Modern Industry Research Articles

Progress Toward Biocarbon Utilization in Electric Arc Furnace Steelmaking: Current Status and Future Prospects

AbstractSteel is an essential material in modern infrastructure and industry, but its production is associated with significant carbon dioxide emissions. Biocarbon utilization in electric arc furnace (EAF) steelmaking represents a promising pathway toward reducing the carbon footprint of steel production. This review draws new perspectives on the current state of biocarbon utilization in EAF steelmaking by collectively examining the literature from multiple scales of testing, from laboratory experiments to industrial trials. The scientific insights from each scale are defined and the results are collectively pooled to give a comprehensive understanding of biocarbon’s performance for EAF applications. Several recent progressions are identified along with critical limitations, such as biocarbon’s high reactivity or low density. However, solution pathways like agglomeration are established from the thorough understanding developed by this study. These insights aim to enhance the progression of biocarbon utilization in the EAF process, ultimately facilitating the development of more efficient and sustainable steelmaking. The proposed areas for future research, such as optimizing key biocarbon properties or improved injection systems, are expected to have significant impact on the next phase of biocarbon adoption. Graphical Abstract

Graphene oxide-polydopamine loaded uniform fibrous membranes via robust multi-channel microfluidic-electrospinning method

The rapid development of modern industries has caused serious water pollution such as dyes wastewater, which brings environmental problems and threatens human health. Thus, methods allowing efficient dyes removal from wastewater are substantially desirable. In this work, we fabricated graphene oxide-polydopamine (GO-PDA) by microfluidics, which enables rapid synthesis of products with excellent uniformity due to the precise control of reaction conditions. In addition, the GO-PDA/ thermoplastic polyurethane (GO-PDA/TPU) composite fibrous membrane was prepared in a high-efficiency manner by microfluidic electrospinning with a four-channel chip. Interestingly, GO-PDA not only improves the mechanical strength and hydrophilicity of the composite fibrous membrane, but also endows the membrane with efficient dye adsorption and separation capacity both for single and mixed dyes. It has been proved that the GO-PDA/TPU composite fibrous membrane exhibits selectivity towards cationic dyes, where the removal efficiency is up to 99 wt%. Moreover, the fibrous membrane could be utilized in a continuous and cyclic manner, which still maintains a high adsorption rate (>95 wt%) after 5 recycling, illustrating outstanding durability and reusability. This work proposes an efficient GO-PDA/TPU composite fibrous membrane towards selective adsorption of cationic dyes, which is of great significance in wastewater purification.

Enhancing International Logistics and Supply Chain Management: Deep Learning Strategies for Enhanced Route Planning and Warehouse Optimization

Logistics and Supply Chain Management (SCM) are both key areas in modern industry and commerce which need better route planning and warehouse optimization. Traditional methods that are in practice have often fall short in of addressing the dynamic complexities of modern logistics which results in inefficient travel times, fuel consumption, and space utilization. To counter these limitations this study introduces an integrated model that combines Long Short-Term Memory (LSTM) networks, Radial Basis Function Neural Networks (RBNN), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for route planning and warehouse optimization. The proposed model employ LSTM to predict traffic patterns and RBNN to optimize space utilization in warehouse. The NSGA-II model is then utilized for multi-objective optimization of minimizing travel times and maximizing warehouse space utilization. In experiment analysis the proposed model achieved the highest accuracy and least variability in predictions, with mean MAE, RMSE, and MAPE values of 0.57, 1.12, and 5.9%, respectively.

Thermally stable thin-film composite nanofiltration membranes derived from 3,3′-diaminobenzidine

The rapid development of modern industrialization has put forward high demands on separation and purification technologies. To reduce energy consumption, improve efficiency, and enhance process flexibility, nanofiltration membranes for high temperature separation have become more and more important. Herein, we report the use of 3,3′-diaminobenzidine (DAB) as the aqueous phase monomer and trimesoyl chloride (TMC) as the organic phase monomer to engineer thermally resistant nanofiltration membranes via interfacial polymerization. The incorporation of DAB enhances the rigidity of the cross-linked polyamide network, reduces segmental thermal motion at high temperature, and improves the thermal stability of the composite membrane. The variations in fractional free volumes of the nanofiltration membranes were analyzed at different temperatures by molecular dynamics (MD) simulation, which have also been confirmed by experiments. The findings reveal that incorporating a rigid monomer into the polyamide layer can significantly enhance the thermal resistance. The resultant composite membrane exhibits outstanding resistance to high-temperature feed solutions, boasting a highly stable rejection rate (97.0 %) to Na2SO4 between 25–85 °C. Remarkably, even under sustained operation at 85 °C for 12 h, the rejection rate is consistently stable. This work presents a novel system for the design of thermally stable nanofiltration membranes for high temperature separation.

