Industrial Management Research Articles

Literature Review on Creative Leadership in Chinese Culture and Arts Industry Managers

With the rapid development of the economy and culture, the creative industry has become the pillar of urban economic growth, and the supply of creative industry personnel has been in short supply. The bottleneck in the development of China’s creative industry is the extreme shortage of creative talent, and the education and training of a large number of creative talents is a great success for China’s creative industry in the future. This study investigated the model of Creative Leadership in China. Xiang (2009) documented that the dual quality superimposing model of cultural industry’s competence can be divided into five dimensions: (1) integrity, responsibility, integrity, fairness, self-control, team spirit, (2) management and management: initiative, public relations, negotiation, marketing, service consciousness. Wang (2016) commented on the status of creative talent competence, judged on the competence of creative talent, conducted empirical research on the competence of creative talent through the design of the research path, and analyzed the data. Li (2007) defines the connotation of the creative industry and the skills of the creative industry, analyses the talents’ ability and quality. At the same time, the concept of competence is introduced, and the competence characteristic model of creative advertising person is established with the example of a creative advertising person. This paper analyzes the strategies for cultivating the competence of creative industrial talents from three levels: society, enterprise, and talent itself.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT (strengths, weaknesses, opportunities, and threats) and PEST (political, economic, social, and technological) analysis, this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities. First of all, based on SWOT analysis, it is found that colleges and universities have rich educational resources and policy support, but they face challenges such as insufficient practical teaching and intensified international competition. External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion, while threats come from global market competition and talent flow. Secondly, PEST analysis reveals the key factors in the macro-environment: at the political level, the state vigorously supports the cultivation of cultural industry management talents; at the economic level, the market demand for cultural industries is strong; at the social level, the public cultural consumption is upgraded; at the technological level, digital transformation promotes industry innovation. On this basis, this paper puts forward a multi-level strategic system covering theoretical education, practical skill improvement, interdisciplinary integration, and international vision training. The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge, practical skills, and global vision, so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

A LSTM-based time series prediction model for China's power supply and its performance evaluation

With the rapid development of China's economy and the acceleration of urbanisation, the stability and sustainability of power supply has become increasingly important. In order to cope with the continuous growth of power demand, accurate power supply prediction has become an essential research direction. In this paper, a time series prediction model for power supply is constructed based on the long short-term memory network (LSTM). Power supply data is organised using methods such as data pre-processing and correlation analysis, and the LSTM model is used to predict future power supply. The performance of the prediction results was evaluated, and the results showed that the model can effectively capture the changing trends of power supply, with high reliability and accuracy, providing important support for scientific management and policy formulation in the power industry.

Sustainable biomass derived natural surfactant of soybean seeds in fly ash industrial waste utilization for carbon storage: Evaluation of environmental impact

The rising carbon emissions challenge global environmental sustainability and climate stability. This study aims to advance climate change mitigation by integrating fly ash and natural surfactant into CO2 sequestration strategies. Fly ash, a by-product of industrial processes, presents an opportunity for innovative reuse. By combining fly ash with a natural surfactant extracted from soybean seeds, both industrial waste management and greenhouse gas reduction can be addressed. The surfactant was characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, surface tension, and electrical conductivity measurements, confirming saponin presence with a critical micelle concentration of 0.3 wt%. Pressure decay studies revealed that a (5 wt% fly ash + NS) demonstrated the highest CO2 absorption, supported by microscopic analysis. Further research on optimizing reaction conditions and kinetics of mineralization could enhance this method’s efficiency and scalability for broader application.

Should the Government Be Paying Investment Fees on $3 Trillion of Tax-Deferred Retirement Assets?

Abstract Under standard assumptions, individuals and the government are indifferent between traditional tax-deferred retirement accounts and “front-loaded” (Roth) accounts. Adding investment fees to this benchmark, individuals are still indifferent, but the government is not. We show that under weak conditions firms charge equal percent fees under both systems, yielding higher dollar fees under Traditional. We estimate that tax deferral increases demand for asset management services by $3.8 trillion, costing the government $23.4 billion in annual fees. In a general equilibrium differentiated-product model, tax deferral produces a larger asset management industry, higher taxes, and lower social welfare. (JEL G11, G23, G28, G51, H21, J32)

