Industrial Tools Research Articles

Analysis of Industrial Performance Indicators Effects On Enhance The Competitiveness of The Food Sector and The Possibility Of Support in The Iraqi Economy (2003-2020)

This study was aimed on analyze the competitiveness of the food industry sector, and showed the level of industrial and competitive performance of these industries, and the possibility of supporting and enhancing their competitiveness in the Iraqi economy for the period 2003-2020. Competitiveness is considered one of the most important indicators of the efficiency of the food industry tool in local and foreign markets, as competitiveness represents the ability of productive sectors of different types to distribute their products and increase their sales level. Therefore, the food industry in Iraq is considered one of the most important sectors of the manufacturing industries that suffer from problems and obstacles including a decrease in the level of competitive and industrial performance. It is believed that the problem of the research is the weak competitiveness of the food industry in Iraq due to the decline in the quality of manufactured products and the weak level of competitiveness of these products compared to imported products. The results of the study showed a decrease and fluctuation in the contribution of the manufacturing sector to the gross domestic product (GDP) in Iraq. The economic policy was not providing the possibility of an investment climate to support and enhance the private sector in industrial investment. The indicators of production requirements such as labor productivity, worker productivity, and the value added index of the food industries sector began to fluctuate between decrease and increase, and to measure the extent of the reciprocal effects between all parts of the standard model and the extent of its contribution to developing the competitive capacity of this sector.

Automated programming approaches to enhance computer-aided translation accuracy.

With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today's fast-paced environment. Computer-assisted translation (CAT) addresses the issue of slow translation speed; however, the quality of CAT translations still requires rigorous evaluation. This study aims to answer the following questions: How do CAT systems that use automated programming fare compared to more conventional methods of human translation when translating English vocabulary? (2) How can CAT systems be improved to handle difficult English words, specialised terminology, and semantic subtleties? The working premise is that CAT systems that use automated programming techniques will outperform traditional methods in terms of translation accuracy. English vocabulary plays a crucial role in translation, as words can have different meanings depending on the context. CAT systems improve their translation accuracy by utilising specific automated programs and building a translation corpus through translation memory technology. This study compares the accuracy of English vocabulary translations produced by CAT based on automatic programming with those produced by traditional manual translation. Experimental results demonstrate that CAT based on automatic programming is 8% more accurate than traditional manual translation when dealing with complex English vocabulary sentences, professional jargon, English acronyms, and semantic nuances. Consequently, compared to conventional human translation, CAT can enhance the accuracy of English vocabulary translation, making it a valuable tool in the translation industry.

Implementing FMEA (Failure Mode and Effect Analysis) in a canned corn manufacturing industry: a case study

Assuring quality and food safety is challenging for many companies, as they affect the sustainability of the business. it was identified lack of studies focused on applying risk assessment tools in the food industry by reviewing the literature. This study aims to describe the implementation of Failure Mode and Effect Analysis (FMEA) to identify potential risks and how to prevent them, to assure quality requirements in a corn food plant. This paper follows a case study approach, based on a qualitative methodology. During the execution of FMEA, the hazards and mitigation controls were identified. The results provide evidences of how FMEA can be effective by showing the vulnerabilities of production flow, controls to implement and how to prioritize actions. FMEA contributed to managing risks found for the company where the case study was conducted. This case study can be followed by other companies and different processing technologies, extrapolating the food industry. Key words: FMEA; quality; hazard; corn; food industry.

Effect of sintering temperature on the properties of ceramic binders synthesized from coal gangue-based geopolymer

ABSTRACT As a by-product of the coal industry, the effective use of coal gangue presents significant implications for environmental protection and sustainable development upon effective utilization. Given the rich content of silicon and aluminum in coal gangue, this study converts it into a precursor for ceramic binders used in grinding wheels through the geopolymerization reaction process. Ceramic binders of the SiO2-B2O3-Al2O3-RO-R2O system were prepared at sintering temperatures ranging from 590 to 740°C, and the performance of each sample was tested. The research found that the flexural strength of the ceramic binder reached a peak of 31 MPa when sintered at 690°C. Additionally, the hardness was observed to be the highest at a sintering temperature of 640°C, reaching 211.8 hV. It was observed that as the sintering temperature rose, wettability tended to decrease. Using techniques including XRD, SEM, and EDS, the effect of sintering temperature on the microstructure and phase transformation of the material was analyzed, and these changes determined the properties of the material. This study not only confirmed the potential value of coal gangue in the abrasive tools industry but also opened up new strategies and technical solutions for the field of solid waste resource utilization.

Intermediate state between steady and breathing solitons in fiber lasers

Ultrafast fiber laser, a vital tool in both science and industry, exhibits two distinct pulse states: the steady soliton (SS) and the breathing soliton (BS). While these states have been extensively studied individually, understanding the complex transition between them is crucial for controlling lasing states effectively. Herein, our experimental observations reveal an intermediate state that toggles between SS and BS, enabled by the dispersive Fourier transform technique. We find that energy hop and decaying breathing processes, driven respectively by the energy quantization effect and Q-switched modulation, govern this transition. Additionally, we observe that the transition between different BS states primarily involves a pure decaying breathing process. Numerical simulations are used to generate similar transition dynamics in a model that combines equations describing the population inversion in a mode-locked laser. This study sheds light on the transition dynamics in non-equilibrium systems, offering insights for intelligently manipulating lasing states.

Development and Evaluation of a Novel Chimeric Genotype VII Newcastle Disease Vaccine: Overcoming Maternal Antibody Interference and Spray Administration.

Newcastle disease virus (NDV) poses a significant threat to the poultry industry, with the emergence of genotype VII NDV leading to extensive outbreaks and economic losses. Vaccination is the primary means of controlling NDV, but the presence of maternal antibodies (MDAs) can interfere with the immunological effect of live virus vaccines. Thus, we constructed a chimeric NDV live virus vaccine, LX-OAI4S, by replacing the extracellular regions of the F and HN genes of the NDV LX strain with the corresponding regions of the A-VII vaccine strain. The chimeric vaccine LX-OAI4S demonstrated high genetic stability, good safety, and strong reproductive capacity in chicken embryos. The LX-OAI4S vaccine induced rapid antibody production in specific pathogen-free (SPF) and commercial chickens via the intranasal and intraocular (IN/IO) routes, with hemagglutination inhibition (HI) antibody titers reaching 4.71 ± 1.03 log2 at 7 days post-vaccination (dpv), significantly higher than those of the two classical vaccine strains La Sota and VG/GA. The LX-OAI4S vaccine group provided effective protection against the challenge of genotype VII NDV virulent strain JS2/06 and inhibited viral shedding. When administered via spray, the LX-OAI4S vaccine elicited high systemic immunity against NDV in both SPF and commercial chickens, effectively protecting against clinical disease and reducing viral shedding. The chickens were exposed to high-dose vaccine for spray vaccination, and no adverse reactions were observed after vaccination. Despite the presence of anti-NDV MDAs in chickens, the NDV-specific antibody titers were significantly greater in the vaccinated groups than in the unvaccinated group. The vaccine exhibited high immunogenicity and the potential to overcome maternal antibody interference. The LX-OAI4S vaccine is a promising candidate for an ND vaccine. Its administration via spray can effectively prevent the occurrence of ND, making it a valuable tool for the poultry industry.

Automated self-adjustment of array probe with a robotic ultrasonic test system

Ultrasonic testing of objects with complex geometries often requires the use of a robotic arm to position the probe perpendicular to the local surface. Using immersion makes it possible to test these objects with standard ultrasonic linear array probes. Here, the probe positions and orientations provided by the robot are used for merging the locally acquired image data into a 3D-reconstruction. The quality of this reconstruction is highly dependent on the alignment of the position of the physical probe with the position used in the digital model. For common industrial tools, the tool center point (TCP) is usually acquired using geometric features of the tools. However, for ultrasonic arrays in immersion, there is a water standoff between the probe and the test object, therefore the TCP is in free space in front of the array and cannot be acquired with the common method. To overcome this challenge, we propose a method that allows the robotic ultrasonic system to automatically self-adjust the position and orientation of the ultrasonic probe using a test block made of steel with defined geometric features as a target for referencing. For each of the six degrees of freedom, a scan and adjustment routine are established using the data based on the actual ultrasound characteristics of the probe. Given a coarse pre-definition of the tool position and the known target test block, minimal human interaction is required to supervise the adjustment method, leading to higher quality reconstructions than with manual adjustment.

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Application of artificial intelligence (AI) as a process analytical technology (PAT) tool in healthcare industry

Process Analytical Technology (PAT) has revolutionized manufacturing by providing real-time insights into process parameters. This guide delves into the synergistic potential of AI and PAT, offering a comprehensive overview of their integration. It explores the way AI algorithms can enhance data analysis, prediction, and control complex manufacturing processes. The document provides a fundamental understanding of AI concepts relevant to PAT, including machine learning, deep learning, and computer vision. Real-world case studies help to illustrate the practical application of AI in diverse industries, such as pharmaceuticals, chemicals, and food processing. By highlighting the benefits of AI-driven PAT, such as improved product quality, increased efficiency, and reduced costs, this guide empowers readers to harness the power of AI for process optimization and innovation. The objective of this review is to provide a base for establishing the regulatory guidelines and adaptation of PAT-controlled applications in the regulations.

Study on the Impact of Green Credit on Carbon Emission Reduction in the Manufacturing Industry

This study aims to explore the impact of green credit on carbon emission reduction in the manufacturing industry. As China actively promotes its “dual carbon” goals, green credit has become increasingly prominent as an essential policy tool in the manufacturing industry. This article first conducts a literature review on green credit and carbon emission reduction in the manufacturing industry, sorting out existing research’s main findings and perspectives. Subsequently, the current challenges and potential development opportunities are revealed by analyzing the development status, problems and future trends of green credit and carbon emission reduction in the manufacturing industry. We further used the SWOT and PEST analyses to conduct an in-depth analysis of the impact of green credit on carbon emission reduction in the manufacturing industry and identified its internal and external factors. Finally, this article puts forward corresponding countermeasures and suggestions, aiming to promote further the application of green credit in the manufacturing industry and promote carbon emission reduction work to achieve more significant results. This study will deepen our understanding of the mechanism and effect of green credit in carbon emission reduction in the manufacturing industry and provide a valuable reference for formulating and implementing relevant policies in the future.

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 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.

Uncovering the taste features: Applying machine learning and molecular docking approaches to predict umami taste intensity of peptides

The umami taste, often described as the fifth basic taste, plays a pivotal role in the culinary world, significantly contributing to the overall flavor profile and consumer satisfaction of food products. Precise prediction and enhancement of umami taste intensity using the identification of umami peptides can lead to groundbreaking advancements in the food industry. This study presents a novel approach to combine machine learning and molecular docking techniques. The machine learning algorithm is based on a cascade algorithm combining CatBoost and BERT models to predict the umami taste intensity of peptides accurately. Our research reveals that combinations of specific amino acids, such as aspartic acid with alanine or glycine and lysine with glycine or histidine, have been identified to enhance the umami taste in foods. The model is available as a user-friendly web server at https://taste.infochemistry.ru. This study contributes to the scientific understanding of taste perception and provides a valuable tool for the food industry to innovate and improve product quality by optimizing umami taste profiles.

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.

Evaluating the performance of triple and double metal gate charge plasma transistors for applications in biological sensors at a dual cavity location

BackgroundBiosensors have become essential tools in biotechnology, environmental monitoring, and healthcare industries due to their ability to detect and analyze biological signals. However, conventional Tunnel Field-Effect Transistors (TFETs) used in biosensors face challenges like reduced ON-state current, random dopant fluctuations, and complex manufacturing processes, which limit their effectiveness. AimThe study aims to investigate the effectiveness of Charge Plasma-based Tunnel Field-Effect Transistors (CP-TFETs) with dual and triple metal gate-dual cavity locations for improving the sensitivity and performance of biosensors. MethodologyThe study compares dual and triple metal gate CP-TFET configurations for signal amplification and detection in biosensors. The CP-TFETs use high-k gate dielectric materials to enhance ON-state current and reduce OFF-state current, while the impact of neutralized and charged substances in the cavities on surface energy, electric field, and energy bands is analyzed. ResultsThe triple metal gate configuration demonstrated superior sensitivity in detecting biomolecules compared to the dual metal gate. By utilizing high-k materials and optimizing the gate work function, the triple metal gate approach achieved higher drain current and reduced OFF-state current, leading to improved overall performance. ConclusionThe triple metal gate CP-TFET outperforms its dual metal counterpart in biosensor applications, offering higher sensitivity, increased ON-state current, and improved detection capabilities, making it a promising approach for enhancing biosensor effectiveness.

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.

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 human ileocecal colorectal adenocarcinoma (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 polymerase chain reaction, which revealed that development continued for 10 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.