Comparison of the basic processes of aerobic, anaerobic, and aerobic-anaerobic coupling composting of Chinese medicinal herbal residues.

Chinese medicinal herbal residues (CMHRs) are waste generated after extracting Chinese medicinal materials, and they can be used as a renewable bioresource. This study aimed to evaluate the potential of aerobic composting (AC), anaerobic digestion (AD), and aerobic-anaerobic coupling composting (AACC) for the treatment of CMHRs. CMHRs were mixed with sheep manure and biochar, and composted separately under AC, AD, and AACC conditions for 42 days. Physicochemical indices, enzyme activities, and bacterial communities were monitored during composting. Results showed that AACC- and AC-treated CMHRs were well-rotted, with the latter exhibiting the lowest C/N ratio and maximal germination index (GI) values. Higher phosphatase and peroxidase activities were detected during the AACC and AC treatments. Better humification was observed under AACC based on the higher catalase activities and lower E4/E6. AC treatment was effective in reducing compost toxicity. This study provides new insights into biomass resource utilisation.

Research on Diagnosis Prediction of Traditional Chinese Medicine Diseases Based on Improved Bayesian Combination Model.

Traditional Chinese Medicine (TCM) clinical intelligent decision-making assistance has been a research hotspot in recent years. However, the recommendations of TCM disease diagnosis based on the current symptoms are difficult to achieve a good accuracy rate because of the ambiguity of the names of TCM diseases. The medical record data downloaded from ancient and modern medical records cloud platform developed by the Institute of Medical Information on TCM of the Chinese Academy of Chinese Medical Sciences (CACMC) and the practice guidelines data in the TCM clinical decision supporting system were utilized as the corpus. Based on the empirical analysis, a variety of improved Naïve Bayes algorithms are presented. The research findings show that the Naïve Bayes algorithm with main symptom weighted and equal probability has achieved better results, with an accuracy rate of 84.2%, which is 15.2% higher than the 69% of the classic Naïve Bayes algorithm (without prior probability). The performance of the Naïve Bayes classifier is greatly improved, and it has certain clinical practicability. The model is currently available at http://tcmcdsmvc.yiankb.com/.