R-matrix Floquet theory for laser-assisted electron-atom scattering

A new version of the R-matrix Floquet theory for laser-assisted electron-atom scattering is presented. The theory is non-perturbative and applicable to a non-relativistic many-electron atom or ion in a homogeneous linearly polarized field. It is based on the use of channel functions built from field-dressed target states, which greatly simplifies the general formalism.Comment: 18 pages, LaTeX2e, submitted to J.Phys.

Colour changes during deep-frying of sesame cracker's dough balls

Sesame crackers (Kuih Bijan), a Malay popular traditional snack in Malaysia, is produced by deep frying sesame coated dough balls that are made from glutinous rice flour, sugar and water as the main ingredients. In this paper, the effect of different frying temperature (150, 170 and 190°C) and frying time (0, 1, 3, 5, and 7 min) on colour change of the uncoated sesame cracker's dough balls using electrical deep-fryer was investigated. The results showed that the mean values of redness (a*), yellowness (b*), chroma (C*), browning index (BI) and total colour change (ΔE) were found to increase significantly (P<0.05) with elevating frying temperature and frying time as shown in 170 and 190°C. However, the change of all these parameters is less significant at 150°C

Breast Cancer Detection Based on Simplified Deep Learning Technique With Histopathological Image Using BreaKHis Database

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety