All known species of Angiosperms in China were evaluated according to the IUCN Red List Categories and Criteria: Version 3.1, Second edition and the Guidelines for Application of IUCN Red List Criteria at Regional and National Levels, Version 4.0. Of the 30,068 species evaluated, 21 species were found Extinct (EX), 9 species were Extinct in the Wild (EW), 10 species were Regionally Extinct (RE), 518 species were Critically Endangered (CR), 1,152 species were Endangered (EN), 1,693 species were Vulnerable (VU), 2,538 species were Near Threatened (NT), 21,132 species were Least Concern (LC), and 2,995 species were Data Deficient (DD). The results show that 3,363 species, representing 11.2% of the evaluated species, were identified as threatened (CR, EN and VU). The main portion of threatened species occurs below 2,000 m elevation in southwestern and southern China. Habitat loss and degradation, over-collecting by humans, and intrinsic factors are the three leading threats to angiosperms in China. Comparisons of the status of taxa on this Red List to those evaluated by Wang & Xie (2004) show changes in the names and categories of some taxa due to land use pressures, the impact of conservation measures to improve the status of some species as well as new information, such as from taxonomic revisions. Therefore, there is a need for future data collection and reevaluation of the red list.
This paper introduces a decomposition method that quantifies the contributions to common prosperity of labor market performance and social policies and extends the idea of shared prosperity to a new measure of inequity in opportunities. The resulting common prosperity indices and opportunity equality indices are then applied to five waves of the Chinese Household Income Project data from 1988 to 2018. This paper shows that the labor market performance and social policies have been improving over the last 30 years and have helped China move towards common prosperity for everyone. The indices developed in this paper allow us to quantify the extent of shared prosperity that a country has achieved and to carry out empirical studies on which policy is working and which is not. It can also help us identify the fundamental causes of inequality and aid us in achieving equality in opportunity among all members of society.
As a corporate social responsibility (CSR) initiative, firms are increasingly disclosing sustainability indicators on online platforms to attract stakeholders’ interests. It is vital to understand what indicators reflect more on a firm’s performance and valuations. This study focuses on deriving value-oriented business intelligence from the voluntary disclosure of sustainability reports. The analysis in this study involves a three-stage approach: (1) Latent Dirichlet allocation (LDA) based topic modeling algorithm to identify and summarize typical contents expressed in various documents, (2) firm’s sustainability maturity modeled as a function of its strategic intent using a latent Markov model (LMM) to estimate the statistical significance and the extent of their relationships, and (3) empirical analysis using random effect linear and non-linear probit models to explore the impact of antecedents and firm performance consequences of three strategic intents. This study highlights using an advanced business analytics approach, specifically with latent Dirichlet allocation (LDA) topic modeling, to codify intangible knowledge embedded in annual sustainability reports to infer a firm’s strategic intent behind voluntary disclosure. In addition, this study aims to analyze the influence of the firm’s sustainability strategic intent on its financial performance. A secondary panel dataset consisting of information on 680 firms in 3 years was constructed by matching the text mined data with information from other sources. Results indicate that, on the one hand, while external stakeholder engagement is the primary motivation behind voluntary disclosure of sustainability reporting, firms are starting to engage internal stakeholders through workforce practices. On the other hand, internal employee-oriented intent has more influence on firm performance than external customer-oriented intent. This study demonstrates a toolset to index firms’ sustainability indicators and evaluates firms’ sustainability practice as an intangible asset and its impact on firms’ financial performance.
Background The COVID-19 pandemic, with all its virus variants, remains a serious situation. Health systems across the United States are trying their best to respond. On average, the health care workforce is relatively homogenous, even though it cares for a highly diverse array of patients. This perennial problem in the US health care workforce has only been accentuated during the COVID-19 pandemic. Medical workers should reflect on the variety of patients they care for and strive to understand their mindsets within the larger contexts of culture, gender, sexual orientation, religious beliefs, and socioeconomic realities. Along with talent and skills, diversity and inclusion (D&I) are essential for maintaining a workforce that can treat the myriad needs and populations that health systems serve. Developing hiring strategies that will help achieve greater workforce diversity remains a challenge for health system leaders. Objective The primary aims of this study were to: (1) explore the characteristics of US health systems and their associations with D&I practices and benefits, (2) examine the associations between D&I practices and three pathways to equip workforces, and (3) examine the associations between the three pathways to better equip workforces and business and service benefits. The three pathways are: (1) improving D&I among existing employees (IMPROVE), (2) using multiple channels to find and recruit the workforce (RECRUIT), and (3) collaborating with universities to find new talent and establish plans to train students (COLLABORATE). Methods During February to March 2021, 625 health systems in the United States were surveyed with the help of a consultant, 135 (21.6%) of whom responded. We assessed workforce talent- and diversity-relevant factors. We collected secondary data from the Agency for Healthcare Research and Quality Compendium of the US Health Systems, leading to a matched data set of 124 health systems for analysis. We first explored differences in diversity practices and benefits across the health systems. We then examined the relationships among diversity practices, pathways, and benefits. Results Health system characteristics such as size, location, ownership, teaching, and revenue have varying associations with diversity practices and outcomes. D&I and talent strategies exhibited different associations with the three workforce pathways. Regarding the mediating effects, the IMPROVE pathway seems to be more effective than the RECRUIT and COLLABORATE pathways, enabling the diversity strategy to prompt business or service benefits. Moreover, these pathway effects go hand-in-hand with a talent strategy, indicating that both talent and diversity strategies need to be aligned to achieve the best results for a health system. Conclusions Diversity and talent plans can be aligned to realize multiple desired benefits for health systems. However, a one-size-fits-all approach is not a viable strategy for improving D&I. Health systems need to follow a multipronged approach based on their characteristics. To get D&I right, proactive plans and genuine efforts are essential.
A multi-machine power system excitation predictive control method using balanced reduced model is presented. First, the theory of empirical Gramians balanced reduction was used to reduce the orders of power system non-linear dynamic model to save the time-solving of open-loop optimisation in model predictive control. Then, it used the minimum deviation of system output(state) and control input as the control objective, using the non-linear reduced system sampling linearisation model as equivalent constraints and the change limits of system output and control input as unequivalent constraints to establish the excitation predictive control model based on reduced model. Next, the interior-point method was used to solve the optimal control problem. Finally, took advantage of a four-machine power system to verify the effectiveness of the predictive control method, and the simulating results show that excitation predictive control method using balanced reduced model for the multi-machine power systems can greatly shorten the optimisation time-calculating, meanwhile maintain the voltages of generator terminals within the set points and have a better control performance than traditional excitation control.
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