Evaluation of reusable cardboard box designs: Biomechanical and perceptual aspects

Reusable cardboard boxes can be ergonomically designed for internal transportation of dry products in industrial settings. In this study we compared the effects of handling a regular commercial box and two cardboard prototypes on upper limb postures through the evaluation of movements, myoelectrical activity, perceived grip acceptability and capacity for reuse. The ergonomic designs provided a more acceptable grip, safer wrist and elbow movements and lower wrist extensors and biceps activity. Biomechanical disadvantages were observed only for one of the prototypes when handling to high surface. The prototypes were durable and suitable for extensive reuse (more than 2000 handlings) in internal industrial transportation. Despite being slightly more expensive than regular cardboard, the prototypes showed good cost-benefit considering their high durability. Relevance to industry: Cardboard boxes can be efficiently redesigned for allowing safer upper limb movements and lower muscle workload in manual materials handling. New designs can also be extensively reused for internal industrial transportation with good cost-benefit. © 2012 Elsevier B.V.

Industrialization in Latin America: from the black box" to the "empty box": a comparison of contemporary industrialization patterns"

Includes bibliography

Caracterização molecular e biológica do Lettuce big-vein associated vírus e Mirafiori lettuce big-vein vírus e estudo da ocorrência em relação à época e sintoma em plantas de alface no Estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Big data and open data as sustainability tools: A working paper prepared by the Economic Commission for Latin America and the Caribbean

The two main forces affecting economic development are the ongoing technological revolution and the challenge of sustainability. Technological change is altering patterns of production, consumption and behaviour in societies; at the same time, it is becoming increasingly difficult to ensure the sustainability of these new patterns because of the constraints resulting from the negative externalities generated by economic growth and, in many cases, by technical progress itself. Reorienting innovation towards reducing or, if possible, reversing the effects of these externalities could create the conditions for synergies between the two processes. Views on the subject vary widely: while some maintain that these synergies can easily be created if growth follows an environmentally friendly model, summarized in the concept of green growth, others argue that production and consumption patterns are changing too slowly and that any technological fix will come too late. These considerations apply to hard technologies, essentially those used in production. The present document explores the opportunities being opened up by new ones, basically information and communication technologies, in terms of increasing the effectiveness (outcomes) and efficiency (relative costs) of soft technologies that can improve the way environmental issues are handled in business management and in public policy formulation and implementation.

An assessment of big data for official statistics in the Caribbean: Challenges and opportunities

The data revolution for sustainable development has triggered interest in the use of big data for official statistics such that theUnited Nations Economic and Social Council considers it to be almost an obligation for statistical organizations to explore big data. Big data has been promoted as a more timely and cheaper alternative to traditional sources of official data, and one that offers great potential for monitoring the sustainable development goals. However, privacy concerns, technology and capacity remain significant obstacles to the use of big data. This study makes a case for incorporating big data in official statitics in the Caribbean by highlight the opportunities that big data provides for the subregion, while suggesting ways to manage the challenges. It serves as a starting point for further discussions on the many facets of big data and provides an initial platform upon which a Caribbean big data strategy could be built.

Previsão estocástica de nível fluviométrico para cidade de Marabá-PA: método de Box-Jenkins

O Município de Marabá- PA, situado na região Amazônica, sudeste do Estado do Pará, sofre anualmente com eventos de enchentes, ocasionados pelo aumento periódico do rio Tocantins e pela situação de vulnerabilidade da população que reside em áreas de risco. A defesa civil estadual e municipal anualmente planeja e prepara equipes para ações de defesa no município. Nesta fase o monitoramento e previsão de eventos de enchentes são importantes. Portanto, com o objetivo de diminuir erros nas previsões hidrológicas para o Município de Marabá, desenvolveu-se um modelo estocástico para previsão de nível do rio Tocantins, baseado na metodologia de Box e Jenkins. Utilizou os dados de níveis diários observados nas estações hidrológicas de Marabá e Carolina e Conceição do Araguaia da Agência Nacional de Águas (ANA), do período de 01/12/ 2008 a 31/03/2011. Efetuou-se o ajustamento de três modelos (M_t, N_t e Y_t), através de diferentes aplicativos estatísticos: o SAS e o Gretl, usando diferentes interpretações do comportamento das séries para gerar as equações dos modelos. A principal diferença entre os aplicativos é que no SAS usa o modelo de função de transferência na modelagem. Realizou-se uma classificação da variabilidade do nível do rio, através da técnica dos Quantis para o período de 1972 a 2011, examinando-se apenas as categorizações de níveis ACIMA e MUITO ACIMA do normal. Para análise de impactos socioeconômicos foram usados os dados das ações da Defesa Civil Estado do Pará nas cheias de 2009 e 2011. Os resultados mostraram que o número de eventos de cheias com níveis MUITO ACIMA do normal, geralmente, podem estar associados a eventos de La Niña. Outro resultado importante: os modelos gerados simularam muito bem o nível do rio para o período de sete dias (01/04/2011 a 07/04/2011). O modelo multivariado N_t (com pequenos erros) representou o comportamento da série original, subestimando os valores reais nos dias 3, 4 e 5 de abril de 2011, com erro máximo de 0,28 no dia 4. O modelo univariado (Y_t) teve bons resultados nas simulações com erros absolutos em torno de 0,12 m. O modelo com menor erro absoluto (0,08m) para o mesmo período foi o modelo M_t, desenvolvido pelo aplicativo SAS, que interpreta a série original como sendo não linear e não estacionária. A análise quantitativa dos impactos fluviométricos, ocorridos nas enchentes de 2009 e 2011 na cidade de Marabá, revelou em média que mais de 4 mil famílias sofrem com estes eventos, implicado em gastos financeiros elevados. Logo, conclui-se que os modelos de previsão de níveis são importantes ferramentas que a Defesa Civil, utiliza no planejamento e preparo de ações preventivas para o município de Marabá.

Previsão da velocidade dos ventos por redes neurais artificiais e arima de box & jenkins

Pós-graduação em Engenharia Elétrica - FEIS

Estudo dos problemas de corte e empacotamento

Pós-graduação em Engenharia Elétrica - FEIS

microRNA156-targeted SPL/ SBP box transcription factors regulate tomato ovary and fruit development

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Distribution and population structure of the flecked box crab Hepatus pudibundus (Decapoda, Brachyura) in the western South Atlantic

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Big, bad, and beautiful: phylogenetic relationships of the horned frogs (Anura: Ceratophryidae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)