Large scale projects increasingly operate in complicated settings whilst drawing on an array of complex data-points, which require precise analysis for accurate control and interventions to mitigate possible project failure. Coupled with a growing tendency to rely on new information systems and processes in change projects, 90% of megaprojects globally fail to achieve their planned objectives. Renewed interest in the concept of Artificial Intelligence (AI) against a backdrop of disruptive technological innovations, seeks to enhance project managers cognitive capacity through the project lifecycle and enhance project excellence. However, despite growing interest there remains limited empirical insights on project managers ability to leverage AI for cognitive load enhancement in complex settings. As such this research adopts an exploratory sequential linear mixed methods approach to address unresolved empirical issues on transient adaptations of AI in complex projects, and the impact on cognitive load enhancement. Initial thematic findings from semi-structured interviews with domain experts, suggest that in order to leverage AI technologies and processes for sustainable cognitive load enhancement with complex data over time, project managers require improved knowledge and access to relevant technologies that mediate data processes in complex projects, but equally reflect application across different project phases. These initial findings support further hypothesis testing through a larger quantitative study incorporating structural equation modelling to examine the relationship between artificial intelligence and project managers cognitive load with project data in complex contexts.
The role of artificial intelligence in achieving the Sustainable Development Goals
KTH Royal Institute Of Technology（瑞典皇家理工学院）；Umeå University（于默奥大学）；Leibniz-Institute of Freshwater Ecology and Inland Fisheries（莱布尼茨淡水生态与内陆渔业研究所）；Basque Centre for Climate Change (BC3)（巴斯克气候变化中心）；University of Otago（奥塔哥大学）；Massachusetts Institute of Technology（麻省理工学院）
Artificial intelligence for sustainable wine industry: AI-driven management in viticulture, wine production and enotourism
University of Szczecin（什切青大学）；Wroclaw University of Environmental and Life Sciences（弗罗茨瓦夫环境与生命科学大学）；University of Wroclaw（弗罗茨瓦夫大学）；WSB Merito University in Wroclaw（弗罗茨瓦夫WSB梅里托大学）；AGH University of Krakow（克拉科夫AGH大学）
Inteligencia Artificial para la conservación y uso sostenible de la biodiversidad, una visión desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)
Instituto de Investigación de Recursos Biológicos Alexander von Humboldt（亚历山大·冯·洪堡生物资源研究所）；Centre for Biodiversity and Environment Research (CBER)（生物多样性与环境研究中心）；Sistema de Información sobre Biodiversidad de Colombia (SiB Colombia)（哥伦比亚生物多样性信息系统）；Instituto de Biociências, Universidade Federal de Mato Grosso do Sul（南马托格罗索州联邦大学生物研究所）
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Mohammed V University of Rabat（拉巴特穆罕默德五世大学）；MENTIS SA（MENTIS SA公司）；University of Nebraska at Kearney (UNK)（内布拉斯加大学凯尔尼分校）；Information Technology University（信息技术大学）；Alfaisal University（费萨尔国王大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Western Michigan University（西密歇根大学）；Alakhawayn University in Ifrane（伊夫兰阿赫瓦因大学）；University of Houston（休斯顿大学）
不同的因素，如热舒适度，湿度，空气质量和噪音有显着的综合影响的可接受性和质量的活动进行的建筑物居住者谁花了他们的大部分时间在室内。在所引用的因素中，热舒适性有助于人类的福祉，因为它与人体的温度调节有关。因此，创造热舒适和节能的环境在建筑物的设计中非常重要，因此供暖，通风和空调系统也非常重要。最近的工作已经针对更先进的控制策略，主要是基于人工智能，具有模仿人类行为的能力。本文系统地综述了建筑物内的智能控制策略，并探讨了它们在室内环境中平衡热舒适性和节能优化的能力。方法.系统的文献综述使用ACM数字图书馆，Scopus，Google Scholar，IEEE Xplore（IEOL），Web of Science和Science Direct（SDOL）以及手动搜索的其他来源检查了同行评审的研究作品。包含以下字符串术语：热舒适性、舒适温度、首选温度、智能控制、高级控制、人工智能、计算智能、建筑、室内和建筑环境。入选标准为：英国，研究监测，主要是在建筑物中的人体热舒适性和能源效率同时基于控制策略，使用智能方法。使用系统性综述和荟萃分析指南的首选报告项目。最初，产生了1，077篇文章，最终有120篇符合纳入标准并进行了审查。
Buyer Artificial Intelligence-Enabled Environmental Governance and Supplier Environmental Controversies: An Organizational Information Processing and Signaling
