Linear Regression Prediction Model Research Articles

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy.

Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm−1 and 1044 cm−1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation () of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400–2000 cm−1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis.

This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors. The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R2 = 0.54, mean square error (MSE)=0.031, mean absolute error (MAE)=0.128). The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2 = 0.52, MSE=0.032, MAE=0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors. Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.

Characteristics of geriatric rehabilitation inpatients based on their frailty severity and change in frailty severity during admission: RESORT

BackgroundFrailty is prevalent amongst geriatric inpatients and worsens clinical outcomes. Determinants of admission frailty and changes in frailty severity are potential targets for intervention. ObjectiveTo identify characteristics associated with frailty severity at admission and change in frailty severity during geriatric rehabilitation. MethodsThe observational, prospective, longitudinal inception cohort REStORing health of acutely unwell adulTs (RESORT) includes geriatric rehabilitation inpatients. Demographic, medical, nutritional, functional, and psychological characteristics were analysed according to admission Clinical Frailty Scale. Linear and multinomial logistic regression prediction models identified admission characteristics predicting change in frailty severity during rehabilitation. ResultsA total 1716 inpatients (median age [IQR] 83.4 [77.7–88.6] years, 56.4 % females) were included. Poorer admission medical, nutritional, functional, and psychological status was associated with more severe frailty. Cardiac admissions, higher Cumulative Illness Rating Scale, greater number of medications, and better Short Physical Performance Battery at admission predicted improved frailty severity. Admission cognitive impairment, delirium, higher Charlson Comorbidity Index, and Hospital Anxiety and Depression Scale anxiety predicted worsened frailty severity. Explained variation was low. ConclusionsFrailer geriatric rehabilitation inpatients have more complicated disease and impaired nutritional, physical, and psychological markers. Admission cognitive impairment, delirium, comorbidity, and anxiety predict worsening frailty during rehabilitation, and require prompt management.

Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.

Effects of Organ System Courses of the First Two Years of Medical School on the Performance of the Comprehensive Osteopathic Medical Licensing Examination of the United States (COMLEX-USA) Level 2-Cognitive Evaluation

Objectives: The Comprehensive Osteopathic Medical Licensing Examination of the United States (COMLEX-USA) Level 2-Cognitive Evaluation (CE) is a board examination that medical students usually take in the third or fourth year of medical school. A few researchers have investigated the prediction of COMLEX Level 2-CE scores based on the performance in third-year clerkships. However, given how close the clerkships are to the board exam, this type of prediction is too late for students to have adequate time to get assistance to prepare for COMLEX Level 2-CE. We aimed to investigate the predictive value of each organ system course during the first two years in predicting COMLEX Level 2-CE performance. Our findings will help students at risk focus on important basic and clinical sciences much earlier before preparing for COMLEX Level 2-CE.Methods: Academic data from students enrolled at Rocky Vista University College of Osteopathic Medicine from 2011 to 2017 were retrieved. Data included the Medical College Admission Test (MCAT) scores, course grades in the first two years of medical school, COMLEX Level 1 scores, and COMLEX Level 2-CE scores. Pearson correlation coefficients, a multiple linear regression model, and a backward stepwise regression model were generated for analysis.Results: The highest correlation with COMLEX Level 2-CE scores was the COMLEX Level 1 score, followed by the performances in the third-semester Cardiovascular System II (CV II) and Renal System II (REN II) courses. Multiple linear regression and backward stepwise regression predictive models found that scores on third-semester CV II and Principles of Clinical Medicine III (PCM III) were the most significant predictors of performance on Level 2-CE. Both models explained 46% of the variance in COMLEX Level 2-CE scores.Conclusions: Performances in third-semester courses are the most important predictors of COMLEX Level 2-CE scores.

A Linear Regression Prediction Model of Infectious Disease Spread Based on Baidu Migration and Effective Distance.

Objective To analyze the relationship between effective distance and epidemic spread trajectory and between arrival time and scale based on the COVID-19 data outbreak in Wuhan and thus to improve the prediction ability of the spread of infectious disease. Methods Up to January 28, 2020, the reporting date, the onset date, and the cumulative number of confirmed cases of COVID-19 in each province and city were collected. Baidu migration data was used to calculate the effective distance from Wuhan city to other regions. The reporting date and onset date of the first diagnosed patient were taken as the arrival time, respectively, to establish a linear regression model of effective distance and arrival time. In different provinces and cities, the logarithm of the cumulative number of confirmed cases with a base of 5 was taken as the criteria to determine the level of the cumulative confirmed cases. Based on this, the linear regression model of effective distance and the level of cumulative confirmed cases in the provincial and municipal units was established. Results The linear correlation between the reporting date of the first confirmed patient and the effective distance was not strong. The coefficients of determination (R2) for cities with and without the cities of Hubei Province were 0.36 and 0.44, respectively. And the linear correlation between the onset date of the first confirmed patient and the effective distance was strong. And the coefficients of determination (R2) for cities with and without the cities of Hubei Province were 0.67 and 0.83, respectively. And the linear correlation between the level of cumulative confirmed cases in the provincial and municipal units and the effective distance was strong, with an R2 of 0.87 and 0.84, respectively. The regression coefficients of each linear model were statistically significant (P < 0.001). Conclusion The effective distance has a good fit with the model of the onset date of the first confirmed patient and the level of cumulative confirmed cases, which can predict the trajectory, time, and transmission range of the epidemic. It can be taken as the reference for the early warning, prevention, and control of sudden acute infectious diseases from a macro perspective.

ANALISA PERBANDINGAN MODEL PREDICTION DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN METODE LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON

Stock is one type of long-term investment that is quite in demand by the public because this investment brings quite a large profit for its investors. However, in relation to this, stock price movements, in general, tend to be non-linear and non-stationary, this is because stock prices can be influenced by several factors whose results can change the pattern of stock price values either up or down, so this can make it difficult to stock prices prediction. In this study, a comparative analysis of prediction models was carried out in predicting stock prices using a technical approach based on past data, while the data used were historical stock prices by taking data samples from three issuers from the Indonesian capital market. There are three methods that were tested in this study, including Linear Regression (LR), Random Forest Regression (RFR), and Multilayer Perceptron (MLP). The test was carried out with two data modeling, namely partitioning which was validated with Cross-Validation, and data modeling with Cross-Validation without partitioning. In this study, the prediction model with LR is able to produce a fairly low error prediction value with the lowest RMSE score of 0.010 and the highest RMSE of 0.012, the lowest MAPE of 1.2%, and the highest of 1.9%, the lowest MAE of 0.006 and the highest. of 0.009, and the highest R2 value was 99.8% and the lowest was 99.6%. It can be concluded that in this study, the Linear Regression prediction model is able to predict historical data on stock prices better than the RFR and MLP models.

Use of artificial neural network to evaluate cadmium contamination in farmland soils in a karst area with naturally high background values

In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham

Shares are securities as proof of ownership of investors in a company. Stocks have a volatile nature, this makes stocks difficult to predict. Stock prediction is an effort to estimate the stock price, especially in the Bank Rakyat Indonesia company that will appear in the future, and to increase investors' profit opportunities in making investment decisions. During the COVID-19 pandemic, Bank BRI's shares experienced significant ups and downs in four months, which illustrates the sensitivity of the stock to an event. Therefore, it is important to predict stock prices to reduce the risk accepted by investors. The prediction itself requires time series data. Time series is data that is collected sequentially from time to time. The method used for time series data is Linear Regression because this method can handle time-series data. Based on these problems, stock prediction research will be conducted at the Bank Rakyat Indonesia company using the Linear Regression method. Bank Rakyat Indonesia share price data were obtained from the investing.com website from the period starting on January 1, 2008, to June 1, 2020. The data is processed starting from preprocessing to determine attributes, remove unnecessary attributes, and change the contents of the data type, then process split data to divide the dataset into training and test data. The attributes used in this study are Date and Price and the distribution of the data used is 60:40, 65:35, 70:30, 75:25, and 80:20. The best ratio is at 80:20 which produces train and test accuracy of 0.89 and 0.91, Then each training data and testing data are entered into the linear regression model for prediction. The error results from the predictions were calculated using MAPE and yielded a percentage of 13.751% for training data, 13.773% for test data, and 13.755% for overall data. The MAPE results indicate that the linear regression method can be used to predict the stock price of BRI Bank.

Optimal pricing for electricity retailers based on data-driven consumers’ price-response

In the present work, we tackle the problem of finding the optimal price tariff to be set by a risk-averse electric retailer participating in the pool and whose customers are price sensitive. We assume that the retailer has access to a sufficiently large smart-meter dataset from which it can statistically characterize the relationship between the tariff price and the demand load of its clients. Three different models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. More specifically, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then, we will insert this model in a quadratic optimization problem which evaluates the optimal price to be offered. This optimization problem accounts for different sources of uncertainty including consumer’s response, pool prices and renewable source availability, and relies on a stochastic and risk-averse formulation. In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive model. This allows us to properly quantify (data-driven) not only the expected value but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward-based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profits are found for the retailer with highly competitive prices and some possible improvements are shown if richer datasets could be available in the future. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers. It has been assumed that the energy procurement of the retailer can be satisfied from the pool and different types of contracts. The obtained results reveal that the risk-aversion degree of the retailer strongly influences contracting decisions, whereas the price sensitiveness of consumers has a higher impact on the selling price offered.

Changes in paediatric Ambulatory Care Sensitive Conditions in Victoria, 2018-20: the COVID-19 effect?

Objectives This study sought to describe the patterns in emergency department (ED) presentations and hospital admissions in children with Ambulatory Care Sensitive Conditions (ACSCs) before and during the coronavirus disease 2019 (COVID-19) pandemic restrictions in Victoria, Australia, to assess if changes in out-of-hospital care resulted in an increase in delayed/severe presentations. Methods This study involved secondary analysis of Victorian Emergency Minimum Dataset and Victorian Admitted Episode Dataset data. Patients (<18 years) attending EDs with a diagnosis of an ACSC were included. We compared the number and severity of ASCS presentations pre-COVID-19 (1January 2018-27 March 2020) and during COVID-19 (28 March-31 October 2020). A linear regression prediction model was built to compare the observed versus predicted presentation number in the 2020 period. Results In total, there were 108 104 paediatric ACSC ED presentations in Victoria during the study period. Females accounted for 51 462/108 104 (47.6%) of all presentations, with a median age of 3 years. A significant decrease in ED presentations was seen in 2020 (41 319 in 2018; 44 978 in 2019; and 21 807 until October 2020), predominantly due to reductions in conditions that are typically mediated by viruses in childhood (i.e. asthma, convulsions/epilepsy and ear, nose and throat conditions). The proportion of high-urgency presentations and those requiring admission was stable in 2020. Conclusions An overall reduction in the number of ED presentations and admissions with paediatric ACSCs was seen in Victoria in 2020, indicating that rates of delayed or more severe presentations did not occur as a result of changes in out-of-hospital care.

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

The multiple regression model is very popular among researchers in both field of social and science because it is easy to interpret and have a well-established theoretical framework. However, the multioutput multiple regression model is actually widely applied in the engineering field because in the industrial world there are many systems with multiple outputs. The ridge regression model and the Multi-Layer Perceptron (MLP) neural network model are representations of the predictive linear regression model and predictive non-linear regression model that are widely applied in the world of practice. This study aims to build multi-output models of a ridge regression model and an MLP neural network whose hyperparameters are determined by a grid search algorithm through the cross-validation method. The hyperparameter that produces the smallest RMSE value in the validation data is chosen as the hyperparameter to train both models on the training data. The hyperparameter in question is a combination of learning algorithms and alpha values (ridge regression), a combination of the number of hidden nodes and gamma values (MLP neural network). In the ridge regression model for alpha in the range between 0.1 and 0.7, the smallest RMSE is obtained for all learning algorithms used. While the MLP neural network model specifically obtained a combination of the number of nodes = 18 and gamma = 0.1 which produces the smallest RMSE. The ridge regression model with selected hyperparameters has better performance (in the RMSE and R2 value) than the MLP neural network model with selected hyperparameters, both on training and testing data.

