Journal of Economy and Technology最新文献

{"title":"Synergizing transformer-based models and financial sentiment analysis: A framework for generative AI in economic decision-making","authors":"Nouri Hicham ,&nbsp;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":"2025-07-23","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}

{"title":"Valuation of a sequential compound option considering electricity generation and transmission expansions","authors":"Gazi Nazia Nur,&nbsp;Cameron A. MacKenzie,&nbsp;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":"2025-07-16","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}

{"title":"A resource-efficient ensemble machine learning framework for detecting rank attacks in RPL-based IoT networks","authors":"Sattenapalli Kalyani,&nbsp;Vydeki D","doi":"10.1016/j.ject.2025.06.003","DOIUrl":"10.1016/j.ject.2025.06.003","url":null,"abstract":"<div><div>The Internet of Things (IoT) links intelligent devices across various sectors, including healthcare, smart cities, and industrial systems, aiming to improve everyday experiences. Despite its benefits, RPL-based routing is commonly adopted in IoT networks operating under low-power and lossy network conditions, which are susceptible to security vulnerabilities, most notably Rank attacks, which distort the routing structure and reduce network performance. Traditional rule-based defenses struggle to scale with dynamic traffic and complex attack patterns, necessitating more adaptive solutions. This paper presents a lightweight, ensemble-based Intrusion Detection System (IDS) that integrates Support Vector Machine (SVM) and XGBoost algorithms to detect Rank attacks in RPL-based IoT environments. A comprehensive dataset was generated by simulating both static and dynamic Rank attack scenarios. Mutual Information and Recursive Feature Elimination (RFE) methods were employed for feature selection. The developed ensemble model exhibited robust performance, reaching an average accuracy of 98.4 %, a precision of 98.2 %, a recall of 97.1 %, an F1-score of 0.97, and a False Positive Rate (FPR) is 1.8 %, an Area Under the Curve (AUC) greater than 0.96 when evaluated using 5-fold cross-validation. Comparative experiments were conducted with traditional machine learning algorithms such as Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF), alongside advanced deep learning architectures including Long Short-Term Memory (LSTM) networks and hybrid models like CNN-LSTM, to effectively demonstrate the superior efficiency and detection capabilities of the proposed approach. Unlike deep models, the proposed solution is resource-efficient and well-suited for deployment on constrained IoT devices. Practical considerations such as latency, computational overhead, and model interpretability are discussed to support real-world applicability. This work introduces one of the initial ensemble learning frameworks tailored for Rank attack detection in RPL, offering both academic insights and engineering relevance for secure IoT deployments.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 171-185"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911911","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}

{"title":"A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data","authors":"Pei Xue ,&nbsp;Tianshun Li","doi":"10.1016/j.ject.2025.06.001","DOIUrl":"10.1016/j.ject.2025.06.001","url":null,"abstract":"<div><div>We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 9-19"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579703","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}

{"title":"Implications of algorithmic bias in AI-driven emergency response systems","authors":"Katsiaryna Bahamazava","doi":"10.1016/j.ject.2025.05.003","DOIUrl":"10.1016/j.ject.2025.05.003","url":null,"abstract":"<div><div>In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory with algorithmic fairness to quantify and systematically analyze how biases in data quality and algorithm design impact resource allocation efficiency, response time equity, healthcare outcomes, and social welfare. Using explicit modeling of emergency-specific variables, which includes time sensitivity and urgency, we demonstrate that biases substantially exacerbate demographic disparities. This could lead to delayed emergency responses, inefficient resource utilization, worsened health outcomes, and significant welfare losses. Our numerical simulations further illustrate the economic viability and effectiveness of bias mitigation strategies, such as fairness-constrained optimization and improved data representativeness, in simultaneously enhancing equity and economic efficiency. The framework presented provides policymakers, healthcare providers, and AI developers with actionable insights and a robust economic rationale for deploying equitable AI-driven solutions in emergency management contexts.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 20-34"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623529","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}

{"title":"Corrigendum to “Federated learning and information sharing between competitors with different training effectiveness” [J. Econ. Technol. (2025) 1–9]","authors":"Jiajun Meng ,&nbsp;Jing Chen ,&nbsp;Dongfang Zhao","doi":"10.1016/j.ject.2025.02.002","DOIUrl":"10.1016/j.ject.2025.02.002","url":null,"abstract":"","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Page 282"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942066","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}

{"title":"The impact of customer boredom on the efficacy of a rewards","authors":"Axel Stock ,&nbsp;Minoo Talebi Ashoori","doi":"10.1016/j.ject.2025.04.001","DOIUrl":"10.1016/j.ject.2025.04.001","url":null,"abstract":"<div><div>In this paper, we study utilizing a game theoretic model, how variety seeking triggered by customer boredom may affect a firm’s rewards program, pricing strategy and profits. Customer Boredom is conceptualized as a utility loss resulting from the purchase of a previously consumed brand. We analyze a two period model where two firms compete for a market of forward looking consumers by selling horizontally differentiated brands. When making the purchase decision in the second period, consumers trade off the utility loss from boredom with the benefits from obtaining the reward offered. In our analysis, we interestingly find that depending on consumers’ discount factor, firm profits either strictly increase or follow a u-shaped relationship with customer boredom. We also consider the case when brands differ in quality and show that under some conditions the high-quality firm’s profits decline while the low quality firm benefits when variety seeking due to boredom increases.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 299-313"},"PeriodicalIF":0.0,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069348","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}

{"title":"Blockchains effects on responsiveness to recalls in the food and beverage industry","authors":"Abbas Keramati ,&nbsp;Bethany Siau ,&nbsp;Tyler Bellitto ,&nbsp;Jafar Heydari ,&nbsp;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":"2025-05-05","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}

{"title":"UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation","authors":"Chunyu Yuan ,&nbsp;Dongfang Zhao ,&nbsp;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":"2025-02-22","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}

{"title":"Remanufacturing facility installation decisions under product sourcing cost uncertainties: A real options approach","authors":"Mohammad Ahnaf Sadat,&nbsp;K. Jo Min","doi":"10.1016/j.ject.2025.02.003","DOIUrl":"10.1016/j.ject.2025.02.003","url":null,"abstract":"<div><div>In this paper, we investigate the strategic decision-making process of a Maintenance Repair and Overhaul (MRO) company considering the installation of a remanufacturing facility under product sourcing cost uncertainties (e.g., purchasing new products from third-party, and remanufacturing used ones). We consider the remanufacturing costs to consist of constant and variable portions. The variable portion is the acquisition cost of used products, which we consider to be correlated with the new product's purchasing costs. Assuming an indefinite lifespan for the remanufacturing facility and equivalent pricing and customer valuation for remanufactured and new products, we employ the real options approach and the quasi-analytical method for problem modeling and solution derivation. The study reveals that the decision to install a remanufacturing facility is influenced by various cost combinations rather than a single threshold. We derive and show the procedure to obtain these cost combinations. Significantly, we discover that unit-based variable subsidies, such as tax exemptions, can effectively reduce this cost threshold, making remanufacturing a more viable option. This insight is crucial for policymakers and businesses, highlighting the role of government incentives in promoting sustainable remanufacturing practices and contributing to the understanding of remanufacturing as a financially viable and sustainable strategy.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 123-142"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611380","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}