The Role of Medical Word-of-Mouth on Sport Therapists: A Patient Psychological Perspectives

In 21st century, more and more people see medical services of sport therapists as a promising industry due to global aging population and high biotechnology development. Hence, understanding medical word-of-mouth (WOM) process among patients in medical services of sport therapists could be interesting and useful. This study would find out the role of WOM on patients psychology when choosing the sport therapists, understand how patients look for sport therapists’ information and which information source is important to them. As a pioneer, this study would like to improve medical WOM marketing of sport therapists. The authors designed a questionnaire based on how patients choosing sport therapists, including questions search importance of different information sources of sport therapists with medical service decision, and respondents’ psychological demographics. We released 253 questionnaires of judo therapy clinics inTaiwan. More than half of the respondents see sport therapists due to others’ recommendations (referral of family, friends and professionals). Women are more likely to seek medical word-of-mouth information than men;and respondents who think choosing sport therapists are important tend to seek medical word-of-mouth information. Further, patients focus on “therapist’s behavior”, “therapist’s skills”, and “therapist’s ethic”.

Dynamic Deep Learning for Enhanced Reliability in Wireless Sensor Networks: The DTLR-Net Approach

In the world of wireless sensor networks(WSNs),optimizing performance and extending network lifetime are critical goals.In this paper,we propose a new model called DTLR-Net(Deep Temporal LSTM Regression Network)that employs long-short-term memory and is effective for long-term dependencies.Mobile sinks can move in arbitrary patterns,so the model employs long short-term memory(LSTM)networks to handle such movements.The parameters were initialized iteratively,and each node updated its position,mobility level,and other important metrics at each turn,with key measurements including active or inactive node ratio,energy consumption per cycle,received packets for each node,contact time,and interconnect time between nodes,among others.These metrics aid in determining whether the model can remain stable under a variety of conditions.Furthermore,in addition to focusing on stability and security,these measurements assist us in predicting future node behaviors as well as how the network operates.The results show that the proposed model outperformed all other models by achieving a lifetime of 493.5 s for a 400-node WSN that persisted through 750 rounds,whereas other models could not reach this value and were significantly lower.This research has many implications,and one way to improve network performance dependability and sustainability is to incorporate deep learning approaches into WSN dynamics.

Real-Time Image and Video Artistic Style Rendering System Based on GPU

Aiming at the practical engineering application of video stylization,in this paper, a GPU-based video art stylization algorithm is proposed, and areal-time video art stylization rendering system is implemented. The four mostcommon artistic styles including cartoon, oil painting, pencil painting and watercolorpainting are realized in this system rapidly. Moreover, the system makesgood use of the GPU’s parallel computing characteristics, transforms the videostylized rendering algorithm into the texture image rendering process, acceleratesthe time-consuming pixel traversal processing in parallel and avoids the loop processingof the traditional CPU. Experiments show that the four art styles achievedgood results, and the system has a good interactive experience.