Mean Square Error Research Articles

Thermal analysis of nonlinear mixed convection effects within wavy enclosure

The analysis in this study focusses on the transfer of heat within a wavy cavity in the presence of nonlinear mixed convection and MHD. We conduct the investigation under conditions where the upper wall of the cavity remains cold, the lower wall receives heat, and the left and right wavy walls remain adiabatic. Using the PDE solver in COMSOL, the non-dimensional system is simplified, employing the finite element method. The study aims to explore the impact of parameters such as nanoparticle concentration, Hartmann number, Reynolds number, Richardson number, and nonlinear mixed convection parameters on fluid flow and heat transfer. An artificial neural network has been employed to examine the behaviour of the Nusselt number within a cavity. The isotherms reveal that the temperature is higher near the lower wall for all parameters due to the heated source. Analysis of the streamline plots indicates that heat increases as it moves towards the upper wall, influenced by the Hartman number and volume fraction parameter. The use of ANN in the study forecasts the accuracy, smoothness, and convergence of the data through plots of mean squared error (MSE), error histogram, regression analysis, and transition state plot. The authors believe that using artificial neural networks to explore the heat transfer rate under the assumptions of MHD and nonlinear mixed convection is a novel approach that has not been previously investigated. Additionally, researchers can utilise ANN concepts to predict intricate cavity formations through multiphysics. This approach will require less human effort and computing time to analyse the heat response of any dataset.

A hybrid model for accurate prediction of composite longitudinal elastic modulus

This research presents a novel hybrid model that integrates a physical-based empirical model with an Artificial Neural Network (ANN) to accurately predict the longitudinal modulus of elasticity for composites under compression. The study focuses on a composite material with a pore inclusion within an ABS plastic matrix, exploring various pore volumes, orientations, and shapes. As part of the proposed hybrid model, a regression-type neural network was trained in MATLAB® to predict and correct discrepancies between the Generalized Stiffness Formulation (GSF) homogenization-based modeling method and the collected compression experimental test results. Using MATLAB® neural network, random error datasets were used to train the feed-forward neural network, and the remaining error datasets were used for validating the performance of the proposed hybrid modeling scheme.The hybrid model demonstrated superior performance, achieving the lowest Mean Error (ME) of 0.1684864, Mean Absolute Error (MAE) of 1.051846, Mean Squared Error (MSE) of 3.500952, and highest R-squared of 0.998797. The proposed hybrid model outperformed both the Generalized Stiffness Formulation (GSF) and standalone ANN models. The significant improvement in prediction accuracy underscores the novelty and robustness of the hybrid approach in composite material modeling. Furthermore, this method can be used to refine any existing physical model by focusing on improving these established models to match experimental results and reducing the discrepancies, which offers a more efficient and attractive strategy for accurate predictions.

ALR-HT: A fast and efficient Lasso regression without hyperparameter tuning

Lasso regression, known for its efficacy in high-dimensional data analysis and feature selection, stands as a cornerstone in the realm of supervised learning for regression estimation. However, hyperparameter tuning for Lasso regression is often time-consuming and susceptible to noisy data in big data scenarios. In this paper we introduce a new additive Lasso regression without Hyperparameter Tuning (ALR-HT) by integrating Markov resampling with additive models. We estimate the generalization bounds of the proposed ALR-HT and establish the fast learning rate. The experimental results for benchmark datasets confirm that the proposed ALR-HT algorithm has better performance in terms of sampling and training total time, mean squared error (MSE) compared to other algorithms. We present some discussions on the ALR-HT algorithm and apply it to Ridge regression, to show its versatility and effectiveness in regularized regression scenarios.

ANN Prediction of Mode I Fracture Energy in Epoxy Adhesive Joints: Adherend, Adhesive, and Strain Rate Effects

In this investigation, the mode I fracture behavior of an epoxy adhesive joint has been investigated to probe the combined effects of adherend Young's modulus and thickness, strain rate, and adhesive length. Experimental fracture testing was conducted on double-cantilever beam (DCB) specimens under three various strain rate thresholds, namely, quasi-static (0.0005 s-1), low (0.015 s-1), and intermediate (0.5 s-1). The specimens were constructed with varying adherend materials (i.e., copper, aluminum), thickness (12-20 mm), and adhesive lengths (25-75 mm). The fracture load was measured in each experiment, and the critical strain energy release rate, JCi, was calculated via a finite element analysis (FEA) in each case. Results with a 95% confidence interval showed adherend thickness had a negligible effect within the investigated range (p-value = 0.462). JCi was significantly impacted by adhesive length (p-value = 0.009) and two other investigated parameters (p-values = 0.000). The development of artificial neural networks (ANNs) with Levenberg-Marquardt (LM) led to the prediction of JCi based on the examined parameters. With a mean absolute percentage error (MAPE) of 8.9% and a mean squared error (MSE) of 683 J2/m4 on unseen test data, the ANN model built in this study was able to predict the JCi in epoxy adhesive joints, indicating its potential to produce an accurate prediction for JCi in epoxy adhesive joints. This work offers insights for precise fracture behavior prediction under mode I stress conditions as well as some helpful information that can be utilized to optimize adhesive joint design.

Measurement of intelligent computing via Levenberg Marquardt algorithm (LMA) for accurate prediction of fluid forces in a transient non-Newtonian thermal flow

Predicting precise results for the quantities of interest in time-dependent Computational Fluid Dynamics (CFD) simulations requires a significant investment of computational resources and time. To get around these issues, CFD simulations have been joined with Artificial Neural Networks (ANN). An optimally configured artificial neural network (ANN) is given the training and validation data sets produced by computational fluid dynamics (CFD). The flow around a cylinder, which is a well-known benchmark problem for incompressible flows, has been taken into consideration by the hybrid-CFD system. The mathematical model is based on the non-stationary Navier-Stokes and energy equations with viscosity. The basic architecture of the ANN model consists of 10 hidden layers, three output levels, and five input layers. Fast second-order LMA, a top-tier approach, was used to train the network. Both the Mean Square Error (MSE) and the coefficient of determination (R) provide statistical evidence that the ANN projected values for the drag and lift coefficients and average Nusselt number obtained from the finite element analysis are accurate. This analysis suggests that ANNs have the potential to significantly cut down on the amount of time and energy needed to run time-dependent simulations.

Evaluating the effectiveness of elastic net model in predicting stock closing price

Abstract. Accurate stock price prediction is important for financial investors, as it directly influences investment strategies and decision-making. In this study, the author focuses on evaluating the effectiveness of the Elastic Net model in forecasting the closing prices of Apple Inc.'s stock. The dataset, spanning from 2014 to 2023, is sourced from Kaggle and includes both historical prices and a range of technical indicators, thereby ensuring the accuracy and reliability of the research data. To enhance the predictive accuracy of the model, this paper carefully selects the 14-day moving average of the closing prices along with a set of relevant technical indicators as the key input variables. This selection aims to capture the underlying trends and patterns in the stock's price. The predictive performance of the Elastic Net model is evaluated using multiple evaluation metrics, including Mean Squared Error (MSE) and R-squared values, which quantify the model's accuracy and explanatory power. Additionally, this paper utilizes the Autocorrelation Function (ACF) plot to analyze the residuals and further validate the model's effectiveness. The results demonstrate that the Elastic Net model is well-suited for handling high-dimensional financial data, exhibiting strong performance in predicting Apple Inc.'s stock prices. However, it also underscores the model's potential problem as a valuable tool for investors seeking to make proper decisions in real-life conditions.

A novel parabolic model driven by double phase flux operator with variable exponents: Application to image decomposition and denoising

This work tackles a novel parabolic equation driven by a nonlinear operator with double variable exponents, aiming to decompose and denoise images. Our primary approach involves enhancing classical models based on variable exponent operators by considering a novel nonlinear operator having a double-phase flux with unbalanced growth. We begin initially by analyzing the theoretical solvability of our model. Employing the Musielak-Orlicz space, we establish a suitable functional framework for investigating the proposed model. Subsequently, we use the Faedo-Galerkin approach to establish the existence and uniqueness of a weak solution for our problem. Furthermore, we provide a demonstration showcasing how our model preserves solution positivity, emphasizing this as a consistent feature of the proposed model. To evaluate our theoretical results, we present various numerical simulations on some grayscale and medical images (Magnetic Resonance Images (MRI)). These simulations covered aspects like decomposition, robustness and behavior sensitivity of model parameters with visual and quantitative comparisons. The obtained numerical results strongly support the efficiency of the proposed model in preserving features, reducing artifacts and deleting noise in comparison to some well-known existing state-of-the-art models. Furthermore, the proposed model quantitatively surpasses the competitive models in terms of several well-known criteria, namely the Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index (SSIM) and the Mean-Square Error (MSE).

Physics-based parameters selection and machine learning based prediction of pool boiling bubble departure diameter

For forecasting the bubble departure diameter in pool boiling heat transfer, this study creates a deep-learning neural network based on physics and a variety of working fluids, manufactured surfaces, and materials subjected to different testing situations. This work analyzes nearly 5,000 data points of bubble departure diameters ranging from 0.2-28.7 mm using neural network input parameters such as saturation temperature, pressure, contact angle, surface roughness, surface tension, liquid density, gas density, wall superheat and heat flux, and other thermophysical properties, predicting their impact on the bubble departure diameter, and also uses them for training neural networks. The best Neural network which is designated as Case-4 is selected on the basis of coefficient of determination(R2), mean absolute error (MAE), and mean-square error (MSE) was used to understand the degree of influence of each input parameter and it was found that θ and Q have the greatest impact on the model. A comparison was also done to correlations proposed by researchers and it was found that neural networks have much better efficiency and accuracy than empirical formulas and thus can be an essential tool to predict the bubble diameter in the future.

Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts

An accurate forecast of short-term reference evapotranspiration (ET0) is crucial for effective farm and irrigation scheduling management. The techniques of forecasting ET0 have progressed from empirical equations to artificial intelligence-based models. However, gaps have remained in comprehensively evaluating the ET0 forecast performance discrepancies between various methods based on weather forecasts in addition to historical meteorological data, as well as investigating the effect of the amount of input variables on ET0 forecast accuracy. Here, the study evaluated the forecast accuracy of five conventional equations calibrated and six machine learning (ML) and deep learning (DL) models in predicting daily ET0 for a lead time of 1–7 days in the North China Plain (NCP). Comparative evaluations indicate that the Analytical Penman-Monteith (APM) is the optimum empirical equation for forecasting ET0, but its accuracy decreases as lead times increase. Conversely, ML and DL models consistently outperform empirical equations, characterized by lower mean absolute error (MAE) and root mean square error (RMSE) as well as correlation coefficient and Nash-Sutcliffe efficiency coefficient approaching 1 across all lead times. Notably, the Bidirectional Long short-term memory (Bi-LSTM) model outperforms other models, maintaining robust and effective forecast performance even on day 7. Moreover, with the increasing input variables, combining weather forecasts and historical meteorological data, the forecast performance of all models is improved, with significant enhancement observed in the Bi-LSTM model. This work provides valuable insights for choosing appropriate models to forecast ET0, holding essential in regional managing farm and irrigation systems.

Bayesian estimation under different loss functions for the case of inverse Rayleigh distribution

In this study, the best parameter estimator for the scale parameter of the inverse Rayleigh distribution was determined based on a comparison of the maximum likelihood estimator (MLE) method, the Bayesian generalized squared error loss function (SELF), the Bayesian linear exponential loss function (LINEX LF), and the Bayesian entropy loss function (ELF). The prior distribution chosen was the non-informative prior, namely the Jeffrey prior, and the informative prior using the exponential distribution. The estimator evaluation method used was based on the smallest value of the Akaike information criterion (AIC), corrected Akaike information criterion (AICc), and Bayesian information criterion (BIC). Based on simulation studies and real data, it was found that the best parameter estimator on the data for the scale parameter of the inverse Rayleigh distribution is the Bayes ELF prior exponential .

Development of a Two-Step EOF Statistical Postprocessing Algorithm to Identify Patterns of Systematic Error and Variance within GEFSv12 Reforecasts

Abstract Analysis of seasonally varying signals of model error biases, as well as the diagnosis of space–time patterns of synoptic error, here is accomplished through the development of a statistical postprocessing algorithm based on time-extended empirical orthogonal functions (EEOFs) built on patterns of variance. This work analyzes GEFSv12 200-hPa geopotential height (Z200) model error against ERA5 reanalysis data. The mean-square error variance between GEFSv12 reforecast and ERA5 grows rapidly after day 7 of a forecast and continues to increase through the end of the 16-day forecast period. At lead time 16, the largest variance occurs in mid- to high-latitude oceanic storm tracks. Variance is highest during hemispheric winter when baroclinic energy is abundant. The seasonal cycle of error shows largest anomalies in the high latitudes during hemispheric winter. After running the titular two-step, space–time EEOF algorithm, a standardized spatial eigenspectrum shows that eigenvalues increase at longer lead times after decreasing in the medium range, demonstrating that the algorithm extracts large-scale signals of systematic error in the long range. Leading wintertime space–time eigenmode pairs include high-latitude blocking structures and Rossby wave trains. Results suggest that the large-scale systematic errors in GEFSv12 Z200 initiate in part from inaccurate representations of phase speeds of atmospheric waves in the polar jet.

Analyzing Cancer Survival Times Using the Exponentiated Fréchet-Gompertz Distribution

Understanding the survival of cancer patients is essential for determining optimal treatment strategies. This research introduces a robust distribution for analyzing survival data known as the Exponential Fr ́echet-Gompertz distribution (EFG). The EFG combines the unique characteristics of the Exponential Fr ́echet and Gompertz distributions, enhancing its efficiency and providing greater flexibility in representing complex datasets. The study specifically investigates the EFG distribution’s effectiveness in modeling cancer patients’ survival times. A comprehensive analysis of the distribution’s properties is presented, including the quantile and quartile functions, shape indices, moments, moment-generating function, characteristic function, mean residual life, mean waiting time, R ́enyi entropy, and order statistics. The parameters of the distribution are derivedusing five distinct methods: Maximum Likelihood Estimation (MLE), Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Cram ́er-von Mises (CVM), and Maximum Product of Spacings (MPS). A Monte Carlo simulation technique is employed to evaluate the performance of these estimation methods. The simulation results indicate that as sample size increases, the meansquare error (MSE) values for all estimators decrease. Notably, the MLE exhibits the lowest MSE, while the MPS has the highest MSE, particularly for smaller sample sizes. Furthermore, the study presents a comprehensive comparison of the effectiveness of these estimation methods in analyzing survival times for various cancer types, including bladder, bone, blood and brain cancer. The results indicate that the EFG distribution is an optimal model for representing the survival times of patients across these different cancers data. Furthermore, the W.LS method yielded superior estimations for most datasets concerning cancer patient survival. Overall, the EFG distribution demonstrates exceptional capability in accurately fitting survival times for cancer patients compared to other competing distributions.

