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Recently, huge concerns have been raised in diagnosing chest diseases, especially after the COVID-19 pandemic. Regular diagnosis processes of chest diseases sometimes fail to distinguish between Corona and Viral Pneumonia diseases through Polymerase Chain Reaction (PCR) tests which are a time-engrossing process that needs convoluted manual procedures. Artificial Intelligence (AI) techniques have achieved high performance in aiding medical diagnostic processes. The innovation of this work lies in using a new diagnostic technique to distinguish between COVID-19 and Viral Pneumonia diseases using advanced AI technologies. This is done by extracting novel features from chest X-ray images based on Wavelet analysis, Scale Invariant Feature Transformation (SIFT), and the Mel Frequency Cepstral Coefficient (MFCC). Support vector machines (SVM) and artificial neural networks (ANN) were utilized to build classification algorithms using 1200 chest X-ray mages for each case. Using Wavelet features, the results of evaluating the SVM and ANN models were 97% accurate, and with SIFT features, they were closer to 99%. The proposed models were very effective at identifying COVID-19 and Viral Pneumonitis, so physicians can determine the best treatment course for patients with the support of this high accuracy. Moreover, this model can be used in hospitals and emergency rooms when a massive number of patients are waiting, as it is faster and more accurate than the regular diagnosis processes as each step takes few seconds on average to complete. [ABSTRACT FROM AUTHOR]

Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application's adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%. [ABSTRACT FROM AUTHOR]

Retinal image segmentation process deals with problems like spurious vascularisation and thin vessel detection. In this paper, a three-step methodology has been proposed for retinal vessel segmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant Herding Optimization (CWEHO) has been proposed to optimize the clip limit and block size values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhance retinal images. In third step, thin and thick vessel segmentation is performed. Top hat transformation, otsu thresholding algorithm, and vessel point selection are applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the match filter is used to extract thin vessels. DRIVE and HRF datasets are used to assess the effectiveness of proposed methodology. The average values of segmentation accuracy, specificity, sensitivity, and Mathew Correlation Coefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650, respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2 using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116, respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology provides better results. [ABSTRACT FROM AUTHOR]

Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice $0.988 \pm 0.003$ 0.988 ± 0.003 , and Hausdorff Distance $4.86 \pm 1.67$ 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice $0.961 \pm 0.005$ 0.961 ± 0.005 and Hausdorff Distance $2.42 \pm 0.79$ 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at . [ABSTRACT FROM AUTHOR]

One of the common eye conditions affecting patients with diabetes is diabetic retinopathy (DR). It is characterised by the progressive impairment to the blood vessels with the increase of glucose level in the blood. The grading efficiency still finds challenging because of the existence of intra-class variations and imbalanced data distributions on the retinal images. Traditional machine learning techniques utilise hand-engineered features for classification of the affected retinal images. As convolutional neural network produces better image classification accuracy in many medical images, this work utilises the CNN-based feature extraction method. This feature has been used to build Gaussian mixture model (GMM) for each class that maps the CNN features to log-likelihood dimensional vector spaces. Since the Gaussian mixture model can be realised as a mixture of both parametric and nonparametric density models and has their flexibility in capturing different data distributions, probabilistic outputs, interpretability, efficient parameter estimation, and robustness to outliers, the proposed model aimed to obtain and provide a smooth approximation of the underlying distribution of features for training the model. Then these vector spaces are trained by the SVM classifier. Experimental results illustrate the efficacy of the proposed model with accuracy 86.3% and 89.1%, respectively. [ABSTRACT FROM AUTHOR]

Canine Hip Dysplasia (CHD) is a congenital disease with a polygenic hereditary component, characterised by abnormal development of the coxo-femoral joint which results in poor coaptation of the femoral head in the acetabulum; the disease rapidly progresses to osteoarthritis of the hip. While dysplasia has been recognised in practically all canine breeds, it is much more common and of concern in medium and large dog breeds with rapid development. Dysplasia in predisposed breeds, particularly the German Shepherd, is the object of screening based on systematic radiological control in some countries. Our collected dataset comprises 507 X-ray images of dogs affected by hip dysplasia (HD). These images were meticulously evaluated using six Deep Convolutional Neural Network (CNN) models. Following an extensive analysis of the top-performing models, VGG16 emerged as the leader, achieving remarkable accuracy, recall, and precision scores of 98.32%, 98.35%, and 98.44%, respectively. Leveraging deep learning (DL) techniques, this approach excels in diagnosing CHD from hip X-rays with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Markerless motion capture has been made available by the development of pose estimation algorithms that provide both an estimate of the location of body keypoints in two-dimensional images and its associated confidence. It seems relevant to use this additional information for three-dimensional (3D) point reconstruction. Yet, it has been little described, nor has its influence on 3D point reconstruction. Eight participants performed a manual material handling task, which was recorded by 10 video cameras. Each video was processed using OpenPose. Different 3D point reconstruction methods were compared: direct linear transform (DLT) and weighted DLT (wDLT), with 10 cameras and with a subset of 4 cameras. For each keypoint, confidence and position deviation from the 3D reconstructed point with 10 cameras projected in the image reference frame were assessed. Results suggest that using confidence information reduces both average and maximum 3D distance between 3D points reconstructed with 4 and 10 cameras. [ABSTRACT FROM AUTHOR]

Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network's input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms. [ABSTRACT FROM AUTHOR]

Facial Emotion Recognition is gaining interest from researchers in various fields due to its numerous applications. The Vision Transformer (ViT) outperforms convolutional neural network (CNN)-based systems significantly in terms of performance when compared to contemporary image categorisation algorithms. In this context, the primary objective of this paper is to present an innovative and efficient deep learning model for facial emotion recognition. This hybrid intelligent model combines the Vision Transformer with Particle Swarm Optimization (PSO). The proposed model accurately identified the majority of emotions in images, achieving an accuracy of 95.93% for the Fer2013plus dataset and 100% for the CK+ dataset, as demonstrated through experiments conducted on both datasets. According to the results, the proposed facial expression recognition approach has either outperformed or closely matched the performance of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper introduces a Computer-Aided Detection (CAD) system for categorizing breast masses in mammogram images from the DDSM database as Benign, Malignant, or Normal. The CAD process involves Pre-processing, Segmentation, Feature Extraction, Feature Selection, and Classification. Three feature selection methods, namely the Genetic Algorithm (GA), t-test, and Particle Swarm Optimization (PSO) are used. In the classification phase, three machine learning algorithms (kNN, multiSVM, and Naive Bayes) are explored. Evaluation metrics like accuracy, AUC, precision, recall, F1-score, MCC, Dice coefficient, and Jaccard coefficient are used for performance assessment. Training and testing accuracy are assessed for the three classes. The system is evaluated using nine algorithm combinations, producing the following AUC values: GA+kNN (0.93), GA+multiSVM (0.88), GA+NB (0.91), t-test+kNN (0.91), t-test+multiSVM (0.86), t-test+NB (0.89), PSO+kNN (0.89), PSO+multiSVM (0.85), and PSO+NB (0.86). The study shows that the GA and kNN combination outperforms others. [ABSTRACT FROM AUTHOR]

Medical imaging research has experienced significant growth over the past decade, particularly in the fields of computer vision and pattern recognition. Computational approaches have been proposed to address the challenges in breast cancer detection, classification and segmentation. However, recent advancements in computational technology such as machine learning (ML) methods have dramatically changed the landscape of breast cancer imaging research. This survey aims to provide a compilation of information for future breast cancer imaging researchers by 1) comprehensively examining how various ML techniques are being used to address the main challenges in breast cancer imaging; 2) providing an in-depth discussion and review of publicly available datasets for the development and evaluation of novel breast cancer detection, classification and segmentation approaches; and 3) outlining current evaluation metrics used by breast cancer imaging researchers. The insights and findings presented in this survey will serve as a valuable resource for researchers and clinicians interested in breast cancer imaging. By providing an overview of the current state-of-the-art techniques and highlighting areas for future research, we hope to facilitate the development of more accurate and effective breast cancer imaging ML techniques, and contribute to advancing our understanding and improving the diagnosis and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a dynamic field intersecting various disciplines, with applications spanning medicine and diagnostics. Our focus on advancing medical imaging has led to groundbreaking progress in brain tumor diagnosis, fostering optimism in treatment. In this pursuit, we introduce HoloBrain, an accessible AR application designed to transform medical education. Utilizing smartphones, HoloBrain enables automatic segmentation, AR visualization, and 3D interaction without the need for head-mounted devices. Emphasizing practicality over hyper-realism, our innovation allows medical students to seamlessly integrate a virtual brain tumor model into their surroundings. This immersive approach facilitates exploration of the 3D representation of an afflicted brain, enhancing educational experiences. Tests in a controlled environment using a phantom head yielded promising results, with our solution demonstrating average errors of 2 mm to 4.5 mm on smartphone displays. These outcomes highlight the precision and effectiveness of our contribution, underlining its potential in transforming medical education and training. HoloBrain represents a step forward in making AR accessible and impactful for aspiring medical professionals, offering a valuable tool for enhancing their understanding of complex medical scenarios. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancements in deep learning, image processing has progressively attracted the attention of medical researchers to achieve accurate diagnoses of numerous human diseases in a non-invasive manner. Image processing systems are among the most helpful and crucial disease identification technologies. Liver cancer is the world's fifth most prevalent and leading cause of cancer deaths. Diagnoses and treatment are essential because of the high mortality rate and recurrence after treatment. Medical image analysis is extremely beneficial in the healthcare industry. In the last several years, creating and developing computer-assisted imaging systems has been increasingly vital to aid physicians or doctors in strengthening their diagnoses. This research presents a deep model to identify cancerous regions in the liver aims using Computerized Tomography (CT) scans from the Kaggle dataset. Basic preprocessing is applied to CT images from the dataset acquired from the Kaggle repository. The performance of the proposed model is evaluated using different performance metrics, and an accuracy score of 94.3% was achieved in tumour identification. [ABSTRACT FROM AUTHOR]

