Supplementing an immunomodulatory feed ingredient to modulate thermoregulation,physiologic, and production responses in lactating dairy cows under heat stress conditions

This study compared vaginal temperature, physiologic, and productive parameters in lactating dairy cows supplemented or not with Omnigen-AF (Phibro Animal Health, Teaneck, NJ) during the summer months in a tropical environment. Thirty-two lactating, primiparous (n = 16) and multiparous (n = 16) pregnant Holstein × Gir cows were ranked by parity, days in milk, body weight, and body condition score (BCS), and assigned to receive (SUPP; n = 16) or not (CON; n = 16) Omnigen-AF (Phibro Animal Health, Teaneck, NJ) at 56 g/cow daily (as-fed basis). During the experimental period (d ?6 to 56), cows were maintained in a single drylot pen with ad libitum access to water and a total mixed ration, and milked twice daily. Cows received Omnigen-AF mixed with 200 g of corn (as-fed basis) after the daily morning milking through self-locking head gates, whereas CON cows concurrently received 56 g of kaolin mixed with 200 g of corn. For feed intake evaluation, cows from both treatments were randomly divided in 4 groups of 8 cows each, and allocated to 8 individual feeding stations for 3 d. Intake was evaluated 4 times per group from d 1 to 56. From d ?6 to 0, d 15 to 28, and d 43 to 56, cow vaginal temperature was recorded hourly. Environmental temperature-humidity index (THI) was also recorded hourly from d 15 to 28 and d 43 to 56. Cows were evaluated for body weight and BCS on d ?6 and 56, individual milk production was recorded daily from d ?6 to 56, and milk samples were collected on d ?6, 0, 7, 14, 21, 28, 35, 42, 49, and 56 for analyses of somatic cell count and milk components. Blood samples were collected on d ?6, ?3, 0, 9, 15, 18, 21, 24, 27, 36, 45, 48, 51, 54, and 56. Results from samples or observations collected from d ?6 to 0 were included as an independent covariate in each respective analysis. Environmental THI was 74.2 ± 0.5 and cows were exposed to THI >68 for 633 h within a total of 672 h of evaluation. Cows assigned to CON had greater vaginal temperature on d 28, 43, 45, and from d 48 to 55 (by 0.38 to 0.52%), as well as greater mean somatic cell count (by 97%) and serum haptoglobin concentrations (by 89%) compared with SUPP cows. Cows assigned to SUPP had greater mean dry matter intake (by 7%), BCS on d 56 (by 11%), and mean serum insulin concentrations (by 35%) compared with CON cows. Hence, SUPP ameliorated hyperthermia, improved nutritional status, and modulated systemic and mammary gland immune parameters in lactating dairy cows exposed to heat stress conditions.     

Influence of glass fiber post translucency on microhardness and dentin bond strength of resin cement at different root levels

The aims of this study were (a) to evaluate the influence of glass fiber post translucency on the hardness of a light-cure resin cement within the root canal; (b) to assess dentin bond strength at different root levels. Fifty human canine roots were randomly divided into five groups. Translucent posts (Exacto, Angelus; White Post DC, FGM; FRC Postec Plus, Ivoclar Vivadent) were used in three groups, opaque posts (Exacto Opaco, Angelus) were used in one group and no posts were used in the last group. The posts were cemented using a light-cure resin cement (Variolink N Base, Ivoclar Vivadent). The roots were cross-sectioned into slices (two from the cervical, two from the middle, and two from the apical thirds) which were then submitted to microhardness and push-out tests. Two-way analysis of variance and Tukey test were performed. Cement microhardness was significantly higher in the translucent post groups when compared to opaque posts and no post. At the apical third, the White Post DC and FRC Postec groups showed higher microhardness values than those in the Exacto Translucido group. The type of glass fiber post did not significantly influence bond strength values. White Post DC and FRC Postec Plus provided higher resin cement microhardness values, especially at the most apical thirds. Bond strength was not dependent on the type of post used. Failure mode analysis suggested superior cement curing when the translucent posts were used.     

A simplified gravitational model to analyze texture roughness

Textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze texture, based on representing states of a simplified gravitational collapse from an image and extracting information from each state using fractal dimension. In this approach, an image evolves in times $t=\{1,2,\dots ,20\}$, each time representing a state, which is explored by the Bouligand–Minkowski method using radius $r=\{3,4,\dots ,8\}$. These parameters allow to create a set of feature vectors, which were extracted from Brodatz's textures and leaf textures. The best classification results were 98.75% and 86.67% of success rate (percentage of samples correctly classified) for these two databases, respectively. These results prove that the proposed approach opens a promising source of research in texture analysis to be explored.     

A Combinatorial Optimization Technique for the Sequential Decomposition of Erosions and Dilations

This paper presents a general algorithm for the automatic proof that an erosion (respectively, dilation) has a sequential decomposition or not. If the decomposition exists, an optimum decomposition is presented. The algorithm is based on a branch and bound search, with pruning strategies and bounds based on algebraic and geometrical properties deduced formally. This technique generalizes classical results as Zhuang and Haralick, Xu, and Park and Chin, with equivalent or improved performance. Finally, theoretical analysis of the proposed algorithm and experimental results are presented.     

A case study on expressiveness and performance of component-oriented parallel programming

Component-oriented programming has been applied to address the requirements of large-scale applications from computational sciences and engineering that present high performance computing (HPC) requirements. However, parallelism continues to be a challenging requirement in the design of CBHPC (Component-Based High Performance Computing) platforms. This paper presents strong evidence about the efficacy and the efficiency of HPE (Hash Programming Environment), a CBHPC platform that provides full support for parallel programming, on the development, deployment and execution of numerical simulation code onto cluster computing platforms.     

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson–Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.