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Background Lumbar disc herniation is seen in 5–15% of patients with lumbar back pain and is the most common spine disorder demanding surgical correction. Spinal surgery is one of the most effective management for these patients. However, current surgical techniques still present complications such as chronic pain in 10–40% of all patients who underwent lumbar surgery, which has a significant impact on patients’ quality of life. Research studies have shown that transcutaneous electrical acupoint stimulation (TEAS) may reduce the cumulative dosage of intraoperative anesthetics as well as postoperative pain medications in these patients. Objective To investigate the effect of pTEAS on pain management and clinical outcome in major spinal surgery patients. Methods We conducted a prospective, randomized, double-blind study to verify the effect of pTEAS in improving pain management and clinical outcome after major spinal surgery. Patients (n = 90) who underwent posterior lumbar fusion surgery were randomized into two groups: pTEAS, (n = 45) and Control (n = 45). The pTEAS group received stimulation on acupoints Zusanli (ST.36), Sanyinjiao (SP.6), Taichong (LR.3), and Neiguan (PC.6). The Control group received the same electrode placement but with no electrical output. Postoperative pain scores, intraoperative outcome, perioperative hemodynamics, postoperative nausea and vomiting (PONV), and dizziness were recorded. Results Intraoperative outcomes of pTEAS group compared with Control: consumption of remifentanil was significantly lower (P < 0.05); heart rate was significantly lower at the end of the operation and after tracheal extubation (P < 0.05); and there was lesser blood loss (P < 0.05). Postoperative outcomes: lower pain visual analogue scale (VAS) score during the first two days after surgery (P < 0.05); and a significantly lower rate of PONV (on postoperative Day-5) and dizziness (on postoperative Day-1 and Day-5) (P < 0.05). Conclusion pTEAS could manage pain effectively and improve clinical outcomes. It could be used as a complementary technique for short-term pain management, especially in patients undergoing major surgeries. Trial registration ChiCTR1800014634, retrospectively registered on 25/01/2018. http://medresman.org/uc/projectsh/projectedit.aspx?proj=183

The greenhouse is a multivariate, nonlinear, time-varying and time-lag system. The establishment of an accurate greenhouse temperature prediction model can effectively ensure the effectiveness of intelligent temperature control. In order to solve the problem of difficulty in modeling the temperature mechanism of greenhouses, this paper proposes a method based on improved Support Vector Regression to establish greenhouse temperature prediction, The PSO algorithm is used to optimize the hyper-parameters of the SVR. Using real greenhouse data to verify the effectiveness of the proposed model. Experimental results show that compared with the traditional BP neural network prediction model, the temperature prediction model in greenhouse based on improved SVR has significantly improved prediction accuracy and better prediction capabilities. Therefore, the greenhouse temperature prediction model based on improved SVR proposed in this paper can well characterize the actual greenhouse system.

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