Biomechanical In Vitro and Finite Element Study on Different Sagittal Alignment Postures of the Lumbar Spine During Multiaxial Daily Motion.

Lumbar lordotic correction (LLC), the gold standard treatment for sagittal spinal malalignment (SMA), and its effect on sagittal balance have been critically discussed in recent studies. This paper assesses the biomechanical response of the spinal components to LLC as an additional factor for the evaluation of LLC. Human lumbar spines (L2L5) were loaded with combined bending moments in flexion (Flex)/extension (Ex) or lateral bending (LatBend) and axial rotation (AxRot) in a physiological environment. We examined the dependency of AxRot range of motion (RoM) on the applied bending moment. The results were used to validate a finite element (FE) model of the lumbar spine. With this model, the biomechanical response of the intervertebral discs (IVD) and facet joints under daily motion was studied for different sagittal alignment postures, simulated by a motion in Flex/Ex direction. Applied bending moments decreased AxRot RoM significantly (all P < 0.001). A stronger decline of AxRot RoM for Ex than for Flex direction was observed (all P < 0.0001). Our simulated results largely agreed with the experimental data (all R2 > 0.79). During the daily motion, the IVD was loaded higher with increasing lumbar lordosis (LL) for all evaluated values at L2L3 and L3L4 and posterior annulus stress (AS) at L4L5 (all P < 0.0476). The results of this study indicate that LLC with large extensions of LL may not always be advantageous regarding the biomechanical loading of the IVD. This finding may be used to improve the planning process of LLC treatments.

Motion preservation surgery for scoliosis with a vertebral body tethering system: a biomechanical study.

PURPOSE: There is a paucity of studies on new vertebral body tethering (VBT) surgical constructs especially regarding their potentially motion-preserving ability. This study analyses their effects on the ROM of the spine. METHODS: Human spines (T10-L3) were tested under pure moment in four different conditions: (1) native, (2) instrumented with one tether continuously connected in all vertebrae from T10 to L3, (3) additional instrumented with a second tether continuously connected in all vertebrae from T11 to L3, and (4) instrumented with one tether and one titanium rod (hybrid) attached to T12, L1 and L2. The instrumentation was inserted in the left lateral side. The intersegmental ROM was evaluated using a magnetic tracking system, and the medians were analysed. Please check and confirm the author names and initials are correct. Also, kindly confirm the details in the metadata are correct. The mentioned information is correct RESULTS: Compared to the native spine, the instrumented spine presented a reduction of less than 13% in global ROM considering flexion-extension and axial rotation. For left lateral bending, the median global ROM of the native spine (100%) significantly reduced to 74.6%, 66.4%, and 68.1% after testing one tether, two tethers and the hybrid construction, respectively. In these cases, the L1-L2 ROM was reduced to 68.3%, 58.5%, and 38.3%, respectively. In right lateral bending, the normalized global ROM of the spine with one tether, two tethers and the hybrid construction was 58.9%, 54.0%, and 56.6%, respectively. Considering the same order, the normalized L1-L2 ROM was 64.3%, 49.9%, and 35.3%, respectively. CONCLUSION: The investigated VBT techniques preserved global ROM of the spine in flexion-extension and axial rotation while reduced the ROM in lateral bending.

A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units.

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings-the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.

Design of a vertical Loss-in-Weight feeder prototype with experimental proof of concept validation.

Loss-in-Weights (LiW) feeders are commonly oriented in a horizontal way. In this work, an experimental proof of concept, including mechanical and electrical design, construction, and operation, of a vertical LiW feeder prototype is performed. In a systematic design process, based on functional design specifications, the semi-automated vertical LiW feeder for dosing a wide range of powders, especially cohesive ones, is developed. The new dosing machine is assessed with regard to a number of key features such as high dosing accuracy, first-in-first-out powder discharge, easily interchange of the powder container, and flexibility in controlling the speed of the auger and stirrer motors independently. An experimental sensitivity analysis to study the functionality of the dosing machine and to investigate the weight variability of the weighing platform, i.e. mass flow rate, and quantity of dosed mass, is carried out. The results of the sensitivity analysis and the powder dosing tests of five diverse powders using different auger and stirrer geometries verified the proof of concept prototype.HighlightsA systematic design approach for validating a proof of concept of a vertical loss in weight feeder is appliedA full mechanical CAD design and implementation along with electric installation and software programming are executedSensitivity analysis approach is performed to validate the functionality of the semi-automated machine and successfully dispense dissimilar powders tested with different process parametersThe machine is characterized with a number of key features: first-in-first-out powder discharge, high dosing accuracy, flexible and modular concept design, flexibility in controlling the speed of the auger and the stirrer independently, lightweight and user-friendly design.

Biomechanical In Vitro Test of a Novel Dynamic Spinal Stabilization System Incorporating Polycarbonate Urethane Material Under Physiological Conditions.

Posterior dynamic stabilization systems (PDSS) were developed to provide stabilization to pathologic or hypermobile spinal segments while maintaining the healthy biomechanics of the spine. Numerous novel dynamic devices incorporate the temperature and moisture dependent material polycarbonate urethane (PCU) due to its mechanical properties and biocompatibility. In this study, standardized pure moment in vitro tests were carried out on human lumbar spines to evaluate the performance of a device containing PCU. An environmental chamber with controlled moisture and temperature was included in the setup to meet the requirements of testing under physiological conditions. Three test conditions were compared: (1) native spine, (2) dynamic instrumentation, and (3) dynamic instrumentation with decompression. The ranges of motion, centers of rotation, and relative pedicle screw motions were evaluated. The device displayed significant stiffening in flexion-extension, lateral bending, and axial rotation load directions. A reduction of the native range of motion diminished the stiffening effect along the spinal column and has the potential to reduce the risk of the onset of degeneration of an adjacent segment. In combination with decompression, the implant decreased the native range of motion for flexion-extension and skew bending, but not for lateral bending and axial rotation. Curve fittings using the sigmoid function were performed to parameterize all load-deflection curves in order to enhance accurate numerical model calibrations and comparisons. The device caused a shift of the center of rotation (COR) in the posterior and caudal direction during flexion-extension loading.

Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

A novel nonlinear nano-scale wear law for metallic brake pads.

In the present work, molecular dynamics simulations were carried out to investigate the temperature distribution as well as the fundamental friction characteristics such as the coefficient of friction and wear in a disc-pad braking system. A wide range of constant velocity loadings was applied on metallic brake pads made of aluminium, copper and iron with different rotating speeds of a diamond-like carbon brake disc. The average temperature of Newtonian atoms and the coefficient of friction of the brake pad were investigated. The resulting relationship of the average temperature with the speed of the disc as well as the applied loading velocity can be described by power laws. The quantitative description of the volume lost from the brake pads was investigated, and it was found that the volume lost increases linearly with the sliding distance. Our results show that Archard's linear wear law is not applicable to a wide range of normal loads, e.g., in cases of low normal load where the wear rate was increased considerably and in cases of high load where there was a possibility of severe wear. In this work, a new formula for the brake pad wear in a disc brake assembly is proposed, which displays a power law relationship between the lost volume of the metallic brake pads per unit sliding distance and the applied normal load with an exponent of 0.62 ± 0.02. This work provides new insights into the fundamental understanding of the wear mechanism at the nano-scale leading to a new bottom-up wear law for metallic brake pads.

Tensile testing of the mechanical behavior of the human periodontal ligament.

BACKGROUND: The periodontal ligament (PDL) plays a key role in alveolar bone remodeling and resorption during tooth movements. The prediction of tooth mobility under functional dental loads requires a deep understanding of the mechanical behavior of the PDL, which is a critical issue in dental biomechanics. This study was aimed to examine the mechanical behavior of the PDL of the maxillary central and lateral incisors from human. The experimental results can contribute to developing an accurate constitutive model of the human PDL in orthodontics. METHODS: The samples of human incisors were cut into three slices. Uniaxial tensile tests were conducted under different loading rates. The transverse sections (cervical, middle and apex) normal to the longitudinal axis of the root of the tooth were used in the uniaxial tensile tests. Based on a bilinear simplification of the stress-strain relations, the elastic modulus of the PDL was calculated. The values of the elastic modulus in different regions were compared to explore the factors that influence the mechanical behavior of the periodontal ligament. RESULTS: The obtained stress-strain curves of the human PDL were characterized by a bilinear model with two moduli (E1 and E2) for quantifying the elastic behavior of the PDL from the central and lateral incisors. Statistically significant differences of the elastic modulus were observed in the cases of 1, 3, and 5 N loading levels for the different teeth (central and lateral incisors). The results showed that the mechanical property of the human incisors' PDLs is dependent on the location of PDL (ANOVA, P = 0.022, P < 0.05). The elastic moduli at the middle planes were greater than at the cervical and apical planes. However, at the cervical, middle, and apical planes, the elastic moduli of the mesial and distal site were not significantly different (ANOVA, P = 0.804, P > 0.05). CONCLUSIONS: The values of elastic modulus were determined in the range between 0.607 and 4.274 MPa under loads ranging from 1 to 5 N. The elastic behavior of the PDL is influenced by the loading rate, tooth type, root level, and individual variation.

Effects of urbanization on heavy metal accumulation in surface soils, Beijing.

Urbanization processes affect the accumulation of heavy metals in urban soils. Effects of urbanization on heavy metal accumulation in soils were studied using Beijing as an example. It has been suggested that the ecological function of vegetation covers shifting from natural to agricultural settings and then to urban greenbelts could increase the zinc (Zn) concentrations of soils successively. The Zn concentration of urban soils was significantly correlated to the percentage of the impervious land surface at the 500m×500m spatial scale. For urban parks, the age or years since the development accounted for 80% of the variances of cadmium (Cd) and Zn in soils. The population density, however, did not affect the heavy metal distributions in urban soils. To conclude, the urban age turned out to be a notable factor in quantifying heavy metal accumulation in urban soils.

Effects of land use intensity on the natural attenuation capacity of urban soils in Beijing, China.

Urban soils are major sinks that provide the services of attenuating and detoxifying environmental pollutants. This significant ecosystem service of urban soil can be evaluated by the natural attenuation capacity (NAC). In this research, we develop a method to calculate the natural pollutant attenuation capacity of urban soils on the basis of 5 chemical and physical measurements. By selecting municipal parks soils for reference, we assessed the spatial and temporal changes of NAC in Beijing city soils under influences of rapid urbanization. Results indicated that NAC was increasingly impacted by land use in the order: parks

Rate-dependent behavior of the amorphous phase of spider dragline silk.

The time-dependent stress-strain behavior of spider dragline silk was already observed decades ago, and has been attributed to the disordered sequences in silk proteins, which compose the soft amorphous matrix. However, the actual molecular origin and magnitude of internal friction within the amorphous matrix has remained inaccessible, because experimentally decomposing the mechanical response of the amorphous matrix from the embedded crystalline units is challenging. Here, we used atomistic molecular dynamics simulations to obtain friction forces for the relative sliding of peptide chains of Araneus diadematus spider silk within bundles of these chains as a representative unit of the amorphous matrix in silk fibers. We computed the friction coefficient and coefficient of viscosity of the amorphous phase to be in the order of 10(-6) Ns/m and 10(4) Ns/m(2), respectively, by extrapolating our simulation data to the viscous limit. Finally, we used a finite element method for the amorphous phase, solely based on parameters derived from molecular dynamics simulations including the newly determined coefficient of viscosity. With this model the time scales of stress relaxation, creep, and hysteresis were assessed, and found to be in line with the macroscopic time-dependent response of silk fibers. Our results suggest the amorphous phase to be the primary source of viscosity in silk and open up the avenue for finite element method studies of silk fiber mechanics including viscous effects.

Criticality in the fracture of silica glass: Insights from molecular mechanics.

The universality of avalanches characterizing the inelastic response of disordered materials has the potential to bridge the gap from micro to macroscale. In this study, we explore the statistics and the scaling behavior of avalanches occurring during the fracture process in silica glass using molecular mechanics. We introduce a robust method for capturing and quantifying these avalanches, allowing us to perform rigorous statistical analyses, revealing universal power laws associated with critical phenomena. The influence of an initial crack is explored, observing deviations from mean-field predictions while maintaining the property of criticality. However, the avalanche exponents in the unnotched samples are predicted correctly by the mean-field depinning model. Furthermore, we investigate the strain-dependent probability density function, its cutoff function, and the interrelation between the critical exponents. Finally, we unveil distinct scaling behavior for small and large avalanches of the crack growth, shedding light on the underlying fracture mechanisms in silica glass.

Tether pre-tension within vertebral body tethering reduces motion of the spine and influences coupled motion: a finite element analysis.

Anterior Vertebral Body Tethering (VBT) is a novel fusionless treatment option for selected adolescent idiopathic scoliosis patients which is gaining widespread interest. The primary objective of this study is to investigate the effects of tether pre-tension within VBT on the biomechanics of the spine including sagittal and transverse parameters as well as primary motion, coupled motion, and stresses acting on the L2 superior endplate. For that purpose, we used a calibrated and validated Finite Element model of the L1-L2 spine. The VBT instrumentation was inserted on the left side of the L1-L2 segment with different cord pre-tensions and submitted to an external pure moment of 6 Nm in different directions. The range of motion (ROM) for the instrumented spine was measured from the initial post-VBT position. The magnitudes of the ROM of the native spine and VBT-instrumented with pre-tensions of 100 N, 200 N, and 300 N were, respectively, 3.29°, 2.35°, 1.90° and 1.61° in extension, 3.30°, 3.46°, 2.79°, and 2.17° in flexion, 2.11°, 1.67°, 1.33° and 1.06° in right axial rotation, and 2.10°, 1.88°, 1.48° and 1.16° in left axial rotation. During flexion-extension, an insignificant coupled lateral bending motion was observed in the native spine. However, VBT instrumentation with pre-tensions of 100 N, 200 N, and 300 N generated coupled right lateral bending of 0.85°, 0.81°, and 0.71° during extension and coupled left lateral bending of 0.32°, 0.24°, and 0.19° during flexion, respectively. During lateral bending, a coupled extension motion of 0.33-0.40° is observed in the native spine, but VBT instrumentation with pre-tensions of 100 N, 200 N, and 300 N generates coupled flexion of 0.67°, 0.58°, and 0.42° during left (side of the implant) lateral bending and coupled extension of 1.28°, 1.07°, and 0.87° during right lateral bending, respectively. Therefore, vertebral body tethering generates coupled motion. Tether pre-tension within vertebral body tethering reduces the motion of the spine.

Ecological risks of polycyclic musk in soils irrigated with reclaimed municipal wastewater.

HHCB (1,3,4,6,7,8-hexahydro-4,6,6,7,8-hexamethylcyclopenta-c-2-benzopyrane) and AHTN (7-acetyl-1,1,3,4,4,6-hexamethyl-1,2,3,4-tetrahydronaphthalene) are found in reclaimed municipal wastewater. They may accumulate in soils receiving long-term application of reclaimed water thus adversely impact the soil biota. We evaluated the extent of their accumulation in receiving soils using HYDRUS-1D based on reclaimed municipal wastewater irrigation data at a public park in Beijing. The potential for ecological harms were assessed according to tested and reported outcomes of acute toxicity tests using wheat (Triticum aetivum L), earthworm (Eisenia fetida) and springtail (Bourletiella hortensis) as target organisms. Results of comparison among EC50 values from wheat, earthworm and springtail showed the EC50 value for root elongation inhibition of wheat germination was the least. Based on the least EC50, predicted no effect concentration in soils were 290 and 320ng /g for HHCB and ATHN, respectively. Comparable results from simulation to experimental and field investigating date validated the using of HYDRUS-1D in the work. Results of risk prediction showed it would take 243 and 666 years for HHCB and AHTN accumulated in soils at current irrigation practice to reach the levels for the resulting risk characterization ratios (RCRs) to cause potential ecosystem harms.

Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation.

BACKGROUND AND OBJECTIVE: Covid-19 infections are spreading around the globe since December 2019. Several diagnostic methods were developed based on biological investigations and the success of each method depends on the accuracy of identifying Covid infections. However, access to diagnostic tools can be limited, depending on geographic region and the diagnosis duration plays an important role in treating Covid-19. Since the virus causes pneumonia, its presence can also be detected using medical imaging by Radiologists. Hospitals with X-ray capabilities are widely distributed all over the world, so a method for diagnosing Covid-19 from chest X-rays would present itself. Studies have shown promising results in automatically detecting Covid-19 from medical images using supervised Artificial neural network (ANN) algorithms. The major drawback of supervised learning algorithms is that they require huge amounts of data to train. Also, the radiology equipment is not computationally efficient for deep neural networks. Therefore, we aim to develop a Generative Adversarial Network (GAN) based image augmentation to optimize the performance of custom, light, Convolutional networks used for the classification of Chest X-rays (CXR). METHODS: A Progressively Growing Generative Adversarial Network (PGGAN) is used to generate synthetic and augmented data to supplement the dataset. We propose two novel CNN architectures to perform the Multi-class classification of Covid-19, healthy and pneumonia affected Chest X-rays. Comparisons have been drawn to the state of the art models and transfer learning methods to evaluate the superiority of the networks. All the models are trained using enhanced and augmented X-ray images and are compared based on classification metrics. RESULTS: The proposed models had extremely high classification metrics with proposed Architectures having test accuracy of 98.78% and 99.2% respectively while having 40% lesser training parameters than their state of the art counterpart. CONCLUSION: In the present study, a method based on artificial intelligence is proposed, leading to a rapid diagnostic tool for Covid infections based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN). The benefit will be a high accuracy of detection with up to 99% hit rate, a rapid diagnosis, and an accessible Covid identification method by chest X-ray images.

