Industrial Tools Research Articles

Role of energy value chain in carbon neutrality: A review

Value chain analysis is an important tool for optimizing operations and decision-making in enterprises. As the concept of sustainable development gains recognition worldwide, research on value chains is increasingly focused on sustainability. Traditionally, energy management and value management have operated in parallel with limited intersections. However, after the 2015 Paris Agreement set the goal of achieving net-zero emissions, carbon management has become integral to national strategies, necessitating a re-evaluation of traditional value chains. In this paper, the “energy value chain” is introduced, a novel concept that integrates energy consumption with value creation and carbon emissions, emphasizing the coupling relationships among “energy flow”, “value flow”, and “carbon flow.” From a review of current value chains in the power, steel, petroleum, and transportation industries, the specific energy value chain for each industry is defined and its rationale and effectiveness are discussed. This integrated analytical method provides a strategic tool for industries or enterprises to optimize energy consumption, reduce carbon emissions, and enhance competitive advantage.

An interdisciplinary approach: Exploring the five dimensions of the tools deployed by EUVABECO

Abstract To effectively address the complexities of vaccination across the EU, EUVABECO’s 14 partners are implementing a comprehensive, interdisciplinary approach. Twelve pilot projects will be implemented from September 2024 to August 2025 in six MS (BE, DE, EL, LV, LU, PL, PT). These pilots will test innovative tools across five domains-medical, social, industrial, modelling and forecasting, and digital-to optimize vaccination outcomes: Medical/Clinical Tool: A decision support system that aids patients and healthcare professionals by providing current, evidence-based information tailored to individuals, thereby facilitating informed vaccination decisions. Social Tool: Data linkage on an individual level, to enhance the targeting and monitoring of vaccine campaigns, and ensuring equitable access to vaccines, especially for vulnerable populations. Industrial Tool: An electronic vaccine leaflet that boosts the flexibility and responsiveness of vaccine distribution by offering dynamic, real-time information in local languages to both healthcare providers and patients - regardless of the source and point of use of the vaccine. Modelling and Forecasting Tool: A disease simulator that models infection dynamics and evaluates the impact of public health interventions, crucial for strategic planning and resource allocation. Digital Tool: A citizen-held digital vaccination card that enables individuals to maintain a comprehensive, accessible record of their own vaccination histories, enhancing continuity of care across borders.

A robust solution for recognizing accurate handwritten text extraction using quantum convolutional neural network and transformer models

Handwritten text extraction from images is challenging due to the variability of styles in handwriting, quality of images, and noise backgrounds. Existing methods often struggle to achieve high accuracy, hindering document analysis, optical character recognition, and data entry applications. We propose a novel approach to improve extraction accuracy, combining Quantum convolutional neural networks (QCNN) and transformer-based neural networks (TextExtractNet) named QTEN. Our method leverages the strengths of both models to recognize and extract handwritten text from images. Experimental results show that our approach achieves a 96 % accuracy rate, outperforming existing methods. This breakthrough has significant implications for automating document processing, data entry, and related applications. Our method's robustness and accuracy make it a valuable tool for industries relying on handwritten document processing, such as healthcare, finance, and government.

Tribomechanical characterization of ceramic reinforced composite coatings for modification of cylinder liner of heavy-duty diesel engines

Heavy-duty diesel engines are essential mechanic power generation tools for industry and transporting freight and passengers. Therefore, increasing engine efficiency and minimizing maintenance and repair costs is crucial. This study examines and characterizes three thermal spray coatings (Cr3C2/NiCr, NiCr, and Al2O3/TiO2) utilized on cylindrical gray cast iron substrate specimens. The coatings were tested for their microstructural (optical microstructure, SEM, EDS, XRD, and AFM topography), mechanical (nanoindentation and nano scratch), and tribological (short and long-distance wear test) properties. Microstructural tests showed that an average coating thickness of 120 to 170 µm was successfully achieved on the cast iron base substrate using different thermal coating methods. The Cr3C2/NiCr coating, observed through the AFM mapping method, achieved the most uniform roughness distribution on the surface. The elasticity of the coatings was measured with a nanoindentation test, and Al2O3/TiO2 coating exhibited high rigidness (177 GPa). The nano scratch test showed that the highest load-carrying capacity was achieved with the Cr3C2/NiCr coated sample. NiCr-coated sample exhibited approximately similar rigidness (61 GPa) to cast iron (67 GPa). Short- and long-distance wear tests showed that the coatings provided significantly better wear resistance than the cast iron base. The coatings’ efficacy was evident. At the long-distance tests, while the cast iron base sample had a wear rate of 19.874 (10−7mm3/Nm), the wear rates of Al2O3/TiO2, Cr3C2/NiCr, and NiCr coatings were 3.835, 4.020, and 6.466 (10−7mm3/Nm), respectively. The Al2O3/TiO2 and Cr3C2/NiCr coatings had superior tribological performance than the NiCr coating, yet the NiCr coating showed much higher wear performance than the uncoated base. The Cr3C2/NiCr coating exhibited superior tribomechanical performance in all the coatings.

