Evaluación de pollos camperos en producción intensiva y semi-intensiva con suplementación de extracto de quillaja y residuos de hortalizas

En la presente investigación se evaluó: la ganancia diaria de peso, consumo semanal, índice de productividad, mortalidad, conversión alimenticia, costo por kg de carne, pigmentación en tarsos, porcentaje de grasa y el efecto del extracto de quillaja como coccidiostato, bajo dos sistemas de crianza, intensiva y semi-intensiva. El extracto de quillaja fue utilizado al 0,1% de inclusión en el alimento. La investigación se llevó a cabo en la provincia del Azuay, cantón Cuenca, parroquia San Joaquín, sector Balzay Bajo. Se utilizaron 300 pollitos camperos de la estirpe Hubbard variedad redbro S de 1 día de edad. Las aves se distribuyeron de forma aleatoria en un diseño de bloques al azar con 3 tratamientos, cada uno con 5 repeticiones y con 20 pollitos por unidad experimental. Los tratamientos fueron: T1: testigo, aves alojadas en sistema intensivo; T2: aves alojadas en sistema intensivo, más una dieta modificada que consistía en la adición de extracto de quillaja al 0,1%; T3: sistema semi-intensivo con la misma dieta del T2, las aves de este tratamiento a partir del día 28 de edad tuvieron acceso a un área verde delimitada, la cual poseía una mezcla forrajera de raigrás-alfalfa y además se adicionaron a su alimentación residuos de hortalizas propias de la zona. La investigación duró 56 días, no se evidenciaron diferencias significativas en ganancia diaria de peso, índice de productividad, índice de conversión, costo por kg de carne y porcentaje de grasa (p>0,05), mientras que consumo semanal y mortalidad, mostraron diferencias significativas (p<0,05). En las demás variables se evidenció mejor intensidad de pigmentación en T3 (p<0,05), mientras que en la infestación por coccidios no se observó diferencia entre tratamientos (p>0,05), lo que indica que la aplicación de extracto de quillaja tuvo un efecto similar al programa anticoccidial utilizado en T1

Sistemas de produção de peixes e bivalves em aquacultura: crescimento e indicadores de qualidade

O presente relatório refere-se ao estágio curricular realizado na Estação Piloto de Piscicultura de Olhão, no período compreendido entre janeiro e julho de 2016, no âmbito do Mestrado Integrado em Medicina Veterinária da Universidade de Évora. Este trabalho divide-se em duas partes. A primeira parte descreve as atividades e respetiva casuística desenvolvidas nas diferentes áreas de produção aquícola. Na segunda parte é abordado o tema “Produção Aquícola de Peixes e Ostras em regime Semi-intensivo”, onde são enquadrados os conceitos relacionados com aquacultura, sistemas de produção e pesquisa de biomarcadores na qualidade e bem-estar animal. São ainda descritas as ações de acompanhamento, durante o estágio, de um projeto de investigação em sistemas de produção em tanques de terra exteriores e um caso clínico. Este relatório atesta a importância da Medicina Preventiva em aquacultura; Abstract: Fish and bivalve aquaculture production systems: growth and quality indicators The following report was elaborated after the externship conducted at the Olhão Pilot Aquaculture Station, between January and July of 2016, in order to fulfill the requirements for a Masters degree in Veterinary Medicine at the University of Évora. This report is divided in two parts. The first part contains a description of the developed activities and the casuistic at the different aquaculture production areas. The second part will focus on the development of the theme “Fish and Oyster aquaculture production in a semi-intensive culture system”, with a theoretical framework about aquaculture concepts, production systems and identifying biomarkers in animal healthcare. At last, are presented and supported the monitoring actions taken in an investigation study case about inshore production systems and a clinic case followed during the externship. This report attests the importance of Preventive Medicine in aquaculture.

Avaliação participativa de indicadores de solo e sanidade de cultivos em sistemas de produção na Comunidade Pé de Serra Cedro, no semiárido brasileiro.

