Milking machines and mastitis.

Mode of access: Internet.

Milking machines and mastitis.

Mode of access: Internet.

Molecular epidemiology of Staphylococcus aureus isolates at different sites in the milk producing dairy farms

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

Aspectos de produção e etio-epidemiológicos referentes às vias de transmissão nas mastites ovinas

Pós-graduação em Medicina Veterinária - FMVZ

Qualidade microbiana e físico-química do leite cru produzido na região de Pardinho, SP

Pós-graduação em Medicina Veterinária - FMVZ

Epidemiologia molecular dos Staphylococcus aureus isolados em diferentes pontos do fluxograma de produção do leite

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo fenotípico e genotípico da produção de biofilme por Staphylococcus aureus isolados de leite bubalino e ambiente de ordenha

Pós-graduação em Microbiologia Agropecuária - FCAV

RB32-270 A Seven Year Study of a Milk Supply

The amount of butter produced by the grain-belt states is evidence that a great many cows are milked by the midwestern farmer. Most of this milk is separated on the farm, the cream is sold, and the skimmilk is fed to hogs and other livestock. As the market for fluid milk has developed, many farmers near the cities have turned to the sale of milk, because it affords a better return for the butterfat sold. Much of the milk produced for sale as fluid milk is produced under practically the same conditions as milk which is produced primarily for the same of cream. The Department of Dairy Husbandry of the University of Nebraska, in conducting its instructional and investigational work, comes in contact with the milk producer. An effort has been made, therefore, to study the relation of milk quality to farm conditions as found among the milk producers or patrons who have delivered milk to the department. The study was carried on in an effort to find possible ways of bettering the conditions without upsetting the economic balance existing between the production of cream and fluid milk.

[Mastitis management in Swiss dairy farms with udder health problems]

The objective of this study was to describe the udder health management in Swiss dairy herds with udder health problems. One hundred dairy herds with a yield-corrected somatic cell count of 200'000 to 300'000 cells/ml during 2010 were selected. Data concerning farm structure, housing system, milking technique, milking procedures, dry-cow and mastitis management were collected during farm visits between September and December 2011. In addition, quarter milk samples were collected for bacteriological culturing from cows with a composite somatic cell count ≥ 150'000 cells/ml. The highest quarter level prevalence was 12.3 % for C. bovis. Eighty-two percent of the pipeline milking machines in tie-stalls and 88 % of the milking parlours fulfilled the criteria for the vacuum drop, and only 74 % of the pipeline milking machines met the criteria of the 10-l-water test. Eighty-five percent of the farms changed their milk liners too late. The correct order of teat preparation before cluster attachment was carried out by 37 % of the farmers only. With these results, Swiss dairy farmers and herd health veterinarians can be directed to common mistakes in mastitis management. The data will be used for future information campaigns to improve udder health in Swiss dairy farms.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Normal Versus Pathological Voice Signals Using Wavelet Analysis and Least Squares Support-Vector Machines

State of Sao Paulo Research Foundation (FAPESP)

Remote control of CNC machines using the CyberOPC communication system over public networks

During the last few years, the evolution of fieldbus and computers networks allowed the integration of different communication systems involving both production single cells and production cells, as well as other systems for business intelligence, supervision and control. Several well-adopted communication technologies exist today for public and non-public networks. Since most of the industrial applications are time-critical, the requirements of communication systems for remote control differ from common applications for computer networks accessing the Internet, such as Web, e-mail and file transfer. The solution proposed and outlined in this work is called CyberOPC. It includes the study and the implementation of a new open communication system for remote control of industrial CNC machines, making the transmission delay for time-critical control data shorter than other OPC-based solutions, and fulfilling cyber security requirements.

Comparison Between Voltage Control Structures of Synchronous Machines

This paper compares the behaviour of two different control structures of automatic voltage regulators of synchronous machines equipped with static excitation systems. These systems have a fully controlled thyristor bridge that supplies DC current to the rotor winding. The rectifier bridge is fed by the stator terminals through a step-down transformer. The first control structure, named ""Direct Control"", has a single proportional-integral (PI) regulator that compares stator voltage setpoint with measured voltage and acts directly on the thyristor bridge`s firing angle. This control structure is usually employed in commercial excitation systems for hydrogenerators. The second structure, named ""Cascade Control"", was inspired on control loops of commercial DC motor drives. Such drives employ two PIs in a cascade arrangement, the external PI deals with the motor speed while the internal one regulates the armature current. In the adaptation proposed, the external PI compares setpoint with the actual stator voltage and produces the setpoint to the internal PI-loop which controls the field current.