Market Demand Research Articles

Asymmetric impact of oil structural shocks on non-ferrous metals supply chains: A groundbreaking multidimensional quantile-on-quantile regression

This paper examines the predictability of various oil price shocks on the returns of non-ferrous metals from the supply and demand sides in the US, UK, and China under different market conditions. An innovative multidimensional quantile-on-quantile (MD-QQ) regression approach is proposed to address this topic, which extends traditional univariate QQ regression to multivariate cases, addressing the limitation of omitted variables in the benchmark QQ method. The findings reveal that the forecasting of oil supply shocks on the non-ferrous metal supply chain is generally insignificant. Nevertheless, oil supply shocks will transmit negative (positive) actions when the non-ferrous metal supply-side (demand-side) is in a bear (bull) market. The implication of oil demand shocks on the non-ferrous metal supply chain is generally positive, while the supply-side market of Chinese non-ferrous metals displays mutability in response to oil demand shocks under certain extreme market conditions. Conversely, oil risk shocks have a negative impact across the entire non-ferrous metal supply chain. Their effects are more pronounced when the non-ferrous metal demand market is bearish, exhibiting asymmetric characteristics. Oil demand shocks and risk shocks have a stronger impact on non-ferrous metal supply chains in the US and UK than in China. Our research not only confirms the estimation bias caused by missing variables in traditional baseline models but also provides new insights into the connection between the non-ferrous metals and oil markets by adopting the fresh method.

Integrated Benefits of Sustainable Utilization of Construction and Demolition Waste in a Pressure-State-Response Framework

This study presents the first application of the pressure-state-response (PSR) model in the comprehensive assessment of construction and demolition waste (CDW) recycling benefits. Unlike traditional methods, the PSR model provides a multi-dimensional analysis that integrates economic, environmental, and social factors, offering a more holistic approach to evaluating the impact of CDW recycling strategies. This model enables stakeholders to better understand the pressures, states, and responses involved in CDW management, providing actionable insights to optimize recycling efforts and support sustainable urban development. Using the pressure-state-response (PSR) logical framework of sustainable economics, this paper systematically analyzed the comprehensive benefit mechanism of the recycling of construction and demolition waste (CDW), and designed a comprehensive benefit evaluation model for CDW recycling. At the same time, taking Chongqing as an example, the management status of construction and demolition waste, the supply and demand matching of sustainable recycling products, and the impact of the input and output of CDW management were analyzed. The results were as follows: (1) The recovery rate of urban manure fluctuated between 0.13 and 0.17, mainly in temporary landfill. (2) Based on the latest market demand data of CDW recycled products, the supply–demand ratio of recycled products fluctuated between 0.11 and 0.21. This change in the supply–demand ratio reflects improvements in recycling technologies, such as the introduction of C2CA technology, which has greatly increased the supply of high-quality recycled materials. In addition, government policies encouraging the use of recycled products in public projects have contributed to this shift, further aligning supply with market demand. (3) The benefit–cost ratio of CDW management reflects new recycling technologies and the improved efficiency of CDW management. The benefit–cost ratio, which currently fluctuates between 0.32 and 0.39, more accurately reflects the current state of CDW management, which is increasingly adopting advanced technologies, resulting in increased efficiency and reduced costs. Based on this, this paper discusses the supply–demand relationship and benefit–cost ratio in CDW management from supply-side and demand-side perspectives, and puts forward corresponding countermeasures and suggestions. The research results provide a clear reference for improving the efficiency of building demolition waste resource utilization, especially in optimizing the balance of market supply and demand, and improving the economic benefits of recycled products. By analyzing the balance between the supply and demand ratio and the benefit–cost ratio, this study helps inform policy makers, businesses, and investors, to promote the sustainable development of CDW recycling projects to maximize resource efficiency, while reducing environmental pressures. These results not only provide practical guidelines for the implementation of CDW recycling projects, but also lay a foundation for future policy formulation and the setting of industry standards.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

PENINGKATAN PRODUKTIVITAS USAHA KERIPIK IKAN KUNIR UD FARAH DENGAN PENERAPAN TEKNOLOGI MESIN VACUUM FRYING SERTA MANAJEMEN KESELAMATAN DAN KESEHATAN KERJA (K3) PADA KARYAWAN

