Showing papers in "Computer Methods in Biomechanics and Biomedical Engineering in 2022"
TL;DR: AIS patients with OP have lower lumbar stability, a higher risk of fracture of lumbars vertebrae, and spinal nerves are more likely to be compressed by intervertebral discs.
Abstract: Abstract Objective: To explore the effect of osteoporosis on the stress, stability, and lumbar intervertebral disc of AIS lumbar vertebrae by finite element method. Better understand the biomechanical characteristics of osteoporotic scoliosis. Methods: Based on the CT images of normal lumbar vertebrae and lumbar vertebrae with AIS, the finite element models were established to simulate the estimated osteoporosis by changing the Young's modulus of cortical bone, cancellous bone, and endplate. Four finite element models of normal lumbar, osteoporotic lumbar, normal AIS lumbar and osteoporotic AIS lumbar were established, and the same load and boundary conditions were applied respectively. The displacement, stress, and intervertebral disc strain of the four models were compared to explore the effect of osteoporosis on the stability and injury risk of AIS. Results: After suffering from osteoporosis, under the same load, the displacement of lumbar spine increases, the stability decreases, and the stability of AIS lumbar spine decrease more obviously, especially under extension load. Suffering from osteoporosis will increase the stress of lumbar spine, AIS lumbar spine increases more obviously, and the stress is more concentrated, Osteoporotic lumbar spine only affects the strain of intervertebral disc when AIS lumbar spine bends on the concave side, resulting in greater strain behind the concave side of intervertebral disc. Conclusions: AIS patients with OP have lower lumbar stability, a higher risk of fracture of lumbar vertebrae, and spinal nerves are more likely to be compressed by intervertebral discs. OP can aggravate the scoliosis of lumbar vertebrae.
12 citations
TL;DR: In this paper , a fractal fractional-order operator was proposed for the Coronavirus (COVID-19) for different infections phases and multiple routes of transmission in the Atangana-Baleanu fractal-fractional operator.
Abstract: Abstract We investigate the dynamical behavior of Coronavirus (COVID-19) for different infections phases and multiple routes of transmission. In this regard, we study a COVID-19 model in the context of fractal-fractional order operator. First, we study the COVID-19 dynamics with a fractal fractional-order operator in the framework of Atangana–Baleanu fractal-fractional operator. We estimated the basic reduction number and the stability results of the proposed model. We show the data fitting to the proposed model. The system has been investigated for qualitative analysis. Novel numerical methods are introduced for the derivation of an iterative scheme of the fractal-fractional Atangana–Baleanu order. Finally, numerical simulations are performed for various orders of fractal-fractional dimension.
12 citations
TL;DR: In this article , the fractional mathematical model for the current pandemic of COVID-19 is investigated, which is composed of four agents of susceptible infectious quarantined and recovered cases respectively.
Abstract: Abstract In the given manuscript, the fractional mathematical model for the current pandemic of COVID-19 is investigated. The model is composed of four agents of susceptible infectious quarantined and recovered cases respectively. The fractional operator of Atangana-Baleanu-Caputo is applied to the considered model for the fractional dynamics. The basic reproduction number is computed for the stability analysis. The techniques of existence and uniqueness of the solution are established with the help of fixed point theory. The concept of stability is also derived using the Ulam-Hyers stability technique. With the help of the fractional order numerical method of Adams-Bashforth, we find the approximate solution of the said model. The obtained scheme is simulated on different fractional orders along with the comparison of integer orders. Varying the numerical values for the contact rate different simulations are performed to check the effect of it on the dynamics of COVID-19.
12 citations
TL;DR: The findings reveal that the use of deep learning methods is satisfactory in affect recognition when a great number of frames is employed, and the proposed hybrid deep model outperforms traditional neural network and deep learning approaches with an average classification accuracy of 93%.
