Search

Electric vehicles (EVs) hold promise for curbing emissions and promoting sustainable urban development in India, aligning with Sustainable Development Goals (SDGs). However, their uptake remains below expectations. This study delves into factors influencing EV adoption, exploring socioeconomic determinants, technical specifications, cost considerations, and policy impacts. It also assesses the role of charging infrastructure accessibility, demographic disparities, and battery end‐of‐life perceptions. The research identifies key socio‐economic factors driving consumer choices and perceptions of EVs through a comprehensive survey and logistic regression analysis. The findings will guide policymakers and businesses in developing effective strategies to promote EV use, supporting SDGs related to sustainable cities, communities, and economic growth. The insights gained from this study can inspire private sector growth and innovation, leading to new business opportunities that benefit companies and consumers. This research represents a crucial milestone in advancing sustainable transportation solutions in India and creating a better future. [ABSTRACT FROM AUTHOR]

Locusts are globally recognized as major pest threats. In the first half of the 20th century, the South American locust caused great economic losses. After the implementation of preventive management, large‐scale upsurges ceased. In 2015, resurgence of S. cancellata led to swarms affecting northern Argentina, Paraguay, and Bolivia, prompting control agencies to address an almost forgotten problem. After six decades without a major locust outbreak, there were limited and outdated studies on this species. This study aims to identify key environmental factors associated with the spatial distribution of S. cancellata oviposition sites. We focus on explanatory variables that represent physical and chemical properties of soil and vegetation cover. To understand the relationships between each potential explanatory variable and the presence‐absence of S. cancellata oviposition sites, we first performed regression analyses applying a linear and quadratic structure for each explanatory variable. Then, we performed comparisons of logistic regression models in a multi‐model inference framework, where CAIC and weights of evidence were analysed. Our results show that the South American locusts chose to lay their eggs in areas with a low proportion of natural forest and flooded grasslands and a high proportion of non‐vegetated areas, where the soils are flat, with neutral pH, and low salinity. We also determined that an increase in the proportion of cultivated areas is associated with an increase in the probability of breeding presence of this species. The locust's habitat falls within the Dry Chaco, a global deforestation hotspot, evidencing a rapid replacing of forests for plantations. Since both the diminish of forest and the increase in cultivated areas are associated with an increase in oviposition sites, we consider that breeding areas will likely increase. The results found herein can be used to map the potential breeding habitats to help preventive management against the South American locust. [ABSTRACT FROM AUTHOR]

Rice grain discoloration (RGD) is a disease of complex aetiology for which there are no resistant varieties. Due to the need to better define the environmental conditions that favour the disease, the aims of this work were to (i) identify the predominant fungi associated, (ii) determine the meteorological variables most closely related, and (iii) develop preliminary weather‐based models to predict binary levels of RGD incidence. After analysing 123 rice grain samples under natural infection conditions from rice‐cropping regions throughout Corrientes province, Argentina, we found that RGD was mainly associated with Alternaria padwickii (14.2%) and Microdochium albescens (13.7%). The strongest associations between weather variables and RGD incidence were observed in a susceptible critical period (Scp) that extended from the rice flowering stage until 870 accumulated degree days (Scp lasting 32 days, ±7 days). The binary response logistic model including the weather variables DPrecT (which combined the effect of the simultaneous daily occurrence of precipitation lower than 12 mm and air temperature between 13 and 28°C), and DDMnT (sum of the exceeding amounts of daily min temperature from 23°C), was the most appropriate, showing prediction accuracy (PA) values of 84.6%. The univariate model that included DPrecT presented a PA of 82.1%. The logistic regression techniques here used to develop weather‐based models to estimate the probabilities of occurrence of binary levels of RGD can not only help to clarify and quantify the environmental effect on the development of RGD but also be useful tools to be included in future management strategies. [ABSTRACT FROM AUTHOR]

Objective: To analyze the changes in the serum levels of CRH and 5‐HT in women with postpartum depression (PPD) and to study the value of the CRH/5‐HT ratio for the prediction of PPD. Methods: This prospective study recruited pregnant women from the Fourth Affiliated Hospital, China Medical University between January 2017 and October 2019. Women were considered for inclusion if they had no history, or no current evidence, of a psychiatric disorder. All women were assessed at postpartum day 10 with the Edinburgh Postnatal Depression Scale (EPDS). Blood samples were obtained at 20 weeks of pregnancy and the levels of CRH and 5‐HT were determined by radioimmunoassay and ELISA. Associations between EPDS score, the demographic variables, and hormone levels were identified using bivariate logistic regression models. Results: A total of 185 women were included. We found that the serum level of both CRH and 5‐HT was significantly correlated with EPDS score; the AUC for CRH was 0.79, and 5‐HT was 0.85, which indicated that both CRH and 5‐HT are a reliable biomarker for PPD. The AUC, specificity, and sensitivity of CRH/5‐HT were 0.92, 0.86, and 0.95, respectively, which were better than those of CRH or 5‐HT individually. Conclusions: We believe that the serum CRH/5‐HT ratio is an excellent biomarker for the prediction of PPD. Serum CRH/5‐HT ratio was more reliable at predicting postpartum depression than serum CRH or 5‐HT levels individually. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high‐frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding. [ABSTRACT FROM AUTHOR]

Salmonella Heidelberg and Salmonella Typhimurium are among the most common serotypes responsible for human salmonellosis in Ontario. Introduction of the Ontario Investigation Tools (OIT) in 2014 allowed for standardized case investigation and reporting. This study compared the risk factors and symptomatology for sporadic S. Heidelberg and S. Typhimurium cases reported in Ontario in 2015, following implementation of the OIT. Multilevel logistic regression models were applied to assess associations between serotype and individual‐level demographic characteristics, exposures and symptoms for sporadic confirmed cases of S. Heidelberg and S. Typhimurium in Ontario in 2015. There were 476 sporadic cases of S. Typhimurium (n = 278) and S. Heidelberg (n = 198) reported in Ontario in 2015. There were significant associations between the odds of the isolate from a case being one of these serotypes, and travel, consumption of sprouts (any type), contact with reptiles and development of malaise, fever or bloody diarrhoea. The S. Typhimurium and S. Heidelberg cases differed in both symptom presentation and risk factors for illness. Case–case comparisons of Salmonella serotypes have some advantages over case–control studies in that these are less susceptible to selection and recall bias while allowing for rapid comparison of cases to identify potential high‐risk exposures that are unique to one of the serotypes when compared to the other. Comparing cases of two different Salmonella serotypes can help to highlight risk factors that may be uniquely associated with one serotype, or more strongly associated with one serotype compared to another. This information may be useful for understanding relative source attribution between common serotypes of Salmonella. [ABSTRACT FROM AUTHOR]

A prognostic model is well calibrated when it accurately predicts event rates. This is first determined by testing for goodness of fit with the development dataset. All existing tests and graphic tools designed for the purpose suffer several drawbacks, related mainly to the subgrouping of observations or to heavy dependence on arbitrary parameters. We propose a statistical test and a graphical method to assess the goodness of fit of logistic regression models, obtained through an extension of similar techniques developed for external validation. We analytically computed and numerically verified the distribution of the underlying statistic. Simulations on a set of realistic scenarios show that this test and the well-known Hosmer-Lemeshow approach have similar type I error rates. The main advantage of this new approach is that the relationship between model predictions and outcome rates across the range of probabilities can be represented in the calibration belt plot, together with its statistical confidence. By readily spotting any deviations from the perfect fit, this new graphical tool is designed to identify, during the process of model development, poorly modeled variables that call for further investigation. This is illustrated through an example based on real data. [ABSTRACT FROM AUTHOR]

Calibration is one of the main properties that must be accomplished by any predictive model. Overcoming the limitations of many approaches developed so far, a study has recently proposed the calibration belt as a graphical tool to identify ranges of probability where a model based on dichotomous outcomes miscalibrates. In this new approach, the relation between the logits of the probability predicted by a model and of the event rates observed in a sample is represented by a polynomial function, whose coefficients are fitted and its degree is fixed by a series of likelihood-ratio tests. We propose here a test associated with the calibration belt and show how the algorithm to select the polynomial degree affects the distribution of the test statistic. We calculate its exact distribution and confirm its validity via a numerical simulation. Starting from this distribution, we finally reappraise the procedure to construct the calibration belt and illustrate an application in the medical context. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The present study describes approaches to improve the performance of empirical models developed from a large nationwide data set to predict sediment toxicity from chemistry for regional applications. The authors developed 4 multiple chemical (PMax) models selected from individual chemical models developed using 1) a previously published approach applied to the nationwide data set; 2) a broader array of response and explanatory variables (e.g., different normalization approaches and toxicity classifications) applied to the nationwide data set; 3) a data set from the New York/New Jersey, USA, region; and 4) both nationwide and regional data sets. The models were calibrated using the regional data set. Performance was tested using an independent data set from the same region. The performance of the final PMax model developed using the calibration process substantially improved over that of the uncalibrated PMax model developed using the nationwide data set. The improvements were achieved by selecting the best performing individual chemical models and eliminating those that performed poorly when applied together. Although the best performing PMax model included both nationwide and region-specific models, the performance of the PMax model derived using only nationwide models was nearly as good. These results suggest that calibrating nationwide models to a regional data set may be both a more efficient and effective approach for improving model performance than developing region-specific models. Environ Toxicol Chem 2014;33:708-717. © 2013 SETAC. This article is a US Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Objective To determine the diagnostic performance of ultrasound-based simple rules, risk of malignancy index (RMI), two logistic regression models (LR1 and LR2) and real-time subjective assessment by experienced ultrasound examiners following the exclusion of masses likely to be judged as easy and `instant' to diagnose by an ultrasound examiner, and to develop a new strategy for the assessment of adnexal pathology based on this. Methods 3511 patients with at least one persistent adnexal mass preoperatively underwent transvaginal ultrasonography to assess tumor morphology and vascularity. They were included in two consecutive prospective studies by the International Ovarian Tumor Analysis (IOTA) group: Phase 1 (1999-2005), development of the simple rules and logistic regression models LRI and LR2, and Phase 2, a validation study (2005-2 007). Results Almost half of the cases (43%) were identified as `instant' to diagnose on the basis of descriptors applied to the database. To assess diagnostic performance in the more difficult `non-instant' masses, we used only Phase 2 data (n = 1036). The sensitivity of LR2 was 88%, of RMI it was 41% and of subjective assessment it was 87%. The specificity of LR2 was 67%, of RMI it was 90% and of subjective assessment it was 86%. The simple rules yielded a conclusive result in almost 2/3 of the masses, where they resulted in sensitivity and specificity similar to those of real-time subjective assessment by experienced ultrasound examiners: sensitivity 89 vs 89% (P = 0.76), specificity 91 vs 91% (P = 0.65). When a three-step strategy was applied with easy `instant' diagnoses as Step 1, simple rules where conclusive as Step 2 and subjective assessment by an experienced ultrasound examiner in the remaining masses as Step 3, we obtained a sensitivity of 92% and specificity of 92% compared with sensitivity 90% (P = 0.03) and specificity 93% (P = 0.44) when using real-time subjective assessment by experts in all tumors. Conclusion A diagnostic strategy using simple descriptors and ultrasound rules when applied to the variables contained in the IOTA database obtains results that are at least as good as those obtained by subjective assessment of a mass by an expert. [ABSTRACT FROM AUTHOR]

