Journal of Economy and Technology最新文献

{"title":"Transforming research with quantum computing","authors":"Sukhpal Singh Gill ,&nbsp;Rajkumar Buyya","doi":"10.1016/j.ject.2024.07.001","DOIUrl":"10.1016/j.ject.2024.07.001","url":null,"abstract":"<div><div>Quantum computing is a novel method of computation that uses the principles of quantum mechanics to handle highly challenging situations in a very short amount of time. Quantum technology has the ability to significantly impact worldwide advancement, even prior to the complete deployment of quantum machines. Quantum technology for communication, computation, and sensors has the capacity to revolutionise many industries, and several nations are making investments in this promising field. This includes research investments from both the public and commercial sectors. This article delves into the recent quantum computing advancements and the potential opportunities made possible by quantum technology in the next few decades. We outline a vision and scientific innovation for embracing the quantum age, as well as explore the pioneering applications of quantum computing. We also highlight software tools and platforms for quantum programming to unlock the power of computing and revolutionize the world. Finally, we identify the groundbreaking impacts of quantum computing on next-generation research and discuss the benefits of unleashing its revolutionary capabilities.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 1-8"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840451","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":"2026-01-01","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":"Evaluating machine learning performance using python for neural network models in urban transportation in New York city case study","authors":"Mohsen Mohammadagha,&nbsp;Saeed Asadi,&nbsp;Hajar Kazemi Naeini","doi":"10.1016/j.ject.2025.11.001","DOIUrl":"10.1016/j.ject.2025.11.001","url":null,"abstract":"<div><div>This study investigates neural network performance optimization for New York City taxi trip duration prediction to address critical gaps in transportation machine learning, where reproducibility, comprehensive diagnostics, and computational efficiency remain underreported. The research addresses limitations in prior literature that emphasize accuracy without standardized preprocessing, leakage prevention, or systematic cost-performance analysis. The objective was to develop a unified, reproducible framework combining an auditable, from-scratch NumPy neural network with production-grade Keras MLPs, systematically benchmarked against classical models under identical preprocessing and data splits. Methodology encompasses four independent phases: theoretical validation using XOR classification, statistical benchmarking through rigorous cross-validation on 1.3 million NYC taxi records, systematic architecture optimization across small/medium/large configurations, and advanced optimization achieving state-of-the-art performance. Key results demonstrate perfect XOR convergence validation (loss reduction from 0.7065 to 0.0198), competitive baseline performance against Random Forest (93.3 %±0.013 vs 90.5 %±0.044 accuracy), optimal medium architecture achieving 0.459 RMSLE, and final proposed model reaching 0.3092 RMSLE—representing 31.8 % improvement over Random Forest (0.4536) and 27.4 % over enhanced Keras baselines (0.4261). The framework incorporates comprehensive residual diagnostics, feature importance analysis, and computational profiling with statistical significance testing. Results establish new benchmarks for NYC taxi duration prediction while providing a methodologically replicable framework for future urban mobility analytics and operational ETA systems.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 266-283"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683671","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 determinants of the Initial Coin Offering (ICO). A cross-country study","authors":"Ana Claudia de A. Moxotó ,&nbsp;Elias Soukiazis ,&nbsp;Paulo Melo","doi":"10.1016/j.ject.2025.07.004","DOIUrl":"10.1016/j.ject.2025.07.004","url":null,"abstract":"<div><div>This study provides a cross-country analysis of the determinants of Initial Coin Offering (ICO) emergence. Our empirical findings indicate a positive correlation between ICO activity and a country's environmental orientation, as well as the quality of its educational and research institutions. Conversely, the emergence of ICOs is negatively impacted by political instability, high country risk, significant bank concentration, high bank default rates, and restricted financial freedom. These results suggest ICOs are more prevalent in environmentally conscious nations, likely driven by demand for sustainable technology. They also function as alternative assets in politically unstable regions where trust in traditional monetary policy is diminished. The study provides valuable insights for entrepreneurs, investors, and policymakers by identifying the key institutional and economic factors that shape the ICO landscape. Future research is encouraged to explore country-specific characteristics and the evolving regulatory framework governing this financing mechanism.