Panarukan District, Situbondo Regency, has marine potential that supports marine fish-based processing industries, including UD FARAH in Paowan Village, a producer of turmeric fish chips. However, UD FARAH's production capacity is still low because it uses traditional equipment such as wood stoves. Technological development is needed to increase production efficiency and quality, which is expected to increase profits and meet growing market demand. The aim of this activity is to improve employee skills in operating Vacuum Frying Machines for the production of turmeric fish chips and strengthen their understanding of occupational safety and health (K3). UD FARAH faces traditional production problems which result in less than optimal product quality and quantity, as well as working conditions that do not meet K3 standards. Solutions include the application of Vacuum Frying Machine technology and K3 training to increase production efficiency and work safety. The program also includes mentoring and evaluation to ensure long-term success. After the service activities, UD FARAH experienced a significant increase in productivity and product quality with the implementation of the Vacuum Frying Machine. Employees' knowledge of technology and K3 also increases, which contributes to work safety and efficiency. In addition, the sustainability program includes marketing strategies and product diversification, such as the development of mango chips, which utilize local resources and open up new market opportunities. It is also hoped that the sustainability of this program will reduce UD FARAH's dependence on one type of product, increase competitiveness and create added value for the local community. This activity succeeded in improving the skills of UD FARAH employees in operating vacuum frying machines and implementing K3, as well as encouraging product diversification and improving marketing strategies to increase productivity and sales.

Linear Programming and Product Mix of Paint Manufacturing Firms in Port Harcourt

This study examined the relationship between linear programming and product mix of paint manufacturing firms in Port Harcourt. This study adopted a correlational survey research design. The population of the study was 10 selected paint manufacturing firms in Port Harcourt as retrieved from Nigerian business directory (2024). 3 managers were selected from each firm multiplied by 10 firms gives us a total of 30 respondents. Structured questionnaire was used in data collection. The result of the Cronbach's Alpha reliability test indicates .804 which is above .70 which implies that the items are reliable. Pearson product moment correlation was used to test the hypotheses using SPSS (statistical package social sciences). The study revealed that there is a significant relationship between Ellipsoid Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. There is a significant relationship between Graphical method Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. There is a significant relationship between Simplex Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. In conclusion, Linear programming is a powerful mathematical tool that can be effectively utilized by paint manufacturing firms in Port Harcourt to optimize their product mix and maximize profits. The paint manufacturing firms should conduct a thorough analysis of market demand for different types of paint products to identify profitable opportunities for product mix optimization.

Effectiveness of Employee Training and Development in Increasing Productivity and Innovation in Multinational Companies

This study explores the effectiveness of employee training and development in enhancing productivity and innovation within multinational companies. Utilizing a qualitative approach, this research employs literature review and library research to analyze existing studies and theoretical frameworks related to employee development strategies. The findings indicate that well-structured training programs significantly contribute to increased employee performance, job satisfaction, and overall organizational productivity. Moreover, continuous professional development fosters a culture of innovation by equipping employees with the necessary skills to adapt to rapidly changing market demands. The review highlights various training methodologies, including on-the-job training, mentoring, and e-learning, demonstrating their impact on enhancing workforce capabilities. Additionally, the study underscores thae importance of aligning training initiatives with organizational goals to maximize effectiveness. This research contributes to the understanding of how employee development can serve as a strategic tool for multinational companies seeking to maintain a competitive edge in the global market. Recommendations for future research include examining the long-term effects of specific training programs on employee retention and organizational success. Overall, this study emphasizes the critical role of employee training and development in driving productivity and innovation in multinational corporations.

