A Study on the Influence of Subjective Exercise Experience on Exercise Persistence of the Elderly

Zijian Zhao,Xiuli Zhang,Yue Chen 아시아건강운동학회 2019 Journal of Asian Society for Health & Exercise Vol.1 No.1

PURPOSE: This paper explores the influence of subjective exercise experience on the persistence of exercise in the elderly in China, and helps to make decisions on how to further improve the persistence of the elderly group to participate in physical exercise. METHODS: The data is randomly collected from a sample of 100 elderly people engaged in physical exercise who were invited to participate in the interview and fill in questionnaires; and the data is processed and analyzed according to two scales, which are "Subjective Exercise Experience Scale" and "Exercise Adherence Scale" in various regions of Henan Province, China. RESULTS: The positive well-being dimensions in the subjective exercise experience of the elderly are positively correlated with the five dimensions of persistence, exercise interest, value judgment, cognitive choice and exercise effort in exercise persistence. There is a significant negative correlation between the psychological distress dimension and the persistence, exercise interest, value judgment, and cognitive choice dimensions in exercise persistence(p <0.01); fatigue dimension is significantly negative correlative with the persistence dimension (p < 0.05) of the exercise persistence; the subjective exercise experience of the elderly has a significant predictive power for their exercise persistence, but there are differences in the predictive power of the various dimensions of exercise persistence. CONCLUSIONS: Positive well-being can positively predict the five dimensions of exercise persistence; psychological distress can negatively predict persistence, exercise interest, value judgment and cognitive choice dimension in exercise persistence. Therefore, by improving positive well-being and reducing psychological annoyance, it can effectively improve the persistence of exercise for the elderly, and thus promote the healthy development of the elderly.

Clustering memory-guided anomaly detection model for large-scale screening of esophageal endoscopic images

Wu Yanbing,Zhao Zijian,Pang Xuejiao,Liu Jin 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.4

A deep learning screening model of esophageal endoscopic images can reduce the burden on endoscopists. However, most deep learning methods require many labeled data with balanced categories, and their ability to deal with new diseases not appearing in the training set is limited. This study elaborated a semi-supervised anomaly detection model for the initial screening of esophageal endoscopic images, requiring a single class of samples as a training set. The reconstruction-based method was used for anomaly detection. The model’s framework was a variational auto-encoder, with two memory modules added in latent space to restrain its generalization ability. In the memory module, a clustering operation was introduced to provide a better distribution of memory vectors, promoting their compactness with encoded features and separation from each other. A detailed description and theoretical substantiation of the proposed model were presented. A dataset containing 7989 esophageal endoscopic images labeled by experienced endoscopists was used for numerical experiments. The proposed model results were compared with those of other auto-encoder-based anomaly detection methods, outperforming them and achieving an area under the curve of 0.8212. The ablation study was also conducted to validate the effectiveness of each model’s part, and new data were successfully incorporated to assess the model feasibility and applicability range.

A Novel Curved-Coil Transmitter for Quasiomnidirectional Wireless Power Transfer

Guo Bochao,Zhao Yubo,Chen Wei,Guo Ni,Tian Zijian 한국전자파학회 2023 Journal of Electromagnetic Engineering and Science Vol.23 No.2

Achieving stable power transfer by merely relying on quasi-omnidirectional couplers is challenging. In this paper, we propose a quasi-omnidirectional wireless power transfer (QWPT) system with a novel curved-coil transmitter to achieve steady transmission performance. A single power source is used to drive the transmitter's current without using a phase and current control methodology. Power is transmitted to the receiver through magnetic resonant coupling at a distance of 50 mm. Moreover, an equivalent circuit model of the curved-coil system is derived and mathematically analyzed. The mutual inductance of the proposed QWPT system is evaluated through analysis and experiments. The experimental results for the resonant coupling system confirm the theoretical analysis of the performance of the curved-coil transmitter and quasi-omnidirectional power transfer.

