Facile Hydrogen-Bonding Assisted Crystallization Modulation for Large-area High-quality CsPbI2 Br Films and Efficient Solar Cells.

The thermally stable inorganic cesium-based perovskites promise efficient and stable photovoltaics. Unfortunately, the strong ionic bonds lead to uncontrollable rapid crystallization, making it difficult in fabricating large-area black-phase film for photovoltaics. Herein, we developed a facile hydrogen-bonding assisted strategy for modulating the crystallization of CsPbI2 Br to achieve uniform large-area phase-pure films with much-reduced defects. The simple addition of methylamine acetate in precursors not only promotes the formation of intermediate phase via hydrogen bonding to circumvent the direct crystallization of CsPbI2 Br from ionic precursors but also widens the film processing window, thus enabling to fabricate large-area high-quality phase-pure CsPbI2 Br film under benign conditions. Combining with stable dopant-free poly(3-hexylthiophene), the CsPbI2 Br solar cells achieve the record-high efficiencies of 18.14 % and 16.46 % for 0.1 cm2 and 1 cm2 active area, respectively. The obtained high efficiency of 38.24 % under 1000 lux illumination suggests its potential in indoor photovoltaics for powering the Internet of Things, etc.

Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.

Background: Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method. Methods: A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function. Results: In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method. Conclusions: Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

GIU-GANs: Global Information Utilization for Generative Adversarial Networks.

Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, because convolutions are limited by spatial-agnostic and channel-specific, features extracted by conventional GANs based on convolution are constrained. Therefore, GANs cannot capture in-depth details per image. Moreover, straightforwardly stacking of convolutions causes too many parameters and layers in GANs, yielding a high overfitting risk. To overcome the abovementioned limitations, in this study, we propose a GANs called GIU-GANs (where Global Information Utilization: GIU). GIU-GANs leverages a new module called the GIU module, which integrates the squeeze-and-excitation module and involution to focus on global information via the channel attention mechanism, enhancing the generated image quality. Moreover, Batch Normalization (BN) inevitably ignores the representation differences among noise sampled by the generator and thus degrades the generated image quality. Thus, we introduce the representative BN to the GANs' architecture. The CIFAR-10 and CelebA datasets are employed to demonstrate the effectiveness of the proposed model. Numerous experiments indicate that the proposed model achieves state-of-the-art performance.

Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.

BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. METHODS: First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. RESULTS: By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. CONCLUSION: In summary, the proposed approach greatly outperforms other competitive methods.

Iridoid glycosides from Radix Scrophulariae attenuates focal cerebral ischemia­reperfusion injury via inhibiting endoplasmic reticulum stress­mediated neuronal apoptosis in rats.

Iridoid glycosides of Radix Scrophulariae (IGRS) are a group of the major bioactive components from Radix Scrophulariae with extensive pharmacological activities. The present study investigated the effects of IGRS on cerebral ischemia­reperfusion injury (CIRI) and explored its potential mechanisms of action. A CIRI model in rats was established by occlusion of the right middle cerebral artery for 90 min, followed by 24 h of reperfusion. Prior to surgery, 30, 60 or 120 mg/kg IGRS was administered to the rats once a day for 7 days. Then, the neurological scores, brain edema and volume of the cerebral infarction were measured. The apoptosis index was determined by terminal deoxynucleotidyl transferase mediated dUTP nick end labeling. The effects of IGRS on the histopathology of the cortex in brain tissues and the endoplasmic reticulum ultrastructure in the hippocampus were analyzed. Finally, the expression of endoplasmic reticulum stress (ERS)­regulating mediators, endoplasmic reticulum chaperone BiP (GRP78), DNA damage­inducible transcript 3 protein (CHOP) and caspase­12, were detected by reverse transcription quantitative polymerase chain reaction (RT­qPCR) and western blot analysis. The volume of cerebral infarction and brain water content in the IGRS­treated groups treated at doses of 60 and 120 mg/kg were decreased significantly compared with the Model group. The neurological scores were also significantly decreased in the IGRS­treated groups. IGRS treatment effectively decreased neuronal apoptosis resulting from CIRI­induced neuron injury. In addition, the histopathological damage and the endoplasmic reticulum ultrastructure injury were partially improved in CIRI rats following IGRS treatment. RT­qPCR and western blot analysis data indicated that IGRS significantly decreased the expression levels of GRP78, CHOP and caspase­12 at both mRNA and protein levels. The results of the present study demonstrated that IGRS exerted a protective effect against CIRI in brain tissue via the inhibition of apoptosis and ERS.

WITHDRAWN: Benchmarking swarm intelligence clustering algorithms with case study of medical data.

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Effects of in-vitro cultured calculus bovis on learning and memory impairments of hyperlipemia vascular dementia rats.

ETHNOPHARMACOLOGICAL RELEVANCE: In-vitro cultured calculus bovis (ICCB) is a quality substitute for natural bezoar which is used for the therapeutic purpose of treating encephalopathy. ICCB has been authorized to use on clinic. The aim of the study is to evaluate the effects and the potential mechanisms of in-vitro cultured calculus bovis (ICCB) on learning and memory impairments of hyperlipemia vascular dementia (HVD) rats. MATERIALS AND METHODS: The HVD model was established by permanent occlusion of bilateral common carotid arteries based on hyperlipemia rats. Learning and memory abilities were evaluated by morris water maze test and shuttle box test. Ultraviolet-visible spectrophotometry (UV-vis) was employed to determine the SOD, MDA and NO in cerebral tissue, as well as the TG in serum. HE staining and toluidine blue staining were employed to evaluate cone cells damage in hippocampus CA1. An immunohistochemistry was used to measure the Bax and Bcl-2 expressions in cerebral tissue. RESULTS: Compared with control group, the abilities of spatial learning and memory and conditional memory were decreased significantly in HVD group (P<0.01, P<0.05). MDA content in cerebral tissue was remarkably increased while the SOD activity and NO content were both decreased (P<0.01). TG content in serum was increased remarkably (P<0.01). And the cone cells in hippocampus CA1 were damaged obviously. Compared with HVD group, ICCB treatment improved the abilities of learning and memory, elevated the SOD activity (P<0.01, P<0.05), reduced the MDA content (P<0.01) as well as the TG content in serum (P<0.01), increased the NO content (P<0.01), improved the damaged cone cells in hippocampus CA1, increased the number of cones cells (P<0.01), decreased the Bax expression, and increased the Bcl-2 expression (P<0.01). CONCLUSION: ICCB could improve the abilities of learning and memory in HVD rats. It might be related to anti-oxidative, regulation of Bax and Bcl-2 expressions, and the alleviation of cone cells damage.