Multivariate Polynomial Regression Research Articles

Geospatial and Socioeconomic Disparities Influencing the Management of Craniosynostosis.

Various social determinants of health have been described as predictors of clinical outcomes for the craniosynostosis population. However, literature lacks a granular depiction of socioeconomic factors that impact these outcomes, and little is known about the relationship between patients' proximity to the care center and management of the condition. This study retrospectively evaluated patients with craniosynostosis who presented to a tertiary children's hospital between 2000 and 2019. Outcomes of interest included age at presentation for surgery, incidence of reoperation, and length of follow-up. Patient addresses were geocoded and plotted on two separate shapefiles containing block group information within San Diego County. The shapefiles included percent parental educational attainment (bachelor's degree or higher) and median household income from 2010. The year 2010 was chosen for the shapefiles because it is the median year of data collection for this study. Multivariate linear, logistic, and polynomial regression models were used to analyze the relationship between geospatial and socioeconomic predictors and clinical outcomes. There were 574 patients with craniosynostosis included in this study. The mean ± SD Haversine distance from the patient's home coordinates to the hospital coordinates was 107.2 ± 321.2 miles. After adjusting for the suture fused and insurance coverage, there was a significant positive correlation between distance to the hospital and age at index surgery (P = 0.018). There was no correlation between distance and incidence of reoperation (P = 0.266) or distance and duration of follow-up (P = 0.369). Using the same statistical adjustments, lower parental percent educational attainment and lower median household income correlated with older age at index surgery (P = 0.008 and P = 0.0066, respectively) but were not correlated with reoperation (P = 0.986 and P = 0.813, respectively) or duration of follow-up (P = 0.107 and P = 0.984, respectively). The results offer evidence that living a greater distance from the hospital and socioeconomic disparities including parental education and median household income may serve as barriers to prompt recognition of diagnosis and timely care in this population. However, the geospatial and socioeconomic factors studied do not seem to hinder incidence of reoperation or length of follow-up, suggesting that, once care has been initiated, longitudinal outcomes may be less impacted.

Big data resolving using Apache Spark for load forecasting and demand response in smart grid: a case study of Low Carbon London Project

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Distribution of Normative Percentiles of Liver Stiffness Measurement Using Ultrasound Shear Wave Elastography in an Adult Asian Indian Population

Abstract Objective The aim of this study was to determine the normative percentiles for liver stiffness measurement (LSM) using shear wave elastography in an adult Asian Indian population as part of the preventive radiology initiative of the Indian Radiological and Imaging Association (IRIA). Methods LSMs were ascertained by two-dimensional (2D) shear wave elastography using the Mindray Resona series of ultrasound machines. The image quality was assessed using the motion stability index (M-STB) and reliability (RLB) map. Ten acquisitions were documented, and an interquartile range-to-median (IQR/M) ratio ≤30% kilopascal (kPa) units was considered a good-quality measurement. A subgroup of the study population without comorbidities was chosen to derive the normative percentile distribution of LSM using a generalized least squares multivariable fractional polynomial regression model that adjusted for sex and body mass index (BMI). The effectiveness of the estimated percentiles was assessed on the entire study population using the greater than 90th percentile value of the LSM as the cutoff for abnormality. Results The study included 852 people who underwent shear wave elastography from June 2022 to July 2023. The magnitude of compensated advanced chronic liver disease (cACLD) and clinically significant portal hypertension (CSPH) was 6.81% (95% confidence interval [CI]: 5.30–8.7) and 4.91% (95% CI: 3.67–6.60), respectively. The normative percentiles were estimated from 282 persons without associated comorbidity and risk factors. The mean age (standard deviation [SD]) of the normal individuals was 40.90 ± 12.92 years, and 210 (71.47%) were males. The mean age (SD) of the 570 persons excluded from the normative percentiles analysis was 47.94 (12.49) years and 72.11% were males. The sex- and BMI-adjusted age-specific 90th percentiles of LSM were 8.76, 8.78, 8.96, 8.97, 9.25, and 9.45 kPa for 18 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 to 70 years, respectively. Conclusion The sex- and BMI-adjusted age-specific 90th percentiles for LSM using shear wave elastography in Asian Indian adults are almost similar to the greater than 9 kPa cutoff proposed by the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement guidelines to discriminate cACLD and CSPH from normal individuals.

