A MORE REPRESENTATIVE GROUP EMOTIONAL INTELLIGENCE MEASUREMENT AND THE INFLUENCE OF GROUP EMOTIONAL INTELLIGENCE ON GROUP PERFORMANCE

Emotional intelligence (EI) is a well established personal characteristic. It has been viewed as a critical factor which can influence an individual's academic achievement, ability to work and potential to succeed. When working in a group, emotion is fundamentally connected to the group members' interaction and ability to work as a team, and the ability of a group member to intelligently perceive and understand other members' emotions could make themselves more effective in their work. This ability is known as Group emotional intelligence (Group EI). Previous research has shown that a group with high Group EI performs better when working as a team than groups with lower Group EI, as well as achieves better outcomes. Despite the acknowledged importance of Group EI, most research presents Group EI using the average of each group members' individual EI. It is argued that this Group EI measurement is less representative, as it does not consider other factors within a group (e.g the composition of a group and the role of each individual). Thus, this research looks into the question of how to more representatively measure Group EI in group work. It is proposed that the use of the weighted average of each individual's EI is more representative if the weight is based on an individual's influence in the group. Recent research also demonstrates that the Group EI is a powerful predictor for the group performance. As the proposed Group EI measurement in this thesis claims to be more representative, it is also necessary to demonstrate the impact of the Group EI using the proposed measurement approach on group performance. This research adopts a multi-methodological research design and a mixed methods approach, involving a combination of both qualitative and quantitative techniques to establish a metric for Group EI and to assess its impact on group performance. It involves 12 case studies on 12 project groups composed of 70 MSc Engineering Management students, and quantitative data collection from individuals as well as groups using two questionnaires. The information recorded from the case study is interpreted to reflect the emotional characteristics of each group, and the data collected using the questionnaires is analysed using statistical analysis. The results demonstrate that the proposed Group EI measurement is more representative than previous approaches. In addition, the results also demonstrate that teams with a high Group EI are more likely to have a better group performance. The findings from this research provide a new method for understanding and measuring Group EI, which will potentially provide new insights into the way team members are selected or prepared for project work. It will be beneficial to those in charge of organising groups if the proposed Group EI measurement approach is adopted

MULTIPLE TESTING CORRECTION IN TIME SERIES ROLLING WINDOW ANALYSIS WITH APPLICATION OF GWAS METHODS

Rolling window is a popular tool in time series analysis. When conducting hypothesis testing on each window simultaneously, multiple testing problem occurs. In the literature in rolling window analysis, it appears that bootstrap is the most frequently used, if not only, method to address the multiple testing issue. This thesis aims to adapt multiple testing correction methods that are popular in genome-wide association study to the time series rolling window context. In particular, some of these methods require the knowledge of the correlation structure of test statistics. In genetics, this structure can be obtained from an external source, which may not exist in time series. To overcome this difficulty, we adopt the AR sieve idea, which enables the computation of correlation structure based on the estimated AR coefficients. We also present the finite sample simulation to illustrate the performance of these methods

Research on Ecological Development of Foreign Language Journals—Based on Foreign language Teaching and Research

A foreign language journal is an ecosystem because each foreign language journal has different columns. This paper takes Foreign Language Teaching and Research as an example to analyze the ecological development of foreign language journals and give some suggestions on the future development of foreign language journals

Photochemical transformation of perfluoroalkyl acid precursors in water using engineered nanomaterials

The production of perfluoroalkyl acids (PFAAs) has been phased out over recent decades; however, no significant decline in their environmental concentrations has been observed. This is partly due to the photochemical decomposition of PFAAs precursors (PrePFAAs) which remain in extensive use. The decomposition of PrePFAAs may be accelerated by the light-activated engineered nanomaterials (ENMs) in water. In light of this hypothesis, we investigated the photochemical transformation of three PrePFAAs, which are 8:2 fluorotelomer sulfonic acid (8:2 FTSA), 8:2 fluorotelomer alcohol (8:2 FTOH), and 2-(N-ethylperfluorooctane-1-sulfonamido ethyl] phosphate (SAmPAP), in the presence of six ENMs under simulated sunlight irradiation. The transformation rates of 8:2 FTSA and 8:2 FTOH were increased by 2–6 times when in the presence of six ENMs. However, most of ENMs appeared to inhibit the decomposition of SAmPAP. The transformation rates of PrePFAAs were found to depend on the yield of reactive oxygen species generated by ENMs, but the rates were also related to compound photo-stability, adsorption to surfaces, and photo-shielding effects. The PrePFAAs are transformed to perfluorooctanoic acid (PFOA) or/and perfluorooctane sulfonate (PFOS) with higher toxicity and longer half-life, PFOA or PFOS and a few PFAAs having shorter carbon chain lengths. Higher concentrations of the PFAAs photodegradation products were observed in the presence of most of the ENMs