Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology.

In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

This paper describes MixGAN-TTS, an efficient and stable non-autoregressive speech synthesis based on diffusion model. The MixGAN-TTS uses a linguistic encoder based on soft phoneme-level alignment and hard word-level alignment approach which explicitly extracts word-level semantic information, and introduces pitch and energy predictors to optimally predict the rhythmic information of the audio. Specifically, we use the GAN to replace the Gaussian function to model the denoising distribution, aiming to enlarge the denoising steps size and reduce the number of denoising steps to accelerate the sampling speed of diffusion model. Diffusion model using GAN can significantly reduce the denoising steps, and to some extent solve the problem of not being able to apply in real-time. The mel-spectrogram is converted into the final audio by the HiFi-GAN vocoder. Experimental results show that the MixGAN-TTS outperforms the other models compared in terms of audio quality and mel-spectrogram modeling capability for 4 denoising steps. The ablation studies demonstrate that the structure of MixGAN-TTS is effective.

Selenium-rich rape Selenium Ziyuan No.1 was used as green manure to study the effects of different amounts of green manure returned to the field on the release characteristics of available selenium in acidic paddy soil in southern China, and to analyze the absorption and transformation of selenium in rice, so as to provide a theoretical basis for planting natural selenium-rich rice in acidic areas of southern China. Six treatments with different amounts of selenium-enriched rapeseed returning (0, 5, 10, 15, 20, and 25t/hm²) were set up. Two rice varieties (selenium-rich rice variety Meixiangzhan 2 and common rice variety Zhongguangxiang 1) were selected. The results showed that (1) with the increase of selenium-rich rapeseed returning amount, the available selenium in soil showed an increasing trend. Over time, soil available selenium showed a significant increasing trend, and the content of soil available selenium reached the maximum at tillering stage, and then decreased. (2) For selenium-rich varieties, when the amount of selenium-rich rapeseed returned to the field was less than 15t/hm², the selenium content in rice grains increased significantly with the increase of the amount of selenium-rich rapeseed returned to the field, then remained basically stable. For conventional varieties, with the increase of the amount of selenium-rich rapeseed returned to the field, the selenium content of rice grains showed an increasing trend, but the overall selenium content was much lower than that of selenium-rich variety. (3) With the increase of the amount of rapeseed returned to the field, the rice yield had an increasing trend, but the maximum rice yields appeared when the amount of selenium-rich rapeseed returned to the field was 15t/hm². Therefore, Se-enriched rape returning could promote the release of available selenium in soil and the enrichment of selenium in rice plants, and significantly increase the selenium content in rice. According to the selenium content and yield of rice, it is suggested that the selenium-rich rice variety Meixiangzhan 2 was chosen and the amount of Se-rich rape returning is 15t/hm².

As wastewater treatment usually involves complicated biochemical reactions, leading to strong coupling correlation and nonlinearity in water quality parameters, it is difficult to analyze and optimize the control of the wastewater treatment plant (WWTP) with traditional mathematical models. This research focuses on how deep learning techniques can be used to model the data from a specific WWTP so as to optimize the required energy consumption. In the operation of a wastewater treatment plant, various sensors are used to record the treatment process data; these data are used to train deep neural networks (DNNs). A long short-term memory with multilayer perceptron network (LMPNet) model is proposed to model the water quality parameters and site control parameters, such as COD, pH, NH3-N, et al., and the LMPNet model prediction error is then measured by criteria such as the MSE, MAE, and R². The experimental results show that the LMPNet model demonstrates great accuracy in the modeling of the control of WWTPs. A life-long learning strategy is also developed for the LMPNet in order to adapt to the environment that may change over time. By developing performance evaluation metrics, the purification performance can be analyzed, and the prediction reference can be provided for the subsequent control optimization and energy saving plan.

This paper demonstrates an improved version of the Salp Swarm Algorithm (SSA) to solve the problems of slow convergence and local minima of the original version. In the population initialization of this scheme, ten chaotic searches with dynamic inertia coefficients are introduced to increase the diversity so that the probability of being trapped in local minima is reduced. Genetic algorithms are then applied to improve the global search ability and convergence speed. The experiments with 12 test functions show that the improved version achieves better accuracy and convergence speed over the original SSA. In the test with robot path planning problem, the proposed algorithm shows improved performance in the average number of iterations, path length, and average number of turns by 69.2%, 19.1%, and 43%, respectively, compared with the original SSA.