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《Information processing & management》2023,60(2):103212
The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. 相似文献
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Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions. 相似文献
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Academics are increasingly expected to produce concrete and directly applicable solutions to hard-to-solve ‘real-world problems’ such as poverty, development, and environmental degradation. However, conventional assessments of the impact of science on society have not yet been adequately adapted to capture the diverse effects of this type of problem-centred research. Drawing on a case study of a large-scale project on (un)sustainable consumption, this paper demonstrates the range, complexity and potential long-term nature of impact in interdisciplinary sustainability research. It thus supports arguments for alternative approaches to impact assessment that question conventional views of translating scientific knowledge into action, value the multi-directionality of science-society relations and recognise diverse forms of engagement between scientists and non-scientific actors through non-academic channels and outputs. The paper also challenges common (mis)conceptions of work practices in a university context by demonstrating the highly innovative and inclusive nature of much sustainability research that seeks to address the needs of diverse communities of actors. It is argued that only radically different ways of conceptualising and measuring short-, medium- and long-term impacts can capture the success or otherwise of social-scientific and interdisciplinary sustainability research. 相似文献
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Recent research call for action on digital sustainability research could potentially contribute to achieving United Nations (UN) Sustainable Development Goals (SDG). In this opinion piece, we specifically focus on artificial intelligence (AI) as a technology that could help achieve digital sustainability. We identify six dimensions related to AI grounded in past literature: sensemaking, relationships among actors in the supply chain, green creativity skills, metrics, strategies, and AI tool improvement. We conceptualize several propositions for these six dimensions, highlighting the nuances associated with AI for digital sustainability to provide clear directions for future research. 相似文献
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Artificial Intelligence tools have attracted attention from the literature and business organizations in the last decade, especially by the advances in machine learning techniques. However, despite the great potential of AI technologies for solving problems, there are still issues involved in practical use and lack of knowledge as regards using AI in a strategic way, in order to create business value. In this context, the present study aims to fill this gap by: providing a critical literature review related to the integration of AI to organizational strategy; synthetizing the existing approaches and frameworks, highlighting the potential benefits, challenges and opportunities; presenting a discussion about future research directions. Through a systematic literature review, research articles were analyzed. Besides gaps for future studies, a conceptual framework is presented, discussed according to four sources of value creation: (i) decision support; (ii) customer and employee engagement; (iii) automation; and (iv) new products and services. These findings contribute to both theoretical and managerial perspectives, with extensive opportunities for generating novel theory and new forms of management practices. 相似文献
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以智能化科研（AI for Science）为核心的第五科研范式已经在多个自然科学和高技术领域得到了广泛应用。与人工智能（AI）在自然科学领域的应用强调发现新原理、新机理和新规律不同,高技术领域更强调用AI技术来发明创造新方案、新工具和新产品,以解决特定的领域问题。文章总结了AI在高技术领域的应用——“技术智能”（AI for Technology）的典型特征和科学问题,并以CPU芯片全自动设计为例介绍过往的成功案例。最后,文章指出技术智能的目标不仅是加速创新流程并减少人工投入,同时也希望其具备更强的创造能力,最终超过人类的水平。 相似文献
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西方可持续性理论研究的最新进展及其趋势 总被引：1，自引：0，他引：1
过去20年来,可持续性概念在生态、经济、政治、社会等领域广泛应用,它代表着人类社会在资源约束条件下,对自身生存与发展的一种哲学化思考.本文拟从以下三个维度对可持续性理论的文献进行研究:关于可持续性概念涵义的演变:可持续性研究中的全球化语境到区域化语境的转化;关于可持续性指标研究的进展,从而总结出可持续性概念的涵义从泛化应用到哲学化理念的演变路径. 相似文献
