Design and simulation analysis of an improved lower limb exoskeleton

The lower extremity exoskeleton robot is a type of power assisted robot which can enhance the human walking function. A fundamental problem in the development of the exoskeleton is the choice of lightweight actuators. Thus in the mechanical structure design in this paper, the linear motor is selected as it greatly reduces the complexity of the mechanical structure. Furthermore, the limit switch inside the motor improves the safety performance. Based on the last version of the exoskeleton, the band positions, length adjusting holes and mechanical limit structures are increased. In addition, a control system based on DSP is designed. Furthermore, a kinematics analysis is carried out using the D-H parameter method and a dynamic analysis is developed using the Newton-Euler method. The driving force of every joint is obtained during the simulation using ADAMS software

Dynamics and kinematics analysis and simulation of lower extremity power-assisted exoskeleton

According to the walking character of lower extremity power-assisted exoskeleton that was designed by our Robotics Laboratory, D-H convention was applied to the kinematics analysis of this exoskeleton model. Lagrangian dynamics was used to analyzing dynamics for the single-foot support model, double-feet support model and double-feet support with one redundancy model respectively. The kinematical equation was obtained and MATLAB was used to verify its validity. Meanwhile, the kinetic equations and torque of each joint were obtained by virtue of ADAMS. Our study provided a theoretical foundation for the control strategies, and optimization design of the mechanical structure and promoted the practical application of this lower extremity power-assisted exoskeleton in further research

Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

A Classification Method for Seed Viability Assessment with Infrared Thermography

This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minutes during the standard five day germination test. After the test, the root length of each sample was measured, which can be used as the viability index of that seed. Each individual seed area in the visible images was segmented with an edge detection method, and the average temperature of the corresponding area in the infrared images was calculated as the representative temperature for this seed at that time. The temperature curve of each seed during germination was plotted. Thirteen characteristic parameters extracted from the temperature curve were analyzed to show the difference of the temperature fluctuations between the seeds samples with different viability. With above parameters, support vector machine (SVM) was used to classify the seed samples into three categories: viable, aged and dead according to the root length, the classification accuracy rate was 95%. On this basis, with the temperature data of only the first three hours during the germination, another SVM model was proposed to classify the seed samples, and the accuracy rate was about 91.67%. From these experimental results, it can be seen that infrared thermography can be applied for the prediction of seed viability, based on the SVM algorithm

Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging

This paper aimed to combine hyperspectral imaging (378–1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability

Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging

This paper aimed to combine hyperspectral imaging (378–1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability

The transcription factor OsbHLH035 mediates seed germination and enables seedling recovery from salt stress through ABA-dependent and ABA-independent pathways, respectively

Abstract Background Many transcription factors (TFs), such as those in the basic helix-loop-helix (bHLH) family, are important for regulating plant growth and plant responses to abiotic stress. The expression of OsbHLH035 is induced by drought and salinity. However, its functional role in rice growth, development, and the salt response is still unknown. Results The bHLH TF OsbHLH035 is a salt-induced gene that is primarily expressed in germinating seeds and seedlings. Stable expression of GFP-fused OsbHLH035 in rice transgenic plants revealed that this protein is predominantly localized to the nucleus. Osbhlh035 mutants show delayed seed germination, particularly under salt-stress conditions. In parallel, abscisic acid (ABA) contents are over-accumulated, and the expression of the ABA biosynthetic genes OsABA2 and OsAAO3 is upregulated; furthermore, compared with that in wild-type (WT) seedlings, the salt-induced expression of OsABA8ox1, an ABA catabolic gene, in germinating Osbhlh035 mutant seeds is downregulated. Moreover, Osbhlh035 mutant seedlings are unable to recover from salt-stress treatment. Consistently, sodium is over-accumulated in aerial tissues but slightly reduced in terrestrial tissues from Osbhlh035 seedlings after salt treatment. Additionally, the expression of the sodium transporters OsHKT1;3 and 1;5 is reduced in Osbhlh035 aerial and terrestrial tissues, respectively. Furthermore, genetic complementation can restore both the delayed seed germination and the impaired recovery of salt-treated Osbhlh035 seedlings to normal growth. Conclusion OsbHLH035 mediates seed germination and seedling recovery after salt stress relief through the ABA-dependent and ABA-independent activation of OsHKT pathways, respectively