A Multimodal Dataset for Mixed Emotion Recognition.

Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus on mixed emotion recognition from multimodal signals, hindering the affective computing of mixed emotions. On this basis, we present a multimodal dataset with four kinds of signals recorded while watching mixed and non-mixed emotion videos. To ensure effective emotion induction, we first implemented a rule-based video filtering step to select the videos that could elicit stronger positive, negative, and mixed emotions. Then, an experiment with 80 participants was conducted, in which the data of EEG, GSR, PPG, and frontal face videos were recorded while they watched the selected video clips. We also recorded the subjective emotional rating on PANAS, VAD, and amusement-disgust dimensions. In total, the dataset consists of multimodal signal data and self-assessment data from 73 participants. We also present technical validations for emotion induction and mixed emotion classification from physiological signals and face videos. The average accuracy of the 3-class classification (i.e., positive, negative, and mixed) can reach 80.96% when using SVM and features from all modalities, which indicates the possibility of identifying mixed emotional states.

Confined-Coordination Induced Intergrowth of Metal-Organic Frameworks into Precise Molecular Sieving Membranes.

Metal-organic framework (MOF) membranes with rich functionality and tunable pore system are promising for precise molecular separation; however, it remains a challenge to develop defect-free high-connectivity MOF membrane with high water stability owing to uncontrollable nucleation and growth rate during fabrication process. Herein, we report on a confined-coordination induced intergrowth strategy to fabricate lattice-defect-free Zr-MOF membrane towards precise molecular separation. The confined-coordination space properties (size and shape) and environment (water or DMF) were regulated to slow down the coordination reaction rate via controlling the counter-diffusion of MOF precursors (metal cluster and ligand), thereby inter-growing MOF crystals into integrated membrane. The resulting Zr-MOF membrane with angstrom-sized lattice apertures exhibits excellent separation performance both for gas separation and water desalination process. It was achieved H2 permeance of ~1200 GPU and H2/CO2 selectivity of ~67; water permeance of ~8 L ⋅ m-2 ⋅ h-1 ⋅ bar-1 and MgCl2 rejection of ~95 %, which are one to two orders of magnitude higher than those of state-of-the-art membranes. The molecular transport mechanism related to size-sieving effect and transition energy barrier differential of molecules and ions was revealed by density functional theory calculations. Our work provides a facile approach and fundamental insights towards developing precise molecular sieving membranes.

Zr-MOF membranes with ultra-fast water-selective permeation towards intensification of esterification reaction.

Well-intergrown polycrystalline UiO-66 membranes were successfully synthesized on a polymeric substrate under mild synthesis conditions of a lower temperature and short synthesis time. The resulted UiO-66 membranes with fast water selective transport channels exhibited unprecedentedly high solvent dehydration performance with a permeation flux of ∼6100 g m-2 h-1 and a separation factor of ∼7500, showing great potential for intensification of esterification reaction.

Kapur's entropy for multilevel thresholding image segmentation based on moth-flame optimization.

Multilevel thresholding is a reliable and efficacious method for image segmentation that has recently received widespread recognition. However, the computational complexity of the multilevel thresholding method increases as the threshold level increases, which causes the low segmentation accuracy of this method. To overcome this shortcoming, this paper presents a moth-flame optimization (MFO) established on Kapur's entropy to clarify the multilevel thresholding image segmentation. The MFO adjusts exploration and exploitation to achieve the best fitness value. To validate the overall performance, MFO is compared with other algorithms to realize the global optimal solution to maximize the target value of Kapur's entropy. Some critical evaluation indicators are used to determine the segmentation effect and optimization performance of each algorithm. The experimental results indicate that MFO has a faster convergence speed, higher calculation accuracy, better segmentation effect and better stability.