Editorial: CO 2 -based energy systems for cooling, heating, and power

© 2022 Li, Su, Xu, Dai, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Peer reviewe

Development of a Recommender System for Dental Care Using Machine Learning

Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013–2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified 8 top features associated with recommendation for dental care. The top 3 features include gum health, number of prescription medications taken, and race. Gum health shows a significantly higher relative importance compared to other features. Demographics, healthcare access, and general health variables were identified as top features related to receiving additional dental care, consistent with prior research. Practicing dentists and other healthcare professionals can follow this model to enable precision dentistry through the incorporation of our algorithms into computerized screening tool or decision tree diagram to achieve more efficient and personalized preventive strategies and treatment protocols in dental care

Rice plants respond to ammonium‐stress by adopting a helical root growth pattern

High levels of ammonium nutrition reduce plant growth and different plant species have developed distinct strategies to maximize ammonium acquisition while alleviate ammonium toxicity through modulating root growth. Up to now, the mechanism underlying plant tolerance or sensitivity towards ammonium remain unclear. Rice uses ammonium as its main N source. Here we show that ammonium supply restricts rice root elongation and induces a helical growth pattern, which is attributed to root acidification resulting from ammonium uptake. Ammonium-induced low pH triggers asymmetric auxin distribution in rice root tips through changes in auxin signaling, thereby inducing a helical growth response. Blocking auxin signaling completely inhibited this root response. In contrast, this root response is not activated in ammonium-treated Arabidopsis. Acidification of Arabidopsis roots leads to the protonation of IAA, and dampening the intracellular auxin signaling levels that are required for maintaining root growth. Our study suggests a different mode of action by ammonium on the root pattern and auxin response machinery in rice versus Arabidopsis, and the rice-specific helical root response towards ammonium is an expression of the ability of rice in moderating auxin signaling and root growth to utilize ammonium while confronting acidic stress

Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program

Short-term residential load forecasting based on LSTM recurrent neural network

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households