Journal of Economy and Technology最新文献_第4页

Techno-economic and emissions comparison of waste-to-fuel via hydrothermal liquefaction, transesterification, and incineration 通过水热液化、酯交换和焚烧将废物转化为燃料的技术经济和排放比较

Journal of Economy and Technology

Pub Date : 2024-11-07 DOI: 10.1016/j.ject.2024.11.002

Muhammad Usman

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.

全球向可持续废物管理和可再生能源的转变引发了人们对从污水污泥中生产生物燃料的兴趣。本研究使用ASPEN Plus V12评估了四种废物转化为生物燃料的工艺，如热液液化（HTL）升级、酯交换和焚烧（有和没有能源回收），以评估其技术经济、能源和环境绩效。经过升级的HTL是最有效的，每年产生~ 4,000,000兆焦耳，排放~ 700吨二氧化碳。酯交换产生~ 2,850,000 MJ/年，由于脂质提取后焚烧，排放~ 1200吨/年。没有能量回收的焚烧效率最低，每年消耗~ 5,000,000 MJ，排放~ 3000吨CO2，能量回收仅产生~ 1,250,000 MJ/年。在财务上，升级后的HTL显示出强大的盈利能力，潜在净现值（NPV）为1.129亿美元（MUS$），而transsesterification的NPV为23.4 MUS$。这两个流程对运营成本都很敏感：50% %的增长可能会使HTL的NPV降至62.7 MUS$，同时使transsesterification陷入亏损。资本成本的降低可能会进一步提高HTL的盈利能力，突显其经济弹性，这与焚烧垃圾在财务上仍然不可行的做法不同。总之，与焚烧相比，HTL升级后的能源输出提高了30% %，排放降低了70% %，使其成为SS管理和生物燃料生产的可扩展、可持续的方法。但是，完整的生命周期评估可以通过确定额外的环境和经济效益进一步提高其潜力。

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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 : 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

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.

这项研究宣布，新的联邦学习结构旨在增强电子健康记录（EHRs）的隐私保护分析，并且该框架中的多个机构允许机构之间的安全协作，允许他们在不直接共享患者数据的情况下训练机器学习副本。我们实施并评估了许多机器学习模型来预测患者的治疗需求，包括逻辑回归、决策树分类器、支持向量分类器、随机森林和堆叠分类器。随机森林分类器以90 %的准确率和80 %的F1分数取得了最好的性能，表明它可以处理复杂和不平衡的数据集。这种基于fl的方法不仅符合HIPAA和GDPR等隐私法规，而且克服了数据共享方面的重大挑战，使其成为罕见疾病研究的理想选择。通过实现跨机构的安全数据聚合，该框架大大提高了研究罕见疾病的能力，并加速了新疗法的发现。未来的方向包括将该框架扩展到医疗保健的其他领域，并结合先进的机器学习技术来进一步增强其能力。这项研究为安全和协作医疗数据分析设定了新标准，并促进了罕见病研究的创新和伦理实践。

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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 : 2024-10-28 DOI: 10.1016/j.ject.2024.10.003

F.K. Sufi

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.

山体滑坡和陆地登陆等自然灾害对全球经济造成了不利影响。最近的滑坡研究严重依赖于文本和图像数据集，尤其是来自美国宇航局（NASA）等来源的数据集，这对灾害领域的机器学习模型开发至关重要。然而，有限的数据集访问和稀缺性构成了重大障碍。本文提出利用生成预训练变压器（GPT）技术综合生成文本和图像数据集，推进GPT在地球科学中的应用。倡导的框架确保了方法的一致性，增强了可重复性，并使研究与目标保持一致。通过对115个具有14个关键参数的综合生成的滑坡事件进行详细的案例研究，我们展示了该框架生成大规模数据集、进行统计分析和增强可视化的能力。此外，我们通过将合成数据与现实世界的数据集（如NASA的全球滑坡目录和吉大港滑坡数据库）进行比较，对合成数据进行了实证验证，显示生成的数据符合预期的现实世界模式。本研究强调了GPT在数据分析方面的有效性及其在帮助不同地球科学研究阶段的潜力。一个综合的框架利用提示工程自主生成数据集和执行分析任务。GPT的可视化功能有效地传达了发现。该研究倡导将基于gpt的技术整合到地球科学工作中，标志着朝着人工智能驱动的灾害管理和数据增强的未来迈出了关键一步。

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引用次数: 0

Global adoption of generative AI: What matters most? 生成式人工智能的全球应用：什么最重要？

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

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.

