Alternative Factor Models and Heritability of the Short Leyton Obsessional Inventory-Children's Version

An alternative models framework was used to test three confirmatory factor analytic models for the Short Leyton Obsessional Inventory-Children's Version (Short LOI-CV) in a general population sample of 517 young adolescent twins (11-16 years). A one-factor model as implicit in current classification systems of Obsessive-Compulsive Disorder (OCD), a two-factor obsessions and compulsions model, and a multidimensional model corresponding to the three proposed subscales of the Short LOI-CV (labelled Obsessions/Incompleteness, Numbers/Luck and Cleanliness) were considered. The three-factor model was the only model to provide an adequate explanation of the data. Twin analyses suggested significant quantitative sex differences in heritability for both the Obsessions/Incompleteness and Numbers/Luck dimensions with these being significantly heritable in males only (heritability of 60% and 65% respectively). The correlation between the additive genetic effects for these two dimensions in males was 0.95 suggesting they largely share the same genetic risk factors.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

The structure of the Beck Hopelessness Scale: A confirmatory factor analysis in UK students

The aim of this paper was to confirm the factor structure of the 20-item Beck Hopelessness Scale in a non-clinical population. Previous research has highlighted a lack of clarity in its construct validity with regards to this population.

Based on previous factor analytic findings from both clinical and non-clinical studies, 13 separate confirmatory factor models were specified and estimated using LISREL 8.72 to test the one, two and three-factor models.

Psychology and medical students at Queen's University, Belfast (n = 581) completed both the BHS and the Beck Depression Inventory (BDI).

All models showed reasonable fit, but only one, a four-item single-factor model demonstrated a nonsignificant chi-squared statistic. These four items can be used to derive a Short-Form BHS (SBHS) in which increasing scores (0-4) corresponded with increasing scores in the BDI. The four items were also drawn from all three of Beck's proposed triad, and included both positively and negatively scored items.

This study in a UK undergraduate non-clinical population suggests that the BHS best measures a one-factor model of hopelessness. It appears that a shorter four-item scale can also measure this one-factor model. (C) 2011 Elsevier Ltd. All rights reserved.

A Dynamic Factor Approach to Mortality Modelling

Longevity risk has become one of the major risks facing the insurance and pensions markets globally. The trade in longevity risk is underpinned by accurate forecasting of mortality rates. Using techniques from macroeconomic forecasting, we propose a dynamic factor model of mortality that fits and forecasts mortality rates parsimoniously.We compare the forecasting quality of this model and of existing models and find that the dynamic factor model generally provides superior forecasts when applied to international mortality data. We also show that existing multifactorial models have superior fit but their forecasting performance worsens as more factors are added. The dynamic factor approach used here can potentially be further improved upon by applying an appropriate stopping rule for the number of static and dynamic factors. 

Midwifery-led antenatal care models: mapping a systematic review to an evidence-based quality framework to identify key components and characteristics of care

Background: Implementing effective antenatal care models is a key global policy goal. However, the mechanisms of action of these multi-faceted models that would allow widespread implementation are seldom examined and poorly understood. In existing care model analyses there is little distinction between what is done, how it is done, and who does it. A new evidence-informed quality maternal and newborn care (QMNC) framework identifies key characteristics of quality care. This offers the opportunity to identify systematically the characteristics of care delivery that may be generalizable across contexts, thereby enhancing implementation. Our objective was to map the characteristics of antenatal care models tested in Randomised Controlled Trials (RCTs) to a new evidence-based framework for quality maternal and newborn care; thus facilitating the identification of characteristics of effective care.

Methods: A systematic review of RCTs of midwifery-led antenatal care models. Mapping and evaluation of these models’ characteristics to the QMNC framework using data extraction and scoring forms derived from the five framework components. Paired team members independently extracted data and conducted quality assessment using the QMNC framework and standard RCT criteria.

Results: From 13,050 citations initially retrieved we identified 17 RCTs of midwifery-led antenatal care models from Australia (7), the UK (4), China (2), and Sweden, Ireland, Mexico and Canada (1 each). QMNC framework scores ranged from 9 to 25 (possible range 0–32), with most models reporting fewer than half the characteristics associated with quality maternity care. Description of care model characteristics was lacking in many studies, but was better reported for the intervention arms. Organisation of care was the best-described component. Underlying values and philosophy of care were poorly reported.

Conclusions: The QMNC framework facilitates assessment of the characteristics of antenatal care models. It is vital to understand all the characteristics of multi-faceted interventions such as care models; not only what is done but why it is done, by whom, and how this differed from the standard care package. By applying the QMNC framework we have established a foundation for future reports of intervention studies so that the characteristics of individual models can be evaluated, and the impact of any differences appraised.

Simplification of structured population dynamics in complex ecological communities

Goldstone's idea of slow dynamics resulting from spontaneously broken symmetries is applied to Hubbell's neutral hypothesis of community dynamics, to efficiently simplify stage-structured multi-species models-introducing the quasi-neutral approximation (QNA). Rather than assuming population-dynamical neutrality in the QNA, deviations from ideal neutrality, thought to be small, drive dynamics. The QNA is systematically derived to first and second order in a two-scale singular perturbation expansion. The total reproductive value of species, as computed from the effective life-history parameters resulting from the non-linear interactions with the surrounding community, emerges as the new dynamic variables in this aggregated description. Using a simple stage-structured community-assembly model, the QNA is demonstrated to accurately reproduce population dynamics in large, complex communities. Further, the utility of the QNA in building intuition for management problems is illustrated by estimating the responses of a fish stock to harvesting and variations in fecundity.

Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis

Energy efficiency improvement has been a key objective of China’s long-term energy policy. In this paper, we derive single-factor technical energy efficiency (abbreviated as energy efficiency) in China from multi-factor efficiency estimated by means of a translog production function and a stochastic frontier model on the basis of panel data on 29 Chinese provinces over the period 2003–2011. We find that average energy efficiency has been increasing over the research period and that the provinces with the highest energy efficiency are at the east coast and the ones with the lowest in the west, with an intermediate corridor in between. In the analysis of the determinants of energy efficiency by means of a spatial Durbin error model both factors in the own province and in first-order neighboring provinces are considered. Per capita income in the own province has a positive effect. Furthermore, foreign direct investment and population density in the own province and in neighboring provinces have positive effects, whereas the share of state-owned enterprises in Gross Provincial Product in the own province and in neighboring provinces has negative effects. From the analysis it follows that inflow of foreign direct investment and reform of state-owned enterprises are important policy handles.

Multi-ionization of helium and lithium using the independent electron and independent event models with intrinsic CDW

Cross sections for the multi-ionization of He and Li are presented for impact energies in the range of 50 to 1000 keV/amu. These are calculated using the eikonal initial state approximation to represent the input and exit channels of the active electrons. The ionization process is simulated in a variety of ways, most notably an attempt to account for the effects of electron correlation via the inclusion of a continuum density of states (CDS) term. Inadequacies, of the CDW formulation at small impact parameters, and of the models themselves, are discussed and conclusions are drawn on what repercussions this has for the cross sections calculated.

Generalized social trust across 22 European countries: A multi-level analysis of four competing models

Concern with what can explain variation in generalized social trust has led to an abundance of theoretical models. Defining generalized social trust as a belief in human benevolence, we focus on the emancipation theory and social capital theory as well as the ethnic diversity and economic development models of trust. We then determine which dimensions of individuals’ behavior and attitudes as well as of their national context are the most important predictors. Using data from 20 countries that participated in round one of the European Social Survey, we test these models at their respective level of analysis, individual and/or national. Our analysis revealed that individuals’ own trust in the political system as a moral and competent institution was the most important predictor of generalized social trust at the individual level, while a country’s level of affluence was the most important contextual predictor, indicating that different dimensions are significant at the two levels of analysis. This analysis also raised further questions as to the meaning of social capital at the two levels of analysis and the conceptual equivalence of its civic engagement dimension across cultures.

Behavioural context of multi-scale species distribution models assessed by radio-tracking

Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the 'central place' and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in . Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats. A Generalised Linear Model (GLM) of roost occurrence using land cover variables with mixed spatial scales indicated roost occurrence was positively associated with woodland on a fine scale and pasture on a broad scale. Habitat selection of radio-tracked bats mirrored the SDM with bats selecting for woodland in the immediate vicinity of individual roosts but avoiding this habitat in foraging areas, whilst pasture was significantly positively selected for in foraging areas. Using habitat selection derived from radio-tracking enables a multi-scale SDM to be interpreted in a behavioural context. We suggest that the multi-scale SDM of . M. nattereri describes a trade-off between the central place and foraging habitat. Multi-scale methods provide a greater understanding of the ecological processes which determine where species occur and allow integration of behavioural processes into SDMs. The findings have implications when assessing the resource use of a species at a single point in time. Doing so could lead to misinterpretation of habitat requirements as these can change within a short time period depending on specific behaviour, particularly if detectability changes depending on behaviour. © 2011 Gesellschaft für ökologie.

Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.