Making sense of the interaction between geopolitics and middle-technology trap: evidence from China’s catching-up CNC machine tool industry

This study proposes a framework to analyze the interaction between geopolitics and the Middle-Technology Trap, drawing on evidence from China's CNC machine-tool industry. Qualitative methods such as interviews have been utilized to identify geopolitical variables that have driven the development of the Chinese CNC machine tools industry. To explore the combined effects of the Middle-Technology Trap and the intense competition between China and the West, this article focuses on an industry that has been significantly influenced by the economic decoupling between China and the US, a phenomenon deeply rooted in recent great power competition. While several industries may illustrate the new status quo of the Middle-Technology Trap, the CNC machine-tool industry is particularly exemplary due to its close interrelation with geopolitics. Accordingly, this study presents a catch-up case that incorporates technology-oriented aspects and factors from strategic competition, underlining three scenarios that geopolitics could influence the result of addresing the Middle-Technology Trap, respectively technological decoupling, the relocation of supply chain, and industrial policy.

Upcycling of Food By-Products and Waste: Nonthermal Green Extractions and Life Cycle Assessment Approach

Food loss and waste constitute a substantial threat to global food system sustainability, representing 38% of energy consumption in the supply chain. The 2030 Agenda for Sustainable Development highlights a vision integrating social, economic, and environmental pillars. Addressing environmental impact requires recycling (destruction for new creations) and upcycling (converting waste into valuable products). This review highlights nonthermal green extractions and sustainable techniques in upcycling raw materials such as olives, red beetroot, sugar beet, and coffee, which are widely used in the food industry. Nonthermal processing efficiently extracts bioactive compounds and utilizes waste. Key approaches for its valorization include life cycle assessment, environmental footprint analysis, energy efficiency strategies, digitalization, and sustainability considerations. However, challenges remain in calculating their environmental impact. Waste and by-product valorization from raw materials address disposal issues, offering economic and environmental benefits. Nonthermal techniques show optimistic opportunities in green extraction and sustainable upcycling. The focus is on raw materials including olives, red beetroot, sugar beet, and coffee byproducts, and possible product development. There are powerful connections offering industry tools for impactful sustainability management and guiding decisions on waste-to-value or ‘upcycling’ products. The review contributes to filling the gap in usage of nonthermal processing in upcycling of waste and by-products.

A pumpless and tubeless microfluidic device enables extended in vitro development of Cryptosporidium parvum

Abstract The enteric parasite Cryptosporidium remains a treatment challenge for drinking water utilities globally due to its resistance to chlorine disinfection. However, the lack of an in vitro culture system for Cryptosporidium that is both cost-effective and reliable remains a key bottleneck in Cryptosporidium research. Here we report that the microfluidic culture of HCT-8 cells under fluid shear stress enables the extended development of Cryptosporidium parvum. Specifically, the growth of C. parvum in a user-friendly pumpless microfluidic device was assessed using immunofluorescence assays, scanning electron microscopy and quantitative PCR, which revealed that development continued for ten days in total. Oocysts produced within the microfluidic device were infective to fresh HCT-8 monolayers, however these oocysts were only present at low levels. We anticipate that such microfluidic approaches will facilitate a wide range of in vitro studies on Cryptosporidium and may have the potential to be further developed as a routine infectivity assessment tool for the water industry.

Using Laboratory Documentation to Extend a Large Language Model to Serve as a Laboratory Assistant

