Fitted Regression Equation Research Articles

Weak Value Amplification Based Optical Sensor for High Throughput Real-Time Immunoassay of SARS-CoV-2 Spike Protein.

The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities.

Machine learning-based aggressiveness assessment model construction for crabs: A case study of swimming crab Portunus trituberculatus

Aggressiveness trait-based selection is crucial for alleviating interspecies cannibalism in economic crab species and enhancing survival rates in aquaculture. However, there is a lack of efficient and simple methods for assessing aggressiveness. In this study, we measured aggressiveness of the swimming crab Portunus trituberculatus through repeated mirror tests and fighting experiments. Factor analysis and the K-means algorithm were used to assess aggressiveness quantitatively and qualitatively. A combination of multiple linear regression and support vector machine (SVM) analyses was employed to construct an aggressiveness assessment model for swimming crabs and explore the relationship between aggressiveness and fighting ability. The results showed significant correlations among repeated aggressive behaviors (attacking, chela extending, defending, crossing, reverse walking, and freezing). Aggression score was significantly correlated with fighting behaviors, and there were significant differences in fighting abilities among different levels of aggressiveness. This suggested that aggressive behaviors are consistent within individuals and that aggressiveness, as a personal trait, affects the fighting ability of swimming crabs. Aggression score (Y) and clustering results of K-means can serve as assessment indicators of aggressiveness. The predictive variables for the quantitative assessment model were relative movement distance (X1) and freezing duration (X2). The adjusted R-square of the optimized quantitative model was 0.72, it also had the smallest Sigma, AIC, MSE, and RMSE values and the best fitting regression equation, which was Y = 0.023X1 – 0.001X2 – 0.002. The predictor variables for the qualitative assessment model were relative movement distance, freezing frequency, and duration. SVM was used to construct the qualitative model, and the prediction accuracy was 92%, sensitivity was 84%, and specificity was 100%, indicating the model has a good classification and prediction effect. The machine learning-based aggressiveness assessment model constructed in this study provides a behavioral method for the selection and high-throughput measurement of economic crab species with excellent aggressiveness traits, giving it important industrial application value.

Study on performance and structure optimization of self-priming pump based on response surface method

ABSTRACT This paper aims to improve the performance of a self-priming pump by optimizing blade geometry parameters, with the head and efficiency as the targeted objectives. Based on the Response Surface Methodology (RSM), the Computational Fluid Dynamics (CFD) were conducted to investigate the pressure distribution characteristics inside the pump chamber and the variations in pump performance parameters. By analyzing the internal pressure field of the pump, it is concluded that adjusting the blade curvature radius and the outlet angle can change the area and uniformity of the low-pressure zones within the fluid to improve the pump performance. The results show that the fitted regression equations indicate that the blade curvature radius and the blade outlet angle have a significant impact on pump performance, while the blade inlet angle has a relatively minor effect. When the outlet angle β 2 = 60 ∘ , the change of the inlet angle β 1 and the curvature radius R has basically no impact on the efficiency. The self-priming pump achieves its optimum performance when β 1 = 50.8 ∘ , β 2 = 52 ∘ and R = 84.9 mm. Compared to the original model, the optimized model’s head increases by 7.12%, H = 34.12 m; and the efficiency improves by 3.40%, η = 63.8.

Research on the integrated structure of electrical automation AC drives and analysis of equipment reliability

Abstract This paper is based on the theoretical knowledge of electrical automation and the composition and basic principles of AC drive systems to determine the overall design of the system integration structure in this paper. The reliability of the system equipment is assessed jointly by combining the Weibull distribution, least-squares regression fitting, and Bayesian network after completing the system integration design. The results show that the fitted regression equation for the equipment failure data is y = 0.3372x − 3.0521, which explains that this data can be used for reliability analysis. The highest a posteriori probability value (0.00899) for the AC motor in the Bayesian network model of the AC drive system indicates that the AC motor should be prioritized for overhauling in the course of system failure to achieve the purpose of intelligent detection and control of the system.