Comparison of Spirometry and Impulse Oscillometry in Thai Childhood Asthma

Background: Impulse oscillometry (IOS), a new respiratory impedance measurement, is increasing in its popularity as a means to assess airway resistance in young children. Its great advantage lies in its effortless and fast performance, making the airway obstruction measurement easier for patients who are not able to perform spirometry tests. However, studies comparing spirometry with IOS in Thai children are rare. Aims: To evaluate the correlation between IOS and spirometry parameters in Thai childhood asthma Methods: Vyntus IOS (JEAGER®, Germany) and spirometry tests (Vyntus SPIRO) were performed in 48 children, aged 5-15 years old, who fulfilled the GINA asthma criteria. The study, approved by the hospital’s ethics committee, was conducted between March 1, 2020 and March 31, 2021 at the Queen Sirikit National Institute of Child Health, Thailand. (TCTR20220527005) Results: Forty-eight childhood asthma patients with a median age of 10.79 (IQR = 8.41, 11.87) years underwent both IOS and spirometry measurements. Male sex was predominant (64.58%), and 77% of patients had well-controlled asthma (C-ACT score ≥20). In our study population, the atopic comorbidities were allergic rhinitis (91.67%), atopic dermatitis (10.42%) and food allergy (10.42%). Moreover, parental asthma was found in 16.67% of the participants. In comparison with spirometry, the percentage change of FEF25-75% was significantly negatively correlated with R5, R10, AX, and mean R5-R20 (r = -0.608, -0.528, -0.500, -0.511, respectively; p &lt;0.001). Likewise, FEV1 was significantly negatively correlated with R15 and R20 (r = -0.520, -0.565, respectively; p &lt;0.001). The linear regression prediction model demonstrated that a 30% increase in FEF25-75% was related to a 22.7% reduction in R5 (p = 0.007). Conclusion: The percentage change of FEF25-75% was found to negatively correlate in statistically significant terms with R5 in Thai childhood asthma. Hence, IOS is an effective and feasible replacement for spirometry as a measurement modality of lung function, especially in young children.

Sparse modeling for small data: case studies in controlled synthesis of 2D materials

Straightforward, interpretable, and modifiable linear-regression prediction models with appropriate accuracy are constructed by sparse modeling coupled with our chemical perspectives as researchers on small data, such as experimental data in laboratories.

Visualized calculation of regional power grid power data based on multiple linear regression equation

Abstract The power generation of distributed power generation systems in regional power grids is mainly affected by external environmental factors, and there are volatility and intermittent power generation issues. This paper establishes a relatively simple multiple linear regression prediction model to predict grid-connected photovoltaic power generation systems. The article uses a hybrid modelling method to filter more important weather forecast input variables through a step-by-step linear regression method to realize the visual calculation of power data. The research results show that the multiple linear regression equation methods proposed in the article have pervasive application value in the actual application of solar photovoltaic power consumption data.

The Effects of Earthquakes on Tourism: Evidence from Turkey

Turkey has been hit by many earthquakes during the last century. A 7.4 earthquake which lasted for 45 seconds struck the Marmara Region on 17 August 1999 at 3:02 am. In addition to its destructive effects such as property losses and personal injuries, it also had negative effects on the tourism industry. Cancellation of package tours for foreign tourists in Turkey had a negative impact on travel agencies, hotels and airlines. The purpose of the present study is to outline the economic impact of the 1999 Earthquake on tourist flows. For this purpose, an event study was performed to evaluate the amount of loss between August 1998 and August 2001 due to the drop in the tourism industry. Additionally, CUSUM and CUSUMSQ test, multiple linear regression (MLR) model and prediction models were employed to identify the patterns of structural change. The results show that the number of tourists decreased by about 20% following the earthquake. The results of the CUSUM and CUSUMSQ estimation analysis based on the control variables suggest that if there had been no earthquakes, the number of future tourists would have been approximately 8% higher than the actual number of tourists who visited Turkey at that time.

