Industrial Requirements Research Articles

Flow-through moisture meter for measuring grain moisture as part of an automated grain moisture control system

The methods of increasing the accuracy and efficiency of the production process, as well as ensuring high quality of final products in the grain processing industry, are investigated. It is shown that to solve the problem of insufficient quality control of grain materials, an automated system for monitoring the properties and characteristics of bulk granular materials is necessary. The features of the construction of automatic and automated systems for monitoring the properties and characteristics of bulk granular materials are considered. Measurement methods and control devices of the main technological parameters used in the management of grain processing facilities are briefly described. It is proposed to include a flow moisture meter in the automated grain moisture control system. The physical properties of grain and grain materials, measurement conditions and instrumentation for grain moisture control are analyzed. The use of devices based on the dielkometric method in the subsystem of an automated process control system is discussed. The application of intelligent sensors of a measuring system with a complex multilevel hierarchy is described. The structure of the interconnection of measuring devices for intellectual support of humidification processes and quality control of the studied materials is given. A functional diagram and metrological characteristics of a prototype grain moisture monitoring device, a flow moisture meter, are presented. The metrological characteristics of this device meet the requirements of the grain processing industry and allow it to be used as part of a humidity control system

Icing Wind Tunnel and Erosion Field Tests of Superhydrophobic Surfaces Caused by Femtosecond Laser Processing

Ice accumulation on lift-generating surfaces, such as rotor blades or wings, degrades aerodynamic performance and increases various risks. Active measures to counteract surface icing are energy-consuming and should be replaced by passive anti-icing surfaces. Two major categories of surface treatments—coating and structuring—already show promising results in the laboratory, but none fulfill the current industry requirements for performance and durability. In this paper, we show how femtosecond laser structuring of stainless steel (1.4301) combined with a hydrocarbon surface treatment or a vacuum treatment leads to superhydrophobic properties. The anti-ice performance was investigated in an icing wind tunnel under glaze ice conditions. Therefore, flexible steel foils were laser-structured, wettability treated and attached to NACA 0012 air foil sections. In the icing wind tunnel, hydrocarbon treated surfaces showed a 50 s ice build-up delay on the leading edge as well as a smoother ice surface compared to the reference. To demonstrate the erosion resistance of these surfaces, long-term field tests on a small-scale wind turbine were performed under alpine operating conditions. The results showed only minor erosion wear of micro- and nano-structures after a period of six winter months.

Enhancing the Mickering emulsifying capacity of agarose microgels by complexation with microamounts of sorbitan monolaurate (Tween-20)

Abstract Using non-amphiphilic polysaccharide to stabilize oil-in-water emulsions is still a challenge in food industry. Polysaccharide microgels showed emulsifying capacity, however, the stability of the formed emulsions is far from the requirements of the food industry. In this work, agarose microgel/Tween-20 suspensions were prepared and further employed in oil-in-water emulsion stabilization. Focuses were taken on the impact of Tween-20 on the emulsifying properties of agarose microgels. Results suggested that Tween-20 could complex with agarose microgels, and microamounts of Tween-20 (0.02 wt%) could apparently improve the emulsifying performance of microgels. Dynamic interfacial pressure and interfacial adsorption of microgels indicated that appropriate amount of Tween-20 (≤0.04 wt%) could benefit the interfacial adsorption of agarose microgels, while excessive Tween-20 (≥0.08 wt%) could compete with agarose microgels for adsorption sites on oil/water interface. Results of this work supporting that surfactant complexing could be a promising solution to prepare polysaccharides based Mickering emulsions.

Fabric4show: real-time vision system for fabric defect detection and post-processing

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry.

Research Paper on Geek-Grid

Geek-Grid is a transformative web application designed to bridge the gap between traditional education and the dynamic needs of modern industry. Powered by React.js and Firebase, Geek-Grid offers personalized learning, industry integrated content and intelligent job search algorithms to prepare learners for the competitive job market. This article explores the motivation behind Geek-Grid, its features, system requirements, and its impact on education and career readiness. The traditional education system often fails to keep pace with the rapidly changing demands of the modern workforce, creating a significant gap between what students learn and what employers need. Geek-Grid is a web application specifically designed to bridge this gap by integrating educational content with real-world industry requirements. Utilizing modern technologies such as React.js and Firebase, Geek-Grid offers a comprehensive learning platform that includes personalized learning paths, video lectures, informative articles, and live job alerts. It also provides recruiters with tools to search for and connect with suitable candidates, making it a dual-ended platform benefiting both students and employers. By leveraging the SERP API, Geek-Grid ensures that job listings are current and relevant, thereby enhancing the job search experience for users. This paper explores the motivation behind Geek-Grid, its innovative features, the technologies used in its development, and its potential impact on education and career readiness. Geek-Grid aims to not only enhance the learning experience but also to make students industry-ready, ultimately contributing to a more competent and prepared workforce.

The Use of Biostimulants in Javanese Turmeric (Curcuma xanthorrhiza Roxb.) Cultivation to Increase Production Competitiveness

Abstract The objective of the research is to increase the productivity and quality of javanese turmeric. The research is a descriptive analytic by using information and data from senior agricultural experts and scientific publications. The agribusiness of javanese turmeric has bright prospects, but there is still problem where javanese turmeric cultivated traditionally so the average productivity and quality is low. According to the regulation from Agency of Food and Drug Supervisory, the production of medicinal plant is required to be high quality to ensure the efficacy and safety of medicinal herb. The efforts to increase productivity and quality in the cultivation of javanese turmeric can be done through the combination of using biostimulant and the application of Good Agriculture Practice (GAP). The use of biostimulant will provide more available nutrients for plant growth, increase plant protection from pathogen, and increase soil water holding capacity where it will lead to increase productivity. The combination of using biostimulant and application of GAP will lead to increase productivity and quality of javanese turmeric production where it can meet the requirement of modern medicinal herb industry. Furthermore, it is expected to increase the competitiveness of production, increase farmer income, and lead to develop farmer economy.

Relation between edge stress, bending strength, surface stress and fracture pattern of thermally toughened glass

Thermally toughened safety glass must meet safety requirements in the building industry. Here, destructive tests are defined in the product standards, which must be carried out on small, standardized format (360 mm × 1100 mm) glass elements to determine the fracture pattern and bending strength. This is costly and not in the interests of sustainability. As part of the quality control of optical anisotropy effects in thermally toughened glass, isochromatic scans that can provide information on the edge stress are acquired. The evaluation of the isochromatics and retardations at the edge with deduction of the edge stress and transfer to bending strength and fracture pattern could provide essential findings for assuring the safety requirements of thermally toughened glass. In this experimental investigation, the surface and edge stress were measured on standardized format thermally toughened safety glass, with different edge processing and glass thicknesses from three different suppliers. Afterwards, the fracture pattern is controlled, or the bending strength is analyzed in a destructive four-point bending test. Conclusively, the results from the photoelastic and destructive tests are compared to determine whether the photoelastic measurement methods used to measure surface and edge stress can be employed as quality control.

Developments in ultrasonic and eddy current testing in the 1970s and 1980s with emphasis on the requirements of the UK nuclear power industry

Developments in ultrasonic and eddy current testing in the 1970s and 1980s with emphasis on the requirements of the UK nuclear power industry

О применении федеральных норм и правил, регулирующих организацию работ на опасных производственных объектах горно-металлургической промышленности

