The Winner Takes it All: Risk Factors and Bayesian Modelling of the Probability of Success in Escaping from Big Cat Predation

The individuals engaged in predation interactions modify their adaptation strategies to improve their efficiency to reach success in the fight for survival. This success is linked to either capturing prey (predator) or escaping (prey). Based on the graphic material available on digital platforms both of public and private access, this research aimed to evaluate the influence of those animal- and environment-dependent factors affecting the probability of successful escape of prey species in case of attack by big cats. Bayesian predictive analysis was performed to evaluate the outcomes derived from such factor combinations on the probability of successful escape. Predator species, age, status at the end of the hunting act, time lapse between first attention towards potential prey and first physical contact, prey species and the relief of the terrain, significantly conditioned (p < 0.05) escape success. Social cooperation in hunting may be more important in certain settings and for certain prey species than others. The most parsimonious model explained 36.5% of the variability in escaping success. These results can be useful to design translatable selective strategies not only seeking to boost predation abilities of domestic felids for pest control, but also, biological antipredator defence in potential domestic prey of big cats.

How do Consumers’ Gender and Rational Thinking Affect the Acceptance of Entertainment Social Robots?

In recent years, the rapid ageing of the population, a longer life expectancy and elderly people’s desire to live independently are social changes that put pressure on healthcare systems. This context is boosting the demand for companion and entertainment social robots on the market and, consequently, producers and distributors are interested in knowing how these social robots are accepted by consumers. Based on technology acceptance models, a parsimonious model is proposed to estimate the intention to use this new advanced social robot technology and, in addition, an analysis is performed to determine how consumers’ gender and rational thinking condition the precedents of the intention to use. The results show that gender differences are more important than suggested by the literature. While women gave greater social influence and perceived enjoyment as the main motives for using a social robot, in contrast, men considered their perceived usefulness to be the principal reason and, as a differential argument, the ease of use. Regarding the reasoning system, the most significant differences occurred between heuristic individuals, who stated social influence as the main reason for using a robot, and the more rational consumers, who gave ease of use as a differential argument.

Informal learning behaviors, interaction and workplace autonomy and readiness to learn

Purpose This paper aims to find a measurement model with better evidence of validity, with data extracted from the Program for the International Assessment of Adult Competencies (PIAAC). To test a parsimonious model in which dispositional and workplace context characteristics are predictors of informal learning behaviors (ILBs). Design/methodology/approach The authors performed exploratory and confirmatory factor analyses to improve the fit of the PIAAC data measurement model. Multiple linear regression was used to examine the prediction of ILBs by one dispositional variable (Readiness to Learn) and two workplace context variables (Autonomy and Interaction in the Workplace). Findings A measurement model emerged with 18 items divided into four factors. The three antecedent variables predicted ILBs. Interaction in the workplace resulted in higher scores, and workplace autonomy resulted in lower scores. Research limitations/implications The small number of items for ILBs prevented a more detailed exploration of predictors of different types of these behaviors. ILBs can be stimulated by policies that promote readiness to learn and that encourage the design of environments that require worker interactions and autonomy. Originality/value Few studies on ILBs in the workplace have investigated the prediction of dispositional and contextual antecedents based on a theoretical model. The findings herein were obtained using a diverse sample of countries, occupations and generations, allowing better generalization. The importance of interpersonal relationships in the workplace for predicting ILBs was emphasized.

Empirical modelling of snow cover duration patterns in complex terrains of Italy

Snow cover duration is a crucial climate change indicator. However, measurements of days with snow cover on the ground (DSG) are limited, especially in complex terrains, and existing measurements are fragmentary and cover only relatively short time periods. Here, we provide observational and modelling evidence that it is possible to produce reliable time-series of DSG for Italy based on instrumental measurements, and historical documentary data derived from various sources, from a limited set of stations and areas in the central-southern Apennines (CSA) of Italy. The adopted modelling approach reveals that DSG estimates in most settings in Italy can be driven by climate factors occurring in the CSA. Taking into account spatial scale-dependence, a parsimonious model was developed by incorporating elevation, winter and spring temperatures, a large-scale circulation index (the Atlantic Multidecadal Variability, AMV) and a snow-severity index, with in situ DSG data, based on a core snow cover dataset covering 97 years (88% coverage in the 1907–2018 period and the rest, discontinuously from 1683 to 1895, from historical data of the Benevento station). The model was validated on the basis of the identification of contemporary snow cover patterns and historical evidence of summer snow cover in high massifs. Beyond the CSA, validation obtained across terrains of varying complexity in both the northern and southern sectors of the peninsula indicate that the model holds potential for applications in a broad range of geographical settings and climatic situations of Italy. This article advances the study of past, present and future DSG changes in the central Mediterranean region.