Protective Effects of Bacillus coagulans JA845 against D-Galactose/AlCl₃-Induced Cognitive Decline, Oxidative Stress and Neuroinflammation

( Xinping Song ),( Zijian Zhao ),( Yujuan Zhao ),( Qing Jin ),( Shengyu Li ) 한국미생물 · 생명공학회 2022 Journal of microbiology and biotechnology Vol.32 No.2

Recently, the efficacy of probiotics in treatment of neurodegenerative disorders has been reported in animal and clinical studies. Here, we assessed the effects of Bacillus coagulans JA845 in counteracting the symptoms of D-galactose (D-gal)/AlCl₃-induced Alzheimer’s disease (AD) in a mice model through behavioral test, histological assessment and biochemical analysis. Ten weeks of pretreatment with B. coagulans JA845 prevented cognitive decline, attenuated hippocampal lesion and protected neuronal integrity, which demonstrated the neuroprotective features of B. coagulans JA845 in vivo. We also found that supplementation of B. coagulans JA845 alleviated amyloid-beta deposits and hyperphosphorylated tau in hippocampus of D-gal/AlCl₃-induced AD model mice. Furthermore, B. coagulans JA845 administration attenuated oxidative stress and decreased serum concentration of inflammatory cytokines by regulating the Nrf2/HO-1 and MyD88/TRAF6/NF-κB pathway. Our results demonstrated for the first time that B. coagulans has the potential to help prevent cognitive decline and might be a novel therapeutic approach for the treatment of neurodegenerative diseases.

Enhancement Method of Weak Transmission Area for Omnidirectional Wireless Power Transfer System

Guo Bochao,Zhao Yubo,Chen Wei,Pan Shan,Tian Zijian 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3

Reducing the weak transmission area (WTA) of omnidirectional transmitter in wireless power transfer is a challenge. In this paper, we propose a method to enhance the WTA with nonorthogonal coils, and two types of transmitters are made, the 90° sandglass Tx and the 20° sandglass Tx. Specifcally, a single power source is utilized to drive the current of Tx without phase and current control methodology. Power is transmitted to the receiver through magnetic resonant coupling in 50 mm. In addition, an equivalent circuit model of the sandglass Tx system is derived and mathematically analyzed. The magnetic induction density of the sandglass Tx system is evaluated via analysis and experiments. Finally, practical experimental results from the resonant coupling system confrm the theoretical analysis.

Attention-based spatial–temporal neural network for accurate phase recognition in minimally invasive surgery: feasibility and efficiency verification

Shi Pan,Zhao Zijian,Liu Kaidi,Li Feng 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.2

Laparoscopic surgery, as a representative minimally invasive surgery (MIS), is an active research area of clinical practice. Automatic surgical phase recognition of laparoscopic videos is a vital task with the potential to improve surgeons’ efficiency and has gradually become an integral part of computer-assisted intervention systems in MIS. However, the performance of most methods currently employed for surgical phase recognition is deteriorated by optimization difficulties and inefficient computation, which hinders their large-scale practical implementation. This study proposes an efficient and novel surgical phase recognition method using an attention-based spatial–temporal neural network consisting of a spatial model and a temporal model for accurate recognition by end-to-end training. The former subtly incorporates the attention mechanism to enhance the model’s ability to focus on the key regions in video frames and efficiently capture more informative visual features. In the temporal model, we employ independently recurrent long short-term memory (IndyLSTM) and non-local block to extract long-term temporal information of video frames. We evaluated the performance of our method on the publicly available Cholec80 dataset. Our attention-based spatial–temporal neural network purely produces the phase predictions without any post-processing strategies, achieving excellent recognition performance and outperforming other state-of-the-art phase recognition methods.

Real-time surgical tool detection in computer-aided surgery based on enhanced feature-fusion convolutional neural network

Liu Kaidi,Zhao Zijian,Shi Pan,Li Feng,Song He 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.3

Surgical tool detection is a key technology in computer-assisted surgery, and can help surgeons to obtain more comprehensive visual information. Currently, a data shortage problem still exists in surgical tool detection. In addition, some surgical tool detection methods may not strike a good balance between detection accuracy and speed. Given the above problems, in this study a new Cholec80-tool6 dataset was manually annotated, which provided a better validation platform for surgical tool detection methods. We propose an enhanced feature-fusion network (EFFNet) for real-time surgical tool detection. FENet20 is the backbone of the network and performs feature extraction more effectively. EFFNet is the feature-fusion part and performs two rounds of feature fusion to enhance the utilization of low-level and high-level feature information. The latter part of the network contains the weight fusion and predictor responsible for the output of the prediction results. The performance of the proposed method was tested using the ATLAS Dione and Cholec80-tool6 datasets, yielding mean average precision values of 97.0% and 95.0% with 21.6 frames per second, respectively. Its speed met the real-time standard and its accuracy outperformed that of other detection methods.