A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping

The modern vehicle insurance industry is increasingly adopting Pay-As-You-Drive (PAYD) insurance models, aligning premium costs with driving behavior. Our study introduces a Bayesian approach to PAYD insurance, leveraging the strengths of Naive Bayes classifiers and Bayesian Networks to handle uncertainty and integrate prior knowledge in risk assessment. The Naive Bayes model achieved an 87.5% accuracy in predicting risk partitions. With the Bayesian Network providing insights into causal relationships through a Directed Acyclic Graph (DAG), we also address the challenges of traditional actuarial models — low interpretability of intra-factor relationships and thus hard to plan for risk management for both provider and policyholder. Our research contributes to optimizing insurance pricing strategies. Still, the causal mapping also dismisses the meaningfulness of using geographic grouping in insurance pricing (discriminatory or not). It reassures the theoretical advantage of the PAYD model over the traditional model, facilitating access to affordable coverage for policyholders.

Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis

Bearings and gears are critical components in modern industry, and cross-domain diagnosis of these elements is of great significance. However, in practical applications, challenges such as insufficient training data and variability between equipment arise. To address this issue, this study proposes an innovative neural network structure, ConvNeXt, and a Multi-Scale Dilated Attention (MSDA) mechanism to improve the accuracy problem caused by inadequate feature extraction. ConvNeXt improves upon traditional convolutional neural networks by introducing a multi-scale attention mechanism to enhance the model's performance and expressiveness. Through parallel multi-channel convolution operations, ConvNeXt can capture dependencies between different channels and reduce the number of parameters. Meanwhile, the MSDA mechanism allows signals to interact and exchange information at different scales, effectively extracting complex features in one-dimensional signals. Experimental results demonstrate a significant performance improvement in one-dimensional signal processing using ConvNeXt and MSDA, better capturing relationships between global and local features in one-dimensional signals and enhancing model accuracy. The joint application of ConvNeXt and MSDA brings new solutions to one-dimensional signal processing, offering potential opportunities for effective monitoring of critical components in rotating machinery. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with an average accuracy of 94.28%, providing reliable support for bearing fault diagnosis.

Application of digital twin technology in monitoring system of pump turbine

The advent of advanced productive forces has catalyzed the ongoing evolution of intelligent and digital technologies within the pump-turbine sector. The integration of digital technologies has not only enhanced production efficiency but also facilitated intelligent operation management, offering novel solutions for pump-turbine design and operation, thereby accelerating the digital transformation in fluid machinery. This study first established the theoretical framework of the digital twin system for pump-turbines, followed by the development of a mathematical model and the construction of a twin virtual model utilizing Proper Orthogonal Decomposition (POD) reduction theory. The model’s accuracy was validated through real-time pressure pulsation data, and exploratory investigations into cavitation prediction were subsequently carried out. To enable the visualization of the digital twin system, this study incorporated Open3D (Three-dimensional computer graphics tools) point cloud technology, effectively rendering the state cloud map of the pump-turbine. Finally, by synthesizing the proposed theories and technologies, the digital twin system’s visualization and monitoring for pump-turbines were successfully implemented through the Unity3D simulation platform. This study offers novel insights into intelligent monitoring and prediction of pump-turbines, holding significant implications for the modernization and intelligent advancement of the hydropower industry.

A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.

The Role of Financial Technology in Enhancing the Efficiency of Legal Services

With the rapid development of financial technology (FinTech), its application in the legal service sector is gradually becoming a key force in driving industry innovation. This paper uses both qualitative and quantitative research methods to explore the interaction and synergistic effects of financial technology, regulatory technology (RegTech), and intellectual property protection (IP Protection) in the legal service industry. The study analyzes the current status of financial technology in contract law, compliance checks, and dispute resolution, and evaluates its actual effect on improving the efficiency of legal services. The study found that financial technology has optimized the contract execution process through technologies such as smart contracts and blockchain, improved the level of automation in compliance work, and provided new ways for dispute resolution. Although facing challenges such as technology integration, data security, and legal regulation, these challenges can be effectively controlled through effective risk management and strategic response. The study proposes a series of strategic recommendations, including technological innovation, policy support, education, and training, to promote the collaborative development of financial technology, regulatory technology, and intellectual property protection in the legal service industry. (Beck, R., Bugnion, F., & Pohl, M., 2018) The conclusion of this paper emphasizes the positive role of financial technology in enhancing the efficiency of legal services and points out strategies to cope with challenges, providing valuable insights and suggestions for legal service providers, technology developers, and policy makers to promote the modernization and digital transformation of the legal service industry.

Synthesis of Thioglycolic Acid-Modified Molybdenum Disulfide Nanosheets for Electrochemical Sensing and Its Application in Molnupiravir Detection

In the modern world, population growth, industrialization, and lifestyle changes have led to a rise in existing and new diseases, increasing global drug consumption. Proper pharmaceutical dosage is vital since drugs are only effective within specific concentration ranges. Therefore, developing reliable analytical methods for drug analysis in pharmaceuticals and biological samples is essential. Electroanalytical methods are particularly advantageous due to their low cost, ease of use, and rapid response. This study introduces a highly sensitive electrochemical sensor based on thioglycolic acid (TA)-decorated metallic phase molybdenum disulfide (MP-MoS2) nanosheets for the selective detection of molnupiravir (MOL), an antiviral drug used in Covid-19 treatment. The TA@MP-MoS2 nanomaterial was characterized using FTIR, TEM, and EIS. Screen-printed carbon electrodes (SPCE) were modified with TA@MP-MoS2 nanosheets to evaluate their electro-chemical and catalytic behaviours towards MOL by cyclic voltammetry (CV) and square wave voltammetry (SWV). The sensor displayed a well-defined electro-oxidation signal for MOL at 0.534 V, with the linear responses in two concentration ranges: 0.50–3.40 μM and 3.40–9.55 μM, and a low detection limit of 22.6 nM. The proposed design that has promising results could be an alternative strategy to fabricate the sensitive sensor for the detection of antiviral agents in real samples.

Water Repellent Coating in Textile, Paper and Bioplastic Polymers: A Comprehensive Review

Water-repellent coatings are essential for enhancing the durability and sustainability of textiles, paper, and bioplastic polymers. Despite the growing use of sustainable materials, their inherent hydrophilicity presents significant challenges. This review explores advanced coating technologies to address these issues, focusing on their mechanisms, properties, and applications. By imparting water resistance and repellency, these coatings improve material performance and longevity. The environmental impact and limitations of current coatings are critically assessed, highlighting the need for sustainable solutions. This review identifies key trends and challenges, offering insights into developing water-resistant materials that align with environmental goals while meeting industry demands. Key focus areas include coating mechanisms, techniques, performance evaluation, applications, environmental impact assessment, and the development of sustainable coating solutions. This research contributes to the development of water-resistant materials that meet the demands of modern industries while minimizing environmental impact.

Разработка комплексного показателя оценки состояния городского электротранспорта для органов исполнительной власти Российской Федерации

This article analyzes 115 cities on the territory of the Russian Federation in which one or more urban electric transport systems operate. It is determined that in the modern industry there is no single comprehensive indicator of the state of urban electric transport, which would allow an assessment of the state of systems not only in relation to their own indicators of previous years, but also in relation to other, similar systems. A comprehensive indicator is proposed that will allow a comprehensive assessment of the state of urban electric transport and interpret it in numerical terms for its application by state and regional authorities, allowing to quickly draw conclusions and make a decision on further adjustment of the functioning of the electric transport system, characterized by a relatively simple formula for calculation and does not require specific competencies for those conducting the analysis. The key indicators of urban electric transport are systematized, including those related to the operated energy infrastructure, linear infrastructure, park facilities, including technical buildings and structures for system maintenance, indicators related to rolling stock and its key technical characteristics, as well as the availability of urban electric transport for residents of the considered locality and passenger flows within this framework systems. The boundaries of the values of the final indicator have been determined, within which the state of urban electric transport will be assessed by state and regional authorities as satisfactory or unsatisfactory and requiring decisions on revising approaches to the development of the system within the framework of strategic transport planning documents.

