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Simple Summary: The meat quality of small ruminants is adversely affected when the feeding mode is changed from grazing to captivity. We found that adding probiotics to lamb feed regulated the intestinal metabolites (SCFAs) of Sunit lambs, acting as signaling factors that affected the gene mRNA expression of MyHC isoforms and the activity of metabolic enzymes, thereby promoting the transformation of muscle fiber types and increasing the proportion of oxidative muscle fibers. Finally, the tenderness of the meat was improved. The development of animal husbandry is closely related to the meat quality of small ruminants. Intestinal metabolites and the muscle fiber types of lambs are important factors that affect their meat quality, but few studies have examined the regulation of the "intestinal muscle axis" by probiotics. In this study, 12 Sunit lambs were divided into a control group (C) and a probiotics group (P). The gene expressions of the myosin heavy chain, metabolic enzyme activity, and short-chain fatty acids in the intestines were analyzed using gas chromatography-mass spectrometry (GC-MS) and quantitative real-time PCR. The results showed that levels of propionic acid and butyric acid in the intestines of group P were significantly higher than in group C (p < 0.05). In addition, probiotics increased the number and area ratio of type I muscle fibers. They also increased the mRNA expression of MyHC IIA and the activity of malate dehydrogenase (MDH) and succinate dehydrogenase (SDH). Propionic acid was negatively correlated with the number ratio of type IIB muscle fibers. Butyric acid was found to be significantly positively correlated with the number ratio of type IIA muscle fibers. Cooking loss, pH24h, and shear force decreased significantly in group P. In conclusion, intestinal metabolites (SCFAs) altered the activity of oxidative-myofibril-metabolizing enzymes and the expression of myosin heavy-chain type IIA, reduced the meat shear values, and improved meat tenderness. This study provides a new basis for improving the production and meat quality of small ruminants. [ABSTRACT FROM AUTHOR]

Since COVID-19 pneumonia broke out, the Chinese government has taken a series of measures to control the spread of the epidemic, which has made the air quality of Taiyuan in February 2020 significantly better than during the same period in previous years. In this paper, the Gray Relational Analysis (GRA) method was first applied to evaluate and analyze the influence of six major pollutants on air quality. Then, the improved seagull optimization algorithm (ISOA) was proposed and combined with Support Vector Regression (SVR) to establish a hybrid predicted model ISOA-SVR. Finally, the proposed ISOA-SVR was utilized to predict air quality index (AQI). The experimental results on two kinds of different data showed that the proposed ISOA-SVR had the better generalization ability and robustness compared with other predicted models. Further, the proposed ISOA-SVR is suitable for the prediction of AQI. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor's diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection. [ABSTRACT FROM AUTHOR]

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal. [ABSTRACT FROM AUTHOR]

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs. [ABSTRACT FROM AUTHOR]