Nonlinear Regression Approach Research Articles

Diabeties Predection Using Biosensors

Biosensors are little investigative gadgets that join a component of natural acknowledgment and a physio-compound transducer to change an organic sign into an electrical perusing. Their specialized allure these days lies in their solid proficiency, high affectability, and consistent limit with regards to estimation. A biosensor embedded in contact central focuses to decide the degree of tear glucose and to hand-off the information to the patient’s body siphon. The siphon can mix insulin, the missing hormone, the necessary degree. This machine evades the distinctive blood checks and implantations for interesting sorts of diabetes that are really basic consistently. Diabetes characteristics are calculated by the sensor and the algorithms of machine learning are used to obtain the solution. Glucose measurements in blood plasma are positive, suggesting that the sensor is designed to estimate physiological blood glucose levels with negligible atomic effects. We are aimed at contributing to the growing biotechnology sector, with a focus on Glucose-Oxidise Biosensor (GOB) modelling from a regression perspective through quantitative learning methods. Blood glucose monitoring was developed as an effective tool for diabetes administration. Since it is suggested to maintain typical levels of blood glucose, a progression of appropriate biosensors of glucose has been developed. The technology of glucose biosensors, including treatment tools, reliable glucose monitoring systems and non-invasive glucose detection frameworks, has been fully developed over the last 50 years. In any case, a few steps are still associated with the achievement of accurate and reliable measurement of glucose. Utilizing a few AI calculations to demonstrate the amperometric reaction of a GOB with subordinate factors under various conditions, for example, temperature, benzoquinone, pH and glucose levels. In particular, kernel-based regression techniques, such as support vector machines, are being used today as one of the best machine learning techniques. Since a GOB reaction’s affectability is emphatically connected with these needy factors, their cooperations ought to be advanced to boost the yield signal for which a hereditary calculation and mimicked tempering is utilized. This dataset is modelled by a nonlinear regression approach which uses a rather simple model of the biosensor performance to allow a very low prediction error. This demonstrates the brief history, basic principles, analytical results, and the current status in clinical practice of glucose biosensors. We report a model that is consistent with the optimization and shows a good generalization error. The results obtained show that the sensor created is a contender for persistent blood glucose checking.

Modelling of Wetting Front Advance and Discharge Change of Subsurface Line Source

Sub-drip irrigation is one of the most important and the latest high-performance irrigation systems that are characterized by a high ability to reduce losses of deep percolation and evaporation from the soil surface. Twelve laboratory experiments were carried out to study the effect of soil texture, dripper depth and initial water content on the wetting pattern, dripper discharge. Further, non-linear regression approach was applied to predict empirical relationships for estimation the wetting pattern dimensions and actual dripper discharge. The results showed a significant match between the observed and estimated values ​​of the wetting front advance and the dripper discharge change. At a specific volume of water application, the size of wetting soil increased (8-20%) with the initial water content increase and (2.5-6.25%) with dripper depth increase and this size decreased (4.5-36%) with the clay content increase. The rate of vertical upward advance decreased with increasing the initial water content. The dripper discharge gradually decreased with the continuation of water application due to positive pressure increase at the dripper opening, and this decrease increased with increasing dripper depth, clay content and the initial water content of the soil.

Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers

In this study, the effect of two water-reducer polymers with smooth and rough surfaces on the workability, and the compression strength of concrete from an early age (1 day) up to 28 days of curing was investigated. The polymer contents used in this study varied from 0 to 0.25% (wt%). The initial ratio between water and cement ( $$ \frac{w}{c} $$ ) was 60%, and it slowly reduced to 0.46 by increasing the polymer contents. The compression strength of concrete was increased significantly with increasing the polymer contents by 24–95% depending on the polymer type, polymer content, $$ \frac{w}{c} $$ , and curing age. Because of a fiber net (netting) in the concrete when the polymers were added which leads to a decrease void between the particles, binding the cement particles, therefore, increased rapidly the viscosity for the fresh concrete and the compression strength of the hardened concrete. This study also aims to establish systematic multiscale models to predict the compression strength of concrete containing polymers and to be used by the construction projects with no theoretical restrictions. For that purpose, 88 concrete samples modified with two types of polymer (44 samples for each modification) has been tested, analyzed, and modeled. Linear, nonlinear regression, M5P-tree, and artificial neural network (ANN) approaches were used for the qualifications. In the modeling process, the most relevant parameters affect the strength of concrete, i.e., polymer incorporation ratio (0–0.25% of cement’s mass), water-to-cement ratio (0.46–0.6), and curing ages (1–28 days). Among the used approaches and based on the training data set, the model made based on the nonlinear regression, ANN, and M5P-tree models seem to be the most reliable models. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the maximum stress (compression strength) of concrete with this dataset.

