Hierarchically porous carbon derived from the activation of waste chestnut shells by potassium bicarbonate (KHCO3) for high-performance supercapacitor electrode

3D hierarchical porous carbon consisting of micropores, mesopores and macropores was successfully prepared through the activation of chestnut shell via KHCO₃. The influence of KHCO₃/chestnut shell ratio on the textural properties was carefully investigated and the structure, micrographs, and surface chemical status of the materials were expressed via X-Ray diffraction (XRD), transmission electron micrographs (TEM), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), respectively. With the optimized amount of KHCO₃, high specific pore surface area (2298 m² g^-1) and high total pore volume (1.51 cm³ g^-1) were achieved, endowing an advantageous electrochemical characteristics for the electrode as ultracapacitors in three-electrode system. At 2 A g^-1, a high specific electric capacity of 387 F g^-1 was reached. The remarkable electrochemical performances are mainly due to hierarchical porous structure with the high specific surface area (SSA) and the eminent total pore volume. It means that this hierarchical porous carbon prepared by activated by using KHCO₃ would have more promising foreground in the field of energy storage.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.     

Progress in the design of zeolite catalysts for biomass conversion into biofuels and bio-based chemicals

This article reviews the recent advances in the development of zeolite catalysts for biomass valorization processes to produce both biofuels and/or bio-based chemicals, which is an emerging and fast expanding field. The work deals with different types of feedstocks, including vegetable oils, lignocellulose and sugars, as well as with a number of relevant intermediates and platform molecules. Transformation of biomass into valuable products is hindered by a number of factors, mainly related to its complex composition, as biomass typically consists of bulky molecules with high oxygen content. Accordingly, biomass processing usually requires the combination of multiple steps and severe conditions, hence concepts like atom efficiency, product selectivity, and catalyst deactivation become of special relevance. A great progress has been achieved in the past years engineering the properties of zeolites for being adapted to the challenges associated to biomass valorization. The possibility of tailoring the main physicochemical properties of zeolites has become now a reality, being the major reason that explains the success achieved by this class of materials in a growing variety of biomass conversion pathways, as those described in this work: catalytic cracking and pyrolysis, hydrotreatments, with special relevance for hydrodeoxygenation processes, as well as in a high number of condensation, isomerization, and dehydration reactions. Thus, the development of hierarchical zeolites, exhibiting enhanced accessibility, and the possibility of introducing and combining in a controlled way different types of active sites (Brønsted and Lewis acid centers, basic sites, and metal phases) are the main basis of the excellent performance of zeolites in numerous biomass conversion routes.     

Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example, the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding. Furthermore, the experimental results based on the real data collected from China Mobile show that the key-attributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.     

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer-aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub-Rhombus Binary Relation Pattern (LS-RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS-RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS-RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI.     

Hierarchical self-assembled hollow hydroxyapatite flower microspheres containing terpene functional groups for efficient drug loading and pH-responsive drug release

In this paper, we fabricated hierarchical self-assembled hollow rose-like flower microspheres (HRFM) and hollow burr-like flower microspheres (HBFM) hydroxyapatite (HAP) using dehydroabietyl phosphate tri-ester (DDPT) as an organic phosphorus source, a regulating agent, and a soft template simultaneously via a one-step solvothermal method. The HBFM and HRFM have been explored for their application in drug delivery, using doxorubicin (DOX) as a drug model. The formation mechanisms of HRFM and HBFM were proposed on the basis of the electrostatic potential diagrams and self-assembled behavior of DDPT organic molecule. After the rosin-based terpene functional groups were incorporated, both HRFM and HBFM exhibited low cytotoxicity against Hela cell, pH-dependent sustained drug release properties, and high drug loading capacity. The drug-loading capacities of HBFM and HRFM were 116.6?mg?g^?1 and 148.3?mg?g^?1, respectively. Thus, the as-prepared HRFM and HBFM are promising for the applications in drug delivery.     

涡流空气分级机叶片间流场的数值模拟研究

根据等速旋转坐标系中的动量方程和连续方程,运用PHOENICS程序对转子叶片间流场进行数值模拟,通过数值模拟的结果分析了转速、风量及转子结构对分级性能的影响。