Abstract: Abstract Emotion recognition has become increasingly utilized in the medical, advertising, and military domains. Recognizing the cues of emotion from human behaviors or physiological responses is encouraging for the research community. However, extracting true characteristics from sensor data to understand emotions can be challenging due to the complex nature of these signals. Therefore, advanced feature engineering techniques are required for accurate signal recognition. This study presents a hybrid affective model that employs a transfer learning approach for emotion classification using large-frame sensor signals which employ a genuine dataset of signal fusion gathered from 30 participants using wearable sensor systems interconnected with mobile devices. The proposed approach implements several learning algorithms such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and several other shallow methods on the sensor input to handle the requirements for the traditional feature extraction process. The findings reveal that the use of deep learning methods is satisfactory in affect recognition when a great number of frames is employed, and the proposed hybrid deep model outperforms traditional neural network (overall accuracy of 54%) and deep learning approaches (overall accuracy of 76%), with an average classification accuracy of 93%. This hybrid deep model also has a higher accuracy than our previously proposed statistical autoregressive hidden Markov model (AR-HMM) approach, with 88.6% accuracy. Accuracy assessment was performed by means of several statistics measures (accuracy, precision, recall, F-measure, and RMSE).
10 citations
TL;DR: Cell resolved blood flow simulations are used to study the pulsatile flow of whole blood through a segmented retinal microaneurysm to resolve the impact of rigid red blood cells, as a result of diabetes, in blood flow.
Abstract: Abstract Blood flow within the vasculature of the retina has been found to influence the progression of diabetic retinopathy. In this research cell resolved blood flow simulations are used to study the pulsatile flow of whole blood through a segmented retinal microaneurysm. Images were collected using adaptive optics optical coherence tomography of the retina of a patient with diabetic retinopathy, and a sidewall (sacciform) microaneurysm was segmented from the volumetric data. The original microaneurysm neck width was varied to produce two additional aneurysm geometries in order to probe the influence of neck width on the transport of red blood cells and platelets into the aneurysm. Red blood cell membrane stiffness was also increased to resolve the impact of rigid red blood cells, as a result of diabetes, in blood flow. Wall shear stress and wall shear stress gradients were calculated throughout the aneurysm domains, and the quantification of the influence of the red blood cells is presented. Average wall shear stress and wall shear stress gradients increased due to the increase of red blood cell membrane stiffness. Stiffened red blood cells were also found to induce higher local wall shear stress and wall shear stress gradients as they passed through the leading and draining parental vessels. Stiffened red blood cells were found to penetrate the aneurysm sac more than healthy red blood cells, as well as decreasing the margination of platelets to the vessel walls of the parental vessel, which caused a decrease in platelet penetration into the aneurysm sac.
8 citations
TL;DR: In this paper , the influence of Arrhenius activation energy and variable velocity slip on MHD blood motion of Seminal nanofluid in a vertical symmetric channel is discussed.
Abstract: Abstract This study sheds light on the influences of Arrhenius activation energy and variable velocity slip on MHD blood motion of Seminal nanofluid in a vertical symmetric channel. In addition, mixed convection, hall current and thermal jump are taken into consideration. The governing system of differential equations with highly nonlinear terms is simplified with facts of long wavelength and low Reynolds number. Pade' approximant and differential transform techniques are combined mathematically to obtain the semi-numerical solutions for the governing system of PDEs. The results are computed and verified graphically with aid of Mathematica 12.3. Physical parameters considered are studied in detail sketchily for the proposed model. Verification/signification of results is approved semi-numerically by comparing the prior results by the newest existing published results by Ahmad et al 2021. Results show that, Velocity of seminal fluid is diminishes with a rise in viscosity-dependent parameter that is a significant feature which can be utilized in controlling the transport of spermatozoa into the cervical canal.
7 citations
TL;DR: A mathematical epidemiological model for the transmission of Hepatitis B virus in the frame of fractional derivative with harmonic mean type incidence rate is proposed in this article , which is then fictionalized by utilizing the Atangana-Baleanu-Capotu ( ) operator with vaccination effects.
Abstract: Abstract A mathematical epidemiological model for the transmission of Hepatitis B virus in the frame of fractional derivative with harmonic mean type incidence rate is proposed in this article. The proposed mathematical model is then fictionalized by utilizing the Atangana–Baleanu–Capotu ( ) operator with vaccination effects. The threshold number R0 is calculated by utilizing the next-generation matrix approach. The existence and uniqueness of solution of the proposed model are proved by utilizing the well-known fixed point theory. For the numerical solution of the proposed model with derivative the well-known Adams–Bashforth–Molton (ABM) method is utilized. Likewise, stability is required in regard of the numerical arrangement. In this manner, Ulam–Hyers stability utilizing nonlinear functional analysis is utilized for the proposed model.
7 citations
TL;DR: This study developed a Wearable-ANN approach for calculating hip joint angles/moments during walking in the sagittal/frontal planes with data from 17 healthy subjects, leveraging one shin-mounted inertial measurement unit (IMU) and a force-measuring insole for data capture.
Abstract: Abstract Quantifying hip angles/moments during gait is critical for improving hip pathology diagnostic and treatment methods. Recent work has validated approaches combining wearables with artificial neural networks (ANNs) for cheaper, portable hip joint angle/moment computation. This study developed a Wearable-ANN approach for calculating hip joint angles/moments during walking in the sagittal/frontal planes with data from 17 healthy subjects, leveraging one shin-mounted inertial measurement unit (IMU) and a force-measuring insole for data capture. Compared to the benchmark approach, a two hidden layer ANN (n = 5 nodes per layer) achieved an average rRMSE = 15% and R2=0.85 across outputs, subjects and training rounds.
6 citations
TL;DR: In this article , the authors proposed a secure authentication to protect the sensible data related to healthcare through IoT network, where the ECG signal from the patients are stored in the cloud in encrypted form, where a modified Elliptic-curve Diffie-Hellman (ECDH) encryption is applied to ensure secure access to the stored data to be used for the analysis of arrhythmia.
Abstract: The Internet of Things (IoT) have become an important part of human in day-to-day life as it permits accesses and manages data flawlessly, the security of data in cloud storage is of great concern in healthcare applications. This paper proposes a secure authentication to protect the sensible data related to healthcare through IoT network. Initially, the Electrocardiography (ECG) signal from the patients are stored in the cloud in encrypted form, where the proposed modified Elliptic-curve Diffie–Hellman (ECDH) encryption is applied to ensure secure access to the stored data to be used for the analysis of arrhythmia. The obtained data for the arrhythmia diagnosis is subjected to classify the attack using the neural network (NN). The weights of the NN are tuned using the proposed hybrid tempest brain optimization algorithm, which integrates the characteristic features of collaborative search agents and the hybrid search agents. The proposed method obtained the values of 95%, 7150, and 111 of detection accuracy, number of genuine users, and information loss of the respectively, which shows the superiority of the proposed method in attack detection and mitigation.
6 citations
TL;DR: To investigate flexibility (ROM), stability, stress condition in implant, implant adjacent bone of the implanted lumbar spine during different physiological movements and loading environments, a finite element intact model with two-level pedicle screw-rod fusion was developed.
Abstract: Abstract Titanium alloy-based Pedicle screw-rod fusion is a very common technique to provide higher fusion regularity than other methods. In recent times, Carbon-fibre-reinforced (CFR)-PEEK rod is used to better reduce the fusion rate. Alternatively, total disc replacement (TDR) is also very common for the non-fusion treatment method for degenerative disc disease (DDD). This study aims to investigate flexibility (ROM), stability, stress condition in implant, implant adjacent bone of the implanted lumbar spine during different physiological movements and loading environments. The finite element (FE) intact model of the lumbar spine (L2-L5) with two-level pedicle screw-rod fusion at L3-L4-L5 and two-level artificial disc replacement was developed. CFR-PEEK was taken for rod for semi-rigid fusion. UHMWPE was taken as core part of the artificial disc. The FE models were simulated under 6, 8 and 10 Nm moments in left right lateral bending, flexion and extension movements. The total ROM was reduced for two-level pedicle screw fixation and increased for the artificial disc replacement model during flexion extension compared to the intact spine. The total ROM was reduced by around 54% and 25% for two-level fixation and increased by 30% and 19.5% for artificial disc replacement spine, under flexion-extension and left-right lateral bending respectively. For screw fixation, the ROM increased by 15% and 18% reduced by 4.5% and 14% for disc replacement at the adjacent segments for flexion-extension and left-right lateral bending.
6 citations
TL;DR: In this article , the numerical results based on the nonlinear dengue fever system are obtained through the ANNs-LMB to reduce the mean square error, the numerical outcomes are proficient to the proportional measures using the topographies of the fitness attained in mean squared error sense, correlation, error histograms and regression.
Abstract: This study is relevant to present the numerical form of the nonlinear dengue fever SIR system are presented using the artificial neural networks along with the Levenberg-Marquardt backpropagation technique, i.e. ANNs-LMB. The procedures of ANNs-LMB are applied with three different sample data scales based on testing, training and authentication. The statistics to solve three cases of the nonlinear dengue fever based on susceptible, infected and recovered system are selected with 75%, 15% and 10% for training, validation and testing, respectively. To find the numerical results of the nonlinear dengue fever system, the reference dataset is designed on the basis of Adams scheme for the numerical solution. The numerical results based on the nonlinear dengue fever system are obtained through the ANNs-LMB to reduce the mean square error. In order to check the exactness, reliability, effectiveness and competence of the proposed ANNs-LMB, the numerical outcomes are proficient to the proportional measures using the topographies of the fitness attained in mean squared error sense, correlation, error histograms and regression.
TL;DR: In this paper , a U-Net variation with 2.5D input layer was used for final segmentation, which achieved 93.82% of Dice similarity coefficient and 88.42% of Jaccard.
Abstract: ABSTRACT Organs at risk (OARs) are healthy tissues around cancers that must be preserved in radiotherapy (RT). Heart is one of the fundamental organs for the full functioning of the human body, protecting this organ in the RT is of paramount importance. For this, the planning process must be careful. Planning begins with manual segmentation by specialists in computed tomography (CT). We propose a deep learning method for heart segmentation from planning CT. The method consists of four steps: (1) material acquisition from a public database; (2) volume standardisation using registration and histogram matching; (3) Volume of Interest (VOI) segmentation using atlas; and (4) a U-Net variation with 2.5D input layer for final segmentation. We tested in 36 CTs and achieved 93.82% of the Dice similarity coefficient, 88.42% of the Jaccard. With the innovation of the proposed method and the promising results, we show that our method effectively uses heart segmentation.
TL;DR: The smart healthcare method using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA–MKELM) approach and the experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
Abstract: Abstract In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA–MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA–MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
TL;DR: A nucleic acid amplification system based on polymerase chain reaction (PCR) was developed to meet the requirements of point-of-care testing (POCT) for nucleic acids and has higher sensitivity than Tianlong PCR instrument.
Abstract: Abstract Nucleic acid testing (NAT) has been widely used in many fields such as medical diagnosis, food safety testing and forensic identification. However, it can only be carried out in professional laboratory because the test process is complicated and rigorous. In this paper, a nucleic acid amplification system based on polymerase chain reaction (PCR) was developed to meet the requirements of point-of-care testing (POCT) for nucleic acids. Firstly, the mechanical structure and electronic control system were designed and constructed. Secondly, an integral separation PID algorithm for temperature control and an intelligent temperature compensation method based on support vector regression (SVR) were proposed. Finally, temperature measurement and biological experiments were performed to prove the stability and availability of the nucleic acid amplification system. The results showed that the system achieved a rapid temperature change velocity of 4.5 °C/s, and the steady-state error was within ± 0.5 °C. The nucleic acids in samples of different concentrations were well amplified, the system can be used for quantitative detection of nucleic acid with the help of a fluorescence detection system, and has higher sensitivity than Tianlong PCR instrument.
TL;DR: A comparative study on the changes in hydrodynamic microenvironment of osteocytes during human body high-intensity exercise at different frequencies revealed the biomechanical mechanism by which exercise has an effect in fighting osteoporosis in astronauts.