ANN, M5P-tree model, and nonlinear regression approaches to predict the compression strength of cement-based mortar modified by quicklime at various water/cement ratios and curing times

This study’s goal is to establish systematic multiscale equations to estimate the compressive strength of cement mortar with a high volume of lime (L) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide tested data and the data gathered in the literature (a total of 392 tested cement mortar modified with lime) have been statically analyzed and modeled. The lime content ranged from 0 to 45% (by cement weight). Depending on literature data the w/c ranged from 0.3 to 0.74, the w/c of 0.5 was selected for this research. The compressive strength of lime-modified cement mortar for up to 28 days ranged from 3 to 75 MPa. The compression strength of the cement mortar reduced with an increasing percentage of lime. The linear and nonlinear regression, M5P-tree, and artificial neural network (ANN) technical approaches were used for the qualifications. In the modeling process, the most relevant parameters affecting the compression strength of cement mortar, i.e. lime (L) incorporation ratio (0–45% of cement’s mass), water-to-cement ratio (0.3–0.74), and curing ages (1 to 28 days). According to the statistical assessment such as R, MAE, and RMSE, the compression strength of cement mortar can be predicted very well in terms of water-to-cement ratio, lime content, and curing age using various simulation techniques. The maximum and minimum error between the actual test results and the outcome of the prediction using NLR and ANN (training dataset) were 0.01–21 MPa and 0.012–9 MPa, respectively, and ranged between 0.03 and 14 MPa and 0.02 and 6 MPa errors, respectively, in terms of tested data. The margin of error in using the nonlinear regression-based model (NLR) and ANN for the training dataset was 1.41 and 1.92, respectively, and it was 2.26 and 2.44 respectively in using the tested dataset. The outcomes of this paper suggest that the nonlinear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The result of the sensitivity investigation was the curing period that is the highest dominating value for the prediction of the compressive strength of cement mortar.

High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach.

Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb2+ in the concentration range of 0.5–2000 ppb with a detection limit of 0.5 ppb.

Cyclic Behavior Assessment of Steel Bar Hysteretic Dampers Using Multiple Nonlinear Regression Approach

Isolators prevent the transfer of earthquake energy to the structure and reduce its relative displacement by increasing the time period of the structure. Most isolators have low damping and hence, in some cases, the auxiliary dampers can play an important role in the increased damping and reduced displacement of the isolation system. In this paper, a new type of steel bar hysteretic dampers (SBHDs) as the auxiliary damper was studied and evaluated in the isolation systems. For this purpose, 1650 numerical models of SBHDs were constructed based on a laboratory sample with the bars of different length, number, and cross section. Also, the role of each of these geometric parameters in the seismic behavior of the dampers was evaluated using the hysteresis curves and the corresponding ideal bilinear curves. Owing to the deficiency of the analytical solutions in the estimation of the nonlinear behavior of SBHDs and to increase their accuracy, some models were proposed using the multiple nonlinear regression (MNLR) methods. The proposed relations can be used to obtain the displacements and the forces corresponding to the first yield point and the ultimate point, ductility ratio, elastic stiffness, post-yield stiffness, effective stiffness, and dissipated energy. The results showed that the proposed equations are able to estimate the cyclic behavior of SBHDs with the proper accuracy and well consistency, and can be used with full confidence.

