Correct Moment Research Articles

2F4-1 Quantification of adduction and abduction corrective moments in the center-bridge knee orthosis

2F4-1 Quantification of adduction and abduction corrective moments in the center-bridge knee orthosis

СТАН ТА ЦИФРОВІ ВЕКТОРИ РОЗВИТКУ РИНКУ СТРАХУВАННЯ УКРАЇНИ

The article examines the current state and digital vectors of the development of the insurance market of Ukraine and identifies promising directions for the digitalization of insurance. The essence of the insurance category is revealed. It is noted that the insurance market in Ukraine lags far behind developed countries. An analysis of the dynamics of the number of participants in the insurance market, the amount of assets of insurance companies, the structure of the insurance portfolio by types of insurance was carried out, and the main trends of the insurance market in Ukraine were revealed. The keys to the stable development of insurance activity in Ukraine have been defined as: quick response to new conditions and customer requests; the ability to adapt, accumulate positive experience, analyze and correct negative moments; develop harmoniously, find a balance between the accumulation of new knowledge and their practical application. Factors influencing and functioning of the domestic insurance market are determined. The features of modern trends in digital insurance tools, as an important mechanism for the growth of the insurance market, are revealed. It was found InsurTech that modern technologies are important for insurance companies. These InsurTech nclude artificial intelligence technologies, new inventions in the field of cyber security, big data analysis («bigdata), blockchain technologies, the Internet of Things (LoT ) and smartphone applications.

Integrated control strategy of semi-active suspension and in-wheel motors for handling limit of electric race car

An integrated control strategy is proposed to deal with extreme driving conditions for race cars using magnetorheological (MR) semi-active suspension and in-wheel motors. A novel controllable boundary is introduced as the vehicle state supervisor based on the phase portrait of yaw rate and side slip angle. Compared with the traditional stable region, it can better cope with the aggressive behaviors of racing drivers. The proposed integrated control algorithm consists of a controllable boundary-based hierarchy direct yaw moment controller (HDYC) and acceleration-based scheduling MR damper controller. The HDYC will generate the correct yaw moment based on the sliding mode method when vehicle states exceed the critical area of the controllable region. The MR damper controller includes vehicle attitude control (VAC) mode and maneuverability control (MC) mode. The switch of control mode is determined by vehicle states as well. According to the analysis of load transfer, the acceleration scheduling strategy based on the “g-g” ellipse is utilized to generate the desired current command. Three typical scenarios are conducted through co-simulation of Carsim and Simulink to verify the control strategy, including accelerating and hard braking test, double lane change test, and duration circuit test. Especially, the limit of actuators is taken into consideration. The simulation results show that the proposed integrated control strategy could reduce the lap time and increase the maneuverability as well as stability of race cars, compared with traditional direct yaw moment control. The ultimate acceleration of the vehicle is improved significantly.

Lateral torsional buckling of unsymmetric beams by FEM with linear elements

As the exact solution of lateral torsional buckling of elastic prismatic beams is practically limited to the simple case of simply supported beams under equal end moments, other loading conditions and boundary conditions require more practical solutions to the problem. As the general solution of lateral torsional buckling of unsymmetric beams is a function of the sign of the bending moment along the axis of the beam, i.e. location of the shear center concerning the compression zone of the cross section, application of the current traditional empirical expressions such as coefficient of the moment, Cb, becomes highly inconsistent and unwarranted to capture the correct critical moment as acknowledged by the American Institute of Steel Construction, AISC. The finite element method, FEM, offers a feasible alternative to overcome these shortcomings. FEM is formulated in its simplest form of linear elements for the general case of lateral torsional buckling of unsymmetric cross sections. Finite element development shows that the characteristic equation is of the nonlinear quadratic eigenvalue problem type. Using the classical polynomial shape functions for beams, FEM proves to be extremely accurate and can overcome the high inconsistencies and discrepancies embedded in the application of the classical methods.

Orthopedic mechanism analysis of growing rod distraction for early-onset scoliosis based on 3D morphological parameters.

Traditional growing rod (TGR) provides a corrective moment for deformed segments to straighten the spine, whose clinical efficacy has proven positive and growth-friendly. However, an insufficient understanding of orthopedic mechanisms can affect the development of clinical strategies. This research attempts to analyze the spine that has undergone four distraction operations: exploring the spinal orthopedic mechanism, including alignment, growth, and morphology. In this study, the spinal morphology curves were illustrated in three human planes to exhibit the changes in spinal alignment. The spinal growth characteristics were measured to discuss the unsynchronized and diminishing growth rate. The spinal deformations were evaluated to indicate asymmetric growth. As a result, the spinal alignment changes indicated the orthopedic process improved, but the re-unbalance occurred after multiple distractions. Then, unsynchronized growth existed in the superior and inferior segments, and the growth rate over every distraction diminished. Finally, asymmetric growth was indicated as the axial/circumferential growth ratio getting greater and the cuneate level approaching normal. Accordingly, a TGR is growth-friendly, but combining the osteotomy fusion of lumbar segments for severe early-onset scoliosis may be an excellent choice to solve the insufficient corrective stimulation. Regarding the distraction process, reshaping before the final fusion can fix the balance loss, and a prolonged distraction frequency fits the law of diminishing return. In conclusion, studying orthopedic mechanisms based on morphological measurement can guide clinical strategy optimization.

A Survey: Approaches for Detecting the Autism Spectrum Disorder

A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous amount of time and money. The early detection of autism might be highly advantageous in regards to treating patients with the right medical treatments at the correct moment in time. It could prevent the individual's illnesses before developing severe and could help in decreasing future expenses associated to a diagnosis that was delayed. Thereby, the requirement to develop a rapid, trustworthy, and simple examination device that can make predictions is essential. Autism Spectrum Disorder (ASD) has been gaining momentum presently more quickly than at any time earlier. Diagnostic evaluation of autistic characteristics is extremely expensive and time-consuming as well. The advancement of algorithms for machine learning (ML) and Artificial intelligence (AI) have made it achievable to identify autism fairly earlier. Although the reality of numerous studies have been carried out performed utilising different techniques, these studies have not contributed to any definitive conclusions regarding the capacity of predicting autism attributes in regards to different age categories. Thereby, the objective of this research is to predict Autism among people of all ages and to provide an effective model for prediction using various ML approaches.

Dirac gauge theory for topological spinors in 3+1 dimensional networks

Gauge theories on graphs and networks are attracting increasing attention not only as approaches to quantum gravity but also as models for performing quantum computation. Here we propose a Dirac gauge theory for topological spinors in dimensional networks associated to an arbitrary metric. Topological spinors are the direct sum of 0-cochains and 1-cochains defined on a network and describe a matter field defined on both nodes and links of a network. Recently in Bianconi (2021 J. Phys. Complex. 2 035022) it has been shown that topological spinors obey the topological Dirac equation driven by the discrete Dirac operator. In this work we extend these results by formulating the Dirac equation on weighted and directed dimensional networks which allow for the treatment of a local theory. The commutators and anti-commutators of the Dirac operators are non vanishing an they define the curvature tensor and magnetic field of our theory respectively. This interpretation is confirmed by the non-relativistic limit of the proposed Dirac equation. In the non-relativistic limit of the proposed Dirac equation the sector of the spinor defined on links follows the Schrödinger equation with the correct giromagnetic moment, while the sector of the spinor defined on nodes follows the Klein–Gordon equation and is not negligible. The action associated to the proposed field theory comprises of a Dirac action and a metric action. We describe the gauge invariance of the action under both Abelian and non-Abelian transformations and we propose the equation of motion of the field theory of both Dirac and metric fields. This theory can be interpreted as a limiting case of a more general gauge theory valid on any arbitrary network in the limit of almost flat spaces.

An Integrated Approach for Yaw Stability of Linear Time Varying Bicycle Model Utilizing Adaptive Model Predictive Control

Accidents during critical maneuvering on roads while overtaking or changing lanes are mainly due to the insufficient generation of the stability matrices of vehicles, including yaw rate. The parameters responsible for stable operation of the vehicle during these driving scenarios may vary, causing improper steering angle input actuation to the vehicle, due to which the desired yaw rate is not generated. To overcome this problem, corrective yaw moments are applied to the yaw dynamics to generate the desired yaw rate and operate the vehicle within the defined stability limit. Therefore - in this paper - to improve the yaw stability of the vehicle, a novel yaw rate gradient-based control approach is proposed for a linear time-varying (LTV) bicycle model. A 2 degrees of freedom bicycle model with the linear approximation for low slip angles in the magic formula tire model is utilized to develop the LTV model. The longitudinal velocity and friction coefficient are chosen as the parameters of interest to be varied during model simulation. Two different critical driving scenarios, including a sine maneuver and a double lane change, are chosen as input actuation with and without corrective yaw moments. The obtained simulation results unveil that, by the application of steering angle with a corrective yaw moment, the yaw stability has effectively been improved by obtaining a feasible adaptive model predictive control solution. Additionally, the root mean square error (RMSE) is calculated to evaluate the performance of the proposed methodology. A RMSE of 0.0768 and 0.0395 for steering without corrective yaw moment (CYM) is decreased to 0.0234 and 0.0214 for sine and double lane change steering input with CYM, respectively. Moreover, the proposed methodology is compared with previous methods and found to have better yaw stability.

"CONTROL OF SHIP FIN STABILIZERS BY A PID CONTROLLER: A NUMERICAL ANALYSIS "

The reduction of ship roll motion is important in order to prevent damage to cargo, to allow the crew to work efficiently and to provide comfort for the passengers. There are passive devices like bilge keels or anti – roll tanks and active devices like gyrostabilizer and stabilizing fins which are commonly used to generate an opposed moment to the wave and wind excitation roll moments in response to the command of a control system. The paper presents numerical results of using a Proportional - Integral – Derivative (PID) controller for damping the roll motion of a fishing boat equipped with a fin stabilizer system and sailing in regular waves. The nonlinear roll equation, which includes B1 type damping and quantic restoring, besides the wave excitation moment and moment generated by fins, was linearized for relative small roll angles. The role of PID controller was to generate the corrective roll moment through the controlled fins in order to stabilize the roll motion. The thresholds for the controller’s gains were determined using the Laplace transform and Routh – Hurwitz criterion. A simple but precise iterative scheme was used for the rapid integration of the system equations of motion in three cases: only proportional (P) component, proportional and derivative (PD) components and full PID controller. The numerical simulations have showed that all the analysed variants achieved roll reduction satisfactorily.

Integrated Lateral and Roll Stability Control of Multi-Actuated Vehicles Using Prioritization Model Predictive Control

This paper presents a prioritization model predictive control for integrated lateral and roll stability control of all-wheel-drive vehicles. The vehicle’s powertrain includes an electric motor and an open differential per axle, which enables generating a corrective yaw moment by front/rear torque shifting. In addition, the brakes can be used in differential mode to further enhance the corrective yaw moment when needed. To ensure vehicle stability, safety limits are defined on the vehicle’s yaw rate, sideslip, and roll motions, considering road angle effects. The controller prioritizes the control actions and objectives based on, respectively, their advantages and their importance, and then combines the priorities such that differential braking, which is defined as the low priority actuation, will not kick in unless the stability limits are violated. The performance of the designed controller is thoroughly evaluated through numerical and full vehicle experimental studies in different driving scenarios on flat and non-flat roads.

