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    Updated Nomogram Incorporating Percentage of Positive Cores to Predict Probability of Lymph Node Invasion in Prostate Cancer Patients Undergoing Sentinel Lymph Node Dissection. 

    Winter, Alexander; Kneib, Thomas; Wasylow, Clara; Reinhardt, Lena; Henke, Rolf-Peter; Engels, Svenja; Gerullis, Holger; Wawroschek, Friedhelm
    Journal of Cancer 2017; 8(14) p.2692-2698
    Objectives: To update the first sentinel nomogram predicting the presence of lymph node invasion (LNI) in prostate cancer patients undergoing sentinel lymph node dissection (sPLND), taking into account the percentage of positive cores. Patients and Methods: Analysis included 1,870 prostate cancer patients who underwent radioisotope-guided sPLND and retropubic radical prostatectomy. Prostate-specific antigen (PSA), clinical T category, primary and secondary biopsy Gleason grade, and percentage of positive cores were included in univariate and multivariate logistic regression models predicting LNI, and constituted the basis for the regression coefficient-based nomogram. Bootstrapping was applied to generate 95% confidence intervals for predicted probabilities. The area under the receiver operator characteristic curve (AUC) was obtained to quantify accuracy. Results: Median PSA was 7.68 ng/ml (interquartile range (IQR) 5.5-12.3). The number of lymph nodes removed was 10 (IQR 7-13). Overall, 352 patients (18.8%) had LNI. All preoperative prostate cancer characteristics differed significantly between LNI-positive and LNI-negative patients (P<0.001). In univariate accuracy analyses, the proportion of positive cores was the foremost predictor of LNI (AUC, 77%) followed by PSA (71.1%), clinical T category (69.9%), and primary and secondary Gleason grade (66.6% and 61.3%, respectively). For multivariate logistic regression models, all parameters were independent predictors of LNI (P<0.001). The nomogram exhibited a high predictive accuracy (AUC, 83.5%). Conclusion: The first update of the only available sentinel nomogram predicting LNI in prostate cancer patients demonstrates even better predictive accuracy and improved calibration. As an additional factor, the percentage of positive cores represents the leading predictor of LNI. This updated sentinel model should be externally validated and compared with results of extended PLND-based nomograms.
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    Supermarket purchase contributes to nutrition-related non-communicable diseases in urban Kenya. 

    Demmler, Kathrin M.; Klasen, Stephan; Nzuma, Jonathan M.; Qaim, Matin
    PloS one 2017; 12(9): Art. e0185148
    BACKGROUND: While undernutrition and related infectious diseases are still pervasive in many developing countries, the prevalence of non-communicable diseases (NCD), typically associated with high body mass index (BMI), is rapidly rising. The fast spread of supermarkets and related shifts in diets were identified as possible factors contributing to overweight and obesity in developing countries. Potential effects of supermarkets on people's health have not been analyzed up till now. OBJECTIVE: This study investigates the effects of purchasing food in supermarkets on people's BMI, as well as on health indicators such as fasting blood glucose (FBG), blood pressure (BP), and the metabolic syndrome. DESIGN: This study uses cross-section observational data from urban Kenya. Demographic, anthropometric, and bio-medical data were collected from 550 randomly selected adults. Purchasing food in supermarkets is defined as a binary variable that takes a value of one if any food was purchased in supermarkets during the last 30 days. In a robustness check, the share of food purchased in supermarkets is defined as a continuous variable. Instrumental variable regressions are applied to control for confounding factors and establish causality. RESULTS: Purchasing food in supermarkets contributes to higher BMI (+ 1.8 kg/m2) (P<0.01) and an increased probability (+ 20 percentage points) of being overweight or obese (P<0.01). Purchasing food in supermarkets also contributes to higher levels of FBG (+ 0.3 mmol/L) (P<0.01) and a higher likelihood (+ 16 percentage points) of suffering from pre-diabetes (P<0.01) and the metabolic syndrome (+ 7 percentage points) (P<0.01). Effects on BP could not be observed. CONCLUSIONS: Supermarkets and their food sales strategies seem to have direct effects on people's health. In addition to increasing overweight and obesity, supermarkets contribute to nutrition-related NCDs. Effects of supermarkets on nutrition and health can mainly be ascribed to changes in the composition of people's food choices.
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    A Conceptual Framework for the Integration of Corporate Social Responsibility and Human Resource Development Based on Lifelong Learning 

