The service of drone filming and photography has been getting more and more popular. However, the service provider does not have enough information about service quality indicators and its weights. Analyzing the weights of service quality indicators by the Fuzzy Analytic Network Process (FANP) combined with Similarity Aggregation Method (SAM) is an important research topic. Therefore, in order to solve this real life problem, based on the SERVQUAL scale, this research analyzes the weights and the rankings from a comprehensive consensus by FANP combined with geometric mean and SAM, and then compares the differences between them. The results reveal that both the comprehensive consensus of experts’ opinions deemed that the most important dimension and indicator are reliability and “Employees are professional and get adequate support to do their jobs well.” The 2nd to 4th indicators from a comprehensive consensus of experts’ opinions are the same but the order is different. They are: “Drone service team’s employees give custom personal attention,” “Drone service team has up-to-date equipment,” and “Drone service team provides service legally, safely, and reliably.” The findings of the research reveal the weights of dimensions and indicators and help us to keep good service quality of filming and photography by drone.

The blended educational method has become a common way of teaching and learning in the post-COVID-19 era. However, the related research on the selection model for the blended design teaching service quality solution is still an important research gap during this period. Therefore, this study proposed a hybrid method of fuzzy analytic network process (FANP) and technique for order preference by similarity to ideal solution (TOPSIS) to analyse the dimensions, indicators and alternatives of blended design teaching service quality. As for the findings of this research, the dimension of assurance is the most vital factor, followed by responsiveness, reliability and empathy. Meanwhile, this research discovered that the top three significant alternatives are “Employees are trustworthy”, “Safe transaction mechanism and environment” and “Personalised needs of customers”. Also, we found that dimensions utilised to evaluate the quality of education service are similar whether in the post COVID-19 era, in the COVID-19 epidemic or prior to the COVID-19 pandemic. The main contribution of this study is to establish a multi-criteria decision-making (MCDM) model for the ranking of the blended design teaching service quality index and solution under a fuzzy environment. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to hybrid design education to maintain good service quality in similar scenarios in the future.

This paper proposed a geological segmentation algorithm to segment different geology, such as rivers, woodlands, and soil, etc. according on differences image feature of geology for aerial photography. In this paper, aerial image capture from low-flying quadcopter unmanned aircraft vehicle (UAV). Using Fuzzy-C-Mean method for aerial image color classification, and estimate some dominant colors of aerial image. And then estimates each dominant color similar distribution map. Finally, shape-based level set method (LSM) is employed to segment the distribution of geological that the dominant color represents. The experimental results show that the proposed algorithm is efficacious and reliable in geological segmentation.

Online English education has become a very common way of educating and learning during the coronavirus pandemic. However, the weight analysis index for the service quality survey of the online English education industry remains a research gap during this period. Thus, this research implemented the analytic network process (ANP) to analyse the index, weight and ranking of online English teaching based on the service quality (SERVQUAL) questionnaire and compare the differences between the dual perspectives of service providers and consumers. Interestingly, this research found that the dimension of responsiveness was considered the most important by service providers. However, consumers deemed the dimension of assurance to be the most significant. Meanwhile, this study discovered that consumers paid more attention to reassurance and safety when they faced problems and transaction procedures during the coronavirus pandemic. In addition, this research found that dimensions utilised to evaluate the quality of online education service are similar whether in the COVID-19 epidemic or prior to the coronavirus pandemic. Thus, it has a certain reference value for evaluating the service quality of online English teaching through the use of dimensions and index weights in the SERVQUAL scale during the coronavirus pandemic. Finally, the findings of this research revealed weights of dimensions and indicators, thereby providing suggestions for maintaining good service quality within online English teaching during the COVID-19 pandemic.

Nowadays, there are more and more robots replace human being’s job. In recent years, the researches accelerate the technology development of multi rotor drone and make it become the flying robot that can self-stabilize and fly according to the waypoints setting by ground station. Therefore, this project designs a system to detect the efficacy of solar panel by UAV integrated with thermal sensors. Firstly, UAV flies above solar panel, takes picture by thermal sensors and transmits to website automatically. Secondly, the system cuts out the area that out of the panel, examine every pixel of the picture and calculates the ratio of red pixels that present the area of overheat. Finally, the system reports the efficacy of solar panel by program that judges from the ratio of overheat, weather condition, etc. By this way, we can save a lot of time and detect the solar panel safely.

Colles fracture is a common type of wrist fracture, typically resulting from falling onto an outstretched hand. Postoperative patients often require long-term self-rehabilitation to restore wrist function. However, traditional rehabilitation relies on medical personnel and lacks real-time feedback and progress tracking, which may lead to poor rehabilitation or even deterioration of the condition. This article proposes an intelligent wearable system based on edge computing to enhance the efficiency of self-rehabilitation, improve system portability, and ensure comprehensive recording of rehabilitation data. The system performs real-time data processing and feedback to assist patients and healthcare providers in monitoring rehabilitation progress and optimizing recovery outcomes. The proposed system integrates an STM32 microcontroller, NanoEdge AI, and a 9-axis inertial sensor to detect and evaluate the accuracy of hand rehabilitation movements, ensuring the precision of self-rehabilitation. All rehabilitation movements are schemed under the guidance of professional physicians to ensure correctness and minimize the risk of secondary injuries caused by improper rehabilitation. Data such as motion records, training duration, and count are transmitted via Bluetooth to the Local database for further analysis. A custom web interface allows healthcare providers to monitor and analyze collected data. This approach improves diagnostic accuracy, supports personalized rehabilitation recommendations, and improves treatment outcomes and patient recovery success rates.