Electrocatalytic activity of LaSr3Fe3O10 and LaSr3Fe3O10-GO towards oxygen reduction reaction in alkaline medium

Oxygen reduction reaction(ORR) plays a critical role in many energy conversion and storage processes.Therein, a comparative study of the electrocatalytic activity for ORR in 0.1 mol/L KOH solution was conducted using layered perovskite-like LaSr_3 Fe_3 O_(10) and LaSr3 Fe_3 O_(10)-graphene oxide(GO) composite as electrodes. Linear sweep voltammetry(LSV) results show that the LaSr3 Fe_3 O_(10)-GO hybrid exhibits higher current density, a more positive onset potential(-0.15 V vs. Hg/HgO) in comparison with LaSr_3 Fe_3 O_(10).The value of the overall transferred electrons for both catalysts implies a dominant two electron process for ORR. Both catalysts under alkalic conditions exhibit a two-step Tafel slope, suggesting a change in the reaction mechanism for ORR. The composite electrode exhibits a higher ORR current density, but inferior durability performances in relative to the LaSr_3 Fe_3 O_(10) electrode.     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.     

Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach

Mobile Networks and Applications - Energy trading mechanism for microgrids has an inherent two-layer architecture, in which the energy trading at the first layer is between a microgrid aggregator...     

水平方向上的大气透过率和热辐射计算模型研究

水平方向上的空气对流和复杂的地面状况会导致大气热辐射计算的不确定度增大，目标发射起始段的红外辐射特性测量有助于实现目标识别。因此，研究水平大气透过率和热辐射对目标的高精度红外辐射特性测量具有十分重要的意义。针对以上问题，首先借鉴垂直廓线理论建立了同层大气“水平温度廓线”模型，然后推导了水平方向计算大气透过率和热辐射的公式，并对其进行了实验验证。最后将MODTRAN软件计算和实验测量的大气热辐射结果进行了比较。实验结果表明，传统软件的热辐射计算精度为8.65% ，而本文方法的计算精度为4.91%。该实验说明，在水平方向上，这种算法是正确的。通过公式推导和实验验证说明了该方法的合理性，为水平方向大气透过率和热辐射的计算提供了一种新的思路。     

A Versatile Theranostic Nanoemulsion for Architecture‐Dependent Multimodal Imaging and Dually Augmented Photodynamic Therapy

To design a clinically translatable nanomedicine for photodynamic theranostics, the ingredients should be carefully considered. A high content of nanocarriers may cause extra toxicity in metabolism, and multiple theranostic agents would complicate the preparation process. These issues would be of less concern if the nanocarrier itself has most of the theranostic functions. In this work, a poly(ethylene glycol)‐boron dipyrromethene amphiphile (PEG‐F₅₄‐BODIPY) with 54 fluorine‐19 (¹⁹F) is synthesized and employed to emulsify perfluorohexane (PFH) into a theranostic nanoemulsion (PFH@PEG‐F₅₄‐BODIPY). The as‐prepared PFH@PEG‐F₅₄‐BODIPY can perform architecture‐dependent fluorescence/photoacoustic/¹⁹F magnetic resonance multimodal imaging, providing more information about the in vivo structure evolution of nanomedicine. Importantly, this nanoemulsion significantly enhances the therapeutic effect of BODIPY through both the high oxygen dissolving capability and less self‐quenching of BODIPY molecules. More interestingly, PFH@PEG‐F₅₄‐BODIPY shows high level of tumor accumulation and long tumor retention time, allowing a repeated light irradiation after a single‐dose intravenous injection. The “all‐in‐one” photodynamic theranostic nanoemulsion has simple composition, remarkable theranostic efficacy, and novel treatment pattern, and thus presents an intriguing avenue to developing clinically translatable theranostic agents.