    Ketschau, Thilo
    Sustainability 2017; 9(9): Art. 1545
    Companies often see themselves as actors in a process of sustainable development that takes place in society. With this self-conception comes the challenge to act in a socially responsible way. The following paper presents a framework to integrate the concepts of Corporate Social Responsibility and Human Resource Development to create an approach that can address this responsibility. The concepts of Corporate Social Responsibility and Human Resource Development are linked by the idea of lifelong learning, incorporating concepts and ideas from the field of education into the framework, which makes it possible to examine the issue of promotion and social advancement irrespective of an individual’s social background. The article lays a foundation for the framework by describing the concepts named above and later on conceptualizes a three-part framework that helps to analyse the development of entrepreneurial structures that enable social commitment through company education. With this framework, an innovative approach to link a corporation’s social and educational engagement for mutual benefit is given an applicable form, with the immanent potential for the development of social sustainability. The research presented in this paper is purely theoretical and its results offer a connection point for practical interventions.
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    One Size Fits All? The Validity of a Composite Poverty Index Across Urban and Rural Households in South Africa 

    Steinert, Janina Isabel; Cluver, Lucie Dale; Melendez-Torres, G. J.; Vollmer, Sebastian
    Social Indicators Research
    Composite indices have been prominently used in poverty research. However, validity of these indices remains subject to debate. This paper examines the validity of a common type of composite poverty indices using data from a cross-sectional survey of 2477 households in urban and rural KwaZulu-Natal, South Africa. Multiple-group comparisons in structural equation modelling were employed for testing differences in the measurement model across urban and rural groups. The analysis revealed substantial variations between urban and rural respondents both in the conceptualisation of poverty as well as in the weights and importance assigned to individual poverty indicators. The validity of a ‘one size fits all’ measurement model can therefore not be confirmed. In consequence, it becomes virtually impossible to determine a household’s poverty level relative to the full sample. Findings from our analysis have important practical implications in nuancing how we can sensitively use composite poverty indices to identify poor people.
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    Economic growth and child malnutrition – Authors' reply 

    Vollmer, Sebastian; Harttgen, Kenneth; Subramanyam, Malavika; Finlay, Jocelyn; Klasen, Stephan; Subramanian, S V
    The Lancet Global Health 2016; 4(12): Art. e903
    not available
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    A review of the ecosystem functions in oil palm plantations, using forests as a reference system 

    Dislich, Claudia; Keyel, Alexander C.; Salecker, Jan; Kisel, Yael; Meyer, Katrin M.; Auliya, Mark; Barnes, Andrew D.; Corre, Marife D.; Darras, Kevin; Faust, Heiko; et al.
    Hess, BastianKlasen, StephanKnohl, AlexanderKreft, HolgerMeijide, AnaNurdiansyah, FuadOtten, FennaPe'er, GuySteinebach, StefanieTarigan, SuriaTölle, Merja H.Tscharntke, TejaWiegand, Kerstin
    Biological Reviews
    Oil palm plantations have expanded rapidly in recent decades. This large-scale land-use change has had great ecological, economic, and social impacts on both the areas converted to oil palm and their surroundings. However, research on the impacts of oil palm cultivation is scattered and patchy, and no clear overview exists. We address this gap through a systematic and comprehensive literature review of all ecosystem functions in oil palm plantations, including several (genetic, medicinal and ornamental resources, information functions) not included in previous systematic reviews. We compare ecosystem functions in oil palm plantations to those in forests, as the conversion of forest to oil palm is prevalent in the tropics. We find that oil palm plantations generally have reduced ecosystem functioning compared to forests: 11 out of 14 ecosystem functions show a net decrease in level of function. Some functions show decreases with potentially irreversible global impacts (e.g. reductions in gas and climate regulation, habitat and nursery functions, genetic resources, medicinal resources, and information functions). The most serious impacts occur when forest is cleared to establish new plantations, and immediately afterwards, especially on peat soils. To variable degrees, specific plantation management measures can prevent or reduce losses of some ecosystem functions (e.g. avoid illegal land clearing via fire, avoid draining of peat, use of integrated pest management, use of cover crops, mulch, and compost) and we highlight synergistic mitigation measures that can improve multiple ecosystem functions simultaneously. The only ecosystem function which increases in oil palm plantations is, unsurprisingly, the production of marketable goods. Our review highlights numerous research gaps. In particular, there are significant gaps with respect to socio-cultural information functions. Further, there is a need for more empirical data on the importance of spatial and temporal scales, such as differences among plantations in different environments, of different sizes, and of different ages, as our review has identified examples where ecosystem functions vary spatially and temporally. Finally, more research is needed on developing management practices that can offset the losses of ecosystem functions. Our findings should stimulate research to address the identified gaps, and provide a foundation for more systematic research and discussion on ways to minimize the negative impacts and maximize the positive impacts of oil palm cultivation.
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    Smoothing Parameter and Model Selection for General Smooth Models 