Abstract Demographic and structural changes in the workforce are resulting in ever smaller cohorts of laboratory medical professionals while medical advancements and an aging population has increased demand for laboratory testing. Furthermore, the inexorable aging of our technologists has resulted in a workforce with a high variation in experience. The need for new tools to help bridge the experience gap and increase productivity are readily apparent. Recently, large language models (LLMs) have emerged as powerful tools in various industries, including healthcare. However, numerous concerns have been raised regarding data security particularly with respect to proprietary and patient data. Furthermore, these models must be adapted to function effectively within the unique context of laboratories. To this end, we present a proof of concept project that uses laboratory documentation to extend a locally run LLM to serve as a laboratory assistant. To develop this tool, we collected and analyzed job aids, SOPs, multidisciplinary guidelines, and classification systems relevant to a particular laboratory at our institution. We then used retrieval augmented generation (RAG) on these documents to locally extend open source large language models to understand laboratory-specific terminology, procedures, and contexts. Preliminary results suggest that the language model can accurately process and generate responses related to specific lab tasks, including sample handling, testing protocols, and data interpretation. For example, it can suggest possible differential diagnoses based on test results and generate suggested follow-up tests even for complex workflows such as leukemia/lymphoma flow cytometry. By using existing specific laboratory documentation as training data, we have demonstrated that it is possible to securely adapt large language models to function effectively within the context of laboratories. This represents an essential step towards integrating advanced technologies into the laboratory. Future work includes evaluating the language model’s performance in different lab settings and expanding its functionality. We envision that this project could evolve into an integral component of laboratories’ digital infrastructure, hopefully enhancing training, knowledge sharing, and operational efficiency.

A study on media and information and traffic convergence in the digital age on consumption

The digital age is the age of productivity innovation. It is the era of industrial transformation and market development to satisfy the public's eyeball economy. Under traditional productivity, new products are often produced and introduced to the market and the public accepts and adapts to the new products, and those products that are not accepted by the public will be eliminated by the market. Now, in the emerging digital era, through data and its analysis, the product and market have begun to analyse the user and market pain points, according to user data to design and develop products for the needs of the user to enjoy at the same time for the data and traffic payment. This is "information consumption" and "data consumption". Poor information is the tool to make money in the current era. How to grasp the information and data of the user group and consumer demand becomes the key. Emerging media is the key to unlocking users' needs. The purpose of this study is to find the role of this key in the integration of information and traffic into consumption. The significance of this study is to deduce the relationship between media and the convergence of information and traffic to consumption in the digital age by analysing the case of the company. The result of this study is that the digital era has created advanced productivity, and the development of productivity has led to innovations in the means and tools of the media industry, which have contributed to the development of user needs. The media is the key to creating and capturing the needs of user groups. It is also the cornerstone for creating "information consumption" and "traffic consumption".

Lipid droplet distribution in Atlantic salmon (Salmo salar L.) oocytes is related to the fertilisation and developmental outcome

At present, there is no simple and practical applicable method to determine the quality of Atlantic salmon oocytes upon collection at the fish farm. Investigating oocyte lipid droplet patterns might be a tool for the aquaculture industry to predict oocyte quality in Atlantic salmon. While examining the lipid distribution in oocyte batches from 16 females of Atlantic salmon with a range in fertilisation rate (6–97 %), five categories of lipid droplet patterns were distinguished. In category 1, the lipid droplets were evenly distributed whereas in the remaining categories lipid droplets showed varying degree of coalescence. Categories 2, 3, 4 and 5 were distinguished based on the area of coalesced lipid droplets. The fertilisation rate was observed to be high (≥ 90 %), in females that had more oocytes from category 1, 2 and 3 whereas females with more oocytes from category 4 and 5 showed a low fertilisation rate (<90 %). A similar pattern was revealed between lipid droplet categories and eyed embryo and hatching success. Staining of salmon oocytes further confirmed the observed lipid droplet patterns, and different sizes of lipid droplets were present in almost all oocytes. The study revealed a significant association between lipid droplet category and fertilisation outcome in oocytes of Atlantic salmon that can be a practical tool for predicting oocyte quality.

Case Study on the Inherent Mechanisms Driving Industrial Innovation through Data Empowerment

In the context of the profound integration of big data, artificial intelligence technologies, and industries, both data resources and digital technologies became crucial pillars supporting the development of industrial innovation. They played key roles in enhancing the innovation capabilities of industries. In this regard, the present study employed the specific case of the intelligent brain of the Taizhou Machine Tool Industry. Utilizing grounded theory, the research integrated relevant data through coding analysis and category extraction to construct a comprehensive theoretical model of the intrinsic mechanisms driving industrial innovation through data empowerment. The study revealed that the realization of data-empowered industrial innovation followed a logical path of 'data resource services data capability spillover industrial innovation achievement.' Specifically, data empowerment operated across three dimensions: industrial resources, production, and ecology, effectively propelling the realization of industrial innovation. From a systematic perspective, this research analyzed the intrinsic mechanisms of data-empowered industrial innovation, deepening the theoretical understanding of industrial innovation. In the context of past industrial digitization transformation, these findings provided valuable insights for guiding industrial innovation development.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.

