The impact of urbanization and economic growth on carbon dioxide emission in sub-Saharan African countries: a perspective from the spatial-temporal approach.

Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

A multifaceted poverty reduction program has economic and behavioral consequences.

This paper examines the causal impact of poverty reduction interventions on the social preferences of the poor. A multifaceted poverty reduction program in China provides a setting for the use of a fuzzy regression discontinuity design. The design compares households with base-year income just below a preset criterion, who were more likely to receive the program treatment, with households just above the criterion. Five years after the program's launch, we conducted a lab-in-the-field experiment to measure the distributional preferences of household heads. Combining quasi-random variation from program rules with administrative census and experimental data, we find both economic and behavioral consequences of the program: It increased household income by 50% 5 y later, increased consistency with utility maximization by household heads, and increased their efficiency preference while reducing selfishness and leaving equality preference unchanged. Our findings advance scientific understanding of social preferences formation and highlight a broad perspective in evaluating poverty reduction interventions.

RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements.

Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤0.4594 m, highlighting the effectiveness of our learning process used in the development of the models.

Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry.

The Ki-67 labeling index (Ki-67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki-67 immunostaining in 200-500 tumor cells. To enable automated Ki-67 LI assessment in routine clinical practice, a framework for automated Ki-67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki-67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A 'heterogeneity TMA' containing three to six samples from different tumor areas in each patient was used to model Ki-67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a 'classical' bright field-based Ki-67 analysis with the mfIHC-based framework. The Ki-67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki-67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient: 0.94 [95% confidence interval: 0.87-0.97]). The analysis of the heterogeneity TMA revealed that the Ki-67 LI of the sample with the highest Gleason score (area under the curve [AUC]: 0.68) was as prognostic as the mean Ki-67 LI of all six foci (AUC: 0.71 [p = 0.24]). The combined analysis of the Ki-67 LI and Gleason score obtained on identical tissue spots showed that the Ki-67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC: 0.82 [p = 0.002]) and quantitative Gleason score (AUC: 0.83 [p = 0.018]). The Ki-67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer-positive biopsies, the sole automated analysis of the worst biopsy was sufficient. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Influence of Culture Capital, Social Security, and Living Conditions on Children's Cognitive Ability: Evidence from 2018 China Family Panel Studies.

The aim of this study was to analyze the influence of economic capital, culture capital, social capital, social security, and living conditions on children's cognitive ability. However, most studies only focus on the impact of family socio-economic status/culture capital on children's cognitive ability by ordinary least squares regression analysis. To this end, we used the data from the China Family Panel Studies in 2018 and applied proxy variable, instrumental variables, and two-stage least squares regression analysis with a total of 2647 samples with ages from 6 to 16. The results showed that family education, education expectation, books, education participation, social communication, and tap water had a positive impact on both the Chinese and math cognitive ability of children, while children's age, gender, and family size had a negative impact on cognitive ability, and the impact of genes was attenuated by family capital. In addition, these results are robust, and the heterogeneity was found for gender and urban location. Specifically, in terms of gender, the culture, social capital, and social security are more sensitive to the cognitive ability of girls, while living conditions are more sensitive to the cognitive ability of boys. In urban locations, the culture and social capital are more sensitive to rural children's cognitive ability, while the social security and living conditions are more sensitive to urban children's cognitive ability. These findings provide theoretical support to further narrow the cognitive differences between children from many aspects, which allows social security and living conditions to be valued.

[Status of soil fertility, nutrient balance, and environmental risk assessment in yam production of North China Plain]. / åŽåŒ—å¹³åŽŸå±±è¯ä¸»äº§åŒºåœŸå£¤è‚¥åŠ›å’Œå »åˆ†å¹³è¡¡çŽ°çŠ¶åŠçŽ¯å¢ƒé£Žé™©è¯„ä»·.