PENERAPAN BLUE ECONOMY DALAM PENGEMBANGAN PENDAPATAN PELAKU USAHA PERIKANAN DI KAMPUNG HAMADI KOTA JAYAPURA

Processed products in Hamadi Village only rely on the type of processed fish smoked produced by Fishery Business Actors, namely POKLAHSAR Mama Papua. Efforts to maximize the acceptance of processed fishery products and meet market demands can be done by improving the appearance of the product, so that old buyers want to buy more products with a new appearance both in terms of shape, size, style, packaging, and taste. This can be done by making other breakthroughs such as value-added processed fishery products. This program used PRA (Participatory Rural Appraisal) method is one of the methods that can be applied. After getting pretest and posttest scores, must conduct an analysis to get the score obtained. The analysis used is uji normalitas gain. This test is used to determine the effectiveness of the treatment given. Implementation of community partnership service activities to POKLAHSAR Mama Papua in Hamadi village with providing training in the manufacture of a variety of processed fishery products. A variety of processed fishery products that have been made such as; Meatballs, Shredded Fish, Suir-Suir and Sambal whose meat is from Yellow Fin Tuna (Thunnus albacares). Furthermore, make packaging for a variety of processed fisheries and don't forget to teach partners about the importance of making simple bookkeeping by making pre-test and post test about bookkeeping knowledge. Then socialization about making NIB (Nomor Izin Berusaha) online and training in making social media in marketing a variety of processed fishery products, This is done to increase income POKLAHSAR Mama Papua in Hamadi village.

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

A cluster approach to matching the competences of data specialists with skills in demand on the labour market

This paper addresses the challenge of aligning the competences of data specialists with skills in demand on the labour market in the rapidly evolving field of data science. Using an open dataset of 3,744 IT job postings, the study applies K-means clustering to identify key skill groupings for data specialist positions. The optimal number of clusters is determined using the elbow method, resulting in four distinct clusters: Data Analyst & Engineer, Data Platform Engineer, Data Science & Engineering Specialist, and Cloud Data Engineer. The research methodology employs unsupervised learning techniques, specifically K-means clustering, to analyze the distribution of skills across job postings. The clusters are visualized using t-distributed Stochastic Neighbor Embedding (t-SNE), providing insights into the relationships between different skill sets. The study reveals that job titles do not always unambiguously define the required skills, emphasizing the importance of focusing on specific skill sets rather than job titles alone. To bridge the gap between specific subject competences academic programs and industry requirements, the paper proposes a novel approach for comparing the proportion of skills in job clusters with the proportion of professional competencies in academic programs. This method is demonstrated using the Information Systems and Technologies Master's program at Kherson State University as a case study. The chi-square test is applied to confirm the statistical similarity between the skill structure of the Data Science & Engineering Specialist cluster and the competency structure of the academic program. The findings highlight the importance of continuous adaptation of profile of academic program to meet evolving industry needs. The proposed approach provides a data-driven framework for universities to align their programs with labor market demands, potentially improving graduate employability in the data science field. The study also underscores the need for personalized learning paths that can be tailored to individual career goals and skill gaps. Future research directions include the development of an artificial intelligence system to form individualized educational trajectories based on the skills required for specific job clusters. This could further enhance the alignment between education and industry needs, preparing students more effectively for the dynamic data science job market.

Biopolymers as Sustainable and Active Packaging Materials: Fundamentals and Mechanisms of Antifungal Activities.

Developing bio-based and biodegradable materials has become important to meet current market demands, government regulations, and environmental concerns. The packaging industry, particularly for food and beverages, is known to be the world's largest consumer of plastics. Therefore, the demand for sustainable alternatives in this area is needed to meet the industry's requirements. This review presents the most commonly used bio-based and biodegradable packaging materials, bio-polyesters, and polysaccharide-based polymers. At the same time, a major problem in food packaging is presented: fungal growth and, consequently, food spoilage. Different types of antifungal compounds, both natural and synthetic, are explained in terms of structure and mechanism of action. The main uses of these antifungal compounds and their degree of effectiveness are detailed. State-of-the-art studies have shown a clear trend of increasing studies on incorporating antifungals in biodegradable materials since 2000. The bibliometric networks showed studies on active packaging, biodegradable polymers, films, antimicrobial and antifungal activities, essential oils, starch and polysaccharides, nanocomposites, and nanoparticles. The combination of the development of bio-based and biodegradable materials with the ability to control fungal growth promotes both sustainability and the innovative enhancement of the packaging sector.