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 284-295"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975953","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":"2026-01-01","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":"Advancing environmental sustainability in the G-7: The impact of the digital economy, technological innovation, and financial accessibility using panel ARDL approach","authors":"Asif Raihan ,&nbsp;Shewly Bala ,&nbsp;Afsana Akther ,&nbsp;Mohammad Ridwan ,&nbsp;Md. Eleais ,&nbsp;Prattoy Chakma","doi":"10.1016/j.ject.2024.06.001","DOIUrl":"10.1016/j.ject.2024.06.001","url":null,"abstract":"<div><div>This study examines the impact of the digital economy, technological innovation, financial accessibility, and urbanization on CO₂ emissions in the G-7 region from 1990 to 2019. The analysis employed Cross-Sectional Dependence (CSD) and Slope Homogeneity tests, revealing the presence of CSD issues and heterogeneous slope coefficients. First- and second-generation panel unit root tests indicated no unit root problem within the dataset, with variables showing mixed integration orders. Panel cointegration tests confirmed that the variables are cointegrated over the long run. To assess the short-run and long-run impacts of the explanatory variables on CO₂ emissions, the study utilized the Panel Autoregressive Distributed Lag (ARDL) model. The findings indicate that the digital economy significantly reduces CO₂ emissions, while economic growth, technological innovation, financial accessibility, and urbanization increase CO₂ emissions in the G-7 region. The robustness of the Panel ARDL results was validated using Driscoll-Kraay standard errors, Augmented Mean Group (AMG), and Common Correlated Effects Mean Group (CCEMG) estimations. Additionally, the Dumitrescu-Hurlin causality test revealed a unidirectional causal relationship between the digital economy and CO₂ emissions, GDP and CO₂ emissions, and CO₂ emissions and technological innovation. Furthermore, bidirectional causality was found between financial accessibility and CO₂ emissions, as well as between urbanization and CO₂ emissions. These findings provide comprehensive insights into the dynamic interactions between economic, technological, and environmental variables in the G-7 region, highlighting the complexity of achieving sustainable development.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 188-205"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141393153","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":"2026-01-01","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":"Unveiling the three-dimensional ecological footprint dynamics in the era of technological revolution","authors":"Muhammed Ashiq Villanthenkodath","doi":"10.1016/j.ject.2024.11.005","DOIUrl":"10.1016/j.ject.2024.11.005","url":null,"abstract":"<div><div>This study integrates technological innovation and economic growth into the environmental degradation function for India. Unlike prior studies, it uses the three-dimensional ecological footprint as a measure of environmental quality for the period span from 1980 to 2022. Empirically, the study employs the Auto-Regressive Distributed Lag (ARDL) model alongside various cointegration regression methods. The results indicate that technological innovation has a positive and significant impact in the long run; however, it exhibits a negative and significant effect in the short run. This finding suggests that, though technological innovation may contribute to environmental degradation over time by increasing the three-dimensional ecological footprint, it can enhance environmental quality in the short term by reducing it. Additionally, the study confirms the Environmental Kuznets Curve (EKC) hypothesis concerning the three-dimensional ecological footprint in the long run, while it does not find support for this relationship in the short run. Finally, the study recommends comprehensive technology and economy-related policies to foster the path to sustainable development.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 238-251"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519735","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":"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 CO₂. 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 CO₂, 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":"2025-11-01","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}

{"title":"Federated learning and information sharing between competitors with different training effectiveness","authors":"Jiajun Meng ,&nbsp;Jing Chen ,&nbsp;Dongfang Zhao","doi":"10.1016/j.ject.2024.12.003","DOIUrl":"10.1016/j.ject.2024.12.003","url":null,"abstract":"<div><div>Federated Learning (FL) is an innovative technique that allows multiple firms to collaborate in training machine learning models while preserving data privacy. This is especially important in industries where data is sensitive or subject to regulations like the General Data Protection Regulation (GDPR). Despite its substantial benefits, the adoption of FL in competitive markets faces significant challenges, particularly due to concerns about training effectiveness and price competition. In practice, data from different firms may not be independently and identically distributed (non-IID) and heterogenous, which can lead to differences in model training effectiveness when aggregated through FL. This paper explores how initial product quality, data volume, and training effectiveness affect the formation of FL. We develop a theoretical model to analyze firms’ decisions between adopting machine learning (ML) independently or collaborating through FL. Our results show that when the initial product quality is high, FL can never be formed. Moreover, when the initial product quality is low, and when data volume is low and firms’ training effectiveness differences are small, FL is more likely to form. This is because the competition intensification effect is dominated by the market expansion effect of FL. However, when there is a significant difference in training effectiveness, firms are less likely to adopt FL due to concerns about competitive disadvantage (i.e., the market expansion effect is dominated by the competition intensification effect). This paper contributes to the literature on FL by addressing the strategic decisions firms face in competitive markets and providing insights into how FL designers and policymakers can encourage the formation of FL.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"3 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093958","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}