This study aims to tackle the implementation of a single deep learning (DL) technique to the brain tumour recognition, segmentation and classification problem based on MR images. We propose utilising the Mask R-CNN approach along with transfer learning models on images. This was done by assigning bounding boxes and successfully constructing a border on each image for the tumour volume in order to distinguish it from neighbouring tissues and structures. By processing the images in this way, a single DL model could accurately identify, classify and segment three different categories of brain tumours (meningioma, glioma or pituitary). ResNet-50 and ResNet-101 network architectures were trained for 1000 epochs, and their classification and segmentation performances were assessed. A higher classification success was achieved for the ResNet-101 backbone pretrained on COCO images (%75 accuracy) in the testing phase. ResNet-50, on the other hand, achieved a higher classification accuracy (%87) when used with the ImageNet dataset. The classification and segmentation successes of meningioma and pituitary tumours were comparable, while glioma tumours could be segmented at relatively lower success rates (41–73% for all models), despite similar classification performances (73–95%) with other tumour types. [ABSTRACT FROM AUTHOR]

A brain tumour is fatal; thus, early discovery is crucial to preserving life. Brain magnetic resonance image (MRI) segmentation can be used to find the tumour area. Radiologic assessments can pinpoint a suspected brain tumour's exact location and size. For future diagnostic and treatment planning, the assessment report is crucial. For the goal of diagnosis, the tumour detection process must be quick and precise. This paper suggests an automated system to distinguish between malignant brain MRIs and those not. Each image is assigned a set of characteristics utilising statistical, edge. Then, to compare the precision of the suggested framework, the Extreme Learning Machine (ELM) classifier is used with various cross-validations on the feature set. The BRATS 2020 dataset is used to validate the suggested technique. The approach has an average accuracy, sensitivity, and specificity of 99.1%, 99.9%, and 99.3%. It can detect tumours more precisely and with less processing time than current techniques. This study suggests a powerful method for identifying brain cancers and predicting the kind of tumour. The suggested method first uses the adaptive histogram equalisation strategy to boost the resolution of BRATS-2020 MRI images. The goal is to improve segmentation performance as a result. After that, the WFCM technique is used to segment the MRI picture. [ABSTRACT FROM AUTHOR]

This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of 'Neural Network (NN) and Support Vector Machine (SVM)' for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively. [ABSTRACT FROM AUTHOR]

Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person's attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people's health. [ABSTRACT FROM AUTHOR]

This paper has aimed to develop the Breast Cancer Detection (BCD) technique along with the mammography reports of the patients. For this proposed framework, the required images are collected from publicly available datasets. Further, the collected images are given into the phase of feature extraction, where the deep features based on texture features, ResNet, morphological features and density features are extracted and fused together. In addition, the weighted fused feature selection is performed with the fused features, in which the weight is optimised with the Modified Hybrid based Leader Optimization (MHLBO) obtained from Hybrid Leader-Based Optimization(HLBO) to elevate the feature performance on classification. Then, breast cancer can be classified with the developed Adaptive Cascaded Deep Network (ACDN) by combing the classification results of, Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Here, the parameter tuning has occurred in all three classifiers using the same enhanced HLBO. Throughout the experimentation, the designed model shows 97% and 96% regarding accuracy and F1-score, respectively. The tentative outcomes show the efficacy of the designed BCD method is compared with other baseline approaches. [ABSTRACT FROM AUTHOR]

Corneal Ulcers are defined as inflammation or even infection. They are one of the most frequent diseases that affect eye health. The proper measurement of corneal ulcer lesions enables the physician to evaluate the treatment effectiveness and decision making. This paper presents a segmentation method that aims to assist doctors in monitoring the treatment of corneal ulcer lesions. We applied a stepwise fine-tuning in U-Net Convolutional Neural Network architecture to train a model with 358 Point-flaky corneal ulcer images. The result from the model using U-Net architecture is then submitted to post-processing operations. Based on experiments performed with 91 Flaky corneal ulcer images, our approach achieved 0.823 of the Dice Coefficient on average, 88.9% of Recall, 99.4% of Specificity, and True Dice Coefficient of 0.835. The results are promising; we then have evidence that the proposed method could generalise the data from the training phase to segment the test data. [ABSTRACT FROM AUTHOR]

The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes. [ABSTRACT FROM AUTHOR]

Some of the viruses may cause lung parenchyma and airway involvement. Usually, viral pneumonia causes ground-glass opacities, bilateral peripheral distribution, consolidation, vascular thickening, and reticular opacity. These features are common in COVID-19 rather than Non-Covid-19 viral pneumonia. However, in advanced cases, COVID-19 viral pneumonia may cause organising pneumonia and fibrosis of the lung. Atypical findings of Non-Covid-19 pneumonia have included central peripheral distribution, pleural effusion, lymphadenopathy, nodules, tree-in-bud opacities, and pneumothorax. Therefore, differentiating Non-Covid-19 pneumonia from COVID-19 pneumonia at chest computed tomography (CT) is necessary. In that case, CT scans of the thorax are one of the essential tools for early identification and future prognosis of viral pneumonia. We have proposed a Computer-Aided Diagnostic (CAD) system that can detect features of chest CT using a Deep Neural Network (DNN) with Long Short-Term Memory (LSTM). Transfer learning using pre-trained DNN models (ResNet50, VGG19, InceptionV3, Xception, DenseNet121, and VGG16) is applied to retain both high-level and low-level features effectively. The deep features are passed to the LSTM layer. The LSTM is utilised as a classifier and detects long short-term dependencies. The proposed method employs a hybrid DNN-LSTM network for automatic detection to take advantage of the uniqueness of the two models. The proposed models are trained with common and different features present in the chest CT of COVID-19 and Non-Covid-19 viral pneumonia. The 5-fold cross-validation (CV) method validated and tested the proposed model. The proposed DNN model's performance is quite improved with LSTM and CV. As a result, the proposed LM-DNN (VGG16+LSTM+CV) model has achieved the classification test accuracy of 91.58% and specificity of 93.86%, which offers superior performance with state-of-the-art. Also, the DenseNet121+LSTM+CV model has reached the classification test accuracy of 90.1% and sensitivity of 92%. [ABSTRACT FROM AUTHOR]

Upper-limb prostheses are either too expensive for many consumers or have a greatly simplified choice of actions. This research aims to enable an improvement to the quality of life for recipients of these devices by providing an understanding of how we use our hands in modern everyday life. To achieve this a taxonomy of grasps used in activities of daily living has been created. A portable motion capture system was developed to collect data. Thirteen participants' hand movements were recorded (totalling 62 hours and 10 minutes of data). From these data, 38 and 22 grasps were observed from the left and right hands, respectively. The portable system effectively captured natural hand motions, which formed an updated taxonomy of grasps. [ABSTRACT FROM AUTHOR]

Skin melanoma is a potentially fatal form of cancer. If left untreated or allowed to spread, it can lead to death or serious disability. Therefore, early diagnosis is crucial for improving patient prognosis and outcomes. Recent advances in machine learning (ML) and deep learning (DL) have greatly contributed to the categorisation and identification of melanoma. The goal of this survey is to evaluate 60 publications that have been submitted in order to create an overview of the melanoma detection process. It examines several feature extraction techniques.The evaluation concentrates on different melanoma detection methods, including 'deep learning (DL)' and 'machine learning (ML)' models. The analysis includes performance metrics and a review of the results obtained from the PH2 dataset, which achieved a high accuracy of 96.5%. Finally, the survey addresses research gaps to facilitate future investigations into melanoma identification strategies. [ABSTRACT FROM AUTHOR]

A virus known as the Coronavirus 2019 (COVID-19) can have serious effects on a variety of body parts. Preventing this spread of the virus requires early detection and proper treatment. Many methods are followed for early detection of this disease based on symptoms and classification for proper treatment. In this paper, classification of COVID-19 using Computed Tomography (CT) scan images with hybrid optimisation-enabled deep learning is researched. Here, a hybrid optimisation algorithm called War Snake Optimisation Algorithm (WSOA) is proposed to train Deep Q-Network (Deep Q-Net). Moreover, the proposed WSOA is formed by integrating War strategy optimisation (WSO) and Snake Optimiser (SO). In this paper, pre-processing is carried out using a median filter, followed by lung lobe segmentation that is done by Generative Adversarial Network (GAN). Furthermore, image augmentation is done by translation, rotation, flipping and scaling. Then, feature extraction is followed after image augmentation, where needed features get extracted to end up with the classification of COVID-19. This classification brings out the patient's health condition of normal and abnormal situations. Moreover, this research work is analysed with three performance metrics, such as accuracy, sensitivity and specificity with values of 93.7%, 94.8% and 89.5%. [ABSTRACT FROM AUTHOR]

A method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard's colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%. [ABSTRACT FROM AUTHOR]

The liver is the largest solid organ in the body that can be anatomically divided into segments. We present in this work a web-based subject-specific human liver atlas based on the Couinaud segments simulated from portal venous (PV) perfusion zones, hepatic arterial (HA) and hepatic venous (HV) trees, as well as biliary drainage. The purpose of the atlas is to provide the modelling community with freely accessible 3D hepatic structures for in silico simulations, which are of tremendous value in yielding novel insights in hepatic circulation, drug transport and clearance. [ABSTRACT FROM AUTHOR]

This study utilised a prototype system which consisted of a person-specific 3D printed jaw tracking harness interfacing with the maxillary and mandibular teeth and custom jaw tracking software implemented on a smartphone. The prototype achieved acceptable results. The prototype demonstrated a static position accuracy of less than 1 mm and 5°. It successfully tracked 30 cycles of a protrusive excursion, left lateral excursion, and 40 mm of jaw opening on a semi-adjustable articulator. The standard error of the tracking accuracy was reported as 0.1377 mm, 0.0449 mm, and 0.9196 mm, with corresponding ${r^2}$ r 2 values of 0.98, 1.00, and 1.00, respectively. Finally, occlusal contacts of left, right, and protrusive excursions were tracked with the prototype system and their trajectories were used to demonstrate kinematic modelling (no occlusal forces) with a biomechanical simulation tool. [ABSTRACT FROM AUTHOR]

Machine learning methods have been extensively employed to predict COVID-19 using chest X-ray images in numerous studies. However, a machine learning model must exhibit robustness and provide reliable predictions for diverse populations, beyond those used in its training data, to be truly valuable. Unfortunately, the assessment of model generalisability is frequently overlooked in current literature. In this study, we investigate the generalisability of three classification models – ResNet50v2, MobileNetv2, and Swin Transformer – for predicting COVID-19 using chest X-ray images. We adopt three concurrent approaches for evaluation: the internal-and-external validation procedure, lung region cropping, and image enhancement. The results show that the combined approaches allow deep models to achieve similar internal and external generalisation capability. [ABSTRACT FROM AUTHOR]

Brain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes' territory localisation. All models' architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications. [ABSTRACT FROM AUTHOR]

In this study, Bi2O3/TiO2 composite was successfully synthesised using the solvothermal method, and the morphology, composition, and surface area of the composite were determined. Comparisons of photocatalytic performance were performed using Bi2O3/TiO2 for the decomposition of Atenolol (ATN) a β-blockers that is generally used to treat disorders such as hypertension and arrhythmias, as a model contaminant under visible light irradiation. The effects of different parameters such as solution pH, catalyst dosage, initial ATN concentration, reaction time, coexisting cations and anions, and turbidity on the degradation efficiency of ATN were investigated. An excellent synergistic effect was observed in the decomposition of ATN with the simultaneous application of Bi2O3/TiO2 and LED compared to single processes (synergistic index value, 4.027). The Bi2O3 catalyst has a much lower photocatalytic performance for the decomposition of ATN than the synthesised Bi2O3/TiO2 composite. The optimal rate for pH, catalyst dosage, and initial concentration of ATN was 7, 400 mg/l and 10 mg/l, respectively. Under optimal conditions, about 68.92% and 22.58% of ATN were degraded after 60 minutes in the presence of Bi2O3/TiO2 and Bi2O3 catalysts, respectively. However, these efficiencies are reduced to 51.83% and 12.4%, respectively, under 50 NTU. The addition of co-existing anions, especially PO43⁻, remarkably reduced the efficiency of ATN removal in the Bi2O3/TiO2/LED process. The presence of cations promoted the degradation of ATN in the Bi2O3/LED process, while the efficiency of ATN degradation was inhibited by the presence of cations in the Bi2O3/TiO2/LED process. The ATN removal efficiency using LED irradiation (49.02%) was much higher than that of UV-C irradiation (27.16%) when the concentration of ATN was 30 mg/L. [ABSTRACT FROM AUTHOR]

In this study, Poly(ethylene terephthalate) (PET) fibres were copolymerised with a mixture of acrylamide (AAm), 2 -hydroxypropyl methacrylate (2-HPMA) monomers in the presence of a mixture of initiators benzoyl peroxide (Bz2O2) −4, 4-azobis-4-cyano-valeric acid(ACV). After determining the optimal graft condition, solution electrospinning was performed. Optimal electrospinning conditions were obtained at (V = 10 kV, TFA/DCM = 4/1, distance from nozzle = 12.5 cm).The best adsorption conditions were determined by batch technique. The adsorption process was in good agreement with the Langmuir isotherm model and the Pseudo-second-order kinetic model. The mentioned findings confirm the homogeneity, monolayer and chemical nature of the adsorption process. Grafted nanofibers had a good performance in removing Ni2+ ions from industrial effluents in Charmshahr and Ghods industrial town. Thermodynamic studies have shown that the process is endothermic, spontaneous and is associated with increasing entropy. [ABSTRACT FROM AUTHOR]

Present work demonstrates the synthesis, characterisation and application of a novel hybrid nanocomposite GO@Fe3O4-MnO2 for adsorption and catalytic degradation of dyestuff malachite green and tartrazine from wastewater. The nanocomposite GO@Fe3O4-MnO2 was characterised by FT-IR, XRD, SEM-EDS and pHzpc. Effects of various optimising parameters such as contact time, solution pH, surfactants, light, adsorbent dose, ultrasonication and coexisting ions were investigated. The removal efficiencies of malachite green and tartrazine were observed as high as 99.9% and 98% under direct sunlight, respectively. The adsorption kinetics follows the pseudo-second order model (R2 = 0.99) where k2 for malachite green and tartrazine were 0.0097 and 0.0011 g mg−1 min−1, respectively, that means adsorption was controlled by chemisorption. Equilibrium adsorption isotherm of these dyes was analysed in the context of Langmuir and Freundlich models, and the maximum adsorption capacity for Langmuir isotherm was found to be 8.0 and 5.1 mg g−1 for malachite green and tartrazine, respectively. The percentage removal was initially low, but when the ultrasound was irradiated, the removal percentage (96% for malachite green and 98% for tartrazine) was drastically increased because of the formation of highly active •H and •OH radicals in the water through the decomposition of water molecules by the formation of hot spots. On treatment with H2O2, advance oxidation processes were observed with high remediation percentage (99.5% for malachite green and 98% for tartrazine) of dye. The synthesised GO@Fe3O4-MnO2 was finally used for the treatment of dyes from real water samples. The used nanocomposite was separated using an external magnet and recycled with 0.1 N HCl or 0.1 N NaOH as desired. [ABSTRACT FROM AUTHOR]

In this study, a cheap, novel, ecofriendly and effective nanosorbent was synthesised with natural materials such as sand beach and eggshell for diazinon determination in natural water. The synthesised nanosorbent was successfully characterised using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), field emission scanning- electron microscopy (SEM), vibrating sample magnetometer (VSM) and Brunauer–Emmett–Teller (BET) methods. Effective parameters were optimised for the extraction of diazinon and determination was done by UV-Vis spectroscopy at 248 nm. The method was linear in the range of 50–400 µg L−1 and the limit of detection was 6.9 µg L−1. Adsorption kinetic and isotherm studies were performed. Also, for measurement of diazinon in water samples of Gilan province, sampling was performed from important points such as river water, Anzali lagoon, well water, agricultural streams, and tap water. The results shows good spiked recovery (97.6–103.4%) in all samples. The accuracy of the method was also confirmed by the student's t -test with HPLC analysis of real samples. [ABSTRACT FROM AUTHOR]

The entrance of heavy metals through human activities causes pollution of water, soil, and plants. The aim of this study was to measure the heavy metals concentration (Hg, Pb, As, and Cd) and their ecological impacts on water, soil, and vegetables in the Kashaf Roud River basin. Hence, 72 samples of irrigation water, 72 samples of soils, and 216 samples of vegetables (dill, leek, and basil) were collected. The contamination levels were evaluated by several ecological indices including Ecological Risks index (ERI), Heavy Metal Evaluation Index (HEI), Contamination factor (Cf), Pollution Index (PI), geo-accumulation index (Igeo), Potential ecological risk index (RI), Pollution load index (PLI), and modified degree of contamination (MDC). Based on obtained results, concentrations of heavy metals in water and soil samples were in the order of Pb>As>Cd>Hg. In addition, Pb and As had the highest concentration in vegetables with the mean total concentrations of 1.60 and 0.08 mgkg−1 wet weight, respectively. According to ERI value (238), water sampling site no. 3 had higher pollution than other places and was classified in the considerable ecological risk. Among the soil samples, the Cf average values followed the declining order of Cd (7.78)>Pb (6.91)>Hg (4.26)>As (2.68), so that Pb and Cd presented high contamination levels. Moreover, all soil samples of the studied region possessed PLI>1, which confirms their contamination with heavy metals. Finally, the trends in the BAF values for heavy metals in various vegetables were in order of Hg>Cd≈Pb>As for Leek and Hg>As> Pb>Cd for Dill and Basil. Concentrations of heavy metals in soil and water samples were within the national and international standard ranges. However, the concentrations of these metals in some vegetable samples exceeded the national and international levels. The utilisation of standard quality effluent for agricultural irrigation requires control measures and good organisational performance. [ABSTRACT FROM AUTHOR]

This study was focused on the design and development of a new heterogeneous catalyst based on polyester (PE) fabric modified with graphene oxide nanosheets decorated with silver metallic nanoparticles (PE-rGO/Ag0), using a simple, easy, and effective approach. PE fabrics were coated by sonication with graphene oxide (GO) nanosheets followed by in situ deposition and reduction of silver nanoparticles over the GO surface, covering the PE fabric and producing PE-rGO/Ag0. The crystalline structure, surface morphology and chemical characteristics of the synthesised materials were identified by X-ray diffraction spectroscopy, scanning and transmission electron microscopy analyses (SEM and TEM) and by Fourier-transform infrared spectroscopy (FTIR), respectively. The catalytic performance of the prepared PE-rGO/Ag0 was assessed during the hydrogenation of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP) in the presence of sodium borohydride (NaBH4). Experimental results indicate that PE-rGO/Ag0 with a surface of 6 cm2 (3x2 cm) showed the highest catalytic activity with an apparent reaction rate constant (Kapp) of 0.65 min−1. In addition, PE-rGO/Ag0 activity resulted to be comparable and significantly higher than previously reported values of silver-based catalysts. Remarkable stability values were achieved during different operating cycles without significant degradation in the catalytic activity, suggesting that PE-rGO/Ag0 can be used as a strong candidate in long-lasting chemical catalytic processes. [ABSTRACT FROM AUTHOR]

