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onin) under drying (D) and cutting-drying (C-D) treatments were significantly higher than those of the other four treatments (P ≤  0.05). Collectively, the above results will not only provide novel processing methods that will improve the yield of active ingredients for S. baicalensis, but also shed light on the optimization of processing technology for the industrial production of medicinal crops.In an attempt to curb the COVID-19 pandemic, several countries have implemented various social restrictions, such as closing schools and asking people to work from home. Nevertheless, after months of strict quarantine, a reopening of society is required. Many countries are planning exit strategies to progressively lift the lockdown without leading to an increase in the number of COVID-19 cases. Identifying exit strategies for a safe reopening of schools and places of work is critical in informing decision-makers on the management of the COVID-19 health crisis. This scoping review describes multiple population-wide strategies, including social distancing, testing, and contact tracing. It highlights how each strategy needs to be based on both the epidemiological situation and contextualize at local circumstances to anticipate the possibility of COVID-19 resurgence. However, the retrieved evidence lacks operational solutions and are mainly based on mathematical models and derived from grey literature. There is a need to report the impact of the implementation of country-tailored strategies and assess their effectiveness through high-quality experimental studies.Feature selection is a critical component in supervised learning to improve model performance. Searching for the optimal feature candidates can be NP-hard. With limited data, cross-validation is widely used to alleviate overfitting, which unfortunately suffers from high computational cost. We propose a highly innovative strategy in feature selection to reduce the overfitting risk but without cross-validation. Our method selects the optimal sub-interval, i.e., region of interest (ROI), of a functional feature for functional linear regression where the response is a scalar and the predictor is a function. For each candidate sub-interval, we evaluate the overfitting risk by calculating a necessary sample size to achieve a pre-specified statistical power. Combining with a model accuracy measure, we rank these sub-intervals and select the ROI. The proposed method has been compared with other state-of-the-art feature selection methods on several reference datasets. The results show that our proposed method achieves an excellent performance in prediction accuracy and reduces computational cost substantially.Most deep language understanding models depend only on word representations, which are mainly based on language modelling derived from a large amount of raw text. These models encode distributional knowledge without considering syntactic structural information, although several studies have shown benefits of including such information. Therefore, we propose new syntactically-informed word representations (SIWRs), which allow us to enrich the pre-trained word representations with syntactic information without training language models from scratch. To obtain SIWRs, a graph-based neural model is built on top of either static or contextualised word representations such as GloVe, ELMo and BERT. The model is first pre-trained with only a relatively modest amount of task-independent data that are automatically annotated using existing syntactic tools. this website SIWRs are then obtained by applying the model to downstream task data and extracting the intermediate word representations. We finally replace word representations in downstream models with SIWRs for applications. We evaluate SIWRs on three information extraction tasks, namely nested named entity recognition (NER), binary and n-ary relation extractions (REs). The results demonstrate that our SIWRs yield performance gains over the base representations in these NLP tasks with 3-9% relative error reduction. Our SIWRs also perform better than fine-tuning BERT in binary RE. We also conduct extensive experiments to analyse the proposed method.In this work, we estimate the total number of infected and deaths by COVID-19 in Brazil and two Brazilian States (Rio de Janeiro and Sao Paulo). To obtain the unknown data, we use an iterative method in the Gompertz model, whose formulation is well known in the field of biology. Based on data collected from the Ministry of Health from February 26, 2020, to July 2, 2020, we predict, from July 3 to 9 and at the end of the epidemic, the number of infected and killed for the whole country and for the Brazilian states of Sao Paulo and Rio de Janeiro. We estimate, until July 9, 2020, a total of 1,709,755 cases and 65,384 deaths in Brazil, 331,718 cases and 15,621 deaths in Sao Paulo, 134,454 cases and 11,574 deaths in Rio de Janeiro. We also estimate the basic reproduction number R 0 for Brazil and its two states. The estimated values ( R 0 ) were 1.3, 1.3, and 1.4 for Brazil, Sao Paulo, and Rio de Janeiro, respectively. The results show a good fit between the observed data and those obtained by the Gompertz. The proposed methodology can also be applied to other countries and Brazilian states, and we provide an executable as well as the source code for a straightforward application of the method on such data.During epidemic outbreaks, there are various types of information about epidemic prevention disseminated simultaneously among the population. Meanwhile, the mass media also scrambles to report the information related to the epidemic. Inspired by these phenomena, we devise a model to discuss the dynamical characteristics of the co-evolution spreading of multiple information and epidemic under the influence of mass media. We construct the co-evolution model under the framework of two-layered networks and gain the dynamical equations and epidemic critical point with the help of the micro-Markov chain approach. The expression of epidemic critical point show that the positive and negative information have a direct impact on the epidemic critical point. Moreover, the mass media can indirectly affect the epidemic size and epidemic critical point through their interference with the dissemination of epidemic-relevant information. Though extensive numerical experiments, we examine the accuracy of the dynamical equations and expression of the epidemic critical point, showing that the dynamical characteristics of co-evolution spreading can be well described by the dynamic equations and the epidemic critical point is able to be accurately calculated by the derived expression.