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简介：AbstractPurpose:Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.Methods:A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.Results:There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.Conclusion:The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

简介：摘要——承插型盘扣式钢管支撑架指的是立杆采用套管承插连接，水平杆和斜杆采用杆端和接头卡入连接盘，用楔形插销连接，形成结构几何不变体系的钢管支撑架。承插型盘扣式钢管支撑架为建筑施工中带来了诸多便利，具有承载能力更高、安全性更强、整体性更好、安装拆卸方便等优点。本文主要讲述了承插型盘扣式钢管支撑架在高大模板支撑体系中的应用。

简介：摘要:口罩机是将多层无纺布通过卷料热压、折叠成型，鼻梁条植入，超声波焊接，废料切除接等工序生产出具有一定过滤性能的功能性口罩，能够有效性的阻隔空气中的灰尘、细菌等污染物。现如今口罩设备不仅是一个单独设备，它需要多台设备有效配合才能完成各种不同的工序。市场上较为流行的口罩机有:平面口罩机、鸭嘴型口罩机、N95口罩机等。本文从平面口罩机安装调试的诸多细节入手，介绍了平面口罩机在安装调试过程中出现的问题、原因并汇总了解决方法。

简介：AbstractBackground:Conventional pressure support ventilation (PSP) is triggered and cycled off by pneumatic signals such as flow. Patient-ventilator asynchrony is common during pressure support ventilation, thereby contributing to an increased inspiratory effort. Using diaphragm electrical activity, neurally controlled pressure support (PSN) could hypothetically eliminate the asynchrony and reduce inspiratory effort. The purpose of this study was to compare the differences between PSN and PSP in terms of patient-ventilator synchrony, inspiratory effort, and breathing pattern.Methods:Eight post-operative patients without respiratory system comorbidity, eight patients with acute respiratory distress syndrome (ARDS) and obvious restrictive acute respiratory failure (ARF), and eight patients with chronic obstructive pulmonary disease (COPD) and mixed restrictive and obstructive ARF were enrolled. Patient-ventilator interactions were analyzed with macro asynchronies (ineffective, double, and auto triggering), micro asynchronies (inspiratory trigger delay, premature, and late cycling), and the total asynchrony index (AI). Inspiratory efforts for triggering and total inspiration were analyzed.Results:Total AI of PSN was consistently lower than that of PSP in COPD (3% vs. 93%, P = 0.012 for 100% support level; 8% vs. 104%, P = 0.012 for 150% support level), ARDS (8% vs. 29%, P = 0.012 for 100% support level; 16% vs. 41%, P = 0.017 for 150% support level), and post-operative patients (21% vs. 35%, P = 0.012 for 100% support level; 15% vs. 50%, P = 0.017 for 150% support level). Improved support levels from 100% to 150% statistically increased total AI during PSP but not during PSN in patients with COPD or ARDS. Patients’ inspiratory efforts for triggering and total inspiration were significantly lower during PSN than during PSP in patients with COPD or ARDS under both support levels (P < 0.05). There was no difference in breathing patterns between PSN and PSP.Conclusions:PSN improves patient-ventilator synchrony and generates a respiratory pattern similar to PSP independently of any level of support in patients with different respiratory system mechanical properties. PSN, which reduces the trigger and total patient’s inspiratory effort in patients with COPD or ARDS, might be an alternative mode for PSP.Trial Registration:ClinicalTrials.gov, NCT01979627; https://clinicaltrials.gov/ct2/show/record/NCT01979627.

简介：摘要：随着旅游城市大理市环保措施的推进，城市建筑物的密集程度大大增加，随之而来的便是建设用地审批的难度越来越大，可以规划的建筑用地也是少之又少，地价也是越来越高。如此唯有在有限且高价的土地上盖出更多的房子方能满足开发商的利益，然而楼层越高，就意味着地下室部分的深度也要越深，方能满足结构稳定性要求。所以，基坑支护，内支撑支护形式在洱海周边建筑得到较大运用。一是深度越挖越深，可占用的面积越来越小，周边高层建筑物密集且距离越来越小。二是只能采用单排或者双排支护桩。三是为防止基坑变形，监测周边建筑物影响，多数情况必须采用内支撑体系，或者内支撑体系与锚索相结合的施工工艺方能满足要求。