THE DETECTION OF EGGSHELL CRACKS USING DIFFERENT CLASSIFIERS

Chicken eggs, which are widely consumed in daily life due to their rich nutritional values, are also used in many products. The increasing need for eggs must be met quickly for various circumstances. Eggs are subjected to various impacts and shaken from production to packaging. In some cases, these effects cause an eggshell to crack. While these cracks are sometimes visible, they are sometimes micro-sized and cannot be seen. The cracks on the egg allow harmful micro-organisms to spoil the egg in a short time. In this study, acoustic signals generated by a mechanical effect to the eggs were recorded for 0.2 seconds at 50 kHz sampling frequency using a microphone. To determine the active part in the collected acoustic signal data, a clipping process was implemented by a thresholding process. Thus, the exactly correct moment of mechanical contact on the eggshell was easily detected. After passing the determined threshold value, statistical parameters such as min, max, difference, mean, standard deviation, skewness and kurtosis were extracted from the data obtained, and 7-dimensional feature vectors were created. Finally, the Common Vector Approach (CVA) is applied on the extracted feature vectors, 100% success rate has been achieved for the test data set. The ANN and SVM classifiers in where the same feature vectors are treated were used for the comparison purpose, and exactly the same classification rates are attained; however, the less number of eggs are tested with the ANN and SVM classifiers in the same amount of time. With the proposed mechanical system and classification methodology, it takes about 0.2008 seconds to determine whether the shells of eggs are cracked/intact. Therefore, the proposed combination of the feature vectors based on statistical features and CVA as a classifier for the detection of cracks on eggshells is notably appropriate especially for industrial applications in terms of speed and accuracy aspects.

Model for Selecting Optimal Dispatching Rules Based Real-time Optimize Job Shop Scheduling Problem

Owing to the randomness of the job release time, it is not possible to obtain all job information in real time during the operation of a manufacturing system. Generating a suitable scheduling strategy at the correct moment is the focus of addressing this disturbance. In this study, the flow time of the job in the manufacturing system was used as a criterion for evaluating the performance of the scheduling strategy. Subsequently, a model was constructed for selecting the optimal dispatching rule (DR) to actively change the scheduling strategies during the production process. The constructed model for selecting the optimal DR included an initial model for selecting the optimal DR and an evaluation model. The initial model for selecting the optimal DR outputs a DR with better performance based on the attributes of the job to be scheduled in the manufacturing system. Meanwhile, the evaluation model is responsible for evaluating the DR output of the initial model for selecting the optimal DR and determining whether the DR needs to be updated; the update process is realized based on simulation technology. Following experimental verification, the constructed model could generate scheduling strategies with superior performance in real-time and realize the update of the historical database. The results of this study will be of reference significance for solving the disturbance problem encountered by the manufacturing system in real time.

Valid properties of truncated Student-t regression model with applications in analysis of censored data

Kim (J. Korean Stat. Soc. 37 (2008) 81–87) introduced an incorrect stochastic representation (SR) for the truncated Student-t (Tt) random variable. By pointing out that the gamma mixture based on a truncated normal distribution actually cannot result in a true Tt distribution, in this paper, we first propose three correct SRs and then recalculate the corresponding moments of the Tt distribution. Different from those derived by following the invalid SR of Kim (J. Korean Stat. Soc. 37 (2008) 81–87), the correct moments of the Tt distribution play a crucial role in parameter estimations. Based on the third SR proposed and the correct expressions of truncated moments, expectation–maximization (EM) algorithms are developed for calculating the maximum likelihood estimates of parameters in the Tt distribution. Extensions to a Tt regression model and a t interval-censored regression model are provided as well. Simulated experiments are conducted to evaluate the performance of the proposed methods. Finally, two real data analyses corroborate the theoretical results.

Vehicle Stability Upper-Level-Controller Based on Parameterized Model Predictive Control

This paper presents an upper-level vehicular stability controller based on parameterized Model Predictive Control (MPC). The proposed system computes the additional moment applied on the vehicle’s yaw axis to improve the lateral stability. In the MPC formulation, the optimization problem is defined as a quadratic programming derived from a linear time-invariant model of vehicle dynamics. The control system is implemented based on a model that considers the rolling movement and on a simpler model that does not consider it, in order to evaluate the effects of using a more representative linear model for more accurate prediction or a simplified model for faster calculation. Constraints are imposed on the optimization problem to deal with the limits in the corrective yaw moment. A parameterized MPC approach is designed to reduce the number of optimization variables, and hence, reducing the computation time required for real-time implementation. Model-in-the-loop simulations are proposed to evaluate the effectiveness of the MPC strategy to avoid steering instability. Simulations are performed for profiling the calculation time, tuning the parameters, and testing algorithm running in an ARM-Cortex A8 on real-time control. Simulation results show that the proposed control strategy is effective in preventing destabilization and demonstrates that even with a longer computation time, the resulting MPC scheme meets the control requirements successfully, even under the presence of model disturbances.