Synthesis of Silver Nanoparticles and Forced Convective Heat Transfer Coefficient Measurement of Silver-Water Nanofluids at Laminar Flow

Heat management in industries and other areas is a major issue currently worldwide. Conventional heat transfer fluids like water and ethylene glycol are not very effective. Nanofluids (NFs), composed of nanoparticles (NPs) dispersed throughout a base fluid (BF), have recently been identified as promising heat management agents for various industrial and other applications. Heat transmission is one of the many uses for AgNPs–water NFs (silver NPs (AgNPs) dispersed in water). The goal of this work was to determine the AgNPs–water NFs' forced convective heat transfer coefficient (HTC) in laminar flow. Due to this, we present the results of the HTC study of AgNPs–water NFs in laminar flow in this paper. First, using AgNO3 as a precursor, a chemical reduction approach was used for facile synthesis of AgNPs in one pot at ambient temperature. Synthesized NPs were examined using ultraviolet-visible spectroscopy (UV-vis), X-ray diffraction (XRD), and transform electron microscopy (TEM) techniques. The average particle size obtained from TEM is 25 nm. The required quantity of AgNPs was added to the water to produce 0.5 and 1.0 vol% of AgNPs–water NFs. Next, zeta potential and dynamic light scattering (DLS), in addition to time-lapse observation and captured picture comparison, were used to assess the stability of the formulated NFs. To calculate the forced convective HTC of prepared NFs in laminar flow, a vertical shell and tube heat transfer apparatus and computer-based data recorder were used. According to the results, in comparison to BF, the HTC of 0.5 and 1.0 vol% NFs was 3.3 and 5 times greater, respectively which encourages the use of AgNPs–water NFs in industry and other heat management. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 13−22

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review

Abstract Recent studies have focused on different aspects of green management, practices, and green consumption in the hotel industry. However, there is a need to explore and better understand the association between indoor environmental quality (IEQ) and green hotel guest’s comfort. Therefore, it is essential to explore the effects of IEQ on the comfort and satisfaction of green hotel guests. This study conducts a comprehensive review of the effects of various IEQ parameters, including indoor air quality, thermal comfort, lighting, visual/view, acoustic comfort, building characteristics, decoration, and indoor greenery, on guest’s comfort and satisfaction in green hotels. Based on previous literature, it was also revealed that most current green building schemes lack of comprehensive evaluation of the performance of IEQ dimensions in green hotels. It was also observed that these IEQ parameters show a significant influence on the hotel guest’s comfort and satisfaction. Based on the findings of the literature review, a conceptual model was developed to represent the relationship between the IEQ parameters and guest’s comfort and satisfaction. The proposed conceptual model can be implemented by the hotel management for a comprehensive assessment of guests’ perceptions toward the IEQ in green hotels. The novelty of this study is based on its findings that establish a more effective IEQ evaluation method and serve as the reference scenario of IEQ, which can be a useful tool for both academician and practitioners and contribute to improving the indoor environmental performance of green hotels through highlighting the key IEQ parameters, which affect the comfort and satisfaction of hotel guests.

At the dawn of the mining and metallurgical industrial management in the Urals

At the dawn of the mining and metallurgical industrial management in the Urals

Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles.

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system's high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry.

How can phase change materials improve vehicle cooling systems for better motor efficiency and thermal management?

Phase change materials, or PCMs, have become an attractive option for improving thermal management in the vehicle industry. With the increasing demand for efficient cooling systems and improved driving ranges in electric vehicles, PCMs offer an innovative approach to managing heat dissipation and improving performance. While PCMs are already applied in industries such as construction, their potential to revolutionize automotive thermal management could lead to advancements in all aspects of the field. They are a perfect option for increasing energy efficiency because of their capacity to collect and release thermal energy. This study explores the application of PCMs in vehicles and assesses their effectiveness in improving cooling systems.

Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.

Determination of antibiotic residues in pork meat from Northeast Mexico by UPLC-MS

ABSTRACT In this study, the aim was to identify and quantify antibiotic residues in pork meat samples of supermarkets and butcher shops of two main cities in northeast Mexico, using Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). The results shown seven antibiotic residues detected in the samples. Ampicillin was the main antibiotic with a higher positive rate (>59%) in supermarkets and butcher shops. Tetracycline and sulfamethazine had a high positive rate (>25%). Finally, the quantification of antibiotics shown a high positive rate of samples that exceed the minimum residues limits (MRL). The high positive rate of antibiotic residues in this study suggests an inadequate management in the food industry and represents a potential risk for the human health in the northeast from Mexico.