本研究调查了136个国家采用生成式人工智能的决定因素，利用2023年的横截面数据，并采用负二项回归模型来解决数据过度分散问题。生成式人工智能是一种变革性技术，可以提高运营效率，推动创新，并在各个领域创造经济价值。主要研究结果显示，IT基础设施、研发投资和公司对新兴技术的投资显著促进了生成式人工智能的采用，而不一致的政府政策可能会阻碍它。该分析确定了关键的决定因素，包括技术基础设施、经济稳定性、监管环境和劳动力准备程度，这些因素对采用率至关重要。该研究为政策制定者、行业领导者和研究人员提供了可操作的见解，倡导制定量身定制的政策，对高速互联网和云服务进行战略投资，并完善政府激励措施，以符合人工智能行业的需求。这项研究的独特之处是提供了一个全面的、跨国的视角来研究影响生成式人工智能采用的因素。

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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 : 2024-10-19 DOI: 10.1016/j.ject.2024.10.001

Frédéric Marty , Thierry Warin

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.

本文从法律和经济两个角度探讨了算法合谋，强调了算法在企业市场决策中的影响力越来越大，以及它们促进反竞争行为的潜力。通过采用强盗算法作为模型（通常用于不确定的决策场景），我们揭示了在没有明确沟通的情况下发生的隐性勾结机制。从法律上讲，主要的挑战在于检测和分类可能的算法信号，特别是当它们作为单边通信时。经济上，在算法驱动决策的背景下，区分理性定价策略和串通模式的任务变得越来越复杂。这篇论文强调了竞争管理机构识别非典型市场行为的必要性。在算法透明度和防止串通之间取得平衡至关重要。虽然监管措施可以减轻串通风险，但它们也可能阻碍算法技术的发展。随着这种形式的勾结在竞争法和经济学讨论中越来越突出，通过像强盗算法这样的模型来理解它变得至关重要，特别是因为这些算法有可能更快地收敛于超竞争性价格均衡。

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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

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.

人工智能（AI）被认为是正在兴起的第六次技术进步浪潮的关键驱动力，对经济产生深远影响。人工智能的出现给众多不同行业带来了重大变革，重塑了现有行业，颠覆了传统商业惯例。这种变革力量与熊彼特的创造性破坏理论不谋而合，即创新会导致旧的技术和商业模式被淘汰，从而引发重大的经济变革。人工智能在第六次浪潮中的作用至关重要，这不仅是因为它在自动化和数据处理领域的直接应用，还因为它具有推动新一轮创新和经济复兴的更广泛能力。在创造性破坏的推动下，这一持续不断的循环对企业的适应和发展提出了挑战，最终将促进经济更加稳健、更具活力。在本文中，作者利用熊彼特的创造性破坏理论，探讨了人工智能促进创新的方式及其对经济扩张的影响。

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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

The rapid advancement of artificial intelligence (AI), particularly with the emergence of Artificial General Intelligence (AGI), has intensified concerns about AI potentially overshadowing human autonomy and disrupting job markets. As AI systems become more capable of performing tasks traditionally handled by humans, there is an urgent need to rethink education to ensure future employability. To stay relevant in an increasingly automated world, the focus should shift toward developing uniquely human skills such as innovation and critical thinking. Educational systems must adapt by emphasizing these higher-order cognitive skills and integrating frameworks like the Digital Sustainable Growth Model (DSGM). By aligning Jungian Cognitive Functions with the innovation process, organizations can develop scalable technologies that not only drive innovation but also optimize talent management. This alignment ensures that human innovation and technological advancements progress together, creating systems that enhance innovative problem-solving and maximize team effectiveness.

人工智能（AI）的飞速发展，尤其是人工通用智能（AGI）的出现，加剧了人们对人工智能可能掩盖人类自主性和扰乱就业市场的担忧。随着人工智能系统越来越有能力执行传统上由人类处理的任务，迫切需要重新思考教育问题，以确保未来的就业能力。要想在自动化程度越来越高的世界中保持竞争力，重点应转向培养人类独有的技能，如创新和批判性思维。教育系统必须做出调整，强调这些高阶认知技能，并整合数字可持续增长模型（DSGM）等框架。通过将荣格认知功能与创新过程相结合，企业可以开发出可扩展的技术，不仅能推动创新，还能优化人才管理。这种调整可确保人类创新与技术进步齐头并进，从而创建出能够增强创新问题解决能力并最大限度提高团队效率的系统。

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

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.

