Custo de produção de leitões em diferentes sistemas de produção: um estudo de caso no Oeste catarinense.

Este texto é o relato do estudo de caso de um estabelecimento suinícola no município de Presidente Castello Branco na região Oeste Catarinense. O objetivo foi estimar o custo de produção de leitões no atual cenário, no qual 33% do consumo de milho são produzidos no estabelecimento e, também, em dois cenários alternativos, um com autossuficiência em milho e o outro especializado em suínos. O estudo de caso utiliza uma abordagem sistêmica do estabelecimento agropecuário. As fontes foram entrevistas, software de gestão e preços fornecidos por órgãos públicos. O custo de produção foi calculado com planilha eletrônica desenvolvida pela rede Interpig. Os três diferentes cenários determinaram diferentes preços do milho, principal componente no custo de suínos. No sistema atual o custo de produção foi de R$88,22 por leitão, enquanto que no sistema especializado foi R$94,97 por leitão e, por fim, R$77,90 por leitão no sistema autossuficiente em milho, que apresentou o menor custo. This text is the report of a case study of a swine farm in the municipality of Presidente Castello Branco in the Western region of Santa Catarina. The objective was to estimate the cost of production of piglets on the actual scenario, in which 33$88,22 per piglet, while on the specialized system it was R$94,97 per piglet and, finally, R$77,90 per piglet on the self-sufficient system, which presented the lowest cost

Potencial produtivo do milho em resposta ao uso de dejetos suínos e adubação química.

Projeto/Plano de Ação: 05.12.50200-03

Incidental findings detected with panoramic radiography: prevalence calculated on a sample of 2017 cases treated at a major Italian trauma and cancer centre

Objectives: This study aimed to assess the prevalence of incidental findings, not strictly related to dentistry, viewed with panoramic radiography. Methods: Panoramic radiographs performed between December 2013 and June 2016 were retrospectively collected. These images were analyzed, searching for incidental findings. All the information collected was statistically analysed Results: A total of 2307 Panoramic Radiograph were analyzed and 2017 of them were included in the study. 529 incidental findings were seen: 255 (48.2%) were ESP (Elongation of Styloid Process), 167 were CAC (Carotid Artery Calcification) (31.57%), 36 were maxillary sinus pathologies (6.8%) and 71 were other incidental findings (13.42%). The total prevalence of IF was 26, 23%., CAC was 8.28% in the total population, and it was higher in women (9.82%) than men (6.54%). 48.5% of CAC were bilateral. When unilateral, the right side showed a higher right side prevalence. The prevalence of ESP was 12.64% in total population (men: 13.82%; women: 11.60%). 84.71% of ESP were bilateral and, when present unilaterally, no side difference was seen. 13.33% of the ESP appeared segmented. The prevalence of maxillary sinus pathologies was 1.78% (men: 2.32%; women: 1.31%). Only 8.33% of these pathologies were bilateral, and, when unilateral, they were mostly present on the right side. Between the 71 other IF (prevalence: 3.52%), sialoliths and tonsilloliths were assessed most frequently. Conclusion: Due to the high prevalence of incidental findings detected with panoramic radiography, dental practitioners should be aware of the various pathologic conditions seen on the panoramic radiographs

Snow metamorphism: a fractal approach

Snow is a porous disordered medium consisting of air and three water phases: ice, vapour and liquid. The ice phase consists of an assemblage of grains, ice matrix, initially arranged over a random load bearing skeleton. The quantitative relationship between density and morphological characteristics of different snow microstructures is still an open issue. In this work, a three-dimensional fractal description of density corresponding to different snow microstructure is put forward. First, snow density is simulated in terms of a generalized Menger sponge model. Then, a fully three-dimensional compact stochastic fractal model is adopted. The latter approach yields a quantitative map of the randomness of the snow texture, which is described as a three-dimensional fractional Brownian field with the Hurst exponent H varying as continuous parameter. The Hurst exponent is found to be strongly dependent on snow morphology and density. The approach might be applied to all those cases where the morphological evolution of snow cover or ice sheets should be conveniently described at a quantitative level

When strategy meets purpose.

