Desenvolvimento e avaliação das características nutricionais, físico-químicas e sensoriais de bolo com diferentes tipos de farinhas e Castanha-do-Brasil (Bertholletia excelsa H. B. K.)

Trabalho de Conclusão de Curso apresentado ao Departamento de Engenharia de Alimentos da Fundação Universidade Federal de Rondônia, campus de Ariquemes, para obtenção do título de Bacharel em Engenharia de Alimentos. Orientador: Prof.ª Dr.a Tânia Maria Alberte.A procura por alimentos mais nutritivos e saudáveis vem despertando interesse por uma expressiva parcela da população. Assim, surge à ideia do enriquecimento de alimentos. Existem diversos trabalhos com substituição da farinha de trigo em alimentos por farinhas alternativas. A utilização de diferentes tipos de farinhas vem sendo empregada nas indústrias no anseio de inovar e agregar valor a produtos já presentes no mercado, utilizando-se diversos tipos farinhas de frutos regionais. Este trabalho estudou o enriquecimento de bolos em relação ao uso da castanha-do-brasil, do açúcar mascavo e das farinhas de açaí, pupunha e tucumã. O bolo de pupunha, teve a maior pontuação no teste de ordenação, diferindo significativamente em relação ao bolo de açaí, porém não apresentando diferença significativa em relação ao bolo de tucumã. O índice de aceitação do bolo de pupunha foi de 77,1 %, apresentando uma boa aceitação pelos consumidores. O bolo de açaí apresentou 31,2 % de umidade, pH 7,4, acidez de 4,1 %, açúcar total 33,9 %, fibra bruta 10,9 %, proteína bruta 9,4 %, lipídios 9,9 % e valor energético de 262,2 kcal.100 g -1 . No de pupunha apresentou 36,3 % de umidade, pH 7,1, acidez de 2,5 %, açúcar total 30,1 %, fibra bruta 3,0 %, proteína bruta 9,4 %, lipídios 14,5 % e valor energético de 288,1 kcal.100 g -1 . O bolo de tucumã apresentou 23,1 % de umidade, pH 7,3, acidez de 4,1 %, açúcar total 35,7 %, fibra bruta 4,9 %, proteína bruta 12,1 %, lipídios 16,9 % e valor energético de 336,2 kcal.100 g -1 . Portanto os bolos elaborados com farinha de pupunha, açaí e tucumã são boas fontes de proteína, fibras, carboidratos e lipídeos. Vale ressaltar a importância de novos estudos em relação às farinhas obtidas, pois estas apresentam características essenciais para o dia-a-dia da população

Performance test of QU-fitting in cosmic magnetism study

QU-fitting is a standard model-fitting method to reconstruct distribution of magnetic fields and polarized intensity along a line of sight (LOS) from an observed polarization spectrum. In this paper, we examine the performance of QU-fitting by simulating observations of two polarized sources located along the same LOS, varying the widths of the sources and the gap between them in Faraday depth space, systematically. Markov Chain Monte Carlo (MCMC) approach is used to obtain the best-fit parameters for a fitting model, and Akaike and Bayesian Information Criteria (AIC and BIC, respectively) are adopted to select the best model from four fitting models. We find that the combination of MCMC and AIC/BIC works fairly well in model selection and estimation of model parameters in the cases where two sources have relatively small widths and a larger gap in Faraday depth space. On the other hand, when two sources have large width in Faraday depth space, MCMC chain tends to be trapped in a local maximum so that AIC/BIC cannot select a correct model. We discuss the causes and the tendency of the failure of QU-fitting and suggest a way to improve it.Comment: 8 pages, 9 figures, submitted to MNRA

A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems

Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants

Flora robotica -- An Architectural System Combining Living Natural Plants and Distributed Robots

Key to our project flora robotica is the idea of creating a bio-hybrid system of tightly coupled natural plants and distributed robots to grow architectural artifacts and spaces. Our motivation with this ground research project is to lay a principled foundation towards the design and implementation of living architectural systems that provide functionalities beyond those of orthodox building practice, such as self-repair, material accumulation and self-organization. Plants and robots work together to create a living organism that is inhabited by human beings. User-defined design objectives help to steer the directional growth of the plants, but also the system's interactions with its inhabitants determine locations where growth is prohibited or desired (e.g., partitions, windows, occupiable space). We report our plant species selection process and aspects of living architecture. A leitmotif of our project is the rich concept of braiding: braids are produced by robots from continuous material and serve as both scaffolds and initial architectural artifacts before plants take over and grow the desired architecture. We use light and hormones as attraction stimuli and far-red light as repelling stimulus to influence the plants. Applied sensors range from simple proximity sensing to detect the presence of plants to sophisticated sensing technology, such as electrophysiology and measurements of sap flow. We conclude by discussing our anticipated final demonstrator that integrates key features of flora robotica, such as the continuous growth process of architectural artifacts and self-repair of living architecture.Comment: 16 pages, 12 figure

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Airborne LiDAR reveals a vast archaeological landscape at the Nan MadolWorld Heritage Site

An airborne LiDAR survey of the Nan MadolWorld Heritage Site and adjacent Temwen Island revealed a complex, irrigated cultivation system, the first found in the Central and Eastern Caroline Islands. This informs the goals of the sustainable conservation project, funded by the U.S. Department of State Ambassadors Fund for Cultural Preservation, that inspired the survey, and expands understanding of Nan Madol and its place in the network of Pacific island interaction and trade. Fieldwork verified the presence, across Temwen, of low, wet, cultivable areas, many of which are connected by water channels or separated by earthen berms. The berms themselves may also have been cultivated. In complexity, labor investment, and organization, the system is comparable to Nan Madol itself, the largest archaeological site in Micronesia, with structures on about 100 artificial islets built of stone and coral on a reef flat. Constructed over a millennium, Nan Madol was the seat of the Saudeleur Dynasty, which persisted from about 1200 to 1600 CE. The cultivation system appears to have been able to provide ample food for consumption, feasting, and redistribution or trade. If the landscape alteration described here proves to date to the time of the Saudeleur Dynasty, it will offer many avenues of research into the economic basis of Nan Madol's regional dominance.This research was funded by the U.S. Department of State via an Ambassadors Fund for Cultural Preservation large grant, award number SLMAQM18GR222

Fostering effective and sustainable scientific collaboration and knowledge exchange: a workshop-based approach to establish a national ecological observatory network (NEON) domain-specific user group

The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges