Research on economic diversification and complexity has made significant advances in understanding economic development processes, but has only recently explored environmental and social sustainability considerations. In this article we evaluate the current state of this emerging literature and reveal 13 research gaps. A total of 35 different keywords and methods from structured literature reviews and network science helped to identify 374 scientific articles between 1988 and 2020 and revealed a fragmented research landscape around three larger network communities: (1) industrial policies, climate change, and green growth; (2) economic complexity and its association with inequality and environmental sustainability; and (3) economic diversification, including studies on livelihood diversification in poor areas. Economic complexity research applies new empirical methods and considers both social and environmental sustainability, but seldom scrutinizes theory and policy. Industrial policy research focuses on green growth policies but tends to omit social sustainability issues and advanced empirical methods. Research on economic diversification in poor regions provides insights on the livelihood diversification of farmers, but is disconnected from the economic complexity and industrial policy research. This review helps to summarize the main contributions and shows pathways for potential mutual learning between these communities for the sake of sustainable development.

Many methods based on biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for deceased individuals, such biometric measurements are not available. In such cases, parts of the human skeleton can be used for identification, such as dental records, thorax, vertebrae, shoulder, and frontal sinus. It has been established in prior investigations that the radiographic pattern of frontal sinus is highly variable and unique for every individual. This has stimulated the proposition of measurements of the frontal sinus pattern, obtained from xray films, for skeletal identification. This paper presents a frontal sinus recognition method for human identification based on Image Foresting Transform and shape context. Experimental results (ERR = 5,82%) have shown the effectiveness of the proposed method.

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