Optimization of Mg-Al Layered Double Hydroxide Film Preparation and Corrosion Resistance Study on AZ91D Mg Alloy by Multivariate Polynomial Regression Fitting.

Layered double hydroxide (LDH) films have received extensive attention for their unique physical barrier function and ion exchange properties, which make them promising candidates for corrosion protection of magnesium alloys. In this paper, we used the multiple polynomial regression fitting method to establish a regression equation for the electrochemical corrosion resistance with the reaction temperature (T), pH, and reaction time (t) of the Mg-Al LDH film on the AZ91D magnesium alloy. The goodness of fit, confidence, and residual analyses confirmed the high accuracy of the model equation. According to the calculation using the fmincon function, the best corrosion resistance of the prepared samples could be achieved when the parameters are T = 135 °C, pH = 12.0, and t = 15 h. Then, the experimental results showed that the corrosion current density (Icorr) of the obtained LDH film under the above conditions could be 1.07 × 10-7 A/cm2, approximately 3 orders of magnitude lower than the magnesium alloy substrate, after immersion in a 3.5 wt % NaCl solution for 180 h, the surface structure of the LDH film did not change significantly, and the Icorr was still 2 orders of magnitude higher than that of the magnesium alloy substrate. Hence, a synergistic effect equation for the reaction temperature, pH, and reaction time on the corrosion resistance of the LDH film on a magnesium alloy surface prepared by the hydrothermal method was obtained. Moreover, using this equation, we obtained an LDH film with good corrosion resistance and durability, providing theoretical guidance for optimizing the process of preparing the LDH film by the hydrothermal method in practical applications.

CFD simulation of cannabidiol delivery through microneedle patches

This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered via the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.

Early Sepsis-Associated Acute Kidney Injury and Obesity.

The prevalence of obesity is increasing in the intensive care unit (ICU). Although obesity is a known risk factor for chronic kidney disease, its association with early sepsis-associated acute kidney injury (SA-AKI) and their combined association with patient outcomes warrant further investigation. To explore the association between obesity, early SA-AKI incidence, and clinical outcomes in patients with sepsis. This nationwide, prospective cohort study analyzed patients aged 19 years or older who had sepsis and were admitted to 20 tertiary hospital ICUs in Korea between September 1, 2019, and December 31, 2021. Patients with preexisting stage 3A to 5 chronic kidney disease and those with missing body mass index (BMI) values were excluded. Sepsis and hospitalization in the ICU. The primary outcome was SA-AKI incidence within 48 hours of ICU admission, and secondary outcomes were mortality and clinical recovery (survival to discharge within 30 days). Patients were categorized by BMI (calculated as weight in kilograms divided by height in meters squared), and data were analyzed by logistic regression adjusted for key characteristics and clinical factors. Multivariable fractional polynomial regression models and restricted cubic spline models were used to analyze the clinical outcomes with BMI as a continuous variable. Of the 4041 patients (median age, 73 years [IQR, 63-81 years]; 2349 [58.1%] male) included in the study, 1367 (33.8%) developed early SA-AKI. Obesity was associated with a higher incidence of SA-AKI compared with normal weight (adjusted odds ratio [AOR], 1.40; 95% CI, 1.15-1.70), as was every increase in BMI of 10 (OR, 1.75; 95% CI, 1.47-2.08). While obesity was associated with lower in-hospital mortality in patients without SA-AKI compared with their counterparts without obesity (ie, underweight, normal weight, overweight) (AOR, 0.72; 95% CI, 0.54-0.94), no difference in mortality was observed in those with SA-AKI (AOR, 0.85; 95% CI, 0.65-1.12). Although patients with obesity without SA-AKI had a greater likelihood of clinical recovery than their counterparts without obesity, clinical recovery was less likely among those with both obesity and SA-AKI. In this cohort study of patients with sepsis, obesity was associated with a higher risk of early SA-AKI and the presence of SA-AKI modified the association of obesity with clinical outcomes.

Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

Machine-learning based process monitoring for automated composites manufacturing

AbstractAutomated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite manufacturing. The interlaminar strength of AFP-manufactured composites depends on the in-situ thermal history during manufacturing. The thermal history is controlled by the choice of processing conditions and improper interfacial temperatures may result in insufficient bonding. Being able to better predict such maintenance issues in real-time is an important focus of smart manufacturing and Industry 4.0 to improve manufacturing operations. The data analysis of real-time temperature measurements during AFP composites manufacturing requires the temperature profiles from Finite Element Analysis (FEA) based simulations of the AFP process to better predict the quality of layup. However, the FEA simulations of the AFP process are computationally expensive. This study focuses on developing a digital tool enabling real-time process monitoring and predictive maintenance of the AFP process. The digital tool constitutes a machine learning-based surrogate model based on results from Finite Element Analysis (FEA) simulations of the AFP process to predict the in-situ thermal profile during AFP manufacturing. Multivariate Linear Regression, Multivariate Polynomial Regression, Support Vector Machine, Random Forest and Artificial Neural Network (ANN)-based models are compared to conclude that ANN based surrogate model performs best by predicting the important parameters of thermal profiles with a mean absolute percentage error of 1.56% on additional test data and reducing the time by four orders of magnitude as compared to FEA simulations. The predicted thermal profile can be compared with the real-time in-situ temperatures during manufacturing to predict the quality of the layup. A GUI application is developed to provide predicted thermal profiles data for analysis in conjunction with real-time temperatures during manufacturing enabling monitoring and predictive maintenance of the AFP process and paving way for the development of a digital twin of the AFP composites manufacturing process.

Analysis of cohesive particles mixing behavior in a twin-paddle blender: DEM and machine learning applications

This research paper presents a comprehensive discrete element method (DEM) examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender. A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%, demonstrating a strong agreement between the results from the experimental tests and the DEM simulation. The main focus centers on systematically exploring how operational parameters, such as impeller rotational speed, blender's fill level, and particle mass ratio, influence the process. The investigation also illustrates the significant influence of the mixing time on the mixing quality. To gain a deeper understanding of the DEM simulation findings, an analytical tool called multivariate polynomial regression in machine learning is employed. This method uncovers significant connections between the DEM results and the operational parameters, providing a more comprehensive insight into their interrelationships. The multivariate polynomial regression model exhibited robust predictive performance, with a mean absolute percentage error of less than 3% for both the training and validation sets, indicating a slight deviation from actual values. The model's precision was confirmed by low mean absolute error values of 0.0144 (80% of the dataset in the training set) and 0.0183 (20% of the dataset in the validation set). The study offers valuable insights into granular mixing behaviors, with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.

Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors.

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

Leachate Treatment via TiO2/UV Heterogeneous Photocatalysis: A Multiple Polynomial Regression Model

Advanced oxidation processes such as TiO2/UV heterogeneous photocatalysis are suitable treatment methods for wastewater with high pollutant loads such as landfill leachates. Optimizing the variables that influence the process is a fundamental aspect. However, in this regard, experimental conditions are limited in terms of resources and time, which is why modeling allows obtaining a general understanding of the phenomenon from a set of experimental data. This work sought to model the photocatalytic process via multivariate polynomial regression, considering variables such as the catalyst concentration, the pH level, and the accumulated energy concerning the percentage of degradation in terms of dissolved organic carbon (DOC). The implemented fitting method resulted in a third-degree polynomial with an R2 of 0,8652, concluding that the model and its conclusions are valid. Moreover, with greater degrees, the model curve overfitted, even with better R2. DOC abatement showed a negative correlation with pH and the catalyst dose, while an opposite trend was observed for the accumulated energy. The model predictions allow inferring that, at low catalyst doses and medium and high pH levels, it is possible to find maximum degradations at low cumulative energies.

