Explore the vast range of titles available.

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.

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.

Young, recently burned forests are increasingly widespread throughout western North America, but forest development after large wildfires is not fully understood, especially regarding effects of variable burn severity, environmental heterogeneity, and changes in drivers over time. We followed development of subalpine forests after the 1988 Yellowstone fires by periodically resampling permanent plots established soon after the fires. We asked two questions about patterns and processes over the past 25 years: (1) Are plant species richness and community composition converging or diverging across variation in elevation, soils, burn severity, and post-fire lodgepole pine (Pinus contorta var. latifolia) density? (2) What are the major controls on post-fire species composition, and has the relative importance of controls changed over time? For question 1, we sampled 10-m2 plots (n = 552) distributed among three geographic areas that differ in elevation and substrate; plots spanned the spectrum of fire severities and were resampled periodically from 1991 to 2013. For question 2, we sampled 0.25-ha plots (n = 72), broadly distributed across areas that burned as stand-replacing fire, in 1999 and 2012. Richness and species composition diverged early on between infertile low-elevation areas (lower richness) and more fertile high-elevation areas (greater richness). Richness increased rapidly for the first 5 yr post-fire, then leveled off or increased only slowly thereafter. Only 6% of 227 recorded species were nonnative. Some annuals and species with heat-stimulated soil seed banks were associated with severely burned sites. However, most post-fire species had been present before the fire; many survived as roots or rhizomes and regenerated rapidly by sprouting. Among the 72 plots, substrate, temperature, and precipitation (the abiotic template) were consistently important drivers of community composition in 1999 and 2012. Post-fire lodgepole pine abundance was not significant in 1999 but was the most important driving variable by 2012, with a negative effect on presence of most understory species, especially annuals and shade-intolerant herbs. Burn severity was significant in 1999 but not in 2012, and distance to unburned forest had no influence in either year. The 1988 fires did not fundamentally alter subalpine forest community assemblages in Yellowstone, and ecological memory conferred resilience to high-severity fire.

1. Reactive oxygen and nitrogen species can damage biomolecules if these lack sufficient antioxidant protection. Maintaining and up-regulating antioxidant defenses and repair of the damaged molecules require resources that could potentially be allocated to other functions, including life-history and signal traits. 2. Identifying the physiological mechanisms causing and counteracting oxidative damage may help to understand evolution of oxidative balance systems from molecular to macroevolutionary levels. This review addresses methodological and statistical problems of measuring and interpreting biomarkers of oxidative stress or damage. 3. A major methodological problem is distinguishing between controlled and uncontrolled processes that can lead either to shifts in dynamic balance of redox potential or cause pathological damage. An ultimate solution to this problem requires establishing links between biomarkers of antioxidant defenses and oxidative damage and components of fitness. 4. Biomarkers of redox balance must correspond to strict technical criteria, most importantly to validated measurement technology. Validation criteria include intrinsic qualities such as specificity, sensitivity, assessment of measurement precision, and knowledge of confounding and modifying factors. 5. The complexity of oxidative balance systems requires that assay choice be informed by statistical analyses incorporating context at biochemical, ecological and evolutionary levels. We review proper application of statistical methods, such as principal components analysis and structural equation modelling, that should help to account for these contexts and isolate the variation of interest across multiple biomarkers simultaneously.

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.

1. Restoration thinning involves the selective removal of stems in woody ecosystems to restore historical or ecologically desirable ecosystem structure and processes. Thinning may also accelerate carbon sequestration in dense regenerating forests. This study considers restoration thinning effects on both structural development and carbon sequestration in a regenerating forest ecosystem. 2. An experimental thinning trial was established in dense Acacia harpophylla regrowth in southern Queensland, Australia. The mean stem density prior to thinning was 17 000 stems ha⁻¹. Four treatments (no thinning and thinning down to 1000, 2000 and 4000 stems ha⁻¹) were applied in a randomized block design. Growth and mortality of a subset of stems was monitored for 2 years. Mixed-effects models and hierarchical Bayesian models (HBMs) were used to test for treatment effects and to explore relationships between neighbourhood density variables and the growth and mortality of stems. The HBMs were subsequently used to parameterise an individual-based simulation model of stand structural development and biomass accumulation over 50 years. 3. The circumference growth rates of stems in thinning treatments were significantly higher than in the control. Woody species diversity and grass cover were also significantly higher in thinning treatments and were strongly negatively correlated with canopy cover. The HBMs confirmed that both growth and mortality were density dependent to some extent. 4. The simulation model predicted a net gain in living above-ground biomass in some thinning treatments (compared with the control treatment) within 20 years after thinning. The 6000 stems ha⁻¹ treatment was predicted to be the optimal thinning density for structural development towards the structure of a nearby mature reference forest. 5. Synthesis and applications. Naturally regenerating woody vegetation provides important habitat for native fauna in fragmented landscapes and represents an efficient means to reinstate habitat connectivity and increase forest area. Many regrowth ecosystems also have considerable potential as land-based carbon sinks. This study demonstrates that restoration thinning can be applied to accelerate stem growth and woody species recruitment and may also accelerate structural development and carbon sequestration in this extensive regrowth ecosystem. The application of restoration thinning to provide dual restoration and carbon benefits should be explored for a wider range of naturally regenerating woody ecosystems.

The effectiveness of relationship education has been supported for youth in correcting faulty relationship beliefs and forming conflict management skills; however, there is very limited research addressing whether relationship education matters for building or modifying relationship standards for romantic partners or relationships. Furthermore, whether and how social climate could add to or moderate curriculum effects has not been considered. Using a sample of 1,808 students nested in 106 high school family and consumer science classes in a southern state, this study examined the impact of a general youth-focused relationship education curriculum and classroom social climate on one ideal standard for relationship partners, warmth/trustworthiness, and one for romantic relationships, intimacy/loyalty. Findings revealed significant and positive curriculum main effects on both standards, while controlling for classroom context. The model for warmth/trustworthiness also showed classroom effects adding to curriculum effects. The role of classroom factors needs further consideration as curriculum effects are examined.

Using a Dutch national sample containing 1,259 triads (two siblings, one parent), we examined whether practical support and emotional support between siblings are enhanced by intergenerational solidarity and how this differs for brothers and sisters. Sibling support was affected by sibling dyad characteristics and by the relationship with the parent. Having a poor relationship and low contact frequency with the parent enhances sibling emotional support, pointing to a compensating mechanism, which is stronger among brothers. Sibling support is also positively related to parental support, suggesting a reinforcing mechanism, especially among sisters. The results contribute new information about influences on sibling support in adulthood and demonstrate the value of including family context variables in research on specific family relationships.

General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low ( r = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.

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.