Uncertain least squares estimation model based on relative error

Uncertain least squares estimation is one of the important methods to deal with imprecise data, which can fully consider the influence of given data on regression equation and minimize the absolute error. In fact, some scientific studies or observational data are often evaluated in terms of relative error, which to some extent allows the error of the forecasting value to vary with the size of the observed value. Based on the least squares estimation and the uncertainty theory, this paper proposed the uncertain relative error least squares estimation model of the linear regression. The uncertain relative error least squares estimation minimizes the relative error, which can not only solve the fitting regression equation of the imprecise observation data, but also fully consider the variation of the error with the given data, so the regression equation is more reasonable and reliable. Two numerical examples verified the feasibility of the uncertain relative error least squares estimation, and compared it with the existing method. The data analysis shows that the uncertain relative error least squares estimation has a good fitting effect.

PRODUCTION FUNCTION FOR MEASURING RETURNS TO SCALE IN THE GARMENT SECTOR: A CASE STUDY OF BANGLADESH

This paper empirically examines whether the production function of the garment sector of Bangladesh is operating under increasing returns to scale. The Cobb-Douglas production function is estimated using the advanced level of econometric techniques based on primary data. GLS estimates indicate that the productivity of labor force is 22.17% and the productivity of capital investment is 61.02%, statistically significant at any significance level. Thus, the production function of the garment sector of Bangladesh is operating under decreasing return to scale. From the GLS estimate of R2 , it is clear that about 99.97% of the total variation of the dependent variable output is explained by the fitted regression equation. Thus, the model fits the data very well. The diagnostic test results show that there are no problems of autocorrelation, heteroscedasticity, autoregressive conditional heteroscedasticity and normality of the random error terms. The test results also confirm that the model is correctly specified. It is also found that the average marginal productivity of labor force is 0.0236 and the average marginal productivity of capital investment is 0.5222 in the garment sector of Bangladesh. Since the marginal productivity of labor force is smaller than the capital investment, it can be said that these factories cannot reduce production costs by shifting resources from capital intensive techniques to labor intensive techniques. As a result, this sector cannot generate additional employment opportunities, which is not a good sign. The CUSUM and CUSUMSQ tests results confirm that the preferred production function of the garment sector of Bangladesh can be used for policy decision-making purposes.

Simulation and analysis of geometric parameters based on Taguchi method in Y-Y microfluidic device for circulating tumor cell separation by alternating current dielectrophoresis

Microfluidic technology has shown a remarkable ability to separate circulating tumor cells (CTC) in microfluidic devices. It can be used more effectively by further understanding the effect of geometric parameters on its separation performance. In this paper, the separation performance of a Y-Y microfluidic device was examined by varying its geometry parameters. In the device, the alternating current dielectrophoresis (AC DEP) method was used to separate CTC. 16 device models with various geometric parameters were created based on the Taguchi method. The geometric parameters included main channel length L, main channel width W, interelectrode angle α, and buffer inlet channel angle β. The electric field, flow field, and cell trajectory in the device were all numerically simulated to analyze the effect of geometric parameters. Signal-to-noise ratio (SNR) was used to determine the order of effect degree and optimal combination of geometric parameters. The results demonstrated that raising the flow velocity in the buffer inlet could enhance the separation purity. The separation purity was affected by the geometric parameters in the order of W> α> L> β. β had the weakest impact on the separation purity and accounted for 7.81%, while W had the most remarkable impact and accounted for 50.48%. It is found that the set of L = 1080 µm, W = 110 µm, α= 60°, and β= 60° is the optimal combination of geometric parameters. A fitting regression equation is found to describe well the effect of these parameters on separation purity. The results may provide a guide for designing microfluidic devices for separating CTC.