A Correlation Study of Plasma and Breast Milk Retinol Concentrations in Breastfeeding Women in China.

Retinol in breast milk is related to plasma concentration among breastfeeding women, but the linear or curvilinear relationships between the two remains unclear. We conducted a cross-sectional study in 403 Chinese breastfeeding women at 42 ± 7 days postpartum. Plasma and breast milk samples were assayed using high performance liquid chromatography to determine the concentration of retinol. Partial Spearman correlation and multivariable fractional polynomial regression were used to examine the relationships between the two retinol concentrations and between plasma retinol concentration and milk-to-plasma (M/P) retinol. The median (interquartile range, IQR) of the retinol concentration in the plasma was 1.39 (1.21, 1.63) μmol/L and 1.15 (0.83, 1.49) μmol/L in the breast milk, respectively. The partial correlation coefficient between them was 0.17 (p < 0.01). A linear relationship was observed with an adjusted regression coefficient of 0.34 (95% CI: 0.19, 0.49). The relationship between the plasma retinol and M/P ratio was nonlinear and segmented at 1.00 μmol/L of plasma retinol. The regression coefficients, below and above the segmented point, were -1.69 (95% CI: -2.75, -0.62) and -0.29 (95% CI: -0.42, -0.16), respectively. Plasma and breast milk retinol were positively correlated, whereas women with a low concentration of plasma retinol showed a stronger capacity of transferring retinol to breast milk.

Prediction of intel CPUs price with regression analysis

CPU stands for the central processing unit. It is a unit that executes the instruction and allows the computer to run programs, making it an essential computer component. When people are looking for a new computer, either buying a built one or building one, CPU takes up a significant portion of the computer budget. The existence of Moores Law indicates that the CPUs price is predictable. This essay constructed the two models, multinomial linear regression and multivariable polynomial regression models, based on parts of Intel CPUs parameter value to predict their recommended sale price. According to the data set used in this research and the built model, the multinomial linear regression model is more accurate in predicting.

Associations between accelerometer‐based physical activity and brain white matter microstructure: Findings from the population‐based Rhineland Study

AbstractBackgroundWhite matter (WM) integrity declines with age and is affected by neurodegenerative diseases. Physical activity promotes neuronal health in animals, and is associated with WM microstructure metrics in humans. However, previous diffusion tensor imaging (DTI) studies were limited by small sample sizes and subjective questionnaire‐based physical activity assessments. Moreover, recently introduced neurite orientation dispersion and density imaging (NODDI) may offer complementary insights. Therefore, we examined the association between accelerometer‐based physical activity and DTI and NODDI measures of WM microstructure in a large population‐based cohort.MethodWe analyzed data of 2,455 participants (57.9% women, mean age: 54.7 years) with valid physical activity and MRI measurements from the Rhineland Study, a prospective cohort study in Bonn, Germany. We recorded physical activity using accelerometers and derived metabolic equivalent (MET)‐hours, step counts and time spent performing sedentary, light‐intensity and moderate‐to‐vigorous (MVPA) activities. DTI (fractional anisotropy (FA), mean diffusivity) and NODDI (orientation dispersion index, neurite density index) measures were obtained from 3T MR images using the microstructure diffusion toolbox. The association of physical activity with WM microstructure was assessed using multivariate polynomial regression, while adjusting for age, sex, education, body mass index and smoking status.ResultPhysical activity was associated with several global and tract‐specific DTI and NODDI measures. Effects were strongest at low physical activity quantities and weakened at higher levels. While higher MET‐hours, step counts and light and moderate‐to‐vigorous intensity activities were related to greater global FA, sedentary time was associated with reduced FA. We observed the strongest tract‐specific effects for moderate‐to‐vigorous intensity activities on the corpus callosum and corona radiata (standardized effects: MVPA on FA corpus callosum, ßlin = 0.0009 [95% CI: 0.0001; 0.0018] and ßqu = ‐0.0006 [95% CI: ‐0.0011; ‐0.0001]; MVPA on FA corona radiata, ßlin = 0.0011 [95% CI: 0.0001; 0.0021] and ßqu = ‐0.0009 [95% CI: ‐0.0014; ‐0.0003]). We obtained similar results for NODDI measures.ConclusionOur findings suggest that WM microstructure and particularly tracts connecting motor regions profit from physical activity. Sedentary individuals may benefit more from additional physical activity than already active individuals. NODDI metrics provide valuable complementary insights into the effects of physical activity on WM microstructure.

