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A hyperspectral imaging system covering 400 - 1000 nm spectral range was applied for vigour detection of waxy maize seeds after artificial aging. After spectral pre-processing, the characteristic wavelength was selected by uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF) methods. The moisture, starch, protein, and fat contents were measured for each grade of seed, and these values were correlated with the spectrum. Finally, the vitality detection model was established by least squares support vector machine (LS-SVM), extreme learning machine (ELM), and random forest (RF). The prediction sets exhibited high classification accuracy (> 99%) for 115 features. The model constructed from the bands significantly correlated with chemical composition (CC), and was better than the classic feature selection methods. The overall results indicated that hyperspectral imaging could be a potential technique to assess seed vigour.

With the rapid development of science and technology, advanced underwater robots have extensively promoted human beings’ abilities to understand, explore, and preserve the oceans. Understanding the mechanism of fish movement has essential scientific and practical significance for advancing underwater robotics technology. On the one hand, it can reveal the secret of efficient swimming of fish and help to understand the behavior of fish; on the other hand, it can provide a basis for the research and development of new underwater robots with low noise, high efficiency, high stability, and high mobility. In this research, to analyze the synergistic propulsion mechanism of fish body and fins, a fish body-fin synergistic propulsion model is established to provide a theoretical basis for such analysis. A fish pose and motion estimation platform is constructed to provide data support. With the help of the constructed platform for quantifying the movement of a koi, fish wave characteristics are analyzed for three koi swimming states (advancing, retreating, and turning), and four koi swimming states (advancing, retreating, turning, and floating up) are summarized and analyzed using the proposed body-fin coordination model. The proposed analysis method provides an innovative idea for fish movement research. The research results can greatly help the designing, developing, and optimizing modern underwater robots. The code and dataset for this research are available inhttps://github.com/wux024/AdamPosePlug.

The semiparametric proportional odds (PO) model is a popular alternative to Cox’s proportional hazards model for analyzing survival data. Although many approaches have been proposed for this topic ...

Our previous studies demonstrated that the natural compound emodin blocks the tumor-promoting feedforward interactions between cancer cells and macrophages, and thus ameliorates the immunosuppressive state of the tumor microenvironment. Since tumor-associated macrophages (TAMs) also affect epithelial mesenchymal-transition (EMT) and cancer stem cell (CSC) formation, here we aimed to test if emodin as a neoadjuvant therapy halts breast cancer metastasis by attenuating TAM-induced EMT and CSC formation of breast cancer cells. Methods: Bioinformatical analysis was performed to examine the correlation between macrophage abundance and EMT/CSC markers in human breast tumors. Cell culture and co-culture studies were performed to test if emodin suppresses TGF-β1 or macrophage-induced EMT and CSC formation of breast cancer cells, and if it inhibits breast cancer cell migration and invasion. Using mouse models, we tested if short-term administration of emodin before surgical removal of breast tumors halts breast cancer post-surgery metastatic recurrence in the lungs. The effects of emodin on TGF-β1 signaling pathways in breast cancer cells were examined by western blots and immunofluorescent imaging. Results: Macrophage abundance positively correlates with EMT and CSC markers in human breast tumors. Emodin suppressed TGF-β1 production in breast cancer cells and macrophages and attenuated TGF-β1 or macrophage-induced EMT and CSC formation of breast cancer cells. Short-term administration of emodin before surgery halted breast cancer post-surgery metastatic recurrence in the lungs by reducing tumor-promoting macrophages and suppressing EMT and CSC formation in the primary tumors. Mechanistic studies revealed that emodin inhibited both canonical and noncanonical TGF-β1 signaling pathways in breast cancer cells and suppressed transcription factors key to EMT and CSC. Conclusion: Natural compound emodin suppresses EMT and CSC formation of breast cancer cells by blocking TGF-β1-mediated crosstalk between TAMs and breast cancer cells. Our study provides evidence suggesting that emodin harbors the potential for clinical development as a new effective and safe agent to halt metastatic recurrence of breast cancer.

Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.

Multivariate interval-censored data arise when each subject under study can potentially experience multiple events and the onset time of each event is not observed exactly but is known to lie in a certain time interval formed by adjacent examination times with changed statuses of the event. This type of incomplete and complex data structure poses a substantial challenge in practical data analysis. In addition, many potential risk factors exist in numerous studies. Thus, conducting variable selection for event-specific covariates simultaneously becomes useful in identifying important variables and assessing their effects on the events of interest. In this paper, we develop a variable selection technique for multivariate interval-censored data under a general class of semiparametric transformation frailty models. The minimum information criterion (MIC) method is embedded in the optimization step of the proposed expectation-maximization (EM) algorithm to obtain the parameter estimator. The proposed EM algorithm greatly reduces the computational burden in maximizing the observed likelihood function, and the MIC naturally avoids selecting the optimal tuning parameter as needed in many other popular penalties, making the proposed algorithm promising and reliable. The proposed method is evaluated through extensive simulation studies and illustrated by an analysis of patient data from the Aerobics Center Longitudinal Study.

This essay revisits the Jesuit churches in Beijing in order to address both the spatial engagement of Christian sacred images and the way Christian and biblical themes were narrated and perceived in the church space. It first discusses the availability of Christian iconographies and their hierarchical usage in gendered church spaces. It then demonstrates the ways the altarpieces and the trompe l’oeil murals were arranged into defined iconographic programs in the reevaluation of their architectural contexts. Through a close reading of contemporary travel writings, this essay concludes by presenting a larger picture of various types of Christian sacred images in Chinese perceptions of Christianity.

Arbitrarily censored data are referred to as the survival data that contain a mixture of exactly observed, left-censored, interval-censored, and right-censored observations. Existing research work on regression analysis on arbitrarily censored data is relatively sparse and mainly focused on the proportional hazards model and the accelerated failure time model. This article studies the proportional odds (PO) model and proposes a novel estimation approach through an expectation-maximization (EM) algorithm for analyzing such data. The proposed EM algorithm has many appealing properties such as being robust to initial values, easy to implement, converging fast, and providing the variance estimate of the regression parameter estimate in closed form. An informal diagnosis plot is developed for checking the PO model assumption. Our method has shown excellent performance in estimating the regression parameters as well as the baseline survival function in a simulation study. A real-life dataset about metastatic colorectal cancer is analyzed for illustration. An R package regPO has been created for practitioners to implement our method.

Considering that there are many irrelevant images in the image data set crawled from the Internet, this paper proposes a new method of cleaning irrelevant images data based on transfer learning. The method consists of two steps that can be done iteratively. Firstly, we use transfer learning to train and get a better image classifier, which makes the single image's recognition effect more accurate. Besides, according to the number of different types of images in the data set, we calculate and determine the threshold value of the image of the minority class and then clean them accordingly. We find that our method can improve the test accuracy of the original data set on VGG16, VGG19, and Inception v3 from 37.41%, 40.62%, and 49.37% to 50.07%, 53.89%, and 65.16%, respectively. Since transfer learning has a good application effect on irrelevant images cleaning, we can use it to create data sets in deep learning.

Clustered interval-censored data commonly arise in many studies of biomedical research where the failure time of interest is subject to interval-censoring and subjects are correlated for being in the same cluster. A new semiparametric frailty probit regression model is proposed to study covariate effects on the failure time by accounting for the intracluster dependence. Under the proposed normal frailty probit model, the marginal distribution of the failure time is a semiparametric probit model, the regression parameters can be interpreted as both the conditional covariate effects given frailty and the marginal covariate effects up to a multiplicative constant, and the intracluster association can be summarized by two nonparametric measures in simple and explicit form. A fully Bayesian estimation approach is developed based on the use of monotone splines for the unknown nondecreasing function and a data augmentation using normal latent variables. The proposed Gibbs sampler is straightforward to implement since all unknowns have standard form in their full conditional distributions. The proposed method performs very well in estimating the regression parameters as well as the intracluster association, and the method is robust to frailty distribution misspecifications as shown in our simulation studies. Two real-life data sets are analyzed for illustration.

To obtain a speaker’s pronunciation characteristics, a method is proposed based on an idea from bionics, which uses spectrogram statistics to achieve a characteristic spectrogram to give a stable representation of the speaker’s pronunciation from a linear superposition of short-time spectrograms. To deal with the issue of slow network training and recognition speed for speaker recognition systems on resource-constrained devices, based on a traditional SOM neural network, an adaptive clustering self-organizing feature map SOM (AC-SOM) algorithm is proposed. This algorithm automatically adjusts the number of neurons in the competition layer based on the number of speakers to be recognized until the number of clusters matches the number of speakers. A 100-speaker database of characteristic spectrogram samples was built and applied to the proposed AC-SOM model, yielding a maximum training time of only 304 s, with a maximum sample recognition time of less than 28 ms. Comparing to other approaches, the proposed method offers greatly improved training and recognition speed without sacrificing too much recognition accuracy. The promising results suggest that the proposed method satisfies real-time data processing and execution requirements for edge intelligence systems better than other speaker recognition methods.

Many traffic sign detection algorithms suffer from poor robustness and low detection efficiency problems under poor environmental conditions, such as low illumination and sudden weather changes. Here, we propose a novel traffic sign detection algorithm based on multi-color space fusion. First, we select sensitive channels that can represent the color information of traffic signs by analyzing the color space. Second, the sensitive channels from the equalized image are extracted and fused to enhance the contrast of the area with the traffic sign colors. Additionally, an adaptive threshold setting method is used to binarize the fused image to acquire the candidate areas of traffic signs. Finally, non-traffic sign areas in the candidate areas are removed on the basis of the features of area, aspect ratio, and eccentricity. Results using two databases demonstrate that our proposed algorithm can effectively improve the detection rate and has a higher robustness under poor environmental conditions.

Partly interval-censored time-to-event data often occur in biomedical studies of diseases where periodic medical examinations for symptoms of interest are necessary. Recent decades have seen blooming methods and R packages for interval-censored data; however, the research effort for partly interval-censored data is limited. We propose an efficient and easy-to-implement Bayesian semiparametric method for analyzing partly interval-censored data under the proportional hazards model. Two simulation studies are conducted to compare the performance of the proposed method with two main Bayesian methods currently available in the literature and the classic Cox proportional hazards model. The proposed method is applied to a partly interval-censored progression-free survival data from a metastatic colorectal cancer trial.

This paper presents a color recognition algorithm for natural scene images based on HSV color space.In this algorithm,different fuzzy membership are constructed on the hue channel,saturation channel and value channel.By establishing the fuzzy rule base,the Mamdani fuzzy inference method is selected,and the color images are divided into eleven kinds according to the human psychological perception of color.This algorithm can accurately identify the color of the image with rich color distribution and partial background color close to the target color. Experiments show that the algorithm's color recognition and segmentation results are in good agreement with human subjective visual perception, which effectively improves the color recognition accuracy of the robot.

Compared with grayscale images, color images with color information have more advantages in target recognition, detection and tracking, but the color of the image can be easily affected to be distorted by light. Aiming at solving the problem of color distortion, a method based on wavelet transform is proposed to eliminate the influence of illumination. Firstly, the V-channel components in HSV space are decomposed by wavelet and the illumination component in the image scene is estimated by reconstructing the approximate coefficients of wavelet decomposition. Secondly, according to the imaging principle model, the illumination component is removed from the original image, and the reflection component that characterizes the color information is preserved. Finally, the normalized distribution function is used to normalize the reflection components, and the image color recovery is achieved in RGB space. The experimental results show that the proposed method can reduce the influence of non-uniform illumination on the image to restore the color and improve the image quality.

Technologies such as identification, detection, and measurement based on multiple industrial cameras have developed vigorously. Most of these technologies require synchronized video signals from multiple cameras, thus a general method of synchronization of multiple industrial cameras has always been an important research direction. To reduce synchronization error and hardware cost of the existing multi-vision sensor synchronization methods, based on multiprocessing or multithreading techniques, this paper designs three synchronous capture schemes for different application scenarios. The proposed schemes are tested for an example system of three industrial cameras with the highest frame rate for 600FPS. For long-time synchronous capture with a low frame rate, a read–write structure is proposed based on multiprocessing, which makes the synchronization error lower than the reciprocal frame rate when the frame rate is lower than 200FPS. For short-time capture with a high frame rate, a read-storage-write structure is used based on multiprocessing, which makes synchronization errors be about 1 ms when the frame rate is lower than 600FPS (theoretically ~ 1000FPS). To relieve the memory usage pressure of the read-storage-write structure, a read-cache-write structure is proposed based on multiprocessing/multithreading, which makes synchronization error be less than 1 ms when the frame rate is lower than 600FPS (theoretically ~ 1000FPS). Experimental results show that the proposed method provides a promising accurate and low-cost solution for general industrial camera synchronization applications.

In allusion to the problem that the near infrared images of early teeth disease detection is low contract, minutiae feature isn’t evident and the images have small target gray range. So the image enhancement approach is proposed, which is disposed by the high frequency filter, linear compensation and the successive mean quantization transform. First, the near infrared images of teeth are filtered by the high frequency filter, the high frequency filter can emphasize on high-frequency components and keep lost low-frequency components, then the near infrared images are linearly compensated, finally the method combined with the successive mean quantization transform to enhance the near infrared images. The experiments show that the enhancement images show high contract and detail feature is obvious. Visually it is better to observe whether the teeth are diseased. Compared with the results of histogram equalization, the proposed method can get better enhancement effect and the particular features are stand out.

To identify the accuracy of the installation position of the spring hooks on both sides of the automobile seat back curtain automatically, this paper presents an automatic detection system for spring hooks on automobile seat back based on feature detection and line segment. Firstly, the backrest image of the automobile is matched by a scale-invariant feature transformation algorithm (SIFT algorithm) to classify different types of automobile seat backs. Secondly, an edge detection method is used to extract the characteristic thread to locate the middle line of the spring hook (or parallel lines) and the side line where the spring hook hooks. Finally, the possibility of the intersection of the spring middle line (or parallel lines) and side line to be inside the side line slot is calculated to determine qualification level of the automobile seats. The experimental results indicated that the system detection accuracy was up to 98.8%. The system meets the practical requirement of automobile industry with high efficiency.

With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female breast cancer study, background mortality is incorporated into the mixture cure proportional hazards (MCPH) model to improve the cure fraction estimation in population-based cancer studies. Here, that patients are "cured" is defined as when the mortality rate of the individuals in diseased group returns to the same level as that expected in the general population, where the population level mortality is presented by the mortality table of the United States. The semiparametric estimation method based on the EM algorithm for the MCPH model with background mortality (MCPH+BM) is further developed and validated via comprehensive simulation studies. Real data analysis shows that the proposed semiparametric MCPH+BM model may provide more accurate estimation in population-level cancer study.

Censored failure time data with a cured subgroup is frequently encountered in many scientific areas including the cancer screening research, tumorigenicity studies, and sociological surveys. Meanwhile, one may also encounter an extraordinary large number of risk factors in practice, such as patient's demographic characteristics, clinical measurements, and medical history, which makes variable selection an emerging need in the data analysis. Motivated by a medical study on prostate cancer screening, we develop a variable selection method in the semiparametric nonmixture or promotion time cure model when interval-censored data with a cured subgroup are present. Specifically, we propose a penalized likelihood approach with the use of the least absolute shrinkage and selection operator, adaptive least absolute shrinkage and selection operator, or smoothly clipped absolute deviation penalties, which can be easily accomplished via a novel penalized expectation-maximization algorithm. We assess the finite-sample performance of the proposed methodology through extensive simulations and analyze the prostate cancer screening data for illustration.