Explore the vast range of titles available.

Mixed-effects multilevel models are often used to investigate cross-level interactions, a specific type of context effect that may be understood as an upper-level variable moderating the association between a lower-level predictor and the outcome. We argue that multilevel models involving cross-level interactions should always include random slopes on the lower-level components of those interactions. Failure to do so will usually result in severely anti-conservative statistical inference. We illustrate the problem with extensive Monte Carlo simulations and examine its practical relevance by studying 30 prototypical cross-level interactions with European Social Survey data for 28 countries. In these empirical applications, introducing a random slope term reduces the absolute t-ratio of the cross-level interaction term by 31 per cent or more in three quarters of cases, with an average reduction of 42 per cent. Many practitioners seem to be unaware of these issues. Roughly half of the crosslevel interaction estimates published in the European Sociological Review between 2011 and 2016 are based on models that omit the crucial random slope term. Detailed analysis of the associated test statistics suggests that many of the estimates would not reach conventional thresholds for statistical significance in correctly specified models that include the random slope. This raises the question how much robust evidence of cross-level interactions sociology has actually produced over the past decades.

1. Labile characters allow individuals to flexibly adjust their phenotype to changes in environmental conditions. There is growing evidence that individuals can differ both in average expression and level of plasticity in this type of character. Both of these aspects are studied in conjunction within a reaction norm framework. 2. Theoreticians have investigated the factors promoting variation in reaction norm intercepts (average phenotype) and slopes (level of plasticity) of a key labile character: behaviour. A general prediction from their work is that selection will favour the evolution of repeatable individual variation in level of plasticity only under certain ecological conditions. While factors promoting individual repeatability of plasticity have thus been identified, empirical estimates of this phenomenon are largely lacking for wild populations. 3. We assayed aggressiveness of individual male great tits (Parus major) twice during their egg-laying stage and twice during their egg-incubation stage to quantify each male's level of seasonal plasticity. This procedure was applied during six consecutive years; all males breeding in our plots during those years were assayed, resulting in repeated measures of individual reaction norms for any individual breeding in multiple years. We quantified among- and within-individual variation in reaction norm components, allowing us to estimate repeatability of seasonal plasticity. Using social pedigree information, we further partitioned reaction norm components into their additive genetic and permanent environmental counterparts. 4. Cross-year individual repeatability for the intercepts (average aggressiveness) and slopes (level of seasonal plasticity) of the aggressiveness reaction norms were 0.574 and 0.516 respectively. The mean of the posterior distributions suggested modest heritabilities (h² = 0.260 for intercepts; h² = 0.266 for slopes), but these estimates were relatively uncertain. Males behaved more aggressively in areas with higher breeding densities, and became less aggressive and less plastic with increasing age; plasticity thus varied within individuals and was multidimensional in nature. 5. This empirical study quantified cross-year individual repeatability, heritability and agerelated reversible plasticity in behaviour. Acknowledging such patterns of multi-level variation is important not only for testing behavioural ecology theory concerning the evolution of repeatable differences in behavioural plasticity but also for predicting how reversible plasticity may evolve in natural populations.

In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.

In order to improve the performance of organic luminescent materials, lots of studies have been carried out at the molecular level. However, these materials are mostly applied as solids or aggregates in practical applications, in which the relationship between aggregation structure and luminescent property should be paid more attention. Here, we obtained five phenothiazine 5,5‐dioxide (O‐PTZ) derivatives with distinct molecular conformations by rational design of chemical structures, and systematically studied their room‐temperature phosphorescence (RTP) effect in solid state. It was found that O‐PTZ dimers with quasi‐equatorial (eq) conformation tended to show stronger π‐π interaction than quasi‐axial (ax) conformers in crystal state, which was more conducive to the generation of RTP. Based on this result, a multi‐level structural model of organic solids was proposed to draw the relationship between aggregation structure and RTP effect, just like the research for the structure‐property relationship of proteins. Using this structural model as the guide, boosted RTP efficiency from 1% to 20% was successfully achieved in the corresponding host‐guest doping system, showing its wide applicability. Five phenothiazine 5,5‐dioxide (O‐PTZ) derivatives with distinct molecular conformations were obtained, and their room‐temperature phosphorescence (RTP) effects were studied. It was found that O‐PTZ dimers with quasi‐equatorial (eq) conformation were more conducive to generate RTP than quasi‐axial (ax)‐ones in crystal state. Accordingly, a multi‐level structural model of organic solids was proposed to draw the relationship between aggregation structure and RTP effect.

Multi-level modeling is currently regaining attention in the database and software engineering community with different emerging proposals and implementations. One driver behind this trend is the need to reduce model complexity, a crucial aspect in a time of analytics in Big Data that deal with complex heterogeneous data structures. So far no standard exists for multi-level modeling. Therefore, different formalization approaches have been proposed to address multi-level modeling and verification in different frameworks and tools. In this article, we present an approach that integrates the formalization, implementation, querying, and verification of multi-level models. The approach has been evaluated in an open-source F-Logic implementation and applied in a large-scale data interoperability project in the oil and gas industry. The outcomes show that the framework is adaptable to industry standards, reduces the complexity of specifications, and supports the verification of standards from a software engineering point of view.

