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Summary: There is conflicting evidence regarding the association between epidural labour analgesia and risk of postpartum depression. Most previous studies were observational trials with limited ability to account for confounders. We aimed to determine if epidural analgesia was associated with a significant change in the incidence of postpartum depression in this randomised controlled trial. We enrolled women aged 21–50 years old with a singleton fetus ≥ 36 weeks gestation. Patients were advised regarding available labour analgesic modalities during enrolment (epidural block; intramuscular pethidine; nitrous oxide; or intravenous remifentanil). On request for analgesia, patients were offered the modality that they had been allocated randomly to first. Blinded investigators recorded patient and obstetric characteristics within 24 h of delivery and assessed for postpartum depression at 6–10 weeks following delivery using the Edinburgh Postnatal Depression Scale (score ≥ 13 considered positive for postpartum depression). The modified intention‐to‐treat population consisted of all patients who received any form of labour analgesia, while per‐protocol consisted of patients who received their randomised modality as their first form of labour analgesia. Of 881 parturients allocated randomly (epidural n = 441, non‐epidural n = 440), we analysed 773 (epidural n = 389, non‐epidural n = 384); 62 (15.9%) of women allocated to epidural group developed postpartum depression compared with 65 (16.9%) women allocate to the non‐epidural group. There were no significant differences in the incidence of postpartum depression between the two groups (adjusted risk difference (95%CI) 1.6 (‐3.0–6.3%), p = 0.49). Similar results were obtained with per‐protocol analysis (adjusted risk difference (95%CI) ‐1.0 (‐8.3–6.3%), p = 0.79). We found no significant difference in the risk of postpartum depression between patients who received epidural labour analgesia and those who utilised non‐epidural analgesic modalities. [ABSTRACT FROM AUTHOR]

Monitoring of vibration in machine tools is becoming a very important application in industry to reduce machine failures, maintenance costs, and dead time. In this paper, we propose a method to identify possible faults based on vibration data from which predictions about the working condition of the machine tools can be made. We used an accelerometer to collect the vibration data from which to analyse the health of machine tools by diagnosing whether they are in good or faulty condition for working. In our experiments, we introduced a machine called the Reliance Electric motor, which has a bearing running inside it. Our research analyses vibration data from components of the bearing including the outer bearing, inner bearing, and rolling element. The experimental results show that our method is highly accurate in diagnosing failures and significantly reduces the maintenance costs of machine tools.