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[This corrects the article DOI: 10.1371/journal.pone.0224794.].

Nietzsche's Mirror introduces the reader to one of the most central and pervasive themes in Friedrich Nietzsche's works—will to power. The book traces Nietzsche's use of the terms \"power,\" \"will,\" and \"will to power\" as they are presented in both the works he authorized for publication and his literary remains, called the Nachlass. The author demonstrates that will to power as it is presented in the Nachlass differs from the way it is presented in the works Nietzsche authorized for publication before his collapse in 1889. Then it is argued that the problems that the Nachlass poses for scholars suggests that the Nachlass material should not be held in the same regard as the works Nietzsche authorized for publication. Because of the discrepancy between the published and unpublished writings, will to power should not be interpreted as a metaphysical principle operating behind the world, since the metaphysical-sounding passages are located in the Nachlass, but rather as a tool for interpreting relations, especially human relations, within the world. The final chapter examines Nietzsche's unique style of writing, which the author calls \"mirror writing.\" Mirror writing is a technique Nietzsche deliberately employs in order to have such visionary themes as will to power, master morality, and eternal recurrence reflect the reader's values back to himself. Since this book is meant to be an introduction to will to power, at the end of each chapter is a list of additional books, so that the reader can delve further into the themes presented in the chapter, such as Nietzsche's biography, ethics, writings on truth, and eternal recurrence.

This book sees that the solution to a number of the problems of contemporary philosophy lies in the development of an alternative to Hume's metaphysics. This alternative would have real causal powers at its centre. This book sets about developing a thorough account of powers that might persuade those who remain, perhaps unknowingly, in the grip of Hume's assumptions. This book shows both that the notion of a power is central and that it could serve to dispel a number of long-standing philosophical problems. This book's account of powers is as realistic as any that has appeared so far, and shows that dispositions are as real as any other properties. Specifically, they do not depend on their manifestations for their existence; nevertheless, they are directed towards such manifestations. The book thus appropriates the notion of intentionality from Brentano and argues that it is the essential characteristic of powers. It offers a persuasive case for the existence of some basic and ungrounded powers, thus ruling out the reducibility of the dispositional to the non-dispositional. However, the book does allow that there are non-power properties as well as power properties. In this respect, the book's final position is dualistic.

Future trends in air pollution and greenhouse gas (GHG) emissions for China are of great concern to the community. A set of global scenarios regarding future socio-economic and climate developments, combining shared socio-economic pathways (SSPs) with climate forcing outcomes as described by the Representative Concentration Pathways (RCPs), was created by the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China's future anthropogenic emission pathways. The DPEC is designed to integrate the energy system model, emission inventory model, dynamic projection model, and parameterized scheme of Chinese policies. The model contains two main modules, an energy-model-driven activity rate projection module and a sector-based emission projection module. The activity rate projection module provides the standardized and unified future energy scenarios after reorganizing and refining the outputs from the energy system model. Here we use a new China-focused version of the Global Change Assessment Model (GCAM-China) to project future energy demand and supply in China under different SSP–RCP scenarios at the provincial level. The emission projection module links a bottom-up emission inventory model, the Multi-resolution Emission Inventory for China (MEIC), to GCAM-China and accurately tracks the evolution of future combustion and production technologies and control measures under different environmental policies. We developed technology-based turnover models for several key emitting sectors (e.g. coal-fired power plants, key industries, and on-road transportation sectors), which can simulate the dynamic changes in the unit/vehicle fleet turnover process by tracking the lifespan of each unit/vehicle on an annual basis. With the integrated modelling framework, we connected five SSP scenarios (SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and three pollution control scenarios (business as usual, BAU; enhanced control policy, ECP; and best health effect, BHE) to produce six combined emission scenarios. With those scenarios, we presented a wide range of China's future emissions to 2050 under different development and policy pathways. We found that, with a combination of strong low-carbon policy and air pollution control policy (i.e. SSP1-26-BHE scenario), emissions of major air pollutants (i.e. SO2, NOx, PM2.5, and non-methane volatile organic compounds – NMVOCs) in China will be reduced by 34 %–66 % in 2030 and 58 %–87 % in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important role in continuous emission reduction until 2050. In contrast, China's emissions will remain at a high level until 2050 under a reference scenario without active actions (i.e. SSP3-70-BAU). Compared to similar scenarios set from the CMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates of emission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in 2020–2030, but their emission ranges become similar in the year 2050.

Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N ≥ 8. With high variance, accurate inference was stable at N ≥ 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc wi) were essential to clearly identify patterns (e.g., simple linear vs. null); R2 or adjusted R2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N ≥ 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R2 or adjusted R2. Insufficient N and R2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N ≥ 25.