Coupled Effect of Granite Sand and Calcium Lignosulphonate on the Strength Behavior of Cohesive Soil

This paper assesses the significance of stabilizing clay soil with calcium lignosulphonate (CLS) and granite sand (GS). Unconfined compressive strength (qc) and hydraulic conductivity (K) are taken as performance indicators and the effect of varying dosages of GS (30%, 40%, and 50%) and CLS (0.5%, 1%, 1.5%, and 2%) at different curing periods on qc and K are examined. The best fit regression equations have been proposed to relate qc and K of untreated clay soil and stabilized clay using GS and CLS. The proposed nonlinear regression equations provide details of experimental data and aid in estimating qc and K very efficiently and reliably for targeted geotechnical applications from a sustainable perspective.

The neglected potential source of microplastics from daily necessities: A study on protective mobile phone cases

Microplastics (MPs) pollution has become a serious global environment problem. It is therefore of practical significance to investigate the MP pollution caused by using plastic materials on a daily basis. In this study, different protective mobile phone cases (PMPCs) were selected as a representative plastic commodity that are in contact with the human body for long periods to explore the generation and transportation of MPs during 3 months of actual use. The average abundances were 1122 particles cm−2 on the PMPC and 314 particles cm−2 on the palm, respectively. There were four main kinds of MPs produced during the use of different PMPCs, which indicated that waste plastics may be recycled and used as raw materials, resulting in a complex PMPC composition. The median sizes of MPs on the surfaces of PMPCs and palms were 28 and 32 µm, respectively, which were smaller than the sizes reported in other studies. The combined effect of ultraviolet ageing and friction was the main reason for MP generation during daily PMPC use. Based on the results of a fitted regression equation and Monte Carlo simulation, the sharply generation of MPs may occur when PMPC was used for approximately 33 days.

Regression models for predicting the inelastic seismic response of steel braced frames

This paper describes the development of regression equations to predict the peak inelastic drift and acceleration response of steel concentrically braced frame (CBF) building structures. A database of Engineering Demand Parameters (EDPs) is developed by performing a large number of nonlinear time-history analysis (NLTHAs) in OpenSEES for a set of 24 case study CBFs. A consistent and realistic set of mechanical models is achieved by designing each case study frame using Eurocode 8. Multi-variable regression models for peak drift and acceleration response at each storey level are fitted to the NLTHA results data. A nonlinear formulation and robust regression are employed to capture the influences of the Eurocode 8 behaviour factor and brace slenderness and overstrength limits on the EDP profiles over the height of a building. Comparison with existing models shows that the fitted regression equations achieve improved agreement with NLTHA results across a wide range of structural parameters. The regression models can be applied in performance-based design or assessment to predict the peak inelastic response of CBF structures without performing additional nonlinear analyses.

Study on Correlation of Physical and Mechanical Properties Indexex of Cohesive Soil in Hilly and Plain Region along the Yangtze River in Anhui Province

In this paper, a large number of geotechnical engineering survey data are collected in hilly and plain region along the Yangtze River in Anhui Province. Based on the statistical analysis and calculation of the experimental data of physical and mechanical properties of cohesive soils (the main quaternary soil layers in the area), the correlation between the liquidity index and water content and other physical and mechanical properties indexes are analyzed, and the fitting regression is carried out respectively. The results show that the liquidity index (IL)) and water content (w) are highly correlated with cohesion (C), compression modulus (Es), compression coefficient (α), natural density (ρ), void ratio (e), and the regression equations have high goodness of fit and good fitting effect; In addition, the fitting regression equations of water content, void ratio and natural density are compared with the theoretical calculation formula, it is found that the calculation results are close, which proves that the fitting regression equations are reliable and can be used in engineering practice.

