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Abstract Background Anxiety may adversely impact mother and her newborn. Music listening is a safe and efficacious treatment that may to reduce perioperative anxiety. The effect on acute pain and pain catastrophizing scores remains unclear. We aimed to determine whether perioperative music listening reduces anxiety, acute pain, and pain catastrophizing scale (PCS) scores following elective cesarean delivery under spinal anesthesia. Methods After randomization into music listening and control groups, baseline patient characteristics, visual analog scale-anxiety (VAS-A) scores, pain scores, PCS total and sub-scores, and music preferences were collected preoperatively. Before surgery, parturients in the experimental group listened to music of their own choice for 30 min. Music listening was continued during administration of spinal anesthesia and cesarean delivery, and for 30 min following surgery. Postoperative VAS-A score, acute pain score, PCS scores, music preferences, satisfaction score, and feedback were recorded. Results We analyzed 108 parturients (music: n = 53; control: n = 55). Music listening was associated with reduced postoperative VAS-A (mean difference (MD) -1.43, 95%CI -0.63 to -2.22), PCS total score (MD -6.39, 95%CI -2.11 to -10.66), PCS sub-scores on rumination (MD -1.68, 95%CI -0.12 to -3.25), magnification (MD -1.53, 95%CI -0.45 to -2.62), and helplessness (MD -3.17, 95%CI -1.29 to -5.06) sub-scores. There was no significant difference in postoperative acute pain scores. The majority (> 95%) of parturients reported “excellent” and “good” satisfaction with music listening, and most provided positive feedback. Conclusion Perioperative music listening was associated with reduced postoperative anxiety and lower pain catastrophizing. Based on the good patient satisfaction and positive feedback received, the use of music listening in the obstetric setting is recommended. Trial registration This study was registered on Clinicaltrials.gov NCT03415620 on 30/01/2018.

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model’s performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy.

Background: Cerebral ischemia-reperfusion injury (CIRI) can cause irreversible brain damage and autophagy has been implicated in the pathophysiology. Increasing serum potassium (K+) levels reduces CIRI, but the relationship between its protective mechanism and autophagy is unclear. In this study, we aimed to find the optimal degree of raising serum (K+) and to investigate the relationship between high (K+) and autophagy and the underlying mechanisms in a cardiac arrest/cardiopulmonary resuscitation (CA/CPR) rat model. Methods: Sprague Dawley (SD) rats were divided into four groups: S group, N group, P group, and Q group. The rats S group and N group were administered saline. The rats P group and Q group were administered 640 mg/kg of potassium chloride (KCl) continuously pumped at 4 mL/h (21.3 mg/(kg·min) and divided according to the electrocardiogram (ECG) changes during the administration of KCl. After 24-h of resuscitation, neural damage was assessed by measuring neurological deficit score (NDS), oxidative stress markers, and pathological staining of the cerebral cortex. The level of autophagy and the expression of mTOR-ULK1-Beclin1 pathway-related proteins were evaluated using transmission electron microscopy (TEM), immunostaining, and western blotting. Results: Our results revealed that high (K+) improved NDS and decreased the oxidative stress markers. The autophagosomes, autolysosomes, and lysosomes were decreased following treatment KCl. Furthermore, the levels of micro-tubule-associated protein 1 light chain 3 (LC3) Ⅱ/Ⅰ, Unc-51-like kinase 1 (ULK1), and Beclin1 were decreased, whereas mTOR expression was increased in the cortex. Conclusion: The results demonstrated that moderate hyperkalemia could alleviate autophagy after CIRI via regulating the mTOR-ULK1-Beclin1 pathway.

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.

The photo-stabilizing effect of post-extraction was evaluated for thermally modified wood. Extracted and non-extracted thermally modified Scots pine (Pinus sylvestris L.) samples were exposed in a xenon weather-ometer for 1008 h, and the surface color and chemical changes were characterized using a chroma meter, attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and X-ray photoelectron spectroscopy (XPS). The results showed that: (1) the weight losses of thermally modified wood were higher than those of unmodified wood after extraction due to the leaching of some low molecular weight compounds that were generated during thermal modification; (2) the photodegradation of thermally modified wood during weathering was hindered by the presence of extractives; and (3) the color change during weathering was a little more severe in sapwood than in heartwood because more extractives were present in heartwood.