The Velocity of Money in a Life-Cycle Model

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is governed by behavior patterns of economic agents and open a way to constructing micro-foundation of it. It is found that time pattern of income and expense for a representative individual can be obtained from a simple version of life-cycle model, and average holding time of money resulted from the individual's optimal choice depends on the expected length of relevant planning periods.Comment: 10 page

Montmorillonite modified by CNx supported Pt for methanol oxidation

A composite support based on nature clay, i.e. montmorillonite (MMT), shows great promise as support materials for Pt electrocatalyst for the methanol oxidation reaction in fuel cell anodes. The reported composite support (CNx-MMT) was prepared via carbonizing MMT which was covered by N-contented polymer. X-ray diffraction and transmission electron microscopy results showed that Pt nanoparticles can be well-dispersed on the composite support with highly dispersed tiny crystal Pt nanoparticles. Cyclic voltammetry measurements showed that the Pt/CNx-MMT has the enhanced electrocatalytic activity in methanol oxidation reaction. The developed Pt catalyst supported on new composite support is catalytically more active for methanol electrooxidation than Pt supported on the conventional carbon support and shows good stability, offering promising potential for application of MMT as support for fuel cell electrocatalysis.Web of Scienc

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

The Velocity of Money in a Life-Cycle Model

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is governed by behavior patterns of economic agents and open a way to constructing micro-foundation of it. It is found that time pattern of income and expense for a representative individual can be obtained from a simple version of life-cycle model, and average holding time of money resulted from the individual's optimal choice depends on the expected length of relevant planning periods.

Changes in the Potential Habitat Distribution of Typical Fire-Resistant Forest Species under Climate Change in the Subtropical Regions of China

Ecological fire prevention forest belts can effectively alleviate the spread of forest fires and reduce the harm caused by forest fires. Exploring the distribution and changes in suitable growth areas for fire-resistant forest species under the effects of climate change can provide effective references for the introduction of ecological fire prevention and tree species preservation in the region. This study is based on the distribution data of six typical ecological fire prevention forest species in the subtropical regions of China. The maximum entropy model (MaxEnt), optimized by the ENMeval data package, was used to analyze the potential relationship between the ecological environment variables and fire prevention forest species. The potential distribution of certain tree species in the historical period and in future periods is simulated. In addition, the area changes, migration trends, and stable areas of tree species under climate change are also discussed. The research results indicated the following: (1) The AUC values of the optimized model are all higher than 0.9, indicating the optimal prediction results. (2) The climate variables that have the greatest impact on the suitable habitat of Schima superba were the annual mean temperature, precipitation of the driest month, and mean diurnal range. Quercus glauca was mainly influenced by the minimum temperature of the coldest month and the precipitation of the warmest quarter. Castanopsis eyrei was mainly influenced by the precipitation of the driest month and the annual precipitation. The distribution of suitable growth areas for Symplocos sumuntia is mainly influenced by the precipitation of the driest month. The distribution of Camellia oleifera was influenced by the minimum temperature of the coldest month. The potential habitat distribution of Photinia serratifolia was greatly influenced by annual precipitation. (3) Until 2090, the expansion degree of the suitable growth area will be Symplocos sumuntia (51.05%) > Schima superba (19.41%) > Camellia oleifera (10.14%) > Quercus glauca (6.80%) > Castanopsis eyrei (2.34%) > Photinia serratifolia (−6.97%). (4) The centroid of Schima superba will migrate northward. Quercus glauca will migrate northeast. The suitable areas for the migration of Symplocos sumuntia and Castanopsis eyrei will move in a northwest direction, with repeated changes in alum migration, as well as with the largest migration span for Castanopsis eyrei. In addition, Camellia oleifera will move southwest. The centroid of Photinia serratifolia will migrate to the southeast. (5) The six fire-resistant tree species in this study were noted to have excellent stability in Guizhou, Hunan, Jiangxi, Fujian, Guangdong, and Guangxi. This conclusion can provide an effective reference for the introduction of ecological fire prevention tree species and the protection of tree species under climate change in subtropical forest-fire-prone areas in China

Comprehensive Decision Index of Logging (CDIL) and Visual Simulation Based on Horizontal and Vertical Structure Parameters

The comprehensive indexes approach based on stand structure parameters is mainly used to select trees for harvest. However, these indexes do not consider the comprehensive impact of horizontal and vertical structures, leading to an incomplete analysis of the forest structure and an inaccurate selection of trees for harvest. To solve this problem, we constructed a comprehensive decision index of logging (CDIL), integrating horizontal and vertical structure parameters which can identify harvest trees more scientifically. In this study, we took the Shanxia Forest Farm in the Jiangxi Province of China as the experimental area and used mixed broadleaf/conifer forests at different ages as our experimental sample. We selected eight horizontal and vertical spatial structure parameters to establish an efficient, objective, and accurate comprehensive decision index of logging. We combined 3D visualization technology to realize the dynamic visualization simulation of the index at different intensities of tending and felling management. The results indicated that the proposed CDIL-index could effectively optimize the forest spatial structure. From the perspective of stand structure adjustment, the optimal thinning intensity was 20%. The average CDIL in each plot decreased by more than 80% after logging, while the change range of each plot was between 30% and 70% after the F index was applied to implement tending and logging. The CDIL was 11.4% more accurate in selecting trees for harvesting than the F index. In this study, the main conclusion is that the CDIL would enable forest managers to more accurately choose trees for harvesting, leading to forest adjustment that would reduce the competition pressure among trees and improve the distribution and health of trees, possibly making the forest structure more stable

Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment