Linear Regression-based Model Research Articles

QPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data.

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQSpeak, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features. High DQSpeak chromatographic peaks have a lower chance of being classified as false-positive and show higher repeatability over multiple measurements. The high efficiency of the algorithm makes it particularly useful for application within processing routines of nontarget screening through chromatography coupled with high-resolution mass spectrometry. qPeaks is integrated into the qAlgorithms nontarget screening processing toolbox and appends a parameter-free chromatographic peak detection and characterization step to it. With qAlgorithms, now high-resolution mass spectra are centroided using the qCentroids algorithms, centroids are clustered to form extracted ion chromatograms (EICs) with the qBinning algorithm, and chromatographic peaks are found on the generated EICs with qPeaks. However, all tools from qAlgorithms can also be used independently.

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

CaTCH: Calculating transcript complexity of human genes

The findings based on whole transcriptome sequencing suggest that alternative splicing occurs in approximately 95% of human multi-exon genes, thus, playing a crucial role in promoting proteome diversity. According to the latest GENCODE annotations, most genes have less than four transcripts, positively correlating with the number of exons. Thus, it is more accurate to measure the splice variant efficiency of a gene with respect to the number of exons, which is a measure of Transcript Complexity (TC). In addition to that, the theoretical number of transcripts is substantially higher than the actual number of transcripts produced by Alternative Splicing Events, and the features restricting this phenomenon need to be explored.In this method, we have extracted the data of various features contributing to TC from different databases. Linear regression is used to identify the determinant features and to train and test the model of TC. The results indicate that exon length is the determining feature of TC, followed by coding potential, presence of chromatin signature, and 5′ splice site dinucleotide, all of which negatively affect a gene's TC, except exon length. To further classify the genes based on TC, random forest is used to identify the determinant features.•The splicing efficiency of a gene can be inferred by the transcript complexity, which is the number of transcripts per exon.•CaTCH is a linear regression-based model to calculate the transcript complexity of human genes, which can be calculated from the exon length, coding potentiality, presence of chromatin signature/s, and 5’ splice site dinucleotide.

Confrontation of linear versus nonlinear approach in Fe2B boridelayer thickness predictions

Kinetic studies of boride layers focus on trying to accurately predict their thicknesses according to some variables using different approaches. In this paper, an approach that is reliant on a multilinear regression is investigated. In doing so, with an engineering perspective, temperature and time are used as the sole variables in predicting a boride layer thickness u. The approach uses experimental data from a boriding process performed on iron substrates of the XC38 steel. A comparison between the proposed linear model and a nonlinear one is seen afterward to scrutinize the results. That nonlinear approach is known as the diffusion model and is based on Fick’s second law, where it uses more variables than the linear approach to estimate its predictions. Ultimately, the comparison elucidated that the use of a linear regression-based model can be an accurate engineering tool to identify boride layer thicknesses, but without interpolating the results outside the scope of the studied interval.

A multiple linear regression-based machine learning model for received signal strength prediction of multiband applications

A multiple linear regression-based machine learning model for received signal strength prediction of multiband applications

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions. Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals. As fuzzy systems have reformed several areas of work, health has also made the most of it. In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption. In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed. Data collection is performed with the WESAD dataset. Duplicated data are eliminated by Blinder Oaxaca Linear Regression-based Preprocessing model. With the application of the Blinder Oaxaca function, energy efficiency is enhanced. Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis. The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution. The result is conducted by using PYHTON application to analyze stress detection with the WESAD dataset. The proposed BO-SWNF method achieved an overall accurate data analysis of 12% with minimum time ensuring 56%improvement and minimizing energy consumption by 54%.

Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy

Abstract Using the global climate model outputs without any adjustment may bring errors in water resources and climate change investigations. This study tackles the critical issue of bias correction temperature in ERA5-Land reanalysis for 10 ground stations in northern Italy using nine machine learning (ML) techniques. Among standalone ML models, XGBoost regression emerged as the most effective standalone ML model, outperforming others across 6 out of 10 stations, while random forest regression, Gaussian process regression, and support vector regression obtained the second to fourth places. In contrast, AdaBoost regression (ABR) achieved the least favorable performance. Furthermore, nine ensemble ML models are proposed to correct bias of the reanalysis of temperature data. The results indicated that the K-nearest neighbors-based ensemble model excelled and secured the top rank in 7 out of 10 stations, while the multiple linear regression-based ensemble model achieved the highest precision in 4 out of 10 stations. Furthermore, other ML-based ensemble models displayed satisfactory results. On the other hand, the ABR-based ensemble model exhibited the lowest accuracy among ML-based ensemble models. The findings highlight the potential of ML-based ensemble models in effectively addressing bias correction in climate data.

Jacobian linear regression and Tate Bryant Euler angle enabled autonomous vehicle LiFi communication sustained IOT

Artificial Intelligence (AI) and the constant paradigm shift in road traffic have led to a need for significant improvement in road safety to minimize traffic accidents. LiFi helps minimize accidents by transmitting data between multiple vehicles (i.e. Vehicle-to-Vehicle (V2V)) and between vehicles and infrastructure (i.e. Vehicle-to-Infrastructure (V2I)) without interference. LiFi uses light to transmit data between devices or vehicles, which ensures efficient data transmission speed and is therefore considered a safe technology. A method called Deep Jacobian Regression and Tate Bryant Euler Recommendation (DJR-TBER) is proposed in this paper based on V2V and V2I autonomous vehicle communication. The proposed method DJR-TBER consists of an input layer, four hidden layers and finally an output layer. Sensors are first used to obtain the information. A linear regression-based speed evaluation model is developed and followed by a Jacobi matrix-based distance evaluation model in the hidden layer. The third hidden layer by developing a distance evaluation model. The use of Laplacian function ensures secure V2I communication for the autonomous vehicle. Finally, a Tate-Bryant-Euler angle-based model for emergency handling is proposed in the hidden layer to optimally consider the aspect of braking in emergency situations and thus increase driving safety.

Performance Assessment Comparison between Physically Based and Regression Hydrological Modelling: Case Study of the Euphrates–Tigris Basin

This paper presents two hydrological models developed for the Euphrates–Tigris Basin in Turkey. The first model is a linear regression-based model allowing the estimation of streamflow based on available hydroclimatic data (precipitation, temperature, evapotranspiration, etc.) with the use of clustering analysis. The second model consists of an elevation-based semi-distributed hydrological model (HBV model), allowing process-based modelling of the watershed. A set of performance metrics identified the HBV model as the best performance in terms of predicting streamflow (NSE = 0.752), while the RCA4-EU regression model of CORDEX showed the most robust performance. The results show the potential of regression models from a computational and data point of view in being integrated into physically based models wherein a hybrid approach might be beneficial. The comparison of conceptual models with statistical analyses of streamflow shows the potential of regression analysis when the regions are clustered in hydro-meteorologically homogeneous groups. The employment of the conceptual model HBV also provides significantly robust streamflow estimation for the region, which is especially important in estimating the hydropower potential of the region’s near future.

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score.

Existing approaches neither provide an accurate prediction of subarachnoid hemorrhage (SAH) nor offer a quantitative comparison among a group of its risk factors. To evaluate the population, hypertension, age, size, earlier subarachnoid hemorrhage, and location (PHASES) and unruptured intracranial aneurysm treatment score (UIATS) scores and develop an Artificial Intelligence-based 5-year and lifetime aneurysmal rupture criticality prediction (ARCP) score for a set of risk factors. We design various location-specific and ensemble learning models to develop lifetime rupture risk, employ the longitudinal data to develop a linear regression-based model to predict an aneurysm's growth score, and use the Apriori algorithm to identify risk factors strongly associated with SAH. We develop ARCP by integrating output of Apriori algorithm and ML models and compare with PHASES and UIATS scores along with the scores of a multidisciplinary team of neurosurgeons. The PHASES and UIATS scores show sensitivities of 22% and 35% and specificities of 76% and 79%, respectively. Location-specific models show precision and recall of 93% and 90% for the middle cerebral artery, 83% and 80% for the anterior communicating artery, and 80% and 80% for the supraclinoid internal carotid artery, respectively. The ensemble method shows both precision and recall of 80%. The validation of the models shows that ARCP performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, and 41 severe, and 1 of which is a critical factor. The PHASES and UIATS are weak predictors, and the ARCP score can identify, and grade, risk factors associated with SAH.

Machine learning-based framework for predicting toxicity of ionic liquids

There has been an increase interest in the application of ionic liquids (ILs) in chemical, biochemical, and other industrial processes. Besides physicochemical properties of ILs, toxicity of ILs is an important parameter that must also be accessed while designing ILs for any specific applications. Consideration of toxicity levels is crucial because of the potential risks to the ecosystem and biome that come with the widespread usage of ILs. Numerous combinations of cations and anions results in a rapidly increasing new IL structures. Toxicity measurements via experiments is not only time consuming but also resource intensive. Hence, a suitable alternative is to develop a metamodel that can predict the toxicity of ILs with reasonable accuracy. The objective of this study is to use machine learning algorithms like multiple linear regression and artificial neural network (ANN) to develop a model that can aid in the prediction of toxicity for any given IL structure. Group contribution-based methods are deployed to extract relevant features for each IL, which are used as inputs and toxicity values as output to develop the metamodel. Based on our results obtained, the linear regression-based model with regularization yields a reliable and a robust metamodel. This can be used as a screening framework to design ILs with low toxicity values for targeted applications.

Comparison of artificial neural network and linear regression model for the leaf morphology of fenugreek (Trigonella foenum graecum) grown under different nitrogen fertilizer doses

Health of a plant and its growth can be assessed by the area of its leaf. The present study envisaged to develop an artificial neural network (ANN) and linear regression-based leaf area estimation model, and to compare the models in statistical terms; thereby obtaining a statistically accurate model suitable for predicting leaf area of fenugreek. Fenugreek (Trigonella foenum graecum) was grown at open field conditions at different doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kgN/ha) of nitrogenous fertilizer using a randomised block design. The morphological parameters in term of leaf area (AP, mm2), leaf width (LW, mm), leaf length (LL, mm), roundness (R), leaf perimeter (LP,mm) and eccentricity (e) using a MATLAB based graphical user interface (GUI) software. Among all the linear regression models, AP =19.66 + 00.62 (LL × LW) (R2 = 0.94, RMSE = 14.09) was found to be the best fitting for fenugreek leaf area estimation. In case of ANN model, the best results obtained when three hidden neurons were considered. The R2, RMSE and MAD values of ANN model during training (0.97, 12.55 and 8.55, respectively) as well as validation (0.97, 10.48 and 7.92, respectively) were found more accurate than the linear regression model. Thus, the proposed ANN model can be used for the estimation of leaf area of fenugreek leaves.

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions.In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable.Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R2 of intensity prediction reached 0.778. Linear regression-based model was identified as unable to fit and predict the intensity, and thus only the variable influence on COVID-19 occurrence can be explained.We found that (1) CO VID-19 was more likely to occur in prosperous cities closer to the epicentre and located on higher altitudes, (2) and the occurrence was higher under extreme weather and high minimum relative humidity. (3) Most air pollutants increased the risk of COVID-19 occurrence except NO2 and O3, and there existed a lag effect of 6–7 days. (4) NPIs (non-pharmaceutical interventions) did not show apparent effect until two weeks after.

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

Multiple linear regression-based machine learning model for received signal strength prediction of multiband applications

Multiple linear regression-based machine learning model for received signal strength prediction of multiband applications

A hybrid model to improve WRF-Chem performance for crop burning emissions of PM2.5 and secondary aerosols in North India

In north India (about 30,000 km2 area), October–November months witness crop residue burning (CRB). This study aims to assess the impact of CRB emissions on PM2.5 and secondary aerosol (SA) at distances far from the sources. Chemical transport model, WRF-Chem (with chemical mechanisms, RADM2 and MADE/SORGAM), was applied to a domain of 840 × 840 km2 (centered around Delhi) with inputs of emissions of anthropogenic sources of EDGAR-HTAP and fire inventory from the National Centre for Atmospheric Research, USA. In parallel, measurements of PM2.5 and its constituents, CO, and NO2 were undertaken in Delhi and Kanpur. The model underestimated PM2.5 and showed poor predictions (r < 0.15) with insignificant formation of secondary organic aerosol (SOA) (< 1% in PM2.5). To improve the model, a linear regression-based hybrid model (HM) was developed that utilized the mechanistic processes of WRF-Chem and observed levels of PM2.5, CO and NO2. The HM performed adequately at multiple sites in Delhi (r = 0.19–0.47) and Kanpur (r = 0.38–0.43). The contribution of CRB to PM2.5 was at 31 ± 16% (in 232 ± 36 μg/m3) in Delhi and 21 ± 15% (in 233 ± 38 μg/m3) in Kanpur. The contribution of SOA in CRB-contributed PM2.5 was 18 ± 9% (Delhi) and 29 ± 21% (Kanpur).