Latent Classes of First Incident Difficulty in Instrumental Activities of Daily Living

Ability to perform instrumental activities of daily living (IADLs) deteriorates during prodromal Alzheimer’s disease (AD), eventually leading to impaired everyday functioning and dementia. Ordering and timing of IADL difficulty onset may identify individuals at greater risk of cognitive impairment, but most studies only consider total number of difficult tasks. Leveraging longitudinal data from the Advanced Cognitive Training in Independent and Vital Elderly (ACTIVE) Study who entered free of any IADL difficulty (N=1266), we hypothesized that a latent class analysis based on timing of first reported IADL task difficulty would reveal class differences in cognitive functioning . Participants were followed until they self-reported at least one IADL difficulty, study completion (10 years), or loss to follow-up. Discrete-time multiple event process survival mixture (MEPSUM) models were used to simultaneously estimate hazards of incident IADL task difficulty across 7 task groups. Two, 3, 4, and 5 latent class models were fit to the data. Both unadjusted and covariate-adjusted models (adjusted for age, sex, race, education, marital status, and general health rating) were fit. Using the 2-class solution as the most parsimonious model, model entropy was 0.855. The model was able to distinguish a class of participants with lower global cognitive factor scores at baseline (Cohen’s D = 0.23, P = 0.04). We conclude that first incident IADL difficulty may be a useful measure in identifying individuals with worse cognitive functioning.

Study on the Anthropometric and Body Composition Indices for Prediction of Cold and Heat Patter

Objectives: Many symptoms of cold and heat patterns are related to the thermoregulation of the body. Thus, we aimed to study the association of cold and heat patterns with anthropometry/body composition.Methods: The cold and heat patterns of 2000 individuals aged 30–55 years were evaluated using a self-administered questionnaire.Results: Among the anthropometric and body composition variables, body mass index (-0.37, 0.39) and fat mass index (-0.35, 0.38) had the highest correlation coefficients with the cold and heat pattern scores after adjustment for age and sex in the cold-heat group, while the correlation coefficients were relatively lower in the non-cold-heat group. In the cold-heat group, the most parsimonious model for the cold pattern with the variables selected by the best subset method and Lasso included sex, body mass index, waist-hip ratio, and extracellular water/total body water (adjusted R2 = 0.324), and the model for heat pattern additionally included age (adjusted R2 = 0.292).Conclusions: The variables related to obesity and water balance were the most useful for predicting cold and heat patterns. Further studies are required to improve the performance of prediction models.

Use of Multi-Seasonal Satellite Images to Predict SOC from Cultivated Lands in a Montane Ecosystem

Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred.

Constrained parsimonious model-based clustering

A new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. This is done in a natural way by filling the gap among models and providing a smooth transition among them. The methodology provides mathematically well-defined problems and is also useful to prevent us from obtaining spurious solutions. Novel information criteria are proposed to help the user in choosing parameters. The interest of the proposed methodology is illustrated through simulation studies and a real-data application on COVID data.

Development and Validation of a Prediction Model for Acute Kidney Injury Among Patients With Acute Decompensated Heart Failure

Background: Acute kidney injury is an adverse event that carries significant morbidity among patients with acute decompensated heart failure (ADHF). We planned to develop a parsimonious model that is simple enough to use in clinical practice to predict the risk of acute kidney injury (AKI) occurrence.Methods: Six hundred and fifty patients with ADHF were enrolled in this study. Data for each patient were collected from medical records. We took three different approaches of variable selection to derive four multivariable logistic regression model. We selected six candidate predictors that led to a relatively stable outcome in different models to derive the final prediction model. The prediction model was verified through the use of the C-Statistics and calibration curve.Results: Acute kidney injury occurred in 42.8% of the patients. Advanced age, diabetes, previous renal dysfunction, high baseline creatinine, high B-type natriuretic peptide, and hypoalbuminemia were the strongest predictors for AKI. The prediction model showed moderate discrimination C-Statistics: 0.766 (95% CI, 0.729–0.803) and good identical calibration.Conclusion: In this study, we developed a prediction model and nomogram to estimate the risk of AKI among patients with ADHF. It may help clinical physicians detect AKI and manage it promptly.

A parsimonious model to validate cost-effectiveness analyses on preventive health care

Background The effect of preventive health care on health expenditures is ambiguous. On the one hand, prevention reduces the costs of future morbidity. On the other hand, prevention leads to costs of life extension. The purpose of this paper is to develop a parsimonious model that determines for a preventive measure of interest whether savings from preventing morbidity are more than offset by the costs of living longer, resulting in a net expenditure increase. Methods A theoretical model was built based on a Weibull survival function. It includes savings and life extension costs over the remaining lifetime. The model was applied to the example of obesity prevention. Results The model shows that the cost consequences of prevention are essentially driven by two factors: i) the relative reduction of morbidity-related costs, which determines the amount of savings from avoiding morbidity; and ii) the hazard ratio of death, which determines the amount of life extension costs. In the application example, the model is able to validate the results of a more complex cost-effectiveness model on obesity prevention. Conclusions This work provides new insight into the lifetime cost consequences of prevention. The model can be used both to check plausibility of the results of other models and to conduct an independent analysis.