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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Background Acute appendicitis (AA) is the most common surgical disease, and appendectomy is the treatment of choice in the majority of cases. A correct diagnosis is key for decreasing the negative appendectomy rate. The management can become difficult in case of complicated appendicitis. The aim of this study is to describe the worldwide clinical and diagnostic work-up and management of AA in surgical departments. Methods This prospective multicenter observational study was performed in 116 worldwide surgical departments from 44 countries over a 6-month period (April 1, 2016–September 30, 2016). All consecutive patients admitted to surgical departments with a clinical diagnosis of AA were included in the study. Results A total of 4282 patients were enrolled in the POSAW study, 1928 (45%) women and 2354 (55%) men, with a median age of 29 years. Nine hundred and seven (21.2%) patients underwent an abdominal CT scan, 1856 (43.3%) patients an US, and 285 (6.7%) patients both CT scan and US. A total of 4097 (95.7%) patients underwent surgery; 1809 (42.2%) underwent open appendectomy and 2215 (51.7%) had laparoscopic appendectomy. One hundred eighty-five (4.3%) patients were managed conservatively. Major complications occurred in 199 patients (4.6%). The overall mortality rate was 0.28%. Conclusions The results of the present study confirm the clinical value of imaging techniques and prognostic scores. Appendectomy remains the most effective treatment of acute appendicitis. Mortality rate is low.

Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control.

This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed.

Item response theory (IRT) measurement models are now commonly used in educational, psychological, and health-outcomes measurement, but their impact in the evaluation of measures of psychiatric constructs remains limited. Herein we present two, somewhat contradictory, theses. The first is that, when skillfully applied, IRT has much to offer psychiatric measurement in terms of scale development, psychometric analysis, and scoring. The second argument, however, is that psychiatric measurement presents some unique challenges to the application of IRT – challenges that may not be easily addressed by application of conventional IRT models and methods. These challenges include, but are not limited to, the modeling of conceptually narrow constructs and their associated limited item pools, and unipolar constructs where the expected latent trait distribution is highly skewed.

We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, that is, without interactions, even the niche character of the experimental time series. Linear noise is associated with growth rate stochasticity, which is related to changes in the environment. This suggests that fluctuations in the sparsely sampled experimental time series may be caused by extrinsic sources.

We evaluated the use of the Rasch model in the scoring and scaling of the context questionnaire scales from the Progress in International Reading Literacy Study 2021 cohort. Focusing on the bullying scale of the Progress in International Reading Literacy Study, we contrasted polytomous item response theory models using free (two-parameter logistic modeling) and fixed (Rasch modeling) discrimination parameters. Our analyses included data from 354,074 students from 63 countries or entities. Across all countries and entities, the Rasch-based model significantly distorted levels of bullying. The rank ordering of countries was consistent between models in 32 of the cases (50.8%), while in other cases there were changes in rank of up to five places. This study provides a robust basis for evidence-based decisions on whether a country is lagging or exceeding the global norm in aberrant behaviors such as bullying.

The height of rice plants is not only an important component of the crop canopy structure but also a crucial pathway for increasing crop yield. In this study, we used a logistic regression model to fit the plant height data of rice varieties cultivated in Taiwan over years and interpret the parameters from the perspective of crop growth. The logistic model has five critical points which allows for the inference of various growth stages: absolute acceleration point (AAP, completion of seedling establishment), maximum acceleration point (MAP, tillering initiation), inflection point (IP, effective tillering), maximum deceleration point (MDP, panicle initiation), and asymptotic deceleration point (ADP, heading). We found that the autumn cropping season reached the point of maximum growth rate earlier (AAP: 5; MAP: 11; IP: 19; MDP: 28; ADP: 34 days) than the spring cropping season, with noticeable advancements in the critical points of IP, MDP, and ADP. According to the model parameters, the period between AAP and ADP is the main growth stage of rice plant height, with the maximum growth rate of autumn crops exceeding that of spring crops. The results showed that there was no significant difference between the early and recent varieties in terms of the time to reach the maximum growth rate and its slope for both cropping seasons. The model can be applied to rice cultivation management to schedule the timing of fertilizer application and irrigation.

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is “a better fit” or more “valid” than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Artificial intelligence currently represents one of the most talked about topics, considering the need for sustainable economic growth at a global level. When it comes to education, artificial intelligence is aimed at enhancing systems, ways of learning, as well as at the results of learning, on the one hand, and training the youth so as to accordingly satisfy the requirements of their future jobs, on the other hand. In this context, research on higher education in Romania was conducted, which analysed the students’ opinion on the social impact of using artificial intelligence in education. As a consequence of performing an opinion poll, answers were collected online from students from prestigious Romanian universities. The data registered for the mentioned objective was processed by applying three statistical and econometric logistic regression models. The results of the first binary logistic model show the respondents’ opinions on the need and importance of enhancing the learning experience by using artificial intelligence in education, considering their gender and level of education. Also, with respect to the two characteristics considered the most significant to the objective of the paper, the following two multinominal logistic models have been developed. The results highlight the way in which the use of artificial intelligence in education influences, on the one hand, the graduates’ prospect for a job and, on the other hand, the society as a whole.