Quantification of brain age using high-resolution 7 tesla MR imaging and implications for patients with epilepsy.

Epilepsy patients exhibit morphological differences on neuroimaging compared to age-matched healthy controls, including cortical and sub-cortical volume loss and altered gray-white matter ratios. The objective was to develop a model of normal aging using the 7T MRIs of healthy controls. This model can then be used to determine if the changes in epilepsy patients resemble the changes seen in aging, and potentially give a marker for the severity of those changes. Sixty-nine healthy controls (24F/45M, mean age 36.5±10.5years) and forty-four epilepsy patients (24F/20M, 33.2±9.9years) non-lesional at 3T were scanned with volumetric T1-MPRAGE at 7T. These images were segmented and quantified using FreeSurfer. A linear regression-based model trained on healthy controls was developed to predict ages using derived imaging features among the epilepsy patient cohort. The model used 114 features with significant linear correlation with age. The regression-based model estimated brain age with mean absolute error (MAE) of 6.6years among controls. Comparable prediction accuracy of 6.9years MAE was seen epilepsy patients. T-test of mean absolute error showed no difference in the prediction accuracy with controls and epilepsy patients (p=0.68). However, average signed error showed elevated (+5.0years, p=0.0007) predicted age differences (PAD; brain-PAD=, predicted minus biological age) among epilepsy patients. Morphological metrics in the medial temporal lobe were major contributors to PAD. Additionally, patients with seizure frequency greater than once a week showed significantly elevated brain-PAD (+8.2±5.3years, n=13) compared to patients with lower seizure frequency (3.7±6.5years, n=31, p=0.033). Morphological patterns suggestive of premature aging were observed in non-lesional epilepsy patients vs. controls and in high seizure frequency patients vs. low frequency patients. Modeling brain age with 7T MRI may provide a sensitive imaging marker to assess the differential effects of the aging process in diseases such as epilepsy.