The Influence of Motivation, Working Environment and Career Development Toward Employees’ Loyalty

<p>This research aims to determine the effect of motivation, working environment, and career development toward employees’ loyalty at the PT Tawada Healthcare Semarang, which is one of the regional offices of PT Tawada Healthcare. PT Tawada Healthcare Semarang Branch is one of PT Tawada Healthcare office branch, a company engaged in medical devices provider that has been established since 1999. PT Tawada Healthcare has 13 branches throughout Indonesia and also provides medical devices in many hospitals in Indonesia.The data collection method used in this research are questionnaires, literature study, and interview. Sampling method used census which all members of the population are sampled. The methods of data analysis used is Multiple Linear Regression, goodness of fit regression equation used are coefficient of determination, F test and T test. The results of this research shows that motivation, working environment, and career development have positive and significant influence on employees’ loyalty. Working environment has a dominant influence in increasing employee loyalty.</p><p><strong>Keywords:</strong> Career Development, Employees’ Loyalty, Motivation, Working Environment.</p>

The impact of estimation methods on phytase phosphorus equivalency for commercial layer hens

An experiment was performed for evaluating calibration curve (CC) and comparing negative and positive controls (CNP) as a major method for estimating of phytase phosphorus equivalence for layer hens. Three hundred sixty 70-wk-old layer hens (W-36Hy-line) were used in a complete randomized design. Evaluated methods were setting the two regression equations for NPP-supplemented and phytase supplemented treatments with two sub-methods, include calibration curve (CC) or exclude phosphorus content of the basal diet (CC-BD) in calculation, and exploring enzyme equivalency by comparing phosphorus deficient diet as a negative and supplemented diet by inorganic phosphorus sources as a positive control group (CNP). Experiment included nine treatments (a phosphorus deficient basal diet contained 0.12% Av. P and 200, 300, 400 and 500 FTU/kg phytase was added to the basal diet, the rest four treatments were included basal diet supplemented with 0.20, 0.27, 0.35 and 0.43% Av. P). Each treatment in the experiment replicated five times, eight birds in each. Results indicated that methods of estimation had a significant effect on phosphorus equivalence estimation (p <0.0001). Fitted regression equations excluding P content of basal diet (CC-BD) estimated rational values than those ignore it (CC) (0.432% vs 0.564% for 500 FTU/kg phytase for layer hens) (p <0.0001). On average, among three methods used, CC method had the highest estimated values (p <0.0001). Regardless of mathematical method, there were no significant differences for egg production performance and egg quality traits served as response criteria (P˃0.05). In conclusion, the phosphorus equivalent value of enzyme varies according to the estimation methods. Hence, using matrix values of enzymes for accurate feed formulation depend on a variety of circumstances and decision making requires comprehensive information.

Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera

The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RRdepth) and tidal volume (TVdepth) estimates. The bias and root mean squared difference (RMSD) accuracy between RRdepth and the ventilator reference, RRvent, across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RRdepth = 0.96 × RRvent + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TVdepth and the reference TVvent across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TVdepth = 0.79 × TVvent—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RRdepth is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TVdepth may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.

Muscles adaptation to aging and training: architectural changes \u2013 a randomised trial

BackgroundTo investigate how anatomical cross-sectional area and volume of quadriceps and triceps surae muscles were affected by ageing, and by resistance training in older and younger men, in vivo.MethodsThe old participants were randomly assigned to moderate (O55, n = 13) or high-load (O80, n = 14) resistance training intervention (12 weeks; 3 times/week) corresponding to 55% or 80% of one repetition maximum, respectively. Young men (Y55, n = 11) were assigned to the moderate-intensity strengthening exercise program. Each group received the exact same training volume on triceps surae and quadriceps group (Reps x Sets x Intensity). The fitting polynomial regression equations for each of anatomical cross-sectional area-muscle length curves were used to calculate muscle volume (contractile content) before and after 12 weeks using magnetic resonance imaging scans.ResultsOnly Rectus femoris and medial gastrocnemius muscle showed a higher relative anatomical cross-sectional area in the young than the elderly on the proximal end. The old group displayed a higher absolute volume of non-contractile material than young men in triceps surae (+ 96%). After training, Y55, O55 and O80 showed an increase in total quadriceps (+ 4.3%; + 6.7%; 4.2% respectively) and triceps surae (+ 2.8%; + 7.5%; 4.3% respectively) volume. O55 demonstrated a greater increase on average gains compared to Y55, while no difference between O55 and O80 was observed.ConclusionsMuscle loss with aging is region-specific for some muscles and uniform for others. Equivalent strength training volume at moderate or high intensities increased muscle volume with no differences in muscle volume gains for old men. These data suggest that physical exercise at moderate intensity (55 to 60% of one repetition maximum) can reverse the aging related loss of muscle mass.Trial registrationNCT03079180 in ClinicalTrials.gov. Registration date: March 14, 2017.

Evaluation of optimal conditions for anionic surfactant removal in wastewater

This study was conducted to find the optimal conditions for removing anionic surfactants in wastewater using the coagulant-flocculant method. Optimal conditions must be found to minimize the amount of metal materials that can cause secondary contamination and to improve performance. Five parameters were selected to investigate their influence on surfactant removal. The ranges of the independent variables were 0.5–5% for coagulant concentration, 0.1–1% for flocculant concentration, and 20–650 mg/L for surfactant concentration; the coagulant type was FeCl3·6H2O or Ca(OH)2; and the pH ranged from 2 to 10. The experimental results were analyzed with Minitab 19.1 to find the optimal conditions to maximize the removal rate of surfactant. In this study, a total of 20 experiments were carried out using a half fractional factorial design (FFD) including two center points with a resolution of 5 and a pseudo-center point. The results demonstrated that coagulant concentration, flocculant concentration, and pH were significant independent variables with respect to surfactant removal. The fitted regression equation confirmed that the surfactant removal rate was maximized when the coagulant concentration was 5%, the flocculant concentration was 0.1%, and the pH was 10.

Influence of Fineness and Vontent of Steel Slag Powder on Properties of Composite Admixture

Taking fluidity and compressive strength of mortar as index, steel slag slag composite admixture replaces cement by 50%. The influence of the fineness and content of steel slag powder on the properties of composite admixture was studied by uniform experimental design. The fitting regression equation between the parameters of steel slag fineness and dosage and the performance indexes is established, and the contour map is drawn. The results show that the predicted value is in good agreement with the measured value, and the correlation coefficient is more than 0.99. Through regression analysis and contour map analysis, it is shown that increasing the fineness of slag powder and reducing the content of slag are beneficial to improving the cementitious activity of slag composite.

Analysis of biomarkers for prediction of idiopathic infertility in Large White Yorkshire boar (Sus scrofa domesticus)

The experiment was conducted to analyze biomarkers like pH, calcium (Ca2+), cholesterol (Ch), estradiol (E2) and vitamin D (Vit-D) in the serum for prediction of idiopathic infertility and its early amelioration. pH of epididymal luminal fluids (ELF) and concentrations of Ca2+, Ch, E2 and Vit-D were measured in serum, as well as in epididymal luminal fluids (ELF) and sperm cytosolic fluids (SCF) from caput, corpus and cauda epididymis of healthy, mature Large Yorkshire breed of boar (n=100). The levels of these bio-molecular components in the serum, in ELF and SCF from different epididymal segments were used statistically to develop a Curve Fit Regression Equations and a prediction equation to predict the quantum of these bio-molecules in the ELF and SCF by estimating their levels in the serum which would have been very difficult otherwise to be estimated in live animals. Further, from the clinical point of view, present findings about the spermatozoa maturation can pave the way for development of specific marker(s) that will assist in the development of new arena for both the prediction and early diagnosis of male infertility and for improving treatment modalities of male infertility, since epididymal dysfunctions are related to cases of idiopathic male infertility.

Unified methods for variable selection and outlier detection in a linear regression

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.