EVOLUTION OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA AND ITS PATH FORWARD

Artificial intelligence (AI) has brought a paradigm shift in patient management in the modern healthcare industry. This narrative literature review aims to explore the evolution of AI in the field of anesthesia and discuss its potential path forward. The increased demand for personalized care, smart decision-making tools, precision in practice, and growth in telemedicine, particularly in the realm of perioperative care, has brought AI to the forefront. By examining the existing research and applications of AI in anesthesia, this review seeks to provide insights into the current state of AI technology, its applications, implications for practicing clinicians, and the challenges and limitations that need to be addressed for its successful integration into perioperative care. This review encourages interdisciplinary teamwork between anesthesiologists, technologists, engineers, and technicians to simplify the complexities of AI implementation, address ethical issues, mitigate risks, and ensure safe perioperative care.

Acoustic Noise Mitigation in Slip Angle Controlled DTC of Open-End Winding Induction Motor Drive Using Dual Randomized AISPWM for EV Application

Low vibration and acoustical noise as well as effective DC link usage are the demands of industries and Electric Vehicles (EV). Direct Torque Control (DTC) of Induction Motors (IMs) meet the EV and other modern industry requirements. Still, flux as well as torque swings lead to higher acoustical noise. Consequently, EVs as well as working place noise have become major concerns, affecting the health of individuals. Efficiency of dc link use is improved by Space Vector Pulse Width Modulation (SVPWM). Yet, SVPWM is not efficient in lowering acoustical noise. Different RPWM techniques can lower acoustic noise. Still, the lower degree of randomisation makes the noise reduction difficult. This work presents a Hybrid Dual Random PWMs (HDRPWMs) based Alternate Inverter Switching (AIS) strategy for Slip-Angle Controlled Direct Torque Control of an Open End Winding IM Drive (OEWIMD). This technique is aimed to lower the acoustical noise for EVs. The intended PWMs seek to show how well HDRRPWMs distribute the acoustical noise spectra in contrast to conventional techniques.

Analyzing demand for virtual reality based adaptive learning design in engineering faculty of universitas negeri Makassar Indonesia

This research aims to analyze the needs of students regarding the application of Virtual Reality (VR) technology in learning at the Faculty of Engineering. The main focus is to understand how VR can support adaptive learning and improve students' practical ability according to individual ability. This research uses a needs analysis method involving a survey of engineering students. Data collection was conducted through questionnaires and observations to identify students' constraints, needs, and interests in VR-based learning. The collected data were then analyzed quantitatively and qualitatively. The results show that 85% of students struggle to understand complex engineering concepts, and 90% show high interest in using VR for skills practice in a safe environment. In addition, students expected a more interactive and personalized learning approach. Important features they expected included automatic adjustment of difficulty levels and immediate feedback during practice. The findings of this study confirm the importance of integrating VR into engineering education, especially in creating an adaptive learning environment that meets individual needs. VR has the potential to improve students' understanding of concepts and practical skills, as well as to support the development of curriculum that are more relevant to the needs of modern industry.

Theoretical Analysis of Viscoelastic Friction System Characteristics of Robotic Arm Brake Based on Fractional Differential Theory

In engineering practice, the nonlinear vibration effect can easily lead to chaos in the system, which will not only reduce the performance of the system but also lead to premature fatigue of components, control failure, and increased safety risks. In view of the core position of the robotic arm in modern industry, this study relies on the robotic arm brake system to explore the theoretical basis of integrated viscoelastic materials as a vibration isolation layer. By analyzing the dynamic characteristics of the friction braking system with fractional differential terms, it aims to provide a new perspective for understanding and controlling the chaotic phenomena of a class of nonlinear friction systems. Firstly, we construct a model of a friction system and analyze its dynamic characteristics in detail. The self-excited vibration of the system under disturbance is studied. The relationship between amplitude and frequency is calculated by a nonlinear approximate analytical algorithm, and the accuracy of this relationship is verified by a numerical algorithm. Then, we compare the differences between non-fractional systems and fractional systems. It is found that with the increase in the fractional order term, the vibration amplitude of the system decreases significantly, which helps to reduce the nonlinear characteristics generated by the friction system and narrow the range of unstable solutions. Secondly, we also study the influence of parameter coefficients on the amplitude–frequency characteristics and analyze the local static bifurcation characteristics through singularity theory. Finally, we study the dynamic bifurcation behavior under different parameter perturbations and find that the change in system parameters will lead to the alternation of periodic motion and chaotic motion.

