Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the Zhijia Jin, Ying Wang, and Mojtaba Jokar contributed equally to this study.

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

This paper constructed an accurate employee satisfaction factors model for measuring employee satisfaction. An employee satisfaction scale with 30 indicators is designed and tested in Chinese Resource-based State-owned Enterprises. Satisfaction surveys are administered and first-hand data of 3,029 respondents are obtained from 27 units. Exploratory factor analysis is used for extracting four employee satisfaction factors from final 29 satisfaction indicators. The four factors are attributed to the culture, job, management, and welfare of the enterprises. Job positions and education level are found to have significant impacts affecting employee satisfaction. Influences of the four factors are discussed with suggestions made to the management. In future research, the relationship between satisfaction, stress, and performance needs further exploration, in addition to the influence of employee stress and factors from other fields.

Abstract:The aim of this paper is to provide a new approach for assessing the input-output efficiency of education and technology for national science and education department. We used the Data Envelopment Analysis (DEA) method to analyze the efficiency sharing activities in education and technology sector, and classify input variables and output variables accordingly. Using the panel data in the education and technology sector of 53 countries, we found that the countries with significant progress in educational efficiency and technological efficiency mainly concentrated in East Asia, especially in Japan, Korea, Taiwan and some other developing countries. We further evaluate the effect of educational and technological efficiencies on national competitiveness, balanced development of the country, national energy efficiency, export, and employment. We found that the efficiency of science and technology has an effect on the balanced development of the country, but that of education has played a counter-productive role; Educational efficiency has a large role and related the country's educational development. In addition, using the panel data analysis, we showed that educational and technological efficiency has different degrees of contributions to the development from 2000 to 2014. It mainly depends on the economic development progress and the push for the education and technological policy. The proposed approach in this paper provides the decision-making support for the education and technological policy formulation, specially the selection of the appropriate education and technological strategies for resource allocation and process evaluation.