FINANCIAL FEASIBILITY OF FOOD PROCESSING INDUSTRY: LESSONS FROM TOFU INDUSTRY IN TRUNAN VILLAGE, MAGELANG

This study aims to examine the financial analysis of the Food Processing Industry in Tofu Center of Trunan Village in an effort to maintain the calculation of operational costs, profit and loss, and investment in minimizing investment losses. In particular, this study aims to calculate the Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP), and Profitability Index (PI) in the Processed Food Industry in the Tofu Center of Trunan Village. The urgency of this research is to maintain the sustainability of the tofu industry in Trunan Village, which is one of the leading MSME centers in Magelang City and in an effort to maintain food security. Based on the results of the analysis it was found that financially the MSME management in the Tofu Center Food Processing Industry in Trunan Village did not fulfill the feasibility aspect. Net Present Value (Rp 2,147,030,794), Internal Rate of Return shows 2%, Payback Period 2 Years 8 months, and Profitability Index 4.10. Keywords: Feasibility Study, NPV, IRR, PP, PI

Environmental Management in Lithium Extraction: Optimization of the Hydrometallurgical Process for Lepidolite with Focus on Eco-Efficiency and Life Cycle Assessment

Objective: Develop and optimize an eco-efficient hydrometallurgical process for lithium extraction from lepidolite, enhancing environmental management in the lithium mining industry. Theoretical Framework: The study is grounded in principles of eco-efficiency, life cycle assessment (LCA), and optimization of chemical processes applied to lithium extraction within the context of organizational environmental management. Method: A fractional factorial design 2^(5-2) was employed for process optimization. Mineralogical characterization was conducted using XRD and ICP-MS. Extraction efficiency, product purity, and environmental parameters were evaluated through LCA. Results and Discussion: Extraction efficiency increased from 91.0 ± 1.5% to 94.5 ± 0.8%, with a product purity of 99.62 ± 0.04%. LCA revealed a carbon footprint of 5.9 ± 0.3 kg CO₂ eq / kg Li₂CO₃ and a water consumption of 38 ± 2 m³/t Li₂CO₃. Implications in relation to conventional extraction methods are discussed. Research Implications: The study provides a framework for implementing more sustainable processes in lithium mining, potentially applicable to the battery industry and natural resource management. Originality/Value: The research innovatively integrates process optimization, LCA, and eco-efficiency principles in lithium extraction, contributing to the sustainable transformation of the lithium mining industry.

The Role of Green Risk Management Approaches in Promoting Green and Sustainable Supply Chain Management

This study assesses how advanced technologies like AI, blockchain, and big data facilitate green risk management in the automotive and electronics industries in the EU. When applied to environmental risk assessment, green purchasing, and eco-design activities, these technologies will optimize Economies of Scale (EoS) and contribute to advancing GSSCM in companies. This review uses systematic literature review and thematic analysis and focuses on using various peer-reviewed sources to understand how these technologies support GSSCM and also reveal gaps and challenges such as the integration of innovative waste disposal technologies and sustainable partnership schemes. Research proves that actual assessment of environmental risk is possible through the implementation of AI in risk assessment; On the other hand, blockchain makes sustainable procurement and reverse logistics more transparent. Thus, some problems like high costs, limited time, and problems with the alignment of stakeholder goals remain. It is recommended that these gaps be overcome by innovating, partnering with industries, and implementing policies that can further improve the position of information systems as the foundation of GSSCM. Thus, policymakers have been urged to explore options like grants or subsidies in an attempt to promote the adoption of these technologies as a way of creating a circular economy. Consequently, this research offers insights that may be beneficial for industry managers and policymakers seeking to improve sustainability within the EU automotive and electronics sectors.

