Conventional Industry Research Articles

Electric Vehicle Adoption: Implications for Employment in South Africa’s Automotive Component Industry

The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition.

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

Shedding light on blue-green photosynthesis: A wavelength-dependent mathematical model of photosynthesis in Synechocystis sp. PCC 6803.

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions for ensemble of parameter sets and found no physiological benefit of a model assuming phycobilisome detachment. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. We suggest gene targets for overexpression under different illuminations to increase the yield. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

The Effect of ESG Performance on the Systematic Risk of Listed Companies in China: A Comparative Analysis of High-Tech Manufacturing Versus Conventional Industries

The continued growth of the ESG (environment, society, and governance) investing trend has garnered great interest from both academics and professionals alike in the past decade. Nowadays, even developing countries like China are seeing a new wave of ESG investment opportunities. However, the research on ESG investing in emerging markets remains relatively lackluster, and fewer ESG studies concentrate on the topic of systematic risk. Therefore, this study wants to provide an analysis of the effect of ESG rating on the systematic risk of listed companies on the A-share stock market in China through the lens of a comparative study between high-tech manufacturing and conventional industries. Based on the fixed-effect model regression result on the quarterly data of 240 companies from 2018 to 2021, this paper discovers a negative correlation between ESG rating and the systematic risk a company is exposed to and a greater effect of ESG performance on systematic risk in high-tech manufacturing industries than conventional industries. Overall, this paper not only presents a new perspective on the correlation between ESG investing and the systematic risk a firm is exposed to but also serves as an inter-industry research that tackles a phenomenon specific to the Chinese capital market.

Peningkatan Pemahaman dan Implementasi Customer Relationship Management (CRM) di Industri UMKM Kebumen

Public service is a real contribution to strengthening the local economic foundation, the conventional industry sector. "Besides, it's still unclear how consumer behaviour varies. CRM can minimize abstract behaviour based on the data obtained. CRMs can provide consumer behaviour information based on data obtained such as purchasing behaviour. Such data can be enabled in UMKM bank data and build decisions such as discounts, product innovations and product sales. Increasing CRM in the context of society is crucial to strengthening the bond between the convenience industry and its consumers. The proposed community enhancement involves a series of holistic activities that are not aimed at improving employee understanding and integrating such methods into everyday practice

Measurement System Analysis of Densometry Technique for the Determination of Porosity and Thickness of Porous Fuel Cell Media

Porosity is one of the critical parameters governing mass transport of reagents and products in the heterogenous architecture of a fuel cell electrode. Techniques for the measurement thereof are required that are rapid, cost-effective, and simple, and yet capable of the highest levels of accuracy, precision, and stability. This body of work presents a comprehensive account of the measurement system analysis (MSA) of the densometer technique for the ex situ determination of total porosity and mean thickness of thin film porous materials by way of hydrostatic principles. The MSA involved a four phased approach which systematically tested several process assumptions before performing gage precision and accuracy studies and, finally, benchmarking of the system against several conventional industry techniques. Results confirmed statistically that the densometry technique, in conjunction with a standardized measurement procedure, can be used for the precise and accurate measurement of porosity as well as thickness across a representative range for porous materials deployed in fuel cells and similar technologies.

Urgensi Manajemen Sumber Daya Insani Pada Industri Halal

Today the halal industry is in a significant upward trend, ranging from banking, travel, fashion to other businesses. Of course this requires the best plan to support the increase in the halal industry in Indonesia, especially in the field of human resources. This paper uses a literature review of relevant references related to the urgency of human resource management in the halal industry. The importance of human resource development in the development of the halal industry is needed to encourage the development of the halal industry so that it can compete with conventional industries.

The Application Status and Energy Saving Analysis of Heat Pump Technology Under Dual Carbon Target

This paper introduces the significance of energy conservation and the current situation of energy utilization in China, and focuses on the development prospect of heat pump technology. Since the proposal of carbon peaking and carbon neutrality has made higher requirements for carbon emission reduction in different industries, the energy structure in China will make important adjustments accordingly. The research focuses on the way to reduce carbon emission in conventional industries and making full use of renewable energy. Among different energy saving and emission reduction technologies, heat pump technology has been widely used in civil, industrial and agricultural areas due to its remarkable performance of energy saving and emission reduction. There are many kinds of heat pump technology, but there are great differences in specific applications. How to make effective use of heat pump technology has become an important direction of our current development. It is a problem we must solve to study the current situation of heat pump technology and then realize energy saving.