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随着人类历史的不断发展进步以及经济水平的大幅度提高,能源严重匮乏以及环境污染成为了摆在人们面前的严峻话题。随着汽车的普及以及空调系统的完善,在汽车空调工作时消耗的功率非常可观。如何在保证空调正常使用功能的前提下,进一步减少能源的消耗、降低污染物排放成为了摆在我们面前的重要研究课题。文章根据多年相关工作经验,详细论述汽车空调的节能与环保。 相似文献
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负责任创新(RRI)旨在科学和技术创新过程中社会行动者和创新者彼此负责,同时会考虑创新过程中的伦理问题及创新产品的可接受性、可持续性和社会需求。其内涵分为行政定义和学术定义。在预期、反思、协商、反应、可持续性、关怀这6种负责任创新框架的基础上引入无私利性,基于负责任创新这7种框架理念解析人工智能技术发展带来的安全、隐私、结果等三方面主要伦理问题,探讨嵌入RRI框架来解决人工智能(AI)在产生、应用和发展等过程中滋生的伦理问题的路径,使AI技术更好地造福人类社会。 相似文献
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Despite heightened interest, integrating artificial intelligence (AI) into businesses remains challenging. Recent surveys show that up to 85 % of AI initiatives ultimately fail to deliver on their promises. Studies on successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey are still lacking. Therefore, this study aims to understand how AI technology, people, and processes should be managed to successfully create value. Building on the resource orchestration perspective, this study analyzes the successful applications of AI at Alibaba's e-commerce fulfillment center. The findings indicate that the key AI resources include data, AI algorithms, and robots. These resources must be orchestrated (e.g., coordinated, leveraged, deployed) to work with other related resources, such as warehouse facilities and existing information systems, to generate strong AI capabilities. The key AI capabilities generated include forecasting, planning, and learning. More importantly, AI capabilities are not independent – they interact and coevolve with human capabilities to create business value in terms of efficiency (e.g., space optimization, labor productivity) and effectiveness (e.g., error reduction). The implications of understanding these social informatics of AI for research and practice are discussed. 相似文献
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AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda. 相似文献
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基于生态系统服务的生态足迹模型构建与应用 总被引：2，自引：0，他引：2
生态足迹(EF)自提出以来得到了世界各国学者的广泛关注。然而,核算内容的不完整导致其无法全面衡量人类活动对生态系统造成的各种影响。本文完善了基于生态系统服务的生态足迹(ESEF)概念与通用模型,并拓展出基于资源供给和废弃物吸纳两大类服务的足迹模型。案例研究则利用基于废弃物吸纳的足迹模型,对太湖流域上游常州市和湖州市水污染物排放的环境影响进行评估。研究表明,就水污染物吸纳而言,当地水环境处于不可持续状态;氮磷营养盐的累积对当地水环境造成的压力远远大于有机物。这些信息能够为当地水环境管理政策的制定提供参考。ESEF概念的提出与模型的建立,有助于实现足迹方法对人类活动影响的全面衡量,并推动足迹理论的进一步发展与完善。 相似文献
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青藏高原自然资产利用的生态空间占用评价 总被引：25，自引：4，他引：25
鲁春霞谢高地成升魁张镱锂 《资源科学》2001,23(6):29-35
生态空间占用是衡量人类对自然资源的利用程度以及自然界为人类提供的生命支持服务功能的一种方法。它根据维持人类的自然资源消费量和同化人类产生的废气物所需要的生产性空间进行估算。并与给定区域实际生物承载力进行比较，衡量区域的持续发展状况。青藏高原作为世界的第三级，有其独特的生物资源和自然景观。本文以青海省为例，评估了青海省自然资产利用的生态空间占用状况。计算结果表明，青海省的人均占用世界平均产量的生态空间为1.8hm2,青海省的自然资产利用程度尚在其实际生物承载力范围之内。青海省的工业生产占青藏高原的3/4，人口占青藏高原总人口的一半左右，因此，原材料和能源消费应该高于青藏高原的平均水平。由此推知，青藏高原的人均生态空间占用小于1.8hm2,而青藏高原实际生物承载力人均8hm2,应该有较大的生态盈余。 相似文献
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全面建设小康社会的能源合理利用与CO2减排 总被引：6，自引：1，他引：6
与世界发达国家的比较,可以发现中国能源消费结构以煤为主,CO2排放总量大但人均量小,单位能耗的CO2排放指标偏高.21世纪头20年是中国全面建设小康社会的关键时期,在人口将继续增长、能耗规模继续增大、以煤为主的能源结构难以根本改变的情况下,如何实现CO2减排,是对中国的严峻挑战.借鉴发达国家的经验教训,该文提出优化产业结构,不断提高新技术产业和第三产业的比例;改善能源结构,提高水电、核能、太阳能及生物能等清洁能源比例;引进和开发能源环保技术,提高能源利用效率;以法律、政策和经济手段,促进能源合理利用和削减CO2的排放量的设想和对策. 相似文献
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数字经济背景下,人工智能（AI）技术的应用正在深入地影响着企业管理变革、业务边界的扩展和管理模式的改变。结合互补资产的观点和组织学习理论,本文提出了一个基于AI应用能力和AI管理能力的分析框架,强调人工智能与人类智慧结合的必要性,阐述了两种能力的功能和作用及其协同对企业效率和创新成本的影响。本文提出,企业必须具备管理AI的能力才能有效应对大数据、数字技术、AI的不断革新及技术带来的组织内部结构和外部环境变化以及风险;企业AI应用与管理能力的有效结合,有利于控制AI应用带来的成本和风险,增强企业在人工人力、协调沟通、和数据搜寻方面的效率,同时降低AI应用带来的数字基建、道德情感、数据安全、组织结构变革方面的成本,进而促进企业的组织学习、对内外部数字技术使能资源的获取和管理以及互补资产的形成,对企业创新绩效发挥正向作用。最后,本文为企业的数字化创新战略提供了新的发展思路。 相似文献
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人类活动的一项基本功能是从自然界不断开采资源,并将其加工成各种产品来满足生产和生活需要,同时向环境中排放污染物.因此,从宏观化学物质循环的角度来看,人类活动就是将地球表层物质不断进行时空转运和形态重构的过程.日益增强的人类活动已经成为影响地球表层物质循环格局、过程及其生态环境效应的主要驱动力,并引发区域性资源短缺和环境... 相似文献
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投资项目可持续发展后评价研究 总被引：11，自引：0，他引：11
可持续发展理论为投资项目持续性后评价提供了理论基础。目前在宏观经济政策评价中，考虑环境因素和资源问题的研究与应用已取得较好的进展，而如何把环境因素纳入微观经济活动的分析与评价之中，尚有待研究。文中对项目的持续性进行分析，在考虑环境和资源因素的基础上，构建基于可持续发展战略的效益概念新内涵、效益指标体系、持续性后评价方程，并以水利工程项目为例，进行了“生态经济效益”评价实证研究。 相似文献