Modern Industry Research Articles

Increasing labour productivity by providing innovative activities in when repair and service sector in livestock industry

At present, the Russian Federation and the Republic of Belarus are significantly behind the developed countries in terms of labor productivity and the share of domestic companies implementing technological innovations in the main sectors of the economy (especially in agriculture). One of the significant factors of this lag is the underestimation in past years of the promising potential of various innovative technologies and, as a consequence the very limited and slow implementation of breakthrough innovations in the most signifi cant sectors of the domestic economy. The decisive turn of the state economic policy of neighboring states in the area of deep technological transformation of all sectors of the economy is aimed at an innovative breakthrough and reaching advanced growth rates in the technological and economic development of fraternal countries. However, along with the introduction of the latest technologies the creation of completely new modern high-tech industries, which will require colossal fi nancial investments, the task of comprehensively and thoroughly exploring internal production reserves, searching for new forms and methods of improving the effi ciency of labour organization at existing enterprises, which will also bring a noticeable economic effect, remains relevant. This article describes in sufficient detail practical activities to improve and streamline accounting, planning, certifi cation, rationalization of workplaces of animal husbandry technical service organizations under the current conditions of the developing market. The results of the studies show that certifi cation and rationalization of workplaces are eff ective tools in fi nding internal production reserves to improve the use of labour resources, fi xed production assets, promotes the replacement of obsolete equipment, improves the relationship between workplaces, improves working conditions, and optimally balances the need for labour with the number of existing workplaces. This will significantly increase the overall efficiency of activities and competitiveness of service enterprises in the fi eld of animal husbandry.

The Importance of Data Analytics in Modern Fashion Industry

The growth of the internet and the abundance of data it currently offers has caused one of the most significant developments in recent years. Fashion data scientists can now examine and benefit from a multitude of information because of the introduction of social media. These tips are invaluable for fashion campaign managers and designers. This paper examined the importance of data analytics in modern fashion industry. The paper collected secondary data from existing journals, articles, and companies’ blogs and websites. The overall result from this study indicates that data analytics have significant roles in modern fashion industry. For one thing, decision making in relation to customers’ needs and expectation, trend, quality, etc are based on data collected through different means. Thus, the fashion retail market is a quickly evolving field, with trends shifting within a few years. Intuition and gut feelings alone are not enough. In order to adapt to shifting consumer tastes and preferences, the fashion industry needs a data-driven, analytical approach.

Effect of Microgravity on Rare Earth Elements Recovery by Burkholderia cepacia and Aspergillus niger

Rare earth elements (REEs) are indispensable in modern industry and technology, driving an urgent demand for innovative, eco-friendly recovery technologies. As space exploration advances, the impact of microgravity on microorganisms has become a focal point, yet the effects on microbial growth and REEss recovery remain uncharted. This study investigates the biosorption of REEs by Burkholderia cepacia (B. cepacia) and Aspergillus niger (A. niger) from a mixed solution containing La, Ce, Pr, Nd, Sm, Er, and Y under varying initial concentrations, pH levels, and microgravity conditions. We observed that the medium’s pH rose with B. cepacia and fell with A. niger when cultured in normal gravity conditions, suggesting distinct metabolic responses. Notably, microgravity significantly altered microbial morphology and metabolite profiles, significantly enhancing REEs recovery efficiency. Specifically, the recovery of B. cepacia of Ce and Pr peaked at 100%, and A. niger achieved full recovery of all tested REEs at pH 1.5 (suboptimal growth conditions). This study pioneers the application of biosorption for the recovery of REEs in microgravity conditions, presenting a promising strategy for future resource exploitation by space biomining.

Field-Free Superconducting Diode Effect and Magnetochiral Anisotropy in FeTe0.7Se0.3 Junctions with the Inherent Asymmetric Barrier.

Nonreciprocal electrical transport, characterized by an asymmetric relationship between the current and voltage, plays a crucial role in modern electronic industries. Recent studies have extended this phenomenon to superconductors, introducing the concept of the superconducting diode effect (SDE). The SDE is characterized by unequal critical supercurrents along opposite directions. Due to the requirement on broken inversion symmetry, the SDE is commonly accompanied by electrical magnetochiral anisotropy (eMCA) in the resistive state. Achieving a magnetic-field-free SDE with field tunability is pivotal for advancements in superconductor devices. Conventionally, field-free SDE has been achieved in Josephson junctions by intentionally intercalating an asymmetric barrier layer. Alternatively, internal magnetism was employed. Both approaches pose challenges in the selection of superconductors and fabrication processes, thereby impeding the development of SDE. Here, we present a field-free SDE in FeTe0.7Se0.3 (FTS) junction with eMCA, a phenomenon absent in FTS single nanosheets. The field-free property is associated with the presence of a gradient oxide layer on the upper surface of each FTS nanosheet, while eMCA is linked to spin splitting arising from the absence of inversion symmetry. Both SDE and eMCA respond to magnetic fields with distinct temperature dependencies. This work presents a versatile and straightforward strategy for advancing superconducting electronics.

