None of the guidelines qualified to be evidence-based clinical practice guidelines as the level of evidence was uniformly rated "low". A newly constructed tool showed good validation, reliability, and internal consistency. This rapid scoping review found two major research gaps lack of systematic review of evidence during their development and insufficient weightage of their impact on surgical services from the global south. These significant issues were addressed by constructing a simple and more representative tool for evaluating rapidly emerging guidelines which also gives the rightful importance of their impact on surgical services from the global south.Terminology is the most basic information that researchers and literature analysis systems need to understand. Mining terms and revealing the semantic relationships between terms can help biomedical researchers find solutions to some major health problems and motivate researchers to explore innovative biomedical research issues. However, how to mine terms from biomedical literature remains a challenge. At present, the research on text segmentation in natural language processing (NLP) technology has not been well applied in the biomedical field. Named entity recognition models usually require a large amount of training corpus, and the types of entities that the model can recognize are limited. Besides, dictionary-based methods mainly use pre-established vocabularies to match the text. However, this method can only match terms in a specific field, and the process of collecting terms is time-consuming and labour-intensive. Many scenarios faced in the field of biomedical research are unsupervised, i.e. unlabelled corpora, and the system may not have much prior knowledge. This paper proposes the TermInformer project, which aims to mine the meaning of terms in an open fashion by calculating terms and find solutions to some of the significant problems in our society. We propose an unsupervised method that can automatically mine terms in the text without relying on external resources. Our method can generally be applied to any document data. Combined with the word vector training algorithm, we can obtain reusable term embeddings, which can be used in any NLP downstream application. This paper compares term embeddings with existing word embeddings. The results show that our method can better reflect the semantic relationship between terms. Finally, we use the proposed method to find potential factors and treatments for lung cancer, breast cancer, and coronavirus.Face threat sensitivity (FTS) is defined as reactive sensitivity to threats to one's social self-worth. In negotiations, such threats may come from a counterpart's competitive behavior. We developed and tested the argument that individuals high in face threat sensitivity, when negotiating with a competitive (vs. cooperative) counterpart, exhibit psychological responses that inhibit them from claiming value in distributive negotiations. Employing a face-to-face interaction paradigm, Study 1 revealed that higher counterpart competitiveness was negatively associated with high (but not low) FTS negotiators' global self-esteem, which in turn led them to be less demanding and obtain worse negotiation outcomes. In Study 2, employing a simulated on-line interaction paradigm, we manipulated counterpart's behavior (cooperative vs. competitive) to establish causality and examined specific aspects of negotiator global self-esteem that may account for the effect. We found that the effect of counterpart's competitiveness on high FTS negotiators' demand levels was mediated by their performance self-esteem, but not by their social self-esteem. In Study 3, we manipulated performance self-esteem to establish it as a causal underlying psychological mechanism. For high FTS negotiators, when performance self-esteem was low, demand levels were significantly lower with a competitive (vs.  https://www.selleckchem.com/screening-libraries.html  cooperative) counterpart. However, when performance self-esteem was high, there was no significant difference in demand levels depending on counterpart's behavior. This finding suggests that negotiating with a competitive (vs. cooperative) counterpart reduces high FTS negotiators' performance self-esteem, which in turn leads them to make lower demands. The implications of these findings are discussed.This paper explores the trends, step changes and innovations that could impact the integration of renewable energy into electricity systems, explores interventions that may be required, and identifies key areas for policy makers to consider. A Delphi approach is used to collect, synthesise, and seek consensus across expert viewpoints. Over sixty experts across a range of geographies including the US, Europe, New-Zealand, Australia, Africa, India and China participated. They identified 26 trends, 20 step changes, and 26 innovations that could lead to major shifts in the design, operation, or management of electricity systems. Findings suggest that key challenges are not technological. Instead they are with delivering an aligned vision, supported by institutional structures, to incentivise, facilitate, and de-risk the delivery of a completely different type of energy system. There is a clear role for government and policy to provide a future energy vision and steer on strategic issues to deliver it; to create space for new actors and business models aligned with this vision; and to create an environment where research, development, demonstration and deployment can promote technologies, system integration and business model innovation at a rate commensurate with delivering net-zero electricity systems.Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R0) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R0 value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown.