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Background/purpose: To establish regression models of physical and equivalent dose in 2 Gy per fraction (EQD 2 ) plan parameters of two kinds of hybrid planning for stage III NSCLC., Methods: Two kinds of hybrid plans named conventional fraction radiotherapy & stereotactic body radiotherapy (C&S) and conventional fraction radiotherapy & simultaneous integrated boost (C&SIB) were retrospectively made for 20 patients with stage III NSCLC. Prescription dose of C&S plans was 2 Gy × 30f for planning target volume of lymph node (PTV LN ) and 12.5 Gy × 4f for planning target volume of primary tumor (PTV PT ), while prescription dose of C&SIB plans was 2 Gy × 26f for PTV LN and sequential 2 Gy × 4f for PTV LN combined with 12.5 Gy × 4f for PTV PT . Regression models of physical and EQD 2 plan parameters were established based on anatomical geometry features for two kinds of hybrid plans. The features were mainly characterized by volume ratio, min distance and overlapping slices thickness of two structures. The possibilities of regression models of EQD 2 plan parameters were verified by spearman's correlation coefficients between physical and EQD 2 plan parameters, and the influence on the consistence of fitting goodness between physical and EQD 2 models was investigated by the correlations between physical and EQD 2 plan parameters. Finally, physical and EQD 2 models predictions were compared with plan parameters for two new patients., Results: Physical and EQD 2 plan parameters of PTV LN CI 60Gy have shown strong positive correlations with PTV LN volume and min distance (PT to LN) , and strong negative correlations with PTV PT volume for two kinds of hybrid plans. PTV (PT+LN) CI 60Gy is not only correlated with above three geometry features, but also negatively correlated with overlapping slices thickness (PT and LN) . When neck lymph node metastasis was excluded from PTV LN volume, physical and EQD 2 total lung V 20 showed a high linear correlation with corrected volume ratio (LN to total lung). Meanwhile, physical total lung mean dose (MLD) had a high linear correlation with corrected volume ratio (LN to total lung) , while EQD 2 total lung MLD was not only affected by corrected volume ratio (LN to total lung) but also volume ratio (PT to total lung). Heart D 5 , D 30 and mean dose (MHD) would be more susceptible to overlapping structure (heart and LN) . Min distance (PT to ESO) may be an important feature for predicting EQD 2 esophageal max dose for hybrid plans. It's feasible for regression models of EQD 2 plan parameters, and the consistence of the fitting goodness of physical and EQD 2 models had a positive correlation with spearman's correlation coefficients between physical and EQD 2 plan parameters. For total lung V 20 , ipsilateral lung V 20 , and ipsilateral lung MLD, the models could predict that C&SIB plans were higher than C&S plans for two new patients., Conclusion: The regression models of physical and EQD 2 plan parameters were established with at least moderate fitting goodness in this work, and the models have a potential to predict physical and EQD 2 plan parameters for two kinds of hybrid planning.

Prediction (regression) equations are widely used, but their reliability as predictive tools is questionable as they provide contradicting results. The key point is that values calculated by regression equations are not precisely defined numbers but lie within a range of possible values in the standard deviation interval, none of which can be considered as the most probable. Ignoring this point leads to illicit/improper calculations, generating wrong results, which may have adverse consequences for human health. To demonstrate this, we applied the equations of Harris and Benedict in a reverse method, i.e. calculating (predicting) the daily energy expenditure in the same subjects used to obtain the equations and comparing values with the original measured data. We used the Bland-Altman and frequency distribution analyses. We found large differences in both individual data and population characteristics, showing that prediction equations are not predictive tools.

Discrete data in the form of proportions with overdispersion and zero inflation can arise in toxicology and other similar fields. In regression analysis of such data, another problem that also may arise in practice is that some responses may be missing. In this paper, we develop estimation procedure for the parameters of a zero-inflated overdispersed binomial model in the presence of missing responses under three different missing data mechanisms. A weighted expectation maximization algorithm is used for the maximum likelihood estimation of the parameters involved. Extensive simulations are conducted to study the properties of the estimates in terms of average of estimates, relative bias, variance, mean squared error, and coverage probability of estimates. Simulations show much superior properties of the estimates obtained using the weighted expectation maximization algorithm. Some illustrative examples and a discussion are given., (Copyright © 2018 John Wiley & Sons, Ltd.)

Understanding the intricate interplay between demographic shifts and the profound influence of social networks on the mental health of older adults is crucial amid our rapidly aging global population. As societies evolve, the significance of social connections intensifies, directly impacting the well-being of older individuals. This empirical research, encompassing 50 individuals aged 60 years and above from urban and rural areas in equal proportions, aims to comprehend sociodemographic characteristics, assess social network resilience, evaluate mental health, and investigate how sociodemographic factors shape mental well-being. Utilizing Chi-Square tests, correlation coefficients, independent samples t-tests, and frequency tables, the study reveals nuanced insights into the complex relationship between social network strength and demographic variables among older adults. The research methodology employs a comprehensive set of tools, including a self-prepared interview schedule for socio-demographic details, the Lubben Social Network Scale (LSNS-R), and the Mental Health Inventory (MHI). These instruments, recognized for their reliability and validity, offer a thorough assessment of social networks and mental health. The study's outcomes emphasize the necessity of tailored approaches that consider diverse sociodemographic factors in addressing the mental health needs of older adults. In conclusion, this research contributes significantly to the understanding of how social networks influence the mental health of older adults within the context of demographic shifts, underscoring the imperative of personalized strategies to effectively cater to the diverse mental health needs of this population in view of social work practice. [ABSTRACT FROM AUTHOR]

Dengue fever remains a significant epidemiological challenge globally, particularly in Brazil, where recurring outbreaks strain healthcare systems. Traditional statistical models often struggle to accurately capture the complexities of dengue case distributions, especially when data exhibit bimodal patterns. This study introduces a novel bimodal regression model based on the log-generalized odd log-logistic exponential distribution, offering enhanced flexibility and precision for analyzing epidemiological data. By effectively addressing multimodal distributions, the proposed model overcomes the limitations of unimodal models, making it well suited for public health applications. Through regression analysis of dengue case data from the Federal District of Brazil during the epidemiological weeks of 2022, the model demonstrates its capacity to improve the fit of the disease rate. The model's parameters are estimated using maximum likelihood estimation, and Monte Carlo simulations validate their accuracy. Additionally, local influence measures and residual analysis ensure the proposed model's goodness-of-fit. While this innovative regression model offers substantial advantages, its effectiveness depends on the availability of high-quality data, and further validation is necessary to confirm its applicability across diverse diseases and regions with varying epidemiological characteristics. [ABSTRACT FROM AUTHOR]

Stem size has the greatest effect on harvester productivity, and stem sizes vary in a forest stand. How these within-stand variations influence harvester productivity is normally not considered in studies or predictions of productivity. This study suggests reasons as to why the current production and/or application of productivity models are prone to bias from stem size variations in a stand, irrespective of whether models were developed from tree-based or stand-based studies. Moreover, it also provides empirical data on the stand stem size variation's influence on stand-based modeling of harvester productivity. Data from 11 harvesters in 347 final fellings and four harvesters in 80 thinnings were used. The mean productivity was 26.7 and 11.0 m3/PMh5 in final felling and thinning, respectively, and the mean stem size explained most of the observed variation. The productivity in final felling decreased with increased levels of stand stem size variation, as well as with increases in the proportion of broadleaf trees in the stand. For thinnings, productivity increased with increases in the proportion of pine trees in the stand, but there was no significant effect of stand stem size variation or other tested factors. The results show that stand stem size variation is a relevant factor to consider when modeling and predicting harvester productivity. [ABSTRACT FROM AUTHOR]

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and noninformatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI. [ABSTRACT FROM AUTHOR]

The need to increase bio-oil yield from biomass and enhance its fuel properties has driven research into optimizing the pyrolysis process. This study investigated the influence of three key process parameters—temperature, heating rate, and nitrogen flow rate—on the pyrolysis of elephant grass (Pennisetum purpureum) in a fixed-bed reactor. Response surface methodology was used to study the impact of the aforementioned variables on bio-oil yield to improve its production efficiency. Proximate analysis of the biomass revealed 79.24 wt% volatile matter, 14.22 wt% fixed carbon, and 5.86% ash, with ultimate analysis showing 45.44% carbon, 5.59% hydrogen, and 40.95% oxygen. The high volatile matter content and favourable carbon and hydrogen percentages indicate that elephant grass is a viable energy source due to its potential for high bio-oil yield and energy content. The resulting bio-oil exhibited a higher heating value of 20.9 MJ/kg, indicating its suitability for various heating applications. A second-order regression model was developed for bio-oil yield, with optimal conditions identified as a temperature of 550°C, a heating rate of 17°C/min, and a nitrogen flow rate of 6 ml/min. The study achieved an optimal bio-oil yield of 59.03 wt%, and the model's high R ² value of 0.8683 from analysis of variance analysis confirmed its predictive accuracy. This research highlights elephant grass as a sustainable feedstock for bio-oil production, offering valuable insights into optimizing pyrolysis conditions to enhance bio-oil yield, thus advancing biofuel technology. [ABSTRACT FROM AUTHOR]

Automotive chassis components, constructed as lap joints and produced by gas metal arc welding (GMAW), require fatigue durability. The fatigue properties of the weld in a lap joint are largely determined by weld geometry factors. When there is no gap or a consistent gap in the lap joint, improving the geometry of the weld toe can alleviate stress concentration and enhance fatigue properties. However, due to machining tolerances, it is difficult to completely eliminate or consistently manage the gap in the joint. In the case of a lap-welded joint with an inconsistent gap, it is necessary to identify the weld geometry factors related to fatigue properties. Evaluating the fatigue behavior of materials and welded joints requires significant time and cost, meaning that research that seeks to predict fatigue properties is essential. More research is needed on predicting fatigue properties related to automotive chassis components, particularly studies on predicting the fatigue properties of lap-welded joints with gaps. This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied in order to build various weld geometries in joints configured in a lap with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S–N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S–N curve using the Basquin model from the S–N curve and the safe fatigue strength were selected as output variables for prediction in order to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed. [ABSTRACT FROM AUTHOR]

Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles (i.e. maximum compressive stress, maximum tensile stress, and blow per foot). In addition, support vector regression, extreme gradient boosting, k nearest neighbor, and decision tree are also used and applied for comparison purposes. In order to train and test these models, among the 4072 datasets collected with 17 model inputs, 3258 datasets were randomly selected for training, and the remaining 814 datasets were used for model testing. Lastly, the results of these models were compared and evaluated using two performance indices, i.e. the root mean square error (RMSE) and the coefficient of determination (R). The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters, specifically 0.044, 0.438, and 0.146; and higher R² values than other implemented techniques, specifically 0.966, 0.884, and 0.977. In addition, the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo (MC) simulation. It can be concluded that the optimized RF model could be used to predict the performance of the pile, and it may provide a useful reference for solving some problems under similar engineering conditions. Graphic Abstract [ABSTRACT FROM AUTHOR]

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The determination of geotechnical correlations and coefficients relies on the assumption of error-free training data used for empirical models, but this assumption may not always hold true. Empirical-risk assessments based on noisy data cannot guarantee the accuracy of regression results. The statistical robustness of empirical-design parameters is influenced both by the soil properties and regression models used. This study proposed a non-stochastic regression method for predicting the hydraulic conductivity of sandy soils based on relevant soil parameters. No changes in content have been made. The approach involved the following steps: data preprocessing, regression-algorithm selection, model optimization, uncertainty estimation, and model selection. The study identified trends in hydraulic conductivity and pore-structure characteristics based on specific model parameters that were derived from empirical data. The paper presents a compilation of a regression model and methods for fine-tuning parametric representations. The prediction results highlighted the best-fitting model and parameter combination with the lowest residuals, comparing favorably to empirical regression models. Machine-learning-based regression models suggest an optimal combination of properties while considering model performance and handling missing values for uncovering the relationships between hydraulic conductivity and multiple, influential, soil properties. [ABSTRACT FROM AUTHOR]

Summary: The time until an event occurs is often known to have a skewed distribution. To model this, a statistical distribution called the two‐parameter flexible Weibull extension (FWE) has been proposed. In this paper, the FWE distribution is used to model datasets through the use of generalised additive models for location, scale and shape (GAMLSS) distributional regression. GAMLSS is the only regression technique that can examine the effects of both categorical and numeric predictors on all the parameters of the distribution used to fit the dependent variable. To make it easier to use the FWE distribution through GAMLSS, the RelDists R package is proposed. A simulation study shows that FWE modelling through GAMLSS provides reliable parameter estimates even in the presence of factors that affect the distribution. [ABSTRACT FROM AUTHOR]

Regression models often require constraints that can be expressed as non-negativity constraints. This could be because it makes sense for the underlying modeling context, or it could be necessary to prevent the fitted values violating the natural constraints of the response distribution. Examples of the latter include log-link binomial regression and additive variance regression, while an example of the former is non-negative linear regression. Non-negativity constraints may apply directly to the regression parameters, in which case we call the model parameter-constrained, or they may apply to the linear predictor, in which case we call the model predictor-constrained. In this article, we show that it is possible to fit the predictor-constrained model using a parameter-constrained method applied to a model with additional dummy parameters, a technique called parameter expansion. This is advantageous because parameter-constrained models are often easier and more reliable to fit. After considering a range of models in which predictor-constrained estimation is desirable, we study the application of parameter expansion to a general regression model that includes the specific models as special cases. We then undertake a more in-depth study of parameter expansion for the log-link binomial model. Simulation results are presented demonstrating the computational advantages of parameter expansion. [ABSTRACT FROM AUTHOR]

In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unit power half-normal distribution provides a robust and flexible alternative for existing models for proportion data. [ABSTRACT FROM AUTHOR]

Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer's perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building's quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables' coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model's reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects. [ABSTRACT FROM AUTHOR]

The spatial location of job opportunities contributes to people’s livelihood outcomes. Privileged communities are well located and have access to advanced educational systems, while poor communities are marginalized with deprived education outcomes. Understanding the influence of spatial mismatch on educational attainment requires information about job locations and education levels. Using a regression model, the study examined the role of spatial mismatch in explaining education attainment in South African metropolitan areas. Data were acquired per sub-place from Stats SA, CSIR, and GeoTerraImage. The regression models in all metros confirmed the relationship between spatial mismatch and educational attainment, which is valuable for area-based and people-based development approaches. The findings presented a significant relationship between spatial mismatch and educational attainment. Through the results, the study concluded that spatial mismatch is one of the significant determinants of educational attainment in South African metro areas. [ABSTRACT FROM AUTHOR]

Abstract.The choice of the censoring mechanism is very important when we are analyzing survival data because, in some practical situations, there is a dependence between the failure and censoring times, contradicting the assumption of non informative censoring. The present article proposes a censored-data regression in the presence of informative censoring based on the generalized odd log-logistic family of distributions under two systematic components. The proposed model is based on the assumption that failure and censoring times are conditionally independent given a frailty. The choice of the widespread odd log-logistic family for failure and censoring times is justified by the fact that it generalizes some distributions already consolidated in the area of survival and, concomitantly, by the fact that it offers great flexibility in data modeling in practice, in addition to the capacity to present various forms of the risk function, among them, bimodal. The maximum likelihood method is used to estimate the model parameters. For different parameter configurations and sample sizes, some simulations are performed to analyze the behavior of the maximum likelihood estimates. Two real data applications illustrate the usefulness of the proposed survival regression with informative censoring. [ABSTRACT FROM AUTHOR]

In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network's connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network's behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour. [ABSTRACT FROM AUTHOR]

The actual application of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is restricted by the bias revealed by ground data. This study established seven regression models (RMs) to generate the adjusted IMERG estimates. Relatively stable parameters of the regression can be gained during the calibration. The calibration was performed by climatological adjustment, building a relationship between the 18-year data series of the original IMERG estimates and simultaneous daily data derived from 687 rain gauges in mainland China. A one-time modeling scheme was designed using all daily precipitation data as a calibration dataset. A two-time modeling scheme was established by dividing the calibration period into cold and warm seasons. Then, the relative bias (RB) and root-mean-square error (RMSE) were the evaluation indicators inspected during the validation. Three rounds of modeling were designed to provide corrections when near-real-time and post-real-time IMERG products produce time series. The main conclusions are as follows: (1) The two-time modeling scheme had a higher proportion than the one-time modeling scheme in having the lowest RMSE and absolute RB values. (2) Compared with the original IMERG estimates, the model-generated estimates in rounds 1 and 2 reduced the magnitude of the RB and RMSE at around 75% and 85% of gauges, respectively. (3) Polynomial RMs were the best-performing models in rounds 2 and 3. (4) The gauges where the RM failed to reduce the magnitude of the RB were mainly found in humid, plain, and low-latitude areas of mainland China. [ABSTRACT FROM AUTHOR]

A damage regression identification method for large and complex transmission tower structures subjected to static loads was proposed based on the substructure model reduction and data-driven method. According to the structural features of the transmission tower and its deformation under self-weight and ice loading, the full finite element model for the tower was reduced by means of the sub-structure method, the possible damage modes were predicted and the damage indexes defined. The substructure modeling method was used to reduce the orders of the structure with different damage states, and the order reduction model library was established. The calibration load was determined based on the loading characteristics of the tower, and the strain sensor layout was designed according to the deformation and failure modes. The deformations of all the reduced-order models under calibration loads were numerically simulated with the finite element method, and a dataset was then created. With the data measured by the strain sensors as input and the damage indexes as output, a damage regression identification model was built by the BP neural network algorithm. With the identification model, the damage locations can be recognized and the damage indexes can be quantified. This work lays a foundation for real-time health monitoring of transmission tower structures. [ABSTRACT FROM AUTHOR]

This article presents accelerated failure time models with and without frailty for modeling multiple systems subject to minimal repair. The study considers the conventional accelerated failure time model, the accelerated failure time model with Gamma frailty, and proposes the accelerated failure time model with weighted Lindley frailty, which has attractive properties such as a closed‐form Laplace transform. The proposed model extends the accelerated failure time model with the intensity function of a power law process. It retains the direct physical interpretation of the original accelerated failure time model, in which the role of covariates is to accelerate or decelerate the time to each repair. This framework includes parametric approaches to model fitting, which we consider for estimating the vector of regression parameters under this model and the parameter in the baseline intensity functions. The methodology is illustrated with a simulation study and a toy example to demonstrate the applicability of these models in the industrial context. [ABSTRACT FROM AUTHOR]

• The principal component analysis was suitable for sludge properties analysis. • The variance contribution of the extracted two principal components was 92.5 %. • Principal component scores visualized characteristics of sludge solids parameters. • The maximum drying rate was positively correlated with the initial moisture content. Convective drying is an effective method for reducing the moisture content of the sludge. Fewer studies have discussed the effect of sludge physicochemical properties on drying compared to air parameters. Eleven types of sludge were collected, and ultimate analysis, proximate analysis, and heat value analysis were performed. Meanwhile, the maximum drying rate (u max) of sludge convection drying at 70 °C was determined. The results showed that the cumulative variance contribution of the two extracted principal components (PCs) was 92.5 %. Then, a regression model of u max was developed based on the extracted PCs. The coefficient of determination of this model was 0.788, and the difference was statistically significant, with a negative correlation between u max and PC2. Further, the principal component score plot enabled the traceability of the integrated sludge, and based on this classification results, the drying characteristics of various types of sludge were discussed, and a high correlation (R2 = 0.9590) between the initial moisture content of sludge and u max was found. Mathematical models between sludge physicochemical properties and drying characteristics can be effectively developed from both sludge composition and type scales. This exploration deepened the knowledge of sludge drying and facilitates the prediction of drying rate. [ABSTRACT FROM AUTHOR]

Reducing fruit losses is one of the most important strategies for improving diets and strengthening food systems. Therefore, improving modern technologies for storing sweet cherries, assessing the potential of the necessary capacities and technologies for fruit storage to reduce post-harvest losses and extend the shelf life of fruits remains relevant. Improvement of pre-cooling methods optimize the shelf life of fresh fruit and contributes to the growth of demand for fruit by consumers in the European market. The object of the study is the technology of pretreatment of sweet cherry fruits using an organic composition. The fruits of the model variety Valerii Chkalov were used as experimental material. The minimum natural losses from microbiological diseases and physiological disorders were recorded on the 40th day of sweet cherry fruit storage. Fruit weight losses during storage of sweet cherries of the Valerii Chkalov variety were minimal when hydro cooling was applied in combination with a protective organic composition of lactic and acetic acid (1.75 %:2.00 %). The optimum concentration of lactic and acetic acids (2.00 %:1.75 %) was determined, which ensures minimal daily fruit losses (0.068 %) under the influence of hydro-cooling during storage of sweet cherries. The analysis of the regression model reveled the optimal value of the average level of daily losses during storage of sweet cherry fruit of Valerii Chkalov variety – 0.0642 % at concentrations of lactic and acetic acid 2.161 % to 1.705 %, respectively. The tendency to minimize sweet cherry fruit losses during storage under the influence of hydrocooling and the optimal concentration of organic acids in the protective composition was identified. The results could be used to improve pre-cooling methods in the food industry to preserve the quality of fruit raw materials in a waste-free fruit supply chain for all relevant stakeholders. [ABSTRACT FROM AUTHOR]

Governments worldwide have invested considerable money and time into creating pedestrian-oriented urban environments. However, generalizing arbitrary standards for walking environments is challenging. Therefore, this study presents a method for predicting walkability scores of evaluations using five regression models, including Multiple linear, Ridge, LASSO regression, SVR, and XGBoost. The models were trained using semantic segmentation, walkability evaluations based on crowdsourcing, and image scores obtained using the TrueSkill algorithm, and their performances were compared. Feature selection was employed to improve the accuracies of the models, which were retrained using the importance of extracted features. Among the five regression models, XGBoost, a tree-based regression model, exhibited the lowest error rate, high accuracy, and greatest performance improvement after retraining. This study is expected to generalize the walking environments preferred by various people and demonstrate that objective walkability evaluations are possible through a computer system rather than through subjective human judgment. [ABSTRACT FROM AUTHOR]

Alternative intersection designs can provide cost-effective solutions to overcome the proven inadequacy of conventional approaches. Several studies have assessed the performance of alternative designs against a range of traffic volumes and geometric design aspects, each in isolation, but a model which can factor in multiple variables into the analysis is the identified research gap. The displaced left-turn – DLT intersection design was found to be the most versatile, efficient, and transferable to locations elsewhere in the world. In this paper, a displaced right-turn intersection – a variant of DLT, was modelled for a range of traffic flows and design conditions. Regression models were developed for Practical Reserve Capacity and Delay as dependent variables with traffic flow, proportion of right-turning traffic, signal cycle time and length of displaced turn as explanatory variables. These models can provide relatively quick preliminary estimates of the performance indicators before committing to resource-consuming junction remodelling works. [ABSTRACT FROM AUTHOR]

In this paper, we consider a transformation in a random variable which follows a bimodal Birnbaum-Saunders distribution. We propose the unit-bimodal Birnbaum-Saunders (UBBS) distribution and investigate some of its important properties, like cumulative distribution function, moments, survival function and risk function. We apply the UBBS distribution to censored data inflated at zero and one. We used the maximum likelihood approach for parameter estimation and to compare the models. Given the flexibility in UBBS distribution modes, our proposal performs best in beta regression models with zero and/or one excess. [ABSTRACT FROM AUTHOR]

Background: Sport plays a significant role in shaping social life, the health of the nation and the development of regions. Studying the impact of the sports industry on the quality of life and regional development can help to better understand how sports influence the social, economic and cultural aspects of people's lives. Objective: This study aimed to investigate the impact of the sports industry on the quality of life and human development in developed and developing countries by analyzing the correlation and modeling of recognized world indices. Methods: The research employed correlation analysis, regression analysis, and scenario modeling, utilizing publicly available international indices to determine the level of impact of sports on the country's development indicators. While investment in sports development may not be a straightforward decision for developing countries facing socio‐economic challenges, the study demonstrated the long‐term benefits of such investments through evidence‐based mathematical regression models and scenario modeling. Results: The study findings revealed that the advancement of the sports industry leads to improvements in the quality of life and human development in most of the studied regions. Conclusion: Moreover, the scenario modeling suggested that the evolution of this field would further enhance the development of the quality of life. [ABSTRACT FROM AUTHOR]

In Poplar plantations, manual tree harvesting techniques (using chainsaws) are still utilized, especially on gentle terrain or for smaller trees, where automated felling may not be a possible or safe option. The most important variables affecting the productivity of motor-manual felling, a global survey show that terrain slope, understory density, distance to trees, and diameter at breast height (DBH) are the most major variables influencing the productivity of manual tree felling. In harvesting activities like felling, delimbing, and bucking, skilled workers are required. Chainsaw use is associated with timber harvesting activities including felling, delimbing, and bucking in forest areas. The chainsaw felling hourly output was 0.4546 m³/h, the delimbing hourly production was 0.482 m³/h, and the bucking hourly production was 0.753 m³/h. As tree DBH increased, chainsaw productivity also increased. In the present study, the utilization rate and chain saw productivity hours (PMH) were 8.25 hours and 71%, respectively. Regression models' calibration and validation procedures for many aspects of tree utilization were used to develop the mathematical models between the above-mentioned process included in utilization as dependent variables and both DBH and total height of trees as independent variables. The coefficient of determination was used to test the efficiency of the calibration and validation of the developed models in the estimation of the dependent variables, the most acceptable Equation No. 11 with a 91.86 coefficient of determination(R2). Planning for sustainable harvesting can be influenced by the primary suggestions for improving motor-manual felling productivity. [ABSTRACT FROM AUTHOR]

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level. [ABSTRACT FROM AUTHOR]

We propose two new regressions, one parametric and other partially linear based on an extended Birnbaum–Saunders distribution. This distribution includes, as special cases, the exponentiated Birnbaum–Saunders, odd log-logistic Birnbaum–Saunders and Birnbaum–Saunders distributions. Several mathematical properties are presented. We adopt the maximum likelihood method to estimate the parameters of the parametric regression model and the penalized maximum likelihood method to estimate the parameters of the partially linear regression model. We study the behavior of the estimators through Monte Carlo simulations considering different scenarios and also extended the quantile residuals for the new regression models to verify the versatility of the parametric regression model. An analysis is carried out using average price data (R$) received by producers and wholesalers collected by Hortifruti/Cepea-Esalq/USP (Brazil). Similarly, the flexibility of the partially linear regression model is proved through an analysis with data on the average price of a hectare of rural property with improvements in the city of Itapeva-SP, Brazil. These applications empirically show that the proposed regression models have a better quality of fit than other existing regression models in the literature. [ABSTRACT FROM AUTHOR]

The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution, such as bimodality, real moments, entropies and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality. [ABSTRACT FROM AUTHOR]

In this paper, we discuss some theoretical results and properties of a discrete version of the Birnbaum-Saunders distribution. We present a proof of the unimodality of this model. Moreover, results on moments, quantile function, reliability and order statistics are also presented. In addition, we propose a regression model based on the discrete Birnbaum-Saunders distribution. The model parameters are estimated by the maximum likelihood method and a Monte Carlo study is performed to evaluate the performance of the estimators. Finally, we illustrate the proposed methodology with the use of real data sets. [ABSTRACT FROM AUTHOR]

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios. [ABSTRACT FROM AUTHOR]

Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three methods. [ABSTRACT FROM AUTHOR]

The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics. [ABSTRACT FROM AUTHOR]

The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) provides an efficient way to monitor the Earth's surface. But the speckle noise that the SAR system generates when acquiring images makes it difficult to understand and interpret SAR intensity features. To automatically analyze SAR images, this paper presents a K-Bessel regression (KBR) model in which a function of the mean intensity response is explained by other features (or covariates) determined in parallel. Some mathematical properties of this regression are derived and discussed in the context of the physical origin of the SAR image. A maximum likelihood estimation procedure is planned and its performance is quantified by Monte Carlo experiments. An application to real data obtained from a polarimetric SAR image of San Francisco Bay is realized. Results show that both the KBR-based processing is more informative than the unconditional approach to describe SAR intensity and that our proposal can outperform the normal and gamma regression models. Finally, it is shown that the KBR model is useful to reproduce the relief signal of one channel from the intensity values of the other. [ABSTRACT FROM AUTHOR]

Hydrologists pursue the long-term goals of studying the relationship between hydrological processes and geomorphological processes, establishing quantitative relationships between these processes and finding ways to directly derive hydrological processes from topographic parameters to reduce the dependence on hydrological data. In this study, the river network of the Haihe River Basin was extracted based on DEM, and 18 representative small basins were selected as samples. Four river network parameters (the river network density, average branch ratio, average length ratio and fractal dimension) and four topographic parameters (the slope, topographic relief, surface roughness and roundness rate) were calculated for 18 small basins. The river network features and topographic features of the basin were analysed. Correlation analysis, one-dimensional linear regression, partial correlation analysis and multiple linear regression (stepwise regression) methods were used to analyse the quantitative relationship between topographic parameters and river network parameters. The analysis results were tested by a significance test, the final goodness of fit of the regression model was good, and the results were reliable. The correlations are spatially heterogeneous and may show variable results in different regions. This study guides river network planning and management, and managers should pay attention to the correlation between river networks and topography in the long term. [ABSTRACT FROM AUTHOR]

Integrating a model-based and data-driven approach to generate sufficient number of training data that include time-varying operating conditions is needed to develop a prognostics and health management technique using machine learning for a reusable rocket engine. A data-driven approach was used to predict the engine behavior, which cannot be determined using a model-based approach. This paper proposes a clustered regression model to predict various operating conditions for a reusable rocket engine. The training data were divided in advance according to the operating conditions using the Gaussian mixture model, and multiple regression models were individually trained on each cluster. By switching the regression models according to the operating conditions, the time-series data comprising various operating conditions, such as the combustion and chill-down phases and transition between phases, were accurately predicted when compared with the prediction results of a single regression model. The prediction results of the present hybrid approach agree reasonably well with the static firing test results of a reusable rocket engine if the explanatory variables are adequately included. By integrating model-based and data-driven approaches, adequate training data can be generated for prognostics and health management of the reusable liquid rocket engine. • This study improves the prediction accuracy of the data-driven approach. • Training data for the data-driven approach comprised varying operating conditions. • The varying operating conditions were classified using a Gaussian mixture model. • The regression models were trained based on each classified operating condition. • The prediction accuracy was improved using multiple regression models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new distribution for survival data. The distribution is a new generalization of the weighted exponential distribution in the Marshall-Olkin family. Statistical properties of the proposed distribution are studied and its submodels are presented. The unknown parameters are estimated via a Bayesian approach. Simulation studies are conducted to assess the performance of the Bayesian estimator. Complete and censored data sets are analyzed to illustrate the potential of the proposed distribution. We also develop a regression model based on the proposed distribution. Finally, we compare the proposed model with other models. [ABSTRACT FROM AUTHOR]

All across the world, the construction industry is a very crucial sector for development. Cost prediction is the process of forecasting the cost of construction projects at an early stage. An effective estimation technique is necessary to reduce the effort, time, and cost. Therefore, the study aims to develop a mathematical model to predict the cost of Ethiopian Roads Authority road projects in the case of Ethiopia. The data were collected from primary and secondary sources. The method of data analysis for the study was multiple regression analysis, and it was done using SP SS and Microsoft Excel. The data were obtained from a road document review and a questionnaire survey of 62 respondents and 15 interviewers who are working in ERA. The major cost-influential factors were found to be: inflation rate, bridges, culverts, project length, width, type and scope, contract duration, and location. As a result, a multiple regression model was developed using nine significant parameters taken from 18 completed road projects. The multiple regression analysis result revealed that the model can perform at a success rate of 83.8%. The MAPE of the model was ±16.44%. The conclusion and recommendations are likely to support and reduce ineffective estimation trends. [ABSTRACT FROM AUTHOR]

We obtain new mathematical properties of the exponentiated odd log-logistic family of distributions, and of its special case named the exponentiated odd log-logistic Weibull, and its log transformed. A new location and scale regression model is constructed, and some simulations are carried out to verify the behavior of the maximum likelihood estimators, and of the modified deviance-based residuals. The methodology is applied to the Japanese-Brazilian emigration data. [ABSTRACT FROM AUTHOR]

The modified primary compression index (MPCI) of municipal solid waste (MSW) is an important parameter for describing the primary consolidation settlement of MSW. It is beneficial to calculate the settlement and storage capacity of landfills accurately by calculating the MPCI of MSW correctly. This article studies the primary consolidation compression characteristics and influencing factors of MSW through a consolidation experiment considering temperature control. The experimental results show that the MPCI increases with an increase in temperature, decreases with an increase in dry weight, increases with an increase in water content, and increases with an increase in organic matter content. According to the above findings, an MPCI regression fitting model was established. The reliability and accuracy of the model was validated by the experimental results of the indoor bioreactor test considering temperature control. [ABSTRACT FROM AUTHOR]

The purpose of the current study is to examine the relationship of ruminative thought styles with Gestalt contact disturbances and unfinished business. The study employed the relational survey model. The study group is comprised of a total of 219 university students; 159 females (72.6%) and 60 males (27.4%), continuing their undergraduate education in the 2021–2022 academic year. The data of the study were collected by using the Gestalt Contact Disturbances Scale, the Unfinished Business Scale and the Ruminative Thought Style Scale. The findings obtained from the analyses revealed that all the variables together predicted the ruminative thought styles of the university students at a statistically significant level and the established regression model was found to be significant. It was found that the ruminative thought styles of the university students were significantly and positively predicted by the contact sub-dimension of Gestalt contact disturbances and significantly and negatively predicted by the post-contact sub-dimension of Gestalt contact disturbances. In addition, it was determined that the living in the past and unexpressed thoughts and feelings sub-dimensions of unfinished business were found to be positive and significant predictors of ruminative thought styles. Contrary to these findings, the full contact and dependent contact sub-dimensions of Gestalt contact disturbances and the procrastination, regret about abilities, attachment to objects, need for acceptance and approval, being subject to injustice, regret about conditions, transference and dreams and negative emotion load sub-dimensions of unfinished business were found to not statistically significantly predict the ruminative thought styles of the university students. As a result, it was determined that the Gestalt contact disturbances and unfinished business of the university students significantly predicted their ruminative thoughts and various suggestions were listed in light of the relevant literature. [ABSTRACT FROM AUTHOR]

The development of an effective method for predicting the transfer of biologics from plasma into breast milk is important to ensure the safe use of medications during lactation. The aim of this study was to develop a regression model that could predict the transfer of monoclonal antibodies (mAbs) and Fc-fusion proteins from plasma into breast milk. By searching various databases, a list of eleven mAbs and Fc-fusion proteins with available information of presence in the breast milk was generated. Physicochemical properties such as the isoelectric point (pI), molecular weight (MW), dissociation constant (Kd), and pharmacokinetic (PK) parameters such as clearance (CL), volume of distribution (Vd), and half-life (T1/2) were collected or calculated. A twovariable non-linear regression analysis and a multivariate regression analysis were employed to establish correlation of milk-to-plasma (M/P) ratios with different combinations of two physicochemical properties. The 3D isoelectric point (pI) of the Fv region and the buried surface area (BSA) between the light and heavy chains (LC_HC) were two factors that emerged as a promising predictor of the milk-to-plasma concentration ratio (M/P). The correlation between M/P ratio, 3D pI of Fv region, and BSA_LC_HC was found to be good with R2 of 0.9058. Other combinations of the physicochemical properties did not show a statistically significant correlation. The multivariate regression model was used to predict the MP ratios for 79 different mAbs. We believe that this regression model could serve as a valuable tool to estimate the M/P ratios of mAbs and Fc-fusion proteins. Further model validation is necessary when the M/P ratios of additional biologics are available. This could inform clinical decision-making and improve the safety of large molecule drug use during lactation. [ABSTRACT FROM AUTHOR]

Experimental flow stress–strain data under different stress states are often used to calibrate the plastic constitutive model of anisotropic metal materials or identify the appropriate model that is able to reproduce their plastic deformation behavior. Since the experimental stress–strain data are discrete, they need to be mathematically returned to a continuous function to be used to describe an equivalent hardening increment. However, the regression results obtained using existing regression models are not always accurate, especially for stress–strain curves under biaxial stress loading conditions. Therefore, a new regression model is proposed in this paper. The highest-order term in the recommended form of the new model is quadratic, so the functional relationships between stress–strain components can be organized into explicit expressions. All the experimental data of the uniform deformation stage can be substituted into the new model to reasonably reproduce the biaxial experimental stress–strain data. The regression results of experimental data show that the regression accuracy of the new model is greatly improved, and the residual square sum SSE of the regression curves of the new model reduced to less than 50% of the existing three models. The regression results of stress–strain curves show significant differences in describing the yield and plastic flow characteristics of anisotropic metal materials, indicating that accurate regression results are crucial for accurately describing the anisotropic yielding and plastic flow behaviors of anisotropic metal materials. [ABSTRACT FROM AUTHOR]

The size of the leaf area can have a profound impact on the growth and life activities of the tree, and then it is related to the change of the biomass of the tree and the development trend of the forest ecology. The research on leaf area can play a key role in the correlation analysis of trees. Based on the analytical wood data of 48 Korean pine plantations in Heilongjiang Province and the leaf area data measured by the drainage method, a basic linear regression model was established, and then random effect parameters were introduced to construct a mixed effect regression model to improve the fitting effect and prediction accuracy. When random effects at the tree level were added, the optimal mixed effect model obtained contained three random effect parameters, namely logarithm of branch base diameter(lnBD), logarithm of branch length (lnSL), and tree height logarithm(lnHT). The R ² a was 0. 86, the MAE was 0. 342 8, and the RMSE was 0. 484 1. The statistical indicators were better than the basic linear model that only included fixed effects. The comprehensive analysis showed that the model can better describe the leaf area of Korean pine branches. Then, based on the model, the crown leaf area of the Korean pine tree was calculated and a mixed effect prediction model was obtained. The mixed model R ² a was 0. 83 and the RMSE was 0. 442 9. Through the test results, it can be seen that the model can predict the crown and leaf area of Korean pine trees better, and provide a better guiding direction for the future management of artificial Korean pine in this area. [ABSTRACT FROM AUTHOR]

At present, sensor-based sorting machines are usually not operated at the optimal operation point but are either overrun or underrun depending on the availability of waste streams. Mathematical approaches for predefined ideal mixtures can be found based on the input stream composition and the throughput rate. This scientific article compares whether and under what conditions these approaches can be applied to sensor-based sorting machines. Existing data for predefined ideal mixtures are compared with newly generated data of real waste on three sensor-based sorting setups in order to make significant statements. Five samples of 3D plastics at regular intervals were taken in a processing plant for refuse-derived fuels. With the comparison of all these results, four hypotheses were validated, related to whether the same mathematical approaches can be transferred from ideal mixtures to real waste and whether they can be transferred to sensor-based sorting machines individually or depending on the construction type. The developed mathematical approaches are regression models for finding the optimal operation point to achieve a specific sensor-based sorting result in terms of purity and recovery. For a plant operator, the main benefit of the findings of this scientific article is that purity could be increased by 20% without substantially adapting the sorting plant. [ABSTRACT FROM AUTHOR]

Background: Endometrial proliferative lesions (EPL) usually refer to endometrial hyperplasia (EH) and endometrial cancer (EC). Among patients with premenopausal EPL who wish to preserve their fertility, only those with EH and early-stage EC have the possibility to undergo fertility preservation therapy. However, there is currently a lack of specific and reliable screening criteria and models for identifying these patients. Methods: This study utilized a retrospective diagnostic study design. The training set included medical record information that met the criteria between August 2017 and October 2022, while the validation set consisted of medical record information that met the criteria from November 2022 to May 2023. The endometrial pathological test served as the gold standard. The serum anti-Mullerian hormone (AMH) level before endometrial sampling and a regression model were employed to predict EPL. Results: The study included a total of 1209 patients with PCOS (1119 in the control group and 90 in the endometrial proliferative lesion group) and 5366 women without PCOS (5249 in the control group and 117 in the proliferative lesion group). In the case of PCOS patients aged 20–39 years, the most effective screening threshold for AMH was found to be a serum AMH level of ≤5.39 ng/mL. The model used for this group was logit(p) = −2.562 − 0.430 × AMH + 0.127 × BMI + 1.512 × hypertension + 0.956 × diabetes −1.145 × regular menstruation. On the other hand, for non-PCOS women aged 20–39 years, the optimal screening threshold for AMH was determined to be a serum AMH value of ≤2.18 ng/mL. The model used for this group was logit(p) = −3.778 − 0.823 × AMH + 0.176 × BMI + 2.660 × diabetes −1.527 × regular menstruation −1.117 × dysmenorrhea. It is important to note that all of these findings have successfully passed internal verification. Conclusion: For PCOS and non-PCOS women aged 20–39 years, the serum AMH test and related multiple regression models were obtained for the warning of EPL. [ABSTRACT FROM AUTHOR]