Investigation and review of FGM functional gradient materials

With the advancement of modern industries and processes, numerous structures are now required to function in thermal environments, leading to a new class of composite materials known as functional gradient materials (FGMs). Initially developed as thermal barrier materials for structural applications in aerospace and fusion reactors, FGMs have gained global recognition as vital composite materials. The overall properties of FMG are unique and different from any of the individual material that forms it. There is a wide range of applications for FGM and it is expected to increase as the cost of material processing and fabrication processes are reduced by improving these processes. Their application is increasingly recognized as a highly effective method for achieving sustainable development across various industries. In this study, an an overview of resents a detailed examination of the development, features, and essential manufacturing processes of FGMs is presented, based on a review of existing literature.

Digital Inclusion Strategy in Modern Cultural Industries through the Prism of Digital Divide

The purpose of the article is to reveal the features of digital inclusion in modern cultural industries of Ukraine and to outline promising strategies for overcoming the digital divide. Research methodology. The analytical method, the method of analysis and synthesis, the dialogical method, the typological method, the systematic method, the method of comparative analysis and the method of scientific generalisation are applied. Scientific novelty. The problem of digital inclusion in the cultural industry has been studied; the theory of digital inclusion in foreign scientific works is considered; the peculiarities of digital inclusion in modern cultural industries of Ukraine were revealed; promising measures on which the strategy for overcoming the digital divide should be based are outlined. Conclusions. The strategy of digital inclusion of cultural industries in Ukraine should be based on a set of measures aimed at overcoming the digital divide, which includes, first of all: providing high-speed Internet to as large a part of the population as possible; all kinds of assistance in the process of improving the digital literacy of potential consumers of a cultural product / service; provision of high-quality technical support; creation of specialised applications and online content, according to the specifics of the activities covered by each cultural industry. Potential forward-looking steps include providing ongoing support to users, reducing professional jargon and offering interpretive resources, and ensuring that technology and training are offered to all users on an equal basis, not just those confident enough to seek help. The study revealed that at the current stage in Ukraine, only libraries not only offer the Internet, but also provide a range of training services from basic digital literacy to the skills needed to use certain services (audio/video streaming service, file sharing services, online platforms), devices and applications that are currently actively used in the system of cultural industries (for example, applications of augmented reality in museum, exhibition activities, performing arts).

Особенности управления изменениями в малом бизнесе

It is clarified that the essential content of the concept of “change” implies a certain procedural phenomenon, including awareness of the need for change, the implementation of change, and the consolidation of the result of change. Various types of changes are considered (there is no classical generally accepted classification yet), as well as the most popular models of change management and the use of these models in small businesses. The results of small business development in our country (modern characteristics and industry structure) are briefly analyzed and the main problems of small business development are formulated. The peculiarities of change management in small business are determined by the peculiarities of the small business itself, considered in the context of the study of the possibility of change management (sensitivity to change, flexibility, the need for innovation, working in a highly competitive environment, short-term existence, facilitated document management, quick reaction, corporate solidarity of staff, risk). For the first time, a feature of small business is highlighted – corporate solidarity of staff. The main features of change management in small business are considered. Communications on any communication platform are particularly important, the mandatory use of new technologies, the possibility of using various change management strategies, the publicity and openness of small businesses, the importance of continuous staff training, and the cascading formation of communication goals at all stages of change implementation are noted. The necessity of using various systems for evaluating the effectiveness of change management results is confirmed, since there simply is no unified system; The possibility, but limited use of Lean technologies is noted, since the reserves for reducing costs in small enterprises are always practically exhausted; certain features of change management in small businesses of various industry affiliations (manufacturing, construction business, services and trade differ in their economies, processes, sizes).

