The pursuit of lightweight, structurally sound, and cost-effective designs is paramount in contemporary engineering, particularly in the development of advanced enclosed mast/sensor systems (AEM/S). A significant challenge in this field is the limited availability of comprehensive data and systematic classification of studies pertaining to these specific mast configurations. This research addresses this gap by initially developing a model of a steel and anti-radar composite mast, subsequently employing Abaqus/CFD and Abaqus/FEM to conduct aerodynamic and finite element analyses, respectively. This initial phase aims to characterize the structural behavior of this mast type. In the second stage, a neural network-based method, coupled with a genetic optimization algorithm, is implemented to determine the optimal dimensions for the mast and its associated casing. This optimization process culminated in a composite mast design, standing 22 meters tall with a 9.59-degree slope, achieving a substantial 50% weight reduction compared to the original steel mast design (reducing the mass from 118 to 27.51 tons). Finite element analysis (FEA) was utilized to assess the mechanical behavior of the initial and optimized designs. The results demonstrate that the optimized design exhibits a more evenly distributed stress profile, with reduced stress concentrations in the lower sections and increased stress levels in the upper antenna region. This stress redistribution suggests improved material utilization and enhanced structural integrity. By minimizing the potential for localized failure, the optimized design demonstrates the feasibility of achieving significant weight reductions without compromising structural performance. These findings underscore the effectiveness of artificial neural network (ANN)-based optimization in creating lightweight and efficient composite structures, providing valuable insights for designing AEM/S systems and other tall structures subjected to dynamic loads. The study highlights the potential of advanced optimization techniques and composite materials in achieving sustainable and high-performance engineering solutions.

This work presents a new approach to designing high-performance and efficient pumps based on dielectric elastomer actuators. By considering the entire system including load from the beginning of the design process, the advantages of dielectric elastomers are specifically utilized to minimize the required space while optimizing performance within that compact volume. The process is model-based, ensuring that every aspect, from individual components to the complete system, is carefully optimized for efficiency and power. This approach represents an important step in the use of dielectric elastomer actuators for real-world applications, as the pump is more powerful over a wide working range compared to the state of the art and can therefore be used in applications where conventional pumps are still commonly employed.

We examine the two-dimensional, steady, and irrotational flow of an inviscid and incompressible fluid emerging from certain cases of flow through solid walls. Surface tension (T) is considered, while gravitational effects are neglected. This problem is complicated by the non-linear boundary condition imposed by the Bernoulli equation on the free surface, which presents challenges when adapting numerical methods used by many researchers. We employ a series truncation method for numerical solution. We computed solutions for various values of the angle (β) between the walls AB and the horizontal, the length of the vertical wall BC, and different Weber numbers. For determining the shape of the free surface, most of the calculations were performed for N=50. We were able to find approximate solutions for  α > 3 To validate our results, they were compared with studies using other numerical methods and in special cases of the angle β, compared with the exact solution in the limit of

This work deals with optimizing the synthesis error in an 8-bar Peaucellier - Lipkin mechanism, for its dimensional synthesis and applications in load-lifting machines. A new method for the formulation of the problem of maximizing the objective function is proposed and makes it possible to obtain from the PSO algorithm a minimum synthesis error e min= 9.07E-06 mm for the generation of the straight trajectory when the search interval for the lengths of the bars is  and a minimum error e min= 1.47E-04 mm when the search interval is . For 10 simulations in case 1 the average convergence time is t m = 55 s with the largest iteration at 10 (for t = 159 s); for 100 simulations in case 2, the t m = 229 s with the largest iteration at 136 (for t = 2294 s). The minimum error of case 1 is compared with the results of authors in the literature on the generation of the right trajectory because the search space is approximately equal. In the literature, e min= 0.648358 mm with the GA-DE algorithm in 2010, e min = 2.3667E-005 mm with the MKH algorithm in 2016, e min = 0.027145 mm with the SAP-TLBO algorithm in 2017, e min = 3.7E-4 with the GA algorithm in 2019. This new method brings a plus, because even when the search space is very large, the algorithm converges quickly and it allows the study to be extended to the generation of circular trajectories by just modifying the ratio between the frame bar and the crank bar. The results of the post-design FEM analysis show that for a 1.4571 steel (X 6CrNiMoTi 17- 12- 2) with a thickness of 50 mm and a joint with a radius of 500 mm, the mechanical device obtained can support a load of 1500 kg.