Shelf life enhancement technique of Musa acuminata in a controlled environment and optimization of process parameters affecting shelf life using genetic algorithm.

An economical and effective storage solution has been designed in this work for the storage of postharvest fruits and vegetables. Musa acuminata or banana has a shelf life of 5-6 days in open uncontrolled environment. This article reports a storage solution of M. acuminata in a controlled enclosure containing titanium oxide (TiO2 )-coated inner walls and irradiated with ultraviolet ray of band "C," an air filtration unit, 5% by volume potassium permanganate (KMnO4 ) solution in a clay pot, grow lights, and activated charcoal granules. The same fruit was kept in an uncontrolled environment too. The percentages of dark spots on banana (M. acuminata) upon storage in controlled and uncontrolled environments have been estimated using an image-processing algorithm. The prediction of dark spots was conducted using multi-linear and multivariate polynomial regression. Experimentation with optimum process parameters obtained with genetic algorithm resulted in a shelf life extension of 6 days as compared to its storage in an uncontrolled environment. The setup can be used in vegetable and fruit markets for the extension of shelf life of postharvest perishable items in a compact and cost-effective manner. The setup does not use any refrigeration process thereby decreasing energy requirement.

Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

Multi-objective optimization of rectangular microchannel heat sink based on entropy generation and hydro-thermal performance using NSGA-II algorithm

In this study, multi-objective optimization of a rectangular microchannel heat sink is implemented based on two objective functions figure of merit (FOM) and entropy generation (Ṡgen). Decision variables are height ratio (α), width ratio (β) and number of channels (N). CFD simulations are conducted for different values of decision variables and the effects of each decision variable on hydro-thermal performance (FOM) and entropy generation are studied separately. To reduce the computational cost in multi-objective optimization process, a fourth-order multivariate polynomial regression model is used to predict the FOMαβ and Ṡgenαβ objective functions at each N. Non-Dominated Sorting Genetic Algorithm II coupled with predicted objective functions (FOM and Ṡgen) is implemented to maximize the FOM and minimize the Ṡgen at each N. Also, the decision variables related to the highest FOM are presented, which is 90% more than the reference point, and the hydrodynamic-thermal performance has improved. Decision variables related to the lowest Ṡgen are presented, which is 19% lower than the reference point. Finally, decision variables are presented to simultaneously satisfied the highest FOM and the lowest Ṡgen where FOM is 57% higher than the reference point and Ṡgen is 9.78% lower than the reference point.

