Non-oxidative Decomposition of CH4 Over CeO2 and CeO2–SiO2 Supported Bimetallic Ni–Mo Catalysts

Catalysis Letters - The development of highly active and durable catalysts for H2 production through CH4 decomposition process is still a great challenge. In this study, CeO2 and CeO2–SiO2...     

Pairwise Alignment of Archaeological Fragments Through Morphological Characterization of Fracture Surfaces

International Journal of Computer Vision - We design a computational method to align pairs of counter-fitting fracture surfaces of digitized archaeological artefacts. The challenge is to achieve an...     

Effect of impeller on sinking and floating behavior of suspending particle materials in stirred tank: A computational fluid dynamics and factorial design study

The present study focuses upon the effect of the impeller on sinking and floating behavior of suspending particles in stirred tank reactor, employing computational fluid dynamics (CFD) simulation where factorial design is used to investigate the main and interaction effects of design parameters on the particle distribution performance of four typical impeller designs. Factorial design results show the effect of diameter and width of the impeller and off-bottom clearance on sinking particles is different from that of floating particles and regression equations for sinking particles and floating particles are achieved separately. Meanwhile, optimal equations which quantitatively reveal the effect of impeller factors on suspension quality and energy input is established for impeller improvement. Besides the development of computational models, the combination of CFD simulation with factorial design method provides a useful approach to gain insight into the suspension behavior of sinking and floating particles, also it guides to optimize the impeller design.     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.     

A hyperspectral GA-PLSR model for prediction of pine wilt disease

Pine wilt disease caused by a forest-invasive alien species, the pine wood nematode (Bursaphelenchus xylophilus) is considered as one of the most destructive pest problems. In recent years, spectroscopic technologies have shown great potentials for the assessment of forest damage due to their nondestructive, noninvasive, cost-effective, and rapidly responsive nature. This paper first identified the hyperspectral characteristics of pine wilt disease by measuring and analyzing the changes in spectral reflectance of healthy and infected Pinus massoniana trees. Then 16 spectral features were extracted from the spectral bands covering the green region (510~580 nm), the red region (620~680 nm), the red edge (680~760 nm), the near-infrared region (780~1100 nm), and coded as genes composing the chromosome of a genetic algorithm (GA). Based on the optimal spectral features with suitable fitness from the GA, a partial least squares regression (PLSR) prediction model was built with highest determination coefficient R²_c?=?0.91, R²_v?=?0.82, relative prediction deviation RPD?=?3.3 and lowest root mean square error RMSE_c?=?0.23, RMSE_v?=?0.33 on the calibration and validation datasets. Compared with other PLSR models, our proposed GA-based approach significantly improves the prediction accuracy with few input spectral features.

     

Improving the security of quantum key agreement protocols with single photon in both polarization and spatial-mode degrees of freedom

Recently, Wang and Ma (Quantum Inf Process 16(5):130, 2017) proposed two interesting quantum key agreement protocols with a single photon in both polarization and spatial-mode degrees of freedom. They claimed that the privacy of participants’ secret keys in the multiparty case is protected against dishonest participants. However, in this paper, we prove that two dishonest participants can deduce the secret key of an honest one using a fake sequence of single photons, without being detected. Also, we propose an additional security detection process to avoid the security loophole in their protocol.