Optimizing the Agricultural Supply Chain Through the Internet + Model: Enhancing Efficiency with a Case Study of TikTok

This study uses the TikTok platform as a case study to explore the application of the Internet+ model in the agricultural supply chain and analyses the impact of Internet+ model on improving supply chain efficiency and reducing costs. With the development of science and technology, Internet+ has become an important trend in modern industry, which deeply integrates traditional agriculture with information technology and promotes the innovation and efficiency of supply chain management. This study not only discusses the practical application of the Internet + model in agriculture but also proposes to solve the bottlenecks in the traditional agricultural supply chain using logistics informatization, intelligent scheduling system, cold chain monitoring and intelligent warehouse management. Through case studies, the effectiveness of Internet technology in optimizing the supply chain, improving the circulation efficiency of agricultural products and reducing costs are verified, providing theoretical and case study support for promoting the development of agricultural modernization.

RANCANG BANGUN ALAT PEMOTONG DAUN BAWANG BERBASIS INTERNET OF THINGS (IOT)

The utilization of Internet of Things (IoT) technology in the agriculture sector continues to evolve, including in the automation of agricultural processing tools. This research aims to design and develop an IoT-based scallion cutting tool integrated with the Blynk application, which functions to control and manage the device in real-time. This tool is equipped with key components such as a cutting motor, sensors, and a microcontroller, all controlled via the IoT network. The Blynk application allows users to adjust cutting parameters, such as motor speed, and receive information about the tool's status. Testing has shown that this tool can operate automatically according to settings provided through the Blynk app, producing consistent scallion cuts that meet the requirements. The implementation of this technology is expected to enhance efficiency, reduce manual labor and time, and improve the productivity and quality of scallion cutting. With the integration of IoT and the Blynk application, this scallion cutting tool offers an innovative solution to support the modernization of the agricultural industry, particularly in scallion processin.

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.

Three-dimensional modeling of 500 kV transmission lines by airborne LiDAR

This study aims to enhance the accuracy of three-dimensional (3D) modeling in the planning, design, operation management, and risk assessment of 500 kV transmission lines. Addressing the limitations of traditional two-dimensional (2D) or low-precision 3D models, a new method based on airborne Light Detection and Ranging (LiDAR) technology is proposed. Initially, high-density, high-precision 3D point cloud data are collected using airborne LiDAR technology. This data is then processed using specialized software for noise removal, terrain classification extraction, and Geographic Information System (GIS) data integration. Subsequently, a Convolutional Neural Network (CNN) is employed to classify the 3D point cloud data to enhance the accuracy of complex structure recognition. Lastly, the point cloud data are converted into linear and planar entities using Computer-Aided Design (CAD) technology, achieving high-precision 3D reconstruction of the 500 kV transmission lines and their complex terrain environment. Experimental results show that the spatial resolution of the constructed model reaches 0.1 m per pixel, with overall geometric accuracy within ±5 cm. Compared to traditional aerial imagery and ground survey methods, the model based on airborne LiDAR technology achieves improvements of 30 % in detail richness and 35 % in terrain adaptability, with a 45 % increase in decision-making efficiency. These improvements significantly enhance the accuracy of line operations, safety assessments, and environmental impact analyses, providing solid data support for the modernization and intelligent development of the power industry.

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.

304L Austenitic Stainless Steel Corrosion Studies in Neutral Environment in Presence of Sulfite Ions

Abstract This paper examined the influence of the sulfite concentration introduced in the electrolyte added to the neutral solution (1 mol L-1 Na2SO4) on the corrosion process of 304L stainless steel material. 304L. The accelerated effect of sulfite ions under corrosion rate of 304L austenitic stainless steel was demonstrated using electrochemical techniques such as linear sweep voltammetry at a low scan rate −1 mV s−1, the Tafel slope method, chronoamperometry, and electrochemical impedance spectroscopy. Electrochemical studies were conducted using a BioLogic SP150 potentiostat/galvanostat. Stainless steels are the most varied and intricate group among all steels. Among them, 304L austenitic stainless steel is one of the most commonly utilized alloys, prized for its excellent corrosion resistance and strength, hygienic and aesthetic properties, affordability, good formability, and effective weldability. [1-3]. In modern industry, the amount of austenitic stainless steel represents only about 2% of the total steel production manufactured globally. This type of steel is commonly employed in chemical engineering, metal extraction, desalination and wastewater treatment plants, the oil and gas sector, transportation and aerospace industries, the food and beverage sector, as well as in architecture and construction engineering. [1].

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.