The main objective of the study was to examine factors that influence employee performance in general and, more specifically, in public enterprises. The research approach was qualitative, with an in-depth literature review and content analysis. The findings of the study reflect that some factors have a positive and some have a negative influence on employee performance. The study also shows a significant relationship between factors and employee performance, which in turn has a multiplier effect on employee development. Recommendations include the need to provide resources for employee training and development, and the strategic aims and objectives of public enterprises should be aligned with the training and development programs.

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.

Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.