Computer-aided diagnosis system based on multi-scale feature fusion for screening large-scale gastrointestinal diseases

Pang Xuejiao,Zhao Zijian,Wu Yanbing,Chen Yong,Liu Jin 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1

For endoscopists, large-scale screening of gastrointestinal (GI) diseases is arduous and time-consuming. While their workload and human factor-induced errors can be reduced by computer-aided diagnosis (CAD) systems, the existing ones mainly focus on a limited number of lesions or specific organs, making them unsuitable for diagnosing various GI diseases in large-scale disease screening. This paper proposes a transformer and convolutional neural network-based CAD system (called TransMSF) to assist endoscopists in diagnosing multiple GI diseases. This system constructs two feature extraction paths with different coding methods to obtain the lesions’ global and local information. In addition, downsampling is implemented in transformer to get global information of different scales, further enriching the feature representation and reducing the amount of computation and memory occupation. Moreover, a channel and spatial attention module with fewer parameters was successfully designed to pay more attention to the target and reduce the loss of important information during spatial dimension transformation. Finally, the extracted feature information is fused through the feature fusion module and then input into the linear classifier for disease diagnosis. The proposed system outperformed that of other state-of-the-art models on two datasets, reaching a 98.41% precision, a 98.15% recall, a 98.13% accuracy, and a 98.28% F1 score on the in-house GI dataset versus a 95.88% precision, a 95.88% recall, a 98.97% accuracy, and a 95.88% F1 score on the public Kvasir dataset. Moreover, TransMSF’s performance was superior to that of seasoned endoscopists. The above results prove that the proposed system is instrumental in diagnosing GI diseases in large-scale disease screening. It can also be used as a training tool for junior endoscopists to improve their professional skills by rendering helpful suggestions.

Robust Tracking: Keeping Adaptivity but Refusing to Drift

Wenhui Dong,Faliang Chang,Zijian Zhao 보안공학연구지원센터(IJSIP) 2013 International Journal of Signal Processing, Image Vol.6 No.4

Preparation and enhanced CO2 adsorption capacity of UiO-66/graphene oxide composites

Yan Cao,Qin Zhong,Yunxia Zhao,Zijian Lv,Fujiao Song 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.27 No.-

New composites of UiO-66 and graphene oxide (GO) were synthesized and tested as CO2 adsorbents atroom temperature. The materials and the parent composite components were characterized using X-raydiffraction (XRD), thermo-gravimetric analysis (TGA), nitrogen adsorption–desorption isothermanalysis, scanning electron microscopy (SEM), and FT-IR spectroscopy. The CO2 isotherms on theUiO-66/GO composites and the UiO-66 were measured by a static volumetric method separately. Experiments of multiple adsorption/desorption cycles were conducted to estimate reversibility of CO2on the UiO-66/GO. The results showed that the BET surface area of the composites was higher than thatof the parent UiO-66, and the adsorption capacities of CO2 on the composites were greatly higher thanthat on the UiO-66 sample. The composite UiO-66/GO-5 exhibited the maximum CO2 uptake of3.37 mmol/g at 298 K and 1 bar, which increased by 48% in comparison with that of the UiO-66, and wasmuch higher than those of the conventional activated carbons and the zeolites. The CO2 adsorptioncapacity was dependent on the BET surface area and the micropore volume of the composites. Finally, theadsorption/desorption cycle experiment revealed that the adsorption performance of UiO-66/GO-5 wasfairly stable, without noticeable degradation in the adsorption capacity of CO2 after 6 cycles. Therefore,this kind of composites has a potential application on CO2 capture technologies to mitigate globalwarming.