跨行政区游憩空间柔性一体化研究 ——以高莱茵河沿岸地区为例

随着城市化与区域一体化的迅速发展,不同行政区之间的联动关系越来越紧密,这种联动关系在城市各功能空间的设计上有所体现,更渗透到了与市民息息相关的游憩空间建构之中。受制于行政管理等方面的原因,在跨行政区的游憩空间一体化实施过程中,往往会采取一系列柔性措施。以欧洲高莱茵河沿岸地区为研究对象,分析高莱茵河沿岸地区游憩空间的发展与现状,总结其游憩空间体系的构建特点,为中国跨城市河流沿岸的游憩空间柔性一体化建构提供策略与建议。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Breeding schemes with optimum-contribution selection or truncation selection for beef cattle destined for use on dairy females

We tested the hypothesis that the size of a beef cattle population destined for use on dairy females is smaller under optimum-contribution selection (OCS) than under truncation selection (TRS) at the same genetic gain (ΔG) and the same rate of inbreeding (ΔF). We used stochastic simulation to estimate true ΔG realized at a 0.005 ΔF in breeding schemes with OCS or TRS. The schemes for the beef cattle population also differed in the number of purebred offspring per dam and the total number of purebred offspring per generation. Dams of the next generation were exclusively selected among the one-year-old heifers. All dams were donors for embryo transfer and produced a maximum of 5 or 10 offspring. The total number of purebred offspring per generation was: 400, 800, 1,600 or 4,000 calves, and it was used as a measure of population size. Rate of inbreeding was predicted and controlled using pedigree relationships. Each OCS (TRS) scheme was simulated for 10 discrete generations and replicated 100 (200) times. The OCS scheme and the TRS scheme with a maximum of 10 offspring per dam required approximately 783 and 1,257 purebred offspring per generation to realize a true ΔG of €14 and a ΔF of 0.005 per generation. Schemes with a maximum of 5 offspring per dam required more purebred offspring per generation to realize a similar true ΔG and a similar ΔF. Our results show that OCS and multiple ovulation and embryo transfer act on selection intensity through different mechanisms to achieve fewer selection candidates and fewer selected sires and dams than under TRS at the same ΔG and a fixed ΔF. Therefore, we advocate the use of a breeding scheme with OCS and multiple ovulation and embryo transfer for beef cattle destined for use on dairy females because it is favorable both from an economic perspective and a carbon footprint perspective.     

Structure–Function Relationship of Retinal Ganglion Cells in Multiple Sclerosis

The retinal ganglion cells (RGC) may be considered an easily accessible pathophysiological site of degenerative processes in neurological diseases, such as the RGC damage detectable in multiple sclerosis (MS) patients with (HON) and without a history of optic neuritis (NON). We aimed to assess and interrelate RGC functional and structural damage in different retinal layers and retinal sites. We included 12 NON patients, 11 HON patients and 14 healthy controls for cross-sectional multifocal pattern electroretinography (mfPERG) and optical coherence tomography (OCT) measurements. Amplitude and peak times of the mfPERG were assessed. Macula and disc OCT scans were acquired to determine macular retinal layer and peripapillary retinal nerve fiber layer (pRNFL) thickness. In both HON and NON patients the foveal N2 amplitude of the mfPERG was reduced compared to controls. The parafoveal P1 peak time was significantly reduced in HON only. For OCT, parafoveal (pfGCL) and perifoveal (pGCL) ganglion cell layer thicknesses were decreased in HON vs. controls, while pRNFL in the papillomacular bundle sector (PMB) showed reductions in both NON and HON. As the mfPERG derived N2 originates from RGC axons, these findings suggest foveal axonal dysfunction not only in HON, but also in NON patients.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

石油化工管道安装常见问题及控制

目前,石油化工已成为我国重要产业之一,是我国社会经济的中流砥柱,因此石油化工业的稳定发展成为社会关注的热点。但随着石油化工产业的蓬勃发展,石油化工管道的安装问题愈加明显,较为常见的有阀门安装、焊接质量和防腐性差等问题。若不及时改善与调整,必然对化工管道的使用寿命与性能产生巨大影响。有鉴于此,通过对相关文献研究以及结合自己多年工作经验情况下,首先针对石油化工管道安装常见的问题进行分析与探讨,然后提出相应措施用以提高石油化工管道的安装质量。     

A Case of Double Standard: Sex Differences in Multiple Sclerosis Risk Factors

Multiple sclerosis is a complex, multifactorial, dysimmune disease prevalent in women. Its etiopathogenesis is extremely intricate, since each risk factor behaves as a variable that is interconnected with others. In order to understand these interactions, sex must be considered as a determining element, either in a protective or pathological sense, and not as one of many variables. In particular, sex seems to highly influence immune response at chromosomal, epigenetic, and hormonal levels. Environmental and genetic risk factors cannot be considered without sex, since sex-based immunological differences deeply affect disease onset, course, and prognosis. Understanding the mechanisms underlying sex-based differences is necessary in order to develop a more effective and personalized therapeutic approach.     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.