STAGES OF DEVELOPMENT OF HIGHER DENTAL EDUCATION IN THE USA

This article examines the stages of development of higher dental education in the United States, focusing on its historical, current, and future aspects. The primary aim of the research is to analyze the key challenges within the dental education system and offer potential solutions to address these issues. To achieve this goal, various research methods were employed, including comparative analysis, literature review, and empirical research. These approaches facilitated the identification of critical trends and persistent problems in dental education. The findings suggest that the integration of modern technologies into the educational process is crucial for improving the quality of dental education. Digital technologies, simulators, and remote learning methods have the potential to enhance the training of future dentists, providing more flexible and interactive learning opportunities. However, unresolved issues remain, such as the standardization of curricula across different educational institutions and the need for educator training to meet new challenges posed by innovative teaching methods. The article also highlights the importance of aligning educational programs with current realities and labor market demands. Moreover, there is a growing necessity to modernize the system by developing interdisciplinary approaches and fostering collaboration between academia, industry, and policy makers. These reforms are essential to ensure that dental education continues to produce highly qualified professionals capable of responding to the evolving needs of both the healthcare system and society at large. The article concludes that ongoing adaptation and innovation in dental education will be vital to addressing the demands of a rapidly changing field.

Openness influences business model innovation: the mediating role of digital capability

PurposeExploring open value, cultivating digital capability (DC) and driving business model innovation (BMI) have become an inevitable choice for enterprises to meet market demand and adapt to environmental changes. However, as one of the situational variables of BMI, the positive or negative influence of openness has not been proved and the path mechanism between DC and BMI is not clear. Based on the dynamic capability theory, this paper takes manufacturing enterprises as an example to explore the internal impact mechanisms of organizational openness on BMI. It extends the analysis by introducing DC as a mediating variable and introducing manufacturing enterprise type (high-tech and non-high-tech) as a moderating variable.Design/methodology/approachA questionnaire survey was conducted using data collected in China, data from 355 manufacturing enterprises were collected to carry out empirical research. Participants were mainly middle and senior managers with a comprehensive grasp of their firms’ information. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the reliability and validity of the sample data, and negative binomial regression analysis was used to test hypothesis relationships.FindingsThe authors find an inverted U-shaped relationship between openness and BMI, and explain that excessive openness may lead to low resource utilization, organizational inertia, cooperation distrust, which will have a negative impact on BMI. DC includes digital resource capability (DRC), digital management capability (DMC) and digital collaboration capability (DCC), which promote BMI and play a mediating role between openness and BMI. Enterprise type has a moderating effect on the relationship between DC and BMI.Research limitations/implicationsThe results of this paper summarize the opportunities and threats of open innovation, help enterprises fully understand the double-edged sword impact of openness, guide manufacturing enterprises to be sensitive to openness and achieve sustainable innovation. By analyzing the path of DRC, DMC and DCC to BMI, managers can improve their understanding of digital-driven value creation process and improve the competitive advantage of enterprises.Originality/valueThis paper presents the relationships among openness, DC and BMI. We find the non-linear effects of openness on DC and BMI, bridging the inconsistent view of positive or negative relationship between openness and organizational change in previous studies. The introduction of DC extends the theory of dynamic capability in the digital age, and opens the “black box” from opening to BMI from the process perspective of DRC, DMC and DCC. From the perspective of enterprise type, this paper provides different choices of capability upgrading and strategic innovation based on openness for high-tech and non-high-tech manufacturing enterprises.

Market demand for metaverse-based sporting events: a mixed-methods approach

ABSTRACT The burgeoning reach of the metaverse has introduced new opportunities for the sport industry. As live sporting events are increasingly being streamed in the metaverse, both industry practitioners and academics face a pivotal question: Which attributes of these digitally mediated events appeal to consumers? We address this question in two phases, from a market demand perspective. In the initial qualitative phase, we conducted an extensive literature review, semi-structured interviews, and netnographic analysis to propose a hierarchical conceptualization of market demand for metaverse-based live sporting events. The conceptualization comprises three higher-order market demand dimensions: traditional core sport demand, VR engagement demand, and metaverse experience demand — each of which comprises four lower-order, more concrete market demand factors. In the subsequent quantitative phase, we analyzed 616 questionnaire responses from metaverse spectators using partial least squares structural equation modeling (PLS-SEM). The structural model results indicated that all three higher-order market demand dimensions significantly influenced metaverse spectators’ future attendance intentions, with consumer satisfaction as a partial mediator. An Importance-Performance Map Analysis (IPMA) revealed that virtual interactions were the most important market demand factor in affecting attendance intentions. Our results augment the theoretical understanding of the market demand concept and its functional mechanism in an emerging metaverse context.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

