Journal of Economy and Technology最新文献

User segmentation under blockchain-based privacy protection 基于区块链的隐私保护下的用户细分

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-09-23 DOI: 10.1016/j.ject.2025.09.002

Jiayao Wang , Hao Dong , Junwu Zhu , Yizhang Wang , Qilin Wu , Dongfang Zhao

{"title":"User segmentation under blockchain-based privacy protection","authors":"Jiayao Wang , Hao Dong , Junwu Zhu , Yizhang Wang , Qilin Wu , Dongfang Zhao","doi":"10.1016/j.ject.2025.09.002","DOIUrl":"10.1016/j.ject.2025.09.002","url":null,"abstract":"<div><div>User segmentation is a marketing strategy that allows companies to more accurately position their products and services, thereby enhancing marketing efficiency and customer satisfaction. However, existing user segmentation approaches continue to face significant challenges related to personal information leakage and data security. To address these issues, this study proposes a data storage architecture that integrates localized differential privacy mechanisms with blockchain technology to ensure user data security and reduce the risk of privacy breaches. Building on this foundation, a clustering algorithm based on a density distance metric, referred to as the KDE-KMeans algorithm, is designed and implemented. In this algorithm, a density distance score is introduced as the core metric for measuring the similarity between samples and cluster centers. This scoring mechanism comprehensively considers the traditional distance as the primary factor in similarity evaluation, while also incorporating the density difference between samples as a secondary factor. Together, these elements form a more detailed and robust similarity evaluation framework.Experimental results demonstrate that the KDE-KMeans algorithm significantly outperforms baseline algorithms in clustering accuracy. Its advantages are especially pronounced when processing datasets with substantial density variations between clusters, highlighting the algorithm’s effectiveness and adaptability in complex data environments.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 307-322"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037155","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

An innovative way of analyzing COVID topics with LLM 用LLM分析COVID主题的创新方法

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2024-11-19 DOI: 10.1016/j.ject.2024.11.004

Fahim Sufi

{"title":"An innovative way of analyzing COVID topics with LLM","authors":"Fahim Sufi","doi":"10.1016/j.ject.2024.11.004","DOIUrl":"10.1016/j.ject.2024.11.004","url":null,"abstract":"<div><div>In the aftermath of the COVID-19 pandemic, international landscapes have been profoundly reshaped, with shifts in political alliances, economic priorities, and socio-cultural norms. Such evolutions, reflected in the vast expanse of digital conversations, particularly on Twitter, necessitate advanced tools for analysis given their impact on policy and strategy. In this context, the presented study underscores the indispensability of Artificial Intelligence (AI) in discerning intricate patterns from voluminous and multifaceted Twitter data on COVID-19. Through an innovative methodology leveraging AI modalities such as language detection, sentiment analysis, topic analysis, Large Language Model (LLM), regression, clustering, this study distills textual features from 152,070 multilingual tweets across 58 languages, spanning 645 days from 15 July 2021–20 April 2023. Our analyses, automatically identify five pivotal COVID-19 discussion topics and expound on four critical factors—tweet language, retweet count, and positive and negative sentiments—that significantly influence these conversations. In essence, the paper's contributions lie in: 1) unveiling an AI-centric autonomous methodology for deep insights into COVID-19 discussions; 2) empirically validating this approach using a diverse, multilingual dataset that resulted in five key discussion areas; and 3) presenting 52 nuanced AI-generated observations that detail factors influencing these discussions. Comparative literature suggests that our approach offers unparalleled depth in AI-driven analytics related to COVID-19 discourse. In summary, this paper underline the pressing need to harness the power of AI-based Tweet analytics as an indispensable tool in formulating strategic decisions pertaining to disaster responses.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 35-56"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662528","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

Valuation of a sequential compound option considering electricity generation and transmission expansions 考虑发电和输电扩建的顺序复合方案的估值

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-07-16 DOI: 10.1016/j.ject.2025.07.002

