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Purpose: The knowledge of the normal geometrical characteristics of the proximal humerus is crucial to the success of its arthroplasty. This important information, is however, limited for the South African population. Therefore, this study investigates the three-dimensional morphometric parameters, specifically examining the intra-ancestral differences within the South African population. Methods: With the aid of geometry extraction techniques, various morphometric characteristics were measured on South African cadaveric humeri originating from three ethnicities including whites, blacks, and mixed in the ratio 1:2:4. Results: There is a significant mean difference in humeral head diameter between blacks and whites and between mixed and whites with a mean difference of $-$4.86, 95% CI ($-9.40$, $-0.32$) and $-4.50$, 95% CI ($-8.56$, $-0.44$), respectively. Similarly, for articular surface diameter, a significant mean difference of $-4.58$, 95% CI ($-9.10$, $-0.0646$) and $-4.32$, 95% CI ($-8.36$, $-0.2854$) were recorded between blacks and whites and between mixed and whites, respectively. Conclusion: The outcome of our study showed that the shape of the South African proximal humerus varies distinctively within the different ethnicities that were measured. The findings from this study may provide the data required to design and develop a new shoulder implant appropriate for South African patients.

Electrocardiogram (ECG) signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of a specimen. Independent component analysis (ICA) of measured ECG signals yields the independent sources, provided that certain requirements are fulfilled. Properly parametrized ECG signals provide a better view of the extracted ECG signals, while reducing the amount of ECG data. Independent components (ICs) of parametrized ECG signals may also be more readily interpretable than original ECG measurements or even their ICs. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG signals for a clear interpretation of ECG data in diagnostic applications. In this work, ICA is tested on the Common Standards for Electrocardiography (CSE) database files corrupted by abrupt changes, high frequency noise, power line interference, etc. The joint approximation for diagonalization of eigen matrices (JADE) algorithm for ICA is applied to three-channel ECG, and the sources are separated as ICs. In this analysis, an extension is applied to the algorithm for further correction of the extracted components. The values of R-peak before and after application of ICA are found using quadratic spline wavelet, which facilitates the estimation of the reconstruction errors. The results indicate that, in most of the cases, the percentage reconstruction error is small at around 3%. The paper also highlights the advantages, limitations, and diagnostic feature extraction capability of ICA for clinicians and medical practitioners. Kurtosis is varied in the range of 3.0–7.0, and variance of variance (Varvar) is varied in the range of 0.2–0.5.

The neoclassical growth model is being analyzed subject to spatially homogeneous perturbations by using the weak nonlinear method of solution and comparing its results to the numerical solution. The latter expands the analytical tools beyond the investigation of Turing instability. The results identify a Hopf bifurcation at a critical value of a controlling parameter, and their comparison to direct numerical solutions show an excellent match in the neighborhood of this critical value and for amplitudes of oscillations that are not too large.

The need for the possible improvements in the proposed algorithm is felt toward more effective filtering in the principal component analysis (PCA) preprocessing stage itself, as well for better variance threshold adjustment. Using composite wavelet transform (WT)-based PCA–ICA methods helps for redundant data reduction as well for better feature extraction. This article discusses some of the conditions of ICA that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. In this analysis, a new statistical algorithm is proposed, based on the use of combined PCA–ICA for the three correlated channels of 12-channel electrocardiographic (ECG) data. This study also deals with the detection of QRS complexes in electrocardiograms using combined PCA–ICA algorithm.

The efficacy of the combined PCA–ICA algorithm lies in the fact that the location of the R-peaks is accurately determined, and none of the peaks are ignored or missed, as quadratic spline wavelet is also used. With (WT)-based methods, PCA and ICA are used not only for preprocessing, but may also be used for postprocessing based on the requirements, whether ICA is used first then PCA or vice versa.

Cell spreading plays an important role in the modulation of physiological functions such as inflammation and cancer metastasis. The Brownian ratchet model and Bell's model have been used to simulate actin dynamics and bond kinetics for focal adhesion dynamics, respectively. In the present study, these models were modified and two additional subcellular mechanisms, integrin and myosin kinetics, were incoporated. An integrin recruitment function was introduced to determine the size of a focal adhesion associated with the substrate stiffness. The relationship between myosin concentration and the actin protrusion velocity was described by a first-order differential equation. Subcellular processes, including cell protrusion, focal adhesion formation, and stress fiber formation, were integrated into an axial-symmetric biophysical model, while inputs to the model were kinematic data from time-lapse experiments. Numerical simulations of the model using the Gillespie algorithm showed that dynamics of cell spreading can be well described by the model.

Walking is daily physical activity and a common way of exercise during pregnancy, but morphological changes can modify the gait pattern. Biomechanical models can help in evaluating joint mechanical loads and kinetics and kinematics during gait, and provide patterns. This study aimed to describe the gait pattern during the second trimester of pregnancy and give an orientation for biomechanical modeling for pregnant women. The ankle and hip joints seem to be more overloaded, mainly in the sagittal and frontal planes, respectively. Results show that pregnant women have a similar walking pattern to the normal gait. This model construction was revealed to be appropriate for describing gait during the second trimester of pregnancy.

Cell migration is crucial for many physiological functions such as wound healing, immuno-response and carcinogenesis. In this study an one-dimensional model of migration of fibroblasts was developed by modeling and integrating five subcellular processes, namely, actin protrusion, focal adhesion formation, stress fiber formation, polarization and retraction. The direction of migration was determined by polarization, which was related to direction of the stiffness gradient of the substrate. By controlling intensity of ultraviolet exposure on type-I collagen, a substrate with a stiffness gradient could be fabricated. Kinematic analyses of positions of the cell front, the nucleus and the cell rear, were utilized as inputs to the model. Simulation results of five live NIH 3T3 fibroblasts showed that the model was capable of simulating fast moving, slow moving and back-and-forth moving of the cells on the substrate.

The dynamic study of frog’s swimming style contributes to the modeling of the nature-inspired robots. To study the torque matrix produced in the joints during continuous modeling, the dynamic model of the Xenopus laevis swimming is reproduced in the coronal plane. The necessary kinematic data for the modeling is extracted from the frog movement graphs and diagrams during swimming. In the dynamic model, legs are considered as a group of rigid links. In order to verify this method, the generated forward force in half a cycle is studied. Unlike the previous studies, the role of geometry, dimensions and mechanical properties of the legs’ fundamental links in generating thrust force is modeled in this study, leading to finding the most proper form for this mechanism design.

With the aim to build an accurate skeletal muscle simulation model, the biomechanical modeling and solution method of skeletal muscle were developed. First, the Mooney–Rivlin model, polynomial model, exponential model, logarithmic model, combined exponential and polynomial model were compared. The biomechanical model of skeletal muscle was built by combining the exponential and polynomial models. Second, the geometric non-linearity problem and material non-linearity problem of the biomechanical model were solved by using the finite element method. The program for this solution was implemented using Visual Studio 2012. Finally, the simulation results were compared to the experimental results. The maximum error between the simulation curve and the experiment stress–strain curve was 0.00149MPa. Finite element simulation for the lateral femoral muscle was conducted using the program developed in this study.

We developed a quantitative biomechanical analysis of the supine bridge exercise by combining biomechanical modeling with kinematic and kinetic measurements recorded with an optoelectronic motion capture system and a grid of force platforms embedded in the ground. The relevant joint angles and joint torques were determined accounting for three exercise variants: the distance L of the feet from upper back, the degree of pelvic elevation, and the change in shear ground reaction force intentionally induced by voluntary isometric knee-flexion/extension efforts. Contrary to the ankle and hip, the knee angle displays a nonmonotonic dependence on pelvic elevation. A voluntary isometric knee-flexion (knee-extension) effort enhances (reduces) the hip extensor torque when the hips are above the level of the ground. Progressive pelvic elevation and decrease in L gradually change the knee flexor torque into a knee extensor torque, while reducing the hip extensor torque, to reach a limit configuration where a knee extensor torque sustains the bridge position with a negligible contribution of the hip extensors. Moreover, in this configuration, a hip flexor torque is needed to counteract the hip extension thrust induced by a voluntary quadriceps effort across the closed kinetic chain constituted by the lower limbs and trunk.

The biomechanical modeling and simulation of human tissue are the basic part of virtual surgery system. As an important human soft tissue, the biomechanical modeling and simulation method of subcutaneous adipose tissue are urgent problems to be solved in virtual surgery system. In this paper, biomechanical modeling and simulation of subcutaneous adipose tissue were completed based on linear elastic and hyperelastic finite element model. First, the deformation process of subcutaneous adipose tissue was divided into two stages: Initial loading stage and later loading stage. Furthermore, the linear elastic model at initial loading stage and the nonlinear elastic model at later loading stage were established, respectively. Then, the biomechanical model of subcutaneous adipose tissue was solved based on the finite element method. Finally, the linear finite element calculation was included in nonlinear finite element calculations, and finite element simulation program of the subcutaneous adipose tissue was compiled in Visual Studio environment. The accuracy of the model was verified by comparing the simulation results with the experimental results. The efficiency of finite element simulation program of the linear and nonlinear hybrid model is evaluated by comparing the simulation time with the single linear model and the hyperelastic model.

This study aims to develop effective predictive models to assess knee replacement (KR) risk in knee osteoarthritis (KOA) patients, which is important in the personalized diagnosis, assessment, and treatment of KOA. A total of 269KOA patients were selected from the osteoarthritis initiative (OAI) public database and their clinical and knee cartilage image feature data were included in this study. First, the clinical risk factors were screened using univariate Cox regression and then used in the construction of the Clinical model. Next, their image features were selected using univariate and least absolute shrinkage and selection operator (LASSO) Cox methods step by step, and then used in the construction of the Image model. Finally, the Image+Clinical model was constructed by combining the Image model and clinical risk factors, which was then converted into a nomogram for better visualization and future clinical use. All models were validated and compared using the metric of C-index. In addition, Kaplan–Meier (KM) survival curve with log-rank test and calibration curve were also included in the assessment of the model risk stratification ability and prediction consistency. Age and three Western Ontario and McMaster Universities (WOMAC) scores were found significantly correlated with KR, and thus included in Clinical model construction. Fifty-eight features were selected from 92knee cartilage image features using univariate cox, and four image features were retained using the LASSO Cox method. Image+Clinical model and nomogram were finally constructed by combining clinical risk factors and the Image model. Among all models, the Image+Clinical model showed the best predictive performance, and the Image model was better than the Clinical model in the KR risk predictive consistency. By determining an optimal cutoff value, both Image and Image+Clinical models could effectively stratify the KOA patients into KR high-risk and low-risk groups (log-rank test: $p<0.05$). In addition, the calibration curves also showed that model predictions were in excellent agreement with the actual observations for both 3-year and 6-year KR risk probabilities, both in training and test sets. The constructed model and nomogram showed excellent risk stratification and prediction ability, which can be used as a useful tool to evaluate the progress and prognosis of KOA patients individually, and guide the clinical decision-making of KOA treatment and prognosis.

The musculoskeletal system, containing bones, cartilage, skeletal muscles, tendons, ligaments, and some other tissues, is a perfect system that undergoes the external and internal load properly and controls the body’s motion efficiently. In this system, skeletal muscle is obviously indispensable. People have been studying the mystery of skeletal muscle mechanics for the last 80 years. Many modeling methods have been used to study skeletal muscles. Among these methods, multi-scale modeling methods are increasingly frequently used in studying musculoskeletal systems, especially those of skeletal muscles. In this review, we summarize the multi-scale modeling methods in studying works of skeletal muscle modeling reported so far. Then, several multi-scale methods of other tissues which possibly could be used in research on skeletal muscle modeling are discussed. Finally, the future research direction and the main challenges of multi-scale skeletal muscle modeling are briefly presented.

Taking advantage of the well-known Komarova’s type model, it is proposed here to analyze again the bone dynamics process within the context of a new parameter introduced in order to act only on the production/removal activities of osteoblasts and osteoclasts (BMU) while saving the net effectiveness of osteoclast-or-osteoblast-derived autocrine or paracrine factors. The effects of this new parameter upon simulation of the bone remodeling cycle as well as stable, intrinsically regulated oscillatory changes in bone cell numbers and bone mass are analyzed and from which unstable oscillations, similar to pathologically accelerated bone remodeling of Paget’s disease appear. One can say that the introduced d parameter, with $0\le d<1$, can be viewed as a new parameter driven by Pathological conditions. On the other hand, the parameter d can probably be linked to the complex mechanisms that regulate communication between osteoblasts and osteoclasts, which are known as critical to bone cell biology.

The superiority of deformable human fingertips as compared to hard robot gripper fingers for grasping and manipulation has led to a number of investigations with robot hands employing elastomers or materials such as fluids or powders beneath a membrane at the fingertips. It is interesting to study the phenomenon of contact interaction with an object and its manipulation. In this paper bond graph modeling is used to model the contact between a grasped object and two soft fingertips. Detailed bond graph modeling (BGM) of the contact phenomenon with two soft-finger contacts which are placed against each other on the opposite sides of the grasped object as is generally the case in a manufacturing environment is presented. The stability of the object is determined which includes friction between the soft finger contact surfaces and the object, the stiffness of the springs is exploited while achieving the stability in the soft-grasping. The weight of the object coming downward is controlled by the friction between the fingers and the object during the application of contact forces by varying the damping and the stiffness in the soft finger.

This paper presents an LQR controller approach for the simulation and controls of an affordable commercial humanoid robot doing a handstand on a high bar, by considering it as an underactuated 3-link inverted pendulum with off-centered masses. The developments presented include: i) the software development for interfacing the Matlab® Real Time Workshop Toolbox® with the humanoid robot servos; ii) the identification of the internal and external dynamic parameter of the humanoid servos and structure, respectively; iii) the dynamics modeling and simulation of the humanoid robot; iv) the deduction of the equations of motion for an underactuated n-link inverted pendulum. Simulation results proved the adequacy of LQR controller.