Bio-Inspired Design of Bipolar Plate Flow Fields for Polymer Electrolyte Membrane Fuel Cells

The flow field of a bipolar plate distributes hydrogen and oxygen for polymer electrolyte membrane (PEM) fuel cells and removes the produced water from the fuel cells. It greatly influences the performance of fuel cells, especially regarding reduction of mass transport loss. Flow fields with good gas distribution and water removal capabilities reduce the mass transport loss, thus allowing higher power density. Inspired by natural structures such as veins in tree leaves and blood vessels in lungs, which efficiently feed nutrition from one central source to large areas and are capable of removing undesirable by-products, a mathematic model has been developed to optimize the flow field with minimal pressure drop, lowest energy dissipation, and uniform gas distribution. The model can be used to perform optimal flow field designs, leading to better fuel cell performance for different sizes and shapes of bipolar plates. Finite element modeling (FEM) based simulations and in-situ experiments were conducted to verify some of the flow field designs obtained using the developed mathematic model

The Estimation of Nodal Power Supply Reliability through the Network Connectivity by Complex Network Method

The paper studies the reliability of the power system from the perspective of node loads. The reliability of the whole system can be estimated by evaluating the power supply reliability of each node. A measure, connectivity observed at load node (Ci), is proposed. Ci is calculated through a recursion equation by evaluating the generation capacity that can be transferred from the further neighbor to the nearest neighbor of load node i. IEEE-30 bus system is taken as a test system. We calculated the index of 7 load nodes at 2 different load levels with different N-1 failures. The test results show that the variation of the index and that of the percentage load shedding at selected load nodes show good consistency

Federated Learning with Imbalanced and Agglomerated Data Distribution for Medical Image Classification

Federated learning (FL), training deep models from decentralized data without privacy leakage, has drawn great attention recently. Two common issues in FL, namely data heterogeneity from the local perspective and class imbalance from the global perspective have limited FL's performance. These two coupling problems are under-explored, and existing few studies may not be sufficiently realistic to model data distributions in practical sceneries (e.g. medical sceneries). One common observation is that the overall class distribution across clients is imbalanced (e.g. common vs. rare diseases) and data tend to be agglomerated to those more advanced clients (i.e., the data agglomeration effect), which cannot be modeled by existing settings. Inspired by real medical imaging datasets, we identify and formulate a new and more realistic data distribution denoted as L2 distribution where global class distribution is highly imbalanced and data distributions across clients are imbalanced but forming a certain degree of data agglomeration. To pursue effective FL under this distribution, we propose a novel privacy-preserving framework named FedIIC that calibrates deep models to alleviate bias caused by imbalanced training. To calibrate the feature extractor part, intra-client contrastive learning with a modified similarity measure and inter-client contrastive learning guided by shared global prototypes are introduced to produce a uniform embedding distribution of all classes across clients. To calibrate the classification heads, a softmax cross entropy loss with difficulty-aware logit adjustment is constructed to ensure balanced decision boundaries of all classes. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both the proposed realistic modeling and the existing modeling of the two coupling problems

PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow

This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single lightweight framework. We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one, thus ensuring privacy. The pre-obfuscation applied can be in diversified form with different strengths and styles specified by users. Along protection, a secret key is injected into the network such that the original image can only be recovered from the protection image via the same model given the correct key provided. Two modes of image recovery are devised to deal with malicious recovery attempts in different scenarios. Finally, extensive experiments conducted on three public image datasets demonstrate the superiority of the proposed framework over multiple state-of-the-art approaches

Liuzijue training improves hypertension and modulates gut microbiota profile

BackgroundLiuzijue training (LZJ) is a traditional exercise integrating breathing meditation and physical exercise, which could prevent and improve hypertension symptoms.PurposeWe aimed to evaluate the therapeutic effect of LZJ on hypertensive patients from the perspectives of blood pressure (BP), vascular endothelial function, immune homeostasis, and gut microbiota.MethodsWe conducted a randomized, controlled, single-blind experiment to assess the effect of 12 weeks LZJ in hypertensive patients. We measured the blood pressure level, vascular endothelial function, serum inflammatory factor concentration, and fecal microbial composition of hypertension patients.ResultsCompared with aerobic training, LZJ has a more significant effect on serum inflammatory factors (IL-6 and IL-10) and gut microbiota. PCoA analysis showed that LZJ tended to transform the gut microbiota structure of hypertensive subjects into that of healthy people. This process involves significant changes in Bacteroides, Clostridium_sensu_stricto_1, Escherichia-Shigella, Haemophilus, Megamonas, and Parabacteroides. In particular, Bacteroides and Escherichia-Shigella, these bacteria were closely related to the improvement of BP in hypertensive patients.ConclusionIn conclusion, our results confirm that LZJ could be used as an adjuvant treatment for hypertensive patients, which could effectively reduce BP, improve the immune homeostasis and gut microbiota structure in patients, and provide a theoretical reference for the use of LZJ in the clinic.Clinical trial registrationhttp://www.chictr.org.cn/listbycreater.aspx, identifier: ChiCTR2200066269

New Reassortant H5N6 Highly Pathogenic Avian Influenza Viruses in Southern China, 2014

New reassortant H5N6 highly pathogenic avian influenza viruses were isolated from apparently healthy domestic ducks in Southern China in 2014. Our results show that the viruses grew efficiently in eggs and replicated systemically in chickens. They were completely lethal in chicken (100% mortality), and the mean death time (MDT) was 6 to 7 days post-inoculation (DPI). The viruses could transmit in chickens by naïve contact. BLAST analysis revealed that their HA gene was most closely related to A/wild duck/Shangdong/628/2011 (H5N1), and their NA genes were most closely related to A/swine/Guangdong/K6/2010 (H6N6). The other genes had the highest identity with A/wild duck/Fujian/1/2011(H5N1). The results of phylogenetic analysis showed that their HA genes clustered into clade 2.3.4.4 of the H5N1 viruses and all genes derived from H5 were Mix-like or H6-like viruses. Thus, the new H5N6 viruses were reassortanted of H5N1 and H6N6 virus. Therefore, the circulation of the new H5N6 avian influenza viruses may become a threat to poultry and human health

Versatile Ratiometric Fluorescent Probe Based on the Two-Isophorone Fluorophore for Sensing Nitroxyl

Nitroxyl (HNO) is closely linked with numerous biological processes. Fluorescent probes provide a visual tool for determining HNO. Due to fluorescence quenching by HNO-responsive recognition groups, most of the current fluorescent probes exhibit an "off-on"fluorescence response. As such, the single fluorescence signal of these probes is easily affected by external factors such as the microenvironment, sensor concentration, and photobleaching. Herein, we have developed a ratiometric fluorescent probe (CHT-P) based on our previously developed two-isophorone fluorophore. CHT-P could be used to determine HNO through ratiometric signal readouts with high selectivity and sensitivity, ensuring the accurate quantitative detection of HNO. Additionally, the probe exhibited low cytotoxicity, was cell permeable, and could be used for ratiometric imaging of HNO in cells. Finally, CHT-P-coated portable test strips were used to determine HNO using the solid-state fluorescence signal readout. </p