Abstract: Abstract Osteoporosis occurs in astronauts after long-term space flight owing to the lack of gravity. The mechanical microenvironment of osteocytes in load-bearing bone are changed during resistance exercise, which prevents massive bone loss in the human body. A cylindrical fluid-structure coupling finite element model for osteons with a two-stage pore structure (i.e., Haversian canal, lacunar-canalicular system) was established with the software COMSOL. In the Earth’s gravity field and in microgravity, considering the effects of pulsating pressure of arterioles, a comparative study was performed on the changes in hydrodynamic microenvironment of osteocytes during human body high-intensity exercise at different frequencies (defined as causing bone to produce 3000 με) and the body is at rest. Positive and negative liquid pressure (with respect to one atmosphere pressure) alternately acted on osteocytes during human exercising, but only positive pressure acted on osteocytes during human resting. The variation range of liquid pressure acted on osteocytes during human exercising was significantly higher than that during resting. The liquid flow velocity around osteocytes during body exercise was about four orders of magnitude higher than that during resting. In microgravity, moderate physical exercise can obviously improve the hydrodynamic microenvironment of osteocytes in load-bearing bone, which could compensate for the lack of mechanical stimulation to osteocytes caused by the lack of gravity, thereby promoting the normal physiological function of osteocytes. To a certain extent, these results revealed the biomechanical mechanism by which exercise has an effect in fighting osteoporosis in astronauts. Graphical Abstract Highlights Positive and negative pressure alternately acted on osteocytes during exercise. Exercise obviously improve the fluid mechanics environment of osteocyte. Physical exercise has an effect in fighting osteoporosis in astronauts. Mechanical environment of osteocytes significantly depend on gravity fields.
TL;DR: In this paper , a mathematical modeling of the novel corona virus (COVID-19) is considered, where a brief relationship between the unknown hosts and bats is described and the interaction among the seafood market and peoples is studied.
Abstract: In this paper, the mathematical modeling of the novel corona virus (COVID-19) is considered. A brief relationship between the unknown hosts and bats is described. Then the interaction among the seafood market and peoples is studied. After that, the proposed model is reduced by assuming that the seafood market has an adequate source of infection that is capable of spreading infection among the people. The reproductive number is calculated and it is proved that the proposed model is locally asymptotically stable when the reproductive number is less than unity. Then, the stability results of the endemic equilibria are also discussed. To understand the complex dynamical behavior, fractal-fractional derivative is used. Therefore, the proposed model is then converted to fractal-fractional order model in Atangana-Baleanu (AB) derivative and solved numerically by using two different techniques. For numerical simulation Adam-Bash Forth method based on piece-wise Lagrangian interpolation is used. The infection cases for Jan-21, 2020, till Jan-28, 2020 are considered. Then graphical consequences are compared with real reported data of Wuhan city to demonstrate the efficiency of the method proposed by us.
TL;DR: The efficiency of proposed method is revealed which makes it highly suitable for heart disease prediction in an efficient manner.
Abstract: Abstract In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people’s lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner.
TL;DR: In this article , the authors presented a Caputo type fractional dynamical model to assess the efficacy of facemask to the community transmission of COVID-19 and established the existence and uniqueness of the solution and subsequently, with the use of the generalized mean value theorem, the positivity and boundedness of the solutions were established.
Abstract: The emergence of highly contagious Alpha, Beta, Gamma and Delta variants and strains of COVID-19 put healthy people on high risk of contracting the infection. In addition to the vaccination strategies, the nonpharmaceutical intervention use of face mask gives protection against the contraction of the virus. To understand the efficacy of such, we present a Caputo type fractional dynamical model to assess the efficacy of facemask to the community transmission of COVID-19. The existence and uniqueness of the solution was established, and subsequently, with the use of the generalized mean value theorem, the positivity and boundedness of the solutions were established. The disease free equilibrium (DFE) was found to be asymptotically stable when the basic reproduction number R0<1. By constructing quadratic Lyapunov function, the equilibria (DFE and Endemic) were found to be globally asymptotically stable.
TL;DR: In this article , the authors evaluated the mechanical properties of composite and ceramic based indirect restorative materials used in dental treatments with scanning nanoindentation test (NT) and fracture test.
Abstract: Abstract The aim is to evaluate the mechanical properties of the composite and ceramic based indirect restorative materials used in dental treatments with scanning nanoindentation test (NT). Finite element analysis (FEA) was applied to investigate the stress distribution. Four hybrid composite materials; Indirect resin composite (IRC), Resin nanoceramic (RNC), Polymer infiltrated ceramic (PIC) and Zirconia-reinforced lithium-di-silicate (ZRC) were divided into two subgroups for NT (n = 20) and fracture test (n = 40). Statistical analyses were performed with independent t-test, ANOVA and post-hoc Tukey tests (p ≤ 0.05). The highest hardness, elasticity and fracture toughness were observed in ZRC (p = 0.001). Frequency of vertical root fractures in RNC and IRC were statistically lower than ZRC (p = 0.032). Reinforced CAD-CAM ceramics revealed higher mechanical properties compared with IRC materials. The FEA model for fracture mechanism of RNC demonstrated lowest stress values and uniform stress distribution amongst all groups, while ZRC and PIC presented the highest fracture toughness.
