Food is essential for human life and has been the concern of many healthcare conventions. Nowadays new dietary assessment and nutrition analysis tools enable more opportunities to help people ...understand their daily eating habits, exploring nutrition patterns and maintain a healthy diet. In this paper, we develop a deep model based food recognition and dietary assessment system to study and analyze food items from daily meal images (e.g., captured by smartphone). Specifically, we propose a three-step algorithm to recognize multi-item (food) images by detecting candidate regions and using deep convolutional neural network (CNN) for object classification. The system first generates multiple region of proposals on input images by applying the Region Proposal Network (RPN) derived from Faster R-CNN model. It then indentifies each region of proposals by mapping them into feature maps, and classifies them into different food categories, as well as locating them in the original images. Finally, the system will analyze the nutritional ingredients based on the recognition results and generate a dietary assessment report by calculating the amount of calories, fat, carbohydrate and protein. In the evaluation, we conduct extensive experiments using two popular food image datasets - UEC-FOOD100 and UEC-FOOD256. We also generate a new type of dataset about food items based on FOOD101 with bounding. The model is evaluated through different evaluation metrics. The experimental results show that our system is able to recognize the food items accurately and generate the dietary assessment report efficiently, which will benefit the users with a clear insight of healthy dietary and guide their daily recipe to improve body health and wellness.
Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.
This study proposed a 14-layer ...convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.
The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.
Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.
Early fault detection and diagnosis can increase the stability, reliability and safety of manufacturing equipment. It can be used for protection against unforeseen emergencies in manufacturing ...system. Recently, fault diagnosis (FD) methods based on deep learning (DL) have become a research hotspot for their excellent performance. However, the training process of deep learning (DL) models is time-consuming because of their high computation complexity. Moreover, most of DL-based FD methods have an assumption the distribution of training datasets in the source domain is the same as that of test datasets in the target domain. However, it is impossible in typical real-world manufacturing applications. In order to cope with these two problems, this paper proposes a FD method based on convolutional neural network (CNN) and transfer learning (TL). Firstly, a CNN model based on LeNet-5 is designed to extract fault features from images which is converted from raw signal data by continuous wavelet transform (CWT), then the performance of the CNN model are improved by fine-tuning which is an effective way of TL. The proposed method is conducted on two well-known datasets and the experimental results show that the proposed method can significantly improve the accuracy and efficiency performance a lot compared with the standard CNN model.
•Groundwater table rising reduced nitrate concentration, while falling aggravated it.•Bacterial biomarkers and functional genes were identified during N transformation.•Groundwater table fluctuation ...affected N transformation mainly in indirectly paths.
Groundwater table fluctuation alters the biochemical environment and nitrogen transformation, leading to periodic changes in groundwater nitrate concentration. A microcosm experiment of groundwater table fluctuation was designed to explore the underlying mechanism. The dissolved oxygen (DO), bacterial community, functional gene groups and associated nitrogen transformation were investigated based on field scenario. The results showed that groundwater rising reduced the average nitrate concentration, while falling aggravated it. Owing to the variations in groundwater table, the DO concentration underwent periodic changes. Furthermore, the abundance and diversity of soil bacteria were enriched and the structure of the microbial community was altered. Nitrogen transformation biomarkers such as Arthrobacter sp., Bacillus sp., Chloroflexi sp. and Acidobacteriota sp. as well as nitrogen transformation functional genes were identified. A structure equation model was established which demonstrated that groundwater table fluctuation altered DO and the dominant bacterial abundance, which in turn modified the functional gene groups and nitrogen transformation. This study broadens the understanding of how and to what extent groundwater table fluctuation affects nitrogen transformation and is vital for assessing and managing risks associated with groundwater nitrate contamination
Human pose estimation localizes body keypoints to accurately recognizing the postures of individuals given an image. This step is a crucial prerequisite to multiple tasks of computer vision which ...include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Therefore, we present this survey article to fill the knowledge gap and shed light on the researches of 2D human pose estimation. A brief introduction is followed by classifying it as a single or multi-person pose estimation based on the number of people needed to be tracked. Then gradually the approaches used in human pose estimation are described before listing some applications and also flaws facing in pose estimation. Following that, a center of attention is given on briefly discussing researches with a significant effect on human pose estimation and examine the novelty, motivation, architecture, the procedures (working principles) of each model together with its practical application and drawbacks, datasets implemented, as well as the evaluation metrics used to evaluate the model. This review is presented as a baseline for newcomers and guides researchers to discover new models by observing the procedure and architecture flaws of existing researches.
Pure high impact polystyrene (HIPS) is widely used, but it is flammable. During combustion, it produces black smoke, drips, and poses other hazards to human safety, making it necessary to study ...flame-retardant HIPS. In this study, nitrogen-phosphorus-silicon flame retardants were prepared by using melamine cyanurate (MC), aluminum hypophosphite (ALHP), and nano-silica (nano-SiO2) to improve the flame retardancy of HIPS. The flame retardancy and mechanism of HIPS composites were studied. The results showed that when the ratio of MC to ALHP was 1:4 and nano-SiO2 was 1.5 wt%, the limiting oxygen index (LOI) value of HIPS composite was 26.9%, and PHRR and THR were reduced to 254 kW/m2 and 85 MJ/m2, respectively. The smoke production decreased by 32%, and the carbon residue increased by 70%. ALHP and nano-SiO2 mainly play a flame retardant role in the condensed phase. MC mainly plays a flame retardant role in the gas phase, as well as the condensed phase.
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•The filling amount was lower than single silicon flame retardant.•The efficiency was higher than single nitrogen flame retardant.•The carbonization effect was better than single phosphorus flame retardant.•MC, ALHP, and nano-SiO2 were compounded and added to HIPS for the first time.•The flame retardant composite is an environmentally friendly material.
Plant basic/helix-loop-helix (bHLH) transcription factors participate in a number of biological processes, such as growth, development and abiotic stress responses. The bHLH family has been ...identified in many plants, and several bHLH transcription factors have been functionally characterized in Arabidopsis. However, no systematic identification of bHLH family members has been reported in potato (Solanum tuberosum). Here, 124 StbHLH genes were identified and named according to their chromosomal locations. The intron numbers varied from zero to seven. Most StbHLH proteins had the highly conserved intron phase 0, which accounted for 86.2% of the introns. According to the Neighbor-joining phylogenetic tree, 259 bHLH proteins acquired from Arabidopsis and potato were divided into 15 groups. All of the StbHLH genes were randomly distributed on 12 chromosomes, and 20 tandem duplicated genes and four pairs of duplicated gene segments were detected in the StbHLH family. The gene ontology (GO) analysis revealed that StbHLH mainly function in protein and DNA binding. Through the RNA-seq and quantitative real time PCR (qRT-PCR) analyses, StbHLH were found to be expressed in various tissues and to respond to abiotic stresses, including salt, drought and heat. StbHLH1, 41 and 60 were highly expressed in flower tissues, and were predicted to be involved in flower development by GO annotation. StbHLH45 was highly expressed in salt, drought and heat stress, which suggested its important role in abiotic stress response. The results provide comprehensive information for further analyses of the molecular functions of the StbHLH gene family.
A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the class mean, improves ...the signal-to-noise ratio in single-particle reconstruction. The averaging step is often compromised because of the outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods are done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a w-estimator of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers.
Traditional Chinese state sacrificial ritual represented a symbolic system of integrating religious belief, divine authority, and political legitimacy. The Northern Stronghold (Beizhen 北鎮, i.e., ...Mount Yiwulü 醫巫閭山) was equal in status to the other four strongholds, which, moreover, served as a strategic military fortress and represented the earth virtue in the early state sacrifice system. In the late imperial era of China, and during the Yuan (1279–1368) and Qing (1644–1911) dynasties in particular, the Northern Stronghold swiftly achieved prominence and eventually became an instrument used by minority ethnic groups, namely the Mongolians and Manchus, when elaborating upon the legitimacy of their political regimes. During the Yuan dynasty, the mountain spirits of the five strongholds (Wuzhen 五鎮) were formally invested as kings and, as a result, were accorded equivalent sacrifices in comparison to those given to the five sacred peaks (Wuyue 五嶽). Given that the Northern Stronghold was located near the northeast of Beijing, the Yuan government considered it the foundation of the state. Thereafter, the Northern Stronghold was regarded as the most important of the five stronghold mountains. In the Ming dynasty (1368–1644), the Northern Stronghold Temple (Beizhenmiao 北鎮廟) was reconstructed as both a military fortress and religious site, while its representation as a significant site for a foreign conquest dynasty diminished and its significance as a bastion of anti-insurgent suppression emerged. By the Qing dynasty, the Northern Stronghold was regarded as an integral component of the geographic origin of the Manchu people and thereby assumed once again a position of substantial political significance. Several Qing emperors visited the Northern Stronghold and left poems and prose written in graceful Chinese to present their high respect and their mastery of Chinese culture. The history of the Northern Stronghold demonstrates how the ethnic minority regimes successfully utilized the traditional Chinese state sacrificial ritual to serve their political purpose.
Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing ...data to generate additional insights.
To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.
This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.
Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.
A total of 755 402 patients (mean SD age, 65 13 years; 495 202 65.5% male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.
In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.
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