Electroanalytical detection of fruit ethylene by a novel electroactive biosensing membrane

Purpose This study aims to present an innovative approach to detect and monitor ethylene gas during fruit ripening. Design/methodology/approach It uses a specialized composite membrane in conjunction with a solid-state electrochemical method. This unique electroactive membrane, composed of polyvinyl alcohol (PVA), chitosan (CHT), lithium chloride (LiCl) and ammonium molybdate (AMO), exhibits synergistic behavior when applied to a microelectrode chip surface. This composite enhances the sensitivity of electrochemical ethylene detection. Empirical experiments were conducted to elucidate the ripening kinetics in various fruit specimens, including apples, pears and mangoes. These fruits released ethylene, which was analyzed using the molybdenum-permeated electroactive biopolymer composite membrane, a critical determinant of ethylene levels. Findings Characterization of the synthesized composite through techniques such as X-ray diffraction and Fourier-transform infrared spectroscopy revealed reduced crystallinity and decreased hydrogen bond interactions upon activation with Mo ions. Field emission scanning electron microscopy images exhibited a distinctive porous surface morphology with spherical microgranules. Energy dispersive X-ray analysis indicated a significant change in the mass or atomic composition of Mo in the composite membrane after Mo ion activation. Electrochemical measurements, including cyclic voltammetry and potentiostatic electrochemical impedance spectroscopy, validated the efficiency of the Mo-activated PVA-CHT-LiCl-AMO membrane, manifesting an impressive 87.79% increase in sensitivity compared to the nonactivated membrane. Practical implications This research work represents a significant advancement in the field of ethylene detection and fruit ripening monitoring. The Mo-activated PVA-CHT-LiCl-AMO membrane offers a reliable and effective solution for real-time ethylene detection, providing an invaluable tool for the horticultural industry to optimize fruit ripening processes, extend shelf life and ensure the delivery of high-quality produce to consumers. Social implications The findings of this study hold great promise for fostering sustainability and efficiency within the global fruit supply chain, ultimately benefiting both producers and consumers alike. Originality/value The implications of this research extend to the fabrication of a sensor based on a solid-state electroactive PVA-CHT-LiCl-AMO composite membrane, which upon Mo-activation exhibits robust electrochemical fruit ethylene detection when exposed to different fruits.

Enhancing thread cutting performance with CNT-embedded micro textured tools

ABSTRACT This paper investigates the impact of a CNT-embedded textured tool on the thread-turning process. The study aims to optimize the efficiency and effectiveness of the thread-turning by utilizing this innovative tool. The experimental research explores the performance of the tool in terms of generated cutting forces. The findings contribute to understanding the potential benefits of integrating CNT-embedded textured tools in the manufacturing industry, particularly for thread-turning operations. This study demonstrates that the linear textured tool embedded with CNT solid lubricant exhibits a significant 36.8% reduction in cutting forces compared to conventional cutting inserts.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.

Tourists’ Experiences During the Covid-19 Pandemic in Plateau State, Nigeria: An Empirical Evaluation Using Chaos Theory

The safety of tourists’ health when visiting any tourism business is critical in tourism management and development. By using chaos theory, this study aimed to understand how 408 tourists in Plateau State, Nigeria, behaved toward tourism-related businesses during the COVID-19 pandemic. Completed and retrieved responses were analyzed descriptively using an inductive method, following a pragmatic approach. The findings of this investigation are consistent with chaos theory. Results from our survey indicate that 93% of tourists took precautions. This suggests that in an otherwise random and chaotic period of the tourism crisis, self-organization brought order and new stability. With a mean score of 40%, safety and hygiene were ranked as the two main influencing factors that changed tourists’ actual behaviour during the pandemic. The alteration in behaviour might be attributed to tourists’ awareness of their risk of catching the COVID-19 virus when visiting tourist attractions. Empirical evidence shows that non-interactive restriction techniques had a significant impact on tourist behaviour. Thus, the impact of the pandemic on tourists’ protective behaviour continues to play a part in tourists’ behaviour during and after the pandemic, implying that the chaos theory approach might be used as a crisis management tool in the tourism industry.