Thermal efficiency of microchannel heat sink: Incorporating nano-enhanced phase change materials and porous foam gradient and artificial intelligence-based prediction

This study investigates the impact of incorporating phase change materials (PCMs), nano-particles (NPs) enhanced PCMs (nePCMs), and porous foam gradients on the thermal performance (TPEF) of microchannel heat sinks (MCHS). Specifically, the effect of different PCM types, hybrid NPs, and spiral microchannels on the TPEF is examined using numerical solutions based on the finite volume method. The results indicate that increasing Re and using spiral microchannels significantly enhance the TPEF. The incorporation of NPs-water and PCM can reduce the thermal resistance (R) of MCHS, with PCM significantly improving the TPEF. Among the PCMs, ENCAPSUL demonstrates the best performance for MCHS. The combination of hybrid nePCM increases TPEF by approximately 9%. The combination of PCM-aluminum oxide-iron oxide NPs exhibits the highest TPFE. The use of a porous medium with PCM can decrease the R by about 60 %, and it improves the TPEF by altering the conduction and convection mechanism. various scenarios of changes in the porosity coefficient have been considered for porous foam gradient and the best performance is achieved for the NYPC mode. Various scenarios of MCHS with PCM-water combinations have been explored, and the best performance is observed when PCM is situated in the microchannel. Additionally, artificial intelligence techniques, such as the Group Method of Data Handling (GMDH), have been utilized to estimate R, and a multivariate polynomial regression (MPR) equation has been developed to calculate R based on input variables. GMDH is more accurate compared to MPR.

From machine learning to semi-empirical formulas for estimating compressive strength of Ultra-High Performance Concrete

Although some machine learning (ML) models have successfully developed for ultra-high-performance concrete (UHPC), they do not provide insights and explicit relations between all input variables and its compressive strength. This paper will address these ambiguities and provide a tool to predict the compressive strength by explainable and interpretable equations and plots. Explicit semi-empirical formulas are derived from a multivariate polynomial regression (Lasso) and automated feature engineering and selection (Autofeat) using a 810 dataset of UHPC with 15 input variables, which are collected from literature. Coefficient of determination R2 = 0.8223 is the same for the first-order degree (linear regression) of both models, however, the Autofeat achieves a better result than Lasso for the third-order degree with R2 = 0.9616 vs. R2 = 0.9503. A comprehensive parametric study is carried out via relative feature importance and partial dependence plots to explain and gain profound insights into the effects of some important input variables on the compressive strength of UHPC. Some details discussions related to these effects with previous studies are also presented. The proposed models not only show better performance with those from reference in terms of R2, especially for Autofeat model but also have explicit relations of compressive strength with 15 input variables. Hence, they can be used as a reliable tool in mixture design optimization of the UHPC.

Does the one-leg rise test reflect quadriceps strength in individuals following anterior cruciate ligament reconstruction?

ObjectiveTo explore if one-leg rise test performance is associated with quadriceps strength following anterior cruciate ligament reconstruction (ACLR). DesignCross-sectional. Participants100 individuals (50 females, 50 males) aged 18–40 years, 9–36 months post-ACLR with ongoing knee symptoms (KOOS4 <80/100). Main outcome measuresNumber of one-leg rise repetitions (using an adjustable-height plinth) and isometric quadriceps strength using isokinetic dynamometry (60° flexion, normalised to body mass). Multivariable fractional polynomial regression models adjusted for sex explored relationships between one-leg rise performance (repetitions) and quadriceps strength (Nm/kg) for each limb. ResultsA non-linear, increasing association between one-leg rise performance and quadriceps strength was observed, with the rate of increase attenuating at higher values of one-leg rise performance. Similar relationships were observed in the ACLR (β = 0.15, 95%CI 0.10 to 0.20; adjusted r2 = 0.51) and contralateral limb (β = 0.14, 95%CI 0.08 to 0.19; r2 = 0.42). ConclusionThe one-leg rise test can be an indicator of quadriceps strength in individuals after ACLR, enabling clinicians to easily monitor quadriceps strength recovery without specialised equipment. With the relationship between one-leg rise performance and quadriceps strength attenuating with a larger number of one-leg rises achieved, other factors (e.g., motivation, endurance) likely contribute to one-leg rise performance at higher values.