TL;DR: It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory System.
Abstract: Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.
TL;DR: In this article , a peridynamic model with diminishing non-locality measure is proposed to provide a non-local bone remodeling framework, which is based on the restrictions of local continuum theory.
Abstract: Abstract Bone remodelling is a complex biomechanical process, which has been studied widely based on the restrictions of local continuum theory. To provide a nonlocal bone remodelling framework, we propose, for the first time, a peridynamic formulation on the macroscale. We illustrate our implementation with a common benchmark test as well as two load cases of the proximal femur. On the one hand, results of our peridynamic model with diminishing nonlocality measure converge to the results of a local finite element model. On the other hand, increasing the neighbourhood size shows to what extent the additional degree of freedom, the nonlocality, can influence the density evolution.
TL;DR: The hydrothermal features of unsteady, incompressible, and laminar hybrid nanofluid motion through a porous capillary are analytically studied in the magnetic field presence to find applications in biomedicine, nanotechnology, and fluid dynamics.
Abstract: Abstract The hydrothermal features of unsteady, incompressible, and laminar hybrid nanofluid motion through a porous capillary are analytically studied in the magnetic field presence. The hybrid nanofluid (GO + ZnO + Blood) is synthesized by blending nanomaterials of graphene oxide and zinc oxide with blood acting as the host fluid. The mathematical model of the flow comprises of a coupled nonlinear set of partial differential equations (PDEs) satisfying appropriate boundary conditions. These equations are reduced to ordinary differential equations (ODEs) by using similarity transformations and then solved with homotopy analysis method (HAM). The impacts of various pertinent physical parameters over the hybrid nanofluid state functions are examined by displaying 2 D graphs. It has been observed that the fluid velocity mitigates with the varying strength of M, A 0, N 0, and N 1. The enhancing buoyancy parameter ϵ augments the fluid velocity. The increasing Prandtl number causes to reduce, while the enhancing A 0, B, and N 2 augment the hybrid nanofluid temperature. The fluid concentration mitigates with the higher Schmidt number values and A 0, and augments with the increasing Soret number strength. The augmenting magnetic field strength causes to enhance the fluid friction, whereas the convective heat transfer increases with the Prandtl number rising values. The rising Sherwood number drops the mass transfer rate of the fluid. The achieved results are validated due to the agreement with the published results. The results of this computation will find applications in biomedicine, nanotechnology, and fluid dynamics.
TL;DR: A new protocol for accurate geometric modelling, bifurcation zone meshing and numerically investigates the arterial network with abdominal aortic aneurysms (AAA) and right internal iliac stenosis (RIIAS) is developed and a realistic arterial model is reconstructed from the computed tomography data of a human subject.
Abstract: Abstract The study of patient-specific human arterial flow dynamics is well known to face challenges like a) apt geometric modelling, b) bifurcation zone meshing, and c) capturing the hemodynamic prone to variations with multiple disease complications. Due to aneurysms and stenosis in the same arterial network, the blood flow dynamics get affected, which needs to be explored. This study develops a new protocol for accurate geometric modelling, bifurcation zone meshing and numerically investigates the arterial network with abdominal aortic aneurysms (AAA) and right internal iliac stenosis (RIIAS). A realistic arterial model is reconstructed from the computed tomography (CT) data of a human subject. To understand the combined effect of the aneurysm and aortoiliac occlusive diseases in a patient, an arterial network with AAA, RIIAS, multiple branches tapering, and curvature has been considered. Clinically significant pulsatile blood flow simulations have been carried out to trace the alteration in the flow dynamics with multiple pathological complications under consideration. The transient blood flow dynamics are investigated via wall shear stress, wall pressure, velocity contour, streamlines, vorticity, and swirling strength. During the systolic deceleration phase, the rhythmic nested rapid secondary oscillatory WSS, adverse pressure gradients, high WSS, and high WP bands are noticed. Also, the above studies will help researchers, clinicians, and doctors understand the influence of morphological changes on hemodynamics in cardiovascular studies.
