Search

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]

Solid waste management is a critical issue in India as the country continues to develop. Accurately estimating the types, quantities, and distribution of solid waste is essential for effective waste management. The methods and processes for managing waste in any city, including collection, transportation, treatment, and disposal, rely heavily on accurate estimations of waste quantities. These estimates are in turn influenced by various factors, including socio-cultural, economic, environmental, political, and technological factors. The research aims to identify specific social and spatial factors that influence solid waste generation in municipal cities of the present times through a literature study. It then undertakes the study of a selected area in the city of Cuttack, Orissa, India, as a case study and formulates a model for quantifying solid waste based on the measurements of derived indicators. The research utilizes both primary and secondary data to achieve its objectives. The analysis revealed that factors such as monthly family income, house occupancy, and occupation have a strong positive correlation with the quantity of solid waste. Conversely, factors such as educational qualification and the incentive system provided to citizens exhibit a negative correlation with the amount of solid waste generated. Based on these factors, the model derived will facilitate the accurate estimation of solid waste generated in similar contexts, thereby aiding efficient waste management. By conducting this case study in Cuttack City, we aim to contribute to the existing body of knowledge on solid waste management in India and provide a comprehensive understanding of the factors affecting waste quantity. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation. [ABSTRACT FROM AUTHOR]

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]

Presently, the main trend in thin-sheet steel production, including the high-strength automobile steel, is to reduce production costs while retaining essential properties and quality. A considerable part of the cost of high-strength steel comes from alloying with expensive chemical elements such as vanadium and niobium. Simply reducing the content of these elements in finished products would compromise the mechanical properties of rolled products, leading to inferior quality and defects. This work, based on statistical analysis for steel grade S355MC, demonstrated the potential to reduce alloying while maintaining the required yield strength by adjusting the hot rolling conditions. It was also revealed that, in addition to the concentration of chemical elements and the hot rolling mode, the thickness of the finished product affects the tensile strength. Therefore, the analysis was performed exclusively on products with a thickness of 4 mm. The study resulted in a regression equation that illustrates the dependence of yield strength on vanadium content and hot rolling parameters. [ABSTRACT FROM AUTHOR]

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City's SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of the automotive industry, innovation is the key driver of success. Companies that actively engage in idea generation and have efficient idea management systems in place often gain a competitive edge. This article explores the importance and necessity of idea generation in the context of the automotive industry in Romania, shedding light on the economic effects it can have. Based on a review of scientific literature, the paperwork adds value by filling in a gap on continuous improvement process, idea management, and idea management system concepts, by describing in detail how Idea Management System can be introduced, and by performing a quantitative analysis, using complex statistical methods and machine learning on big data collected to observe the effects of Idea Management System use on the results and the level of employee involvement, as well as finding out a predictor of potential savings for automotive company. Very few academic papers take into consideration the economic effect of Idea Management System especially for the company' performance (KPIs), focusing rather on performance metrics for idea management. [ABSTRACT FROM AUTHOR]

Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at the store and product levels. To capture different big data characteristics in sales forecasting data, such as seasonal and trend variations, this study develops a hybrid model combining adaptive trend estimated series (ATES) with a deep neural network model. ATES is first used to model seasonal effects and incorporate holiday, weekend, and marketing effects on sales. The deep neural network model is then proposed to model residuals by capturing complex high-level spatiotemporal features from the data. The proposed hybrid model is equipped with a feature-extraction component that automatically detects the patterns and trends in time-series, which makes the forecasting model robust against noise and time-series length. To validate the proposed hybrid model, a large volume of sales data is processed with a three-dimensional data model to effectively support business decisions at the product-specific store level. To demonstrate the effectiveness of the proposed model, a comparative analysis is performed with several state-of-the-art sales forecasting methods. Here, we show that the proposed hybrid model outperforms existing models for forecasting horizons ranging from one to 12 months. [ABSTRACT FROM AUTHOR]

In the rapidly changing climate of arid desert regions, evaluating the comprehensive characteristics of humidity levels is crucial for agricultural, urban, and infrastructural planning, as well as for minimizing potential public health impacts. We investigated variability and trends of humidity levels in major Saudi Arabian cities during 1982–2022, focusing on the influence of meteorological factors such as average, maximum and minimum temperature, rainfall, and windspeed. Employing the Probability Density Function and descriptive statistics, variability of climatic factors was analyzed. The Mann–Kendall Test (MKT) and Innovative Trend Analysis (ITA) were employed to identify monthly and annual trends. The magnitude and changing patterns were determined by calculating Sen's Slope and ITA slope. Findings of the MKT and ITA showed similar trends in humidity levels across all the cities. ITA result revealed that humidity in Riyadh and Taif decreased at a rate of 0.012% and 0.016% per year, respectively, while increased in Jeddah, Makkah, and Madinah at a 0.05 confidence level. The influence of climatic factors on humidity was assessed using Pearson's correlation coefficients, multiple regression model, and wavelet transform coherence (WTC) for each city, pinpointing temperature as the key driver of humidity variability. The dominance of temperature features was corroborated by strong power spectrums in the WTC across various time periods and scales. The in-depth analysis of humidity dynamics in this study provides critical insights for the development of climate-resilient infrastructure and formulation of public health strategies in Saudi Arabian cities. [ABSTRACT FROM AUTHOR]