Taking the main production area of yam in North China Plain as the research area, we analyzed the status of soil fertility and fertilizer application in yam production through field investigation and tracking monitoring, examined soil nutrient balance using the input-output model of nutrients in agricultural system, and assessed the environmental risks in the yam planting system. The results showed that: 1) the contents of soil organic matter and total N were extremely low, and the contents of available P and available Zn were both low; both nitrate and available Cu contents were at the middle level, the contents of soil slowly available K, available S, and exchangeable Ca and Mg were all extremely high, the contents of available K, available Fe, and available Mn were all at high level; 2) The nitrogen (N), phosphorus (P2O5), and potassium (K2O) inputs were 575-943 kg·hm-2, 341-981 kg·hm-2, and 655-1219 kg·hm-2 during the whole growth period of yam, with chemical fertilizer accounting for 83.0%, 88.6%, and 91.3%, respectively; The input imbalance between organic and inorganic fertilizer, as well as the excessive nutrients input were prominent; 3) The surplus rate of soil nitrogen, phosphorus and potassium reached 271.14 kg·hm-2, 466.34 kg·hm-2, and 739.97 kg·hm-2, with corresponding surplus ratio of 48.7%, 258.1%, and 324.5%, respectively, which all exceeded the environmental safety threshold and were classified as moderate risk, severe risk, and severe risk, respectively. The overall environmental risk caused by chemical fertilizer application in yam production had reached severe risk level.

The impact of LCTI on China's low-carbon transformation from the spatial spillover perspective.

China has conducted a long-term low-carbon technology innovation (LCTI), but there was a faster increase of CO2 emission in 2017 and 2018 than in 2016, which has lead scholars to doubt the effect of LCTI on CO2 emission. This paper builds a spatial auto regression (SAR) model with provincial panel data from 2011 to 2017 to calculate the spatial spillover effect of China's LCTI on regional CO2 emission. The results show that regional LCTI can reduce the local CO2 emission, but will increase the CO2 emission of adjacent regions due to spatial spillover effect. This produces the uncertainty of the promotion effect of LCTI on China's low-carbon transformation. Therefore, this paper suggests innovation resources should be appropriately and evenly distributed among regions to avoid their agglomeration in few regions.

Corrigendum to "Time barrier to trade: Data on 190 economies' export and import time, 2005-2018" [Data in Brief 22 (February 2019) 508-515].

[This corrects the article DOI: 10.1016/j.dib.2018.11.054.].

Time barrier to trade: Data on 190 economies' export and import time, 2005-2018.

This data article presents data on time to export and import across 190 economies, for the years 2005-2018. The data can foster research on international trade, and are of great academic and political value given the growing awareness and importance of time as a trade barrier. The data are publicly available at https://www.doingbusiness.org/data. A subset of the data is used in the related research data article, "Time barrier to export for OECD countries" (Li, 2018). Data on the number of documents required in these economies to export and import are also presented, for the years 2005-2015.

Benzene risk assessment: does new evidence on myelodysplastic syndrome justify a new approach?

Epidemiologic findings play an important role in benzene risk assessment, which is utilized to guide the selection of recommended benzene exposure levels to prevent adverse health effects. For decades, excess leukemia risk, especially that in the Pliofilm® cohort, has been the focus of benzene risk assessment. While more stringent benzene standards, often ≤1 ppm, have been promulgated to protect workers from developing leukemia, recent epidemiologic studies have reported elevated risk of myelodysplastic syndrome (MDS). This report aims to examine whether the use of new data on MDS is scientifically warranted in future benzene risk assessments. First, we reviewed current benzene guidelines, regulations, and underlying risk assessments in developed countries. Second, we examined current epidemiologic literature on benzene and MDS, which identified seven studies with simultaneous measures of MDS risk and benzene exposure and 17 studies on MDS in populations potentially exposed to benzene. Next, we examined the potential of the MDS data to serve as the basis of future benzene risk assessments, by comparing its quality and risk estimates with those used in current benzene standards. We conclude from the current literature that there is strong evidence that MDS can be caused by benzene, and the MDS data from the pooled petroleum study should be further examined in future benzene risk assessments. We recommend that future MDS-based benzene risk assessment use total MDS as the endpoint, take into consideration the full exposure period, and examine a range of benzene exposure metrics, including the role of peak, intermittent benzene exposures.