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.

The novelty of this research is surveying the effects of CuO nanoparticles in jeffery-hamel flow with the high levelized of magnetic field in converging and diverging channels using quasi-linearization method (QLM) and also the meshless method which based on radial basis functions. In order to evaluate the convergence analysis of the method, error estimations are made by a residual function denoted. Also, the ability of the present method is shown by comparing it with the numerical method to solve this problem, which is in good agreement. Effects of multivariable parameters is analyzed on magnetic field, nanoparticles volume fraction and angle of converging and diverging channels. The obtained results show that at angles or Reynolds number of greater in divergent channels, backflow occurs so the high levelized of magnetic field is eliminated this phenomenon. Increasing viscous force is due to velocity reduction close to the walls of converging and diverging channels.

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.

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 paper deals with micromagnetic measurements for online detection of strain-induced α’-martensite during plastic deformation of metastable austenitic steel AISI 304L. The operating principles of the sensors are Barkhausen noise (MBN) and eddy currents (EC), which are suitable for detection of microstructure evolution due to formation of ferromagnetic phases. Nevertheless, the description of the calibration and transformation models of the micromagnetic measurements into quantitative α’-martensite fractions is beyond the scope of this paper. The focus will be put on the qualification of different micromagnetic methods as well as of different measurement systems under conditions similar to the real ones during production, which is crucial for implementation of a property-controlled flow forming process. The investigation was carried out on tubular specimens produced by flow forming, which have different content of α’-martensite. To characterize the sensitivity of the sensors, different contact conditions between sensors and workpieces were reproduced. MBN sensors are suitable for detecting amount of α’-martensite, but the measurements are affected by the surface roughness. This entails that the calibration models for MBN sensors must take account of these effects. EC sensors show a closer match with the amount of α’-martensite without having major affectation by other effects.

s: Driven by the ”dual-carbon” target, it’s best time for great development for zero-carbon energy, which is a huge role in promoting the emission reduction of commercial vehicles. Based on zero-carbon (Green Electricity, Green Hydrogen and Green Ammonia), this paper carries out the research about carbon emissions prediction between zero-carbon energy and diesel in light-duty trucks and different penetration projects of zero-carbon energy from 2020 to 2060, and the changes in the quantity of light-duty trucks (including stock, increment, scrap and total amount). The calculation results show that CO2 emissions from Diesel is the largest, and shows a gradual upward trend in general, and reaches peak value in 2055 (1.793 billion tons). Based on different penetration projects for Green Electricity trucks, CO2 emissions of peaking is in 2035. As penetration increases, CO2 emissions gradually declines. Reduction of CO2 emissions of Project_1 is 19.94% will stabilizes after 2055, however CO2 emissions of Project_4 stabilizes after 2040 and 15 years earlier than Project_4’s, and CO2 emissions is nearly zero. The change rule of CO2 emissions from ”Diesel + Green Hydrogen” and ”Diesel + Green Ammonia” is similar, and CO2 emissions from ”Diesel + Green Ammonia” is higher than that of ”Diesel + Green Hydrogen”. The permeability of Project_4 changes greatly, and the results is a large reduction. Based on 2020’s data, and as gray ammonia is fuel, the results show that the current overall emission level can be reduced only when the CO2 emissions level for the production of ash ammonia reduces less than 30% of the current level.

Additive Manufacturing(AM) has revolutionized the production industry by offering design freedom with shorter lead times and reduced material wastage. However, the damage tolerance of AM parts is a significant concern due to their microstructural and geometric complexities, which affect their mechanical performance. This article aims to provide a comprehensive overview of the manufacturing parameters affecting the damage tolerance of components produced by AM specifically selective laser melting(SLM). Detailed discussions are presented on the effects of manufacturing attributes on the microstructure, defects, and mechanical characteristics of AM parts. Depending on these aspects, basic concepts are studied and critically explained specifically for AM materials. The basic criterion for damage-tolerant component design, the criterion for fatigue and fracture properties, and the effect of the defects on fatigue life are critically presented. In addition, the effect of different types of gradation on the crack growth behavior of AM samples processed by SLM is also investigated in depth. There is currently a lack of a specific review study in the literature that establishes a connection between process attributes and metallographic properties, and their impact on the damage behavior of additively manufactured parts. This gap in research highlights the need for a comprehensive review to bridge this knowledge deficit and provide valuable insights for understanding the relationships between manufacturing processes, material characteristics, and the structural integrity of additively manufactured components. This review concludes by addressing the challenges and opportunities in designing and qualifying AM parts for damage tolerance.

