The ability of NAO robot to play tic-tac-toe board game using its hands is greatly determined by the kinematics model of NAO's arms and the use of trajectory planning for manipulation tasks. In this paper, kinematics models for NAO's upper body are presented. The forward kinematics models are designed by using the modified Denavit-Hartenberg convention. The inverse kinematics models of 5-joints NAO's arms are obtained by using analytical inverse transform technique in which provide unique and feasible solutions. The movement of NAO's arms while playing tic-tac-toe is constructed by using the proposed 3-Dimensional Cartesian Space trajectory planning method. The test result shows that the implemented trajectory planning method along with kinematics model are able to make NAO robot plays tic-tac-toe well by using its hands.

In the digital era, transforming public services through the application of innovative technologies to form a Smart Government has become a priority for governments at various levels. This study presents a systematic review of smart government frameworks and architectures, focusing on identifying and analyzing the key components and features that enhance public service delivery. Specifically, the study examines the impact of smart government initiatives on public services, the challenges and barriers faced, and the solutions implemented across different regions. The findings highlight the importance of integrating advanced information technology, ensuring system interoperability, and fostering stakeholder engagement and collaboration. Major challenges include data security issues, technological infrastructure limitations, and resistance to change within government organizations. This study also identifies gaps in the current literature and provides recommendations for future research, aimed at improving the efficiency and responsiveness of public services through smart government innovations. The insights from this research are intended to assist policymakers, IT practitioners, and academics in designing and implementing effective smart government frameworks and architectures for the transformation of public services.

In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of Things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness.

Data collecting is important issue for health care problem. Many people are used heart rate for identify how much calories burned. Heart rate data is collected using direct observation method. Direct observation method using tools calls smart watch. This data is used for further research in heart rate time series.

SHESOP is an intelligent health system designed to monitor health condition of the user and provide information from the data saved in system. SHESOP integrate three subsystems such as web, mobile and wearable devices. One of implementation SHESOP system applied on smart chair in car aims to monitor heart rate and stress level of the driver. Implementation of the system in the car will provide convenience to the driver to take a decision on the way by the condition of the driver's heart health. Electronic stethoscope utilizes a microphone and an electronic circuit as an amplifier and signal conversion of acoustic signals into digital. To facilitate physicians in analyzing the status of the heart, the output can be visualized in the form of spectrum and stored into a digital audio format. Results of analyzed heart sounds can determine the condition of the driver's heart health and stress levels using heart rate variability (HRV) method. The parameters used in the processing using time domain series. An assessment of the condition of heart health and stress level indicators based on standard SDNN.

In this paper discussed the development of heart disease prediction using machine learning (in this case the Artificial Neural Network or ANN). There are 13 variables that can determine heart disease according to Miss Chaitrali paper. Prediction of a person's heart disease one year ahead is performed by studying the model heart rate data. Data is taken by using tool such as smart mirror, smart mouse, smart phones and smart chair. Heart rate data were collected through the Internet and collected in a server. Learning in this system is performed for a period of one year to get enough data to make predictions. Predictive of future heart disease in one year can increase a person's awareness of heart disease itself. The system is also expected to reduce the number of patients and the number of deaths from heart disease.