Restirring and Reheating Effects on Microstructural Evolution of Al–Zn–Mg–Cu Alloy during Underwater Friction Stir Additive Manufacturing

Friction stir additive manufacturing (FSAM) can be potentially used for fabricating high-performance components owing to its advantages of solid-state processing. However, the inhomogeneous microstructures and mechanical properties of the build attributed to the complex process involving restirring and reheating deserve attention. This study is based on the previous research of the underwater FSAMed 7A04 aluminum alloy and adopts a quasi in situ experimental method, i.e., after each pass of the underwater FSAM, samples were taken from the build for microstructural observation to investigate the restirring and reheating effects on microstructural evolution during the underwater FSAM. Fine-grain microstructures were formed in the stir zone during the single-pass underwater FSAM. After restirring, the grain size at the bottom of the overlapping region decreased from 1.97 to 0.87 μm, the recrystallization degree reduced from 74.0% to 29.8%, and the initial random texture transformed into a strong shear texture composed of the C {110}<11¯0>. After reheating, static recrystallization occurred in the regions close to the new additive zones, increasing the grain size and recrystallization degree. This study not only revealed the microstructural evolution during the underwater FSAM but also provided a guideline for further optimization of the mechanical properties of the Al–Zn–Mg–Cu alloy build.     

探讨ICU获得性肺部感染的病因分析

目的探讨ICU院内获得性肺部感染的病因。方法183例危重病患者均来自于2004年1月~2006年4月入住怀化医学高等专科学校附属医院暨怀化市第三人民医院ICU的患者。结果183例危重病患者中发生院内获得性肺部感染47例,感染率25.68%。年龄愈大、住院时间愈长、采用手术治疗、气管插管或切开的患者,肺部感染率愈高。结论ICU院内获得性肺部感染率较高,应引起临床医务人员的高度重视。     

MAGE-A3基因与MDR1基因在非M3型急性白血病患者中的表达及其临床意义

目的观察MAGE-A3与MDR1在非M3型急性白血病（AL）患者中的表达及其与治疗反应的关系。方法应用逆转录-聚合酶链反应法观察77例初治非M3型AL患者MAGE-A3和MDR1的表达,结合临床观察。结果MAGE-A3及MDR1在非M3型AL患者中表达阳性率分别为50．6％与23．3％。MAGE-A3表达阳性与阴性者MDR1阳性率分别为46．2％与13．2％（P〈0．01）。MAGE-A3表达阴性与阳性者治疗完全缓解（CR）率分别为86．8％与64．1％（P〈0．05）;MDR1表达阴性与阳性者CR率分别为83．3％与56．5％（P〈0．05）。MAGE-A3和MDR1表达均为阳性者与均为阴性CR率分别为55．6％与87．9％（P〈0．05）。结论非M3型AL MAGE-A3表达阳性者MDRI有较高的表达率,MAGE-A3及MDR1表达均为阴性者治疗缓解率高。提示联合检测MAGE,A3和MDR1对判断初治非M3型AL的疗效有良好的预测作用,可作为临床判断疗效的一项有意义的指标。     

上海世博会园区内伤害流行病学特征分析

[目的]了解中国2010年上海世博会（简称“上海世博会”）园区内伤害流行病学特征。[方法]利用“中国2010年上海世界博览会园区内就诊异常情况监测和报告系统”（后称“就诊监测报告系统”）,观察上海世博会园区内伤害病例,对其伤害原因、伤害性质等流行病学特征进行调查分析。[结果]从2010年5月1日至10月31日,伤害监测系统共收集12505例伤害病例。5月份游客伤害发生率最高（月均3．01／万）,在世博局采取健康宣教以及改善园区内设施等一系列干预措施后,游客伤害发生率逐步下降,从5月初的4．50／万降低到最后一周的0．80／万。伤害分别是≥65岁和0～14岁游客的第1和第3位就诊原因。游客的主要伤害原因是跌倒和坠落。但是园区内最主要的伤害性质是挫伤和擦伤。[结论]上海世博会园区内伤害发生特征表明,在大型活动中,及时进行健康干预能有效地降低伤害的发生。     

Initial satisfying experience of total retroperitoneal laparoscopic radical nephroureterectomy: a retrospective comparative research

