Nazarbayev University Repository (NUR) is an institutional electronic archive designed for the long-term preservation, aggregation, and dissemination of scientific research outcomes and intellectual property produced by the Nazarbayev University community and affiliated organizations.
Recent Submissions
HOW DO CTHRC1 AND MATRIX METALLOPROTEINASES AFFECT PATHOLOGIC ACTIVITIES OF FIBROBLAST-LIKE SYNOVIOCYTES IN RHEUMATOID ARTHRITIS?
(Nazarbayev University School of Medicine, 2023) Kozhdan, Kamilya
Rheumatoid arthritis (RA) is an intricate autoimmune condition impacting approximately 1% of the global adult population. One of the key characteristics of this disease is the tendency of activated fibroblast-like synoviocytes (FLS) to invade and damage joint tissues. Recently, there has been a significant focus on discovering biomarkers that can precisely forecast the development of RA. One potential set of biomarkers includes collagen triple helix repeat-containing protein 1 (CTHRC1) and metalloproteinases (MMPs) proteins associated with migration and invasiveness of fibroblast-like synoviocytes, which are responsible for instigating inflammation and joint destruction in RA.
The objective of this work is to investigate how CTHRC1 and MMPs are associated with the movement and invasive properties of FLS in the progression of RA. The study aims to assess whether the expression of these proteins can be used to anticipate the development of RA. The research will entail evaluating how CTHRC1 and MMP levels affect FLS pathogenesis. The conventional diagnostic markers for RA were used to show the correlation between potential biomarkers expression and disease activity. The results of this study could be significant for developing new targets for treatment and therapies for RA.
ADAPTING TO LEARNER’S COGNITIVE DIFFERENCES USING REINFORCEMENT LEARNING
(Nazarbayev University School of Engineering and Digital Sciences, 2023-05-02) Nurgazy, Symbat; Issa, Ilyas; Kassymbekov, Saparkhan; Kuangaliyev, Zholaman
This report explores the benefits and challenges of
creating adaptive robotic systems by integrating open-source
software and reinforcement learning algorithms. The aim is to
develop robotic systems that adapt to cognitive differences of
the child to increase the engagement time and develop social
skills using robot-assisted therapy. The Furhat and NAO robots
are used as the platforms for receiving inputs and outputs,
while a reinforcement learning agent selects the order of ac-
tivities. The state space includes the pose landmarks from the
Mediapipe and OpenPose libraries to recognize the engagement
level and some static parameters such as age, autism level,
verbality, and the co-diagnosis of Attention Deficit Hyperactivity
Disorder. The actions space contains the act of changing the
current activity. In the current stage, we have eight types of
activities the robot choose to interact with the child. Furhat
robot is programmed to have a full pipeline for a conversational
robot: automatic speech recognition, text-to-speech, machine
translation, and OpenAI language model. The results of this project provide insight into the potential of using open-source software and reinforcement learning algorithms to create more advanced interactive robots and highlight the importance of continued research in the field of Human-Robot Interaction.
EXAMINING UNDERGRADUATE STUDENTS’ ENGLISH LANGUAGE SPEAKING ANXIETY AND THEIR STRATEGY USE AT AN EMI UNIVERSITY IN KAZAKHSTAN
(Nazarbayev University Graduate School of Education, 2024-05-21) Shorman, Mariya
Speaking is one of the most anxiety-causing skills in learning a foreign language. Foreign Language Speaking Anxiety (FLSA) significantly affects language performance, so many students struggle in their learning process. Despite the fact that FLSA is a common phenomenon, there is a lack of studies on FLSA in the context of Kazakhstan. This mixed-method research study sought to investigate a group of second-year Undergraduate students’ challenges while speaking English across different settings and fourth-year students’ Language Learning Strategies (LLSs) they adopt to face these challenges at university using English as a Medium of Instruction (EMI) in Kazakhstan.
The current study answered two research questions with subquestions: 1) What level of Foreign Language Speaking Anxiety do second-year Kazakhstani students experience while speaking English in EMI classes? Sub-questions: (a) Does gender affect the participants’ FLSA in class? (b) Does school type affect the participants’ FLSA in class? (2) What strategies do fourth-year students use to reduce FLSA?
Data were gathered using two data collection tools: Horwitz et al.’s (1986) Foreign Language Classroom Anxiety Scale (FLCAS) for second-year students and semi-structured individual interviews for fourth-year students. The results showed that second-year students had a moderate level of FLSA and fourth-year students use mainly cognitive and metacognitive LLSs. This research filled the gap in the literature on FLSA, revealing results that have not been found previously in Kazakhstani context.
Keywords: Foreign Language Speaking Anxiety (FLSA), Language Learning Strategies (LLSs), English as a Medium of Instruction (EMI), mixed-method
FINAL PROJECT REPORT DOCUMENT– SPRING 2024
(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-20) Duzelbay, Alisher; Bakkali, Nurseiit; Issa, Yeldar; Serikbayev, Yermakhan; Bulatov, Yernur
The NU Life Hub project aims to address the fragmentation of information and resources for Nazarbayev University (NU) students by offering a centralized platform designed specifically for their needs. The project addresses students' challenges in finding and participating in campus activities, accessing a convenient marketplace to fulfill their needs, and staying informed about various campus events. The NU Life Hub solution will be a comprehensive platform that combines event management, marketplace, and community engagement functions to improve the overall university experience.
During the project, extensive research was conducted to understand existing solutions and approaches to address similar challenges university communities face. This analysis informed the design and development of the NU Life Hub, ensuring that best practices were embedded into the platform and critical challenges were effectively addressed.
DEVELOPMENT OF BRAIN-BASED SMART-HOME/TYPING SYSTEM
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Yergaliyeva, Aiana; Berikbol, Arnur; Seiilkhan, Arsen
Our project combines EEG-EOG signals to develop an efficient Brain-Computer Interface (BCI) spelling system for Virtual Reality (VR) and Mixed Reality (MR) environments. This hybrid speller enables users to spell using brain activity by leveraging multi-modal signals and various classification strategies. Aimed at improving the quality of life for individuals with motor disabilities, such as spinal cord injuries, ALS, locked-in syndrome, and the elderly, our BCI system provides an alternative communication channel. Focusing on the well-established P300 Row-Column (RC) speller paradigm, we incorporate convolutional neural network (CNN) classification techniques for enhanced performance. Additionally, we use mixed reality glasses to improve user comfort and EEG signal quality. Our methodology includes comprehensive experimental procedures, from environment setup to data analysis and iterative refinement. By advancing BCI technology and integrating VR and MR interfaces, our project seeks to promote accessibility and inclusivity, enabling individuals of all abilities to communicate and participate more fully in social, educational, and professional activities.