Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision making and the uncertainty associated with the energy supply and demand. Artificial Intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision making processes in the power grid can be cast as classic, though challenging, machine learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.Comment: To be published in Patterns (Cell Press

Promotors or inhibitors? Role of task type on the effect of humanoid service robots on consumers’ use intention

Despite the pervasiveness of service robots in hospitality industry, it is unclear how highly human-like service robots elicit aversive effect on consumers’ use intention in addition to discomfort and when the aversive effect can be mitigated. Three experimental studies were conducted, showing that highly human-like service robots elicit greater consumer discomfort and decrease task attraction toward robots, in turn weakening consumers’ use interaction. Moreover, this research identified that emotional-social tasks (vs. mechanical tasks) mitigated the aversive effects of highly human-like service robots on consumers’ responses. The research extends the uncanny valley and mind perception theories and offers some guidelines for employing service robots with different degree of anthropomorphism. 尽管服务机器人在酒店业中非常普遍, 但尚不清楚除了不适之外,类似人类的服务机器人在多大程度上会对消费者的使用意图产生厌恶效应, 以及厌恶效应何时可以缓解. 进行了三项实验研究, 结果表明, 高度人性化的服务机器人会引起消费者更大的不适感, 降低对机器人的任务吸引力, 进而削弱消费者的使用互动. 此外, 这项研究还发现, 情感社交任务, 与机械任务相比, 减轻了高度人性化的服务机器人对消费者反应的厌恶效应. 这项研究扩展了神秘谷和心智感知理论, 并为使用具有不同程度拟人化的服务机器人提供了一些指导

Estimation of a New Canopy Structure Parameter for Rice Using Smartphone Photography.

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R2) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields

Assessment of network module identification across complex diseases

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