The Western Ghats (WG) of India are basically north-to-south oriented mountains with three distinct meteorological divisions. These mountains exhibit the characteristic features of precipitation and distribution during the summer monsoon season and possess latitudinal variations. It is a well-known fact that sea surface temperature (SST) combined with the El Niño-Southern Oscillation (ENSO) enacts a predominant role in the precipitation over the entire Western Ghats during the summer monsoon season. Whereas the Niño regions affect the variability of the Western Ghats' precipitation in an asymmetric relationship. Nevertheless, the simulation of precipitation has been evidenced to be difficult. The current study attempts to predict the seasonal precipitation over the coastal region and the Western Ghats of Karnataka. The relationship between summer monsoon precipitation (SMP) and SST is examined up to eight seasons by conducting the correlation analysis with three seasons that lag before the onset of the monsoon season. The significant and positively correlated lagged Niño indices with the SMP index are identified as the predictors. The selected predictors are used for predicting the SMP by using statistical models, the multiple linear regression model and the artificial neural network (ANN) model. The statistical models are based on the combined lagged indices and the principle component as the predictor. The results of the statistical models on comparison suggest that neural network models have a better predictive skill than the linear regression models. Neural network models with combined lagged indices being used as predictors are slightly better, but a few more climatic parameters must be verified and the usage of this method on other meteorological divisions of the West Coast of India needs to be further investigated. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an advanced machine learning regression technique recently developed to anticipate the prevalence of malaria in Tripura using a real-world data. The proposed structure uses nine different regression methods, such as multilayer perceptron (MLP), random forest, support vector regressor, gradient boosting regressor, Bayesian ridge, kernel ridge, extreme gradient boost regressor (XGB), light gradient boosting machine (LGBM regressor and linear regression, to predict malaria using the most affecting factors of malaria diseases, namely temperature, humidity, precipitation, month, and years as input. Furthermore, to opt out the best suited technology for malaria cases prediction, the range of value method (ROVM)–multi-criteria decision methods (MCDM) technique has been applied, considering various statistical measurements as criteria. Ultimately, a comparison of various MCDM techniques reveals that MLP, XGB, and RF emerge as the top three choices. MLP regression, with root-mean-square error (RMSE) value of 0.03357, yields the lowest RMSE value, and the coefficient of determination (R2) is 0.97616, yielding the maximum among other regressions. To effectively battle the illness in Tripura, it could be useful for continuing intervention tactics by governmental, profit and nonprofit organizations. [ABSTRACT FROM AUTHOR]

This research paper aimed to establish the optimal conditions for lead (Pb(II)) ions from industrial wastewater (IW) using response surface methodology and central composite design (CCD) approach. A fixed-bed column study was designed by using a novel and reusable biocomposite material, Amanita vaginata (A. vaginata) immobilized on Amberlite XAD-4 resin (A&XAD-4@AV), to find out biosorption behavior of Pb(II) ions. Various experimental variables such as optimum pH value, biocomposite material amount, flow rate, and Pb(II) ions initial concentration were investigated, which significantly affected the Pb(II) ions removal efficiency. The maximum Pb(II) removal efficiency of A&XAD-4@AV biocomposite material was determined as 11.2 mg g−1 under optimum conditions. The reusability of A&XAD-4@AV biocomposite material was found to be suitable. For checking model adequacy, a regression model was derived using CCD to predict the responses and analysis of variance (ANOVA) and lack of fit test was performed. It was observed that the quadratic model, which was controlled and proposed, originated from experimental design data. The ANOVA showed a high coefficient of determination (R2 = 0.9896, R2Adj = 0.9800, R2Pred = 0.9633). Finally, developed and optimized method was applied effectively to wastewater for determining Pb(II) ions levels. The current study indicated that the prepared A&XAD-4@AV is an efficient material to remove of Pb(II) ions from IW. [ABSTRACT FROM AUTHOR]

Aromatic heterocyclic compounds have become highly sought-after for their diverse medicinal and biological uses. Their synthesis and adaptability have led to a surge in research interest, as they play a crucial role in the creation of over 90% of groundbreaking medications. These compounds bridge the gap between biology and chemistry, driving significant scientific progress. Heterocycles are utilized across various fields, including pharmaceutical chemistry and biochemistry. This article introduces the development of quantitative structure–property relationship models using ve-degree-based Sombor indices. Some linear regression models have been fitted that can be used for prediction purposes. Specifically, these models aim to predict the fluorescence properties of aromatic heterocyclic species based on their structural features. Such models provide researchers with the ability to estimate the fluorescence behavior of new molecules without the need for experimental measurements. It is noted that the maximum excitation wavelength (λ e x ) on the Sombor index type four (ST4) has the highest coefficient of determinations ( R 2 = 0.872 ) and the smallest residual standard error ( s = 53.673 ). Thus, the physical property λ e x can be predicted by the sombor index ST4 at the best level. [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]

The Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent on rainfall patterns. The São Francisco River Basin (SFRB) is a critical component of the BES, playing a key role in electricity generation. However, climate extremes have increasingly impacted energy production in recent decades, posing challenges for HPP management. This study, explores the relationship between extreme precipitation events in the SFRB and two crucial energy variables: Stored Energy (STE) and Affluent Natural Energy (ANE). We analyze the spatial distribution and trends of 11 extreme precipitation indices and investigate the seasonality, trends, and correlations between these energy variables and the extreme indices. Our findings reveal downward trends in both ANE and STE. Additionally, we identify a seasonal pattern influenced by extreme precipitation rates at various time scales. The results indicate that it is possible to estimate ANE and STE efficiently by employing three machine learning (ML) algorithms (Random Forest, Artificial Neural Networks and k-Nearest Neighbors) using extreme precipitation data. These results offer valuable insights for the strategic planning and management of the BES, aiding in decision-making and the development of energy security. [ABSTRACT FROM AUTHOR]

Lean production philosophy, sustainability approach, and the fourth industrial phase are significant concerns for organizations and in an overall manner for the public. The study examined the relationship between lean production philosophy and sustainability approach in the context of the fourth industrial phase in the selected manufacturing firms. To address this, the study proposed an underlying regression model, with four hypotheses, to gauge and quantitatively estimate the relationship between lean production philosophy and sustainability approach in the context of the fourth industrial phase. Therefore, to mathematically affirm the model and hypotheses, the study gathered 528 valid surveys from selected manufacturing firms in Lagos State utilizing purposive sampling model. The study outcome shows that: (i) lean production philosophy is connected and associated with sustainability approach, and (ii) The fourth industrial phase has a moderating role that determines its functionality. This outcome is a significant decision framework that helps manufacturing organizations optimize their frameworks in line with the dynamics of the fourth industrial phase and sustainability. [ABSTRACT FROM AUTHOR]

Heart disease is one of the most serious health issues that people faced today. Almost 50 million people worldwide suffer from cardiovascular problems. Electrocardiogram (ECG) signals are more important for diagnosing and monitoring patients with various cardiovascular diseases. This article proposes a novel SANN (Siamese Adversarial Neural Network-based Cardiac Arrhythmia Detection) CAD model for detecting different arrhythmias. First, an optimized Savitzky-Golay (OS-G) digital filter model is used to enhance and smooth the ECG signal. OS-G filtering uses a Tunicate Swarm Optimization (TSO) algorithm to adjust the polynomial order and window size to preserve the characteristics of P, Q, R, S, T, and U waves. A hybrid feature extraction process called Pathfinding Least Absolute Shrinkage and Selection Vector Regression (PLASSVR), which integrates regression analysis and pathfinding optimization, is employed to extract the significant features. The extracted features are fed to a novel Siamese neural network based on the Generative Adversarial Network (SNN-GAN) to classify the signals for arrhythmia disease detection. Furthermore, the Gannet Optimization Algorithm (GOA) is applied in the SNN-GAN model to change the hyperparameters. The proposed SANN CAD model is implemented in the Python platform via the MIT-BIH Arrhythmia Database. The performance of the SANN-CAD model is evaluated against various evaluation criteria and compared to traditional classifiers, using optimization for matching and no optimization for matching. The maximum classification accuracy that the SANN CAD model achieves is 99.74% and 98.23% for with and without optimization, and is superior to conventional classifiers. [ABSTRACT FROM AUTHOR]

Introduction: Personal knowledge about the own disease, a key component of health literacy (HL), may have a considerable impact on treatment outcomes. The purpose of this study was to investigate whether the patients' knowledge about the surgical procedure, risks, and aftercare, as well as the satisfaction with the preoperative level of information, has an influence on the health-related quality of life (HRQoL) after primary total hip arthroplasty (THA). Materials and methods: A total of 176 patients (68.3 ± 10.3 years, 60.8% female) were evaluated. HRQoL was assessed prior to surgery as well as one and twelve months after THA using the 12-item Short Form Questionnaire. Following standardized surgical informed consent, HL was assessed preoperatively using a self-constructed quiz score, while information satisfaction was measured with a single-item rating scale. Sociodemographic and clinical characteristics, including pain (VAS), functionality (WOMAC), and psychological distress (PHQ-4), were also assessed at baseline. Multiple linear regression analyses were performed to examine whether HL, satisfaction with information, age, social class, WOMAC, VAS, and PHQ-4 predict HRQoL at one and twelve months post-surgery. Results: The average HL quiz score was 23 ± 5.1 out of a possible 33 points. Social class index significantly influenced HL (p < 0.001). A weak correlation between HL and age (r = 0.23, p = 0.01) and no correlation between HL and psychological distress (p = 0.868) were observed. One month after THA, physical HRQoL was significantly predicted by the WOMAC index (p = 0.031) and subjective satisfaction with information (p = 0.022), but not by HL. After twelve months, only the WOMAC was a significant predictor (p < 0.001) of physical HRQoL. Conclusion: Although subjective satisfaction with the patient's preoperative level of information had a significant effect on the physical HRQoL at one month after THA, the influence of osteoarthritis severity outweighed this effect after twelve months. HL had no direct influence on HRQoL. These results suggest that patient satisfaction, rather than knowledge, predicts HRQoL. [ABSTRACT FROM AUTHOR]

