This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

Objectives: The unprecedented COVID-19 pandemic has intensified the stress on blood banks and deprived the blood sources due to the containment measures that restrict the movement and travel limitations among blood donors. During this time, Malaysia had a significant 40% reduction in blood supply. Blood centers and hospitals faced a huge challenge balancing blood demand and collection. The health care systems need a proactive plan to withstand the uncertain situation such as the COVID-19 pandemic. This study investigates the psychosocial factors that affect blood donation behavior during a pandemic and aims to propose evidence-based strategies for a sustainable blood supply. Study design: Qualitative design using focus group discussion (FGD) was employed. Methods: Data were acquired from the two FGDs that group from transfusion medicine specialists (N = 8) and donors (N = 10). The FGD interview protocol was developed based on the UTM Research Ethics Committee’s approval. Then, the data was analyzed using Nvivo based on the General Inductive Approach (GIA). Results: Analysis of the text data found that the psychology of blood donation during the pandemic in Malaysia can be classified into four main themes: (i) reduced donation; (ii) motivation of donating blood; (iii) trends of donation; and (iv) challenges faced by the one-off, occasional, and non-donors. Conclusions: Based on the emerging themes from the FGDs, this study proposes four psycho-contextual strategies for relevant authorities to manage sustainable blood accumulation during the pandemic: (1) develop standard operating procedure for blood donors; (2) organize awareness campaigns; (3) create a centralized integrated blood donors database; and (4) provide innovative Blood Donation Facilities.

 Landscape architects, who guide planning and design decisions by understanding the socio-cultural expectations, functional needs, and social behaviors of the community, create ideal spaces for people by integrating natural, social, cultural, and aesthetic factors with a holistic design approach in urban public areas. Public open green spaces are important urban areas that have a positive impact on people’s physical, mental, and emotional health. In this context, the concept of personal space, its impact on individuals, and related perception studies have been examined. In landscape design, criteria that affect individuals’ personal space distances and personal space perceptions have been identified, providing a basis for sustainable landscape design projects in public open and green spaces.

Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.

The study examined the socio-demographic factors affecting access to and utilization of social welfare services in Yenagoa Local Government Area of Bayelsa State, Nigeria. Quantitative and qualitative approaches were adopted to select 570 respondents from the study area. Probability and non-probability sampling techniques were adopted in the selection of communities, and respondents. The quantitative data were analyzed using frequency distribution tables and percentages, while chi-square statistic was used to determine the relationship between socio-demographic variables and access to and utilization of social welfare services. The qualitative data were analyzed in themes as a complement to the quantitative data. This study reveals that although all the respondents reported knowing available social welfare services, 44.3% reported not having access to existing social services due to factors connected to serendipity variables, such as terrain condition, ethnicity and knowing someone in government. Therefore, the study recommends that the government and other stakeholders should push for the massive delivery of much-needed social welfare services to address the issue of welfare service deficit across the nation, irrespective of the ethnic group and whether the community is connected to the government of the day or not, primarily in rural areas.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.