TL;DR: The multi-layersclear aligner is better than the single-layer clear aligner in tooth movement, stress distribution of periodontal ligament and mechanical loading of alveolar bone, especially when the ratio of soft layer to hard layer is more than 50%.
Abstract: Abstract Background: In the invisible orthodontic treatment, composite thermoforming film materials have become the focus of orthodontic clear aligners. The orthodontic efficacy of clear aligners which consisted of multi-layers materials remains unclear. This study aims to evaluate the biomechanical effects of various multi-layers of clear aligners on en-mass retraction of maxillary anterior teeth. Methods: A patient-specific 3D non-linear finite element model numerical analysis was constructed to simulate the en-mass retraction of maxillary anterior teeth with clear aligner after extraction of the first premolars. Four kinds of multi-layers clear aligners with different proportion of film materials were simulated. The biomechanical responses of four different clear aligners on invisible orthodontics were calculated. The tooth displacement in all directions, the hydrostatic pressure of periodontal ligament, the orthodontic deformation of clear aligner, and the stress distribution of alveolar bone were compared and investigated. Results: In all experimental models, the maximum equivalent deformation of alveolar bone, the vector displacement of tooth and the compressive/tensile stress of periodontal ligament decreased with the increase of soft layer thickness. The elastic strain of clear aligners also decreased with the increase of the ratio of soft/hard layers. Conclusions: The multi-layers clear aligner is better than the single-layer clear aligner in tooth movement, stress distribution of periodontal ligament and mechanical loading of alveolar bone, especially when the ratio of soft layer to hard layer is more than 50%. Moreover, the side effects of the multi-layers clear aligner are significantly less than those of the single-layer one.
TL;DR: An enhanced Recurrent Neural Network is proposed by tuning certain parameters using the proposed TS-SFO for predicting heart disease with the help of extracted statistical features and test results show that the flexible design and subsequent tuning of RNN hyper-parameters can achieve a high prediction rate.
Abstract: Abstract The main intention of this proposal is to design and develop a new heart disease prediction model via WBAN using three stages. The first stage is data aggregation, in which data is scheduled in Time Division Multiple Access manner based on priority level, and the data from the public benchmark datasets are collected representing WBAN. In the second stage, a channel selection is performed using a developed hybrid metaheuristic algorithm named Tunicate Swarm-Sail Fish Optimization (TS-SFO) Algorithm. The main intention of the suggested channel selection algorithm is to solve the multi-objective problem based on certain constraints like Reference Signal Received Quality, Signal to Noise Ratio and channel capacity. The third stage is the heart disease prediction stage, in which the feature extraction and prediction are performed. The data transmitted in the selected channel is used for the feature extraction phase, where the weighted entropy-based statistical feature extraction is developed and extracts the essential statistical features. Then, an enhanced Recurrent Neural Network (RNN) is proposed by tuning certain parameters using the proposed TS-SFO for predicting heart disease with the help of extracted statistical features. Test results show that the flexible design and subsequent tuning of RNN hyper-parameters can achieve a high prediction rate.
TL;DR: The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively, and the proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.
Abstract: Abstract Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.
TL;DR: The results showed good agreements of TFCFs between the predictions based on the proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities, which are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA.
Abstract: Abstract Accurate prediction of tibiofemoral contact force (TFCF) during daily living activities is significant for understanding the initiation, progression, and treatment of knee osteoarthritis (KOA). However, the diversity of target activities, prediction accuracy, and computational efficiency of the current musculoskeletal simulations need to be further improved. In this study, a subject-specific musculoskeletal model considered the tibiofemoral alignment, medial-lateral contact locations, secondary tibiofemoral and all patellofemoral motions, and knee ligaments was proposed to predict the TFCFs during the five daily activities (normal walking, sit-to-stand, stand-to-sit, stair ascent, and stair descent) in OpenSim software. The standing lower-limbs-full-length radiograph, local radiograph of knee joint, motion capture data, and force plate data of eighteen subjects were acquired as the input data of the musculoskeletal model. The results showed good agreements of TFCFs between the predictions based on our proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities (RMSE: 0.16 ∼ 0.31 BW, R2: 0.88 ∼ 0.97, M: −0.11 ∼ −0.02, P: 0.03 ∼ 0.10, and C: 0.04 ∼ 0.14). Additionally, the order of the peak total and lateral TFCFs from low to high was normal walking, stair ascent and stand-to-sit, and stair descent and sit-to-stand (P < 0.05), and the peak medial TFCF was stand-to-sit, sit-to-stand, normal walking, stair ascent and stair descent (P < 0.05). The outcomes of this study are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA.