Making large-size fail-safe steel by deformation-assisted tempering process

Synergistically improving the strength and toughness of metallic materials is a central focus in the field of physical metallurgy. Yet, there is a noticeable lack of research in strengthening and toughening large-size metal components, whereas those components are extensively used in the modern industry. In this work, a deformation-assisted tempering (DAT) process was proposed to create a novel microstructure in 1.4 tons low-alloyed plain steel. After DAT treatment, the steel contains low dislocation density but high density of low-angle subgrain boundaries and dispersed spherical nano carbides. Such microstructure enables a much better combination of tensile strength and fracture toughness compared to the small-size quench and temper steels. The significant improvement in low-temperature impact toughness is due to the occurrence of delamination and subsequent large plastic deformation at the notch tip. The DAT process can provides a new strategy for the development of large-size fail-safe steel with excellent strength and fracture resistance.

Development of intelligent mechatronic motion module of mobile industrial robot

Modern industry is inextricably linked with mobile industrial robots, which plays a key role in production processes and warehouse logistics. The intensive introduction of mobile industrial robots has increased the need to improve the energy efficiency of robotic installations due to their significant impact on overall production costs. At the same time, there is a pressing need to reduce the time required to develop and implement mobile industrial robots. This leads to the need to improve and optimize the properties of all components that make up a mobile industrial robot. Currently, electric drives are most widely used in mobile industrial robots. Research shows that the use of modern high-tech solutions in the construction of electric drive control systems can improve the energy efficiency of mobile industrial robots. In turn, the introduction of an intelligent component into mechatronic motion modules allows to reduce the time required for their construction and integration into mobile industrial robots. The purpose of the study is to increase the energy efficiency of an intelligent mechatronic motion module by using a multi-motor electric drive in the movement system of a mobile industrial robot and to develop a stable and energy-efficient intelligent automatic control system based on a neural network, taking into account the nonlinear properties of the control object under conditions of uncertainty.

Precision Electrochemical Micro-Machining of Molybdenum in Neutral Salt Solution Based on Electrochemical Analysis

Molybdenum is an important material in modern industry, widely used in extreme environments such as rocket engine nozzles and microelectrodes due to its high melting point, excellent mechanical properties, and thermal conductivity. However, as a difficult-to-machine metal, traditional machining methods struggle to achieve the desired microstructures in molybdenum. Electrochemical machining (ECM) offers unique advantages in manufacturing fine structures from hard-to-machine metals. Studies have shown that molybdenum exhibits a fast corrosion rate in alkaline or acidic solutions, posing significant environmental pressure. Therefore, this study investigates the electrochemical machining of molybdenum in neutral salt solutions to achieve high-precision microstructure fabrication. First, the polarization curves and electrochemical impedance spectroscopy (EIS) of molybdenum in NaNO3 solutions of varying concentrations were measured to determine its electrochemical reaction characteristics. The results demonstrate that molybdenum exhibits good electrochemical reactivity in NaNO3 solutions, leading to favorable surface erosion morphology. Subsequently, a mask electrochemical machining technique was employed to fabricate arrayed microstructures on the molybdenum surface. To minimize interference between factors, an orthogonal experiment was used to optimize the parameter combination, determining the optimal machining process parameters. Under these optimal conditions, an array of micro-groove structures was successfully fabricated with an average groove width of 110 μm, a depth-to-width ratio of 0.21, an aspect ratio of 9000, and a groove width error of less than 5 μm.

Optimizing Organizational Efficiency: Strategic Requirement of Modern Industrial Asset Level Management

Asset Hierarchy Management (AHM) is not just a theoretical concept but a practical tool for optimizing operations in modern businesses, particularly in sectors with intricate infrastructures such as manufacturing, utilities, and healthcare. This research paper explores the significance of AHM in standardizing and enhancing organizational efficiency. It traces AHM's evolution, delineates its benefits, and provides strategies for implementation. By emphasizing the tangible benefits of AHM, the paper aims to instill optimism in the audience about its potential impact. The paper outlines prerequisites and steps for developing a robust asset hierarchy, stressing transparent governance and integration with technology solutions like Enterprise Asset Management (EAM) software. Furthermore, it presents a detailed framework for implementing AHM in manufacturing plants, highlighting the importance of defining clear asset categories, hierarchical levels, and relationships. By adhering to best practices and leveraging advanced tools, organizations can effectively manage assets, drive operational excellence, and achieve sustainable growth.