The bar properties (elastic modulus, density, Poisson's ratio, and sound speed) necessary for exploiting bar impact tests were calibrated via two methods called the 1D method (which combines the iterative dispersion correction with 1D approximation) and 3D method (which resorts to the simulation-based reverse engineering of the strain profile in a circular 3D bar). The two methods cross-verified one another for a maraging steel bar with 19.1 mm in diameter. This paper also considers the method of calibrating measured strain and finally presents a method of determining the impact velocity with reference to a reliably measured strain profile for a bar calibrated via the 1D method. [ABSTRACT FROM AUTHOR]

This paper presents a novel Flexnertia metastructure concept to perform vibration suppression through coupling rotational inertia to structural flexural motion. Theoretical analysis and experimental test of the proposed metastructure with emphasis on dissipating structural flexural motion is exhibited, showing vibration control performance. The experimental and numerical results are in good agreement, both confirming that the average overall response of the metastructure is significantly reduced. The attenuation becomes most pronounced in the low-frequency range where structures tend to suffer most due to high response around the regime of the first flexural modes. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced composite multilayer damping structures with different types of stiffeners demonstrate excellent mechanical properties. This paper deals with the bending properties analysis of carbon fiber reinforced composite multilayer damping structures with different types of stiffeners for the first time. A theoretical model was constructed to investigate the bending performance of the structure under static loading based on structural stress transfer mechanism between stiffener and composite plate. Moreover, the theoretical solution was compared with the numerical simulation results to confirm their validity. Finally, structural parameters were considered to discuss the change law of the structural center deflection. [ABSTRACT FROM AUTHOR]

Bi-directional re-entrant anti-tetrachiral (BRATC) structure with negative Poisson's ratio is developed in this paper to meet the requirement in the variation of morphing wing area. Inherited from the anti-tetrachiral (ATC) structure, the BRATC has the potential of lightweight and out-of-plane stiffness properties, which is suitable for simultaneous deformation and load-bearing capability in the design of morphing wings. At the same time, inspired by the design idea used in re-entrant anti-tetrachiral (RATC), the BRATC structure has improved the potential of deformability by relaxing the rotation constraints of the ligaments. The effect of the geometric parameters on the equivalent mechanical properties of BRATC, such as elastic modulus and Poisson's ratio was investigated by using the finite element method. The relation between geometric parameters and the mechanical properties is then obtained. Finally, mechanical properties of the BRATC structure are tested with the aid of additive manufacturing technology in the tensile experiment. [ABSTRACT FROM AUTHOR]

This paper intends to enhance the corrosion resistance of coolant pipes in thermal fatigue conditions and lateral hydrostatic pressure in critical engineering environments. This research applies to circular coolant pipes under hydrostatic pressure of the LBE (lead-bismuth eutectic) boundary in nuclear power plants. The resistance against corrosion caused by liquid LBE is mainly formed by Fe12Cr2Si solid solutions coatings on the pipe. The silicon concentration in Fe12Cr2Si can interact with LBE as an effective oxidized compound such as SiO2 and Fe2SiO4 when the silicon concentration is above 1.25 wt%. The oxide film formed on the coating can resist the LBE corroding in the Fe12Cr2Si structure. The primary material for constructing a coolant pipe is T91 ferritic-martensitic alloys, and the surface anti-corrosion coating is Fe12Cr2Si solid solution. With a high-strength structure, FGC (functionally graded composite material) ensures that the pipe resists the corrosion from Liquid LBE. This study evaluates the thermal fatigue failure of coolant pipes and the silicon concentration when the pipe model is under thermal shock testing by a CO2 laser. The research result indicates that the FGM (functionally graded material) structure performs better in effectively raising the margin of safety and reserving the silicon concentration on the inner surface higher than 1.25 wt% as compared to the FGC structure. [ABSTRACT FROM AUTHOR]

Reservoir water sensitivity damage means that the contact between the external fluid and reservoir rock lead to the hydration, expansion, dispersion, and migration of clay minerals, thereby reducing the formation permeability and causing damage to the reservoir. In this study, the transformation of smectite to illite (S-I) is promoted by endogenous microorganisms to achieve anti-swelling of the clay minerals and reduce the water sensitivity damage of the reservoir. The microbial community reducing Fe(III) was enriched from the samples of Karamay Oilfield, Xinjiang, and microbial community diversity was analyzed by the 16S rRNA gene sequencing. The Fe(II) ion concentration in the interaction medium was monitored, and the maximum iron reduction rates reached 60.2%. The structural changes of smectite before and after microbial treatment were observed by Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The result demonstrated that the reduction of Fe(III) was accompanied by the transformation of S-I. There may be some endogenous microorganisms that transform S-I in low permeability reservoirs. This study is of great significance for oilfields to realize clay mineral anti-swelling and eliminate reservoir water sensitivity damage by endogenous microorganisms. [ABSTRACT FROM AUTHOR]