Post-harvest regeneration is driven by ecological factors rather than wood procurement intensity in eastern Canadian forests

Abstract Biomass from surplus forest growth that is not harvested for wood supply of conventional industries can be an important source of feedstock for bioenergy. Its procurement can be integrated with little effort into current harvest operations. However, the increasing harvesting intensity to meet greater demand for biomass procurement can impact forest ecosystem functions because of its direct and indirect effects on woody debris and the regeneration of next-rotation stands. In this context, we aimed to determine the relationships between wood procurement intensity, woody debris inputs, and regeneration success over 2 years after harvesting. We tested four treatments of increasing wood procurement intensity using a randomized block design within six experimental sites along a gradient of varying forest characteristics of boreal and temperate forests. We assessed stand characteristics in terms of standing trees, woody debris, and regeneration pre- and post-harvest. We used mixed effects models to evaluate (i) the effects of wood procurement intensity and pre-harvest stand characteristics on the volume and cover of woody debris and (ii) the specific influence of woody debris on the presence of suitable planting microsites post-harvest. Furthermore, we used principal component regressions to explore the relationships between harvesting intensity and the presence of natural regeneration and competing vegetation as a function of pre- and post-harvest stand characteristics (iii). Our results showed that increasing wood procurement intensity reduced the volume of post-harvest woody debris while having a limited effect on regeneration. Increasing harvesting intensity had a negligible effect on suitable planting microsites in hardwood-dominated stands but it increased their presence in conifer-dominated stands. Natural regeneration and competing vegetation were mainly related to stand characteristics, and only broadleaf regeneration was sensitive to harvest intensity. We conclude that the relationships between wood procurement intensity and regeneration are complex and rely mainly on stand characteristics rather than wood procurement needs.

Monopsony, Job Tasks and Labour Market Concentration

Abstract This paper extends the monopsony literature by taking a task-based approach and estimating the causal effect of concentration on labour market outcomes. Using detailed employer–employee data from Norway, we find that our job task-based measure shows lower degrees of concentration than conventional industry- and occupation-based measures. Exploiting mass lay-offs as exogenous shocks to local labour demand, we show that workers who experience mass separations in more concentrated markets have substantially worse subsequent labour market outcomes than workers in less concentrated markets. Our results point to the existence of employer market power that is driven by the concentration of skill demand across firms.

Bacterial chondronecrosis with osteomyelitis lameness in broiler chickens and its implications for welfare, meat safety, and quality: a review.

The exponential increase in global population continues to present an ongoing challenge for livestock producers worldwide to consistently provide a safe, high-quality, and affordable source of protein for consumers. In the last 50years, the poultry industry has spearheaded this effort thanks to focused genetic and genomic selection for feed-efficient, high-yielding broilers. However, such intense selection for productive traits, along with conventional industry farming practices, has also presented the industry with a myriad of serious issues that negatively impacted animal health, welfare, and productivity-such as woody breast and virulent diseases commonly associated with poultry farming. Bacterial chondronecrosis with osteomyelitis (BCO) lameness is one such issue, having rapidly become a key issue affecting the poultry industry with serious impacts on broiler welfare, meat quality, production, food safety, and economic losses since its discovery in 1972. This review focuses on hallmark clinical symptoms, diagnosis, etiology, and impact of BCO lameness on key issues facing the poultry industry.

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review.

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

NOVEL METHODOLOGY FOR REAL-TIME STRUCTURAL ANALYSIS ASSISTANCE IN CUSTOM PRODUCT DESIGN

Mass-customization is related to optimizing the balance between flexibility, strongly required by the customer-focused industries and manufacturing efficiency, which is critical for market competitiveness. In the conventional industries, the process of designing, validating and manufacturing a product is long and expensive. Some of the common approaches for addressing those issues are parametric product modeling and Finite Element Analysis (FEA). However, the costs involved are still relatively high because of the very special expertise needed and the cost of the specialized software. Also, the specific design of the product cannot be validated in a real-time, which often leads to making hard compromises between the specific customer requirements and the structural properties of the product in its exploitation. In this paper, we propose the novel methodology for real-time structural analysis assistance for custom product design. We introduce the concept of so-called compiled FEA model, a Machine Learning (ML) model, consisting of dataset of characteristic product parameters and associated physical quantities and properties, selected ML algorithms and the sets of associated hyperparameters. A case study of creating a compiled FEA model for the case of internal orthopedic fixator is provided.

COMPARATIVE ANALYSIS OF FINANCIAL PERFORMANCE OF PT. BANK SYARIAH INDONESIA AND PT. BANK MANDIRI (PERSERO) AS THE PARENT AND CONTROLLING SHAREHOLDERS OF THE COMPANY

The desire for a new business line to increase new income is one of the goals of conventional banks opening Islamic business units (UUS) or Islamic Commercial Banks (BUS). This goal will be achieved if the transfer of knowledge (marketing knowledge, technological knowledge and experience) and commitment to increasing resources (human resources, capital, infrastructure) from the parent company to the subsidiary companies goes well. In fact, the market share of Islamic banking is still very small compared to the conventional industry, only recorded at 7.09% and there is a large discrepancy in the performance of Islamic Business Units and Islamic Commercial Banks compared to the performance of their parent companies. The author is interested in conducting this research, with the consideration that Bank Syariah Indonesia (BSI) is the largest Islamic bank and Bank Mandiri as the holding company and controlling shareholder. The author wants to know how the performance comparison of both. Supposedly if shareholder commitment is high to its subsidiaries, then total assets, equity, net income and financial performance ratios in this case CAR, NPL, NIM, ROA, BOPO and LDR are not much different. The sample data is the financial performance of BSI and Bank Mandiri as of Quarter I 2021 to Quarter IV 2022. The columnogorov-smirnov test is used to test data normality and independent sample t-test to test hypotheses, with the SPSS 25 program. As a result, there is no simultaneous significant differences between BSI and Bank Mandiri in CAR, NPL, NIM, ROA, BOPO and LDR or all financial performance indicators, but partially there are significant differences for all of these financial performance indicators

Implementation of AR and VR using 5G in conventional industry applications

Various industry verticals have tedious tasks that are vital in terms of maintenance, operations and customer service. These repetitive activities use traditional methods to achieve the goal, however, the methods are not efficient, as they are time consuming and resource heavy. Emerging technology of Augmented Reality (AR) and Virtual Reality (VR) brought the drastic change for example in Bridge check-up, Big Box stores, Health care, Supply-chain management. But these sharp change in our life demands more work in less time with precision and high resolution. To implement AR and VR under 5G there are challenges, such as: how to get fidelity and high resolution, where Drone or Robot-assisted bridge inspection is required. The objective of this research work is twofold: 1) meet the challenges and 2) figure out optimize solutions.

Industry 5.0: analyzing the challenges in implementation using grey influence analysis

PurposeThe author explores the challenges to implementation of Industry 5.0 in the manufacturing sector, considering the developing economy context and studying the causal relationships among factors using an advanced causal modelling technique, the Grey Influence Analysis (GINA). The challenges were further classified based on importance and their influencing power.Design/methodology/approachThe author uses the novel causal modelling technique of GINA to study and understand the influence relations among the challenges to implementation of Industry 5.0.FindingsBased on the results from the expert response-based study in the Indian manufacturing industry, it is seen that the Regulatory challenges (RGC) appear to be the most important challenge that needs to be tackled first, while implementing Industry 5.0. Integration with existing systems and Ethical challenges (ETC) emerge as the second and third most important in the category of challenges, as per the results from the GINA analysis.Research limitations/implicationsThe RGC and the ETC need to be addressed prior to implementation and it is necessary to check whether the technologies comply with regulations and whether it creates serious job displacements. While implementation, the challenges with integration to existing systems can be appropriately tackled.Practical implicationsAs a practical implication of the study, the author suggests that a proactive and reactive approach can be adopted to implement the Industry 5.0 concepts to reality. The RGC can be viewed for the adoption of technology, integration challenges can be viewed by understanding the existing systems, and ETC can be addressed by understanding the workforce in combination with technologies.Originality/valueThe shift in focus on sustainability and resilience has transformed the conventional industries to think beyond efficiency and productivity, toward being more responsible to society. The study analyzes the challenges to implementation of Industry 5.0 and the causal relations among them considering an expert response-based study.

