Physicochemical Characterization and Simulation of the Solid–Liquid Equilibrium Phase Diagram of Terpene-Based Eutectic Solvent Systems

The characterization of terpene-based eutectic solvent systems is performed to describe their solid–liquid phase transitions. Physical properties are measured experimentally and compared to computed correlations for deep eutectic solvents (DES) and the percentage relative error e_r for the density, surface tension, and refractive index is obtained. The thermodynamic parameters, including the degradation, glass transition and crystallization temperatures, are measured using DSC and TGA. Based on these data, the solid–liquid equilibrium phase diagrams are calculated for the ideal case and predictions are made using the semi-predictive UNIFAC and the predictive COSMO RS models, the latter with two different parametrization levels. For each system, the ideal, experimental, and predicted eutectic points are obtained. The deviation from ideality is observed experimentally and using the thermodynamic models for Thymol:Borneol and Thymol:Camphor. In contrast, a negative deviation is observed only experimentally for Menthol:Borneol and Menthol:Camphor. Moreover, the chemical interactions are analyzed using FTIR and ¹H-NMR to study the intermolecular hydrogen bonding in the systems.     

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.     

Simple cuprous iodide complex‐based crystals with deep blue emission and high photoluminescence quantum yield up to 100%

Luminescent cuprous complexes have attracted much attention due to their low cost, rich photophysical properties, and hence extensive applications in various fields. In this work, we report the synthesis, structure and photophysical properties of a simple and highly efficient deep blue emission cuprous iodide complex, namely CuI (PPh₃)₂(t‐BuPy), where PPh₃ and t‐BuPy stand for triphenylphosphine and 4‐tert‐butylpyridine, respectively. The complex was synthesized with a one‐pot method, and showed a super high photoluminescence quantum yield up to 100% and a maximum emission wavelength at 454 nm in crystals at room temperature. Based on density functional theory calculation, the emission likely comes from iodide to 4‐tert‐butylpyridine charge transfer and some copper to 4‐tert‐butylpyridine charge transfer excited states. Two reference complexes, CuI (PPh₃)₂(IQu) (IQu = isoquinoline) and Cu₂I₂(PPh₃)₂(t‐BuPy)₂, were also synthesized, and the photophysical properties of the three compounds in various forms such as crystalline powder, thin film and solution at both room temperature and 77 K were studied for comparison. These results give clues on how the N‐heteroaromatic ligand (4‐tert‐butylpyridine vs. isoquinoline), coordinating style (mononuclear vs. binuclear), sample form (crystalline powder vs. thin film vs. solution) and temperature (room temperature vs. 77 K) affect the photophysical properties of luminescent cuprous iodide complex.     

Handwritten digit recognition based on ghost imaging with deep learning

We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging(GI) with deep neural network, where a few detection signals from the bucket detector, generated by the cosine transform speckle, are used as the characteristic information and the input of the designed deep neural network(DNN), and the output of the DNN is the classification. The results show that the proposed scheme has a higher recognition accuracy(as high as98% for the simulations, and 91% for the experiments) with a smaller sampling ratio(say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has slightly better performance with a lower complexity and non-locality property.The proposed scheme provides a promising way for remote sensing.     

Rolling resistance and sinkage analysis by comparing FEM and experimental data for a grape transporting vehicle

In this study a 2D FEM model was developed to analyze ruts formation, rolling resistance, and power loss for a grape transporting cart aimed to replace the use of heavy tractors while harvesting grape. The model was supported by experiments in a vineyard in South Italy. Cone penetration tests were conducted to estimate frictional and cohesive properties in three soil conditions: firm, soft, and wet saturated. A tractor pulled test rig for a single wheel was developed to measure rolling resistance and sinkage, and complete the selection of the soil parameters. Completed the model, the analysis was conducted for a range of different wheel dimensions, and the outputs analyzed through response surfaces. The results showed the different impacts that width and diameter have on ruts formation and rolling resistance for different soil conditions. Wider wheels determined a main reduction of the sinkage, while the width contribution to the rolling resistance was affected by the total soil volume deformed. Larger diameters led to lower rolling resistance, with a higher impact on more deformable soils. Contact stress was compared with the thresholds recommended in the literature to determine the acceptable designs. This analysis represents a tool to select the running gear dimensions.     

以乙醇为主线的“化学方程式”单元复习课教学

分析了九年级化学“化学方程式”的有关教研类文献,提出在单元复习阶段要基于学科思想系统整合教学内容,以乙醇为教学主线,在知识之间进行整体性联通,引导学生深度思考,提高学科知识的整合能力,帮助学生建构学科观念,呈现了可借鉴的教学设计。     

Information Bottleneck Classification in Extremely Distributed Systems

We present a new decentralized classification system based on a distributed architecture. This system consists of distributed nodes, each possessing their own datasets and computing modules, along with a centralized server, which provides probes to classification and aggregates the responses of nodes for a final decision. Each node, with access to its own training dataset of a given class, is trained based on an auto-encoder system consisting of a fixed data-independent encoder, a pre-trained quantizer and a class-dependent decoder. Hence, these auto-encoders are highly dependent on the class probability distribution for which the reconstruction distortion is minimized. Alternatively, when an encoding–quantizing–decoding node observes data from different distributions, unseen at training, there is a mismatch, and such a decoding is not optimal, leading to a significant increase of the reconstruction distortion. The final classification is performed at the centralized classifier that votes for the class with the minimum reconstruction distortion. In addition to the system applicability for applications facing big-data communication problems and or requiring private classification, the above distributed scheme creates a theoretical bridge to the information bottleneck principle. The proposed system demonstrates a very promising performance on basic datasets such as MNIST and FasionMNIST.     

使用TensorRT进行深度学习推理

TensorRT是一个高性能的深度学习推理平台。它包括一个深度学习推理优化器和运行时为深度学习推理应用程序提供低延迟和高吞吐量。给出了一个使用TensorRT快速构建计算管道的例子,实现通过TensorRT执行智能视频分析的典型应用。该示例演示了使用片上解码器进行解码、使用片上标量进行视频缩放和GPU计算的4个并发视频流。为了演示的简单性，只有一个通道使用NVIDIA TensorRT执行对象标识，并在标识的对象周围生成包围框。该示例还使用视频转换器函数进行各种格式转换，使用EGLImage来演示缓冲区共享和图像显示。最后采用GPU卡V100对ResNet网络进行TensorRT加速性能的实际测试,结果表明TensorRT能够使吞吐量提升大约15倍。     

Synthesis and characterization of Pd(II)–vitamin B6 complex supported on magnetic nanoparticle as an efficient and recyclable catalyst system for C–N cross coupling of amides in deep eutectic solvents

Vitamin B₆–Pd(II) immobilized onto magnetic nanoparticles have been successfully prepared and applied for C–Xcross-coupling reactions with aryl halides in green deep eutectic solvents. The results prove that the Fe₃O₄@vitamin B₆–Pd(II) magnetic nanoparticles show high catalyst activity and good stability. It was also revealed that this complex can be recycled up to five times without any significant loss in catalytic activity.     

关注大概念的教学设计与实践——以“气体摩尔体积”为例

以高中化学中的“气体摩尔体积”概念教学为例,分析了基于大概念教学的必要性,建构了“解读基本概念教学价值,编写基本理解和基本问题,创设有层递性的学习活动”的大概念教学的模式,有利于更好地凸显学科概念本身的价值和意义,形成具有持久价值和迁移价值的关键性的方法、思维和能力。