TL;DR: The simulation results of this study could provide a reference for helmet and mounted devices design and offer a proposal for the protection of pilots during ejection.
Abstract: The helmet plays an important role in protection of pilot's head and enhances the pilot's capabilities and performance significantly with the use of mounted devices such as the Night Vision Goggle (NVG). However, the use of helmet-mounted devices might increase the risk of injury due to the increased helmet weight and change in the centre of gravity of head. In this study, four helmets with different combinations of mounted devices were modelled in a validated human head-neck multi-body model to analyse their effects on the pilot's neck injury during simulated ejection. The probability of neck injury was evaluated and predicted using the Nij neck injury criteria and human injury risk curves, considering the tolerance of injury for upper and lower cervical segment. It was demonstrated that the helmet-mounted devices would increase the compression force and bending moment on cervical spine, especially for the lower cervical segments with higher Nij. In the cases with Night Vision Goggle, Nij of the lower cervical segment reached 0.54, which exceeded the requirement in aviation filed. For the cases with Visor, excessive extension occurred, resulting in a high Nij. The simulation results of this study could provide a reference for helmet and mounted devices design and offer a proposal for the protection of pilots during ejection.
TL;DR: A FE model is proposed that examines all phases of tissue deformation, including puncture, cutting, needle-tissue interaction, and various stress-strain values for BMB needle during interaction and shows the potential of this technique to estimate bone damage and tissue deformed for multiple needle dimensions, coefficient of friction, and penetration speeds.
Abstract: Abstract The main aim of this work is to use a finite element technique (FEM) to gain understanding about the bone marrow biopsy (BMB) needle insertion process and needle-tissue interactions in the human iliac crest. A multi-layer iliac crest model consists of stratum corneum, dermis, epidermis, hypodermis, cortical, and cancellous bone has been established. This paper proposes a FE model that examines all phases of tissue deformation, including puncture, cutting, needle-tissue interaction, and various stress-strain values for BMB needle during interaction. The results explain the needle-tissue interface and show the potential of this technique to estimate bone damage and tissue deformation for multiple needle dimensions, coefficient of friction, and penetration speeds. The insertion and extraction force of conical-shaped needles in the multi-layered iliac crest model decreased by 18.92% and 37.5%, respectively, as the needle diameter reduced from 11 G to 20 G. It has also been found that the significant insertion motion raises the deformation of the tissue due to the augmented frictional forces but reduces the strain perpendicular to the penetration direction closer to the needle tip. The simulation outcomes are helpful for the optimal design of fine biopsy needles used to perform the bone marrow biopsies.
TL;DR: In this article , a non-integer order derivative of the Leishmania model is proposed to solve the ACL problem and conditions for the local stability of the no-disease as well as the disease endemic states are derived in terms of the threshold quantity.
Abstract: Abstract Very recently, Atangana and Baleanu defined a novel arbitrary order derivative having a kernel of non-locality and non-singularity, known as AB derivative. We analyze a non-integer order Anthroponotic Leshmania Cutaneous (ACL) problem exploiting this novel AB derivative. We derive equilibria of the model and compute its threshold quantity, i.e. the so-called reproduction number. Conditions for the local stability of the no-disease as well as the disease endemic states are derived in terms of the threshold quantity. The qualitative analysis for solution of the proposed problem have derived with the aid of the theory of fixed point. We use the predictor corrector numerical approach to solve the proposed fractional order model for approximate solution. We also provide, the numerical simulations for each of the compartment of considered model at different fractional orders along with comparison with integer order to elaborate the importance of modern derivative. The fractional investigation shows that the non-integer order derivative is more realistic about the inner dynamics of the Leishmania model lying between integer order.