Service Life Design for Concrete Engineering in Marine Environments of Northern China Based on a Modified Theoretical Model of Chloride Diffusion and Large Datasets of Ocean Parameters

In this study,through experimental research and an investigation on large datasets of the durability parameters in ocean engineering,the values,ranges,and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed.Based on a modified theoretical model of chloride diffusion and the reliability theory,the service lives of concrete structures exposed to the splash,tidal,and underwater zones were calculated.Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed,and a cover thickness requirement was proposed.In addition,the effects of the different time-varying relationships of the boundary condition(Cs)and diffusion coefficient(Df)on the service life were compared;the results showed that the time-varying relationships used in this study(i.e.,Cscontinuously increased and then remained stable,and Dfcontinuously decreased and then remained stable)were beneficial for the durability design of concrete structures in marine environment.

Dataset of Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi Arabia.This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment.The methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face regions.The raw images in the dataset consist of a total of 4613 frames obtained fromvideo sequences.The processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented data.The dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 images.It portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes.We have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal context.This can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

ROBUST DEPENDENCE MEASURE FOR DETECTING ASSOCIATIONS IN LARGE DATA SET

In this paper, we proposed a new statistical dependency measure for two random vectors based on copula, called copula dependency coefficient （CDC）. The CDC is proved to be robust to outliers and easy to be implemented. Especially, it is powerful and applicable to high-dimensional problems. All these properties make CDC practically important in related applications. Both experimental and application results show that CDC is a good robust dependence measure for association detecting.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space,fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions,creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks(CE-GAN).The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions,and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution,initial reservoir performance and time-varying well control,thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions.The experimental results in basic(egg model)and actual water-flooding reservoirs show that the model predictions align well with numerical simulations.In the basic reservoir model validation,the median relative residuals for pressure and oil saturation are 0.5%and 9.0%,respectively.In the actual reservoir model validation,the median relative residuals for both pressure and oil saturation are 4.0%.Regarding time efficiency,the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models,respectively,compared with traditional numerical simulations.The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.

An Efficient Algorithm for Distributed Outlier Detection in Large Multi-Dimensional Datasets

The distance-based outlier is a widely used definition of outlier. A point is distinguished as an outlier on the basis of the distances to its nearest neighbors. In this paper, to solve the problem of outlier computing in distributed environments, DBOZ, a distributed algorithm for distance-based outlier detection using Z-curve hierarchical tree （ZH-tree） is proposed. First, we propose a new index, ZH-tree, to effectively manage the data in a distributed environment. ZH-tree has two desirable advantages, including clustering property to help search the neighbors of a point, and hierarchical structure to support space pruning. We also design a bottom-up approach to build ZH-tree in parallel, whose time complexity is linear to the number of dimensions and the size of dataset. Second, DBOZ is proposed to compute outliers in distributed environments. It consists of two stages. 1） To avoid calculating the exact nearest neighbors of all the points, we design a greedy method and a new ZH-tree based k-nearest neighbor searching algorithm （ZHkNN for short） to obtain a threshold LW. 2） We propose a filter-and-refine approach, which first filters out the unpromising points using LW, and then outputs the final outliers through refining the remaining points. At last, the efficiency and the effectiveness of ZH-tree and DBOZ are testified through a series of experiments.

A novel microfluidic flow-cytometry for counting numbers of single-cell β-actins

As a house keeping protein with stable expressions, β-actin is used as a loading control in normalization of western blotting. However, the actual numbers of β-actins at the single-cell level remain elusive. Based on a homedeveloped flow cytometry, single-cell numbers of β-actin from 8 cell types(subtypes) and 2 tumour patient samples were quantified as 9.62 ± 4.29 × 105(A549, Ncell= 14,242), 6.46 ± 3.34 × 105(Hep G2, Ncell= 35,932),1.58 ± 0.90 × 106(MCF 10 A, N6 cell= 16,650), 1.08 ± 0.48 × 10(HeLa, Ncell= 26,151), 7.60 ± 4.34 × 105(PC3, Ncell= 11,922), 1.10 ± 0.72 × 106(SACC-83, Ncell= 13,616), 8.58 ± 4.54 × 105(CAL 27, Ncell= 7271),9.00 ± 4.69 × 105(CAL 27-LN2, Ncell= 6222), 8.26 ± 4.48 × 105(Oral Tumour Patient I, Ncell= 359), and8.19 ± 5.12 × 105(Oral Tumour Patient II, Ncell= 175), and were analyzed by statistical approaches including one-way analysis of variance, neural network based pattern recognition and Bayesian estimation, with varied expressions of β-actins among different cell types located. The dataset reported in this study may serve as a reference in future studies of quantitative protein analysis.

Clothing identification via deep learning:forensic applications

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals.An item such as clothing is a visual attribute because it can usually be used to describe people.The method proposed in this article aims to identify people based on the visual information derived from their attire.Deep learning is used to train the computer to classify images based on clothing content.We first demonstrate clothing classification using a large scale dataset,where the proposed model performs relatively poorly.Then,we use clothing classification on a dataset containing popular logos and famous brand images.The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%.Finally,we evaluate clothing classification using footage from surveillance cameras.The system performs well on this dataset,labelling 70%of the test images correctly.