Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet.

The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale.

AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images.

In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires certain disease recognition abilities through continuous training, thereby assisting clinical physicians in disease diagnosis. This study aims to develop an object detection network that accurately locates and classifies the abnormal parts in spinal x-ray photographs.

Application of PVAR model in the study of influencing factors of carbon emissions.

Based on the panel data of China from 2003 to 2017, this paper applies the panel vector autoregressive (PVAR) model to the study of the influencing factors of carbon emissions. After the cross-section dependence test, unit root test and cointegration test of panel data, the dynamic relationship between energy consumption, economic growth, urbanization, financial development and CO emissions is investigated by using PVAR model. Then, we used the impulse response function tool to better understand the reaction of the main variables of interest, CO emissions, aftershocks on four factors.

Factor decomposition for ecological pressure of the whole industrial energy carbon footprint: a case study of China.

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward.

Decoupling trend and emission reduction potential of CO emissions from China's petrochemical industry.

This paper aims to study the decoupling status and emission reduction potential of China's petrochemical industry from 1996 to 2019. Firstly, the IPCC method is used to calculate the CO emissions of the petrochemical industry in China, then the logarithmic mean Divisia index (LMDI) method is used to identify the influencing factors of CO emissions, then the decoupling index is constructed to analyze the dependence of economic development on CO emissions, and finally the emission reduction potential model is established by using the influencing factors to reflect the CO emission reduction potential of the petrochemical industry. The results reveal that (1) the CO emissions can be divided into two stages of slow decline (1996-2000), (2015-2019), and one stage of rapid growth (2000-2015).

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model.

Illuminant direction estimation for a single image based on local region complexity analysis and average gray value.

Illuminant direction estimation is an important research issue in the field of image processing. Due to low cost for getting texture information from a single image, it is worthwhile to estimate illuminant direction by employing scenario texture information. This paper proposes a novel computation method to estimate illuminant direction on both color outdoor images and the extended Yale face database B.