Effects of inorganic mulching on soil hydrothermal environment and tomato characters in the presence of unheated greenhouse cultivation.

The present study was conducted by cultivating tomato (Solanum lycopersicum 'Provence') using varied inorganic mulching to investigate soil hydrothermal environment and tomato characters under unheated greenhouse cultivation in the cold zone of China. A total of 6 different treatments were adopted: no mulching (control), white film mulching (white film), black film mulching (black film), the white film with hole mulching (white hole), the black film with hole mulching (black hole), and snake skin bag mulching (snake skin). Inorganic mulching treatment significantly improved soil temperature and moisture, water use efficiency, tomato yield, and reduced soil water consumption. There was no significant difference observed in the variation of daily mean soil temperature between different mulching treatments, and the variation was in the range of 1.95-2.20 °C, which was significantly lower compared with the control (3.42 °C). The daily mean soil moisture varied significantly after different mulching treatments, with the highest level achieved by snake skin (23.37%), followed by black hole (22.55%), white hole (22.08%), white film (21.48%), black film (20.12%), and control (18.78%) in descending order. According to the research results, plastic-hole mulching, which include white hole and black hole treatments, performed better in maintaining soil temperature and moisture.

Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China.

Analysis of the probability of extreme precipitation events leading to rainstorm and flood disasters can aid in disaster prevention policy development. Using daily precipitation data from 16 meteorological stations from 1960 to 2019, we calculated eight extreme precipitation indices to analyze the spatio-temporal characteristics of extreme precipitation in the Fen River Basin (FRB) through ensemble empirical mode decomposition and Kriging interpolation. Extreme precipitation events and disasters were defined based on a combination of the antecedent precipitation index (API) and extreme precipitation on the event day and classified; extreme precipitation and the API were ranked from small to large and classified into dry, wet, and moderate (mod) precipitation periods, respectively, yielding nine extreme precipitation event categories. The probability of disasters caused by different types of extreme precipitation events was calculated using a binomial distribution. The results are as follows: (1) between 1960 and 2019, except for extreme precipitation period length, which continuously increased, the extreme precipitation indices changed from a downward to an upward trend since the 1980s. All extreme precipitation indices showed similar interannual variation over short periods and different interdecadal variation over long periods. (2) The extreme precipitation indices showed latitudinal and zonal spatial divergence patterns, but different spatial characteristics were observed around the 1980s. (3) More than 70% of extreme precipitation events in the midstream and downstream fell into four categories: "dry-dry," "dry-mod," "mod-dry," and "mod-mod." (4) A single category VII (VIII) extreme precipitation event in the midstream (downstream) had a maximum probability of causing disaster of 14%. When more than four extreme precipitation events occurred in a year, the probability of one disaster was the highest and that of four or more disasters was < 0.1%. The probability of rainstorm and flood disasters increased gradually with increasing frequency of annual extreme precipitation events.

Effects of organic mulching on moisture and temperature of soil in greenhouse production of tomato under unheated greenhouse cultivation in the cold zone of China.

In the cold zone of China, winter is cold and long and has a short duration of sunshine. Unheated earthen-wall solar greenhouses are used for tomato production in winter in this region. This was an experimental investigation of different organic mulching materials (newspaper, bran, and grass) on the soil temperature, soil moisture, tomato yield, fruit quality, and water use efficiency. Organic mulching variously improved soil temperature, soil moisture, water use efficiency, and tomato yield, which is very important for greenhouse winter cultivation in this cold zone. Organic mulching regulated the soil temperature, with daily soil temperature ranges of bran, newspaper, and grass treatments being 1.6, 1.9, and 2.1°C lower than for bare land, respectively. Compared to bare land, newspaper mulching had little effect on soil temperature and fruit quality, but increased soil moisture (14.1%) and water use efficiency (WUE: WUEb, 31.3%; WUEy, 30.6%), and greatly increased yield (81.8%) and biomass (82.7%); bran mulching greatly increased soil temperature, moisture (16%), and WUE (WUEb, 60.1%; WUEy, 44.3%) and increased biomass (30.2%) and yield (17.3%); grass mulching greatly increased soil temperature and moisture (20.9%) and increased biomass (17.9%), yield (11.2%), and WUE (WUEb, 20.5%; WUEy, 13.6%). In addition, organic mulching had a good water retention effect on soil layer above 30 cm. The total soil water consumption during tomato growth was in the following order: newspaper (103 mm) > bare (74 mm) > grass (73 mm) > bran (60 mm). Soil water consumption mainly occurred in the 0- to 10-cm soil layers.

Vigilance estimation using truncated l1 distance kernel-based sparse representation regression with physiological signals.

BACKGROUND: With a large number of accidents caused by the decline in the vigilance of operators, finding effective automatic vigilance monitoring methods is a work of great significance in recent years. Based on physiological signals and machine learning algorithms, researchers have opened up a path for objective vigilance estimation. METHODS: Sparse representation (SR)-based recognition algorithms with excellent performance and simple models are very promising approaches in this field. This paper aims to study the adaptability and performance improvement of truncated l1 distance (TL1) kernel on SR-based algorithm in the context of physiological signal vigilance estimation. Compared with the traditional radial basis function (RBF), the TL1 kernel has good adaptiveness to nonlinearity and is suitable for the discrimination of complex physiological signals. A recognition framework based on TL1 and SR theory is proposed. Firstly, the inseparable physiological features are mapped to the reproducing kernel Krein space through the infinite-dimensional projection of the TL1 kernel. Then the obtained kernel matrix is converted into the symmetric positive definite matrix according to the eigenspectrum approaches. Finally, the final prediction result is obtained through the sparse representation regression process. RESULTS: We verified the performance of the proposed framework on the popular SEED-VIG dataset containing physiological signals (electroencephalogram and electrooculogram) associated with vigilance. In the experimental results, the TL1 kernel is superior to the RBF kernel in both performance and kernel parameter stability. CONCLUSIONS: This demonstrates the effectiveness of the TL1 kernel in distinguishing physiological signals and the excellent vigilance estimation capability of the proposed framework. Moreover, the contribution of our research motivates the development of physiological signal recognition based on kernel methods.

Analysis of Altered Brain Dynamics During Episodic Recall and Detection of Generalized Anxiety Disorder.

Numerous blood oxygenation level-dependent (BOLD) imaging studies have shown that generalized anxiety disorder (GAD) can lead to abnormal activation of specific brain regions in patients. However, these methods lack sufficient temporal resolution to explain the underlying brain dynamics of GAD. The electroencephalogram (EEG) microstate allows us to explore brain dynamics at the subsecond level. We performed microstate analysis and source localization on the EEG data of 15 GADs and 14 healthy controls (HCs). We found two kinds of noncanonical microstate topologies (MS-4 and MS-5) in the episodic recall tasks. Compared with HCs, the duration and coverage of MS-5 were significantly reduced in GADs and positively correlated with the GAD-7 scores. The results of source localization showed obvious activation in the prefrontal lobe, parietal lobe, temporal lobe, and fusiform gyri. Moreover, we propose an improved capsule network to capture EEG spatial features and combine them with temporal parameters of microstates for more reliable GAD detection. The sensor-level EEG data and the source-level EEG data obtained by source reconstruction are used as input to the model. The optimal configuration combined the spatial features of source-level data with microstate features and achieved the highest classification accuracy. Collectively, the statistical results indicated remarkable differences in dynamic brain parameters between the two groups, and patients with GAD may have abnormalities in their higher sensory cortex that affect the processing of anxiety signals. Furthermore, our proposed fusion framework provides a reliable method for GAD automatic detection.

fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls.

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuous-wave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC) and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (CBV, COE, |L | and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.

Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex.

As a promising non-invasive technique, functional near-infrared spectroscopy (fNIRS) can easily detect the hemodynamic responses of cortical brain activities. This paper investigated the multiclass classification of motor imagery (MI) based on fNIRS; ten healthy individuals were recruited to move an object using their imagination. A multi-channel continuous-wave fNIRS equipment was applied to obtain the signals from the prefrontal cortex. A combination of ensemble empirical mode decomposition and independent component analysis method was used to solve the signal-noise frequency spectrum aliasing issues caused by the Mayer wave (0.1 Hz), then the signal means features were extracted as an input of linear discriminant analysis and support vector machine (SVM) classifier. The SVM classifier shows better classification results, and the average accuracies of four directions, up-down and left-right were 40.55%, 73.05%, and 70.7%, respectively, using oxygenated hemoglobin (8-21 s). This paper demonstrated that Brodmann area 4 was activated, which is consistent with previous conclusions. Furthermore, we found that the orbitofrontal cortex is also involved in MI and O2sat can also serve as a classified index.