On generic G-prevalent properties and a quantitative K-S theorem for Cr diffeomorphisms of the circle

We will consider a convex subset of a metric linear space and a certain group of actions G on this set, that allow us to define the notion of Haar zero measure on sets that have zero Haar measure for the translation (by adding) invariant HSY prevalence theory. In this way, we will be able to define the meaning of G-prevalent set according to the pioneering work of Christensen. Our setting considers problems which take into account the convex structure and this is quite different from the previous results on prevalence which consider basically the linear additive structure. In this setting, we will show a kind of quantitative Kupka–Smale theorem, and also we generalize a result about rotation numbers which was first considered by J.-C. Yoccoz (and, also by M. Tsujii). Among other things we present an estimation of the amount of hyperbolicity in a setting that we believe was not considered before.     

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.     

水泵转子径向圆跳动检测数据修正

针对应用千分表和电涡流传感器进行水泵转子径向圆跳动检测时,检测结果出现正弦变化规律的现象,分析了安装偏心误差对检测结果的影响原理,建立了相应的数学模型.运用最小二乘拟合法计算出变化曲线的幅值及初相位,从而消除偏心误差实现误差分离.结果表明,当将所提方法应用在水泵转子径向圆跳动检测的工程实践中时,可以有效消除安装偏心引起的误差,从而提高检测精确度.拟合平方和误差约为0. 000 9,所提方法可以广泛应用于轴类工件的检测实践.     

Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example, the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding. Furthermore, the experimental results based on the real data collected from China Mobile show that the key-attributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.     

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer-aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub-Rhombus Binary Relation Pattern (LS-RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS-RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS-RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI.     

A simple and sensitive aptasensor with rolling circle amplification for viable Cronobacter sakazakii detection in powdered infant formula

Cronobacter sakazakii is a foodborne, emerging opportunistic pathogen that causes severe bacteremia, necrotizing enterocolitis, and sepsis with a mortality rate of up to 80%. In this study, we developed a simple and sensitive fluorescent turn-off aptasensor with rolling circle amplification assay for viable C. sakazakii detection in powdered infant formula. The results showed that the proposed aptasensor has good performance and specificity for detecting viable C. sakazakii in pure culture and powdered infant formula samples within 3 h. Under the optimal reaction conditions, there is a linear relationship between fluorescent intensity at 490 nm and logarithmic concentration of C. sakazakii in the range of 2.7 × 10⁵ to 2.7 × 10² cfu/mL, with a limit of detection of 2.7 × 10² cfu/mL in pure culture. The proposed aptasensor achieved a recovery of 104 to 111% in pure culture, and 96 to 107% in spiked powdered infant formula samples. The proposed aptasensor does not require complicated DNA extraction steps or antibodies, and can be performed at 37°C, making it a convenient and sensitive strategy for C. sakazakii detection.     

“流空间”视角下的城市空间网络结构研究——以成渝双城经济圈为例

推动区域协调发展已经成为构建高质量发展国土空间布局的客观需要和解决发展不平衡问题的内在需求，以城市群为核心的经济圈建设成为驱动高质量发展的重要空间层级。为实现成渝双城经济圈的区域一体化及区域多层级协调发展，将流动的生产资源与城市空间结构相结合，基于人流、信息流、技术流、经济流四大流动资源，采用社会网络分析方法，分别从点、线、面3个维度对成渝双城经济圈空间网络结构特征进行分析。研究表明：经济圈内部呈现以成都、重庆为双核心的结构，区域间相互作用，城市中心性整体表现出内部优于外部的空间特征，核心城市对外围城市未充分发挥带动作用；圈内城市链接多表现出高度不对称性，链接对称地区主要集中在成渝中轴地区，城市间的联系有待加强，经济圈外围城市与外界联系较少；区域内部小团体现象明显，团体间联系密度存在差异，整体来看城市集群格局尚未形成，跨省域、跨地区的联系不够，成渝双城经济圈主要是西侧地区围绕成都发展。最后，为构建成渝经济圈一体化及区域多层级协调发展的空间格局，建议突破行政区域限制，构建多领域的跨区域共享协作通道；加强中部地区发展动力，提高外围城市的资源吸收能力；注重经济圈内城市交通建设及信息融合发展，突出核心城市的引领作用和外溢属性，促进城市间技术交流与经济融合。     

资源循环利用对有色金属工业发展的影响

资源循环利用是把全社会已经使用过的物品、边角料、废弃物作为一种资源,经过技术处理重新服务于人类。而循环经济则是一种新的经济模型。资源循环利用对有色金属工业可持续发展意义重大,值得深入研究。本文对有色金属资源循环的必要性及资源循环型社会建立的条件进行了初步分析,以期引起人们的思考。