Against the backdrop of global warming, rapid urbanization has caused the aggregation of urban building spaces and the heat island effect is becoming increasingly serious, hindering sustainable urban development. In order to investigate the potential and methods of green roofs in different types of neighborhoods to mitigate the urban heat island effect, this study used multivariate data for surface temperature inversion and local climate zone (LCZ), and the potential of green roofs to reduce the heat island effect was evaluated by combining LCZ zoning and ENVI-met prediction model. Finally, a multi-scenario analysis with economic factors was conducted to derive the optimal implementation path for green roofs. The results show that in LCZs 1-9, the green roof can reduce the daytime average air temperature by a maximum of 0.41 °C for 0.5 m of the LCZ8 roof and 0.37 °C for 1.2 m of the LCZ6 pedestrian. Based on the surface cooling efficiency of LCZ green roofs get the best green roof construction order: LCZ3, LCZ6, LCZ8 > LCZ2, LCZ5, LCZ7 > LCZ1, LCZ4, LCZ9. The construction of green roofs for the heat island areas within the fifth ring road of Beijing can reduce the area of high-temperature and sub-high-temperature zones by 52.55% and 29.17%, respectively, compared with the area without green roof construction. The study clarifies the technical methodology system of cooling efficiency of green roofs in different types of neighborhoods and the reduction of the urban-scale heat island effect, which provides a reference for the planning of green roofs for urban buildings.

The Tucker-Lewis index (TLI; Tucker & Lewis, 1973), also known as the non-normed fit index (NNFI; Bentler & Bonett, 1980), is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research. It augments information provided by other indices such as the root-mean-square error of approximation (RMSEA). In this paper, we develop and examine an analogous index for categorical item level data modeled with item response theory (IRT). The proposed Tucker-Lewis index for IRT (TLIRT) is based on Maydeu-Olivares and Joe\'s (2005) [Formula: see text] family of limited-information overall model fit statistics. The limited-information fit statistics have significantly better Chi-square approximation and power than traditional full-information Pearson or likelihood ratio statistics under realistic situations. Building on the incremental fit assessment principle, the TLIRT compares the fit of model under consideration along a spectrum of worst to best possible model fit scenarios. We examine the performance of the new index using simulated and empirical data. Results from a simulation study suggest that the new index behaves as theoretically expected, and it can offer additional insights about model fit not available from other sources. In addition, a more stringent cutoff value is perhaps needed than Hu and Bentler\'s (1999) traditional cutoff criterion with continuous variables. In the empirical data analysis, we use a data set from a measurement development project in support of cigarette smoking cessation research to illustrate the usefulness of the TLIRT. We noticed that had we only utilized the RMSEA index, we could have arrived at qualitatively different conclusions about model fit, depending on the choice of test statistics, an issue to which the TLIRT is relatively more immune.

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the \"ground truth\" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.

Collection and analysis of students\' writing samples on a large scale is a part of the research agenda of the emerging writing analytics community that promises to deliver an unprecedented insight into characteristics of student writing. Yet with a large scale often comes variability of contexts in which the samples were produced-different institutions, different purposes of writing, different author demographics, to name just a few possible dimensions of variation. What are the implications of such variation for the ability of automated methods to create indices/features based on the writing samples that would be valid and meaningful? This paper presents a case study in system generalization. Building on a system developed to assess the expression of utility value (a social-psychology-based construct) in essays written by first-year biology students at one postsecondary institution, we vary data parameters and observe system performance. From the point of view of social psychology, all these variants represent the same underlying construct (i.e., utility value), and it is thus very tempting to think that an automatically produced utility-value score could provide a meaningful analytic, consistently, on a large collection of essays. However, findings from this research show that there are challenges: Some variations are easier to deal with than others, and some components of the automated system generalize better than others. The findings are then discussed both in the context of the case study and more generally.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.