It is challenging for decision‐makers (DMs) to deal with uncertainties in multi‐level agricultural water resource systems, where DMs independently make decisions but have different levels of power. In this paper, we model the multi‐level agricultural water resources system under deep uncertainties as a Stackelberg game, use multi‐level programming to solve equilibrium water allocation problems, and introduce robustness metrics into multi‐level programming to balance solution feasibility and model optimality within uncertain environments. The approach is applied to a shallow groundwater area with three decision levels, pursuing, from the top level to the bottom one, high food production, fair water allocation, and increased economic benefit. The model generated a series of optimal equilibrium solutions with different robustness degrees. DMs can choose “rational” solutions according to their acceptable costs, oriented robustness degree, expected objective values, and advance risk assessment of uncertainties. Among these solutions, we capture a critical point with high objective values and strong robustness, where DMs can accomplish both objective optimality and solution robustness with a low cost. The proposed approach in this study provides a posterior decision support to consider solution robustness while designing policies in multi‐level agricultural water resource systems under deep uncertainties. Key Points An introduction of robust measure into multi‐level programming balances solution robustness and model optimality There is a critical point that decision makers can achieve high objective values and strong robustness with low costs

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Two studies were conducted to address the potential nonlinear relationship between emotional exhaustion and voice. Study 1 developed and tested a model rooted in conservation of resources theory in which responses to emotional exhaustion are determined by individual-level and group-level conditions that influence the perceived safety and efficacy of voice and drive prohibitive voice behaviors by giving rise to either resource-conservation-based or resource-acquisition-based motivation. Specifically, there was a curvilinear (U-shaped) relationship between emotional exhaustion and prohibitive voice under conditions of (i) high job security and (ii) high interactional justice climate, but a linearly negative relationship when these resources were low. Study 2 replicated and extended these findings to include an empirical examination of these effects on promotive versus prohibitive voice. Results confirmed the findings of Study 1, provided evidence of differences in the nomological networks of promotive and prohibitive voice, and indicated that prohibitive voice is more salient to the experience of high emotional strain. Implications of the findings and areas for future research are discussed.

Collaborative management of the water‐energy‐food‐ecosystems (WEFE) nexus can contribute significantly to sustainable development. However, multiple decision‐making levels with diverse preferences and multiple uncertainties in different forms pose intractable challenges to the management process. In this study, a novel optimization method named as multi‐level chance‐constrained fuzzy programming (MCFP) is developed to jointly manage the WEFE nexus. MCFP has advantages in evaluating trade‐offs among multiple competitive decision makers, solving decentralized planning problems with hierarchical structure, and tackling uncertainties expressed as randomness and vagueness. MCFP is then applied to the WEFE nexus in Central Asia, where five countries, 43 states, six water sources, and eight water users are involved over a long‐term planning horizon (2021–2050). A set of scenarios are designed to reflect decision‐making preferences based on different irrigation efficiencies, food, ecological and electricity demands as well as constraint‐violation probability and system credibility levels. The major findings are: (a) the proportion of agricultural water allocation would reduce to 45.4%–56.6% by 2050 to save more water for ensuring ecological restoration and energy supply; and (b) in order to balance water demands and support regional sustainable development, policymakers should sacrifice some of the benefits, set strict arable land limits for cereal crops, improve irrigation efficiency through adopting drip and sprinkler irrigation, and avoid the effects of the irrigation efficiency paradox. The findings are helpful for policymakers in gaining insight into the interrelationships of water, energy, food and ecosystems as well as making decisions for collaborative management of the WEFE nexus system. Plain Language Summary In Central Asia, water scarcity, food crisis, energy insecurity and ecological degradation are closely linked. Economic development and diminishing resources further exacerbate these problems. Therefore, a “nexus” concept is needed to analyze trade‐offs and synergize management to promote the efficient use of resources. However, the management process faces the problems of uncertain information and multiple decision makers with different preferences, which adds to the complexity of the management process. To address the above issues, this study develops a collaborative management model for the water‐energy‐food‐ecosystems (WEFE) nexus in Central Asia based on a multi‐level programming framework. Besides, the agricultural irrigation paradox is also considered in the developed model. The results find that to ensure food security and ecological restoration, decision makers need to forego some of the benefits and allocate more water resources to cereal crops and ecological uses. In addition, it is recommended that managers increase advanced irrigation methods, such as drip and sprinkler irrigation, but that care be taken to curb the irrigation efficiency paradox while increasing irrigation efficiency. Key Points We proposed an optimization method to plan the water‐energy‐food‐ecosystems nexus for Central Asia over a long‐planning horizon The uncertain information expressed as randomness and vagueness were tackled during the planning process The agricultural irrigation paradox is considered during the collaborative management, thus providing decision support to managers

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.