Internationalisation and students' intercultural competence development

Ph. D. ThesisIn response to marketisation agendas, a considerable body of research now focuses on more values-based and inclusive aspects of higher education (HE) internationalisation. Examples include concepts such as internationalisation at home, internationalisation of the curriculum and students’ internationalised experiences. However, relatively little is known about intercultural competence (IC) as a learning outcome of HE internationalisation, and there is a lack of studies on different student cohorts regarding their IC development (e.g. students from different disciplines, home and international students). The aim of this research was to (a) investigate student and staff perceptions of internationalisation on a ‘home’ campus, and (b) examine whether their international and intercultural experiences contribute to the development of IC. This study adopted a longitudinal mixed methods approach, including a two-stage self-report survey (October and May) and three rounds of semi-structured interviews (October, February, June). The Multicultural Personality Questionnaire (i.e. MPQ) was used to measure students’ IC development over time, while the interviews were designed to monitor students’ intercultural experiences at three stages. In total, 227 students from three disciplines (Business, Education, and Engineering) took part in a pre- and post- survey. Fourteen students and five staff members participated in semi-structured interviews. Findings revealed that staff from both ‘soft’ and ‘hard’ disciplines hold similar instructional beliefs, acknowledging the importance of international elements in their teaching and aiming to prepare their students with skills that enable them to work with colleagues from different cultural groups. On the other hand, students’ attitudes towards their experience of internationalisation at the host university changed from positive towards less satisfied after nine months of studies. The study suggests that the degree of internationalisation at a university is not merely reflected in its number of international students (ISs) and the internationalised curriculum, but also in home and international students’ social integration in and out of class. Regarding students’ IC development, findings indicated that although students mostly claimed that they became more open-minded and empathetic towards people from other cultural groups, those from the Engineering discipline demonstrated a significant decrease in open-mindedness (OM). This was mainly related to having ‘negative intergroup contact’ resulting from working in mixed culture groups, lack of social contacts, or experiencing social segregation in and out of class. In addition, ISs showed a significant increase in flexibility (FL) over time. This indicates that ISs have become more adapted both academically and socio-culturally after a period of nine months of studying. The study informed a conceptual model of HE internationalisation that integrates the exploration of student and staff perceptions and experience (i.e. as a process) and the measurement of students’ IC development (i.e. as a learning outcome)

Effect of the Sodium Silicate Modulus and Slag Content on Fresh and Hardened Properties of Alkali-Activated Fly Ash/Slag

This paper presents the results of an experimental study performed to investigate the effect of activator modulus (SiO2/Na2O) and slag addition on the fresh and hardened properties of alkali-activated fly ash/slag (AAFS) pastes. Four activator moduli (SiO2/Na2O), i.e., 0.0, 1.0, 1.5, and 2.0, and five slag-to-binder ratios, i.e., 0, 0.3, 0.5, 0.7, 1.0, were used to prepare AAFS mixtures. The setting time, flowability, heat evolution, compressive strength, microstructure, and reaction products of AAFS pastes were studied. The results showed that the activator modulus and slag content had a combined effect on the setting behavior and workability of AAFS mixtures. Both the activator modulus and slag content affected the types of reaction products formed in AAFS. The coexistence of N-A-S-H gel and C-A-S-H gel was identified in AAFS activated with high pH but low SiO2 content (low modulus). C-A-S-H gel had a higher space-filling ability than N-A-S-H gel. Thus, AAFS with higher slag content had a finer pore structure and higher heat release (degree of reaction), corresponding to a higher compressive strength. The dissolution of slag was more pronounced when NaOH (modulus of 0.0) was applied as the activator. The use of Na2SiO3 as activator significantly refined the pores in AAFS by incorporating soluble Si in the activator, while further increasing the modulus from 1.5 to 2.0 prohibited the reaction process of AAFS, resulting in a lower heat release, coarser pore structure, and reduced compressive strength. Therefore, in view of the strength and microstructure, the optimum modulus is 1.5

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific tasks, it is still a black box that lacks interpretability, far from how humans perceive the world. Inspired by the grandmother-cell hypothesis in neuroscience, this work investigates how population encoding and spiking timing-dependent plasticity (STDP) mechanisms can be integrated into the learning of spiking neural networks, and how a population of neurons can represent a symbol via guiding the completion of sequential firing between different neuron populations. The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network. Moreover, we introduced the Reward-modulated spiking timing-dependent plasticity (R-STDP) mechanism to simulate the biological reinforcement learning process and completed the related reasoning tasks accordingly, achieving comparable accuracy and faster convergence speed than the graph convolutional artificial neural networks. For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge. For the field of artificial intelligence, this paper indicated the exploration direction for realizing a more robust and interpretable neural network by constructing a commonsense knowledge representation and reasoning spiking neural networks with solid biological plausibility

Differentiation of Glial Cells From hiPSCs: Potential Applications in Neurological Diseases and Cell Replacement Therapy.

Glial cells are the most abundant cell type in the central nervous system (CNS) and play essential roles in maintaining brain homeostasis, forming myelin, and providing support and protection for neurons, etc. Over the past decade, significant progress has been made in the reprogramming field. Given the limited accessibility of human glial cells, in vitro differentiation of human induced pluripotent stem cells (hiPSCs) into glia may provide not only a valuable research tool for a better understanding of the functions of glia in the CNS but also a potential cellular source for clinical therapeutic purposes. In this review, we will summarize up-to-date novel strategies for the committed differentiation into the three major glial cell types, i.e., astrocyte, oligodendrocyte, and microglia, from hiPSCs, focusing on the non-neuronal cell effects on the pathology of some representative neurological diseases. Furthermore, the application of hiPSC-derived glial cells in neurological disease modeling will be discussed, so as to gain further insights into the development of new therapeutic targets for treatment of neurological disorders

Predictable forward performance processes : infrequent evaluation and applications to human‐machine interactions

We study discrete‐time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market. The key step in the construction of these processes is to solve a linear functional equation of higher order associated with the inverse problem driving the evolution of the predictable forward process. We provide sufficient conditions for the existence and uniqueness and an explicit construction of the predictable forward process under these conditions. Furthermore, we find that these processes are inherently myopic in the sense that optimal strategies do not make use of future model parameters even if these are known. Finally, we argue that predictable forward preferences are a viable framework to model human‐machine interactions occurring in automated trading or robo‐advising. For both applications, we determine an optimal interaction schedule of a human agent interacting infrequently with a machine that is in charge of trading

Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement

AIMS The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement. METHODS AND RESULTS The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age. CONCLUSIONS In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification

Human Behavior Cognition Using Smartphone Sensors

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