The article considers the regulatory and legal control of industrial safety as to the organization of safe work at hazardous production facilities of the mining and metallurgical industry of the Russian Federation. The historical aspects of the development of the regulatory framework of occupational safety in the Russian Federation that earlier divided the requirements for the organization of safe work into occupational safety and industrial safety have been mentioned. The analysis of the systems of regulatory and legal framework of the international and Russian legislations, as well as the industrial practices of occupational safety management implies that the activities to ensure the organization of safe work must be constantly developing. The evaluation of the Russian legislation has demonstrated that the tight links between the legal areas of occupational safety and industrial safety did not consider some aspects of the requirements for industrial safety regulation. General industrial safety requirements provide for the development of regulatory and legal acts such as federal norms and rules that contain more detailed requirements for ensuring the safety of workflows, employees, and other specific types of activities. In order to resolve the issues of the organization of safe work, the Federal norms and rules «Ensuring industrial safety in the organization of work at hazardous production facilities of the mining and metallurgical industry» approved by the Order of Rostechnadzor on November 13, 2020, № 440 (hereinafter FNP), were developed. When developing the Federal norms and rules for industrial safety, the entire range of requirements for the safety of the organization of various jobs has been analyzed; the requirements for the work organization at hazardous production facilities of the mining and metallurgical industry have been formulated. Earlier, these jobs were regulated only by the requirements contained in the regulatory and legal acts of occupational safety. The article refers to the sections of the Federal norms and rules for industrial safety where classification of the types of jobs at hazardous production facilities at the current stage are explained as the basis for further development of legal regulation. Explanations for the following sections are provided: on the work organization during operation, maintenance, and repair of equipment and building construction and repairs; on determining and controlling high-risk jobs; on organizational and engineering measures regulating high-risk jobs; on work permits for high-risk jobs; and on the decision-making when choosing safety tools for the work organization. The current aspects of regulatory and legal control over high-risk jobs contained in the Federal norms and rules for industrial safety will be a significant contribution to the understanding of occupational safety processes as to their use by senior officials of enterprises in order to ensure further reduction of accident rate at enterprises of the mining and metallurgical industry of the Russian Federation.

Flexible Cartridge Sorting Line Using Parallel Robot “Bulletsort Topcodpet”

Abstract Nowadays the automation of some industrial processes within the military system is a necessity, as this trend is accelerated by technological advances and thus must respond to the increasingly complex contemporary challenges of global security. Sorting is an extremely important process in almost any industrial area of use and it is essential that it is done in the right way so that productivity and flexibility of operations is increased. Over time, sorting with industrial robots has become an increasingly widespread and sophisticated technology, which has led to benefits in various industrial fields and manufacturing processes. This technology involves the use of robots with articulated arms or other mechatronic devices that follow the principle of modular interchangeability and are designed to pick, handle and sort different parts in an efficient and more accurate way. The desire to maximize flexibility, adaptability to changing industry requirements coupled with increased productivity has made robot arm sorting a necessary component of today's military industrial and manufacturing processes, and this technology continues to develop, opening up new opportunities in manufacturing automation. This paper presents the principle design of a flexible manufacturing line for sorting cartridges, served by a parallel robot with two degrees of freedom in the kinematic chain structure.

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

Mobile-YOLO: An accurate and efficient three-stage cascaded network for online fiberglass fabric defect detection

The tolerance for different defects in the production process of fiberglass fabric varies. For acceptable defects, only relevant information needs to be recorded and printed, while for intolerable defects, the loom needs to be stopped promptly for handling. To address the issue of inefficient production of fiberglass fabric due to this situation, this paper proposes a task-modularized cascade network model for quality inspection of fiberglass fabric. The three stages cascaded detection network is composed of the improved MobilenetV3-small and the enhanced You Only Look Once v8 Nano (YOLOv8n). For MobilenetV3_small, the Squeeze- and-Excitation(SE) module in the Block layer with a 5 × 5 convolutional kernel is replaced with the Efficient Channel Attention (ECA) mechanism, enhancing channel information while making the model more lightweight. For YOLOv8n, a Global Attention Module (GAM) is added to the network, enhancing the ability to capture global information of input data, making the model more perceptual. Additionally, a Similarity Attention Module (SimAM) is added between the neck and head networks, enabling the network to better capture relevant information in the images, improving the quality of feature maps and enhancing network performance. Finally, a large number of defect images of fiberglass fabric were collected from an industrial site, and different datasets for the three stages were created. Numerous experiments were conducted, and when using all three stages for detection, the accuracy rate reaches 99.4%, with a parameter count of 16.8M and Frames Per Second (FPS) of 186. The cascade network has been successfully applied in the industrial field of fiberglass fabric and meets industrial production requirements.

Sustainable Electrolytes: Design Principles and Recent Advances.

Today, rechargeable batteries are omnipresent and essential for our existence. In order to improve the electrochemical performance of electric fields, the introduction of electrolytes with fluorine (F)-based inorganic elemental compositions is a direction of exploration. However, most fluorocarbons have a high global warming potential and ozone depletion potential, which do not meet the sustainability requirements of the battery industry. Therefore, developing sustainable electrolytes is a viable option for future battery development. Although researchers have made much progress in electrolyte optimization, little attention has been paid to developing low-toxic and safe electrolytes. This review aims to elucidate the design principles and recent advances in this direction for solvents and salts. It concludes with a summary and outlook on future research directions for the molecular design of green electrolytes for practical high-voltage rechargeable batteries.

Novel Aluminum Alloy Tailored for Additive Manufacturing: Structural Characterization and Qualification Perspectives

The recent advances achieved in additive manufacturing (AM) technology demonstrate the potential to realize customized metal components, ensuring weight reduction opportunities. These benefits make AM attractive for high-cost aerospace applications, especially where high geometric complexity is required. In the context of an EU research scenario, the H2020 MANUELA (Additive Manufacturing Using Metal Pilot Line) project promotes the development of new technologies for design optimization by enabling the application of novel materials in AM. This paper illustrates recent advances in a new aluminum alloy (Al-HS1) with high strength emphasizing all of the characterization steps at the coupon level. This material has been employed in the re-engineering of a conventional hydraulic manifold using a powder bed fusion-laser beam (PBF-LB) process. Both the simulations and structural tests allowed for proving its compliance and technological maturity with industrial standards and applicable airworthiness requirements.

Industrial Based Learning for Improving Problem Solving Skill for Technical Workforce Labour

Objective: The research aims to develop the industrial based learning in vocational diploma program improving problem solving skill and actual work in the workplace, assess the problem solving skill of students, evaluate the efficiency of learning of the industrial based learning in vocational diploma program for actual work in the workplace, analyze advanced abilities of students, evaluate the industrial requirements for project workpieces of students and evaluate student’s achievement. Method: Experimental research model followed IBL integrated with ADDIE with MIAP and questioning teaching method and 7 steps of problem based learning: PBL that comprise of determination of population and sampling group, competency creation and topic analysis and creation of research tools. Results and Discussion: Industrial based learning improve problem solving skill for technical workforce labour, the efficiency of IBL were above 80/80 established criteria. The advanced abilities increased more than before learning and abilities that met their requirements, the project workpieces were good level. Originality/Value: Industrial based learning was applied Phradabos school and other school which implement diploma program, improved problem solving skill of Phradabos students which is necessary skill for workforce labour.

Examining the Future of Translator Education: An In-depth Review of Educational Approaches in the Arab Context

This paper examines the pedagogical and curricular structures used in Arab translation education. It identifies a significant disparity between these pedagogical interventions and the specific requirements of the translation sector, namely in the Arabian Gulf region. The objective is to perform a comprehensive analysis that compares these practices with international norms, emphasizing the immediate necessity for modernization to improve the quality and applicability of translation education in the Arab environment. The research aims to identify disparities between existing educational methods and the requirements of the industry. It also seeks to suggest improvements for curriculum creation by drawing on global best practices. This study makes a significant contribution to the field by providing valuable insights for educators, students, and stakeholders in translation education. It serves as a professional resource for enhancing Arab translation programs to meet the demands of the global market. By conducting thorough study and comparative research, the goal is to support pedagogical innovations that link academic preparation with industry requirements, thus enhancing the professionalization of translation education.

Dynamic Landscape of Higher Education, The Intersection of Student Skills and Industry Demand

