difference analysis
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Background: To assess the impact of study-at-home during the COVID-19 pandemic on myopia development in Chinese schoolchildren.Methods: This historical cohort involved two groups with a total of 154 children. The exposed group was formed from 77 children aged 8 to 10 years who studied at home in the 7-month period during the COVID-19 pandemic (follow-up period: January – August 2020) and did not study at home in the 7-month period before the COVID-19 outbreak (baseline period: July 2019 – January 2020). Seventy-seven children who did not undergo study-at-home (baseline period: 7 months in 2015, follow-up period: 7 months in 2016) were included in the control group. Cycloplegic refraction, axial length and uncorrected visual acuity were measured 3 times. The questionnaire mainly focused on collecting visual habits.Results: Myopia progression was similar between the two groups in the baseline period. However, in the follow-up period the exposed group had a greater change in refraction toward myopia (−0.83 ± 0.56 D) than the control group (−0.28 ± 0.54 D; p < 0.001). In addition, the exposed group exhibited a significantly greater change in refraction toward myopia in the follow-up period (−0.83 ± 0.56 D) than in the baseline period (−0.33 ± 0.46 D; p < 0.001). Difference-in-difference analysis indicated that study-at-home accelerated the change in refraction toward myopia (t = −0.567; p < 0.001).Conclusions: During the COVID-19 pandemic study-at-home accelerated the change of refraction toward myopia in children.

A series of finite difference analysis of the soft ground treatment with PVDs application has been performed with the application of the newly developed consolidation model. The model utilizes the concept of multi-compression indices and coefficients of consolidation to investigate the behaviors of the soft ground deposit on pore water pressure dissipation, surface and sub-layered ground settlement and to validate the newly developed CONSOPRO finite-difference procedure. Furthermore, the pre-consolidation pressures of the soft ground deposit are characterized with the combination of the piezocone penetration profiling and constant rate of strain consolidation tests under 0.02 %/min. on undisturbed samples which were retrieved at the investigated site, Saigon Premier Container Terminal (SPCT) in the South of Vietnam. On the comparison of the back-calculation results to the field observing data, the correlation between the coefficients of consolidation determined by constant-rate-of-strain (CRS) consolidation tests and those from piezocone dissipation tests, which were carried out after the soil improvement, is developed.

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

This paper estimates the impact of the capture of leaders of criminal organizations on the labor market in municipalities where these organizations operated between 2004 and 2006. The difference-in-difference analysis compares different employment outcomes in cartel locations and the rest, before and after the capture of cartel leaders. The results show that captures caused a decrease in nominal wages and paid employment in cartel municipalities. Using Economic Census Data, I find that captures also caused a fall in the number of establishments and had a negative impact on other establishment outcomes. This document focuses exclusively on the impact of the capture of leaders of criminal organizations on the labor market until 2011 without studying other possible consequences, and thus does not make an integral assessment of this policy