•We compare batteries deployed in 4500 individual households with 200 communities.

•Using real demand, PV data and locations we form community microgrids.

•We find that community batteries are more effective for distributed PV integration.

•Internal rates of return depend on the number of PV households.

 

Community energy storage: A smart choice for the smart grid?
Edward Barbour, David Parra, Zeyad Awwad, Marta C.González

Applied Energy
Volume 212, 15 February 2018, Pages 489-497

Source: www.sciencedirect.com

People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators.

 

Hashemian B, Massaro E, Bojic I, Murillo Arias J, Sobolevsky S, Ratti C (2017) Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE 12(11): e0187031. https://doi.org/10.1371/journal.pone.0187031

Source: journals.plos.org

Nearly 500 years ago, in what we know call Mexico, a disease started rippling through the population.

 

It bore the name cocoliztli, meaning ‘pestilence,’ and it killed between five and 15 million people in just three years. As many plagues were at the time, it proved deadly and mysterious, burning through entire populations. Occurring centuries before John Snow’s work on cholera gave rise to epidemiology, data on the disease’s devastation was sparse. Over the years, researchers and historians attempted to pin the blame for the illness on measles, plague, viral hemorrhagic fevers like Ebola, and typhoid fever—a disease caused by a variation of the bacteria Salmonella enterica.

 

In a paper published this week in Nature Ecology & Evolution, researchers present evidence that the latter was the most likely candidate in this cast of microbial miscreants. The study was pre-printed in biorxiv last year. The researchers detected the genome of a different variety of Salmonella enterica (the specific variety is Paratyphi C) in teeth of individuals buried in a cemetery historically linked to the deadly outbreak.

 

The researchers used a technique called MALT (MEGAN Alignment Tool) to analyze DNA left behind in the pulp of the teeth. MALT takes a sample of material, in this case from a tooth, and compares it to 6,247 known bacterial genomes. The results identified Salmonella enterica in 10 burials associated with the epidemic.

Source: www.popsci.com

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system’s structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.

 

Complexity, Development, and Evolution in Morphogenetic Collective Systems
Hiroki Sayama

Source: arxiv.org