Special Section on Mechanics of Electrochemical Energy Storage and Conversion

The increasing population growth, depletion of natural resources, and rising energy demand have sparked enormous research endeavors in electrochemical energy storage and conversion. For example, rechargeable lithium-ion batteries are ubiquitous in everyday life. Mechanics plays a critical role in designing a wide range of energy technologies. The emerging field of electro-chemo-mechanics, the interplay of mechanics and electrochemistry, is crucial for understanding the coupled physiochemical processes. The electrochemical phenomena can govern the mechanical response such as stress generation, deformation, fracture initiation/propagation, elasticity, and plasticity. Similarly, mechanical phenomena also influence the electrochemical properties such as device reliability and durability. Therefore, the in-depth mechanical study of electrochemical systems is urgently necessary for fundamental science and technological applications. Over the past few years, there has been significant progress in modeling, theories, and experimental characterizations of mechanical aspects of energy storage and conversion. This timely special issue addressed some recent advances in electro-chemo-mechanics. We have selected eight papers covering a wide range of issues in batteries and fuel cells such as (i) deformation, microstructural changes, creep, overcharge detection and prevention, optimization of structural parameters in batteries and (ii) temperature and load variations, metal-free cathode catalyst in fuel cells. The selected papers cover a gamut of electrochemical-mechanics centric research in energy storage and conversion.In this issue, Adams et al. used ex-situ X-ray microtomography to study cycling-induced microstructural changes in alloy anodes for Lithium-ion Batteries. They showed that besides mechanical degradation of electrodes, excessive volume expansion could also influence transport networks in the active materials and supporting phases of the electrode. Dienemann et al. measured the time- and temperature-dependent mechanics of two thicknesses of commercial lithium anodes inside a dry industrial room, where battery cells are manufactured at high volume. Furthermore, they examined the anisotropic microstructure of lithium anodes and its effect on bulk creep mechanics. Zaidi et al. proposed a robust passivity-based control scheme for 1.26 KW proton exchange membrane fuel cell under temperature and load variations. Li et al. performed experiments to study overcharge detection and prevention with temperature monitoring of Li-ion batteries. Furthermore, they developed a linear regression-based machine learning model to validate the experimental results. Nguyen et al. experimentally studied the deformation behavior of single prismatic battery cell cases and cell assemblies loaded by internal pressure. They demonstrated that the use of nominal cell casting characteristics significantly underestimates the resistance provided by the cell case to counter swelling of the active battery components. Bao et al. performed combined experiments and computation (computational fluid dynamics and multi-objective optimization method) to study structural parameters of the air-cooled system for the lithium battery pack. Woodcox et al. performed density functional theory (DFT) calculations to investigate 19 CaxSn1−x structures (six bulk materials and 13 alloys) as potential battery anodes. They found that for stable/metastable compositions of CaxSn1−x, those sharing the same chemical composition (stoichiometry) also share remarkably similar material properties, indicating that such materials would be advantageous for uses in battery anodes. Miao et al. developed a two-dimensional, two-phase transport model to investigate the transport characteristics in direct methanol fuel cells (DMFCs) using platinum group metal (PG)-free cathode catalysts. To achieve the projected performance target of 300 mW/cm2 peak power density, catalytic activities of both the anode and cathode catalysts need to be improved by one order of magnitude compared with the state-of-the-art commercial catalysts.We believe that this special issue will be a valuable contribution to the energy storage and conversion literature and open new frontiers for the researchers. The interdisciplinary field of electro-chemo-mechanics is rapidly evolving. Our special issue will motivate many researchers to implement novel techniques such as artificial intelligence, digital twins, and automated advanced manufacturing in this field and initiate new collaborations between experimentalists, theorists, and modelers. We would like to thank all contributors to this special section issue for submitting their latest high-impact work addressing the mechanics aspects of electrochemical energy storage. Also, special thanks would go out to the invited reviewers for helping us further enhance the quality of the articles. A special issue of ASME Applied Mechanics Reviews is also being planned for the second half of 2022 and developed in collaboration with the ASME Journal of Electrochemical Energy Conversion and Storage, dedicated to state-of-the-art reviews that highlight contributions of the discipline of electrochemical energy conversion and storage to research in applied mechanics.

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

Passive stiffness of fibrotic skeletal muscle in mdx mice relates to collagen architecture.

The amount of fibrotic material in dystrophic mouse muscles relates to contractile function, but not passive function. Collagen fibres in skeletal muscle are associated with increased passive muscle stiffness in fibrotic muscles. The alignment of collagen is independently associated with passive stiffness in dystrophic skeletal muscles. These outcomes demonstrate that collagen architecture rather than collagen content should be a target of anti-fibrotic therapies to treat muscle stiffness. Fibrosis is prominent in many skeletal muscle pathologies including dystrophies, neurological disorders, cachexia, chronic kidney disease, sarcopenia and metabolic disorders. Fibrosis in muscle is associated with decreased contractile forces and increased passive stiffness that limits joint mobility leading to contractures. However, the assumption that more fibrotic material is directly related to decreased function has not held true. Here we utilize novel measurement of extracellular matrix (ECM) and collagen architecture to relate ECM form to muscle function. We used mdx mice, a model for Duchenne muscular dystrophy that becomes fibrotic, and wildtype mice. In this model, extensor digitorum longus (EDL) muscle was significantly stiffer, but with similar total collagen, while the soleus muscle did not change stiffness, but increased collagen. The stiffness of the EDL was associated with increased collagen crosslinking as determined by collagen solubility. Measurement of ECM alignment using polarized light microscopy showed a robust relationship between stiffness and alignment for wildtype muscle that broke down in mdx muscles. Direct visualization of large collagen fibres with second harmonic generation imaging revealed their relative abundance in stiff muscles. Collagen fibre alignment was linked to stiffness across all muscles investigated and the most significant factor in a multiple linear regression-based model of muscle stiffness from ECM parameters. This work establishes novel characteristics of skeletal muscle ECM architecture and provides evidence for a mechanical function of collagen fibres in muscle. This finding suggests that anti-fibrotic strategies to enhance muscle function and excessive stiffness should target large collagen fibres and their alignment rather than total collagen.