Corrosion in circulating cooling water systems of power plants significantly compromises equipment reliability and energy efficiency. This study investigates the anticorrosive performance of 1-hydroxyethylidene-1,1-diphosphonic acid (HEDP) on carbon steel (St-20) commonly used in such systems. Bench tests simulating the operating conditions of a power plant were conducted using circulating cooling water and carbon steel coupons. The corrosion rate was assessed at HEDP concentrations of 2.0, 5.0, and 10.0 mg/dm 3 using weight loss methods over 1000 hours of exposure. Results demonstrated a concentration-dependent reduction in corrosion rate, with a maximum inhibition efficiency of 58% observed at 10.0 mg/dm 3. Statistical analysis confirmed a strong positive correlation between HEDP concentration and inhibition efficiency. Although HEDP effectively reduced corrosion, the occurrence of moderate biological contamination was observed. This unexpected microbial growth indicates that HEDP alone does not prevent biofouling. Furthermore, environmental implications related to phosphonate discharge and the potential for eutrophication must be carefully considered when applying HEDP in power plant cooling systems.

This article presents a circular array using four-elements dipole with beam-switching capabilities for 5G communications. The beam can switch in four directions by controlling the switch states implemented in the arms of each dipole. The different antenna modes lead to different radiation behaviors. The first is a directional behavior containing 4 radiation modes depending on the dipole fed into the network. The second type of radiation has a double beam pattern according to the two fed dipoles. According to the simulated results, a corresponding overall bandwidth of 932.3 MHz (S11 < - 10 dB) can be obtained with a peak gain of 3.15 dBi and an overall efficiency of 80%. The proposed antenna is fabricated and measured to verify the simulation results. This low-cost, efficient, and easy-to-design antenna array can be very useful for 5G beam switching applications.

A simple wideband magneto-electric (ME) dipole antenna is presented in this letter. In particular, two pairs of L-shaped slots with different sizes are successively etched out of the pair of horizontal patches of electric dipole, which can enlarge the bandwidth of ME dipole antenna without increasing the antenna size. Two new resonant frequency points are generated respectively at the lower and upper frequency region. To improve the impedance matching characteristics, two vertical rectangular patches are loaded in the middle of the horizontal portion of the Г-shaped feed structure. A prototype is fabricated and measured, and an overlapped bandwidth of 101.3% (from 1.36 GHz to 4.15 GHz) in terms of VSWR≤2 is achieved, which can simultaneously cover the n50 (1.432GHz-1.517GHz), n51(1.427GHz-1.432GHz), and n78 (3.300GHz to 3.800GHz) bands of sub-6G. It should be highlighted that other excellent properties of ME dipole antenna, such as stable radiation pattern, high gain, and low cross-polarization level (CRPL), are maintained, which makes the proposed ME dipole antenna a good candidate for sub-6G applications.

To address the difficulty of dynamically assessing overhead transmission lines under varying meteorological conditions through manual inspections and drone monitoring, this study proposes an evaluation method for the operating status of overhead transmission lines based on the Analytic Hierarchy Process (AHP). A mathematical model based on AHP is first established to perform weighted processing of five meteorological factors, generating a comprehensive meteorological dataset. A simulation model for the LGJ-300/70 type conductor is then developed under the temperature field, where temperature distribution during operation is determined based on different meteorological data. Stress variations are observed, and evaluation criteria are established by calculating displacement deviations in the two-dimensional transmission line model. The transmission line is considered to be in a normal operating state when the maximum displacement deviation is ≤ ±0.63 mm. This demonstrates that the AHP-based evaluation method can effectively enable dynamic assessment of the operating status of overhead transmission lines.

This article reports on the optimisation of forging process parameters using Deform 3D simulation software and Taguchi methodology. The process parameters considered were temperature, die velocity, and temperature and friction coefficient. The workpiece material was 42CrMo4 steel used to manufacture the engine connecting rod. The output responses were maximum stress, maximum strain, maximum tensile stress and damage. The simulation design had three levels for each process parameter. The study used Taguchi L9 orthogonal array. Taguchi’s analysis gave two sets of optimal conditions. Case 1: temperature (1200℃), die velocity (100 mm/sec), die temperature (250℃) and friction coefficient (0.2) from S/N ratios analysis. Case 2: temperature (1200℃), die velocity (115 mm/sec), die temperature (215℃) and the friction coefficient (0.2) for multiple response prediction. The ANOVA results indicate that the most significant factors were deformation temperature (influencing maximum stress and tensile stress) and friction coefficient (influencing maximum strain and damage). The die temperature and velocity had a minimal effect on the output responses. The confirmation test results show slight variation in output responses when using the two cases of optimal conditions for the predictive regression and simulation model. However, maximum tensile stress had a higher percentage error. The error results were 10.895% (Case 1) and 18.512% (Case 2). An industrial forging simulation of an engine connecting rod validated the optimal conditions. This study showed that numerical simulation and Taguchi methodology can effectively and efficiently optimise the forging process. The approach provides the basis for future industrial analyses and optimisation.

