Search Results (634)

The accurate prediction of the state of health (SOH) for lithium-ion batteries is a key factor for improving the performance of battery management systems (BMS). However, traditional point prediction methods are difficult to effectively eliminate errors due to the uncertainty of variables and application environments. This paper presents a model for predicting the interval of lithium-ion batteries based on health indicators (HIs) during charging, which addresses the limitations of current point prediction in practical applications. First, twelve HIs are extracted from the current and voltage variables of the charging process. Secondly, feature selection is performed by random forest (RF) training, and the selected HIs are dimensioned using partial least squares (PLS). Finally, a long short-term memory network (LSTM) combined with quantile regression (QR) is used to derive the quantile values of the prediction points and each quantile is employed as input information for Gaussian kernel density estimation (KDE) to obtain the SOH probability density distribution. The experimental results based on the NASA PCOE Li-ion battery dataset and CALCE Li-ion battery dataset show that the SOH interval coverage is more than 90% and the average width of the interval is less than 0.294. Full article

Abundant information exists on fluoride intake and excretion in populations exposed to fluoridated water, but not fluoridated salt, where fluoride is eaten through a combination of foods and beverages. This study assessed associations between dietary patterns, fluoride intake and excretion in Mexican women exposed to fluoridated salt. We estimated dietary fluoride intake and excretion (mg/day) from 31 women using 24-h recalls (ASA24) and 24-h urine collections (HDMS diffusion method) and assessed agreement among both estimates of exposure with a Bland-Altman plot. Dietary patterns among the sample were explored by Principal Component Analysis and associations between these patterns and both fluoride intake and excretion were estimated. using Quantile Regressions. Median dietary fluoride intake and excretion were 0.95 and 0.90 mg/day, respectively, with better agreement at values below 1.5 mg/day. We identified three dietary patterns: “Urban Convenience”, “Plant-based” and “Egg-based”. The “Urban Convenience” pattern, characterized by dairy and convenience foods was associated with an increase of 0.25 mg and 0.34 mg of F in the 25th and 50th percentiles of intake respectively, (p < 0.01), and a marginal 0.22 mg decrease in urinary fluoride (p = 0.06). In conclusion, in this sample of Mexican women, a dietary pattern rich in dairy and convenience foods, was associated with both fluoride intake and excretion. Full article

Under the assumption of missing response data, empirical likelihood inference is studied via composite quantile regression. Firstly, three empirical likelihood ratios of composite quantile regression are given and proved to be asymptotically ${\chi }^2$. Secondly, without an estimation of the asymptotic covariance, confidence intervals are constructed for the regression coefficients. Thirdly, three estimators are presented for the regression parameters to obtain its asymptotic distribution. The finite sample performance is assessed through simulation studies, and the symmetry confidence intervals of the parametric are constructed. Finally, the effectiveness of the proposed methods is illustrated by analyzing a real-world data set. Full article

Introduction/Aim: Central sensitivity to thyroid hormones refers to the responsiveness of the hypothalamic–pituitary–thyroid (HPT) axis to changes in circulating free thyroxine (fT4). Although dose–response relationships between thyroid hormones per se and urinary iodine (UI) levels have been observed, central sensitivity to thyroid hormones in relation to UI remains unexplored. The aim of the present study was to evaluate central sensitivity to thyroid hormones (by means of the Thyroid Feedback Quantile-based Index [TFQI], which is a calculated measure, based on TSH and fT4, that estimates central sensitivity to thyroid hormones) in pregnancy and to assess whether it differs according to gestational age and/or iodine intake. Materials and Methods: One thousand, one hundred and two blood and urine samples were collected from pregnant women (with a mean age ± SD of 30.4 ± 4.6 years) during singleton pregnancies; women with known/diagnosed thyroid disease were excluded. Specifically, TSH and fT4, anti-thyroid peroxidase antibodies and UI were measured in each trimester and at two months postpartum, while the TFQI was calculated for all the study samples. After the elimination of outliers, statistical analysis was conducted with analysis of variance (ANOVA) for the variables versus time period, while Pearson’s correlation was used to assess the TFQI versus UI. Results: The mean TFQI index ranged from −0.060 (second trimester) to −0.053 (two months postpartum), while the corresponding UI was 137 and 165 μg/L, respectively. The TFQI-UI correlation was marginally negative (Pearson r: −0.323, p: 0.04) and significantly positive (r: +0.368, p: 0.050) for UI values over 250 μg/L, in the first and the second trimesters of pregnancy, respectively. Discussion: The TFQI is a new index reflecting central sensitivity to thyroid hormones. A lower TFQI indicates higher sensitivity to thyroid hormones. In our sample, the TFQI was mainly positively related to iodine intake in the second trimester of pregnancy (following the critical period of organogenesis). Thus, the observed changes in the TFQI may reflect the different ways of the central action of thyroid hormones, according to the phase of pregnancy. These results have the potential to enhance our comprehension of the changes in the HPT axis’ function via variations in central sensitivity to thyroid hormones and its interplay with nutritional iodine status during pregnancy. Full article