From classroom to operating room: What attracts medical students to neurosurgery

BackgroundThe specialties chosen by medical students after graduation are critical to their professional trajectories. This study investigates the perceptions of medical students at Jazan University in southwestern Saudi Arabia toward neurosurgery, focusing on the factors that influence their inclination to pursue this specialty. MethodsAn observational cross-sectional study was conducted among 342 medical students from the Faculty of Medicine at Jazan University during the 2022–2023 academic year. The participants included students ranging from second- to sixth-year levels. ResultsAmong the 342 students, 207 (60.5 %) were males, and 135 (39.5 %) were females, with 169 (49.4 %) expressing interest in neurosurgery. Over time, there was a trend of decreasing interest in neurosurgery. The primary reasons for choosing this specialty were market demand for neurosurgeons and potential for substantial income, while stress was the main deterrent. Multivariate analysis revealed that third-, fourth-, and fifth-year students were significantly less likely to pursue neurosurgery, with reductions of 89 % [OR=0.11, 95 % CI: (0.04–0.29), p < 0.001], 83 % [OR=0.17, 95 % CI: (0.07–0.43), p < 0.001], and 74 % [OR=0.26, 95 % CI: (0.11–0.61), p = 0.002], respectively. The strongest motivating factors for choosing neurosurgery were research opportunities [OR=2.37, p < 0.001] and the prestige associated with the specialty [OR=2.35, p < 0.001]. ConclusionsThe study provides valuable insights into medical students’ perceptions and career aspirations related to neurosurgery in southern Saudi Arabia. It highlights key factors such as research opportunities, prestige, and competitiveness that motivate students to consider neurosurgery as a specialty. The study also addresses common misconceptions and stereotypes about neurosurgery, promoting a more accurate understanding of the field among students. These findings have significant implications for medical education, career guidance, and healthcare policy, helping to shape initiatives that enhance patient care and support the development of neurosurgery.

Techno-economic feasibility of CO2 utilization for production of green urea by Indian cement industries

The cement industry contributes substantially to global carbon emissions, necessitating urgent adoption of mitigation pathways aligned with climate goals. This study investigates the technical and economic viability of capturing carbon dioxide (CO2) emissions from cement plants in India and using that captured CO2 to produce green urea. The research aims to determine the feasibility of the CCUS approach as a practical way to reduce CO2 emissions from cement production while also boosting India's self-sufficiency in producing urea, a crucial fertilizer. The research uses a multi-criteria analysis (MCA) framework to assess various factors, including the technological readiness of CCUS technologies, the capital and operating costs of setting up and running such a system, the market demand for green urea, and the potential reduction in CO2 emissions. The study focused on 32 major cement plants in India, using data from their annual reports to inform the MCA model with an assumption of a post-combustion Monoethanolamine (MEA) scrubbing capacity CO2 capture capacity of 0.5 million tonnes per year per plant and used existing data and modeling techniques to analyze the feasibility of converting the captured CO2 into urea. The key findings indicate that this approach is promising and projects a 12.4% reduction in CO2 emissions from the cement industry if these 32 plants implement the CCUS technology. Additionally, it highlights a favorable market for the green urea produced, with an estimated output of 21.82 million tonnes per year, enough to offset a significant portion of India's reliance on urea imports. Financially, the study projects a six-year payback period and a 16.7% return on investment. It concludes that implementing CCUS technology to create green urea in Indian cement plants is technically and economically feasible. The approach has the potential to significantly reduce CO2 emissions while also supporting India's goal of self-sufficiency in urea production. However, further investigation and pilot-scale projects are recommended to validate the model's findings and optimize various aspects, such as managing flue gas impurities and leveraging waste heat for energy efficiency.