A phase field finite element study and evaluation of sulfide stress cracking in DCB specimen testing

The challenges presented by sour environments rich in hydrogen sulfide (H2S) underscore the necessity for a comprehensive understanding of material behavior under such conditions. The cracking susceptibility of metals and alloys used for subsurface equipment in downhole oil and gas exploration operations is particularly concerning. The NACE Double Cantilever Beam (DCB) test has emerged as a widely used quality assurance tool in the petroleum industry, leveraging fracture mechanics principles to assess the environment-assisted cracking (EAC) resistance of metals and alloys. The DCB test evaluates the fracture toughness KISSC of materials in H2S-containing environments via assessment of the crack arrest, which serves as a vital parameter in structural integrity assessments to mitigate the risk of service-related failures from sulfide stress cracking (SSC). However, various studies suggest that different test parameters, such as arm displacement and initial notch, significantly influence KISSC. This work presents a detailed numerical investigation on a comprehensive simulation of the DCB test, examining the effects of different test parameters on KISSC prediction. A coupled deformation-diffusion phase field framework is adopted to simulate SSC in DCB specimens arising from a complex interplay between material deformation, hydrogen diffusion, and fracture. The numerical results show good agreement with experimental results reported in the literature and provide deeper insights into the factors affecting crack growth and arrest in DCB testing.

PECULIARITIES OF MARKETING AND BRANDING IN THE CONFECTIONERY AND CAFE INDUSTRY

The purpose of this article is to determine the essence and theoretical features of marketing and branding in the confectionery and cafe industry, as well as the challenges associated with marketing activities in these establishments. The main research methods used in the article include induction, synthesis, analysis, comparison, and generalization. The specific features of marketing activities in confectioneries and cafes have been identified. The impact of marketing efforts on service delivery in these establishments has been studied. Key problems arising from marketing activities and brand promotion in confectioneries and cafes have been identified. Various tools used in marketing efforts within this industry, which constitute the marketing mix or marketing complex, have been explored. The peculiarities of developing a marketing plan for confectioneries and cafes using the “5R+1C” integrated marketing model, and elements of the marketing mix specific to the food industry, are discussed. The ecosystem of the internet marketing strategy for confectioneries and cafes is outlined. The necessity for increasing social media activity (SMM) and enhancing market recognition, as well as developing a user-friendly website with online table reservations and sweet order functionalities, has been emphasized. Directions for optimizing the digital strategy effectiveness for confectioneries and cafes, focused on improving brand recognition among target audiences, are proposed. Factors influencing the selection of Google’s contextual media network for the promotion of confectioneries and cafes are outlined. The required changes for social media pages and websites to enhance brand promotion are discussed. International experiences regarding the efficacy of business tools in the food industry, incorporating marketing concepts, are also explored. The main idea of advertising campaigns, centered around creating favorable conditions for customer purchases and replicating the brand’s style in every part of the establishment, is reviewed. The specifics of consumer loyalty, shaped by the brand’s appearance, communication, establishment design, and available offerings, are also examined.

PROBLEMS OF MANAGING TECHNOLOGICAL REGIMES IN GAS DISTRIBUTION NETWORKS AND MODERN APPROACHES TO THEIR SOLUTION

This article systematically discusses the problems of managing technological regimes in gas distribution networks and modern approaches to their resolution. The scientific-theoretical and practical aspects of managing gas distribution networks (GDN) are analyzed. Based on the obtained results, a scientifically grounded comparative explanation of the existing contradictions in modern and traditional gas distribution networks is provided, and the role of key factors in the emergence of these contradictions is investigated. It is shown that technological backwardness in traditional GDNs increases energy consumption and operational costs, thereby reducing the economic efficiency of the system and increasing the risk of accidents. The research suggests decision-making methods for solving management problems in traditional GDNs by maintaining the technical-structural level and implementing telemetry systems. It is indicated that the widespread use of telemetry and telemechanics tools in the gas distribution industry enhances the safe delivery of gas to end-users and significantly reduces service costs. Another focus of the research is on the issues related to the processing of spatial information for the management of the technical components of gas distribution systems. The article also analyzes scientific research dedicated to the application of modern IT methods and tools in the management of GDNs, investigating management and forecasting models using neural networks and intelligent methods. Keywords: Gas distribution networks, modern GDN networks, traditional GDN networks, GIS systems, digital technologies, technogenic risks, neural networks, forecasting methods

Study the Potential of Mineral Ceramics Based on Aluminium Oxide in Replacing Metal Alloys

Modern metal alloys used in the tooling industry often consist of expensive and rare metals, impacting affordability and sustainability. This research explores the potential of mineral ceramics, mainly composed of readily available aluminum oxide (Al2O3), as an alternative for high-performance engineering ceramics. This research highlights the advantages and disadvantages of mineral ceramics over various metal alloys. Further exploration could refine properties and expand industrial adoption, contributing to a more responsible and efficient materials landscape.