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 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.

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46-50.91 doses per 100 people.

Initially found in Hubei, Wuhan, and identified as a novel virus of the coronavirus family by the WHO, COVID-19 has spread worldwide at exponential speed, causing millions of deaths and public fear. Currently, the USA, India, Brazil, and other parts of the world are experiencing a secondary wave of COVID-19. However, the medical, mathematical, and pharmaceutical aspects of its transmission, incubation, and recovery processes are still unclear. The classical susceptible-infected-recovered model has limitations in describing the dynamic behavior of COVID-19. Hence, it is necessary to introduce a recursive, latent model to predict the number of future COVID-19 infection cases in the USA. In this article, a dynamic recursive and latent infection model (RLIM) based on the classical SEIR model is proposed to predict the number of COVID-19 infections. Given COVID-19 infection and recovery data for a certain period, the RLIM is able to fit current values and produce an optimal set of parameters with a minimum error rate according to actual reported numbers. With these optimal parameters assigned, the RLIM model then becomes able to produce predictions of infection numbers within a certain period. To locate the turning point of COVID-19 transmission, an initial value for the secondary infection rate is given to the RLIM algorithm for calculation. RLIM will then calculate the secondary infection rates of a continuous time series with an iterative search strategy to speed up the convergence of the prediction outcomes and minimize the maximum square errors. Compared with other forecast algorithms, RLIM is able to adapt the COVID-19 infection curve faster and more accurately and, more importantly, provides a way to identify the turning point in virus transmission by searching for the equilibrium between recoveries and new infections. Simulations of four US states show that with the secondary infection rate ω initially set to 0.5 within the selected latent period of 14 days, RLIM is able to minimize this value at 0.07 and reach an equilibrium condition. A successful forecast is generated using New York state\'s COVID-19 transmission, in which a turning point is predicted to emerge on January 31, 2021.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11071-021-06520-1.

Objective: Acupuncture is one of the most widely used treatments of complementary and alternative medicine (CAM) within the military\'s health system. The success of CAM integration is partially dependent on both providers\' and patients\' perceptions that acupuncture is health-promoting. The aim of this research was to identify turning points, or changes, across treatments that enhanced or inhibited physicians\' and patients\' perception of acupuncture as health-promoting. Materials and Methods: Using a retrospective-interview approach, interviews were conducted with 15 family medicine physicians practicing medical acupuncture in a family medicine setting and with 17 patients (N = 32). Turning points were separated into 2 groups (health-promoting or health-inhibiting). Similarities and differences between perspectives were noted. Results: Patients and physicians identified two changes that enhanced their perspective of acupuncture as health-promoting: (1) observed health changes and (2) pain-medicine/narcotic reduction/elimination. Patients identified their ability to fulfill personal or professional roles, whereas physicians identified (1) training experiences and (2) enhanced relationships with patients. Health-inhibiting changes in perspective were identified as logistical constraints/barriers by both parties, although their perspectives differed to some degree. Turning points that were viewed as health-inhibiting treatment were identified as clinical challenges by physicians and as a lack of consistency in care by patients. Conclusions: The insight from these findings can help identify areas where medical acupuncture can be improved to promote successful integration in conventional medicine settings, as well as how providers can tailor communication with patients about acupuncture.

Existing socio-technical systems tend to be intransigent to change. Decarbonisation, on the other hand, is an imperative, leading to an obvious conflict between the need for, and highly effective resistance to, change. Moreover, the abandonment of fossil fuel-based technologies in favour of more sustainable alternatives will require substantial reallocation of government\'s operational expenditure, particularly in countries like South Africa with high per capita greenhouse gas emissions and low per capita income. In this article, it is argued that reallocation will require more than niche experimentation and destabilisation of the present socio-technical regime. Based on a study of South Africa\'s budget processes, it is concluded that change will only occur when four separate pre-conditions converge, namely a rapidly growing environmental problem capable of leading to civil unrest, a supportive and recently developed policy framework, decreasing techno-economic costs for its solution, and strong political support from an effective ministry or minister. Turning points for transition, although infrequent, can be reached through strategic attention to these pre-conditions. A modified Kingdon multiple streams approach, which introduces the additional dimension of techno-economic feasibility, is proposed as a useful framework for anticipating when and how to act in order to mobilise sufficient public resources for decarbonisation.

In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments\' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.