Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses.

Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations

This article introduces an improved sales percentage method to quantitatively calculate the evaluation process of the corporate sales cash flow percentage method in order to obtain more evidence-based financial data and increase the accuracy of the evaluation results. At the same time, the paper uses SPSS to perform regression analysis on related financial indicators and sales revenue and obtains quadratic regression equations and linear regression equations. The thesis predicts other financial index data based on the predicted future sales revenue, uses the revised linear regression equation to obtain the company's future net cash flow and calculates the company value.

A Quadratic Regression Model to Quantify Plantation Soil Factors That Affect Tea Quality

Tea components (tea polyphenols, catechins, free amino acids, and caffeine) are the key factors affecting the quality of green tea. This study aimed to relate key biochemical substances in tea to soil nutrient composition and the effectiveness of fertilization. Seventy tea samples and their corresponding plantation soil were randomly collected from Xinyang City, China. The catechins, free amino acids, and caffeine in tea were examined, as well as the soil pH, nitrate (NO3--N), ammonium (NH4+-N), available phosphorus (AP), available potassium (AK), and soil organic matter (SOM). The ordinary kriging was employed to visualize the spatial variation characteristic by ArcGIS. A quadratic regression model was used to analyze the effects of the soil environment on the tea. The results showed that the soil pH of the study area was suitable for cultivating tea plants. The relationship between soil pH and tea polyphenols and catechins presented the U-shape curve, whereas the soil pH and NH4+-N and the free amino acids, the soil pH, and caffeine presented the inverted U-shape curve. Soil management measures could be implemented to control the soil environment for improving the tea quality. The combination of the macro metrological model with individual experimentation could help to analyze the detailed influence mechanisms of environmental factors on plant physiological processes.

Evaluation of axial length to identify the effects of monocular 0.125% atropine treatment for pediatric anisometropia

The aim of the study is to determine the effects of monocular 0.125% atropine daily treatment on the longer axial length (AL) eyes in children with pediatric anisometropia. This was a retrospective cohort study. The charts of children with anisometropia (aged 6–15 years) who had a > 0.2-mm difference in AL between the two eyes were reviewed. Children who received monocular treatment of 0.125% atropine in the eye with longer AL were included for final analysis. The main outcome measure was the difference in AL between the two eyes after treatment. Regression analysis was used to model the changes in AL according to the time of treatment in both eyes. Finally, forty eyes in 20 patients (mean age 10.2 years) were included in the analyses. During the treatment period, AL was controlled in the treated eyes (p = 0.389) but elongated significantly in the untreated eyes (p < 0.001). The difference in AL between the treated and untreated eyes decreased from 0.57 to 0.22 mm (p < 0.001) after the 1-year treatment period. In the regression model, the best fit for the relationship between changes in AL and time during the treatment period in the treated eyes was the quadratic regression model with a concave function. In conclusion, these data suggest that 0.125% atropine daily is an effective treatment to reduce the interocular difference of AL in eyes with axial anisometropia. This pilot study provides useful information for future prospective and larger studies of atropine for the treatment of pediatric axial anisometropia.

Performance and egg quality of laying hens fed pequi pulp flour

The objective of this study was to evaluatethe effects of including pequi pulp flour (PPF) in laying hendiets onperformance and egg quality.A total of 240 Hisex Brown strain were randomly assigned into 5 groups and were fed diets containing PPF at the level of 0%, 0.125%, 0.250%, 0.375%, and 0.5%. The experiment lasted63 days (3 periods of 21 days).The variables of performance andeggquality were submitted to linear and quadratic regression analysis at the level of 5% probability.Theoutcomes suggest that neither performance nor egg quality wasaffected (p > 0.05) by different levels of PPF.Dietary inclusion ofPPFtested in the present study wasnot sufficient to demonstrate improvement in the productive performance of birdsas well as in egg quality.Because of the lack of responses, additional research is needed to evaluate higher levels of PPF in the diets for laying hens.

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather: A Case of Rawalpindi Pakistan

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.