Gazi Nazia Nur, Cameron A. MacKenzie, Kyung Jo Min

{"title":"Valuation of a sequential compound option considering electricity generation and transmission expansions","authors":"Gazi Nazia Nur, Cameron A. MacKenzie, Kyung Jo Min","doi":"10.1016/j.ject.2025.07.002","DOIUrl":"10.1016/j.ject.2025.07.002","url":null,"abstract":"<div><div>An integrated model considering both generation and transmission expansions is needed for long-term planning in the electrical sector because of the interlinked nature of these decisions. Our paper presents a sequential compound option framework to assist decision-makers in the electric power industry in evaluating generation and transmission expansion investments. By incorporating electricity demand uncertainty into the decision-making process, this framework offers a structured approach for assessing short-term generation decisions and long-term transmission decisions in a coordinated manner. Assuming electricity demand follows geometric Brownian motion (GBM), we employ a binomial lattice model to map uncertain demand and evaluate the value of the compound option. The locational marginal price (LMP), which reflects the physical constraints of the power network, is used as the basis for valuation in our model, and reductions in LMP resulting from expansions serve as the measure of project benefit. This integrated approach enables decision-makers to assess the feasibility of generation and transmission expansion projects within a unified framework and determine the optimal timing for exercising the underlying options.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 57-76"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703400","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

Synergizing transformer-based models and financial sentiment analysis: A framework for generative AI in economic decision-making 基于转换器的模型和金融情绪分析的协同作用：经济决策中生成人工智能的框架

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-07-23 DOI: 10.1016/j.ject.2025.07.003

Nouri Hicham , Nassera Habbat

{"title":"Synergizing transformer-based models and financial sentiment analysis: A framework for generative AI in economic decision-making","authors":"Nouri Hicham , Nassera Habbat","doi":"10.1016/j.ject.2025.07.003","DOIUrl":"10.1016/j.ject.2025.07.003","url":null,"abstract":"<div><div>This study introduces a new way to analyze financial sentiment by combining advanced transformer-based models with generative artificial intelligence (AI) to better understand the language and context of financial discussions. The objective is to enhance the predictive accuracy of market behavior through improved understanding of investor sentiment. The proposed sentiment analysis framework leverages six domain-specific datasets: Social Sentiment Indices (X-Scores), Fin-SoMe, SemEval-2017 Task 5, Fin-Lin, Sanders, and Taborda. These datasets, primarily sourced from social media, reflect diverse investor perspectives. Generative AI models, like GPT-3.5 and GPT-4, are used to create more data, and the meaning of words is enhanced using techniques like BERT and Word2Vec. The model is trained with a cross-entropy loss function and fine-tuned using Few-shot Learning, Chain-of-Thought reasoning, and ReAct strategies, ensuring computational efficiency. Experimental results show consistent improvements across all datasets in accuracy, precision, recall, specificity, and F1 score. The use of generative AI and transformer architectures makes the model stronger and better at understanding how investors feel in real financial situations. This research contributes to the field of explicable AI in finance by demonstrating the impact of domain-adapted models and generative techniques in advancing sentiment analysis. The findings offer practical value for investors and analysts seeking data-driven insights into market dynamics and decision-making processes.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 146-170"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827816","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

Encrypted intelligence: A comparative analysis of homomorphic encryption frameworks for privacy-preserving AI 加密智能：保护隐私的人工智能同态加密框架的比较分析

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-08-29 DOI: 10.1016/j.ject.2025.08.001