Abstract 14166: Epigenetic Biomarkers Correlate With Post-stroke Long-term Memory Recovery

Introduction: Epigenetic markers including 5-metylcytosine (5mC) can reflect disease severity and treatment effect. Stroke is a multifactorial disease causing aberrant DNA methylation (DNAm) and memory damage. Hypothesis: Models built for 5mC biomarker panel signatures may be useful for quantitative prediction of post-stroke long-term memory recovery in response to treatment. Methods: Stroke was induced with a middle cerebral artery occlusion (MCAO)(18 rats: sham(6), stroke(6), and stroke with treatment (6)). Xe-liposomes were intravenously administrated repetitively for 3 days after MCAO. At day 35, memory was assessed and scaled using a novel objective recognition test. Brain hippocampus and cortex tissue samples were prepared for whole-genome bisulfite sequencing (WGBS) to generate quantitative DNA methylation datasets in response to repetitive intermittent Xe administration. The resulting WGBS data were evaluated and quantitatively associated with memory recovery. Results: A post-stroke long-term memory outcome model was generated with scaled data using the following inputs: (1) good recovery, (2) moderate recovery, (3) edge, (4) light damage, and (5) severe damage. Differential DNAm 5mC analysis was performed comparing MCAO affected hemisphere with sham and Xe-treated samples. Initial 5mC differential analysis identified 6,175 (Cortex), and 9,037 (Hippocampus) CpG sites that were different between groups. Subsequently the LASSO algorithm was applied and selected 22 CpG sites that correlated with the long-term memory recovery scale. PCA analysis using the 22 CpGs demonstrated separation of memory outcomes. We have also built a predictive linear regression model of the 22 CpGs to assess memory loss or recovery in response to treatment. Analysis showed 7 out of 22 CpGs are conserved in humans (orthologs) for 30 bp regions. Conclusions: Epigenetic 5mC marker panels have the potential for quantitating post-stroke memory damages/recovery.

Policies, population and impacts in metro ridership response to COVID-19 in Changsha

To secure the city against the transmission of COVID-19, metro ridership control is an important task of the metro corporation on the premise of meeting the basic travel demand as far as possible. First off, this paper describes the influence mechanism of COVID-19 on metro ridership in Changsha, including an analysis into the correlation among policies, population, and metro ridership. Secondly, this paper verifies the influence of governmental macro-policy on population mobility and metro train working diagram, and thereby on metro ridership, based on the actual data during Jan 12th to May 6th, 2020 and year-ago data (2019). And then the Difference-in-Difference (DID) model is used to verify the effect of policies on metro ridership in Changsha. Results also show the effectiveness of policy chain on the limit of metro ridership, which bears a strong correlation to the number of confirmed COVID-19 cases. A linear regression prediction model is built to predict metro ridership based on cumulative net inflow of population index, metro carrying capacity and confirmed COVID-19 cases. This paper is expected to provide reference for Metro Corporation to control ridership on the premise of meeting the basic travel demand amid the explosive outbreak of the epidemics.

Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand

The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input variables used in the developed neural network model were the bearing capacity of square footing, thickness ratio, friction angle ratio and the output were the bearing capacity of E-shaped footing on layered sand. Multiple layer perceptron (MLP) and multiple linear regression (MLR) prediction models were used for the determination of error metrics and the ultimate bearing capacity of E-shaped footing resting on layered sand. Finally, for the ANN model development, a model equation was developed with the assistance of weights and biases, based on the MLP and MLR model using open-source WEKA and Anaconda software respectively. Sensitivity analysis has been performed on the data sets which correlates the various input variables with the output variable of both the models. The coefficient of determination (R2) comes out to be 0.99 and 0.98 for the MLP and MLR models respectively indicating that both the models were able to predict the bearing capacity for the E shaped footing with acceptable accuracy.

Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.