Prediction of Temperature and Loading History Dependent Lumbar Spine Biomechanics Under Cyclic Loading Using Recurrent Neural Networks.

Extended-duration cyclic loading of the spine is known to be correlated to lower back pain (LBP). Therefore, it is important to understand how the loading history affects the entire structural behavior of the spine, including the viscoelastic effects. Six human spinal segments (L4L5) were loaded with pure moments up to 7.5 Nm cyclically for half an hour, kept unloaded for 15 min, and loaded with three cycles. This procedure was performed in flexion-extension (FE), axial rotation (AR), and lateral bending (LB) and repeated six times per direction for a total of 18 h of testing per segment. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) was trained to predict the change in the biomechanical response under cyclic loading. A strong positive correlation between the total testing time and the ratio of the third cycle to the last cycle of the loading sequence was found (BT: [Formula: see text] =  0.3469, p = 0.0003, RT: [Formula: see text] =0.1988, p  =   0.0377). The moment-range of motion (RoM) curves could be very well predicted with an RNN ([Formula: see text]=0.988), including the correlation between testing time and testing temperature as inputs. This study shows successfully the feasibility of using RNNs to predict changing moment-RoM curves under cyclic moment loading.

Impact of poroelasticity of intraluminal thrombus on wall stress of abdominal aortic aneurysms.

BACKGROUND: The predictions of stress fields in Abdominal Aortic Aneurysm (AAA) depend on constitutive descriptions of the aneurysm wall and the Intra-luminal Thrombus (ILT). ILT is a porous diluted structure (biphasic solid-fluid material) and its impact on AAA biomechanics is controversially discussed in the literature. Specifically, pressure measurements showed that the ILT cannot protect the wall from the arterial pressure, while other (numerical and experimental) studies showed that at the same time it reduces the stress in the wall. METHOD: To explore this phenomenon further a poroelastic description of the ILT was integrated in Finite Element (FE) Models of the AAA. The AAA model was loaded by a pressure step and a cyclic pressure wave and their transition into wall tension was investigated. To this end ILT's permeability was varied within a microstructurally motivated range. RESULTS: The two-phase model verified that the ILT transmits the entire mean arterial pressure to the wall while, at the same time, it significantly reduces the stress in the wall. The predicted mean stress in the AAA wall was insensitive to the permeability of the ILT and coincided with the results of AAA models using a single-phase ILT description. CONCLUSION: At steady state, the biphasic ILT behaves like a single-phase material in an AAA model. Consequently, computational efficient FE single-phase models, as they have been exclusively used in the past, accurately predict the wall stress in AAA models.

Identification of heavy metal pollutants using multivariate analysis and effects of land uses on their accumulation in urban soils in Beijing, China.

In order to evaluate the current state of the environmental quality of soils in Beijing, we investigated contents of 14 metals in Beijing urban soils inside the 5th ring road by even grids sampling. Statistic analyses were conducted to identify possible heavy metal pollutants, as well as the effects of land uses on their accumulation. Our results revealed that the urban soils in Beijing were contaminated by Cd, Pb, Cu, and Zn. Land uses and urbanization ages affected the accumulation of the four heavy metals in soils significantly. Soils in industrial areas have the highest average Cu and Zn contents, while Pb contents in park areas and Cd in agricultural areas are the highest. The accumulations of Pb and Zn in urban soils increase significantly with sampling plots approaching the city center. And Pb, Cd, and Zn contents in soils in traffic areas also tend to increase in the city center. However, residential areas have the lowest contents of all the four heavy metals.

Generative adversarial network based data augmentation for CNN based detection of Covid-19.

Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.

An experimental-numerical method for the calibration of finite element models of the lumbar spine.

We present a systematic and automated stepwise method to calibrate computational models of the spine. For that purpose, a sequential resection study on one lumbar specimen (L2-L5) was performed to obtain the individual contribution of the IVD, the facet joints and the ligaments to the kinematics of the spine. The experimental data was prepared for the calibration procedure in such manner that the FE model could reproduce the average motion of the 10 native spines from former cadaveric studies as well as replicate the proportional change in ROM after removal of a spinal structure obtained in this resection study. A Genetic Algorithm was developed to calibrate the properties of the intervertebral discs and facet joints. The calibration of each ligament was performed by a simple and novel technique that requires only one simulation to obtain its mechanical property. After calibration, the model was capable of reproducing the experimental results in all loading directions and resections.