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MODEL PREDICTIVE CONTROL AND IMITATION LEARNING ALGORITHMS FOR ROBOT MOTION PLANNING IN PHYSICAL HUMAN-ROBOT INTERACTION
(Nazarbayev University School of Engineering and Digital Sciences, 2024-08-07) Aigerim Nurbayeva
This PhD thesis focuses on the design and testing of safe robot motion planning algorithms for human-robot workspace sharing. These algorithms are based on the use of nonlinear model predictive control (NMPC), a model-based method for motion planning relying on numerical optimization. The contribution of the thesis can be split into two main areas. The first area consists of the approximation of NMPC laws using deep neural networks (DNNs), often referred to as “imitation learning”. This is motivated by the fact that the execution of NMPC laws might require a considerable amount of time, which restricts the performance of the closed-loop system. Calculating the output of a DNN for a given input is instead a much faster process. Therefore, replacing the optimization solver of NMPC with a DNN can reduce computation times, thus improving performance. It is crucial, though, to suitably train the DNN to imitate the NMPC law in order to improve performance and at the same time guarantee safety. The final result obtained in this area consists of using the so-called dataset-aggregation approach for DNN training, together with properly designed safety filters, which ensure that the safety constraints imposed in the NMPC problem also hold for the robot motion generated by the DNN. The second area consists of the extension of a previously defined NMPC law in terms of stabilizing terminal constraints. The most common
approach for guaranteeing closed-loop stability in an NMPC problem is the imposition of terminal constraints, i.e., the prediction of the system motion is required to satisfy certain conditions at the end of the prediction horizon. Specifically, in a previous approach, the “point terminal constraint” was used, in which the prediction of the robot motion had to exactly reach the desired goal configuration at the end of the prediction horizon. In this thesis, this condition is relaxed by imposing that a given set, rather than a given point, is reached in the state space for the predicted robot motion. The imposition of this new condition allows for an enlargement of the domain of attraction, i.e., the NMPC law can find a solution for reaching the goal configuration from a wider set of initial configurations. All the proposed motion planning strategies were tested experimentally on a UR5 collaborative manipulator.
MULTISCALE MODELING OF CHEMICAL STABILITY AND TRANSPORTATION OF OH- ION FOR CHITOSAN-BASED BIOCOMPOSITE ANION EXCHANGE MEMBRANE FUEL CELLS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-08) Karibayev, Mirat
Anion Exchange Membrane Fuel Cells are obtaining popularity in current research due to their promising advancements, which include low production costs, the ability to use catalysts free of platinum group metals, moderate operating temperatures, and high power densities. However, the primary challenge of Anion Exchange Membranes is associated with chemical instability of the quaternary ammonium head groups in alkaline conditions and elevated temperatures, which also led to a decrease in the diffusion of hydroxide ions. This study used Density Functional Theory calculations, ab initio Molecular Dynamics simulations, and conventional all-atom Molecular Dynamics simulations to examine the chemical stability of different chemical structures, including quaternary ammonium head groups, quaternized chitosan head groups, and Deep Eutectic Solvent supported quaternized chitosan head groups as well as the diffusion of hydroxide ions. This research work consisted of the following four main objectives: i) the degradation mechanisms of different quaternary ammonium head groups under different hydration levels via the Density Functional Theory method, ii) the diffusion of hydroxide ion via different quaternary ammonium head groups under different hydration levels via conventional all-atom Molecular Dynamics simulations, iii) the degrataion mechanisms of various quaternized chitosan head groups and the diffusion of hydroxide ions under different hydration levels and temperatures via the Density Functional Theory method and conventional all-atom Molecular Dynamics simulations, and finally iv) explore the degradation mechanisms and diffusion mechanisms of hydroxide ion via Deep Eutectic Solvents supported tetramethylammonium head group and quaternized chitosan head group under different hydration levels and temperatures via Density Functional Theory calculations and ab initio Molecular Dynamics simulations....
KNOWLEDGE OF PERINATAL DEPRESSION AND ATTITUDES TOWARDS ITS SCREENING AMONG RESIDENTS OF MEDICAL UNIVERSITIES IN KAZAKHSTAN: A CROSS-SECTIONAL STUDY
(Nazarbayev University School of Medicine, 2024) Togzhan, Yerlankyzy
Background: Perinatal depression (PD), a non-psychotic depressive condition, can begin during pregnancy (antenatal depression) and last for up to a year after birth (postnatal depression). Antenatal depression is 26.3% and postnatal depression is 27.6% prevalent worldwide. This study aims to assess the level of knowledge and awareness regarding PD as well as the attitudes of residents in Kazakhstani medical universities on its screening. Our study also aimed to explore how participants' demographics, knowledge, and attitudes about PD relate to their intention to screen for PD in the future.
Methods: A cross-sectional online questionnaire covering 87 residents was conducted among residents from medical universities and centers in Kazakhstan. Knowledge questions were developed based on previous studies (Jones et al., 2011; Chrzan-Dętkoś & Walczak-Kozłowska, 2020). Data was analyzed using the Stata software version 17. We assessed the relationships between dependent and independent variables using the Fisher exact test, chi-square, t-test, and bivariate analyses. Results: Results revealed a moderate level of knowledge about antenatal and postnatal depression, highlighting significant gaps in understanding risk factors and treatment modalities. Despite this, attitudes toward screening were generally positive, with a notable intention among residents to screen for PD in their future practice. Significant factors influencing the intention to screen were the residency program, personal acquaintance with PD-affected individuals, postnatal knowledge scores, attitudes, and interest in training, barriers included Ministry of Health directives and insufficient training. Conclusion: Recommendations include the development of educational resources and integrating mental health education into medical curricula to equip future physicians to manage PD effectively.
INVESTIGATING APOPTOSIS AS A POTENTIAL PATHWAY FOR ANTICANCER MECHANISM OF PHENFORMIN
(Nazarbayev University School of Medicine, 2024) Amangelsin, Yernar
Apoptosis is an essential cellular process that maintains cellular homeostasis. Dysregulation of apoptosis can lead to uncontrolled cell proliferation, which may result in the development of cancer. Targeting apoptosis has emerged as a promising strategy for cancer treatment. Biguanides, including metformin and phenformin, are known to activate AMP-activated protein kinase (AMPK) and have been identified as potential drugs for cancer prevention. It has been highlighted that phenformin has a greater antitumor activity compared to metformin. Because phenformin is more lipophilic and hence can easily enter the cells. Studies have proposed that phenformin's antiproliferative activity is attributed to its ability to induce cellular apoptosis. However, the anticancer activity and mechanism of apoptosis induction of phenformin can vary depending on the type of cancer. In light of this, we sought to evaluate the antiproliferative activity of phenformin on various cancer cell lines and to explore the role of apoptosis in phenformin's anticancer activity...
UNDERSTANDING THE IMPACT OF COVID-19 PANDEMIC ON THE MENTAL HEALTH OF THE FRONTLINE MEDICAL WORKERS IN A POST-PANDEMIC PERIOD: A SCOPING REVIEW
(Nazarbayev University School of Medicine, 2024-05-02) Danbayeva, Samal
The COVID-19 pandemic has brought unprecedented challenges to global healthcare systems, with frontline medical workers facing immense pressure and strain. This scoping review examines the impact of the pandemic on the mental health of healthcare workers in the post-pandemic period, focusing on studies published from 2021 onwards. Through a systematic literature search, utilizing the Arksey and O'Malley framework, relevant articles were identified from databases including PubMed, Embase, Nazarbayev University Library, and Google Scholar. A total of 20 articles were analyzed, comprising cross-sectional studies, qualitative research, and systematic reviews. The findings reveal a significant prevalence of mental health conditions among healthcare workers post-pandemic, with post-traumatic stress disorder (PTSD), burnout, anxiety, and depression emerging as primary concerns. Factors contributing to mental health challenges include high stress levels, moral dilemmas, and inadequate support systems. The review highlights the importance of implementing interventions to support healthcare workers' mental health, such as regular screening, access to counseling services, and resilience training. Organizational support and policy changes are also crucial to mitigate workplace stressors and foster a supportive environment. Despite the importance of this topic, the available literature on post-pandemic mental health remains limited, emphasizing the need for further research and comprehensive meta-analyses. Addressing the mental health needs of healthcare workers is essential not only for their well-being but also for ensuring the provision of quality patient care.