The study introduces a computational method that combines Legendre polynomials with Gauss-Lobatto points to solve nonlinear coupled differential equations, focusing on the Williamson fluid model with the existence of mixed convection and permeability under mixed boundary conditions. The nonlinear governing equations were transformed to ordinary differential equation (ODE) from partial differential equation (PDE), applying the appropriate similarity conversions. By using Legendre polynomials as trial functions and collocating residual equations with Gauss-Lobatto points, the system is solved with Mathematical software. The technique was validated by comparing the obtained solution with an existing literature and further validation was done with Runge-Kutta of order 4 via shooting method. Validation against the Shooting Runge-Kutta method showed minimal discrepancies, confirming the method’s accuracy. Graphical analysis indicated that an increase in the Grashof number enhances velocity, while higher porosity raises temperature but reduces fluid velocity. This approach offers an efficient and precise solution for complex nonlinear equations, with broader potential applications in fluid dynamics.

Polylactic acid (PLA), a biodegradable polymer, is gaining attention as a sustain- able alternative to steel in civil engineering, yet its fracture behavior under complex loading remains underexplored. This study examines crack propagation in PLA sam- ples with dual keyhole notches under tensile loading, integrating experimental tests, finite element simulations, and machine learning predictions. Six PLA specimens (200×50×10 mm, crack length 10 mm, angles 60° and 70°, notch radii 0.5–2 mm) were tested experimentally, while 400 samples (angles 1°–80°, radii 0.5–4 mm) were simu- lated in ABAQUS. Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models, implemented in MATLAB, analyzed the results. Experimental peak stress reached 33.3 N/mm 2 (0.5 mm notch, 60°), while simulations predicted up to 65.225 N/mm 2 (0.5 mm, 1°). ANN with Bayesian Regularization outperformed other models, offering precise predictions of crack behavior. These findings provide a fracture criterion for PLA, advancing its potential in sustainable structural applications.

The application of deep learning methods in the research on intelligent recognition of fault images in Electric Multiple Units (EMUs) represents a crucial approach to alleviating the heavy workload of Trouble of moving EMU Detection System (TEDS) inspections and safeguarding train safety. In light of the current TEDS image data’s problems such as irregular formats, substantial quality disparities, and uneven sample distribution, which pose challenges for deep learning, we proposes a method for constructing an intelligent recognition dataset of TEDS EMU fault images. By integrating the characteristics of TEDS images with the prior knowledge of EMU operations, a unified data acquisition protocol and standardized dataset construction techniques are devised. Consequently, an intelligent recognition dataset of TEDS EMU fault images is established. This dataset accomplishes effective data integration and standardization and has been successfully implemented in the research on automatic Recognition Algorithms for TEDS EMU faults. It accelerates the rapid progress of intelligent recognition technology for TEDS fault images and lays a robust technical foundation for ensuring the safety of EMU operations.

Ball mills are commonly used in chemical and mineral processing industries for particle size reduction. In mineral processing, cylindrical drums with wear resistant materials are used various operation scales. Polygon shaped mills have found application by artisanal and small-scale miners (ASM) due technological and economic reasons. Evaluating this kind of mills is key in improving or replacing them based on technological data. A modest improvement in grinding efficiency and mill durability can lead to significant economic and environmental benefits. In this study, the performance of polygonal shaped mills was evaluated and compared with cylindrical profiles considering power draw, collision energy dissipation, relative wear and operational stability. Discrete element method (DEM) was used to carry out simulations to describe mechanical behavior of grinding media and the interaction with the mill walls. Polygonal shaped mills without lifters promoted more interparticle interaction with limited centrifuging of particle even at higher speeds compared to cylindrical mills without lifters. Installation of lifters in polygon increased inter-particle collisions and reduce power draw and relative wear. Cylindrical mill with lifters had high interparticle collisions similar to polygonal profile with much lower cumulative relative wear and high operational stability.

The detection of distant small ship targets in the marine environment is a critical and challenging issue that urgently needs to be addressed in the realization of accurate marine information control in the complex environment. It is of great significance for monitoring Marine environment and safeguarding maritime sovereignty In the process of acquiring target information on ships at sea, the images captured typically contain information of moving targets within dynamic scenes. Traditional, singular methods are inadequate for obtaining complete information on these moving targets. Based on this, the article proposes an integrated method combining motion target detection algorithms, edge detection operators, and deep learning-based target detection algorithms. This method constructs an improved motion target detection algorithm to achieve comprehensive information acquisition and detection of the position, size, and type of moving ship targets in complex marine environments. Experimental simulation has validated the network performance and practical value. The network has been deployed on an Nvidia Jetson TX2 development board for real-world testing, confirming its performance in detecting dynamic ship targets in actual marine environments, and providing a viable technical approach and theoretical support for enhancing the refined target selection capability.

This study assesses the potential for water reuse from treated wastewater in the Rio dos Sinos and Rio Sapucaí watersheds in Brazil, applying the Analytic Hierarchy Process (AHP). The criteria evaluated include treatment efficiency, proximity to demand, supply capacity, and legal and institutional frameworks. An interactive dashboard was developed to support decision-making. Results show higher reuse potential in Rio dos Sinos for BOD removal >80%, meeting up to 46% of industrial or 17% of irrigation demand; the municipalities of Esteio, São Leopoldo, and Sapucaia do Sul are highlighted as high-potential areas. Rio Sapucaí shows better performance with 60-80% BOD removal, meeting 100% of industrial or 68% of irrigation demand, with Pouso Alegre identified as a high-potential municipality. Further development of the evaluation matrix and cost estimations are recommended.

This paper proposes a sub-optimum Kuhn-Munkres-based resource allocation algorithm to maximize the number of connected links and total throughput served by ultra-dense networks consisting of densely distributed co-channel access points and user equipment. In the proposed seven-step algorithm, users are first assigned to access points supporting higher data rates considering the interference of all access points. Then, only the interference of the selected access points is considered and users connected to these access points that meet the minimum throughput threshold level are found. Afterward, considering the interference of the access points assigned in the first run and the remaining access points selected in the next runs, new users are connected to the remaining selected access points. Simulations in MATLAB for a service area of 250 meters by 250 meters including randomly distributed 250 access points and different numbers of 25 to 250 users, show more connected users and total throughput, and much less processing time of the proposed algorithm than those for Genetic, particle swarm optimization, cuckoo search, and grey wolf optimization algorithms. By changing the number of users from 10% to 100% of the number of access points, the proposed algorithm increases the number of connected users by 10% to 48%, 47% to 96%, 57% to 109%, and 22% to 58%, and the total throughput by 20% to 52%, 44% to 86%, 50% to 105%, and 22% to 69% compared to the four mentioned algorithms. Due to the lower complexity order, it experiences at least 99% less processing time.

The variable and unpredictable output from distributed generation (DG) like wind and solar creates new reliability concerns for distribution networks. Integrating DG on a large scale can unbalance the power supply and compromise quality, making accurate reliability assessment essential. This paper puts forward a new assessment method using a Transformer network. The method first utilizes an improved minimum path algorithm designed for grids with DG. This algorithm models the scenario where a section of the grid, if cut off from the main supply, can form an operational island, using its local DG to power essential loads. Secondly, the Transformer network is innovatively applied to classify reliability indices into specific intervals, transforming a difficult prediction challenge into a more manageable classification task. This overcomes the problem of non-smoothness in reliability data. We demonstrate the method’s effectiveness on Feeder 4 of the IEEE RBTS 6-node test system. The proposed framework enables dynamic monitoring and proactive warnings against operational risks in the grid.

The analysis of the composition of alkali-excited concrete in the civil engineering industry is of great significance for material development. However, there is no machine learning method suitable for alkali excited concrete composition analysis. Therefore, in this study, we use the test results of alkali slag concrete specimens as research samples. The composition of broken specimen blocks was analyzed by developing an ensemble learning algorithm called MLP-RF. Finally, by comparing the prediction results of this experiment with the prediction results of other scholars and optimizing the results, it is found that the ensemble learning algorithm has high accuracy and certain universality. This provides a feasible method to analyze the composition of alkali-excited concrete using machine learning.