In various applications, observed variables are missing some information that was intended to be collected. The estimations of both loading and path coefficients could be biased when ignoring the missing data. Inverse probability weighting (IPW) is one of the well-known methods helping to reduce bias in regressions, while belonging to a promising but new category in structural equation models. The paper proposes both parametric and nonparametric IPW estimation methods for dynamic structural equation models, in which both loading and path coefficients are developed into functions of a random variable and of the quantile level. To improve the computational efficiency, modified parametric IPW and modified nonparametric IPW are developed through reducing inverse probability computations but making fuller use of completely observed information. All the above IPW estimation methods are compared to existing complete case analysis through simulation investigations. Finally, the paper illustrates the proposed model and estimation methods by an empirical study on digital new-quality productivity. Full article

The field of statistical extreme value theory (EVT) focuses on estimating parameters associated with extreme events, such as the probability of exceeding a high threshold or determining a high quantile that lies at or beyond the observed data range. Typically, the assumption for univariate data analysis is that the sample is complete, independent, identically distributed, or weakly dependent and stationary, drawn from an unknown distribution F. However, in the context of lifetime data, censoring is a common issue. In this work, we consider the case of random censoring for data with a heavy-tailed, Pareto-type distribution. As is common in applications of EVT, the estimation of the extreme value index (EVI) is critical, as it quantifies the tail heaviness of the distribution. The EVI has been extensively studied in the literature. Here, we discuss several classical EVI-estimators and reduced-bias (RB) EVI-estimators within a semi-parametric framework, with a focus on RB EVI-estimators derived from generalized means, which will be applied to both simulated and real survival data. Full article

In lifetime tests, the waiting time for items to fail may be long under usual use conditions, particularly when the products have high reliability. To reduce the cost of testing without sacrificing the quality of the data obtained, the products are exposed to higher stress levels than normal, which quickly causes early failures. Therefore, accelerated life testing is essential since it saves costs and time. This paper considers constant stress-partially accelerated life tests under progressive Type II censored samples. This is realized under the claim that the lifetime of products under usual use conditions follows Vtub-shaped lifetime distribution, which is also known as log-log distribution. The log–log distribution is highly significant and has several real-world applications since it has distinct shapes of its probability density function and hazard rate function. A graphical description of the log–log distribution is exhibited, including plots of the probability density function and hazard rate. The log–log density has different shapes, such as decreasing, unimodal, and approximately symmetric. Several mathematical properties, such as quantiles, probability weighted moments, incomplete moments, moments of residual life, and reversed residual life functions, and entropy of the log–log distribution, are discussed. In addition, the maximum likelihood and maximum product spacing methods are used to obtain the interval and point estimators of the acceleration factor, as well as the model parameters. A simulation study is employed to assess the implementation of the estimation approaches under censoring schemes and different sample sizes. Finally, to demonstrate the viability of the various approaches, two real data sets are investigated. Full article

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty. Full article

Utilizing panel quantile regression on an unbalanced dataset for 30 European countries from 2008 to 2018, this article seeks to investigate how the age of cabinet members influences e-waste recycling rates in European countries, alongside other relevant factors. Prior research has overlooked the age of cabinet members as a determinant of e-waste recycling. By addressing this gap, this study introduces a novel factor that could impact e-waste recycling rates. Thus, this study provides insights into how the demographic characteristics of parliament members, particularly the age of cabinet members, impact environmental improvement, as indicated by e-waste recycling rates. Estimation results indicate the existence of a nonlinear relationship (i.e., an inverted U-shaped environmental Kuznets curve) between the age of cabinet members and the e-waste recycling rate, rather than a linear relationship. The calculated average turning point age is 49.087, indicating that the e-waste recycling rate increases as the age of cabinet members rises until reaching 49.087, after which the e-waste recycling rate declines. Overall, this study underscores the importance of the demographic characteristics of parliament members, particularly the age of cabinet members, in shaping e-waste recycling policies and environmental sustainability efforts. It emphasizes that the age of cabinet members and generational perspectives can influence their awareness, understanding, and commitment to addressing contemporary challenges such as e-waste. Full article

