水-油连续螺旋折流板换热器热力性能研究

利用计算流体力学(Computational Fluid Dynamic, CFD)方法，针对连续螺旋折流板换热器建立物理模型和数学模型，在管侧介质为水和壳侧介质为原油条件下，研究不同原油流量及螺旋角对螺旋折流板换热器内部流场、换热性能及阻力性能的影响，并拟合了水油换热时螺旋折流板换热器的Nu、f与Re的关联式。结果表明：22°螺旋角的螺旋折流板换热器与其它较小螺旋角换热器对比，壳侧压降和换热系数逐渐减小，综合换热性能最佳。通过对壳侧原油为层流状态下的阻力系数和对流换热系数关系式进行拟合，更好地指导水-油连续螺旋折流板换热器的热力设计。     

针对多种处理痕迹的数字语音取证算法

现有的数字语音取证研究主要集中于对单一的某种操作进行检测，无法对不相关的操作进行判断。针对该问题，提出了一种能够同时检测经过变调、低通滤波、高通滤波和加噪这四种操作的数字语音取证方法。首先，计算语音的归一化梅尔频率倒谱系数（MFCC）统计矩特征；然后通过多个二分类器对特征进行训练，并组合投票得到多分类器；最后使用该多分类器对待测语音进行分类。在TIMIT以及UME语音库上的实验结果表明，归一化MFCC统计矩特征在库内实验中均达到了97%以上的检测率，且在对MP3压缩鲁棒性测试的实验中，检测率仍能保持在96%以上。     

涡流空气分级机熵产与分级性能分析

利用热力学第二定律中的熵产理论对涡流空气分级机各不可逆因素引起的熵产进行分析，通过粉料分级试验对其分级性能进行验证，获得了黏性熵产、湍流熵产和壁面熵产分布特点及操作参数对熵产和分级精度的影响规律。熵产分析结果表明，涡流空气分级机内湍流熵产和壁面熵产占总熵产的比例高达56.41%和43.11%，湍流熵产主要产生于转笼叶片间和转笼内部，进风口和细粉出口壁面剪切引起较大壁面熵产；此外，转笼转速和进口风速变化分别仅对转笼区域和切向进风口区域内气流运动熵产影响较大，进口风速-转笼转速处于8.6m/s、 800r/min和18m/s、1200r/min操作工况附近时，涡流空气分级机内总熵产/总能变化率较小，分级流场稳定性较高，对粗、细颗粒分离有利，该工况下分级机的粉料分级试验效果较好，说明熵产理论可用于涡流分级机内流动分析及其操作参数的优化匹配。     

MSCT多期增强扫描在肾脏透明细胞癌中的诊断价值

目的探讨多排螺旋CT(MSCT)多期增强扫描在肾脏透明细胞癌诊断中的规律性,提高对该肿瘤诊断水平。方法回顾性分析2016年1月~2018年6月14例均经手术切除病理证实为肾脏透明细胞癌患者的CT影像资料,所有病例均行MSCT平扫及多期动态增强扫描。结果14例肾脏透明细胞癌形态大多数呈圆形或者类圆形,瘤体最大径范围1.7cm^11.3cm,平均(4.8±1.4)cm,13例CT平扫时肿瘤密度不均匀,呈等低密度,1例呈等密度;12例出现坏死囊变,钙化1例,伴肾盂、肾盏侵犯2例;动态增强扫描可见12例包膜完整,1例均匀性强化,13例不均匀性强化;引入对比剂后皮质期,出现10例病灶强化幅度大于肾皮质,而仅4例低于肾皮质,三期平均CT值分别为110±30Hu,90±15Hu及75±14Hu,其中在实质期出现13例肿瘤强化幅度小于肾实质,仅1例大于肾实质。结论引入对比剂动态增强扫描后,肾脏透明细胞癌在皮质期明显不均匀强化,实质期强化幅度下降显著,呈现出典型的“快进快出”血流动力学强化方式,且肿瘤容易出现坏死、囊变为其特点。     

煤制天然气中氢对X80钢螺旋焊管力学性能的影响

近年来,我国多项煤制天然气示范工程投入运行,X80钢螺旋焊管常用于天然气长输管道建设,其与煤制天然气的相容性直接影响了长输管道的服役寿命和安全可靠性。为研究煤制天然气对国产X80钢螺旋焊管的力学性能影响,分别从埋弧螺旋焊管的母材和螺旋焊缝处取样,在总压为12 MPa,氢气分数为0,1vol%,2.2vol%,5vol%的模拟环境中分别进行慢应变速率拉伸试验和疲劳裂纹扩展速率试验。试验结果表明:含氢量5vol%以下时,煤制天然气对国产X80管线钢强度性能影响很小,但对塑性性能有一定影响,对疲劳裂纹扩展性能影响很大,煤制天然气中氢对母材的疲劳裂纹扩展性能劣化影响比螺旋焊缝严重。     

内插螺旋立式上行管流场及传热性能的实验研究

通过粒子图像测速流场实验与传热实验相结合，研究了内插螺旋立式上行管的螺旋节距、丝径、中径比等结构参数在不同Re下对流场、阻力及传热性能的影响。结果表明，内插螺旋能够有效扰动和混合管内流体，使管内形成多个纵向旋涡的流体结构、增大管壁附近液体涡量，有利于强化传热。当Re相同时，管内平均流速v、Nu和综合换热性能PEC均随丝径增大而增大，随中径比减小而增大；随节距增大，3种参数均出现增大的趋势，节距大于20 mm后开始减小。管内流体的阻力f随丝径和节距增大而减小，随中径比增大而增大。综合比较，在较低Re时，节距p=20 mm、丝径e=1.6 mm、中径比D/d=0.75时综合传热效果最好。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.     

<i>In Silico</i> analysis and linking of metabolism-related genes with the immune landscape in head and neck squamous cell carcinoma

Metabolic reprogramming and immunologic suppression are two critical characteristics promoting the progression of head and neck squamous cell carcinoma (HNSCC). The integrative analysis of all the metabolism-related genes (MRGs) in HNSCC is lacking and the interaction between the metabolism and the immune characteristics also requires more exploration to uncover the potential mechanisms. Therefore, this study was designed to establish a prognostic signature based on all the MRGs in HNSCC. Genes of HNSCC samples were available from the TCGA and GEO databases while the MRGs were retrieved from a previous study. Ultimately 4 prognostic MRGs were selected to construct a model possessing robust prognostic value and accuracy in TCGA cohorts. The favorable reproducibility of this model was confirmed in validation cohorts from GEO databases. The risk score calculated by this model was an independent prognostic factor that further classified these HNSCC patients into high-/low-risk groups. GSEA analyses and somatic mutations indicated the low-risk group could activate several anti-tumor pathways and possessed lower TP53 mutation. The results of ESTIMATE, single-sample GSEA, CIBERSORT, and some immune-related molecules analyses suggested the low-risk group exhibited lower metabolic activities and higher immune characteristics. The Spearman correlation test implied most metabolic pathways with tumor-promoting function were negatively correlated with the immune activity, indicating a plausible approach of combining the anti-metabolism and the immunotherapy drugs in the high-risk group to enhance therapeutic effects than applied separately. In conclusion, this prognostic signature linking MRGs with the immune landscape could promote the individualized treatment for HNSCC patients.