1. Knowledge of the ecology of carabid species is largely restricted to adults, although larval mortality is assumed to be the key factor in overall mortality. As low-mobility larvae are unable to avoid unfavourable conditions, habitat selection of reproducing adults should be clearly affected by the habitat factors which determine offspring performance. 2. The present study examines the key habitat factors governing the distribution patterns of Bembidion velox larvae and adults on the river banks of the River Elbe by means of habitat suitability models. The validity of the determined habitat factors for offspring performance and survival was tested in laboratory experiments. 3. In the field, B. velox adults as well as larvae show a strong association with semi-terrestrial, sandy, open soil habitats. 4. In the laboratory, overall mortality of larvae reared in different substrates was lowest in sand of medium grain size mixed with fine and coarse sand. The first larval instars in particular reacted sensitively to variations in grain size. Furthermore, flood resistance of eggs was demonstrated, as 90% of larvae hatched under permanently flooded conditions. 5. Short development times were recorded, with 4–7 days for hatching of young larvae from eggs after oviposition and approximately 28 days for the development of adults from newly hatched larvae. This probably increases the survival probability for the pre-imaginal stages in dynamic habitats. 6. In conclusion, it can be said that reproductive success is strongly dependent on oviposition site selection by adults as this reflects the ecological demands of the immature stages. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of mortality in the developed world, and prognostic assessment of cancer patients is indispensable in medical care. Medical researchers are accustomed to using regression models to predict patient outcomes. Neural networks have been proposed as an alternative with great potential. Nonetheless, empirical evidence remains lacking to support the application of this technique as the appropriate method to investigate cancer prognosis. Utilizing data on patients from two National Cancer Institute of Canada clinical trials, we compared predictive accuracy of neural network models and logistic regression models on risk of death of limited-stage small-cell lung cancer patients. Our results suggest that neural network and logistic regression models have similar predictive accuracy. The distributions of individual predicted probabilities are very similar. On occasion, however, the prediction pairs are quite different, suggesting that they do not always give the same interpretations of the same variables. [ABSTRACT FROM AUTHOR]

Summary Aim We modelled the relationship of breeding evidence for five species of forest songbirds (ruby-crowned kinglet (Regulus calendula ) Blackburnian warbler (Dendroica fusca ), black-throated blue warbler (Dendroica caerulescens) , bay-breasted warbler (Dendrioca castanea ) and Connecticut warbler (Oporornis agilis )) and a variety of macro-climate variables to examine the importance of climate as a factor determining distribution of breeding in these species and to assess the usefulness of spatial predictions generated from these models. Location Modelling was conducted over the entire province of Ontario, Canada, an area of ≈900,000 km2 . Methods Data on the distribution of breeding in the province was derived from the Breeding Bird Atlas of Ontario. We used logistic regression to model the relationship between the probability of breeding (assessed in 10 km×10 km blocks) and estimates of a variety of climate variables at the same scale. Models were selected that had the least number of explanatory variables while at the same time having close to the best possible classification accuracy. Results The final models for these five species had from one to six explanatory variables and an overall concordance of 70.4% to 86.3% indicating a good classification accuracy. Results from subsampling 50% of the original data ten times indicate that (1) the classification accuracy of the model for data used to generate the model is not very sensitive to the specific observations used to generate the model (2) the classification accuracy of test data is close to the classification accuracy of the model data and (3) the classification accuracy of the test data is not dependent on the specific observations used to generate the model. We generated a spatial prediction of the probability of occurrence of each species for Ontario using the relationships defined by the logistic regression models and using 1 km gridded estimates of the necessary climate... [ABSTRACT FROM AUTHOR]