Decomposing variance in co-rumination using dyadic daily diary data

Co-rumination is the process of perseverating on problems, negative thoughts, or feelings with another person. Still unknown is how co-rumination unfolds within the daily lives of romantic couples. Using a variance decomposition procedure on data from a 14-day dyadic daily diary, we assess how much co-rumination varies over time and whether it is a couple- or individual-level process. Results revealed that within-person fluctuations in co-rumination contributed most (~33%) to the total variance and that these fluctuations could be reliably assessed using multi-item summary scores. Although time-invariant between-couple differences account significantly for the total variance (~14%) and can be reliably assessed, there is little within-couple agreement on the extent to which co-rumination fluctuates on a daily level. More research is needed to understand when and why perceptions of daily co-rumination diverge within couples, and how this informs theory on co-rumination and similar ostensibly dyadic constructs.

Defect extremal surface for reflected entropy

Defect extremal surface is defined by extremizing the Ryu-Takayanagi formula corrected by the quantum defect theory. This is interesting when the AdS bulk contains a defect brane (or string). We introduce a defect extremal surface formula for reflected entropy, which is a mixed state generalization of entanglement entropy measure. Based on a decomposition procedure of an AdS bulk with a brane, we demonstrate the equivalence between defect extremal surface formula and island formula for reflected entropy in AdS3/BCFT2. We also compute the evolution of reflected entropy in evaporating black hole model and find that defect extremal surface formula agrees with island formula.

Evidences of soil warming from long-term trends (1951–2018) in North Rhine-Westphalia, Germany

Soil temperature (ST) is an important property of soils and driver of below ground biogeochemical processes. Global change is responsible that besides variable meteorological conditions, climate-driven shifts in ST are observed throughout the world. In this study, we examined long-term records in ST by a trend decomposition procedure from eleven stations in western Germany starting from earliest in 1951 until 2018. Concomitantly to ST data from multiple depths (5, 10, 20, 50, and 100 cm), various meteorological variables were measured and included in the multivariate statistical analysis to explain spatiotemporal trends in soil warming. A significant positive increase in temperature was more pronounced for ST (1.76 ± 0.59 °C) compared with air temperature (AT; 1.35 ± 0.35 °C) among all study sites. Air temperature was the best explanatory variable to explain trends in soil warming by an average 0.29 ± 0.21 °C per decade and the trend peaked during the period from 1991–2000. Especially, the summer months (June to August) contributed most to the soil warming effect, whereby the increase in maximum ST (STmax) was nearby fivefold with 4.89 °C compared with an increase of minimum ST (STmin) of 1.02 °C. This widening between STmax and STmin fostered enhanced diurnal ST fluctuations at ten out of eleven stations. Subsoil warming up to + 2.3 °C in 100-cm depth is critical in many ways for ecosystem behavior, e.g., by enhanced mineral weathering or organic carbon decomposition rates. Thus, spatiotemporal patterns of soil warming need to be evaluated by trend decomposition procedures under a changing climate. Graphical abstract

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

Inequality of gender, age and disabilities due to leprosy and trends in a hyperendemic metropolis: Evidence from an eleven-year time series study in Central-West Brazil

The present study aimed to investigate the epidemiological situation of leprosy (Hansen’s Disease), in a hyperendemic metropolis in the Central-West region of Brazil. We studied trends over eleven years, both in the detection of the disease and in disabilities, analyzing disparities and/or differences regarding gender and age. This is an ecological time series study conducted in Cuiabá, capital of the state of Mato Grosso. The population consisted of patients diagnosed with leprosy between the years 2008 and 2018. The time series of leprosy cases was used, stratifying it according to gender (male and female), disability grade (G0D, G1D, G2D, and not evaluated) and age. The calendar adjustment technique was applied. For modeling the trends, the Seasonal-Trend decomposition procedure based on Loess (STL) was used. We identified 9.739 diagnosed cases, in which 58.37% were male and 87.55% aged between 15 and 59 years. Regarding detection according to gender, there was a decrease among women and an increase in men. The study shows an increasing trend in disabilities in both genders, which may be related to the delay in diagnosis. There was also an increasing number of cases that were not assessed for disability at the time of diagnosis, which denotes the quality of the services.