Aadit Shah, Surindernath Sivakumar, Prabakaran N

{"title":"Encrypted intelligence: A comparative analysis of homomorphic encryption frameworks for privacy-preserving AI","authors":"Aadit Shah, Surindernath Sivakumar, Prabakaran N","doi":"10.1016/j.ject.2025.08.001","DOIUrl":"10.1016/j.ject.2025.08.001","url":null,"abstract":"<div><div>This paper presents a comparative study of various homomorphic encryption models to evaluate their qualitative and quantitative benefits and drawbacks in performing computations on encrypted data. Within the framework of ethical AI, the study focuses on enhancing privacy, secrecy, and security, addressing limitations in existing privacy-preserving solutions such as differential privacy and secure multi-party computation. To provide context, related encryption paradigms such as symmetric, asymmetric, hybrid, and multi-party computation are also discussed. The review synthesizes findings from recent literature, comparing schemes based on key performance metrics including encryption and decryption speed, memory consumption and quantum resistance. Published benchmark results and case studies are used to highlight trade-offs between privacy guarantees and computational feasibility. The study highlights the practicality of homomorphic encryption for real-world applications, providing information on its potential to advance privacy-preserving AI while maintaining computational feasibility. The paper also surveys practical applications of homomorphic encryption in machine learning, secure data analytics, and federated learning, along with emerging challenges such as quantum-safe cryptography and hardware acceleration. This review serves as a consolidated reference for researchers and practitioners seeking to select appropriate encryption techniques for AI applications, providing both a broad overview of the field and a focused discussion on homomorphic encryption’s capabilities and limitations.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 252-265"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623301","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

AI-based decision support systems in Industry 4.0, a review 工业4.0中基于人工智能的决策支持系统综述

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2024-08-28 DOI: 10.1016/j.ject.2024.08.005

Mohsen Soori , Fooad Karimi Ghaleh Jough , Roza Dastres , Behrooz Arezoo

{"title":"AI-based decision support systems in Industry 4.0, a review","authors":"Mohsen Soori , Fooad Karimi Ghaleh Jough , Roza Dastres , Behrooz Arezoo","doi":"10.1016/j.ject.2024.08.005","DOIUrl":"10.1016/j.ject.2024.08.005","url":null,"abstract":"<div><div>The development of modern technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) resulted in a new era of industrial automation and data interchange, which is known as Industry 4.0. AI-based decision support systems (DSS) play a crucial role in this paradigm by enhancing the integration and processing of IoT and sensor data to optimize operations, improve productivity, and enable predictive maintenance. Machine learning models analyze production data and visual inspections to identify defects and ensure product quality. This review paper explores the transformative role of AI in enhancing DSS within Industry 4.0, highlighting key technologies including machine learning, deep learning, and natural language processing. It explores a number of applications, including supply chain optimization, energy management, predictive maintenance, quality control, and production planning, showing how AI-driven DSS can significantly boost operational dependability, cut costs, and improve efficiency. The article also discusses the AI-based DSS's architecture and implementation, with a focus on data management, user interface design, and IoT integration. Furthermore, it examines the challenges related to data quality, technical integration, and human factors, offering potential solutions and strategies for effective deployment. The study highlights the continued development of AI technologies and their potential to support autonomous decision-making in industrial settings by identifying new trends and areas for further research. This comprehensive review aims to provide valuable insights for researchers and practitioners, fostering a deeper understanding of the capabilities and future potential of AI-based DSS in Industry 4.0.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 206-225"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415081","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

Impact of entrepreneurial leadership on employees’ innovative behavior: A mediation analysis of organizational motivation to innovate and employees’ creativity 企业家领导对员工创新行为的影响：组织创新动机与员工创造力的中介分析

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-09-18 DOI: 10.1016/j.ject.2025.09.001