Stormwater Filtration Performance for the Ecosol Storm Pit (Class 2): Statistical Analysis of Field Data

An independent field performance evaluation for a secondary stormwater filtration device, named the Ecosol Strom Pit (Class 2), was performed between May 2017 and July 2019 in an urban catchment in Queensland, Australia. During the testing period, a total of 37 rainfall events were recorded, of which between 15 and 21 events were evaluated as qualifying for the purposes of characterizing the removal efficiency performance of the device. A statistical analysis of the event mean concentrations (EMCs) of the flow streams through the device indicate a statistically significant difference between the influent and effluent streams. A variety of pollutant removal evaluation metrics, including concentration-based and total load-based metrics, were utilized in this study to characterise the efficacy of the device. Two new approaches are proposed for facilitation the analysis: a nonlinear regression approach to more effectively deal with nonlinear patterns in the influent and effluent data and the regression of concentration (ROC), which is an added concentration-based metrics. In summary, the removal efficiencies of the Ecosol Storm Pit (Class 2) were evaluated to be 72–74% for total suspended solids (TSS), 45–50% for total phosphorus (TP), 41–45% for total nitrogen (TN), 27–32% for total heavy metals (THM), 79–85% for total petroleum hydrocarbons (TPH), and 80–88% for total recoverable hydrocarbons (TRH).

ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash

This study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of fly ash (FA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with FA) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear and Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar, i.e. fly ash (class C and F) incorporation ratio (0−70% of cement's mass), water-to-binder ratio (0.235–1.2), and curing ages (1–365 days). According to the correlation coefficient (R), mean absolute error and the root mean a square error, the compressive strength of cement mortar can be well predicted in terms of w/b, fly ash, and curing time using various simulation techniques. The results of this study suggest that the Non-linear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.

NIR associated to PLS and SVM for fast and non-destructive determination of C, N, P, and K contents in poultry litter.

Using near-infrared (NIR) spectroscopy for poultry litter characterization can be a rapid, non-destructive, and low-cost alternative. This study aims to estimate the C, N, P, and K content in poultry litter samples using for first time NIR spectroscopy. For these purposes, the building models were carried out using Partial Least Squares (PLS) and Support Vector Machines (SVM) methods. A total of 160 litter samples were analyzed in poultry houses of different rearing systems, seeking the highest possible variability in their chemical composition. NIR spectroscopy, combined with PLS and SVM methods, is an alternative method for non-destructive C, N, P, and K determination in poultry samples. The regression models using SVM provide better accuracy for all elements, laying the basis for the nonlinear regression approach's application. The K determination on poultry litter using NIR was possible only by the SVM model (R2=0.8620 and RPD=2.7330). Conclusively, the predictive ability was improved using the SVM method.

Numerical investigation of occupant injury risks in car-to-end terminal crashes using dummy-based injury criteria and vehicle-based crash severity metrics

Guardrails were designed to deter vehicle access to off-road areas and consequently prevent hitting rigid fixed objects alongside the road (e.g. trees, utility poles, traffic barriers, etc.). However, guardrails cause 10 % of deaths in vehicle-to-fixed-object crashes, which recently attracted attention in the highway safety community on the vehicle-based injury criteria used in regulations. The objectives of this study were to investigate both full-body and body-region driver injury probabilities using finite element (FE) simulations, to quantify the influence of pre-impact conditions on injury probabilities, and to analyze the relationship between the vehicle-based crash severity metrics currently used in regulations and the injury probabilities assessed using dummy-based injury criteria.A total of 20 FE impact simulations between a car (Toyota Yaris) with a Hybrid III M50 dummy model in the driver seat and an end terminal model (ET-Plus) were performed in various configurations (e.g. pre-impact velocities, offsets, and angles). The driver’s risk of serious injuries (AIS 3+) was estimated based on kinematic and kinetic responses of the dummy during the crashes. A non-linear regression approach was used to compare the injury probabilities assessed in this study to the vehicle-based crash severity metrics used in the testing regulations. In particular, the US Manual for Assessing Safety Hardware (MASH) guideline and European procedures (EN1317) were used for the study.All the recorded dummy-based injury criteria values pass the Federal Motor Vehicle Safety Standard (FMVSS) 208 limits which indicated a low driver risk of serious injury. Overall, the pre-impact vehicle velocity showed to have the highest influence in almost all injury probabilities (59 %, 79 %, 62 %, and 44 % in full-body, head, neck, and chest injuries, respectively). The offset between vehicle midline and the guardrail barrier was the most important variable for thigh injuries (56 %). The assessed injury probabilities were compared to vehicle-based severity metrics. The full-body and chest injuries showed the highest correlation with Occupant Impact Velocity (OIV), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) (R2 > 0.6). Lower correlations of thigh injuries were recorded to OIV (R2 = 0.59) and THIV (R2 = 0.46). Meanwhile, weak correlations were observed between all the other regressions which indicated that no vehicle-based criteria could be used to predict head and neck injuries.Car-to-end terminal crash FE simulations involving a dummy model were performed for the first time in this study. The results pointed out the limitations of the standard vehicle-based injury methods in terms of head and neck injury prediction. The dummy-based injury assessment methodology presented in this study could supplement the crash tests for various impact conditions. In addition, the models could be used to design new advanced guardrail end terminals.

Silent Speech Interface Using Ultrasonic Doppler Sonar

Some non-acoustic modalities have the ability to reveal certain speech attributes that can be used for synthesizing speech signals without acoustic signals. This study validated the use of ultrasonic Doppler frequency shifts caused by facial movements to implement a silent speech interface system. A 40kHz ultrasonic beam is incident to a speaker's mouth region. The features derived from the demodulated received signals were used to estimate the speech parameters. A nonlinear regression approach was employed in this estimation where the relationship between ultrasonic features and corresponding speech is represented by deep neural networks (DNN). In this study, we investigated the discrepancies between the ultrasonic signals of audible and silent speech to validate the possibility for totally silent communication. Since reference speech signals are not available in silently mouthed ultrasonic signals, a nearest-neighbor search and alignment method was proposed, wherein alignment was achieved by determining the optimal pair of ultrasonic and audible features in the sense of a minimum mean square error criterion. The experimental results showed that the performance of the ultrasonic Doppler-based method was superior to that of EMG-based speech estimation, and was comparable to an image-based method.

Flood frequency estimation in New Zealand using a region of influence approach and statistical depth functions

A region of influence approach in which statistical depth functions are used to select gauged catchments having similar flood generation behaviour to a given ungauged basin coupled with nonparametric regression on catchment characteristics is employed to estimate mean annual and a quantile flood of 100 year return period in New Zealand basins. This approach may be applied internationally. Relative and root mean square errors assessed by jack-knife resampling are too large to be useful for detailed design in ungauged catchments but are much smaller than those resulting from a conventional multiple nonlinear regression approach. The distribution of errors for samples of sites indicates that large error values result from contributions from a relatively small number of statistical outliers Although these are all natural catchments, the outlier basins have particular physical characteristics such as moraines, karst features and pumice sediments which affect their flood generation processes. Relative errors in estimates for an ungauged basin may be predicted using a relationship involving differences in area, rainfall and statistical depth between the ungauged catchment and gauged similar catchments. Improved analytical techniques and selection and measurement of catchment characteristics should reduce error magnitude but not perhaps sufficiently for use in detailed design. Parallel development based on rainfall-runoff models is desirable but the present lack of requisite information largely concerning rainfalls means that attainment of desired accuracy is a distant prospect.

Estimating the soil water retention curve: Comparison of multiple nonlinear regression approach and random forest data mining technique

This study evaluates the performance of the random forest (RF) method on the prediction of the soil water retention curve (SWRC) and compares its performance with those of nonlinear regression (NLR) and Rosetta-based pedotransfer functions (PTFs), which has not been reported so far. Fifteen RF and NLR-based PTFs were constructed using readily-available soil properties for 223 soil samples from Iran. The general performance of RF and NLR-based PTFs was quantified by the integral root mean square error (IRMSE), Akaike’s information criterion (AIC) and coefficient of determination (R2). The results showed that the accuracy of the RF-based PTFs was significantly (P < 0.05) better than the NLR-based PTFs, and that the reliability of the NLR-based PTFs was significantly (P < 0.01) better than the RF-based PTFs and all of the Rosetta-based PTFs. The average values of the IRMSE, AIC and R2 of the RF method were 0.041 cm3 cm−3, −16997.7, and 0.987, and 0.053 cm3 cm−3, −15547.5, and 0.981 for the training and testing steps of all PTFs, respectively, whereas the values for the NLR method were 0.046 cm3 cm−3, −16616.4, and 0.984, and 0.048 cm3 cm−3, −16355.6, and 0.983 for the training and testing steps, respectively. The PTF5 of the RF and NLR methods, with inputs of sand and clay contents, bulk density, and the water content at field capacity and permanent wilting point, had the greatest R2 values (0.987 and 0.989, respectively), and the lowest IRMSE values (0.039 and 0.032 cm3 cm−3, respectively) compared to other PTFs for the testing step. Overall, the RF method had less reliability for the prediction of the SWRC compared to the NLR method due to overprediction, uncertainty of determination of forest scale and instability in the testing step. These findings could provide the scientific basis for further research on the RF method.