Effectively realizing corporate purpose requires mediating, integrating, and balancing the interests of different corporate stakeholders and their goals. And the trade-offs that result from such a balancing act need to be acknowledged, measured, and communicated as the organization reports the efforts and performance connected to the creation of long-term sustainable value. Moving from the articulation of a company’s purpose to execution requires strategy and capital allocation with a comprehensive management system able to link sustainability strategies with financial returns. It involves reconciling competitiveness and sustainable growth within the context of an inclusive business model to take advantage of the opportunities and face the challenges of the market. Importantly, this requires the development of new measurement practices that can capture whole processes of long-term value creation—taking into account the social and environmental externalities produced by its operations and, especially, its products and services, as well as how the multiple and heterogeneous resources provided by the company’s stakeholders contribute to its financial and nonfinancial performance. Today, the investment community broadly recognizes the need to better understand how material environmental, social, and governance (ESG) issues matter to financial performance

Energy Decisions Within an Applied Ethics Framework: Analysis of Five Recent Controversies

Everywhere in the world, and in every period of human history, it has been common for energy decisions to be made in an ethically haphazard manner. With growing population pressure and increasing demand for energy, this approach is no longer viable. We believe that decision makers must include ethical considerations in energy decisions more routinely and systematically. To this end, we propose an applied ethics framework that accommodates principles from three classical ethical theories—virtue ethics, deontology, consequentialism, and two Native American ethics (Lakota and Navajo)—all considered from the perspectives of the impacted communities. We illustrate this framework by evaluating five recent energy decisions: the Dakota Access Pipeline, the Navajo Nation’s possible transition from coal to solar, hydraulic fracturing in Pennsylvania, uranium mining in Virginia, and the construction of the Xiaolangdi Dam in China. An applied ethics framework is preferable to existing ethical analyses because it can serve to sharpen arguments for (un)ethical decisions and action. Rather than treat ethical reasoning as a matter of opinion, we argue that applying ethical principles in a universal and standardized way adds rigor to energy sector decisions by presenting a position available for objective scrutiny. Because our framework identifies which aspects of a targeted action (if any) must adjust to improve ethical merit, it can serve as a practical tool for improving decision-making as we enter a new era of energy transitions

Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho

Open Boundaries for the Nonlinear Schrodinger Equation

We present a new algorithm, the Time Dependent Phase Space Filter (TDPSF) which is used to solve time dependent Nonlinear Schrodinger Equations (NLS). The algorithm consists of solving the NLS on a box with periodic boundary conditions (by any algorithm). Periodically in time we decompose the solution into a family of coherent states. Coherent states which are outgoing are deleted, while those which are not are kept, reducing the problem of reflected (wrapped) waves. Numerical results are given, and rigorous error estimates are described. The TDPSF is compatible with spectral methods for solving the interior problem. The TDPSF also fails gracefully, in the sense that the algorithm notifies the user when the result is incorrect. We are aware of no other method with this capability.Comment: 21 pages, 4 figure

Autotuning Algorithmic Choice for Input Sensitivity

Empirical autotuning is increasingly being used in many domains to achieve optimized performance in a variety of different execution environments. A daunting challenge faced by such autotuners is input sensitivity, where the best autotuned configuration may vary with different input sets. In this paper, we propose a two level solution that: first, clusters to find input sets that are similar in input feature space; then, uses an evolutionary autotuner to build an optimized program for each of these clusters; and, finally, builds an adaptive overhead aware classifier which assigns each input to a specific input optimized program. Our approach addresses the complex trade-off between using expensive features, to accurately characterize an input, and cheaper features, which can be computed with less overhead. Experimental results show that by adapting to different inputs one can obtain up to a 3x speedup over using a single configuration for all inputs