Scalable and Revocable Attribute-Based Data Sharing With Short Revocation List for IIoT

The cooperative works between connected smart devices in the Industrial Internet of Things (IIoT) have greatly made the growth in productivity and economics for the conventional industry. However, due to the introduction of the communication network, the budding IIoT also confronts the unprecedented cyber threats. To prevent the data from being intercepted by malicious intruders, we propose an efficient and fully secure data sharing work with a short revocation list (DS-SRL) for IIoT. The DS-SRL not only enables flexible access control to the massive data in IIoT but also provides a direct revocation approach for handling the potential issues of key disclosure and membership expiring in application scenarios. Particularly, compared with existing directly revocable ABE works, the revocation list in the DS-SRL scheme will keep constant size even with the increasing number of users. Thus, the consumption for computing and disseminating the revoke-related part of ciphertext are low. This resource-saving merit makes our DS-SRL scheme suitable for IIoT where the smart devices are weak in the ability of both processing and storage. The DS-SRL works without boundary such that the public parameters involved in the system require no predefinitions and can be dynamically adjusted after deployment. Furthermore, the proposed DS-SRL work is demonstrated to be fully secure under the decisional linear assumption. Hence, it owns high flexibility, scalability, and security, which are essential and desirable in real-life applications. Finally, the superior feasibility, efficiency, as well as effectiveness of our DS-SRL work are fairly confirmed by the detailed performance evaluation.

Evaluation and prediction of salt effects on pig muscle by deep UV and machine learning

The salting process for meat transformation is a crucial step in conventional industry. Recent developments in label-free spectrometry techniques combined with machine learning hold great promise for high-precision salt processing. In this study, we applied UV fluorescence to characterize salting treatments in pig's Teres major muscle and predict NaCl concentrations. t-SNE analyses based on spectral measurements revealed clear differences between NaCl-free and salted treatments. However, salt treatments were not clearly identified. We then highlighted and exploited a variability seen in the emission spectra at the wavelengths 300, 318, and 360 nm, which reflected structural or compositional changes. Using this information, predictive models could accurately identify the five salted treatments with a high specificity and sensitivity or predict salt concentrations. This study paves the way toward the possibility for industrials to precisely adjust NaCl concentrations with precision during processing.

Digital agricultural education in Hungary

With the advent of the digital era in the middle of the 20th century and the German government's introduction of Industry 4.0 in 2011, conventional industries shifted into an economy built on massive Internet and Information and Communication Technologies (ICT). The ICTs such as big data, cloud computing, the Internet of Things (IoT), mobile devices, artificial intelligence, 5G network, etc., are not only being used in a variety of businesses but also by an increasing number of people globally. There is no doubt that the use of digital technology in a variety of sectors has effectively increased productivity and decreased costs. Digital agriculture is one of the logical conclusions of Industry 4.0 growth. However, implementing ICT in agriculture may be severely hampered by the risks associated with digital agriculture. One of the main obstacles is unclear Digital Agricultural Education (DAE), a market gap formed by the explosive rise of digital agriculture but with little research. In this exploratory study, we used content analysis as a research methodology, aimed to comprehend Hungary's efforts in digital agriculture education and investigate the challenges by presenting practical examples.

Novel, high-yielding lettuce phenotype generated by urea amine nitrogen nutrition displays contrasting traits to those of nitrate- or ammonium-fertilized plants

Fertilizing intact plants with particular forms of nitrogen (N), at equal concentrations of total N, gives rise to N-form-specific morphological and physiological phenotypes. Urea amine nitrogen is only transiently available in soil, thus its effects on plants have not been as well-defined. By stabilizing the N source urea amine, we have begun to characterize a third phenotype in addition to those recognized for nitrate and ammonium. We show that non-limiting rates of stabilized amine nitrogen (SAN) induce specific traits in greenhouse-grown lettuce, that lead to increased yields compared to controls treated with conventional industry standard fertilizers, un-stabilized urea, calcium nitrate or ammonium nitrate. These were supplied at the same total weight of N, via the soil or foliarly, to seedlings of Lactuca sativa L. cv ‘Tom Thumb’ and/or ‘Lollo Rosso’. Initially tap root length, lateral root development and leaf chlorophyll content are increased by SAN. As plants mature, leaf biomass increases at a faster rate under SAN nutrition, and larger harvest weights are finally attained, and/or a given head weight is achieved more rapidly. We conclude that plants respond positively to the maintenance of nitrogen in ureic-amine form, by initially partitioning more biomass to roots whilst increasing photosynthesis. These changes allow provision of more soil-sourced and carbon-based resources to the leaves of maturing plants. Leafy vegetable yields can be increased using stabilized urea amine fertilizer. We identify proxy traits for yield in young seedlings, which could be utilized in breeding and phenotyping programs for a wide range of species.