Aiming at the optimal scheduling problem of virtual power plant ( VPP ) with multiple uncertainties on the source-load side, this paper proposes a two-stage stochastic robust optimal scheduling method considering the uncertainty of the source-load side. This method combines the characteristics of robust optimization and stochastic optimization to model the source-load uncertainty differentiation. The Wasserstein generative adversarial network with gradient penalty ( WGAN-GP ) is used to generate electric and thermal load scenarios, and then K-medoids clustering is used to obtain several typical scenarios. The min-max-min two-stage stochastic robust optimization model is constructed, and the column constraint generation ( C & CG ) algorithm and dual theory are used to solve the problem, and the scheduling scheme with the lowest operating cost in the worst scenario is obtained.

The operation of offshore vessels is directly related to the use of flexible pipelines. The article describes in detail the analysis of power, geometric, kinematic and technological parameters that affect the main operational characteristics of these pipes during their operation under water, in rough sea conditions and under wind loads. The article describes the degree of dependence between loads on the pipeline and velocity of the oncoming flow, the geometry of the pipe and the distance to the rigid surface of the ship’s hull or the seabed. It was determined how the flow velocity during parametric oscillations of the pipeline in unrestricted flow and near the surface of the seabed affects the change in its drag and lift coefficients. An estimate of operational limits of flexible pipeline instability was obtained. On the base of changes in distributed load, specific values for safe lengths of pipelines were formulated. It has been stated that the probability of failure-free operation of a flexible pipeline at a level of 0.99 is directly determined by the frequency of its oscillation. Its numerical values should always be at a level that does not exceed 3.7% of the frequency of pipe dynamic oscillations.

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.

Under the background of ”dual carbon”, we must develop clean energy, build integrated energy systems (IES), and optimize their operation to achieve carbon neutrality. This study presents a strategy for the IES, whose goal is to reduce the carbon content of the power system, which aims to constrain the carbon emission of IES further, improve energy conversion efficiency, and reduce the carbon content of IES. First of all, we focus on the current situation and the improvement of the economic efficiency of the carbon trading market and introduce a tiered and hierarchical electricity carbon trading mechanism to promote IES to control carbon emissions more effectively. Then, we refine the two-stage operation process of power to gas (P2G) and propose a P2G process. This novel approach can significantly enhance the efficiency of hydrogen energy utilization and save operational expenses. In addition, this paper proposes a flexible supply and demand dual response mechanism. On the supply side, energy efficiency is improved through cogeneration with adjustable heat and power ratio. On the demand side, the efficiency of the cogeneration system in terms of energy should be improved through a flexible supply and demand side dual response mechanism. With this mechanism, the environmental protection and economic viability of IES can be further enhanced.

The interaction between the structure and soil is the key factor for infrastructure stabilization in permafrost engineering. In this study, the shear mechanical behavior of the interface between concrete and permafrost under different normal stiffness, temperature and water content is investigated by using the interface direct shear test under temperature-controlled conditions. The results show that with the increase of temperature and water content, the initial shear stiffness of the interface shear stress-shear displacement curve gradually increases, and the interface shear strength gradually increases. Different normal stiffnesses have a small effect on the morphology of the interfacial shear stress-shear displacement curve, but have a significant effect on the peak shear strength. The peak shear strength increases significantly with the increase of normal stiffness, and this trend is more obvious with the decrease of temperature. The corresponding interfacial cohesion and friction angle also increase with the increase of normal stiffness.

