The blockchain is a ledger of accounts and transactions that are written and stored by all participants. It promises a reliable source of truth about the state of farms, inventories and contracts in agriculture, where the collection of such information is often incredibly costly. The blockchain technology can track the provenance of food and thus helps create trustworthy food supply chains and build trust between producers and consumers. As a trusted way of storing data, it facilitates the use of data-driven technologies to make farming smarter. In addition, jointly used with smart contracts, it allows timely payments between stakeholders that can be triggered by data changes appearing in the blockchain This article examines the applications of blockchain technology in food supply chains, agricultural insurance, smart farming, transactions of agricultural products for both theoretical and practical perspectives. We also discuss the challenges of recording transactions made by smallholder farmers and creating the ecosystem for utilizing the blockchain technology in the food and agriculture sector.

No abstract

Featured Application: The method presented in this study can be applied in many fields, such as mental health care, entertainment consumption behavior, society safety, and so on. For example, in the mental health care field, an automatic emotion analysis system can be constructed with our method to monitor the emotional variation of the subjects. With accurate and objective emotion analysis results from EEG signals, our method can provide useful treatment effect information to the medical staff.Abstract: The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.

We demonstrate the assembly of a mononuclear metal center, a hexanuclear cluster, and a V-shaped, trapezoidal tetracarboxylate linker into a microporous metal–organic framework featuring an unprecedented 3-nodal (4,4,8)-c lyu topology. The compound, HIAM-302, represents the first example that incorporates both a primary building unit and a hexanuclear secondary building unit in one structure, which should be attributed to the desymmetrized geometry of the organic linker. HIAM-302 possesses optimal pore dimensions and can separate monobranched and dibranched alkanes through selective molecular sieving, which is of significant value in the petrochemical industry.

Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. In this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patients. A tenfold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three-category classification. The result of EEG feature analysis indicates that the differences of spatial distributions and amplitude ranges in the alpha rhythm (especially at approximately 10 Hz) among three categories might be distinctive attributes for depression discrimination.