Journal of Economy and Technology最新文献_第2页

Examining the impacts of information and communication technology (ICT) on national development and wellbeing: A global perspective

Journal of Economy and Technology Pub Date : 2024-11-28 DOI: 10.1016/j.ject.2024.11.006

Ming-Yi Wu

引用次数: 0

The role of parental circumstances and luck in shaping socioeconomic success: A simulation-based analysis of talent

Journal of Economy and Technology Pub Date : 2024-11-16 DOI: 10.1016/j.ject.2024.11.003

Hana Hebishima , Shin-ichi Inage

{"title":"The role of parental circumstances and luck in shaping socioeconomic success: A simulation-based analysis of talent","authors":"Hana Hebishima , Shin-ichi Inage","doi":"10.1016/j.ject.2024.11.003","DOIUrl":"10.1016/j.ject.2024.11.003","url":null,"abstract":"<div><div>This study investigates the interplay between parental circumstances, talent, and luck in shaping long-term socioeconomic success through an agent-based simulation model. Building on prior research on the influence of socioeconomic status (SES) and parental circumstances on educational outcomes, the model simulates how parental circumstances enhance innate talent through education and examines how this talent interacts with luck throughout an individual’s life. The simulation is divided into two phases: an educational phase and a working phase. Our results reveal that while parental circumstances and education amplify talent and increase the potential for success, cumulative luck plays the most decisive role in determining savings at age 60. A strong positive correlation is observed between cumulative luck and lifetime savings, whereas the direct influence of talent, even when enhanced through education, remains limited. Additionally, favorable parental circumstances elevate baseline savings, even for individuals experiencing misfortune, underscoring the importance of early educational advantages. These findings highlight that although talent and parental support are essential for fostering success, luck ultimately dominates in shaping financial outcomes. The study offers critical policy insights, advocating for equitable access to education and strategic investments to mitigate the disproportionate impact of luck, promote social mobility, and reduce structural inequalities across generations.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 223-236"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Techno-economic and emissions comparison of waste-to-fuel via hydrothermal liquefaction, transesterification, and incineration

Journal of Economy and Technology Pub Date : 2024-11-07 DOI: 10.1016/j.ject.2024.11.002

Muhammad Usman

{"title":"Techno-economic and emissions comparison of waste-to-fuel via hydrothermal liquefaction, transesterification, and incineration","authors":"Muhammad Usman","doi":"10.1016/j.ject.2024.11.002","DOIUrl":"10.1016/j.ject.2024.11.002","url":null,"abstract":"<div><div>The global shift toward sustainable waste management and renewable energy has sparked interest in biofuel production from sewage sludge (SS). This study evaluated four waste-to-biofuel processes like Hydrothermal Liquefaction (HTL) with upgrading, Transesterification, and Incineration with and without energy recovery using ASPEN Plus V12 to assess their techno-economic, energy, and environmental performance. HTL with upgrading emerged as the most efficient, generating ∼4,000,000 MJ/year and emitting ∼700 tonnes/year of CO<sub>2</sub>. Transesterification yielded ∼2,850,000 MJ/year, emitting ∼1200 tonnes/year due to post-lipid extraction incineration. Incineration without energy recovery was least efficient, consuming ∼5,000,000 MJ/year and emitting ∼3000 tonnes/year of CO<sub>2</sub>, with energy recovery yielding only ∼1,250,000 MJ/year. Financially, HTL with upgrading demonstrated strong profitability with a potential Net Present Value (NPV) of 112.9 million US dollars (MUS$), while Transesterification achieved an NPV of 23.4 MUS$. Both processes were sensitive to operating costs: a 50 % increase could reduce HTL’s NPV to 62.7 MUS$, while pushing Transesterification into a loss. Capital cost reductions could further boost HTL’s profitability, highlighting its economic resilience, unlike incineration, which remained financially unviable. In summary, HTL with upgrading offered 30 % higher energy output and 70 % lower emissions than incineration, making it a scalable, sustainable approach for SS management and biofuel production. However, a complete life cycle assessment could further enhance its potential by identifying additional environmental and economic benefits.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 237-250"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research

Journal of Economy and Technology Pub Date : 2024-11-06 DOI: 10.1016/j.ject.2024.11.001

Karthik Meduri , Geeta Sandeep Nadella , Akhila Reddy Yadulla , Vinay Kumar Kasula , Mohan Harish Maturi , Steven Brown , Snehal Satish , Hari Gonaygunta

{"title":"Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research","authors":"Karthik Meduri , Geeta Sandeep Nadella , Akhila Reddy Yadulla , Vinay Kumar Kasula , Mohan Harish Maturi , Steven Brown , Snehal Satish , Hari Gonaygunta","doi":"10.1016/j.ject.2024.11.001","DOIUrl":"10.1016/j.ject.2024.11.001","url":null,"abstract":"<div><div>This research announces that the fresh federated learning structure is designed to enhance the privacy-preserving analysis of electronic health records (EHRs), and multiple institutions in this framework permit secure collaboration among institutions, allowing them to train machine-learning replicas without directly sharing patient data. We implemented and evaluated numerous machine-learning models to forecast patient treatment needs, including Logistic Regression, Decision-Tree-Classifiers, Support-Vectors-Classifiers, Random-Forests, and Stacking-Classifiers. The Random Forest classifier achieved the best performance with an accuracy of 90 % and an F1 score of 80 %, demonstrating that it handled complex and imbalanced datasets. This FL-based approach not only complies with privacy regulations such as HIPAA and GDPR but also overcomes significant challenges in data sharing, making it ideal for rare disease research. By enabling secure data aggregation across institutions, the framework significantly enhances the ability to study rare diseases and accelerates the discovery of new treatments. Future directions include extending this framework to other areas of healthcare and incorporating advanced machine-learning techniques to enhance its capabilities further. This research sets the new standard for secure and collaborative healthcare data analysis and promotes innovation and ethical practices in rare disease research.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 177-189"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global adoption of generative AI: What matters most?

Journal of Economy and Technology Pub Date : 2024-10-28 DOI: 10.1016/j.ject.2024.10.002

Hassnian Ali , Atta ul Mustafa , Ahmet Faruk Aysan

{"title":"Global adoption of generative AI: What matters most?","authors":"Hassnian Ali , Atta ul Mustafa , Ahmet Faruk Aysan","doi":"10.1016/j.ject.2024.10.002","DOIUrl":"10.1016/j.ject.2024.10.002","url":null,"abstract":"<div><div>This study investigates the determinants of generative AI adoption across 136 countries, leveraging cross-sectional data from 2023 and employing a negative binomial regression model to address data overdispersion. Generative AI is a transformative technology that enhances operational efficiency, drives innovation, and creates economic value across sectors. Key findings reveal that IT infrastructure, R&D investments, and company investment in emerging technologies significantly foster generative AI adoption, while misaligned government policies may hinder it. The analysis identifies crucial determinants, including technological infrastructure, economic stability, regulatory environments, and workforce readiness, as pivotal to adoption rates. The study provides actionable insights for policymakers, industry leaders, and researchers, advocating for tailored policies, strategic investment in high-speed internet and cloud services, and refining government incentives to align with AI sector needs. This research uniquely contributes by offering a comprehensive, cross-country perspective on factors influencing generative AI adoption.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 166-176"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deciphering algorithmic collusion: Insights from bandit algorithms and implications for antitrust enforcement

Journal of Economy and Technology Pub Date : 2024-10-19 DOI: 10.1016/j.ject.2024.10.001

Frédéric Marty , Thierry Warin

{"title":"Deciphering algorithmic collusion: Insights from bandit algorithms and implications for antitrust enforcement","authors":"Frédéric Marty , Thierry Warin","doi":"10.1016/j.ject.2024.10.001","DOIUrl":"10.1016/j.ject.2024.10.001","url":null,"abstract":"<div><div>This paper explores algorithmic collusion from both legal and economic perspectives, underscoring the increasing influence of algorithms in firms’ market decisions and their potential to facilitate anti-competitive behaviour. By employing bandit algorithms as a model—typically used in uncertain decision-making scenarios—we shed light on the mechanisms of implicit collusion that occur without explicit communication. Legally, the primary challenge lies in detecting and categorizing possible algorithmic signals, particularly when they function as unilateral communications. Economically, the task of distinguishing between rational pricing strategies and collusive patterns becomes increasingly complex in the context of algorithm-driven decisions. The paper stresses the need for competition authorities to identify atypical market behaviours. Striking a balance between algorithmic transparency and the prevention of collusion is critical. While regulatory measures could mitigate collusive risks, they might also impede the development of algorithmic technologies. As this form of collusion gains prominence in competition law and economics discussions, understanding it through models like bandit algorithms becomes essential, especially since these algorithms have the potential to converge more rapidly toward supra-competitive prices equilibria.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 34-43"},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creative destruction and artificial intelligence: The transformation of industries during the sixth wave 创造性破坏与人工智能：第六次浪潮中的产业变革