Several correlations are available to determine the shear wave velocity using cone penetration test (CPT) data. Available correlations are applied for the studied region, which shows the requirement for developing a new correlation for the study area. This study uses CPT, standard penetration test (SPT), and multichannel analysis of surface wave (MASW) data to formulate correlations for predicting void ratio (e) and shear wave velocity (VS). The estimated void ratio at various depths was taken from the SPT bore log available for the site. A regression model has been formulated for predicting e from normalized cone tip resistance (Qtn). In developing the shear wave velocity prediction model, two types of cone data are used: mechanical and electrical. In the prediction model of VS, various parameters, such as cone tip resistance (qc), soil behavior type index (IC), effective stress ( σ 0 ′ ), e, and depth (z), are considered. The correlation regarding shear wave velocity gives a good prediction with both CPT cones. A cone factor (KC) is introduced in the developed correlation for predicting ‘e’. The proposed correlations allow design soil parameters to be easily obtained from cone penetration test data. [ABSTRACT FROM AUTHOR]

Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia. [ABSTRACT FROM AUTHOR]

Particulate matter has a diverse range of effects on human health and climate due to which it has emerged as a key parameter in monitoring air quality. The current study explores and estimate the concentration of particulate matter (PM2.5) from MODIS AOD product over the city of Kolkata for a period of 3 years (2019–2021). PM2.5 concentration dataset was acquired from seven CPCB ground stations spread throughout Kolkata. Further, the study utilized the 1-km MODIS AOD product and meteorological parameters from MERRA-2. Considering the statistical analysis of data, four regression models were derived and considered for PM estimation. Daily estimated PM2.5 concentrations were compared against respective observations. The developed models were evaluated with the help of statistical methods. Model-2 based on the multi-linear regression equation was found to be the best fit model having a strong positive correlation between the estimated and observed PM2.5 values (R = 0.814). The root mean square error (RMSE) was estimated at 22.54 µg/m3. The estimated PM2.5 values were able to capture the trend of PM2.5 concentrations on the ground level. The normalized mean bias (NMB) value was − 0.315 and the mean absolute error was 18.94. The mean absolute percent error is estimated at around 5.16%. The results demonstrated that the developed model thus can be used to study the particulate matter concentration over areas where ground-based observation sites are sparse on the city level. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Wasser- und Abfallwirtschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The water-regulating capacity of nature-based solutions (NBSs) plays a crucial role in providing a full range of ecosystem services and enhancing the resilience of urban systems. This work focuses on the hydrological performance of a particular NBS, the so-called blue-green roof (BGR). The BGR is designed to collect infiltrated rainfall in a water storage layer beneath the soil to support vegetation maintenance, enhance evapotranspiration and cooling, and minimize runoff and drainage system load. The study aims to evaluate the hydrological performance of the BGR at global and event scale and, for the first time, to model climatic factors (easy to measure using common sensors) that affect its stormwater retention capacity. The data collected over 2 years and 2 months at a 5-min resolution from a pilot study in Central Italy were analysed. Additionally, a new climatic index called AWWP-x (Antecedent Wet Weather Period index) was introduced and calculated. Results show that the BGR has an overall relevant retention rate (67.1%), although the value depends on the rainfall of the observed period. Approximately 50% of the rainfall events did not produce any runoff, and during the dry season, all events were totally absorbed by the BGR. Four climatic variables were identified as significant factors for predicting BGR retention performance (R2 = 0.50). The results suggest that AWWP-130 (number of days to reach 130 mm cumulative precipitation) could be a possible proxy for the BGR stormwater retention rate. In general, this study demonstrates the potential for evaluating, planning, and designing NBSs by considering the annual and interannual climatic variability of the investigated specific location. [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 influence of the reamer's elastic guide plates on the quality of the machined hole is studied by means of a mathematical model. Recommendations are made for reducing the defects (fracturing) to an acceptable level. [ABSTRACT FROM AUTHOR]

Abstract Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease.

Implementing intelligent irrigation and adjusting the irrigation system is essential in today's agricultural system to control the amount of water required for the plant. This study focuses on data obtained from sensors measuring (soil temperature and humidity, temperature and humidity of ambient and light) and image processing of plant leaves. To analyze the data, two models were implemented including a regression model in SPSS software and another model by genetic programming in MATLAB 2018 software. The optimal model was a combined model of sensors and images in genetic programming with higher R2 and lower standard error of 0.88 and 0.03, respectively. This model was superior to the regression model which had an R2 and standard error of 0.86 and 0.21, respectively, so this optimal model was selected to adjust the microcontroller for the intelligent irrigation system. The following year by replanting the crop, the intelligent irrigation system was presented as the superior system with 11% water saving compared to the previous year (irrigation by the user). Also, no changes were observed in the yield and color indicators of the plant at the level of 5%, which indicates the superiority of the intelligent irrigation system and its high accuracy of this system. [ABSTRACT FROM AUTHOR]

The octopus fishery is of major economic interest in the Moroccan Mediterranean, exploited by fishing boats using mainly octopus jigs and coastal trawlers using atomic bottom trawls. The current study, carried out in 2019, aims to quantify and characterize the nominal and effective fishing effort by fleet, and to assess the exploitation level in the octopus fishery using surveys with fishermen and catch and fishing effort data analysis. Respectively for artisanal boats and coastal trawlers, the depth of exploited areas was 24.7 m and 110 m (between Hoceima and Saidia), 124 m (between Fnideq and Cala Iris). Likewise, the annual fishing effort was 45,592 days (35,346 effective days) and 6385 days (3467 effective days). The fishing effort conversion factors were 0.78 and 0.54, proving that artisanal boats are more efficient than coastal trawlers in terms of time strictly dedicated to fishing. The total standardized annual fishing effort (FT) was 69,394 fishing days (53,799 effective days). The changing relationship Catch-FT during 2010–2019 was analyzed using linear regression Catch = 1,063,728.02 + 28.1FT (R2 = 0.55), implying that the Maximum Sustainable Yield was exceeded since 2015. Catch per Unit Effort (CPUE) was influenced by FT, Gear, and Year variables as demonstrated by the multiple regression equation: CPUE =− 14,299 − 0.0012FT + 7.17Year − 22.82Gear (R2 = 0.82). These results might serve as baseline data in population dynamic studies contributing to octopus fishery management. [ABSTRACT FROM AUTHOR]

The objective of this paper was to examine the effect of such deposited copper electroforming process control parameters as the applied voltage, the process time, the filler content, and the copper sulfate concentration on the surface roughness. For this purpose, an electroforming setup was made using a copper sulfate electroforming bath. A full factorial method was used to design the experiments with four control factors, each of them of two levels. After conducting each experiment, the prepared copper electroformed samples were characterized using a contact profilemeter and measuring certain surface roughness parameters, namely, the average roughness, the root mean square roughness, the average maximum height of the profile, and the autocorrelation length. Finally, the regression modelling was used to obtain a predictive model based on the measurements data. Also, the Pareto ANOVA analysis was performed for each surface roughness parameter to evaluate the contribution ratio of the control factors. The results demonstrated that a different control factor is dominant for each surface roughness parameter. [ABSTRACT FROM AUTHOR]

This article experimentally examines the crashing performance and failure mechanisms of jute fiber (J)-reinforced epoxy/aluminum (Al) hybrid pipes with circular cutout. Wet wrapping by hand lay-up was used to manufacture the designed hybrid pipes, and they were tested under quasi-static axial loads. As crash indicators, hybrid pipes' initial peak load ( F ip ), total absorbed energy (AE), mean crash load ( F m ), specific energy absorption (SEA), and crash force efficiency (CFE) were evaluated. On these indicators, the effect of the number J-plies (P) and circular cutout parameters, i.e., diameter (D) and number (N) of holes, was evaluated. To make statistical predictions about the crash indicators, mathematical regression equations were used. Furthermore, the variance of analysis (ANOVA) was also adapted to determine the percent contribution of each parameter to crash indicators. The experimental outcomes revealed a significant relationship between the studied parameters and the crashing performance as well as the failure mechanisms. Results showed that D is the maximum impelling parameter on the values of F ip and AE with contribution percents of 50.73 and 62.18%, respectively, followed by P with contribution percents of 37.76 and 27.91%, respectively. While P is the highest influencing parameter on the values of F m , SEA, and CFE with contribution percents, respectively of 59.87, 45.77, and 61.98%, followed by D for F m and SEA with contribution percents of 34.66 and 34.83% but N for CFE with a percent of 27.05%. [ABSTRACT FROM AUTHOR]

Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models' pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease. [ABSTRACT FROM AUTHOR]

Incremental sheet forming (ISF) is a promising die-less metal forming process where a rotating tool applies incremental pressure on the sheet to be deformed. However, a lack of available knowledge about the effect of process parameters on the produced part hinders the process from being widely used in the industry. One aspect to be considered is the effect of process parameters on the mechanical properties of the final product. For instance, hardness is a mechanical property that indicates the resistance of a material to elastoplastic deformation. This work investigates the effects of tool rotational speed, vertical step down, and feed rate on the hardness of high carbon steel (AISI 1075) sheet. A full-factorial experimental design for the three process parameters establishes a set of 27 experiments for a cone-forming process. Micro-hardness of the material is found to increase considerably from 185 to 252 HV due to strain hardening during the ISF process. Tool rotational speed and feed rate parameters have an insignificant effect on the hardness (p values of 0.829 and 0.538, respectively). Vertical step down of the tool is identified as the most influential parameter on hardness, with a p value of 0.001. [ABSTRACT FROM AUTHOR]

Kawasaki disease (KD) is a pediatric vasculitis of unknown etiology which is mainly associated with the development of coronary artery aneurysms. The etiology of KD seems to be multifactorial, but there is rare research on the association between KD and potential environmental risk factors. So, we would like to examine the correlation between KD and potential environmental risk factors in West China. We included KD patients in Chengdu from 2015 to 2021 and analyzed the correlation between air pollution indexes and climate condition indexes. The autocorrelation of the data was eliminated by first-order difference, the risk factors were screened by stepwise regression with AIC criterion, and the multiple regression model was established. Random forest and Winsorize were used to test the robustness of the screening results, and it was found that particulate matter with a diameter less than or equal to 2.5 μm (PM2.5) had a significant positive effect on the incidence of KD. In addition, several variables were positively correlated with KD incidence, but not statistically significant. The GAM model was used to explore the nonlinear correlation between PM2.5 and KD incidence. The results showed that PM2.5 concentration was positively correlated with KD incidence, and the effects varied among different concentration levels of PM2.5. Fisher's exact test was used to explore the influence of PM2.5 on the incidence of coronary tumors. It is found that PM2.5 may be a risk factor for it. This study suggested that exposure to high concentrations of PM2.5 may significantly increase the risk of KD. The evidence for the association between other environmental factors and KD incidence, as well as the association between PM2.5 and coronary tumors, was limited and needed further verification. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) pollution is one of the most significant air quality concerns in industrial cities. The present study is an attempt to provide urban air pollution maps based on PM 2.5 parameters in Isfahan city in different seasons between 2018 and 2019 using ground-truth data and MODIS, which is capable of monitoring air quality at different scales (e.g., local, national, and global). Linear regression was established between the data obtained from the earth stations and Aerosol Optical Depth to produce an air pollution map based on PM 2.5 in Isfahan. Also, inverse distance weighting (IDW) was used to explore PM 2.5 concentrations and their spatial mapping by ground-level data. A spatial analysis of Isfahan's pollution levels revealed that, in most seasons of the year, the southern and central regions have higher levels of PM 2.5 pollution, while the eastern regions have lower levels of PM 2.5 pollution. Results showed that in 2019, summer had the highest and winter had the lowest R2. The results indicated that the MODIS images can be used for air quality monitoring. [ABSTRACT FROM AUTHOR]

The study focuses on computing the optimized foot profile for a walking leg mechanism using artificial neural network (ANN), genetic algorithm, and regression approaches. The technique adopted in this work is the benchmark approach and acts as a tool for complex problems. A mathematical model using regression and ANN is developed for the 8-link coplanar mechanism. Optimum link lengths are obtained to minimize the objective function (error). The output response is the foot length with a minimum foot height of 124 mm for obstacle clearance. A neural network is designed with seven neurons (one neuron/link) in the input layer. Optimum neurons in the hidden layer are determined based on the output obtained through simulation. A single neuron is used to represent the foot profile length at the output layer. The foot lengths obtained from the regression model and ANN are compared and validated with a genetic algorithm for the data sets of 100, 200, 300, 400, and 500. Simulation studies of the walking leg mechanism revealed a difference of 19%, 22.4%, and 5.23% in the foot profile by ANN and mathematical, ANN and regression model, and mathematical and regression approach respectively. This paper reveals that different approaches viz., ANN, mathematical and regression models generate dissimilar foot profiles. [ABSTRACT FROM AUTHOR]

Porosity is a key control index for ensuring the compaction quality of rockfill dams with asphalt concrete cores. The porosity of the asphalt mixture is an important factor for an asphalt concrete core dam to obtain adequate strength and control the core deformation. However, the compaction quality (i.e., porosity) of the asphalt core wall inevitably exhibits spatial differences during actual construction process. To analyze the quantitative relationship between the porosity of the asphalt concrete core and the Duncan-Chang model parameters, triaxial tests under different confining pressures were carried out with asphalt concrete samples of different porosities. Based on the test results, the applicability of the Duncan-Chang model was assessed. Aiming at the limitation of the Duncan-Chang model, which is unable to characterize the strain softening and shear dilatancy of materials, a modified constitutive model of the asphalt concrete core was proposed. The determination method of the proposed model parameters was provided and verified by triaxial test results. A regression relationship was established between the porosity of the core material and the parameters of the modified constitutive model. This study provides a prerequisite for conducting refined finite element analysis on the mechanical performance of the asphalt core rockfill dams, taking into account the spatial differences in compaction quality of the asphalt concrete core. [ABSTRACT FROM AUTHOR]