5-methylcytosine (m 5C) is a widely known epigenetic moderation in RNA types. Methyltransferases catalyze the genesis of m5C. This site of RNA plays a crucial role in many biological activities. For many years in DNA, the synthetic process and biological role of m 5C sites have remained the concentrating domain for researchers. Recently, many characters of RNA m 5C sites have been discovered, but it is still considered in their infancy. The accurate and systematic detection and classification of m 5C remains a challenging task. The existence of m 5C sites shows a thriving role in numerous organic activities. Machine learning techniques are alternatives to laboratory experiments, which will ease the m 5C site’s identification in Homo sapiens. This article presents a novel computational model named m 5C-TNkmer to extract RNA sequences. The model is enriched with the k-mer feature extraction technique. Four subdatasets of the primary data set are created: DNC, TNC, tetra-NC, and penta-NC. The results highlighted that m 5C-TNKmers achieved 96.15% accuracy. The suggested technique is a talented one that will help scientists correctly identify RNA m 5C sites and their modification. It provides a clue to better understanding genetic function and controlling roles.

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.

The goal was to evaluate the hysteresis performance of segmented buckling resistant braces with low yield point steel LYP160, the monotonic tensile and cyclic loading tests of LYP160 were conducted and the corresponding laws have been obtained. According to the load-displacement curves of the specimen, the low yield point steel was characterized by good ductility and energy absorption ability. With the consideration of Chaboche model on the materials, the cyclic loading was simulated by ABAQUS and the cyclic hardening parameters of low yield point steel were obtained. On this basis, the hysteretic properties of buckling resistant braces under cyclic loads were simulated and analyzed. After the analysis and comparison of buckling resistant braces specimens with isotropic core plate and segmented variable section core plate, it can be found that: When the buckling resistant braces with isotropic core plate was loaded to L/100, the lateral deformation of BRB would reach 17mm and the seriously squeezed would observe on lateral constraint members. The buckling resistant braces would fail due to the accumulation of deformation on lateral constraint members at both ends of BRB. When the segmented buckling resistant braces was applied, the core plate with variable section would yield first in the middle part, other parts could still continue to consume energy due to the action of the limit plate. It would avoid the phenomenon that other parts could not continue to consume energy after the core plate failed at one point first. Under the action of cyclic loads, the stiffness of the segmented buckling resistant brace was constant and the better ability to consume energy was reflected.

The essential factor in developing multi-robot systems is the generation of an optimal path for task completion by multiple robots. This paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence-based methods. Among the heuristic approaches, bio-inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real-time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI-based approaches. In real-time applications, AI-based approaches are highly implemented in comparison to heuristic and classical approaches. The findings from this review can help researchers select the appropriate approach to overcome the limitations in designing efficient multi-robot systems.

This study utilizes plant-derived fibers including Mikania, wool, kapok, sugarcane and pineapple as strengthening additions to fabricated and tested reinforced polyester mixtures. This study aimed to examine the mechanical qualities and potential applications of these natural materials in boosting the effectiveness of polyester composites. The composites were formed by including natural fibers into a polyester base using a hands-on preparation method. The finished samples received a complete assessment, covering tensile and impact testing as well as SEM analysis. The findings showed that incorporating natural materials like Mikania vines, wool, Kapok fuzz, sugarcane fibers, and pineapple leaves had a notably positive effect on the mechanical features of the plastic composites. The tensile strength of the material combined pineapple was particularly high, achieving a maximum stress of 42.4822 N/mm 2 and a maximum stretch of 4.27950%. Similarly, that same composite exhibited the greatest maximum stretch of 3.75753% and the highest maximum stress of 77.4922 N/mm 2 during the bending experiment. The substances exhibiting the strongest impact endurance, as determined by the impact assessment, were the ones including Kapok, Mikania, wool, pineapple, and sugarcane. The kapok provided an impact toughness of 1.2 Newton meters, which was somewhat elevated. This research offers a precious understanding of using natural fibers as strengthening additives in polyester composites, demonstrating their flexible perspective for many uses. The discoveries present hopeful possibilities for additional examination and progression in the area of composite materials, consequently advancing the development of sustainable materials.

The research proposes a hybrid algorithm model that combines model-driven and data-driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi-objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi-source heterogeneous data, selection of key sub-parameters using Principal Component Analysis (PCA), enhanced K-means clustering analysis, determination of structural component target thresholds, time-dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double-layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub-parameters that have significant impacts on the performance of structural components. Enhanced K-means clustering analysis is carried out to effectively group and classify the identified key sub-parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi-parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan-Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double-layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and the bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.