Journal of Economy and Technology Pub Date : 2024-09-30 DOI: 10.1016/j.ject.2024.09.004

Ramazan Uctu , Nadide Sevil Halici Tuluce , Mustafa Aykac

{"title":"Creative destruction and artificial intelligence: The transformation of industries during the sixth wave","authors":"Ramazan Uctu , Nadide Sevil Halici Tuluce , Mustafa Aykac","doi":"10.1016/j.ject.2024.09.004","DOIUrl":"10.1016/j.ject.2024.09.004","url":null,"abstract":"<div><div>Artificial intelligence (AI) is considered to be a key driver in the emerging sixth wave of technological advancement, one that has profound economic implications. The emergence of AI has led to significant changes in a wide range of different sectors, the reshaping of existing sectors, and the disruption of traditional business practices. This transformative power aligns with Schumpeter's theory of creative destruction, in which innovations are seen to cause older technologies and business models to become obsolete, leading to significant economic shifts. The role of AI in the sixth wave is crucial not only because of its immediate applications in the area of automation and data processing but also because of its broader capacity to drive a new cycle of innovation and economic renewal. This ongoing cycle, driven by creative destruction, challenges businesses to adapt and evolve, ultimately contributing to a more robust and dynamic economy. In this article, the authors explore the ways in which AI promotes innovation and its effect on economic expansion, using Schumpeter's theory of creative destruction.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"2 ","pages":"Pages 296-309"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging the digital sustainable growth model (DSGM) to drive economic growth: Transforming innovation uncertainty into scalable technology 利用数字可持续增长模式（DSGM）推动经济增长：将创新的不确定性转化为可扩展的技术

Journal of Economy and Technology Pub Date : 2024-09-19 DOI: 10.1016/j.ject.2024.09.003

Ahmed Shalaby

引用次数: 0

Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability 新兴经济体采用农业 4.0 的挑战：对智能农业和可持续性的影响

Journal of Economy and Technology Pub Date : 2024-09-16 DOI: 10.1016/j.ject.2024.09.002

Md Hasibul Islam , Md. Zahidul Anam , Mohammad Rashedul Hoque , Maksuraton Nishat , A.B.M. Mainul Bari

{"title":"Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability","authors":"Md Hasibul Islam , Md. Zahidul Anam , Mohammad Rashedul Hoque , Maksuraton Nishat , A.B.M. Mainul Bari","doi":"10.1016/j.ject.2024.09.002","DOIUrl":"10.1016/j.ject.2024.09.002","url":null,"abstract":"<div><p>To ensure food security in this age of production and supply disruption, the agricultural sectors of emerging economies are gradually adopting more smart technologies to achieve sustainability. However, literature on the challenges of adopting Agriculture 4.0-based smart farming technologies is still very limited. This research, therefore, explores the contextual interrelation among the challenges to adopting Agriculture 4.0-based smart technologies in the agricultural production system from a developing country's perspective and prioritizes the identified challenges. A case study was conducted in Bangladesh, an emerging economy, where data was collected through interviews and focus group discussion sessions. A total of 21 challenges were finalized as relevant to the country's context. The Interpretive Structural Modeling (ISM) technique was deployed to develop a hierarchical structure depicting the challenges' interrelations. The challenges were later ranked based on their relevant weight using the Best-Worst Method (BWM). This study finds technological complexity, lack of collaboration among different stakeholders, inadequate support from the government, and lack of action plans to have very high driving power. Challenges such as high initial investment and operational costs, lack of skilled workforce, and farmers' resistance were found to be dependent challenges. This study is expected to contribute by providing a deeper insight into the challenges of adopting Agriculture 4.0 in emerging economies so that practitioners can take effective mitigating measures to streamline the plant-based agricultural production systems to promote food security and sustainability.</p></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"2 ","pages":"Pages 278-295"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949948824000416/pdfft?md5=242f7f4d20e45dcd47e9f166bc4c68ee&pid=1-s2.0-S2949948824000416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM technologies and information search 法律硕士技术和信息搜索

Journal of Economy and Technology Pub Date : 2024-08-29 DOI: 10.1016/j.ject.2024.08.007

Lin Liu , Jiajun Meng , Yongliang Yang

引用次数: 0