Institute of Applied Technology

Urban areas are expanding as the population grows uninterruptedly. Developing urban areas with proper planning and initiatives provides sustainable and healthy accommodation for the inhabitants. Proper policies and strategies are required to drive urban development. Proper initiatives need to be taken for developing an adequate living environment. Emerging technologies are playing leading roles in urban development. Artificial intelligence (AI) is able to make changes and develop automated decision-making solutions in every domain. The addition of AI provides robust solutions for urbanization. Smart city is the ultimate solution for progressive and advanced urban development. A healthy environment offers a healthy place for living. As the population in a smart city is growing continuously, a smart city offers a better, sustainable environment for an excellent living ambience. This study gives views about AI and its impact on sustainability concerns in the context of smart cities and urban development. Many issues regarding sustainability, in addition to environmental impact on urban development, are explored in this study.

Fully homomorphic encryption (FHE) enables privacy-preserving machine learning (PPML) at the cost of intensive computational overhead, which necessitates the use of domain-specific accelerators. To achieve comprehensive support for leveled FHE, this article presents a reconfigurable multi-scheme FHE processor that supports both client-side encryption/decryption and server-side evaluation. First, a reconfigurable processing element (RPE) design for modular arithmetic and a reusable data generator for polynomial sampling are developed to support the various operations in FHE. Second, a configurable RPE array supporting polynomial operations and a decoupled automorphism unit (DAU) necessary for homomorphic rotations are proposed to accelerate the FHE primitives with complex dataflow. Finally, an on-chip data generation strategy and a cache-aware operation scheduling (CAOS) method are introduced to alleviate the memory bottleneck in the end-to-end execution of FHE applications. The chip is fabricated in a 28-nm process and tested with end-to-end execution. Targeting a lightweight parameter set with polynomial degree<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=4096$</tex-math> </inline-formula> at 128-bit security level, the proposed chip achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.05~\mu $</tex-math> </inline-formula>J per encryption on the client side and provides a throughput of 8.72 kHMul/s on the server side. In terms of the number theory transform (NTT) operation, the chip demonstrates the highest throughput and best area efficiency compared with state-of-the-art solutions.

Silicone rubber, in general, possesses super soft physical behavior, which is not suitable for structural applications. Therefore, this study aims to introduce an innovative biocomposite material combining Leucaena and Silicone, named LeuSiC, to establish its physical and mechanical properties for possible medical applications. Various Leucaena fiber compositions ranging from 0 wt% to 16 wt% were mixed with pure silicone rubber, where density, compression set, and uniaxial tensile behavior were experimentally investigated following ASTM standards. The Ogden hyperelastic constitutive was employed to quantify the tensile behavior of LeuSiC via material constants, µ and material exponent, α. Additionally, the tensile properties of LeuSiC were also predicted using Artificial Neural Network (ANN). The results revealed that the material constants, µ value, increased with higher Leucaena fiber composition, indicating stiffness increment. In contrast, increasing fiber composition reduced the tensile strength and flexibility of LeuSiC. In terms of prediction using ANN, the results proved the capability of the constructed neural network model, where the error was less than 0.4%. The quantified and predicted properties of µ and α range from 5.4 to 55.9 kPa and 2.16 to 3.0 respectively, suggest that LeuSiC has the potential to mimic and be made into synthetic connective tissues.

Infection with Hepatitis B Virus (HBV) has been a serious public health issue worldwide. It caused more than one million fatalities per year. The mathematical modelling of the disease allows better understanding of the transmission of the disease and help the government policy makers to choose the best control strategies. With this inspiration, we proposed a novel dynamic model by incorporating infection-age structure to imitate the transmission of HBV, especially the age heterogeneity in horizontal and vertical (mother-to-child) transmission modes. We also discussed its impact on control measures and analyzed the dynamics of waning immunity and reinfection. We conducted sensitivity analysis to evaluate the effectiveness of each control measure. Our research concentrates on HBV acute patient cases in the United States data from Centre for Disease Control and Prevention (CDC). Our findings show that a mixed approach by including vaccination, medication and periodic health assessments can effectively control HBV transmission. Among these measures, we found that early vaccination with a single-dose vaccine of US$50 is the most cost-effective control strategy.

This thesis presents a bibliometric analysis of the integration of Artificial Intelligence (AI) in teacher education, utilizing CiteSpace software to explore academic publications in the China National Knowledge Infrastructure (CNKI) which is the most influential academic database in China. With the rapid development of AI technologies and their potential impact on education, this study aims to systematically map the evolution of AI in teacher education research within the Chinese academic context. By analyzing a comprehensive dataset of published articles, the study identifies key trends, research hotspots, and emerging themes in the field. The bibliometric indicators, such as publication frequency, citation patterns, co-authorship networks, and keyword co-occurrence, are examined to reveal the scholarly landscape and to track the growth of AI -related educational research. The findings highlight the major contributors to the field, the development trajectory of AI in teacher training, and the collaboration networks that shape this growing domain. Additionally, this research offers insights into the challenges and opportunities that AI presents to teacher education in China, suggesting directions for future research and policy development. Overall, the study underscores the importance of AI integration in shaping the future of teacher education and provides a framework for understanding its impact through bibliometric analysis.

ObjectiveThis study aims to understand the status of depression and anxiety among Chinese university students through epidemiological investigation of a large sample size and provide a foundation for identifying individuals at risk of psychological crisis and developing targeted intervention strategies.MethodsSurvey participants were selected using a multi-stage sampling approach, which incorporated elements of stratification and cluster sampling. The main participants consisted of 49,717 university students from 106 Chinese universities. Self-report questionnaires were used to assess depression and anxiety levels retrospectively.ResultsOf the 49,717 questionnaires collected, 41,620 were deemed valid after a rigorous data cleaning process, resulting in a validity rate of 83.7%. Among the respondents, 58.6% were female. The detection rates for depression, anxiety, and comorbidity of depression and anxiety among the students were 9.8%, 15.5%, and 6.5%, respectively. The detection rate of depression varied across different grades (V = 0.119) and locations of universities (V = 0.117). There were great differences in the detection rate of depression and anxiety symptoms among university students in terms of physical health (including self-rated health and vision) and health behaviors (including sleep, smoking and drinking), and physical health and healthy behaviors were important factors in the detection rate of depression and anxiety symptoms among college students.ConclusionPsychological health issues among Chinese university students are relatively severe, with higher grade students exhibiting particularly concerning signs.