COMPARISON OF NONLINEAR MODELS FOR DESCRIBING THE GROWTH OF LOCAL CHICKENS RAISED IN TOGO

Growth is a trait of economic importance in chicken genetic improvement that is often described by mathematical models having biologically significant parameters. The local chicken,commonly raised in rural areas of Togo, is generally described as a slow-growing chicken, but little is known about his growth parameters. Therefore, this study was designed to determine the nonlinear model that best describes the growth curve of local chickens. The Gompertz, Logistic and Richards growth models were used to fit growth weights recorded from 126 males and 150 female chickens using GCFIT program of SIMFIT package (version 8.1.4).Coefficient of determination (R2), mean square error (MSE), Akaikes information criterion (AIC) and Bayesian information criterion (BIC) were used as criteria for models comparison. By estimating the highest value of R² and the lowest values of MSE, AIC and BIC, Gompertz model appeared to be the best-fitting model for local chickensgrowth. The estimated asymptotic weight was 1992 g for males and 1367 g for females. Thus, male and female chickens weighed 732.86g and 502.98g and aged 10.57 weeks and 9.77 weeks respectively at inflection point. Maturation rates of the chicks were similar (0.139 week-1 for males and 0.141 week-1for females). The growth parameters of this model may therefore be useful in defining selection criteria for local chickens in Togo.

APPLICATION OF SUPPORT VECTOR MACHINE AND ORDINARY KRIGING (SVM-OK) TO SPATIAL INTERPOLATION OF POROSITY ATTRIBUTE OF AGBABU BITUMEN DEPOSIT

The lack of geostatistical modelling of volumetric characteristics of Nigeria's long-discovered heavy oil and bitumen deposits in Agbabu was accountable for the discrepancies in the hydrocarbon-in-place calculations. The absence of spatial variability evaluation in such efforts is responsible for the inconsistency and divergence that define the impacts of the existing attempts to estimate the porosity attribute. Due to its optimum features and simplicity of estimate, Kriging is reported as the most generally used approach to evaluate the connection between variables (inputs and output). However, Kriging reveals that the relationship between variables requires a linear form, which is frequently not the case. Thus, the necessity for a procedure with no fixed shape is built based on information acquired directly from the data. Consequently, support vector regression, a form of the support vector machine, is employed as a choice in contrast to kriging. This research is a comparative application of support vector machine and kriging (SVM-OK) to the spatial interpolation of the porosity property of the Agbabu bitumen field. The porosity values from existing data are trained, cross-validated, and verified with the help of the support vector machine, and they are interpolated by ordinary kriging. The geographical patterns of the predictions have been checked visually using graphs based on these three diagnostics: variance of the error terms and correlation of the error terms. The findings of this study illustrate the performance of the combination of the Support vector machine and conventional kriging in the spatial interpolation of volumetric characteristics in the Agbabu bitumen deposit. The approach exhibited great predictive performance. SVM-kriging will be employed in reservoir characterisation with the least mean absolute error (MAE) and mean squared error (MSE).

Analysis of the Effectiveness of Multi-Armed Bandit Algorithms in Crop Pricing

This study explores the application of multi-armed bandit (MAB) algorithms in dynamic pricing of crops, with a focus on evaluating the adaptive upper confidence bound (asUCB) and Thompson Sampling (TS) algorithms. Through simulation experiments on historical data, the study analyzed the performance of these algorithms in fitting actual market price trends and responding to future price fluctuations. The results indicate that the asUCB algorithm performed particularly well in both the training dataset and simulation tests, demonstrating low mean squared error (MSE) and minimal cumulative regret, reflecting its rapid convergence and stable pricing capabilities. In contrast, although the TS algorithm initially performed slightly less effectively, it demonstrated unique advantages in dealing with market volatility due to its strong adaptability. This study demonstrates the potential application of MAB algorithms in dynamic pricing, providing valuable insights for pricing strategies in the agricultural product market.

Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network.

