Central bank digital currency (CBDC) is the digital representation of a specific country's currency that is issued by the government of that country. It is a virtual representation of the currency in the form of a digital token based on Decentralized ledger technology (DLT). The growing interest in cryptocurrency and the declining need for cash in payments have paved the way for CBDC since they offer solutions to the problems created by conventional cash. However, CBDC is still in its infancy and developing; not many countries have invested in this. This paper covers 14 CBDC projects and analyzes them according to different factors such as availability, operating model, transactions, architecture, framework, anonymity, and security. As the current work undertaken on CBDC is in the exploration stage, the analysis could be subject to change with time, as new technology and techniques emerge. In this study, various scholarly articles and reports were used to gain information about the current trend of CBDC and their different projects. Furthermore, we have examined the essence of a CBDC.

The research of mobile health (mHealth) application interventions has attracted considerable attention among researchers. The convenience and ubiquity of smartphones makes them an ideal vehicle by which to use mHealth APPs for the self-monitoring of one’s health throughout the day. This study utilized the tenets of the extended unified theory of acceptance and use of technology (UTAUT2) as our theoretical foundation. We also considered innovativeness and network externality in seeking to investigate the determinants of one’s intention to adopt a chatbot for health and weight management. The health chatbot running on the Line™ APP platform features artificial intelligence (AI) technology to facilitate accurate analysis and health consultations in near real-time. In the analysis of 415 responses, the proposed model explained 87.1% of variance in behavioral intention. Habit was the independent variable with the strongest performance in predicting user intention, followed by performance expectancy, social influence, network externality, and innovativeness. Social influence affects user intention through performance expectancy. This study provides academics and APP developers with insight into the primary determinants of user attitudes toward the adoption of an AI-powered health chatbot.

This paper mainly discusses the network reliability of an online-food delivery system with stochastic capacity, named the stochastic online-food delivery system (SODS). The capacity that is the number of available food delivery persons in each region is stochastic because the food delivery workers can choose their shift. Transportation time and loading space constraints are considered in this paper. The transportation time consists of the waiting time at restaurants, and the food delivery worker’s driving time to reach the customer’s location cannot exceed the time constraint. The space weight denotes the proportion of the commodity’s space in the delivery box. Each food delivery worker will deliver multiple commodities under the space constraint. This paper evaluates the network reliability of the SODS. Network reliability is defined as the probability of meeting the commodity requirements within space and time constraints. Network reliability can be used as a performance indicator to present the SODS system status. A case of an online-food delivery system in front of Taipei railway station in Taiwan is presented to emphasize the management implications of network reliability.

The comparator is a crucial module in Analog-to-Digital Converters (ADC) design and Input Output circuits. In this design, a technique to diminish dynamic power of the comparator is proposed. The traditional latch circuit with two cross coupled inverters is modified. A comparable high-speed, energy-efficient N-MOS transistor is designed and used as input for both the preamplifier phase and latch phase. NMOS transistors are inherently superior to PMOS transistors in view of power consumption. A special clock circuit is designed to control two phases, namely the preamplifier phase and the latch phase. This clock circuit produces considerable benefits during the pre-amplification phase. The designed cross coupled circuit enhances the speed and reduces power consumption when compared with the conventional CMOS comparator. The proposed comparator is simulated in standard cadence 90nm technology. Simulation results shows that the proposed modified comparator design is suitable for high speed and low power application.