Modeling the Impedance Behavior of Ionic Conductors (AgPO&lt;sub&gt;3&lt;/sub&gt;)&lt;sub&gt;1-x&lt;/sub&gt;(Ag&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;x&lt;/sub&gt; Glass System Using Artificial Neural Network

The dielectric permittivity and conductivity of (AgPO₃)_1-x(Ag₂SO₄)_x compound was investigated at different concentrations of (Ag₂SO₄). The effect of concentration on AC conductivity and permittivity as well as temperature and frequency was investigated in order to model this behavior. Multidimensional mathematical models were as proposed to predict the impedance components <img src=image/15115401_01.gif> and the dielectric permittivity components <img src=image/15115401_02.gif> of the glass system as a function of temperatures, frequencies and concentrations. Artificial Neural Network (ANN) and nonlinear regression approaches were set as curve fitting techniques in order to construct models based on 1700 points of data. This model can be then used to predict these proprieties at any concentration and therefore helping the product designer to choose the proper mixing and temperature conditions. For ANN, 20, 50, and 100 nodes in a single hidden layer neural network were considered. Although data results of the two approaches showed a good alignment with experimental data, the ANN model with twenty nodes was able to predict the outputs with lower MSE values range from 0.008 to 0.012 for impedance and from 0.006 to 0.008 for dielectric losses than the regression technique. Moreover, R² values for the neural network were over 99% in both training and testing of impedance and dielectric permittivity while R² values for non-linear regression vary between 73.86% to 94.75%. The proposed ANN model can be of a great help to find the optimal dielectric permittivity and conductivity of (AgPO₃)_1-x(Ag₂SO₄)_x compound when dealing with a specific application.

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

In this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear, and consequently, the common linear approaches fail to provide satisfactory performance. The calibration and verification processes of the RRKHS for one- and multi-day ahead forecasting R-R models were demonstrated using daily rainfall and streamflow measurement from a mountainous catchment located in the Black Sea region, Turkey. The efficacy of the new approach in each forecasting scenario was compared with those of other benchmarks, namely radial basis function artificial neural network and multivariate adaptive regression splines. The results illustrate the superiority of the RRKHS approach to its counterparts in terms of different performance indices. The range of relative peak error (PE) is found as 0.009–0.299 for the best scenario of the RRKHS model, which illustrates the high accuracy of RRKHS in peak streamflow estimation. The superior performance of the RRKHS model may be attributed to its formulation in a very high (possibly infinite) dimensional space which facilitates a more accurate regression analysis. Based on the promising results of the current study, it is expected that the proposed approach would be applied to other similar environmental modeling problems.

Flicker spreading in a transmission network

This paper reports the flicker spreading in the transmission network. Chapter 1 presents introduction containing brief background and key concepts, followed by description of the corresponding instrumentation in Chapter 2. Key contribution of the paper is elaborated in Chapters 3 and 4. Chapter 3 reports measurements of the flicker magnitude along the 400 kV, 220 kV and 110 kV transmission grid for various distances from flicker origin on 400 kV grid, and Chapter 4 gives cost-effective predictive model, enabling estimation of the flicker magnitude for arbitrary selected origin-to-spot distance base on non-linear regression approach. Paper is extension of the work presented at Smagrimet 2019 conference.

