safety implementation (SI) on overall project success (OPS) was analyzed using Partial Least Square- Structural Equation Modelling (PLS-SEM). Subsequently, Synthetic Fuzzy Evaluation (SFE) was employed to determine the criticality and importance of each SPA grouping for construction projects. The PLS-SEM analysis indicates that SI has a moderate impact on OPS, with an R² value of 45.4%. Moreover, the findings of the SFE highlight that the SLRC group is the most significant in enhancing the safety implementation of the construction industry.

Solar energy is one of the renewable and clean energy sources. Accurate solar radiation (SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were developed in this study. Next, a data pre-processing technique named multivariate variational mode decomposition (MVMD) was used to construct the MVMD-GRU and MVMD-LSTM hybrid models. To better test the performance of proposed simple and hybrid models, four stations located in the Illinois State of the USA (i.e., Dixon Springs, Fairfield, Rend Lake, and Carbondale) were considered as the study sites. Whole the simple and hybrid models were established under two different strategies, i.e., local and external. In the local strategy, SR of each location was estimated using the minimum and maximum air temperatures from the same station. While, minimum and maximum air temperatures as well as SR data from the nearby station were utilized in external strategy to estimate SR time series of any target site. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) metrics were used when evaluating the models performances. The overall results revealed that the proposed MVMD-GRU and MVMD-LSTM hybrid models illustrated better SR estimates compared to the simple GRU and LSTM in both the local and external strategies. The values of error metrics obtained for the superior hybrid models (i.e., MVMD-LSTM) during the testing period were as: RMSE = 2.532 MJ/m².day, MAE = 1.921 MJ/m².day, R² = 0.916 at Dixon Springs; RMSE = 2.476 MJ/m².day, MAE = 1.878 MJ/m².day, R² = 0.921 at Fairfield; RMSE = 2.359 MJ/m².day, MAE = 1.780 MJ/m².day, R² = 0.924 at Rend Lake; RMSE = 2.576 MJ/m².day, MAE = 1.941 MJ/m².day, R² = 0.914 at Carbondale. Therefore, the coupled models proposed in this study can be possibly recommended as suitable alternatives to the simple deep learning models with a reliable precision in estimating SR time series.