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in data acquisition from living beings, which has a significant impact on the classification procedure. The purpose of this research work is to advance ECG signal classification results by employing numerous denoising methods and, in turn, boost the accuracy of cardiovascular diagnoses. To simulate realistic conditions, we added various types of noise to ECG data, including Gaussian, salt and pepper, speckle, uniform, and exponential noise. To overcome the interference of noise from environments in the obtained ECG signals, we employed wavelet transform, median filter, Gaussian filter, and the hybrid of the wavelet and median filters. The proposed hybrid denoising method has better results than the other methods because of the use of wavelet multi-scale analysis and the ability of the median filter to avoid the loss of vital ECG characteristics. Thus, despite a certain proximity in the values, the hybrid method is significantly more accurate and reliable, as evidenced by the mean squared error (MSE), mean absolute error (MAE), R-squared, and Pearson correlation coefficient. More specifically, the hybrid approach provided an MSE of 0.0012 and an MAE of 0.025, the R-squared value for this study was 0.98, and the Pearson correlation coefficient was 0.99, which provides a very good resemblance to the original ECG confirmation. The proposed classification model is based on the modified lightweight CNN or MLCNN that was trained using the noisy and the denoised data. The findings demonstrated that by applying the denoised data, the testing accuracy, precision, recall, and F1 scores achieved 0.92, 0.91, 0.90, and 0.91 for the datasets, while the noisy data achieved 0.80, 0.78, 0.82, and 0.80, respectively. In this study, the signal quality and denoising methods were found to enhance ECG signal classification and diagnostic accuracy while encouraging proper preprocessing in future studies and applications for real-time ECG for cardiac care.

Impact of greenhouse gas emission, renewable energy, and economic growth on health expenditure in Southeast Asia: A comparative analysis of econometric models

Purpose — The study explores the effects of greenhouse gas emissions, renewable energy, and economic growth on health expenditures across Southeast Asia while comparing the performance of different econometric models for accuracy in analysis.Method — To analyze the relationships among variables, the study employs three econometric models, the Autoregressive Distributed Lag Model, the co-integration Model, and the Quantile Regression Model, using annual data from 2000 to 2020.Findings — The results reveal that greenhouse gas emissions and GDP significantly influence health expenditure in all three models. However, the significance of renewable energy consumption varies, with only the quantile regression model indicating a significant relationship with health expenditure. Model comparison based on Mean Squared Error (MSE) suggests that the autoregressive distributed lag (ARDL) model provides the most accurate predictions. Also, it found that there is a short-run and long-run causal effect of GHG and GDP on health expenditure, as well as health spending on GDP.Implication — This study helps to understand how economic growth, environmental factors, and healthcare spending interact to develop sustainable policies to address health and environmental problems in Southeast Asia.Originality — This research contributes to the body of knowledge examining the impact of economic and environmental factors on health expenditures in Southeast Asia through a comparative analysis of different econometric models.

Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Deep Learning for Sustainable E-Waste Management: Leveraging Transfer Learning for Classification

The rapid rise of electronic waste (commonly referred to as "e-waste") has become a world's growing challenge which should be managed by creative approaches. The number of e-waste produced is estimated to be 53.6 million metric tons in 2019. From this we can see that the seriousness of the issue direly calls upon taking the measures to prevent the environmental and public health risks associated with this expanding crisis [1]. Since a lot of the e-waste may contain hazardous materials such as mercury, lead and cadmium, which can impact the health and the environment if not treated properly, the mismanagement of it increases the problem [2]. In the case of e-waste, there is wide assortment of the electronic devices and components hence, it becomes difficult to classify them into their product categories properly. Sorting processes can't keep up with the pace of production waste as a result of being tedious, error-prone, and slow. This research employs deep learning approaches to segregate E-waste items using images for automated category. Utilizing contemporary models like VGG16, DenseNet121, InceptionV3, MobileNetV3, and ResNet50, the research designs classification systems that have these great attributes. Dataset building (training and assessment) become easy when an extensive dataset of 3000 images from 10 different types of equipment is developed. This research study helps to offer useful implications for managing current methods of electronic waste disposal and developing sustainable circular economies with quantitative analyzing of model performance factors that include accuracy, precision, F1- score, mean squared error (MSE), and mean absolute error (MAE).