Artificial Photosynthesis Would Unify the Electricity-Carbohydrate-Hydrogen Cycle for Sustainability

Sustainable development requires balanced integration of four basic human needs – air (O2/CO2), water, food, and energy. To solve key challenges, such as CO2 fixation, electricity storage, food production, transportation fuel production, water conservation or maintaining an ecosystem for space travel, we wish to suggest the electricity-carbohydrate-hydrogen (ECHo) cycle, where electricity is a universal energy carrier, hydrogen is a clean electricity carrier, and carbohydrate is a high-energy density hydrogen (14.8 H2 mass% or 11-14 MJ electricity output/kg)carrier plus a food and feed source. Each element of this cycle can be converted to the other reversibly & efficiently depending on resource availability, needs, and costs. In order to implement such cycle, here we propose to fix carbon dioxide by electricity or hydrogen to carbohydrate (starch) plus ethanol by cell-free synthetic biology approaches. According to knowledge in the literature, the proposed artificial photosynthesis must be operative. Therefore, collaborations are urgently needed to solve several technological bottlenecks before large-scale implementation

Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering

Existential rules, also known as data dependencies in Databases, have been recently rediscovered as a promising family of languages for Ontology-based Query Answering. In this paper, we prove that disjunctive embedded dependencies exactly capture the class of recursively enumerable ontologies in Ontology-based Conjunctive Query Answering (OCQA). Our expressive completeness result does not rely on any built-in linear order on the database. To establish the expressive completeness, we introduce a novel semantic definition for OCQA ontologies. We also show that neither the class of disjunctive tuple-generating dependencies nor the class of embedded dependencies is expressively complete for recursively enumerable OCQA ontologies.Comment: 10 pages; the full version of a paper to appear in IJCAI 2016. Changes (regarding to v1): a new reference has been added, and some typos have been correcte

Model Selection for Gaussian Mixture Models

This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified EM algorithm is developed to simultaneously select the number of components and to estimate the mixing weights, i.e. the mixing probabilities, and unknown parameters of Gaussian distributions. Simulations and a real data analysis are presented to illustrate the performance of the proposed method