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With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients ( R 2 ) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future. [ABSTRACT FROM AUTHOR]

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy. [ABSTRACT FROM AUTHOR]

Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897–0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the "severity psychosis" hypothesis. [ABSTRACT FROM AUTHOR]

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793–0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615–0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context. [ABSTRACT FROM AUTHOR]

Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample size problem. GAN can generate samples by the competition between a generator and a discriminator. However, it is difficult to generate high-quality samples for HSIs with complex spatial–spectral distribution, which may further degrade the performance of the discriminator. To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed. In CA-GAN, the generator and the discriminator not only compete but also collaborate. The shallow to deep features of real multiclass samples in the discriminator assist the sample generation in the generator. In the generator, a joint spatial–spectral hard attention module is devised by defining a dynamic activation function based on a multi-branch convolutional network. It impels the distribution of generated samples to approximate the distribution of real HSIs both in spectral and spatial dimensions, and it discards misleading and confounding information. In the discriminator, a convolutional LSTM layer is merged to extract spatial contextual features and capture long-term spectral dependencies simultaneously. Finally, the classification performance of the discriminator is improved by enforcing competitive and collaborative learning between the discriminator and generator. Experiments on HSI datasets show that CA-GAN obtains satisfactory classification results compared with advanced methods, especially when the number of training samples is limited. [ABSTRACT FROM AUTHOR]