In today's dynamic job market, higher education students are confronted with the challenge of sifting through numerous career options that align with their skills, interests, and educational background. To tackle this hurdle, "Dynamic Landscape Of Higher Education, The Intersection Of Students Skills And Industry Demand" introduces a personalized job recommendation system tailored explicitly for higher education students. This system utilizes advanced recommendation algorithms and data analysis techniques to provide students with targeted job suggestions based on their academic qualifications, skills, interests, and career preferences. With a user-friendly interface, students can effortlessly input their information and receive tailored recommendations that evolve over time through continuous learning from user feedback. By offering data-driven insights and facilitating informed career decisions, this system empowers students to explore diverse career pathways and seamlessly transition from education to employment. The user interface of this system is designed to be intuitive and user-friendly, enabling students to easily input their information and preferences to receive tailored job recommendations. The recommendation system is adaptive, continuously learning from user feedback and interaction, ensuring that the recommendations provided evolve over time to better meet the changing needs and preferences of students. Recognizing the widening gap between industry requirements and graduating students' skills, this project proposes a comprehensive job recommendation system to bridge this divide and enhance placement opportunities. Leveraging advanced algorithms and machine learning techniques, the system evaluates students' skill sets and matches them with relevant job roles, facilitating a smooth transition from academia to the workforce. This multifaceted approach includes data collection, algorithm development, user interface design, and integration with existing campus placement systems. Collaborative efforts with academic institutions and industry partners aim to provide personalized job recommendations tailored to individual students' abilities and aspirations, with a focus on fairness and equity. Key components of the project include the design and implementation of an intuitive user interface, data-driven skill assessment methodologies, and continuous refinement of the recommendation algorithm based on user feedback and industry insights. Ethical considerations regarding data privacy and security are carefully addressed, reflecting a commitment to responsible use of student data. Ultimately, the success of the job recommendation system is measured by its ability to match students with suitable employment opportunities and its broader impact on career readiness and long-term professional success. By empowering students with the tools and resources needed to navigate the complexities of the modern job market, this project aims to catalyze positive change and foster a more inclusive and equitable future for all stakeholders involved.

Lithium extraction from salt lake brine by four-stage ion-distillation of flow electrode capacitive deionization

Herein, a novel, highly efficient, and low-energy consumption four-stage ion-distillation of FCDI (ID-FCDI) device was developed that combined four commercial monovalent selective membranes with four flow electrode channels for high selectivity to extract Li+ ions from salt lake. It exhibited excellent separation factor (SMg2+Li+ = 11247.27), high enrichment ratio (4.95 times), super purity of Li+ ion solution (99.97 %), and low molar energy consumption (Em = 0.21 kWh mol−1) at mass ratio of Mg2+/Li+ = 1:1. The mathematical model calculation revealed that the excellent selective separation effectiveness of the as-proposed ID-FCDI system is due to the unique property of the monovalent selectivity membranes, in which the transmembrane rate of lithium ions is ten times that of magnesium ions under the same condition. Furthermore, the separation mechanism of the as-proposed ID-FCDI device for Li+/Mg2+ ions was determined by the high electrosorption capacity of the flow electrode for Li+ ions (1.14 times higher than Mg2+ ions), low diffusion resistance (1.413 Ω), and high diffusion coefficient of Li+ ions (2.83 times faster than Mg2+ ions) by electrochemical measurement. On this basis, 3.92 times of lithium was successfully enriched in the natural salt lake brine of Golmud (mass ratio of Mg2+/Li+ = 79.29), and the separation factor was 6307.17 with a 99.64 % purity of Li+ ion solution and an Em of 0.20 kWh mol−1. Finally, the Li2CO3 product (99.66 %) was precipitated via the reaction between Na2CO3 and the enriched Li+ ion solution, consequently fulfilling the battery-level application of the industrial purity requirements. These findings highlight that this device is promising and profitable for lithium extraction from salt lake in industrial production.

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Digitalization of Accounting Profession: A Decade of Bibliometric Analysis

The Fourth Industrial Revolution (IR4.0) has altered the global industry requirement to replace human labour with greater digital software and robotic function automation. Similar developments are anticipated in the accounting sector as well, with the shift towards digitalization of the accounting industry. The accounting profession gets more difficult in a digital world because accountants must have greater knowledge of information technology and accounting information systems. To better comprehend this problem, the present study examines the development of research articles on the digitalization of the accounting profession by analysing data from the Scopus database between 2014 and 2023, using bibliometric analysis. The analysis involved looking for patterns in research publications to establish the document type; languages; subject area, publication categories by authors, countries and sources titles, citation metrics and keywords. For this reason, 182 relevant documents were examined in Mac 2024 and the data were analysed using software of Microsoft Excel, VOSviewer, Harzing’s Publish or Perish. This study offers some insights regarding the future development and advancement of the digitalization of the accounting profession topic in the academic literature of Business, Management and Accounting; Economics, Econometrics and Finance; Social Sciences and Decision Sciences, as well as provides helpful information for academics and practitioners looking to analyse and delve deeper within this particular field of research.