Increasing use of an endotracheal intubation safety checklist in the emergency department

Endotracheal intubation (ETI) is a high-risk procedure often performed in the emergency department (ED) in critically unwell patients. The fourth National Audit Project by The Royal College of Anaesthetists found...

Effect of the electronic charge gap on LO bond-stretching phonons in undoped La2CuO4 calculated using LDA+U

Typical density-functional theory calculations that wrongly predict undoped cuprates to be metallic also predict Cu-O half- and full-breathing phonon energies that are significantly softer than observed, presumably because of weak on-site Coulomb repulsion on the Cu 3d orbitals. We used DFT+U calculations with antiferromagnetic supercells of La$_2$CuO$_4$ to establish correlation between the on-site repulsion strength, tuned via adjusting the value of U, and phonon dispersions. We find that breathing and half-breathing phonons reach experimental values when U is tuned to obtain the correct optical gap and magnetic moments. We demonstrate that using distorted supercells within DFT+U is a promising framework to model phonons in undoped cuprates and other perovskite oxides with complex, interrelated structural and electronic degrees of freedom.

Adaptive Shrinkage Estimation of High Dimensional Moment Condition Model with Smooth Structural Changes

Structural change is a long-standing problem in time series econometrics and macroeconomics, and financial time series are likely to be affected by structural instability due to changes in preferences, technologies, policies, etc. Most of the existing literature on moment condition models assumed that structural parameters were fixed over the entire sample period. To allow for time-varying structural parameters in the high dimensional moment condition models, this paper proposes a shrinkage local generalized method of moment (SLGMM) which simultaneously achieves parameter estimation and moment selection. We show that our method consistently selects the correct moment conditions and the SLGMM estimator possesses the oracle property, that is, it is as efficient as the time-varying GMM estimator based on all valid moment conditions. Moreover, we establish the consistency and asymptotic normality of the SLGMM estimator. A Monte Carlo simulation and an empirical application on asset pricing are conducted to show the merits of the newly proposed method.

Flowering Dynamics in Alfalfa (Medicago sativa L.) Based on Heat Units

Objective: To describe the sequential pattern of reproductive development of Medicago sativa L. (flowering dynamic) using the relation between principal floral stem nodes and accumulated growing degree day (GDD) in different environmental conditions, and to determine the use of this relationship as a predictor of the seed harvesting date. Design/Methodology/Approach: It was carried out in random blocks and factorial arrangement of 2?5, with two repetitions. Each repetition was made up of 30 plants. Results: The flowering dynamic followed the same pattern between varieties, with a linear relation between GDD and number of floral nodes. The year A0 was different from the years A1 and A2. Therefore, A1 with cutting, and A2 with and without cutting presented less GDD during the flowering stage. In contrast, A0 showed more GDD during this period. In the different environmental conditions, the linear curves of flowering dynamic turned erratic after the tenth floral node (300 GDD) due to a low rate of plants with more than 10 floral nodes along the principal stem. Study Limitations/Implications: It is necessary to have a meteorological station near the alfalfa seed production site, in order to specify the GDD required for predicting the correct moment to harvest seeds. Findings/Conclusions: The reproductive development of M. sativa presents a linear relation between the number of floral nodes and GDD, which allows the prediction of the optimal seed harvesting date, in any environmental condition present.

Frequency domain bootstrap methods for random fields

This paper develops a frequency domain bootstrap method for random fields on Z2. Three frequency domain bootstrap schemes are proposed to bootstrap Fourier coefficients of observations. Then, inverse-transformations are applied to obtain resamples in the spatial domain. As a main result, we establish the invariance principle of the bootstrap samples, from which it follows that the bootstrap samples preserve the correct second-order moment structure for a large class of random fields. The frequency domain bootstrap method is simple to apply and is demonstrated to be effective in various applications including constructing confidence intervals of correlograms for linear random fields, testing for signal presence using scan statistics, and testing for spatial isotropy in Gaussian random fields. Simulation studies are conducted to illustrate the finite sample performance of the proposed method and to compare with the existing spatial block bootstrap and subsampling methods.

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.