Devising methods for planning a multifactorial multilevel experiment with high dimensionality

This paper considers the task of planning a multifactorial multilevel experiment for problems with high dimensionality. Planning an experiment is a combinatorial task. At the same time, the catastrophically rapid growth in the number of possible variants of experiment plans with an increase in the dimensionality of the problem excludes the possibility of solving it using accurate algorithms. On the other hand, approximate methods of finding the optimal plan have fundamental drawbacks. Of these, the main one is the lack of the capability to assess the proximity of the resulting solution to the optimal one. In these circumstances, searching for methods to obtain an accurate solution to the problem remains a relevant task. Two different approaches to obtaining the optimal plan for a multifactorial multilevel experiment have been considered. The first of these is based on the idea of decomposition. In this case, the initial problem with high dimensionality is reduced to a sequence of problems of smaller dimensionality, solving each of which is possible by using precise algorithms. The decomposition procedure, which is usually implemented empirically, in the considered problem of planning the experiment is solved by employing a strictly formally justified technique. The exact solutions to the problems obtained during the decomposition are combined into the desired solution to the original problem. The second approach directly leads to an accurate solution to the task of planning a multifactorial multilevel experiment for an important special case where the costs of implementing the experiment plan are proportional to the total number of single-level transitions performed by all factors. At the same time, it has been proven that the proposed procedure for forming a route that implements the experiment plan minimizes the total number of one-level changes in the values of factors. Examples of problem solving are given

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.

On the Relationship between Economic Dynamics and Female Entrepreneurship: Reflections for the Visegrad Countries

The gender gap in entrepreneurship has been observed for a long time, explained by both female-specific and gender-neutral factors, but none of these explanations is generally accepted. The aim of the paper is to assess the effect of internal economic dynamics on female entrepreneurship. Economic dynamics is a persistent process affected simultaneously by both endogenous and exogenous factors of a different time horizon, with the development trend and the business cycle as the most important time perspectives. The decomposition procedure of time series is implemented to extract trend and cyclical fluctuations, after which the Vector Error Correction Model (VECM) method is used to estimate models showing the impact of economic dynamics on female entrepreneurship in the long- and medium-run. The study concerns the countries of the Visegrad Group, including Czechia, Hungary, Poland, and Slovakia, and is based on quarterly data from the years 1998 to 2020. The results show that, although the economic dynamics impact female entrepreneurship, to some extent, it is not the most dominant factor. The impact of economic dynamics on female entrepreneurship is much stronger in the trend perspective than in the business cycle perspective. The nature of the effect of economic dynamics on female entrepreneurship is also country-specific.

Did Residential Racial Segregation in the U.S. Really Increase? An Analysis Accounting for Changes in Racial Diversity

A recent study by UC Berkeley's Othering & Belonging Institute (Menendian, Gailes, and Gambhir 2021) came to an astonishing conclusion: Of large metropolitan areas in the U.S., 81% have become more segregated over the period 1990-2019. This finding contradicts the recent sociological literature on changes in segregation in the U.S., which has generally found that racial residential segregation has slowly declined since the 1970s, especially between Blacks and Whites. The major question then is: What accounts for this difference? This paper answers this question in two parts. First, it shows that the preferred segregation measure of the Berkeley study, the “Divergence Index” (Roberto 2015), is identical to the Mutual Information Index M (Theil and Finizza 1971; Mora and Ruiz-Castillo 2009; Mora and Ruiz-Castillo 2011), a measure that is mechanically affected by changes in racial diversity. Given that the U.S. has become more diverse over the period 1990 to 2019, it is not surprising that this index shows increases in segregation. Second, by making use of a decomposition procedure developed in Elbers (2021), the paper shows that once the changes in segregation are decomposed into components that account for the changing racial diversity of the U.S., the findings are in line with the sociological literature. Residential racial segregation as a whole has declined modestly in most metropolitan areas of the U.S., although segregation has increased slightly when focusing on Asian Americans and Hispanics.