    Wood, Simon N.; Pya, Natalya; Säfken, Benjamin
    Journal of the American Statistical Association 2017; 111(516) p.1548-1563
    This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (e.g., two stage zero inflation models, and Gaussian location-scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood. Supplementary materials for this article are available online.
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    Managing risks in the Indonesian seaweed supply chain 

    Mulyati, Heti; Geldermann, Jutta
    Clean Technologies and Environmental Policy 2016; 19(1) p.175-189
    Seaweed supply chains in Indonesia, especially carrageenan and agar products, are subject to risks arising both inside the participating companies and in their external networks. Uncertainties in yield, quality, price, and infrastructure in one part of the supply chain can affect the whole chain. To ensure a sustainable seaweed industry, an appropriate supply chain risk management (SCRM) is needed. There are four critical steps in SCRM: identifying seaweed supply chains, identifying and categorizing risks, assessing risks, and mitigating risks. To identify seaweed supply chains, we conducted field research, in-depth interviews, and literature studies. The field survey was conducted in the Provinces of South Sulawesi, West Java, East Java, Banten, and West Nusa Tenggara. The seaweed supply chains were modeled by the software Umberto to get a better understanding of material and energy flows between the key members. To identify and categorize the risks, we started with the risks mentioned in the existing literature works, and then applied the Delphi method to analyze the potential risk sources, their causes, and their impacts. To assess risks, we used a semiquantitative approach through the face-to-face interviews to generate a risk map showing the likelihood, and impact of adverse events. Afterward, the risk intensity was categorized based on the value of the responses and classified into five categories: negligible, marginal, critical, most critical, and catastrophic risks. The mitigation strategies considered sustainability (environment, economy, and social) and risks criteria. Multi-criteria decision analysis was used to evaluate these strategies.
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    Development of mathematical competency in different German pre-vocational training programmes of the transition system 

    Weißeno, Simon; Seeber, Susan; Kosanke, Janna; Stange, Constanze
    Empirical Research in Vocational Education and Training 2016; 8(1)
    Background: Mathematical competency is central to life in modern society, and it is particularly important for many occupations and professions. In Germany, young people with insufficient mathematical skills experience significant difficulties securing a training position within the dual system, and subsequently, they often enrol in prevocational programmes of the transition system. Thus, the various one-year pre-vocational training programmes aim to provide support for enhancing mathematical skills. Currently, there is a lack of information regarding whether fundamental competencies are effectively developed within the context of these pre-vocational training. Methods: Therefore, this paper examines how competencies develop and are enhanced over the course of 1 year, based on data (N = 1.258) from three different 1-year pre-vocational programmes. Growth was based on a multidimensional mathematical competency construct measured at two distinct points: at the beginning and at the end of the pre-vocational training. Results and discussion: Incorporating selected background variables, the results of the stable and valid measurement indicate that, on average, mathematical competencies did not change over the course of 1 year. However, when development was considered in greater depth, a second dimension became visible. Specifically, the mathematical competencies of one group of young people were lower after completing the prevocational programme than they were before, whereas another group achieved recognizable improvements in their competencies. Keywords: Prevocational education and training, Mathematical competency, Development of mathematical competency
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    Measuring the Biodegradability of Plastic Polymers in Olive-Mill Waste Compost with an Experimental Apparatus 

    Castellani, Francesco; Esposito, Alessandro; Stanzione, Vitale; Altieri, Roberto
    Advances in Materials Science and Engineering 2016; 2016 p.1-7: Art. 6909283
    The use of biodegradable polymers is spreading in agriculture to replace those materials derived from petroleum, thus reducing the environmental concerns. However, to issue a significant assessment, biodegradation rate must be measured in case-specific standardized conditions. In accordance with ISO 14855-1, we designed and used an experimental apparatus to evaluate the biodegradation rate of three biopolymers based on renewable resources, two poly(𝜀-caprolactone) (PCL) composites, and a compatibilized polylactic acid and polybutyrate (PLA/PBAT) blend. Biodegradation tests were carried out under composting condition using mature olive-mill waste (OMW) compost as inoculum. Carbon dioxide emissions were automatically recorded by infrared gas detectors and also trapped in saturated Ba(OH)2 solution and evaluated via a standard titration method to check the results. Some of the samples reached more than 80% biodegradation in less than 20 days. Both the experimental apparatus and the OMWcompost showed to be suitable for the cases studied.
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    Spline-based procedures for dose-finding studies with active control. 

    Helms, Hans-Joachim; Benda, Norbert; Zinserling, Jörg; Kneib, Thomas; Friede, Tim
    Statistics in medicine 2015-01-30; 34(2) p.232-248
    In a dose-finding study with an active control, several doses of a new drug are compared with an established drug (the so-called active control). One goal of such studies is to characterize the dose-response relationship and to find the smallest target dose concentration d(*), which leads to the same efficacy as the active control. For this purpose, the intersection point of the mean dose-response function with the expected efficacy of the active control has to be estimated. The focus of this paper is a cubic spline-based method for deriving an estimator of the target dose without assuming a specific dose-response function. Furthermore, the construction of a spline-based bootstrap CI is described. Estimator and CI are compared with other flexible and parametric methods such as linear spline interpolation as well as maximum likelihood regression in simulation studies motivated by a real clinical trial. Also, design considerations for the cubic spline approach with focus on bias minimization are presented. Although the spline-based point estimator can be biased, designs can be chosen to minimize and reasonably limit the maximum absolute bias. Furthermore, the coverage probability of the cubic spline approach is satisfactory, especially for bias minimal designs.
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    Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011. 

    Tahden, Maike; Manitz, Juliane; Baumgardt, Klaus; Fell, Gerhard; Kneib, Thomas; Hegasy, Guido
    PloS one 2016; 11(10): Art. e0164508
    In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence.
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    Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain 

    Laura, Ríos-Pena; Carmen, Cadarso-Suárez; Thomas, Kneib; Manuel, Marey-Pérez
    Procedia Environmental Sciences 2015; 27 p.123-126
    Studies on causes and dynamics of wildfires make an important contribution to environmental. In the north of Spain, Galicia is one of the areas in which wildfires are the main cause of forest destruction. The main aim of this work is to model geographical and environmental effects on the risk of wildfires in Galicia using flexible regression techniques based on Structured Additive Regression (STAR) models. This methodology represents a new contribution to the classical logistic Generalized Linear Models (GLM) and Generalized Additive Models (GAM), commonly used in this environmental context. Their advantage lies on the flexibility of including spatial and temporal covariates, jointly with the other continuous covariates information. Moreover, these models generate maps of both structured and the unstructured effects, and they plotted separately. Working at spatial scales with a voxel resolution level of 1Km x 1Km per day, with the possibility of mapping the predictions in a color range, the binary STAR model represents an important tool for planning and management for the prevention of wildfires. Also, this statistical tool can accelerate the progress of fire behavior models that can be very useful for developing plans of prevention and firefighting.
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    Markov-switching generalized additive models 

    Langrock, Roland; Kneib, Thomas; Glennie, Richard; Michelot, Théo
    Statistics and Computing
    We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation.We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate.
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    Structured Additive Regression Models: An R Interface to BayesX 

    Umlauf, Nikolaus; Adler, Daniel; Kneib, Thomas; Lang, Stefan; Zeileis, Achim
    Journal of Statistical Software 2015; 63(21)
    Structured additive regression (STAR) models provide a exible framework for modeling possible nonlinear e ects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of e ects, e.g., for geographical or spatio-temporal data, allowing for speci - cation of complex and realistic models. BayesX is standalone software package providing software for tting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently speci ed using R's formula language (with some extended terms), tted using the BayesX binary, represented in R with objects of suitable classes, and nally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing tted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.
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    Laboratory experiments in innovation research: a methodological overview and a review of the current literature 

    Brüggemann, Julia; Bizer, Kilian
    Journal of Innovation and Entrepreneurship 2016; 5(1)
    Innovation research has developed a broad set of methodological approaches in recent decades. In this paper, we propose laboratory experiments as a fruitful methodological addition to the existing methods in innovation research. Therefore, we provide an overview of the existing methods, discuss the advantages and limitations of laboratory experiments, and review experimental studies dealing with different fields of innovation policy, namely intellectual property rights, financial instruments, payment schemes, and R&D competition. These studies show that laboratory experiments can fruitfully complement the established methods in innovation research and provide novel empirical evidence by creating and analyzing counterfactual situations.
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    Bilateral Trade Flows and Income Distribution Similarity. 

    Martínez-Zarzoso, Inmaculada; Vollmer, Sebastian
    PloS one 2016; 11(5): Art. e0128191
    Current models of bilateral trade neglect the effects of income distribution. This paper addresses the issue by accounting for non-homothetic consumer preferences and hence investigating the role of income distribution in the context of the gravity model of trade. A theoretically justified gravity model is estimated for disaggregated trade data (Dollar volume is used as dependent variable) using a sample of 104 exporters and 108 importers for 1980-2003 to achieve two main goals. We define and calculate new measures of income distribution similarity and empirically confirm that greater similarity of income distribution between countries implies more trade. Using distribution-based measures as a proxy for demand similarities in gravity models, we find consistent and robust support for the hypothesis that countries with more similar income-distributions trade more with each other. The hypothesis is also confirmed at disaggregated level for differentiated product categories.
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    Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale. 

    Buck, Christoph; Kneib, Thomas; Tkaczick, Tobias; Konstabel, Kenn; Pigeot, Iris
    International journal of health geographics 2015; 14(1): Art. 35
    BACKGROUND: Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA. METHODS: We simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to [Formula: see text]  years) and school children (6-9.9 years), and were adjusted for age, body mass index (BMI), education and safety concerns of parents, season and valid weartime of accelerometers. RESULTS: Association between intensity measures and MVPA strongly differed by network-distance, with stronger effects found for larger network-distances. Simple intensity revealed smaller effect estimates and smaller goodness-of-fit compared to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. CONCLUSION: We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial scales and kernel intensity methods might reduce methodological limitations in assessing opportunities for PA in the built environment.
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    Do Transfer Costs Matter for Foreign Remittances? A Gravity Model Approach 

    Ahmed, Junaid; Martínez-Zarzoso, Inmaculada
    Economics: The Open-Access, Open-Assessment E-Journal 2016; 10(4): Art. 10.5018
    Using bilateral data on remittance flows to Pakistan for 23 major host countries, this is the first study that examines the effect of transaction costs on foreign remittances. The authors find that the effect of transaction costs on remittance flows is negative and significant; suggesting that a high cost will either refrain migrants from sending money back home or make them remit through informal channels. They also find that remittances are facilitated by the existence of migrant networks and improvements in home and host country financial services. Distance, which has been used in previous studies as an indicator of the cost of remitting, is found to be a poor proxy.
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    Economic and ecological trade-offs of agricultural specialization at different spatial scales 

    Klasen, Stephan; Meyer, Katrin M.; Dislich, Claudia; Euler, Michael; Faust, Heiko; Gatto, Marcel; Hettig, Elisabeth; Melati, Dian N.; Jaya, I. Nengah Surati; Otten, Fenna; et al.
    Pérez-Cruzado, CésarSteinebach, StefanieTarigan, SuriaWiegand, Kerstin
    Ecological Economics 2016; 122 p.111-120
    Specialization in agricultural systems can lead to trade-offs between economic gains and ecosystem functions. We suggest and explore a conceptual framework in which economic gains can be maximized when production activities are specialized at increasingly broader scales (from the household to the village, region or above), particularly when markets for outputs and inputs function well. Conversely,more specialization likely reduces biodiversity and significantly limits ecosystem functions. When agricultural specialization increases and moves to broader scales as a result of improved infrastructure and markets or other drivers, ecosystem functions can also be endangered at broader spatial scales. Policies to improve agricultural incomes may influence the level of specialization at different scales and thus affect the severity of the trade-offs. This paper takes Jambi province in Indonesia, a current hotspot of rubber and oil palm monoculture, as a case study to illustrate these issues.We empirically show that the level of specialization differs across scales with higher specialization at household and village levels and higher diversification towards the province level. We discuss ways to resolve trade-offs between economic gains and ecological costs, including landscape design, targeted policies, and adoption of longterm perspectives.
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