Digital Migration Infrastructure in return-writing: visualizing the migration landscape of India.

Digitization has carved out the migration patterns of immigrants over the recent years of increased technological interventions in human mobility. Migration infrastructures, which typically refer to the physical, commercial, humanitarian, and governmental modes of operation, are multidimensional in nature. Digital infrastructures are equally important to the physical ones as digital technologies facilitate these migration processes through agents like hardware, software, and mediating actors. Amongst the multiple forms of migration, the concept of return-writing and nostalgia-struck-returnees encompass individuals whose life trajectories run parallel to the homeland. The narrative of return to the homeland emerges aβs a dominant motif in literature due to the rising trends of globalization, the writers' reflection on their own migrant experiences, and publishing trends meeting the demand of the global book market. The objective is to assess the role of digital migration infrastructures in return migrations to India through a close reading of the selected texts and review of postcolonial literary theories by using conditional operation in Python. The study here explores the varied nuances of return migration with a primary focus on the external conditions of travel in migration literature. The paper aims to analyze the genre of return-writing in Indian English literature, through three novels over a period of two decades, i.e., from 2000-2023. The selected texts, beginning with Amit Chaudhuri's A New World (2000), Gun Island (2019) by Amitav Ghosh, and Devika Rege's novel Quarterlife (2023), offer a panoramic view of return migration. These novels are extensive in the time period of technological interventions and in depiction of return migration. The Python code examines the extent of existence of a set of digital migration infrastructure keywords by analyzing the content of the novels and creates bar plots and charts to offer a visual representation of the classification results. The resulting trend traces the increased intervention of Digital Migration Infrastructure in the recent migration literature.

THE HISTORY OF THE FORMATION OF THE HIGHER EDUCATION SYSTEM IN MODERN RUSSIA UNTIL 2016

This article is devoted to the analysis of the key stages of the development of higher education in Russia after the collapse of the USSR. The paper examines the factors that influenced the transformation of the system, from political and economic changes to social and cultural influences. The author examines the main reforms of higher education carried out in the 90s and in the 2000s, with an emphasis on their impact on the structure, content and financing of educational programs. The article analyzes the changes in the role of the state and the private sector in education, as well as the impact of globalization and international standards. Special attention is paid to the processes that have influenced the availability of higher education, the quality of training of specialists and their demand in the labor market. The article highlights the main problems faced by the higher education system in Russia during this period and analyzes the prospects for its further development.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Exploring the landscape of digital servitization: A systematic review

Background Digital servitization is a strategic transformation where companies adopt a service-focused approach in response to the prevailing trend of digitalization. Utilizing digital technology, businesses manage product and service operations and develop new value propositions. Further research into digital servitization is essential for maintaining competitiveness and meeting evolving market demands globally. This study aims to comprehend the evolution of digital services from theoretical and practical viewpoints and examines how these challenges are tackled. Methods Utilizing a systematic literature review methodology, the study adopts the PRISMA approach to identify 26 pertinent articles from a pool of 340 papers obtained through a SCOPUS database search. These papers were published between 2019 and 2024 and were retrieved using the keywords (‘digitalization OR digital AND servitization OR servitization’). The selection process involved scrutinizing titles, abstracts, and keywords based on predefined criteria. Results The findings reveal that current digital servitization research emphasizes five determinant variables: digitization, servitization, manufacturing servitization, process innovation, and product innovation. Additionally, response variables influenced by digital servitization were identified, including firm competitiveness, firm performance, financial performance, firm profitability, and sustainable performance. Discussion The results of this review point to inconsistencies, underscoring the necessity for additional research into the factors influencing digital servitization across companies beyond the manufacturing sector. This highlights the importance of gaining a deeper understanding of digital servitization strategies and their impacts across various industries. By expanding the scope of research to include a broader range of sectors, researchers can provide more comprehensive insights into the complexities and nuances of digital servitization adoption. This broader perspective enables a more thorough examination of the challenges and opportunities associated with implementing digital servitization strategies, ultimately contributing to a richer understanding of its implications for businesses across diverse industries.