Tropical dry forests are complex and fragile ecosystems with high anthropogenic intervention and restricted reproductive cycles. They harbor unique richness, structural, physiological and phenological diversity. This research was carried out in the upper Magdalena valley, in four forest fragments with different successional stages. In each fragment, four permanent plots of 0.25 ha were established and the light habitat associated with species richness, relative abundance and rarity was evaluated, as well as the forest dynamics that included mortality, recruitment and diameter growth for a period of 5.25 years. In mature riparian forest, species richness was found to be higher than that reported in other studies for similar areas in the Cauca Valley and the Atlantic coast. Values of species richness, heterogeneity and rarity are higher than those found in drier areas of Tolima. Forest structure, diversity and dynamics were correlated with light habitat, showing differences in canopy architecture and its role in the capture and absorption of radiation. The utilization rate of photosynthetic effective radiation in the forest underlayer with high canopy density is low, which is related to the low species richness, while the underlayer under light is more abundant and heterogeneous.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

The journey towards better healthcare sustainability in Asian nations demands a comprehensive investigation into the impact of urban governance, poverty, and female literacy on infant mortality rates. This study undertakes a rigorous exploration of these key factors to pave the way for evidence-based policy interventions, utilizing data from a panel of six selected Asian countries: Pakistan, China, India, Indonesia, Malaysia, and the Philippines, spanning the years 2001 to 2020. The findings reveal that adequate sanitation facilities, higher female literacy rates, and sustained economic growth contribute to a reduction in infant mortality. Conversely, increased poverty levels and limited women’s autonomy exacerbate the infant mortality rates observed in these countries. The Granger causality analysis validates the reciprocal relationship between urban sanitation (and poverty) and infant mortality rates. Furthermore, the study establishes a causal relationship where female literacy rates Granger-cause infant mortality rates, and conversely, infant mortality rates Granger-cause women’s autonomy in these countries. The variance decomposition analysis indicates that sustained economic growth, improved female literacy rates, and enhanced women’s empowerment will likely impact infant mortality rates in the coming decade. Consequently, in low-income regions where numerous children face potentially hazardous circumstances, it is imperative to allocate resources towards establishing and maintaining accessible fundamental knowledge regarding sanitation services, as this will aid in reducing infant mortality rates.

Leukemia is a major public health problem in China, but epidemiological studies on leukemia in China are still insufficient. This study aims to analyze leukemia's disease burden and risk factors in China from 2010 to 2021 and provide a basis for leukemia prevention and treatment. Using data from the Global Burden of Disease (GBD) database, trends in the burden of leukemia in China from 2010 to 2021 were analyzed. Additionally, epidemiological differences by gender and age groups were explored. In 2021, there were 531,000 leukemia patients in China, with 106,000 new cases and 59,000 deaths. Compared to 2010, the mortality rate and disability-adjusted life years (DALYs) per 100,000 population in 2021 decreased by 5% and 18%, respectively, while the incidence and prevalence rates increased by 12% and 29%, respectively. Gender and age stratification indicated that males had higher rates across all indicators than females, and elderly individuals faced higher leukemia mortality and DALYs. The most significant decrease in DALYs was observed in children and adolescents under 20. The highest burden of leukemia for males was found in the 85–90 age group, while for females, it was in the 70–74 age group. Major risk factors for leukemia included smoking, high BMI, and exposure to carcinogens, benzene, and formaldehyde. The overall burden of leukemia in China showed a decreasing trend, with significant gender and age differences. More measures are needed to reduce leukemia mortality, particularly focusing on the prevention and treatment of leukemia in males and the elderly.

The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.

Demographic policy is one of the key tasks of almost any state at the present time. It correlates with the solution of pressing problems in the economic and social spheres, directly depends on the state of healthcare, education, migration policy and other factors and directly affects the socio-economic development of both individual regions and the country as a whole. Many Russian and foreign researchers believe that demographic indicators very accurately reflect the socio-economic and political situation of the state. The relevance of the study is due to the fact that for the progressive socio-economic development of any country, positive demographic dynamics are necessary. The main sign of the negative demographic situation that has developed in modern Russia and a number of countries, primarily European, is the growing scale of depopulation (population extinction). The purpose of this work was to analyze the existing demographic policy of Russia and compare demographic trends in Russia and other countries. The work uses methods of statistical data analysis, comparison of statistical indicators of fertility, mortality, natural population decline, migration, marriage rates in Russia and the Republic of Srpska, methods of retrospective analysis, research of the institutional environment created by the action of state and national programs “Demography”, “Providing accessible and comfortable housing and public services for citizens of the Russian Federation”, “Strategy of socio-economic development for the period until 2024”, Presidential decrees, etc. Research has shown that despite measures taken to overcome the demographic crisis, Russia’s population continues to decline. According to the Federal State Statistics Service of the Russian Federation (Rosstat), as of 1 January 2023, 146.45 million people lived in Russia. By 1 January 2046, according to a Rosstat forecast published in October 2023 the country’s population will decrease to 138.77 million people. To solve demographic problems in the Russian Federation, a national project “Demography” was developed and approved. The government has allocated more than 3 trillion rubles for its implementation. However, it is not possible to completely overcome the negative trend. The authors proposed a number of economic and ideological measures within the framework of agglomeration, migration, and family support policies that can be used within the framework of socio-economic development strategies and national programs aimed at overcoming the demographic crisis.