Journal of Economy and Technology最新文献_第3页

UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation UCM-NetV2：一种高效、准确的皮肤病变分割深度学习模型

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2025-02-22 DOI: 10.1016/j.ject.2025.02.001

Chunyu Yuan , Dongfang Zhao , Sos S. Agaian

{"title":"UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation","authors":"Chunyu Yuan , Dongfang Zhao , Sos S. Agaian","doi":"10.1016/j.ject.2025.02.001","DOIUrl":"10.1016/j.ject.2025.02.001","url":null,"abstract":"<div><div>Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes an efficient deep learning model, UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel \"cyber-structure\" com- bining Multilayer Perceptron and CNN layers, improving prediction accuracy while maintaining an ultra-lightweight design with only 0.046 million parameters. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in accuracy and com- putational efficiency, achieving up to 67 times faster inference speeds than U-Net and requiring less than 0.04 GFLOPs. These advancements make skin lesion analysis more accessible, particularly in resource-limited settings, enabling proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 is on <span><span>https://github.com/chunyuyuan/UCMV2-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 251-263"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854789","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

Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review 针对经济预测和中小企业增长复杂性的创新机器学习方法：全面回顾

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2025-01-04 DOI: 10.1016/j.ject.2025.01.001

Mustafa I. Al-Karkhi , Grzegorz Rza̧dkowski

{"title":"Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review","authors":"Mustafa I. Al-Karkhi , Grzegorz Rza̧dkowski","doi":"10.1016/j.ject.2025.01.001","DOIUrl":"10.1016/j.ject.2025.01.001","url":null,"abstract":"<div><div>Economic forecasting and small and medium-sized enterprises (SMEs) growth prediction have become essential tools for guiding policy, business strategy, and economic development in an increasingly data-driven world. This paper reviews recent advancements in economic regression and SME growth forecasts, with a focus on the application of machine learning (ML) techniques. Specifically, the findings highlight that the integration of ensemble methods and deep learning models has achieved significant improvements in prediction accuracy, while interpretability tools such as SHAP and LIME enhance transparency and user trust. It provides a structured analysis of diverse methodologies that includes ensemble methods, deep learning models, and interpretability tools to evaluate their effectiveness and limitations in addressing the complexities of economic and SME data. This review categorizes studies by regional focus to highlight unique challenges in different economic landscapes and the adaptability of various forecasting models. Key challenges—such as imbalanced data, feature selection, and the integration of real-time data—were identified as critical factors for enhancing prediction reliability and applicability. By comparing existing surveys and identifying gaps, this review presents actionable insights and proposes future research directions that emphasize the need for integrative models that combine Explainable Artificial Intelligence (XAI) with cross-regional data fusion for more accurate and adaptable economic forecasts. These integrative models have the potential to achieve greater regional generalizability by the offering of better decision-making tools for policymakers. The findings underscore the transformative role of ML and XAI in economic forecasting and offer valuable guidance for researchers and decision-makers to optimize forecasting models for business growth and economic planning.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 109-122"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600456","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

ChatGPT and CLT: Investigating differences in multimodal processing ChatGPT和CLT：研究多模态加工的差异

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2024-12-07 DOI: 10.1016/j.ject.2024.11.008

Michael Cahalane, Samuel N. Kirshner

引用次数: 0

Deciphering algorithmic collusion: Insights from bandit algorithms and implications for antitrust enforcement 破解算法合谋：从强盗算法的见解和对反垄断执法的影响

Journal of Economy and Technology Pub Date : 2025-11-01 Epub 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":"2025-11-01","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

The role of parental circumstances and luck in shaping socioeconomic success: A simulation-based analysis of talent 父母环境和运气在塑造社会经济成功中的作用：基于模拟的人才分析

Journal of Economy and Technology Pub Date : 2025-11-01 Epub 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":"2025-11-01","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

Blockchains effects on responsiveness to recalls in the food and beverage industry 区块链对食品和饮料行业召回反应的影响

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2025-05-05 DOI: 10.1016/j.ject.2025.05.001

Abbas Keramati , Bethany Siau , Tyler Bellitto , Jafar Heydari , Tanya Panchal

{"title":"Blockchains effects on responsiveness to recalls in the food and beverage industry","authors":"Abbas Keramati , Bethany Siau , Tyler Bellitto , Jafar Heydari , Tanya Panchal","doi":"10.1016/j.ject.2025.05.001","DOIUrl":"10.1016/j.ject.2025.05.001","url":null,"abstract":"<div><div>Blockchain technology, by revolutionizing the way businesses use data, is shifting the cost-responsiveness frontier. While the most popular application of blockchain is cryptocurrency, nowadays it is touching many other businesses including the food and beverage industry. This paper is a short survey in assessing the usefulness of blockchain technology in the food and beverage supply chain, with a narrow focus on the impact on the product recalls. While recalls are crucial in the food and beverage industry, as they deal with public health, they happen frequently and therefore an efficient and responsive recall process is essential. This paper investigates whether US companies utilizing blockchain technology experience shorter recall durations. Data from Food and Drug Administration (FDA) recall datasets, specifically targeting companies implementing blockchain technology, are analyzed using statistical analysis methods. The results reveal that companies adopting blockchain technology have significantly shorter recall times, demonstrating their usefulness in food and beverage recalls, along with its other advantages. This study highlights the potential of blockchain in improving recall management within the food and drink industry and provides applicable insights for food and beverage supply chain managers.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 283-298"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068898","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

Strategies to achieving deep decarbonisation in power generation: A review 实现发电深度脱碳的战略：综述

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2024-04-13 DOI: 10.1016/j.ject.2024.04.003

Abdullah Alotaiq

{"title":"Strategies to achieving deep decarbonisation in power generation: A review","authors":"Abdullah Alotaiq","doi":"10.1016/j.ject.2024.04.003","DOIUrl":"10.1016/j.ject.2024.04.003","url":null,"abstract":"<div><div>Transitioning to low-carbon power generation is crucial for combatting climate change and achieving sustainability goals. This review delves into the literature on deep decarbonisation strategies within the power sector, examining the economic costs and associated challenges. Methodologically, over 2000 studies were screened, with 30 systematically reviewed to extract insights into transitional strategies and cost implications. The literature underscores the pivotal role of power generation in reducing greenhouse gas emissions, emphasising the urgency of substantial emissions reductions by 2050. While faster transitions to low-carbon power may incur higher costs initially, delaying the transition could lead to significant economic and environmental consequences. Two primary strategies emerge, one centred on renewable energy sources and another incorporating diverse low-carbon technologies. However, hybrid approaches that combine various technologies are highlighted as optimal for minimising costs and enhancing flexibility. In conclusion, this study highlights the importance of identifying cost-effective pathways for power sector decarbonisation, with implications for future research and policymaking to address the challenges effectively.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 22-33"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140784595","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

Business confidence in the shift to renewable energy: A country-specific assessment in major Asian economies 向可再生能源转型的商业信心：对亚洲主要经济体的国别评估

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2024-08-12 DOI: 10.1016/j.ject.2024.08.002

Irsan Hardi , Ghalieb Mutig Idroes , Yoshihiro Hamaguchi , Muhlis Can , Teuku Rizky Noviandy , Rinaldi Idroes

{"title":"Business confidence in the shift to renewable energy: A country-specific assessment in major Asian economies","authors":"Irsan Hardi , Ghalieb Mutig Idroes , Yoshihiro Hamaguchi , Muhlis Can , Teuku Rizky Noviandy , Rinaldi Idroes","doi":"10.1016/j.ject.2024.08.002","DOIUrl":"10.1016/j.ject.2024.08.002","url":null,"abstract":"<div><div>The growing awareness of the importance of transitioning to sustainable energy sources emphasizes the necessity of fostering business optimism toward renewable energy, as businesses wield significant influence in driving innovation and scaling up renewable energy deployment. Therefore, this study investigates the impact of business confidence on long-term renewable energy generation in selected major Asian economies: China, Japan, South Korea, Indonesia, and Turkey. Through country-specific assessments, we utilized three methods capable of yielding long-term empirical results: Fully-Modified OLS (FMOLS), Dynamic OLS (DOLS), and Canonical Cointegrating Regressions (CCR). The study also conducted a robustness check by utilizing the Robust Least Squares (RLS) method, preceded by multiple preliminary tests, to ensure the validity and reliability of the results. The findings show that businesses in all selected countries exhibit confidence toward long-term renewable energy. However, there are variations in the confidence level, with businesses in Japan, South Korea, and Turkey demonstrating high confidence while those in China and Indonesia show low confidence. The study also found a trade-off between business confidence levels and energy consumption. In Japan, South Korea, and Turkey, high business confidence correlates with a negative impact of energy consumption, while in Indonesia, low business confidence is aligned with a positive effect of energy consumption on renewable energy. This suggests that business confidence and energy consumption dynamics influence renewable energy development. Policy recommendations tailored to each of the selected countries are provided to address these findings, aiming to enhance business trust and optimism in renewable energy within major Asian economies.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 44-68"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402647","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

From innovation capability to green innovation capability: Evidence from Chinese big tech firms 从创新能力到绿色创新能力：来自中国大型科技公司的证据

Journal of Economy and Technology Pub Date : 2025-11-01 Epub Date: 2025-01-11 DOI: 10.1016/j.ject.2025.01.003

Martín Hernani-Merino , Jorge Tello-Gamarra , David Mayorga , Julio Zevallos

引用次数: 0

Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research 利用联邦学习对罕见疾病研究中的多机构电子健康记录进行隐私保护分析

Journal of Economy and Technology Pub Date : 2025-11-01 Epub 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":"2025-11-01","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