Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

A Natural Wind Defrosting, Nano-coated Antibacterial Self-cleaning Energy-saving Health Air-cooled Refrigerator

The air-cooled frost-free household refrigerator is popular in the market because of its large size and frost-free size. However, the evaporator defrost process consumes a large amount of electrical energy to limit the wide spread of this refrigerator, at the same time because of its structural problems, resulting in its evaporator, air duct can not be artificially cleaned, leading to the growth of bacteria, pollution of food storage. This research has developed a self-cleaning energy-saving health refrigerator that uses indoor natural wind defrosting, ultra-hydrophilic nano-titanium dioxide coating photocatalytic sterilization and sterilization. After experimental comparison, under the same operating time of the same operating conditions, the refrigeration mode saves 1.5%, the defrost process saves 95%, reduces the amount of frosting by 23%, the temperature changes of the freezer is less than 7 ℃ , and the desterilization rate of nano-coated reaches 80%

Carrier Dynamics in Submonolayer InGaAs/GaAs Quantum Dots

Carrier dynamics of submonolayer (SML) InGaAs/GaAs quantum dots (QDs) were studied by micro-photoluminecence (MPL), selectively excited photoluminescence (SEPL), and time-resolved photoluminescence (TRPL). MPL and SEPL show the coexistence of localized and delocalized states, and different local phonon modes. TRPL reveal shorter recombination lifetimes and longer capture times for the QDs with higher emission energy. This suggests that the smallest SML QDs are formed by perfectly vertically correlated 2D InAs islands, having the highest In content and the lowest emission energy, while a slight deviation from the perfectly vertical correlation produces larger QDs with lower In content and higher emission energy.Comment: 12 pages, 5 figure

A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation

Electrolysis of metal oxides in MgCl2 based molten salts with an inert graphite anode

Eletrolysis of solid metal oxides has been demonstrated in MgCl2-NaCl-KCl melt at 700 oC taking the electrolysis of Ta2O5 as an example. Both the cathodic and anodic processes have been investigated using cyclic voltammetry, potentiostatic and constant voltage electrolysis, with the cathodic products analysed by XRD, SEM and the anodic products by GC. Fast electrolysis of Ta2O5 against a graphite anode has been realized at a cell voltage of 2 V , or a total overpotential of about 400 mV. The energy consumption was about 1 kWh/kg-Ta with a nearly 100% Ta recovery. The cathodic product was nanometer Ta powder with sizes of about 50 nm. The main anodic product was Cl2 gas, together with about 1 mol% O2 gas and trace of CO. The graphite anode was found to be an excellent inert anode. These results promise an environment-friendly and energy efficient method for metal extraction by electrolysis of metal oxides in MgCl2 based molten salts

SAM-Deblur: Let Segment Anything Boost Image Deblurring

Image deblurring is a critical task in the field of image restoration, aiming to eliminate blurring artifacts. However, the challenge of addressing non-uniform blurring leads to an ill-posed problem, which limits the generalization performance of existing deblurring models. To solve the problem, we propose a framework SAM-Deblur, integrating prior knowledge from the Segment Anything Model (SAM) into the deblurring task for the first time. In particular, SAM-Deblur is divided into three stages. First, We preprocess the blurred images, obtain image masks via SAM, and propose a mask dropout method for training to enhance model robustness. Then, to fully leverage the structural priors generated by SAM, we propose a Mask Average Pooling (MAP) unit specifically designed to average SAM-generated segmented areas, serving as a plug-and-play component which can be seamlessly integrated into existing deblurring networks. Finally, we feed the fused features generated by the MAP Unit into the deblurring model to obtain a sharp image. Experimental results on the RealBlurJ, ReloBlur, and REDS datasets reveal that incorporating our methods improves NAFNet's PSNR by 0.05, 0.96, and 7.03, respectively. Code will be available at \href{https://github.com/HPLQAQ/SAM-Deblur}{SAM-Deblur}.Comment: Under revie

The study on the impact of short video tourism Vloggers at social media platform on online sharing intention

COVID-19 has caused significant damage globally, including tourism. This study adopts the quantitative research method, selects 588 samples from tourists watching short videos to investigate the antecedents and effects of parasocial interaction between tourists and short video tourism Vloggers, and analyses them with partial least squares. Based on parasocial relationship theory, this study investigates the antecedents of parasocial relationships between tourists and short video tourism Vloggers and their willingness to share short video tourism. Results show that the consistency of values, entertainment motivation, and emotional engagement positively impact the parasocial relationships between tourists and short video tourism Vloggers and affect the online sharing intention through the parasocial relationship. The consistency of values can directly affect sharing intention. As an intermediary variable, parasocial relationship positively impacts value congruence, entertainment motivation, emotional engagement, and sharing intention. This study introduces parasocial relationship into the research of tourism short video Vloggers, which enriches the literature. Furthermore, this introduction provides new marketing strategies and suggestions for the sustainable development of tourism

Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce \underline{Rob}ust \underline{in}terpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11\% higher fidelity (evaluated on the classifier), 67.22\% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31\% less affected by out-of-distribution examples than that of LEMNA.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023