A low rank robust kernel ridge regression classifier for power quality disturbance pattern recognition

This paper presents the development of a new low rank robust kernel ridge regression (KRR) classifier for power quality (PQ) disturbance pattern recognition. It is well known that the kernel methods are used extensively for regression and classification problems using support vector machines (SVM) by mapping the original nonlinear data into a high-dimensional space thereby increasing the generalization performance, and accuracy of classification. On the other hand, the nonlinear kernel ridge regression approach is known for its simple implementation, fast processing speed and accuracy in comparison to the widely used least square support vector machine (LS-SVM). However, for large scale training patterns, the size of kernel matrix becomes large thereby the execution time becomes prohibitive. Besides, the presence of noise and outliers in the data affects the accuracy of the conventional KRR classifier. This paper, therefore, attempts to develop a dimensionally reduced but robust KRR classifier by choosing a random set of support vectors from the training subset to reduce substantially the training time at the cost of a slight loss in accuracy. Further, the KRR algorithm is modified by using a new objective function to minimize the mean and variance of the error to provide robustness and accuracy. For applying this new classifier to power quality disturbance events three relatively new signal processing techniques like the Short-time modified Hilbert Transform (STMHT), Morphological filters, and Fourier kernel S-transform are used to extract the relevant features from the data samples. The simulation results imply that the proposed methods have a higher recognition rate compared with other established techniques such as ELM and Poly SVM while classifying the PQ disturbances. A PC integrated hardware assembly has been used to verify the PQ events classification in real-time.

Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics.

Proportional and simultaneous est-imation of finger kinematics from surface EMG based on the assumption that there exists a correlation between muscle activations and finger kinematics in low dimensional space. We employ Manifold Relevance Determination (MRD), a multi-view learning model with a nonparametric Bayesian approach, to extract the nonlinear muscle and kinematics synergies and the relationship between them by studying muscle activations (input-space) together with the finger kinematics (output-space). This study finds that there exist muscle synergies which are associated with kinematic synergies. The acquired nonlinear synergies and the association between them has further been utilized for the estimation of finger kinematics from muscle activation inputs, and the proposed approach has outperformed other commonly used linear and nonlinear regression approaches with an average correlation coefficient of 0.91±0.03. There exists an association between muscle and kinematic synergies which can be used for the proportional and simultaneous estimation of finger kinematics from the muscle activation inputs. The findings of this study not only presents a viable approach for accurate and intuitive myoelectric control but also provides a new perspective on the muscle synergies in the motor control community.

International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model

This paper explores the relationship between international economic policy uncertainty (EPU) and stock market return of Bangladesh. The study considers economic policy uncertainty of six big trading partners of Bangladesh: US, Canada, EU, China, Russia, and India. We apply time-varying linear (Break-least Square) and non-linear (Markov-Switching) regression approaches by using monthly data from January 2003 to April 2019. Our findings indicate the following. Firstly, The break-least square captured four structural breaks in the capital market of Bangladesh. Secondly, economic policy uncertainty from major importing countries (China and India) affect stock market returns of Bangladesh more significantly than major exporting countries (US and EU). Thirdly, EPU has a greater negative influence on stock returns during high volatility than low volatility regime. A number of policy measures have been recommended.

US non-linear causal effects on global equity indices in Normal times versus unconventional eras

This paper examines global spillover Quantitative Easing (QE) impacts of US on stock market indices by employing apart from a classical Granger causality, also a non-linear quantile regression approach for detecting causality on the tails of the stock indices’ distributions. Before US unconventional monetary policies, the non-linear causal relationship between the Fed’s balance sheet and stock markets is found to be not significant for most indices over the middle level of quantiles, whereas significant causal relationships are present in tails. There is non-linear evidence that the Fed’s assets were mainly influenced by US, Latin American, Australian and some Asian indices while the other stock indices did not Granger cause balance sheet enlargement for any quantile interval before 2008. Nevertheless, almost all domestic and foreign stock indices are found to exert feedback effects during US QE. We argue that US total assets exert non-linear causality towards all stock indices under scrutiny at least at one level of quantile and tighter links of the Fed mainly with advanced economies are found, as impacts on the upper quantile are stronger during US QE.