Distribution transformers need to be designed for low no-load loss. Wrong estimations of no-load losses can have a tremendous impact on the ownership cost. The utility relies on the estimated no-load loss that are provided by transformer manufactures along with the design based on the data sheets of the core material. Finally, the verification is done by the test results. By performing the conventional open-circuit test, total no-load loss is found, and the separation of these losses is not done. There is an approximation that hysteresis loss and eddy current loss equally contribute to the total no-load loss. Also, the loss testing is done on different frequencies or temperatures after the transformer is ready, which comes at a heavy cost. The assessment of the no-load losses with harmonic content is not practically possible. In this work a 315kVA distribution transformer design is developed in 3-D CAD and the no-load losses are estimated by the finite element method (FEM). The performance of the simulation by FEM is verified by the test results on the actual transformer. The effects from the change in thickness of the laminations on the no-load loss is verified with the available theory. The instantaneous value of core loss is analyzed for fundamental and content of harmonics. The effect of percentage content of a harmonic component in the voltage on the no-load loss is analyzed for an odd and even harmonic. The transient finite element method is proposed to separate the hysteresis and eddy current loss, as well as evaluate the effect of harmonics on the loss performance. A simulation-based design improvement for lower total cost of ownership is proposed. The utility can alter the design for the best characteristic of a system where harmonics are inevitable. This can result in the choice of magnetic material, which is a trade-off between material, the cost of operation and predict performance, so as to have reasonable total cost of ownership (TCO).

Teamwork plays an essential role in determining the outcome of software engineering projects, especially when software is being developed by large teams in geographically distributed environments. To understand the successful development of these types of projects, it is important to assess the required teamwork skills that would help in resolving possible problems and avoiding failure. However, it is still not clear how to assess teamwork skills. In this paper, we propose an analytical framework based on a machine learning algorithm to study teamwork skills and factors that influence the success/failure of software engineering projects. For this purpose, we conduct our study on the Software Engineering Teamwork Assessment and Prediction (SETAP) dataset using a machine learning algorithm to extract the relevant features. The dataset provides quantitative data of team activity measures related to the software engineering process and the product at the different software development lifecycle phases. The results show that each of the software lifecycle phases requires different teamwork skills. The results demonstrate the efficiency of the approach; that has predicted team outcomes by accuracy score greater than 90% for process and product data.

Soil uncertainty plays a crucial role in the analysis and design of any geotechnical structure. Reliability based probabilistic method of analysis is particularly suitable to address the situation arising due to soil uncertainties. This paper presents a reliability based probabilistic method of analysis of a finite slope by considering the uncertainty in the cohesion and angle of internal friction of the soil. The reliability index β and its corresponding probability of failure Pf of the slope are determined using different methods namely Monte-Carlo simulation (MCS) method, First order second moment (FOSM) method and Subset simulation (SS) method. The reliability based probabilistic slope stability analysis has been developed and carried out in a MS-Excel spreadsheet. This spreadsheet mainly analyzes three different models i.e. deterministic model for calculating the factor of safety of the slope, uncertainty model for generating random samples of uncertain parameter of the soil and uncertainty propagation using subset simulation. The stability analysis is carried out using the ordinary method of slices. It is found that the SS method has shown better performance in terms of efficiency and accuracy as compared to the MCS method and FOSM method. Moreover, the study also demonstrate that the SS method can help in understanding the problem and can better assess the risk involved.

Non-Destructive Testing & Evaluation (NDT&E) is used to test the object for detection of voids/cracks that are generated due to various circumstances at the time of production and making of various materials. Among several NDT&E techniques, Non-stationary thermal wave imaging (NSTWI) is an attractive and significant method for testing those materials without changing their serviceability. NSTWI is a whole filed, non-contact and non-invasive testing modality. The present research work is focused on detecting subsurface irregularities/defects that arose during the manufacturing phase by employing a clustering based post-processing approach using Barker Coded Thermal Wave Imaging (BCTWI). The Fuzzy c-means (FCM) & K-means clustering algorithms were intended to classify and detect the defects in the test samples. Fuzzy c-means clustering enhances a better detectability compared to that of the K-means clustering by eliminating the initial counter problems that arise during detection. The obtained experimental results using Fuzzy c-means & K-means clustering algorithms are validated using Signal to Noise Ratio (SNR) as a performance metric. Fuzzy c-means provide an enhanced detection when compared with that of k-means. The obtained SNR values prove that the FCM provides the very nearer value of the defects compared to that of K-means. Thus, fuzzy c-means with Barker coded Thermal Wave Imaging can provide effective detection of the anomaly.