This study presents a broad set of prior experimental data for shale membrane efficiency when interacting with water-based and oil-based drilling fluids. The assessment of the experimentally measured membrane efficiencies of shale reveals that shales are leaky semi-permeable membranes. Results suggest that shale permeability and cation exchange capacity play a determining role in its membrane efficiency, as higher cation exchange capacity and lower permeability are strongly consistent with higher membrane efficiency of shale. Furthermore, it has been found that the ratio of hydrated ionic size relative to shale pore throats' size regulates the movement of ions into the shale, thus directly controlling its membrane efficiency. Ions with large hydrated diameters exhibit higher membrane efficiencies than those with small hydrated diameters. Results also show that the measured membrane efficiencies of oil-based muds were higher than those reported for shales. While shale gets its membrane quality from its tiny pores and inherent surface charge, oil-based mud gets its membrane quality from the stability of its emulsion. We believe that when using oil-based muds to drill shale formations, the emulsifier assumes responsibility for screening out ions, however, if the emulsion stability is breached, shale will become the regulator for ions flow. [ABSTRACT FROM AUTHOR]

Shale is a complex porous medium with multi-scale pores and well-developed fracture networks. This paper aims to use modeling and numerical simulation methods to study the transport of shale gas in a complex multi-scale matrix-fracture system. In this study, mathematical modeling and programming was used to establish digital models of shale gas multi-scale matrix and 3D discrete fracture network. Based on the transport mechanisms of shale gas, this study derives the mathematical models of shale gas transport in different transport media, and uses the finite element method to solve and analysis the transport of shale gas in the multi-scale matrix-fracture system. The model is verified by real shale gas field data. The results show that the fractal dimension of organic pores have great effects on shale gas transport. When the fractal dimension is greater than 1.4, the increase on gas production is particularly obvious. Compared with organic pores, the effect of fractal dimension of inorganic pores is smaller. The existence of the fracture network has an effect of up to 25% on gas production, and the optimal fracture density is 200. [ABSTRACT FROM AUTHOR]

This novel research presents an investigation on petroleum products dehydration using the adsorption method by low-cost adsorbents. To investigate the performance of proposed process intended to separate emulsified water, synthetic stabilized water emulsions in different petroleum products (Gasoline, Kerosene, and Gas oil) which equal 300, 600, and 800 ppm were prepared. Afterwards, under batch conditions, natural zeolite and activated carbon as economic adsorbents were utilized for the dehydration process. The characteristics of adsorbent was examined using SEM and XRD analysis. The obtained results from BET analysis also depicted that the specific surface area of activated carbon was considerably more than that of zeolite. Results showed that both zeolites and activated carbon can effectively purify petroleum products from undesirable emulsified water. The comparison of the adsorbents' efficiency indicated that activated carbon has a higher affinity than zeolite for water in gasoline and gas oil, however, they had almost the same adsorption capacity in kerosene dehydration. The maximum water removal percentage of gas oil, kerosene and gasoline using zeolite was 84.84, 92.17, and 88.64, respectively. Dehydration by activated carbon also led to 86.44, 93.43 and 90.2 per cent water removal from gas oil, kerosene and gasoline, respectively. [ABSTRACT FROM AUTHOR]

Due to the significant advantages of methane sensitivity and area-type leakage detection, tunable diode laser absorption spectroscopy (TDLAS) gas detection has been promoted as an effective method for microleakage monitoring in oil and gas stations. However, the output of the TDLAS detector is the integral concentration (IC). Based on the relevant research, the alarm threshold and risk are assessed via the gas concentration rather than the IC. How to evaluate the concentration of leaked gas clouds based on IC from TDLAS detectors is still a challenge. To address this problem, the characteristics of IC and the influence of the laser path, wind speed and leakage rate were studied via computational fluid dynamics (CFD). A neural network classification model (NNCM) was proposed to obtain the probability distribution of the maximal concentration along the laser path (Cmax). The results indicated that the IC is strongly correlated with the Cmax. Considering the accuracy and operability, the NNCM with input features of IC, wind speed and angle of laser path was selected. Field tests showed that the developed model achieved the concentration evaluation of leaked gas clouds. Additionally, the NNCM can also quantify the uncertainty of the results, which avoids misjudgments caused by deviations. [ABSTRACT FROM AUTHOR]

Chinese voluntary associations (CVAs) are often portrayed as “old people’s clubs” and extant studies have largely neglected younger generations’ experiences in these associations. Drawing on ethnographic fieldwork with younger Singaporean Chinese who engage with CVAs, this paper explores how they negotiate Chineseness in and through the spaces of CVAs. This paper contributes to extant literature in two ways. First, it sheds light on the role of migrant associations in shaping younger generations’ ethnic identity and belonging. CVAs act as sites of knowledge, relationships and connections to ancestral places. Yet at the same time, CVAs are spaces where the younger generations experience alienation during their encounters with older generations. Second, the paper responds to larger scholarly concerns about what China’s “rise” means for the Chinese diaspora. We approach CVAs as microcosms of Singapore’s balancing act in the contemporary geopolitical landscape where China’s presence is increasingly felt. [ABSTRACT FROM AUTHOR]