Journal of Economy and Technology最新文献

Objective

Little research has been done on the user-centered document ranking approach, especially in a crowdsourcing chart review environment. As the starting point of designing and implementing the next generation of Electronic Medical Record (EMR) search engines, a systematic user study is needed to better understand the users' needs, challenges, and future research directions of EMR search engines.

Materials and methods

One primary observation during the user study is the need for a ranking method to better support the so-called "early stopping" reviewing strategy (i.e., reviewing only a subset of EMRs of one patient to make the final decision) during the clinical chart reviews. The authors proposed two novel user-centered ranking metrics: "critical documents" and "negative guarantee ratio," to better measure the power of a ranking method in supporting the “early stopping” requirements during clinical chart reviews.

Results

The evaluation results show that i) traditional information retrieval metrics, such as the precision-at-K, have limitations in guiding the design and development of EMR search engines to better support clinical chart reviews; ii) there is not a global optimal ranking method that fits the needs of different chart reviews and different users; iii) a learning-to-rank approach cannot guarantee a stable and optimal ranking for different chart reviews and different users; and iv) A user-centered ranking metric, such as the negative guarantee ratio (NGR) metric is able to measure the “early-stopping” performance of ranking methods.

Conclusions

User-centered ranking metrics can better measure the power of ranking methods in supporting clinical chart reviews. Future research should explore more user-centered ranking metrics and evaluate their impact on real-world EMR search engines.

Natural and manmade disasters like landslides, floods, earthquake, cyclone, shooting, riots have detrimental effect in precious life, infrastructure, and economy. This study addresses the need for a comprehensive analysis of Generative Pre-Trained Transformers (GPT) in the context of open-source disaster intelligence, a topic where existing literature remains fragmented. Employing a systematic approach, a query scheme incorporating 11 at. keywords was devised, resulting in the acquisition of 53 relevant studies. These studies were meticulously reviewed and synthesized to propose six dimensions of GPT-based open-source disaster intelligence, yielding critical insights into disaster management strategies. Within these 6 dimensions, 24 studies were categorized under “Social Media Analytics for Disaster Response” dimension, 7 on “Disaster Prediction,” 11 on “Disaster Management,” 5 on “Disaster Support Via Technology”, 3 on “Climate Change and Disaster Communication,” and 5 studies were classified under the “General Disaster Analysis” dimension. Leveraging advanced methodologies and machine learning driven tools such as PRISMA, Litmaps, and VOSviewer, this research not only identifies key trends and collaborative efforts but also provides valuable bibliographical insights for researchers and practitioners in the field. For example, the co-citation analysis demonstrated a total of 3703 authors, among whom 51 authors garnered a minimum of 10 citations, leading to the identification of 3 distinct clusters. By addressing a critical research gap and offering a methodologically robust examination, this study contributes significantly to the advancement of knowledge in GPT-based open-source disaster intelligence, facilitating informed decision-making and enhancing disaster response strategies worldwide.

This study presents an archetype-based approach to designing effective strategies for low-carbon transitions in the power sector. To achieve global energy transition goals, a renewable energy transition is critical, and understanding diverse energy landscapes across different countries is essential to designing effective renewable energy policies and strategies. Using a clustering approach, this study identifies 12 power system archetypes based on several features, including energy and socio-economic indicators of 187 UN countries. Each archetype is characterised by distinct challenges and opportunities, ranging from high dependence on fossil fuels to low electricity access, low economic growth, and insufficient contribution potential of renewables. Archetype A, for instance, consists of countries with low electricity access, high poverty rates, and limited power infrastructure, while Archetype J comprises developed countries with high electricity demand and installed renewables. The study findings have significant implications for renewable energy policymaking and investment decisions, with policymakers and investors able to use the archetype approach to identify suitable renewable energy policies and measures and assess renewable energy potential and risks. Overall, the archetype approach provides a comprehensive framework for understanding diverse energy landscapes and accelerating decarbonisation efforts for the power sector.

There are many factors that persuade or discourage business owners or managers of small and medium-sized firms to adopt sustainable policies. It is important to understand how different factors are interlinked to influence the business owners’/managers’ decisions for sustainability. Therefore, the gap between policies and their implementation in the workplace causes the failure of the adoption of sustainable policies in business practices. To provide a clear picture, the present study intends to investigate the factors that might foster/hinder the adoption of sustainable policies in business practices. The study provides a systematic literature review on sustainable business policies from 2015 to 2023. We consider what are the key factors supporting or obstructing the successful implementation of a sustainability framework in the business practices in SMEs. Our findings reveal that data-centered solutions, sensor aided cyber physical systems, green consumption, vendor support, technological roadmap, gain legitimacy on the use of data analytics, and acceleration of digital transformation are the most significant enablers for sustainable business practices in SMEs. In addition, cloud enterprise resource planning is found to be the most significant enabler for developing countries. The barriers are mainly related to the implantation of the enablers. Among those major barriers are security of the data storage, lack of trust, lack of top management support, resistance to change to the new technology, lack of skilled workforce, data breaches, and poor network security. Our findings have key policy implications for practitioners and policy makers.

The underlying objective of the study is to investigate the energy efficiency improvement potentiality of total technological innovation and its components, such as residential and non-residential technological innovations, by controlling for economic growth and energy prices in China. To this end, the study employs Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Fully Modified Ordinary Least Squares (FMOLS) based on the unit root testing and Johansen and Juselius cointegration test outcomes. Thus, the results confirm the long-run relationship among the series under investigation. Further, it shows that total technological innovation and its components, i.e., residential and non-residential technological innovations, have the potential to improve energy efficiency. However, economic growth and energy prices are reducing energy efficiency. Moreover, these findings are stable while applying the other alternative methods for robustness. Therefore, the study suggests policy recommendations aimed at promoting technology that may be either residential or non-residential technological innovations to increase energy efficiency. Additionally, it urges designing national-level energy policy to boost economic progress by conserving energy along with measures to stabilize energy prices.

ChatGPT is an Artificial Intelligence (AI)-powered Natural Language Processing (NLP) tool that comprehends and produces text in response to given commands. It can be adopted for various requirements, like answering our inquiries, assisting us with content creation, translating languages, and more. The fourth industrial revolution, called "Industry 4.0," denotes a new production age focused on automation, digitalisation, and real-time connectivity of production systems. ChatGPT can help Industry 4.0 in a variety of ways. ChatGPT and AI-driven process optimisation is poised to revolutionise Industry 4.0 by enhancing productivity, quality assurance, and efficiency. For developing this paper, various articles on ChatGPT/ AI for Industry 4.0 were identified through Scopus, ScienceDirect, Google Scholar and ResearchGate. Industry 4.0 progresses due to the incorporation of cutting-edge technology like AI, Machine Learning (ML), and NLP and Manufacturing operations are changing. The ChatGPT language model is becoming well-known for daily use because of its promising applications. In the framework of Industry 4.0, it promises to revolutionise processes to assist advancement in boosting business productivity and efficiency. This paper studies the major need for ChatGPT for Industry 4.0. Various associated features, traits and versatile competencies of ChatGPT for Industry 4.0 are identified and briefed. Finally, it identifies and discusses the significant applications of ChatGPT for Industry 4.0. ChatGPT is a very flexible and efficient method for creating human-machine interfaces and automatically generating text, which provides proper knowledge and guidance to the employee. Applications for ChatGPT include chatbots, virtual assistants, automated customer care, language translation, and content production. In future, it will become an effective tool for enhancing communication and automating processes in Industry 4.0.

In contemporary times, nations across the globe, encompassing both developed and emerging economies, are actively pursuing the objective of achieving sustainable economic growth. China, as a prominent emerging nation, holds a significant position in the worldwide environment. However, it is imperative to acknowledge that China's ecological impact is substantial, as evidenced by its considerable contribution of almost one-third of the total global carbon emissions in the year 2021. The encouraging aspect lies in the emergence of China as a significant economic power, which may be attributed to the positive impact of technological advancements. The scientific literature suggests that the use of novel technologies has the potential to contribute significantly to mitigating environmental degradation. The objective of this study is to investigate the impact of China's economic growth and technological advancements on ecological footprint throughout the period spanning from 1985 to 2020. This study utilizes the ecological footprint concept as a means to assess environmental degradation in China. The ARDL cointegration methodology is utilized to estimate the long- and short-term impacts, and the outcomes are validated through the application of further cointegration regression techniques. Furthermore, the pairwise Granger causality analysis is employed to examine the causal links among the variables. The empirical evidence suggests that economic expansion exacerbates environmental degradation in China, whereas technological progress plays a mitigating role in addressing this issue. The results of the study also demonstrated a bidirectional causality between economic growth and technological innovation, suggesting that China's economic growth and technological progress mutually reinforce one another. The paper proposes green technical solutions that have the potential to mitigate environmental degradation while maintaining economic growth. Sustaining long-term economic prosperity necessitates the implementation of both carbon emission taxation and strategic investments in environmentally beneficial technologies.

Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired by the structure and function of the human brain. In the context of supply chain management, ANNs can be used for demand forecasting, inventory optimization, logistics planning, and anomaly detection. ANNs help companies to optimize their inventory levels, production schedules and procurement activities in terms of productivity enhancement of part production. By considering multiple variables and constraints, ANNs can identify the most efficient routes, allocate resources effectively, and reduce costs. Furthermore, ANNs can identify anomalies as well as abnormalities in supply chain data, such as unexpected demand patterns, quality issues and disruptions in logistics operations in order to minimize their impact on the supply chain. ANNs can also analyze supplier performance data, including quality, delivery times and pricing in order to assess the reliability and effectiveness of suppliers. This information can support decision-making processes in supplier evaluation and selection processes. Moreover, ANNs can continuously monitor supplier performance, raising alerts for deviations from predefined criteria to provide safe and secure supply chain in part production processes. By analyzing various data sources, including weather conditions, and political instability, ANNs can identify and mitigate risks in terms of safety enhancement of supply chain processes. Artificial neural networks in supply chain management is studied in the research work to analyze and enhance performances of supply chain management in process of part manufacturing. New ideas and concepts of future research works are presented by reviewing and analyzing of recent achievements in applications of artificial neural networks in supply chain management. Thus, productivity of part manufacturing can be enhanced by promoting the supply chain management using the artificial neural networks.

Escalation of extreme weather events represents substantial threat to power system infrastructure. Mobile emergency generators (MEGs) can form part of a flexible restoration strategy against such destructive events. However, with continued expansion of distribution networks, quantification of the impact of MEGs has become increasingly challenging owing to extreme-weather-event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. First, we propose a distance measure to represent the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs under different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of the entire distribution network under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structural features, and power grid resilience. The proposed method enables analytic assessment of the impact of MEG deployment on the resilience of distribution networks, and provides beneficial insights to help formulate efficient measures for enhancing resilience. Case studies demonstrated that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive potential failure scenarios.