Yohannes Mekonnen Yesuf , Ziska Fields

{"title":"Impact of entrepreneurial leadership on employees’ innovative behavior: A mediation analysis of organizational motivation to innovate and employees’ creativity","authors":"Yohannes Mekonnen Yesuf , Ziska Fields","doi":"10.1016/j.ject.2025.09.001","DOIUrl":"10.1016/j.ject.2025.09.001","url":null,"abstract":"<div><div>Academic literature widely recognizes the importance of entrepreneurial leadership (EL) within agricultural research institutes. However, empirical investigations into how leadership styles influence employees’ innovative work behavior in this sector remain scarce. In particular, scholars have paid limited attention to how leaders in agricultural research institutes shape and enhance employees’ innovation-related behaviors. This study fills that gap by examining how EL influences employees’ innovative behavior (EIB), emphasizing organizational motivation to innovate (OMI) and creativity as the key mechanisms that leaders use to promote innovation among employees in Ethiopian agricultural research institutions. Using a proportionate systematic random sampling method, we drew the sample from the Amhara Agricultural Research Institute (ARARI). The study employed validated questionnaires to examine the hypothesized relationships among EL, EIB, OMI, and employees’ creativity (EC). The findings reveal that OMI serves as a mediator between EL and EIB. Moreover, EC mediates the relationship between OMI and EIB. The study discusses the insights derived from the findings and offers recommendations for fostering creative behavior among staff in agricultural research institutes. Doing so contributes to the literature on entrepreneurial leadership and innovative behavior, particularly relevant to agricultural research institutes in Ethiopia’s developing economy. It also extends previous empirical research by examining how EL influences EIB in agricultural research settings, with OMI and individual creativity as key mediators.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 296-306"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975952","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

A framework for integrating GPT into geoscience research 将GPT整合到地球科学研究中的框架

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2024-10-28 DOI: 10.1016/j.ject.2024.10.003

F.K. Sufi

{"title":"A framework for integrating GPT into geoscience research","authors":"F.K. Sufi","doi":"10.1016/j.ject.2024.10.003","DOIUrl":"10.1016/j.ject.2024.10.003","url":null,"abstract":"<div><div>Natural disasters like landslides and landfalls have a detrimental effect on global economy. Recent landslide research has heavily relied on textual and image datasets, particularly from sources like NASA, essential for machine learning model development in disaster domains. Nonetheless, limited dataset access and scarcity present significant obstacles. This paper proposes leveraging Generative Pre-Trained Transformer (GPT) technology to synthetically generate textual and image datasets, advancing GPT applications in geoscience. The advocated framework ensures methodological consistency, enhances reproducibility, and aligns research with objectives. Through a detailed case study of 115 synthetically generated landslide events with 14 key parameters, we demonstrate the framework’s capacity to generate large-scale datasets, perform statistical analysis, and enhance visualization. Moreover, we empirically validate the synthetic data by comparing it with real-world datasets, such as NASA’s Global Landslide Catalog and the Chittagong Landslide Database, showing the generated data’s adherence to expected real-world patterns. This study highlights GPT's efficacy in data analysis and its potential to aid various geoscience research phases. A comprehensive framework utilizing prompt engineering autonomously generates datasets and performs analytical tasks. GPT's visualization capability effectively communicates findings. This research advocates for integrating GPT-based technologies in geoscience endeavors, marking a pivotal step toward the future of AI-driven disaster management and data augmentation.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 226-237"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465649","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

Corrigendum to “Asynchronous distributed charging protocol for plug-in electric vehicles” [J. Econ. Technol., (2026) 29–47] 插电式电动汽车异步分布式充电协议的修正[J]。经济学。抛光工艺。，（2026） 29-47]

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-10-25 DOI: 10.1016/j.ject.2025.09.003

Yunfan Zhang , Yifan Su , Yue Chen , Feng Liu

引用次数: 0

Corrigendum to “Next generation of electronic medical record search engines to support chart reviews: A systematic user study and future research direction” [Journal of Economy and Technology (2024) 22–30] “支持图表审查的下一代电子病历搜索引擎：系统的用户研究和未来研究方向”的勘误表[经济与技术杂志（2024）22-30]

Journal of Economy and Technology Pub Date : 2026-01-01 Epub Date: 2025-10-25 DOI: 10.1016/j.ject.2025.09.004

Cheng Ye, Daniel Fabbri

引用次数: 0