BackgroundRadical nephroureterectomy (RNU) is the principal method for treatment of high-risk upper urinary tract urothelial carcinoma (UTUC). The transperitoneal approach is associated with poor disease progression, but the distal ureter-bladder cuff (DUBC) resection through retroperitoneal laparoscopic approach is difficult. This study proposed a modulated RNU technique, namely, total retroperitoneal laparoscopic radical nephroureterectomy (tRLRNU), with its advantages of DUBC resection and requiring fewer trocars etc. The efficiency, safety, and short-term impacts were retrospectively compared with total transperitoneal laparoscopic radical nephroureterectomy (tTLRNU).MethodsTotal of 12 patients who received tRLRNU and 28 patients who received tTLRNU were enrolled. The choice of surgical approach was random and their data were retrospectively analyzed. During tRLRNU, the laparoscope was versed towards the caudal direction and a retroperitoneal laparoscopic ureterectomy was performed. The bladder cuff was entirely transected and the bladder incision was sutured. The tRLRNU cases were compared with the tTLRNU cases in terms of general clinical data, pathologic parameters, peri-operative parameters, adjuvant therapy, and short-term outcomes. The independent samples t-tests, chi-square tests, and Fischer exact tests were used to analyze the differences.ResultsThere were no significant differences in the basic patient characteristics between the 2 groups. The data were comparable. There were significantly fewer trocars utilized in tRLRNU group compared to tTLRNU group (P=0.0008). tRLRNU group experienced less blood loss (98.33±61.32 versus 170.71±121.32 mL; P=0.017), smaller drainage volume (182.08±163.60 versus 1,924.82±3,370.02 mL; P=0.011), and shorter extubation time (5.67±1.07 versus 8.57±6.96 days; P=0.040) compared to tRLRNU group. There were no statistically differences in the other peri-operative parameters, including whole operation time, transfusion, visceral and vascular injuries, open conversion, post-operative bleeding, recovery time of intestinal function, and discharge time. The patient outcomes in tTLRNU group at 6 months were significantly worse than that of tRLRNU group by comparing progression-free survival, progression survival and mortality (P=0.039).ConclusionsThe tRLRNU was potentially safer, minimally invasive, and more effective compared to the tTLRNU. Due to the small sample size, short follow-up time and no randomization of the study, future comparative studies are warranted to further analyze long-term outcomes of tRLRNU.     

Study on Toughening and Temperature Sensitivity of Polyurethane Cement (PUC)

Polyurethane cement (PUC) is now commonly used in the reinforcement of old bridges, which exhibit various issues such as poor toughness, temperature-sensitive mechanical properties, and brittle failure. These problems can lead to the failure of the reinforcement effect of the PUC on old bridges in certain operating environments, leading to the collapse of such reinforced bridges. In order to alleviate these shortcomings, in this study, the toughness of PUC is improved by adding polyvinyl alcohol (PVA) fiber, carbon fiber, and steel fiber. In addition, we study the change law of the flexural strength of PUC between −40 °C and +40 °C. The control parameters evaluated are fiber type, fiber volume ratio, and temperature. A series of flexural tests and scanning electron microscope (SEM) test results show that the flexural strength first increases and then decreases with the increase in the volume-doping ratio of the three fibers. The optimum volume-mixing ratios of polyvinyl alcohol (PVA) fiber, carbon fiber, and steel fiber are 0.3%, 0.04% and 1%, respectively. Excessive addition of fiber will affect the operability and will adversely affect the mechanical properties. The flexural strength of both fiber-reinforced and control samples decreases with increasing temperature. Using the flexural test results, a two-factor (fiber content, temperature) BP neural network flexural strength prediction model is established. It is verified that the model is effective and accurate, and the experimental value and the predicted value are in good agreement.     

Microstructure of Dolostones of Different Geological Ages and Dedolomitization Reaction

Dolostone is widely distributed and commonly used as concrete aggregates. A large number of studies have shown that there are significant differences in the expansibility of different dolostones, and the key factors determining the expansibility of alkali carbonate rocks have not been clarified. In this paper, rocks were selected from five different geological ages: Jixianian, Cambrian, Ordovician, Devonian, and Triassic ages. The ordering degree and the content of MgCO₃ of dolomites in rocks of different geological ages were determined by X-ray diffraction (XRD). The degree of dedolomitization reaction in rocks cured in 80 °C, 1 mol/L NaOH solution was determined by quantitative X-ray diffraction (QXRD). The morphology of dolomites in rocks was determined by a polarizing microscope. The products of the dedolomitization reaction were determined by field emission electron microscopy (FESEM-EDS). According to the test results, the following conclusions are drawn. There is a good positive correlation between ordering degree and the molar fraction of MgCO₃ of dolomites. When the MgCO₃ mole fraction of dolomites varies from 47.17% to 49.60%, the higher the MgCO₃ mole fraction, the greater the ordering degree of dolomite. By analyzing the degree of the dedolomitization reaction of different dolostone powders cured at 80 °C in 1 mol/L NaOH solution, it is found that the older the geological age of dolostone, the slower the dedolomitization reaction rate and the lower the degree of dedolomitization reaction. The lower the ordering degree of dolomite crystal in the same geological age, the faster the rate of dedolomitization reaction and the higher the degree of dedolomitization reaction.     

Predictive modeling of 30-day readmission risk of diabetes patients by logistic regression,artificial neural network,and EasyEnsemble

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and Easy Ensemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features(4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods(oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.