Under the dual influences of precipitation and human activities, urban runoff has significantly changed. It is important to evaluate how precipitation characteristics and human activities may affect city runoff in the aspect of alleviating urban flooding problems. The impact of precipitation concentration and human activities on urban runoff was studied by hydro-statistical methods, to provide a scientific basis for the evolution regularity of urban runoff and the urban flood control. The investigation of sudden alterations in runoff was carried out utilizing two approaches—the method of Mann–Kendall test and the technique of double accumulation curve. Then, a regression model was used to analyze the relationship between the precipitation concentration and runoff during different periods. Using the SCRCQ method, we examined how changes in runoff are influenced by the concentration of precipitation and human activities. This research took Guangzhou city as a case study, analyzing the alteration in runoff from 1970 to 2020. The results indicated a rise in runoff of 132.9 mm between 2013 and 2020, attributable to the combined impacts of precipitation concentration and human activities. Specifically, human activities contributed to a runoff increase of 141.4 mm, while the decrease in precipitation concentration resulted in a runoff decrease of 8.5 mm. Human activities displayed a significant contribution rate of 94% to runoff, whereas the contribution rate of precipitation concentration was -6%. This research provides a scientific basis for alleviating urban floods. [ABSTRACT FROM AUTHOR]

The accurate prediction of strip crown is the precondition of the shape preset model in hot strip rolling. In this study, a new hybrid strip crown forecasting model is proposed in combination with extreme learning machine (ELM) and the industrial data. The production data of 1780 mm hot strip rolling are collected by the on-site data acquisition system to form dataset. Principal component analysis (PCA) is used to reduce the dimension of the input data for modeling samples. To improve the prediction accuracy of ELM, an improved PSO based on S-curve decreasing inertia weight (SDWPSO) is proposed to optimize the initial weights and biases of ELM. Finally, the optimal ELM model and simple production dataset are used to establish a strip crown prediction model of hot strip rolling named PCA-SDWPSO-ELM. The comprehensive performance of the proposed hybrid PCA-SDWPSO-ELM prediction model is evaluated by MAE, MAPE and RMSE. The superiority of the proposed model is also proved by comparing the prediction results with those of the other three comparison models. The research shows that the hybrid PCA-SDWPSO-ELM method can solve the problem of nonlinear and strong coupling in traditional engineering. It is suitable for parameter prediction and optimization in the iron and steel manufacturing industry, especially in the process of shape control in hot strip rolling. [ABSTRACT FROM AUTHOR]

In this article, the discrete distribution named Poisson distribution was compounded with a continuous distribution, i.e., NXLindley distribution to generate the Poisson–NXLindley distribution. Its basic mathematical properties were explored and a general expression for its ordinary factorial moments, moment generating function, dispersion index, coefficient of variation, skewness, and kurtosis. The proposed distribution is over-dispersed since its mean is less than the variance. This feature opens a new opportunity to model over-dispersed count datasets. The maximum likelihood approach is used for the estimation of the unknown parameter of the proposed model. Three real-life datasets are used to show the applicability of the new distribution. Additionally, the count regression model based on the Poisson–NXLindley distribution is introduced. The new model efficiently analyzed considered datasets than the competitive one-parameter discrete distribution. [ABSTRACT FROM AUTHOR]

The efficient use of solar energy requires an accurate assessment of the incoming solar radiation. The study involves comparing the accuracy of monthly means of the global solar radiation flux from the ERA5 reanalysis database, the SYN1deg satellite-based observation database, climate reference data, and data of regression models for seven settlements in the east of Russia. The study employs two well-known regression models, including parameters of extraterrestrial solar radiation, total cloudiness, air humidity, minimum and maximum air temperature, atmospheric pressure, and a new regression model, which additionally includes parameters of low-level cloudiness and sun elevation angle. The accuracy of databases and regression models is evaluated by comparing their data with ground measurements of weather stations. The indices of the mean absolute error, root-mean-square error, and mean bias error are calculated. The comparison shows that the data of climate reference books for the period from 1937–1957 to 1980 have the smallest deviation from the estimates of the monthly mean flux of global solar radiation for 2006–2020 at most of the points discussed. The ERA5 monthly mean estimates of the global solar radiation flux are more accurate than the SYN1deg data at five of the seven points considered. The new regression model proposed in the study makes it possible to provide greater accuracy of monthly estimates of the global solar radiation flux compared to the data of SYN1deg and ERA5 for most of the points considered. [ABSTRACT FROM AUTHOR]