Transforming reservoir management in petroleum processing: a synergistic approach with IoT and Blockchain for enhanced emissions monitoring and regulatory reporting

This article proposes an innovative method to transform reservoir management in petroleum processing through the integration of the Internet of Things (IoT) and Blockchain technologies. In response to increasing demands for strict emissions monitoring and regulatory compliance, our approach aims to increase the precision, transparency, and security of data collection and reporting. In our proposed framework, we leverage IoT technology to deploy sensor networks across reservoirs and processing plants. The collected data is integrated into a Blockchain. This creates an immutable and transparent ledger for emissions-related information. Blockchain’s decentralized and tamper-proof features preserve data integrity and facilitate secure information sharing between stakeholders, including regulators and operators. Considering the specific scalability and performance requirements for data management in the petroleum industry, we propose a 4-layer system architecture. Our study examines the practical implementation and benefits of this innovative synergy.

Uncertainty tolerance moderates the link between job insecurity and service performance: evidence from China’s hotel frontline employees

The pandemic crisis outbreak has impacted the hospitality industry tremendously. This study investigated the effect of job insecurity on service performance and examined the mediating role of cognitive appraisal and the moderating effect of uncertainty tolerance from the perspectives of cognitive appraisal theory of stress. The study collected a questionnaire from 316 hotel employees at three time points, with a one-month interval between the two time points. The SPSS PROCESS macro and MPLUS are used to test the research hypotheses. The results highlighted that the mediating role of challenge-hindrance appraisal, affirming that job insecurity positively impacts service performance via increasing challenge appraisal, and job insecurity negatively impacts service performance via increasing hindrance appraisal. Furthermore, the results show that uncertainty tolerance moderates the relationship between job insecurity and cognitive appraisal and also moderated the indirect effect of job insecurity on service performance through cognitive appraisal. The research results provide implications for human resource management in the hospitality industry.

Exploring Differences in Leadership Behaviors and in the Perceived Work Environment Between Younger and Older Managers in a Swedish Mining Company

AbstractThis study examines differences in leadership behaviors and in the perceived work environment between older and younger managers in a large Swedish mining company. The leadership behaviors and work environment of young managers in the mining industry will become more important in the future due to the changes taking place in the industry. Such changes include the older generation of managers reaching retirement age, while the industry generally finds it difficult to recruit a younger generation of employees. Moreover, rapid technology-centered transition, such as the green transition of the mining industry, is underway which may put young managers in particular under pressure. Meanwhile, research on young managers in the mining industry more broadly is sparse. Data for this study was mainly gathered via a questionnaire, complemented by data from interviews. The questionnaire included 48 validated self-rate questions on background variables, social and organizational working conditions, health, and leadership behavior. The survey was answered by 216 managers (response rate: 63%). Interviews were carried out with ten young managers (35 years or younger). The study found no statistically significant differences between younger and older managers with regards to workload, working hours, health, and work satisfaction. We found that older managers rate themselves higher, at a statistically significant level, for change-oriented leadership behaviors. However, when age is analyzed together with other variables that are significantly different between the two groups, only managerial experience and managerial positions show statistically significant associations with change-oriented and healthy and effective leadership behaviors. The interviews indicate that the young managers encounter challenges that are related to their experience and training, but few are related to their age. Given the influence of managerial experience on leadership behaviors, leadership training appears to be an important area of intervention, and conditions must be improved to enable change-oriented leadership among managers in the mining industry, with a greater focus on young managers.

Strategic Management of Luxury Brands: A Case Study of Louis Vuitton’s Market Performance and Consumer Loyalty

This study explores the critical role of brand strategy management in the luxury goods industry, focusing on Louis Vuitton (LV) as a case study. With the continuous development of the global economy and heightened consumer expectations, competition in the luxury market has intensified. Brand strategy management, encompassing positioning, communication, maintenance, and extension, emerges as a vital competitive tool that influences market performance and consumer loyalty. This research aims to uncover the unique aspects of LV's brand strategy, its impact on market performance and consumer loyalty, and the challenges the brand faces. Through an in-depth literature review and case analysis, the study identifies key success factors of LV's brand strategy, providing strategic recommendations for other fashion brands to enhance their market competitiveness. The findings contribute to the existing literature by filling gaps in the understanding of luxury brand management and offering practical insights for industry practitioners. Additionally, the study highlights the importance of innovation and adaptation in maintaining brand prestige and relevance in an ever-evolving market landscape, emphasizing how LV's strategic decisions have solidified its position as a leader in the luxury sector. This comprehensive examination not only sheds light on LV's enduring success but also offers valuable lessons for emerging brands aiming to navigate the complexities of the luxury market.