在这个生产和供应混乱的时代，为确保粮食安全，新兴经济体的农业部门正逐步采用更多智能技术来实现可持续性。然而，有关采用基于农业 4.0 的智能农业技术所面临挑战的文献仍然非常有限。因此，本研究从发展中国家的角度出发，探讨了在农业生产系统中采用基于农业 4.0 的智能技术所面临挑战的背景相互关系，并对所发现的挑战进行了优先排序。在新兴经济体孟加拉国开展了一项案例研究，通过访谈和焦点小组讨论收集数据。最终确定了与该国国情相关的 21 项挑战。采用了解释性结构建模（ISM）技术，以建立描述挑战相互关系的层次结构。随后，使用最佳-最差法（BWM）根据挑战的相关权重对其进行排序。本研究发现，技术复杂性、不同利益相关者之间缺乏协作、政府支持不足以及缺乏行动计划等挑战具有很强的驱动力。研究还发现，高昂的初始投资和运营成本、缺乏熟练劳动力以及农民的抵触情绪等挑战也是依赖性挑战。本研究有望深入探讨新兴经济体采用农业 4.0 所面临的挑战，从而帮助从业人员采取有效的缓解措施，简化以植物为基础的农业生产系统，促进粮食安全和可持续发展。

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引用次数: 0

Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT 新闻分类的高级计算方法：神经网络和CNN与GPT集成的研究

Journal of Economy and Technology

Pub Date : 2024-09-15 DOI: 10.1016/j.ject.2024.09.001

Fahim Sufi

In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios.

在一个信息泛滥的时代，高效的新闻分类势在必行。本研究探讨了神经网络和卷积神经网络（CNN）与生成预训练变形器（GPT）的复杂集成，以提高新闻分类的精度和效率。新闻的快速数字化传播需要先进的计算方法，能够准确分类和预测事件，包括金融和经济事件。利用机器学习和自然语言处理（NLP）的最新进展，本研究利用大型语言模型（llm），如GPT和BERT，以其出色的理解和生成类人文本而闻名。在232天的时间里，我们的方法将33,979篇新闻文章分类为教育和；学习、健康&；医学与科学技术，进一步细分为32个不同的子类别。为了评估，使用真阳性（TP）、真阴性（TN）、假阳性（FP）、假阴性（FN）、精度、召回率和F1-Score等指标对5000篇文章的样本进行评估。与已有研究相比，本文方法的平均准确率为0.986 (Precision)，召回率为0.987 (Recall)， F1-Score为0.987 （F1-Score）。这项研究提供了大量的实际贡献，提供了对新闻来源贡献的详细见解，有效的异常检测，以及使用神经网络进行预测趋势分析。理论贡献是深刻的，展示了GPT与cnn和递归神经网络的数学集成。这种集成推进了计算新闻分类，并举例说明了复杂的数学框架如何增强大规模文本数据分析，标志着在现实世界场景中应用先进计算方法的关键进步。

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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

With the booming of LLM technologies (e.g., ChatGPT), people’s goals and behaviors in information search have been reshaped significantly. This paper attempts to conceptually discuss how LLM technologies might revolutionize these important aspects in information search and provides a comprehensive analysis of the technological advancements and capabilities of ChatGPT, highlighting its potential to disrupt traditional search engines like Google. In addition, this paper contrasts ChatGPT’s conversational approach with Google’s link-based search model, offering a detailed examination of the implications for online search advertising and user behavior and explaining why Google is concerned about ChatGPT as well as its potential reactions.

随着 LLM 技术（如 ChatGPT）的蓬勃发展，人们的信息搜索目标和行为发生了重大变化。本文试图从概念上探讨 LLM 技术可能如何彻底改变信息搜索中的这些重要方面，并对 ChatGPT 的技术进步和功能进行了全面分析，强调了其颠覆谷歌等传统搜索引擎的潜力。此外，本文还将 ChatGPT 的对话方式与谷歌基于链接的搜索模式进行了对比，详细分析了其对在线搜索广告和用户行为的影响，并解释了谷歌关注 ChatGPT 的原因及其潜在反应。

引用次数: 0