This study investigates the effect of 1% sodium hydroxide-treated and untreated Ronier fibers (Borassus aethiopum) fibers on concrete’s mechanical and durability properties. Ronier fibers were incorporated into concrete at proportions of 0.5%, 1%, and 1.5% by volume. The fibers treated with 1% sodium hydroxide (NaOH) underwent chemical modification to enhance their bonding with the cement matrix and improve their durability in aggressive environments. The performance of the concrete was assessed through tests for compressive strength, tensile strength, flexural strength, durability properties such as water absorption, and resistance to sulfuric acid attacks. The results indicated that treatment with 1% sodium hydroxide significantly improved the performance of the fibers. Among the variations studied, concrete reinforced with 1% treated fibers demonstrated the best combination of mechanical and durability properties. The treated fibers increased the concrete’s tensile and flexural strength while maintaining adequate compressive strength. Durability assessments showed that 1% treated fibers effectively reduced water absorption and enhanced resistance to sulfuric acid, improving the concrete’s performance in aggressive environments. Although higher dosages of fibers (1.5%) provided better crack control, they slightly reduced workability and compressive strength.

Bio-based materials play a significant role in developing efficient engineering materials because of availability, recyclability and eco-friendliness. Products from Borassus flabellifer are found in both urban and rural locations in Bangladesh, and its fruits, leaf stems, and leaves are utilised in domestic applications, while some, mainly the fruit shells (husk), are discarded as agricultural waste. The objective of this study is to characterise the physical and chemical properties of the Borassus husk fibre experimentally, and it has been found that the density of the husk fibre is 0.74 g/cm 3, water absorption is 47.25%, moisture regain is 4.41%, and porosity is 34.58%. Scanning electron microscopy (SEM) analysis reveals cross-linked, non-uniform, cylindrical tubular fibers. Fourier transform infrared spectroscopy (FTIR) confirms the presence of hemicellulose, cellulose, lignin, and other components, aligning with the composition of natural bio-fibers such as jute, sisal, and flax. This indicates the material’s potential as an alternative natural fiber for lightweight engineering applications.

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.

Over the past decade, significant advancements in computer vision have been made, primarily driven by deep learning-based algorithms for object detection. However, these models often require large amounts of labeled data, leading to performance degradation when applied to tasks with limited datasets, particularly in scenarios involving moving objects. For instance, real-time detection and detection of humans in agricultural settings pose challenges that demand sophisticated vision algorithms. To address this issue, we propose SB-YOLO-V8, an optimized YOLO-based Convolutional Neural Network (CNN) designed specifically for real-time human detection in citrus farms. The proposed model is trained using images and videos of human workers captured by autonomous farm equipment. The preprocessing stage involves employing data augmentation techniques and Synthetic Minority Over-sampling Technique (SMOTE) to enhance object detection performance and prevent overfitting. SB-YOLO-V8 incorporates Binary ALO optimization for improved feature extraction, enabling high-quality data extraction for classification purposes. The architecture comprises both the YOLO-based CNN and an aggregator module for classification and feedback, respectively. Evaluation metrics, including frame per second (FPS), model performance, and efficiency, demonstrate the proposed model outperforms variances of YOLO such as YOLO-V8, YOLO-V7, YOLO-V6, YOLO-V4 and YOLO-V3 with an average FPS of 13.63 and a precision of 91%. In effect, the proposed SB-YOLO-V8 presents an efficient solution for real time human detection in challenging visual scenarios.

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.

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.

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.

As standardization work is ongoing for the upcoming generation of cellular networking (6G), exploring new technologies for low-latency, high-bandwidth fronthauling technologies becomes a crucial aspect for the overall system performance. Free Space Optics (FSO) has emerged as a light beam-based wireless communication technology that can be fulfill such requirements with the possibility of surpassing certain radio technologies. Consequently, in this paper, we investigate the possibility of employing FSO as a feasible solution for 6G fronthaul networks because of its promising characteristics in providing high bandwidth, low latency, and high flexibility. We review the main features of FSO and its primary use-cases in the 6G fronthaul. Building on these, we highlight the challenges that face the implementation of FSO in a 6G fronthaul environment, such as the influence of atmospheric conditions on signal dependability. In the form of recent solutions and techniques, we enlist possibilities to overcome these challenges. Furthermore, we outline potential future research directions including network slicing and resource allocation strategies for FSO-based 6G fronthaul networks to offer a complete picture. This way our comprehensive analysis aims to provide valuable insights into the prospects of employing FSO in the development of robust and efficient 6G fronthaul networks.

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.