Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio

Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly

Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture

Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy

Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy

Importance of Solvent Removal Rate on the Morphology and Device Performance of Organic Photovoltaics with Solvent Annealing

Solvent vapor annealing has been widely used in organic photovoltaics (OPV) to tune the morphology of bulk heterojunction active layer for the improvement of device performance. Unfortunately, the effect of solvent removal rate (SRR) after solvent annealing, which is one of the key factors that impact resultant morphology, on the morphology and device performance of OPV has never been reported. In this work, the nanoscale morphology of small molecule (SM):fullerene bulk heterojunction (BHJ) solar cell from different SRRs after solvent annealing was examined by small-angle neutron scattering and grazing incidence X-ray scattering. The results clearly demonstrate that the nanoscale morphology of SM:fullerene BHJ especially fullerene phase separation and concentration of fullerene in noncrystalline SM was significantly impacted by the SRR. The enhanced fullerene phase separation was found with a decrease of SRR, while the crystallinity and molecular packing of SM remained unchanged. Correlation to device performance shows that the balance between pure fullerene phase and mixing phase of SM and fullerene is crucial for the optimization of morphology and enhancement of device performance. Moreover, the specific interfacial area between pure fullerene phase and mixing phase is crucial for the electron transport and thus device performance. More importantly, this finding would provide a more careful and precise control of morphology of SM:fullerene BHJ and offers a guideline for further improvement of device performance with solvent annealing