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Abstract Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study.

Abstract This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1st and 2nd order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.

Abstract This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique’s performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.

Vertebrate fossils/remains became recently significant in various study fields for determining the ecological biodiversity. However, with the great abundance of fossils/remains and their classes, there is a difficulty in identifying and detecting these classes. Hence, in this paper, an accurate machine learning classification technique is presented to differentiate automatically some types of 3D vertebrate remain images. A computed tomography (CT) scanner is utilized to construct a dataset of 3D images of some vertebrate remains found in Egypt. Adaptive enhancement and segmentation processes are applied to the dataset. The different selected geometric features are then extracted. Thus, the extracted features are classified using suitable machine learning classifiers (SVM, KNN, DTs). The automatic detection for the remains class, according to the extracted features, is obtained using the confusion matrix for the training and testing data points and the receiver operating characteristic (ROC) curve. The results confirmed an accurate technique with high performance.

Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.

Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of “you only look once” (YOLOv5) and “ResNet50” is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features.

Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis.

Space-angular fractional radiative transfer equation has been derived depending on the invariance of the functional potential and the Euler–Lagrange functional of the radiative-transfer intensity. Pomraning–Eddington technique is employed to unzip the space-fractional radiative transfer through a clumpy medium. The medium is considered as an anisotropic scattering participating finite slab with specular reflecting boundaries. The fractional differential operator is taken in terms of the Jumarie Riemann–Liouville representation. The resultant space-fractional equation has diffusion parameter depends on both the single scattering albedo and the anisotropic parameter of the medium. Laplace transformation method is used to solve the obtained space-fractional differential equation, whose solution is obtained in terms of the Mittag–Leffler function. Numerical calculations are carried out to investigate the effects of the fractional exponent and the other radiative-transfer parameters on the radiation density and net flux through the medium. In addition, the reflectivity and transmissivity from the medium boundaries are calculated and represented graphically. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.

Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on many organs and tissues of the body, identify its symptoms in the short and long terms, in addition to recognize the role of diagnosis imaging in COVID-19. Principally, the quick evolution of active vaccines act an exceptional accomplishment where leaded to decrease rate of death worldwide. However, some hurdels still have to be overcome. Many proof referrers that infection with CoV-19 causes neurological dis function in a substantial ratio of influenced patients, where these symptoms appear severely during the infection and still less is known about the potential long term consequences for the brain, where Loss of smell is a neurological sign and rudimentary symptom of COVID-19. Hence, we review the causes of olfactory bulb dysfunction and Anosmia associated with COVID-19, the latest appropriate therapeutic strategies for the COVID-19 treatment (e.g., the ACE2 strategy and the Ang II receptor), and the tests through the follow-up phases. Additionally, we discuss the long-term complications of the virus and thus the possibility of improving therapeutic strategies. Moreover, the main steps of artificial intelligence that have been used to foretell and early diagnose COVID-19 are presented, where Artificial intelligence, especially machine learning is emerging as an effective approach for diagnostic image analysis with performance in the discriminate diagnosis of injuries of COVID-19 on multiple organs, comparable to that of human practitioners. The followed methodology to prepare the current survey is to search the related work concerning the mentioned topic from different journals, such as Springer, Wiley, and Elsevier. Additionally, different studies have been compared, the results are collected and then reported as shown. The articles are selected based on the year (i.e., the last three years). Also, different keywords were checked (e.g., COVID-19, COVID-19 Treatment, COVID-19 Symptoms, and COVID-19 and Anosmia).

Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches. [ABSTRACT FROM AUTHOR]

Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features. [ABSTRACT FROM AUTHOR]

AA 6061 reinforced with four different volume fractions of SiCp (5, 10, 15, and 20% ) were produced by powder metallurgy processing using hot compaction (HC) temperature ranging from 400-500ºC. The effect of HC temperature and the volume fraction of the SiC reinforcement were investigated for the degree of consolidation, hardness profile, and microstructural evolutions. Microhardness measurement were taken to assess the variation in hardness across the diameters of the disks and following a rectilinear grid pattern to give color-coded maps of the hardness distributions. The highest Vickers hardness results were produced for AA6061 reinforced with 15% SiC. Increasing SiC volume fraction impeded grain boundaries diffusion, which resulted in lower grain coarsening of the AA6061 matrices. Comparing the relative densities and Vickers hardness of the hot compacted samples, the HC temperatures ranging from 400 -500 ºC had insignificant influence on the densification and hardness results.

Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical study of fully developed turbulent flow in a channel at friction Reynolds numbers of 1000, 2000 and 5200. For the momentum equation, Reynolds Averaged Navier-Stokes (RANS) approach was used, where the Reynolds stress was modeled using Prandtl's mixing length model with Van Driest's modification to capture the damping effect near wall. The velocity profile was computed by solving the non-linear ODE for momentum using Euler's method. The results were compared with the latest available DNS data. [ABSTRACT FROM AUTHOR]

Vertebrate fossils/remains became recently significant in various study fields for determining the ecological biodiversity. However, with the great abundance of fossils/remains and their classes, there is a difficulty in identifying and detecting these classes. Hence, in this paper, an accurate machine learning classification technique is presented to differentiate automatically some types of 3D vertebrate remain images. A computed tomography (CT) scanner is utilized to construct a dataset of 3D images of some vertebrate remains found in Egypt. Adaptive enhancement and segmentation processes are applied to the dataset. The different selected geometric features are then extracted. Thus, the extracted features are classified using suitable machine learning classifiers (SVM, KNN, DTs). The automatic detection for the remains class, according to the extracted features, is obtained using the confusion matrix for the training and testing data points and the receiver operating characteristic (ROC) curve. The results confirmed an accurate technique with high performance. [ABSTRACT FROM AUTHOR]

Here, we investigate the effectiveness of the optimal homotopy asymptotic method (OHAM) with a symbolic computational method for constructing the approximate solution for quantum Zakharov-Kuznetsov equation that is derived to describe the in magnetized plasma in ion acoustic waves. The results reveal that the method is explicit, effective and easy to use. The proposed method is a strong and easy to use analytic tool for nonlinear problems and does not need small parameters in the equations. The results obtained here reveal that the proposed method is very effective and simple for solving nonlinear problems. The basic ideas of this approach can be widely employed to solve other strongly nonlinear evaluation form equations arising in physics. [ABSTRACT FROM AUTHOR]

Here, an extended discrete tanh function method with a computerized symbolic computation is used constructing a new exact travelling wave solutions of nonlinear differential difference equations of special interest in physics, namely, Hybrid equation, Toda lattice equation and Relativistic Toda lattice difference equations. As a result, we obtain many kinds of exact solutions which include soliton solutions, periodic solutions and rational solutions in a uniform way if solutions of these kinds exist. The method is straightforward and concise, and it can also be applied to other nonlinear difference differential equations in physics. [ABSTRACT FROM AUTHOR]

Radiation transfer through a volcanic aerosol medium has been studied. The radiation transfer properties of the medium as scattering, absorption and extinction coefficients are calculated using the Mie scattering theory. Average coefficients over the size parameter and the radiation wavelength are calculated. The radiation heat fluxes for volcanic eruption ash medium are calculated using the Variational Pomraning–Eddington approximation and compared with those obtained from the Galerkin method. The comparison showed very good agreement. [ABSTRACT FROM PUBLISHER]

A theoretical investigation has been made of obliquely propagating nonlinear electrostatic shock structures. The reductive perturbation method has been used to derive the Korteweg-de Vries-Burger (KdV-Burger) equation for dust acoustic shock waves in a homogeneous system of a magnetized collisionless plasma comprising a four-component dusty plasma with massive, micron-sized, positively, negatively dust grains and non-extensive electrons and ions. The effect of dust viscosity coefficients of charged dusty plasma of opposite polarity and the non-extensive parameters of electrons and ions have been studied. The behavior of the oscillatory and monotonic shock waves in dusty plasma has been investigated. It has been found that the presence of non-extensive parameters significantly modified the basic properties of shock structures in space environments. [ABSTRACT FROM PUBLISHER]

The properties of radiation transfer through a plane-parallel atmospheric aerosol medium has been studied. It has been done by employing Mie theory to calculate the radiation transfer scattering parameters of the medium in the form of extinction, scattering, and absorption efficiencies. Then, the equation of radiative transfer through a plane-parallel atmosphere of aerosol has been solved for partial heat fluxes using two different analytical techniques, namely, the Variational Pomraning -Eddington approximation and Galerkin technique. Average efficiencies over log-normal and modified gamma size distributions are calculated. Therefore, the radiative properties of Carbon, Anthracite, Bituminous, Lignite, and Fly ash have been calculated. The obtained numerical results show very good agreement with each other in addition to the previous published work. [ABSTRACT FROM AUTHOR]

Abstract: We make use of Jacobi elliptic functions to construct periodic wave solutions for the dispersive long wave equations. In the limit cases the multiple soliton solutions are also obtained. The properties of some periodic and soliton solutions for the dispersive long wave equations are shown by some figures. As a result, we can successfully obtain the solitary wave solutions that can be found by the previous work. [Copyright &y& Elsevier]

Abstract: By using the solutions of elliptic equation, a direct method is described to construct the exact traveling wave solutions for two-dimensional KdV–Burgers and Boussinesq equations. More Jacobi elliptic function solutions are obtained. These solutions can be degenerative to hyperbolic function solutions and trigonometric function solutions when the modulus m of Jacobi elliptic function is driven to limit 1 and 0. The results include solitary wave solutions, periodic wave solutions and shock wave solutions. Many new results are presented. [Copyright &y& Elsevier]

Abstract: The properties of radiation through an aerosol medium have been achieved. This has been done by employing Mie scattering theory to calculate the radiation transfer scattering parameters in the form of extinction, absorption and scattering efficiencies. The equation of radiative transfer for the heat flux through a plane parallel atmosphere of aerosol has been solved. The aerosol size distributions are found in practical systems. Average efficiencies over size distribution for spherical particles of complex refractive index are calculated. Therefore, the radiative properties of stratospheric aerosols have been done. The obtained results found to be in a good agreement with the previous work. [Copyright &y& Elsevier]

The Lie technique has been employed to obtain the solutions of the dynamic equation of aerosols. This has been achieved when the rate of growth due to condensation of particles, removal rates of aerosol particles and diffusion process are taken into consideration. In such a case, we have determined a family of new solutions in this paper. [Copyright &y& Elsevier]

Here, we propose an improvement ansatz in three-wave method, then applying this ansatz to an extended generalization of Vakhnenko equation, we obtain a new periodic type of three-wave solutions including periodic two-solitary solution, doubly periodic solitary solution and breather two-solitary solution, respectively. These results show that the three-wave type of ansatz approach is an effective and simple method for seeking three-wave solutions and two-wave solutions of higher dimensional nonlinear evolution equations. [ABSTRACT FROM AUTHOR]

The article deals with the problem of a sharp corner, the tip of which is located on the bi-material interface. The paper presents a qualitative and quantitative description of singular stress fields occurring in the tip area of such a stress concentrator. The qualitative description was obtained by solving the problem of the plane theory of elasticity with appropriately defined boundary conditions. To obtain a quantitative description, it was necessary to determine the values of generalised stress intensity factors (GSIFs). The GSIFs were determined using the developed analytical-numerical method. The calculations were made for various load variants (uniaxial/biaxial tension load, shear load) and notch positions (single/double edge-notched plate, centre-notched plate). Additionally, the impact of notch geometry (height and opening angle) and relative stiffness (Young's moduli ratio of both components of bi-material) on GSIFs was investigated. It has been noticed that with a decrease in the relative stiffness and an increase in the notch angle or its height, the normalised GSIFs values increased. The obtained results were compared with the data available in the literature and their satisfactory agreement with those presented by other scientists was found. [ABSTRACT FROM AUTHOR]