Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

Although recent research suggests that, for community youth, greater specific (episodic) detail in self-relevant turning point memory narratives predicts depressive symptoms over time, no research has investigated whether the narratives\' specificity similarly predicts depression. Therefore, we investigated whether recalling a specific (unique, 24-hour or less) turning point narrative predicted youth depressive symptoms concurrently and across 6 months (Study 1), and, for a subset of participants, three years (Study 2). We also examined whether the valence of the implication of the experience for self (the resolution) explained additional variance and interacted with memory specificity. For Study 1 (N = 320, M = 16.9 years, 81% female), a specific (rather than a non-specific) turning point predicted greater depressive symptoms concurrently but not longitudinally, whereas a negative resolution predicted both concurrent and longitudinal depressive symptoms. The moderation result showed that a specific turning point predicted escalating depressive symptoms across six months when the resolution was negative. Study 2 (N = 68) additionally showed that a specific turning point predicted increased depressive symptoms three years later. These findings contrast with research suggesting that specific memories are related to better mental health and highlight the complexity of the role of memory in emerging youth depression.

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Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 to 37°C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 μm (PM2.5, an indicator of human activities) increase by twofold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events.

Although research findings show that the personal memories of people who are depressed are characterized by sparse episodic detail, under some circumstances, the opposite pattern emerges. Specifically, a recent study (Salmon et al., 2021) has shown that for community youth, greater episodic detail in a highly self-relevant narrative (a life turning point) predicted increased depressive symptoms concurrently and one year later. In a new longitudinal study of young people (N = 320 at Time 1, M = 16.9 years; 81% female) followed up over six months, we aimed to replicate and extend this finding. In Study A, we compared the turning point with a narrative about a conflict event, to establish whether the detail in a turning point memory uniquely predicted depressive symptoms. Supporting the first hypothesis, at both time-points, greater episodic detail was concurrently positively associated with depressive symptoms for turning point narratives only. Contrary to our second hypothesis, greater detail did not predict increased depressive symptoms longitudinally. The reverse pattern was significant, however, in that greater initial depressive symptoms predicted greater detail uniquely in the turning point narrative six months later. In Study B, we determined that the concurrent association between episodic detail and depressive symptoms in turning points (but not conflict events) was exacerbated by linguistic markers of self-focus (greater I-talk and lower distancing language). These findings suggest that greater detail in a turning point narrative may uniquely signify risk of psychological distress when youth narrate the experience with heightened self-focus.

The response of vegetation growth to soil moisture varies greatly from space and time under climate change and anthropogenic activities. As an important grain producer in China, the vegetation growth and grain production of North China are constrained by the region\'s water resources. With the significant increase in vegetation greenness in North China over the last 40 years, it is essential to explore the changes in soil moisture constraints on vegetation growth to water management. However, to what degree vegetation growth responds to soil moisture and how the response varies spatiotemporally in North China remain unclear. In this study, the response patterns of vegetation growth to soil moisture at different depths and the spatiotemporal trend patterns of their relationships were explored thoroughly based on long time series remote sensing data in North China over the past 40 years. The results showed that compared to forests, the growth of grasslands and crops with one maturity per year and two maturity per year in North China was more constrained by soil moisture. Due to the combined effects of climatic conditions and human activities, vegetation growth in North China has been significantly less constrained by soil moisture over the last 40 years. This was especially seen in one maturity per year crop and natural vegetation in Shanxi and central Shandong. However, with the significant increase in temperature, potential evapotranspiration and water demand of the crop, the moisture constraints on vegetation growth in North China have begun to show an increasing trend since the early 2000s, especially for irrigated crop in central and southern North China. These findings highlight a comprehensive understanding of the vegetation response to soil moisture from the time-varying perspective and provide a theoretical basis for water management and appropriate planning of agricultural water use in North China.

The objective of this paper is to examine the overreaction behavior of 20 commodity futures based on intraday data from November 20, 2019 to June 3, 2020 with a focus on the impact of the Covid-19 pandemic. A dynamic and non-parametric approach is applied on intraday data for four different frequencies (from 1 min to 1 h) and two different sub-periods (pre-Covid-19 pandemic and during Covid-19 pandemic) in order to detect overreaction behavior which is defined as a large change of prices followed by proportional price reversals. Our empirical findings show that the overreaction hypothesis is confirmed for the considered commodity futures. Furthermore, both the number and the amplitude of overreactions is higher during the Covid-19 pandemic. Our findings also indicate that soft and metal commodities show much less overreactions than precious metals and especially energy commodities. In particular, crude oil futures exhibit a different overreaction behavior compared to other commodities since it has a higher number of negative than positive overreactions during the Covid-19 pandemic. We also find that the data frequency is independent of the overreacting behavior in both periods as the results continuously improve when having more observations due to higher frequencies. Finally, we find that extreme overreactions during the Covid-19 pandemic provide a great potential for profitable trading returns, which can be exploited by traders.

The hazard function plays an important role in cancer patient survival studies, as it quantifies the instantaneous risk of death of a patient at any given time. Often in cancer clinical trials, unimodal hazard functions are observed, and it is of interest to detect (estimate) the turning point (mode) of hazard function, as this may be an important measure in patient treatment strategies with cancer. Moreover, when patient cure is a possibility, estimating cure rates at different stages of cancer, in addition to their proportions, may provide a better summary of the effects of stages on survival rates. Therefore, the main objective of this paper is to consider the problem of estimating the mode of hazard function of patients at different stages of cervical cancer in the presence of long-term survivors. To this end, a mixture cure rate model is proposed using the log-logistic distribution. The model is conveniently parameterized through the mode of the hazard function, in which cancer stages can affect both the cured fraction and the mode. In addition, we discuss aspects of model inference through the maximum likelihood estimation method. A Monte Carlo simulation study assesses the coverage probability of asymptotic confidence intervals.

In rapidly developing countries, it is imperative to study the changes in municipal solid waste (MSW) generation for planning waste management and treatment. This study took the largest 11 economies in the world as cases, comprising half of the global population, analyzed the variations of definition of MSW among these economies. Based on the Environmental Kuznets Curve (EKC) hypothesis and using parametric model, Feasible General Least Squares (FGLS) regression, and nonparametric models, Generalized Additive Mixed Models (GAMMs), it was expected that the change features and its socioeconomic drivers of total MSW generation and per capita MSW (PCMSW) since the 1960s would be determined. Efforts were also made to find the turning/stabilizing point in the relationship between PCMSW and per capita gross domestic product (PCGDP) in each economy. It shows that population has the most important impact on total MSW, however, the economic indicators might be ignored. The United States and Germany have the highest PCMSW generation, while China and India indicate the lowest. The turning/stabilizing point in the relationship between PCMSW and PCGDP perfermed in most developed economies, Singapore and Korea reached the turning point around 1990, while for other developed economies it was 2000. Germany came to a stabilizing point in 1990, and with some arbitrary, so did the United States. The developing economies seem to be still in their early stage of the potential EKC. In developed economies, heterogeneous time effects on PCMSW seem to be more significant than heterogeneous income effects, which is contrary to developing economies.

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.