The main objective of the paper is to assess the relationship between firms’ financial resilience and firms’ strategic sustainable development vulnerabilities, in the context of implications of the COVID-19 pandemic on firms’ business environment. Background: The last decade has emphasized an increase in business models’ uncertainty and risk exposure. The COVID-19 pandemic has highlighted the awareness in this direction, especially in a changing context, that looks more and more for corporate sector operations’ orientation towards sustainable development. The question we would address in this paper is how the nexus between corporate sustainability performance and corporate financial resilience is affected by management decision through process improvements, product quality assurance, or managers’ preference to improve corporate financials by earnings management practice instead, especially in the context of specific corporate financial risk management. Methods: The data are extracted from the Refinitiv database. The sample is limited to 275 European Union listed firms, selected based on data availability. The empirical analysis consists of an OLS multiple regression. For robustness purposes, a quantile regression model is estimated as well. Results: The approach considers implications of the pandemic on firms’ business environment and earnings management accounting based policies and strategies as well. The result suggests that alignment to sustainability frameworks lead to the deterioration of firms’ financial resilience. Similar results show the negative impact of firms’ financial vulnerability (credit default risk) on firms’ financial resilience. Instead, the risk of bankruptcy, firms’ liquidity, or high product quality and business process improvement determine the positive impact on firms’ financial resilience. Conclusions: The study highlights several insights both for management and policy makers. First, the results underline the relevance of management’s choice for earnings management on ensuring firms’ financial resilience, which ask for better corporate governance and high-quality and effective institutional regulatory and enforcement mechanisms. Second, the paper brings evidence on the impact of the COVID-19 pandemic on firms’ financial sustainable development. Third, the study emphasizes the importance of the efforts of corporate process improvements and high-quality products on generating value-add, by looking on the relevance of those drivers on the level of corporate economic value-add, a measure that limits the impact of discretionary management accrual-based accounting choices on our discussion. Full article

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. Full article

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm², droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h⁻¹), and spraying norm (250–400 L h⁻¹). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm² (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R² = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R² = 0.437–0.503). Full article

Various stakeholders seek effective methods to communicate the potential impacts of tropical cyclone (TC) rainfall and subsequent flood hazards. While current methods, such as Intensity–Duration–Frequency curves, offer insights, they do not fully capture TC rainfall complexity and variability. This research introduces an innovative workflow utilizing GPM-IMERG satellite precipitation estimates to cluster TC rainfall spatial–temporal patterns, thereby illustrating their potential for flood hazard assessment by simulating associated flood responses. The methodology is tested using rainfall time series from a single TC as it traversed a 500 km diameter buffer zone around Dominica. Spatial partitional clustering with K-means identified the spatial clusters of rainfall time series with similar temporal patterns. The optimal value of K = 4 was most suitable for grouping the rainfall time series of the tested TC. Representative precipitation signals (RPSs) from the quantile analysis generalized the cluster temporal patterns. RPSs served as the rainfall input for the openLISEM, an event-based hydrological model simulating related flood characteristics. The tested TC exhibited three spatially distinct levels of rainfall magnitude, i.e., extreme, intermediate, and least intense, each resulting in different flood responses. Therefore, TC rainfall varies in space and time, affecting local flood hazards; flood assessments should incorporate variability to improve response and recovery. Full article

Global warming has become a big problem around the world, and it is because of what people do. As a possible answer, countries are looking for ways to keep their economies growing and invest in technologies that use clean energy. Therefore, the notion of carbon neutrality has emerged as a crucial policy strategy for nations to attain sustainable development. This study expands the existing discussions on carbon neutrality by investigating the influence of key factors, including green innovation, financial development, natural resources depletion, trade openness, institutional quality, growth, and urbanization on the progress made towards attaining a carbon neutral state in the BRICS nations. This study considers the Method of Moment Quantile-Regression (MM-QR) and Prais–Winsten correlated panel corrected standard errors (PCSEs) estimators to investigate the study objectives over the period of 1990–2021. Under the investigated outcomes, this study validated the significant role of urbanization and growth in carbon neutrality. On the other hand, this study finds the positive role of openness, green innovation, resource depletion, institutional quality, and financial development on environmental deterioration. However, under a systematic analysis, this study utilizes different proxies of the financial sector, for instance, financial complexity, financial efficiency, financial stability, and domestic credit by financial sector, and provides interesting outcomes. Based on these outcomes, this study also provides suggestions to attain desired levels of sustainability. Full article

China’s agricultural sector is transitioning from extensive management to intensive management, and land transfer brings about changes in land use and management methods, which may encourage the agricultural sector to enter a sustainable development track, but this mechanism has not been effectively proven. Using the SBM-GML index to construct a green total factor productivity index to measure the level of sustainable agricultural development in each province (or autonomous region or municipality directly under the central government) and provincial panel data from 2010 to 2022, we applied a panel interactive fixed-effects model to empirically test the impact of land transfer on sustainable agricultural development, with a focus on analyzing the heterogeneity and related mechanisms of this impact. The results indicate that (1) land transfer significantly promotes sustainable agricultural development, and this conclusion still held true after robustness tests such as controlling for regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation. (2) The mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development. (3) The heterogeneity analysis found that the higher the quantile of agricultural development level is, the weaker the role of land transfer in promoting sustainable agricultural development, indicating that land transfer has a greater impact on areas with poor agricultural development foundations, and areas with poor agricultural development foundations are more likely to obtain sustainable development space through land transfer. The impact of different land transfer methods and land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer methods such as exchange and transfer, market-oriented transfer methods such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development. Full article