RESUMO: A avaliação participativa permite inferir se os sistemas agrícolas necessitam de melhorias e quais conhecimentos podem promover incrementos. Esse trabalho objetivou avaliar diferentes sistemas de produção na Comunidade Pé de Serra Cedro, Sobral-CE, utilizando metodologia participativa com agricultores locais através de indicadores de qualidade do solo e sanidade dos cultivos. Os sistemas de produção avaliados foram quatro, com as seguintes descrições: sistema tradicional: plantio de culturas anuais, com queima da área a 3 anos; sistema tradicional + esterco; sistema tradicional + esterco + leucena; e sistema roçado agroecológico. A inserção de práticas edáficas como aplicação de estercos e implantação de leguminosas incrementaram sistemas de produção tradicionais no semiárido cearense, mesmo em curto prazo de avaliação. A utilização de roçados agroecológicos diferenciou-se para indicadores do solo e sanidade dos cultivos em relação aos manejos tradicionais, com indicador médio para atributos do solo de 8,6 e para sanidade de cultivos em 8,4. [Participatory evaluation indicators of soil and crop shealth in production systems in comunity Pé de Serra Cedro in brazilian semi-arid]. Abstract: Participatory evaluation allows us to infer that the agricultural systems need to be improved and what knowledge can promote increments, thus aimed to evaluate different production systems in the Community Pé de Serra Cedro, Sobral, state of Ceará, Brazil, using participatory methodology with local farmers on soil properties and health of crops. The production systems evaluated were four, with the following descriptions: traditional system: planting annual crops, with burning area to 3 years; traditional system + manure; traditional system + manure + Leucaena leucocephala; and scuffed agroecological system. Inserting soil practices such as manure application and implementation of incresead legumes traditional production systems in semiarid region, even in short-term assessment. The use of agroecological scuffed differed for soil propertie sand health of crops over traditional managements with value 8.6 and 8.4 to soil and plant indicators, respectively.

Semi-active control for structural vibration based on predictive algorithm [Chinese title = 基于预测算法的结构振动半主动控制]

Based on Newmark-β method, a structural vibration response is predicted. Through finding the appropriate control force parameters within certain ranges to optimize the objective function, the predictive control of the structural vibration is achieved. At the same time, the numerical simulation analysis of a two-storey frame structure with magneto-rheological (MR) dampers under earthquake records is carried out, and the parameter influence on structural vibration reduction is discussed. The results demonstrate that the semi-active control based on Newmark-β predictive algorithm is better than the classical control strategy based on full-state feedback control and has remarkable advantages of structural vibration reduction and control robustness.

Semi-analytic method of contact modelling

An algorithm to improve the accuracy and stability of rigid-body contact force calculation is presented. The algorithm uses a combination of analytic solutions and numerical methods to solve a spring-damper differential equation typical of a contact model. The solution method employs the recently proposed patch method, which especially suits the spring-damper differential equations. The resulting semi-analytic solution reduces the stiffness of the differential equations, while performing faster than conventional alternatives.

Semi-supervised intelligent surveillance system for secure environments

This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of people utilising biometrics and in particular soft-biometrics; the monitoring of crowds; and activity recognition. Recent advances in these computer vision modules and capability gaps in surveillance technology are also highlighted.

Analysis of the build-up of semi and non volatile organic compounds on urban roads

Vehicular traffic in urban areas may adversely affect urban water quality through the build-up of traffic generated semi and non volatile organic compounds (SVOCs and NVOCs) on road surfaces. The characterisation of the build-up processes is the key to developing mitigation measures for the removal of such pollutants from urban stormwater. An in-depth analysis of the build-up of SVOCs and NVOCs was undertaken in the Gold Coast region in Australia. Principal Component Analysis (PCA) and Multicriteria Decision tools such as PROMETHEE and GAIA were employed to understand the SVOC and NVOC build-up under combined traffic scenarios of low, moderate, and high traffic in different land uses. It was found that congestion in the commercial areas and use of lubricants and motor oils in the industrial areas were the main sources of SVOCs and NVOCs on urban roads, respectively. The contribution from residential areas to the build-up of such pollutants was hardly noticeable. It was also revealed through this investigation that the target SVOCs and NVOCs were mainly attached to particulate fractions of 75 to 300 µm whilst the redistribution of coarse fractions due to vehicle activity mainly occurred in the >300 µm size range. Lastly, under combined traffic scenario, moderate traffic with average daily traffic ranging from 2300 to 5900 and average congestion of 0.47 was found to dominate SVOC and NVOC build-up on roads.

Modulation of depth-dependent properties in tissue-engineered cartilage with a semi-permeable membrane and perfusion : a continuum model of matrix metabolism and transport

The functional properties of cartilaginous tissues are determined predominantly by the content, distribution, and organization of proteoglycan and collagen in the extracellular matrix. Extracellular matrix accumulates in tissue-engineered cartilage constructs by metabolism and transport of matrix molecules, processes that are modulated by physical and chemical factors. Constructs incubated under free-swelling conditions with freely permeable or highly permeable membranes exhibit symmetric surface regions of soft tissue. The variation in tissue properties with depth from the surfaces suggests the hypothesis that the transport processes mediated by the boundary conditions govern the distribution of proteoglycan in such constructs. A continuum model (DiMicco and Sah in Transport Porus Med 50:57-73, 2003) was extended to test the effects of membrane permeability and perfusion on proteoglycan accumulation in tissue-engineered cartilage. The concentrations of soluble, bound, and degraded proteoglycan were analyzed as functions of time, space, and non-dimensional parameters for several experimental configurations. The results of the model suggest that the boundary condition at the membrane surface and the rate of perfusion, described by non-dimensional parameters, are important determinants of the pattern of proteoglycan accumulation. With perfusion, the proteoglycan profile is skewed, and decreases or increases in magnitude depending on the level of flow-based stimulation. Utilization of a semi-permeable membrane with or without unidirectional flow may lead to tissues with depth-increasing proteoglycan content, resembling native articular cartilage.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

A semi-local method for iterative depth-map refinement

We present an iterative hierarchical algorithm for multi-view stereo. The algorithm attempts to utilise as much contextual information as is available to compute highly accurate and robust depth maps. There are three novel aspects to the approach: 1) firstly we incrementally improve the depth fidelity as the algorithm progresses through the image pyramid; 2) secondly we show how to incorporate visual hull information (when available) to constrain depth searches; and 3) we show how to simultaneously enforce the consistency of the depth-map by continual comparison with neighbouring depth-maps. We show that this approach produces highly accurate depth-maps and, since it is essentially a local method, is both extremely fast and simple to implement.

Molecular structure of the phosphate mineral brazilianite NaAl3(PO4)2(OH)4 : a semi precious jewel

The phosphate mineral brazilianite NaAl3(PO4)2(OH)4 is a semi precious jewel. There are almost no minerals apart from brazilianite which are used in jewellery. Vibrational spectroscopy was used to characterize the mol. structure of brazilianite. Brazilianite is composed of chains of edge-sharing Al-O octahedra linked by P-O tetrahedra, with Na located in cavities of the framework. An intense sharp Raman band at 1019 cm-1 is attributed to the PO43- sym. stretching mode. Raman bands at 973 and 988 cm-1 are assigned to the stretching vibrations of the HOPO33- units. The IR spectra compliment the Raman spectra but show greater complexity. Multiple Raman bands are obsd. in the PO43- and HOPO33- bending region. This observation implies that both phosphate and hydrogen phosphate units are involved in the structure. Raman OH stretching vibrations are found at 3249, 3417 and 3472 cm-1. These peaks show that the OH units are not equiv. in the brazilianite structure. Vibrational spectroscopy is useful for increasing the knowledge of the mol. structure of brazilianite.