This study used a regression model, experimental data from a viscosity measurement device, and a computational technique to investigate the relationship between lubricant viscosity and surface-attached adhesion rates. Three types of used lubricants were identified and evaluated using viscosity and surface adhesion measuring instruments using three different sizes of steel balls, namely 3 mm, 3.5 mm, and 4 mm. The experimental results were then analysed using a simple linear regression model with parameter estimation. At a significance level of 0.05, the results revealed that the lubricant viscosity and the rate of surface adhesion were linearly correlated. The results of this research could be utilized by industry to control the usage of lubricating oil in industrial parts, make judgments about changing or analysing the quality of the heat-reducing lubricant at the interface between mechanical components, and so on. To maintain lubrication efficiency and increase the service life of industrial parts. As a result of fulfilling this desired goal, the regression equations generated during this study can be used to estimate surface-attached adhesion rates in other circumstances. The viscosity range utilised in this case study corresponds to the normal viscosity of the unused lubricant. The surface-attached adhesion rate can thus be accurately selected by manufacturers, and the lubrication effectiveness can also be monitored. This is another indirect waste reduction in the manufacturing chain. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a chronic disease characterized by producing abnormal levels of blood glucose concentration. Currently, the most widely accepted method for glucose monitoring is invasive, however, despite its great reliability, it can be uncomfortable and traumatizing for the youngest users. The objective of this study is to provide an alternative method that allows a non-invasive estimation of blood glucose levels with an elevated level of confidence. In this work, 187 records were performed on people without any declared pathology; the concentration of blood glucose and the amplitude of the PPG signals of 525 nm, 660 and 940 nm were measured simultaneously. 70% of the data was used to train a regression model based on a fine Gaussian support vector machine, while the remaining 30% is used to validate the results. The regression model using the support vector machine was able to locate 95.38% of the estimates with an error of less than 15%, showing a standard error of 7.01 mg/dL and a MARD of 6.99%. The model presented here allows non-invasive estimation of blood glucose levels with reliability comparable to minimally invasive devices currently on the market. [ABSTRACT FROM AUTHOR]

This paper describes an experimental investigation, modelling and optimization during abrasive water jet machining (AWJM) of glass fibre reinforced polymer composite. Four process parameters namely water pressure, stand-off distance, traverse rate and abrasive mass flow rate are considered to study their influence on maximum delamination length (Max. DLL), surface roughness (Ra) and kerf taper (Kt). The second-order regression models are developed for the maximum delamination length, surface roughness and kerf taper in AWJM of glass fibre reinforced polymer composite using response surface methodology based central composite design approach. From the regression models, it is revealed that delamination decreases with an increase in abrasive mass flow rate, with a decrease in traverse rate. Surface roughness decreases with increase in water pressure and decrease in traverse rate. Kerf taper decreases with increase in water pressure; and decrease in traverse rate and stand-off distance. Further, response surface methodology based desirability function is performed to minimize the Max. DLL, Ra and Kt and the desirability values were found for Ra = 0.936, Kt = 0.942 and Max. DLL = 1 with a combined desirability rating of 0.959 which was reasonably good and acceptable. From the confirmation test of multi-response optimization, it was obvious that the percentage error at optimum level of process parameters for Ra, Kt, and Max. DLL were less than 6.312%, 7.229%, and 4.318%, respectively. [ABSTRACT FROM AUTHOR]

Rutting is a typical distress of asphalt pavement related to material, structural, loading, and environmental conditions of the pavement. This work presents a thorough and targeted synthesis of literature on current predictive models for rutting development in asphalt pavement, including the mechanical model, empirical model, machine learning model, and their combinations. By introducing and comparing the characteristics, advantages, and limitations of different model types, we focused on suitable approaches that predict rutting given the available information in the corresponding studies. Furthermore, we conducted a practitioner survey to identify performance deterioration models used by various highway agencies for asphalt pavement and to capture insights and experiences of users on the existing models in terms of reliability, precision, input and output parameters, consideration of maintenance and rehabilitation history, implementation considerations, etc. This review sheds light on the developing trend of predictive models for rutting and other distresses of asphalt pavement. [ABSTRACT FROM AUTHOR]

In this paper, we establish the complete convergence of the weighted sums for martingale differences, which is the interesting supplements for some known results. As statistical applications, when errors are martingale differences, the least square estimator in the errors-in-variables regression model and the regression function estimator in nonparametric regression model are studied and their strong consistency is obtained. [ABSTRACT FROM AUTHOR]

To solve the problems of low evaluation accuracy and satisfaction in the complex evaluation index differences of hybrid education models in schools, this paper designs a multimodal teaching quality evaluation for hybrid education based on stepwise regression analysis. The proposed method introduces a stepwise regression analysis algorithm by constructing multimodal reliability fusion, and gradually analyzes the evaluation indicators of hybrid teaching modes based on a multiple linear regression model. By combining sensitivity and specificity, ensure the accuracy of evaluation methods and achieve teaching quality evaluation of hybrid education models. The experimental results show that for the hybrid teaching mode, the satisfaction level of the quality evaluation method using multimodal teaching is above 94.7 points, the evaluation efficiency reaches 1.1s, the evaluation accuracy reaches 97.0%, and the sensitivity and specificity reached above 0.94, which has a superior teaching quality evaluation effect. The proposed method effectively improves the accuracy of teaching quality evaluation in the hybrid joint education model, and has feasibility and effectiveness, which can provide reliable reference for schools. [ABSTRACT FROM AUTHOR]

The Gini index does not give a strictly consistent scoring function. Therefore, simply maximizing the Gini index may lead to a wrong model choice. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for strictly consistent scoring if we restrict it to the class of auto-calibrated regression models. That is, on the class of auto-calibrated models we know that the true model maximizes the Gini index. [ABSTRACT FROM AUTHOR]