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Nigeria is one of the fastest growing African economies, yet struggles with armed conflict, poverty, and morbidity. An area of high concern is how this situation affects vulnerable families and their children. A key pathway in improving the situation for children in times of conflict is to reinforce maternal agency, for instance, through education. However, the state of the art of research lacks a clear understanding of how many years of education is needed before children benefit. Due to mother's differing social context and ability, the effect of maternal education varies. We study the heterogeneous treatment effects of maternal agency, here operationalized as length of education, on severe child undernutrition in the context of armed conflict. We deploy a repeated cross-sectional study design, using the Nigeria 2008 and 2013 Demographic and Health Survey (DHS). The sample covers 25,917 children and their respective mothers. A key methodological challenge is to estimate this heterogeneity inductively. The causal inference literature proposes a machine learning approach, Bayesian Additive Regression Trees (BART), as a promising avenue to overcome this challenge. Based on BART-estimation of the Conditional Average Treatment Effect (CATE) this study confirms earlier findings in that maternal education decreases severe child undernutrition, but only when mothers acquire an education that lasts more than the country's compulsory 9 years; that is 10 years of education and higher. This protective effect remains even during the exposure of armed conflict.

The paper explores the degree to which exposure to natural disasters and poor governance (quality of governance) is associated with absolute child poverty in sixty-seven middle- and low-income countries. The data is representative for about 2.8 billion of the world´s population. Institutionalist tend to argue that many of society's ills, including poverty, derive from fragile or inefficient institutions. However, our findings show that although increasing quality of government tends to be associated with less poverty, the negative effects of natural disasters on child poverty are independent of a country´s institutional efficiency. Increasing disaster victims (killed and affected) is associated with higher rates of child poverty. A child´s estimated odds ratio to be in a state of absolute poverty increases by about a factor of 5.7 [95% CI: 1.7 to 18.7] when the average yearly toll of disasters in the child´s country increases by one on a log-10 scale. Better governance correlates with less child poverty, but it does not modify the correlation between child poverty and natural disasters. The results are based on hierarchical regression models that partition the variance into three parts: child, household, and country. The models were cross-sectional and based on observational data from the Demographic Health Survey and the Multiple Indicator Cluster Survey, which were collected at the beginning of the twenty-first millennium. The Sustainable Development Goals are a principle declaration to halt climate change, but they lack a clear plan on how the burden of this change should be shared by the global community. Based on our results, we suggest that the development agencies should take this into account and to articulate more equitable global policies to protect the most vulnerable, specifically children.

Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the complex interactions between human activities and natural resources. Existing global urban-rural datasets such as such as GHSL-SMOD, GHS Degree of Urbanisation, and GRUMP are often spatially coarse, methodologically inconsistent, and poorly adapted to heterogeneous regions such as Africa, which limits their usefulness for policy and research. Their coarse grids and rule-based classification methods obscure small or informal settlements, and produce inconsistencies between countries. In this study, we develop a DeepLabV3-based deep learning framework that integrates multi-source data, including Landsat-8 imagery, VIIRS nighttime lights, ESRI Land Use Land Cover (LULC), and GHS-SMOD, to produce a 10 m resolution urban-rural map across the African continent from 2016 to 2022. The use of Landsat data also highlights the potential to extend this mapping approach historically, reaching back to the 1990s. The model employs semantic segmentation to capture fine-scale settlement morphology, and its outputs are validated using the Demographic and Health Surveys (DHS) dataset, which provides independent, survey-based urban-rural labels. The model achieves an overall accuracy of 65% and a Kappa coefficient of 0.47 at the continental scale, outperforming existing global products such as SMOD. The resulting High-Resolution Urban-Rural (HUR) dataset provides an open and reproducible framework for mapping human settlements, supporting UN Sustainable Development Goal (SDG) 11—Sustainable Cities and Communities—and enabling more context-aware analyses of Africa’s rapidly evolving settlement systems, which indirectly support other SDGs, such as SDG 1 (No Poverty), by distinguishing human settlement types. We release a continent-wide urban-rural dataset covering the period from 2016 to 2022, offering a new source for high-resolution settlement mapping in Africa.oxy_aqreply_end

Little research has examined associations between disaster-related home loss and multiple domains of health and well-being, with extended long-term follow-up and comprehensive adjustment for pre-disaster characteristics of survivors. We examined the longitudinal associations between disaster-induced home loss and 34 indicators of health and well-being, assessed post-disaster. We used data from a preexisting cohort study of Japanese older adults in an area directly impacted by the 2011 Japan Earthquake ( and , depending on the outcomes). The study was initiated in 2010, and disaster-related home loss status was measured in 2013 retrospectively. The 34 outcomes were assessed in 2020 and covered dimensions of physical health, mental health, health behaviors/sleep, social well-being, cognitive social capital, subjective well-being, and prosocial/altruistic behaviors. We estimated the associations between disaster-related home loss and the outcomes, using targeted maximum likelihood estimation and SuperLearner. We adjusted for pre-disaster characteristics from the wave conducted 7 months before the disaster (i.e., 2010), including prior outcome values that were available. After Bonferroni correction for multiple testing, we found that home loss (vs. no home loss) was associated with increased posttraumatic stress symptoms ( ; 95% CI: 0.35, 0.65), increased daily sleepiness (0.38; 95% CI: 0.21, 0.54), lower trust in the community ( ; 95% CI: , ), lower community attachment ( ; 95% CI: , ), and lower prosociality ( ; 95% CI: , ). We found modest evidence for the associations with increased depressive symptoms, increased hopelessness, more chronic conditions, higher body mass index, lower perceived mutual help in the community, and decreased happiness. There was little evidence for associations with the remaining 23 outcomes. Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors. https://doi.org/10.1289/EHP10903.

Twelve various quinoxaline derivative dyes, so coded from Q 1–1 to Q 1–12 , have been investigated as sensitizers in dye-sensitized solar cells (DSSCs). The simulation study has been performed using the density functional theory and time dependent (TD–DFT) methods. All derivatives have the same backbone structures, with two oxygens in fixed positions, and varying numbers and/or positions of hydroxyl groups. The TD–DFT calculations have been soundly useful to predict various dye excitation energies and absorption spectra. The influence of varying numbers and positions of oxygen atoms and hydroxyl groups on optical properties for the free dyes has been studied. With such variation, DSSC characteristics such as light-harvesting efficiency (LHE), electronic injection driving force (ΔG inj ) and dye regeneration spontaneity (ΔG reg ) have all been studied. Among the series, the Q 1–7 @ ZnTiO 3 interface shows the highest (ΔG inj ) (−2.829 eV) and LHE (0.677). The results indicate that hydroxyl group (OH) numbers and positions influence the DSSC microscopic properties, and consequently the macroscopic properties such as short circuit current density ( J SC ) and open circuit potential ( V OC ).

[This corrects the article DOI: 10.1371/journal.pone.0153296.].

Food entitlement decline (FED) and food availability decline (FAD) are two approaches to explaining famines that have different policy implications. One focuses on the systemic level, whereas the other is concerned with the individual level. They therefore analyse relatively distinct causal mechanisms. Thus, an important question is whether these approaches can be reconciled. Another related question is how FAD- and FED-based explanations relate to classical Malthusian views about rapid food requirement increase (FRI). This paper analyses these questions and argues that these three approaches can indeed be reconciled within a single framework by outlining the causal sources of FAD, FED and FRI. This task requires, among other things, the separation of ontological categories and empirical measures. As a consequence of this argument, the paper suggests that there are only seven possible ontological combinations of how a famine situation can arise as a direct cause. Simultaneously, it maintains that there are virtually an infinite number of ways in which these combinations may act as indirect causes (rooted in economic, political and social conditions). The analysis is exemplified by the Bengal famine of 1943 because that famine is a well-known case. The wider research and policy applicability of this general account are discussed but have yet to be tested in relation to other scarcity cases (water, land, fish). This synthesis is made possible by the incorporation of critical realist interventions into economic theory.

“Atoms are the new bits”. That is the latest buzz arising from the Californian trade press. What do we get when this dictum is sampled with the old rallying cry: “Information wants to be free”? We suggest that the predominant, bounded critique of intellectual property is thereby destabilised. Constitutive of that critique was the exceptionality attributed to information goods (bits) vis-a-vis tangible goods (atoms). It was thus intellectual property could be presented as something altogether different from private property. We recognise that this way of framing the issue has had tactical advantages, but contend that it has stood in the way of a deeper understanding of what intellectual property is. When the critique of proprietary software is expanded by an emerging movement for open hardware development, however, the boundary between intellectual property and property as such crumbles. This enables us to renew our critique of the political economy of information.

Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables—over 90 variables—included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.

The problem of scarcity is often talked about, but it is rarely clearly defined. In this article, two different views of scarcity are outlined: absolute and relative scarcity. These two are respectively exemplified by Malthus's and Robbins's views of scarcity. However, both of these views tend to naturalize and universalize scarcity, and thus overlook abundance and sufficiency, which are important states in the social provisioning process. It is argued that this is due to ignorance of the sociocultural causal underpinnings of scarcity, abundance, and sufficiency (SAS). The introduction of these mechanisms enables further conceptual differentiation of SAS (e.g., quasi-, artificial-, natural-).