Infant and young child growth and nutrition in urban informal settlements in Mumbai, India

The overarching question addressed in the thesis is: What are the relationships between socioeconomic position, parental characteristics, and infant and young child growth and nutrition in urban informal settlements (slums) in Mumbai, India? I answer this question using data from the SNEHA Centres Infant Nutrition Cohort study, an epidemiologic birth cohort of 978 infants born between March 2013 and March 2014 in 20 informal settlements in Mumbai, and followed up till April 2016. After introducing the topic in Chapter 1, I present a systematic review of longitudinal studies in Chapter 2 to identify the determinants of linear growth in infancy and early childhood. Chapter 3 details the cohort’s study design, implementation, and data collection procedures. In Chapter 4 I describe how I used these data to derive my main study variables. Chapter 5 presents a profile of the cohort at birth, outlining key infant, parental and household socioeconomic characteristics. I also investigate patterns and predictors of missing data and non-response in longitudinal data. In Chapter 6 I identify the determinants of linear growth between 0-37 months using the SITAR model to fit growth curves to 16 753 length measurements for 944 children. I quantify the relationship between parental anthropometry and child growth. In Chapter 7 I describe infant and young child feeding practices, and investigate the relationships between baseline characteristics and longitudinal feeding patterns using discrete-time survival and dynamic autoregressive models. In Chapter 8 I investigate whether the relationship between predominant breastfeeding (0-5 months) and predicted length at 24 months is mediated by consumption of animal source foods at 6-23 months using causal mediation analysis. Chapter 9 begins with a summary of the main findings of my research. I discuss the empirical and methodologic implications of my study

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Energy Supply within Sustainable Agricultural Production: Challenges, Policies and Mechanisms

Providing the security of a broad-based energy and slowing the speed of climate change are the main challenges today of the basic of legal framework to stimulate the development of alternative energy sources. Energy from renewable sources is one part of the system, which not only enables to provide energy self-sufficiency, but also contributes to the reduction heating of the Earth’s atmosphere. International climate agreements indicate the need to intensify the prevention of global warming and accelerate the reduction in CO2 emissions. The implementation of such challenging plans as outlined in the European Green Deal or "Fit for 55," among others, entails the almost complete elimination of GHG emissions in the energy sector, which can be very challenging for some member states. In the EU, the preferred direction of development of RES use is distributed generation and increasing the share of the use of by-products and organic waste for the production biofuels. This creates great opportunities for rural areas, which until the last century were identified with agriculture and the production of food or raw materials. While the role of agriculture will not diminish, as incomes are rising in relatively poor countries with a high elasticity of demand for food, these areas will increasingly perform a number of other important functions as well. The production of energy raw materials and energy, which is no longer a mere idea, but is becoming, thanks to the development of new technologies, a mainstream energy sector that can make contribution to improving energy security and achieving climate neutrality

Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure

Electricity Consumption, Export and Production: Evidence from Malaysian Manufacturing Sector

Study on the impact of energy on economic development becomes the new interest in the economy since the industrial revolution where greater amount of energy have been used in industrial production with high scale. Numeral studies have been done at the micro and macro levels to discover the role of energy and its impact on economic growth. However, little have been done to explore the essence of energy in a particular sector especially the energy based sector like manufacturing sector. This paper investigates the relationship between electricity consumption, export and production in Malaysia’s manufacturing sector in a multivariate framework. This study has two objectives. The first objective is to discover the existence of long-run relationship among the variables and the second objective is to examine the short-run causality among the variables. This is a time series analysis with the sample period covers from 1980-2010. Johansen and Juselius cointegration test is employed to discover the long-run relationship while Vector Error Correction Model (VECM) Granger causality test will be used to find out the causal relationship. We found that GDP of manufacturing sector, electricity consumption of the manufacturing sector, export of manufacturing sector, labor of the manufacturing sector and capital of the manufacturing sector are cointegrated in the long run. The VECM results show unidirectional causality running from electricity consumption of manufacturing sector to GDP of the manufacturing sector and from electricity consumption of manufacturing sector to labor of the manufacturing sector. Hence, these results indicate electricity is essential in the manufacturing sector. Keywords: Electricity consumption, output, Granger causality, cointegratio