Food intake, milk production, and tissue changes of Holstein-Friesian and Jersey x Holstein-Friesian dairy cows within a medium-input grazing system and a high-input total confinement system

Although interest in crossbreeding within dairy systems has increased, the role of Jersey crossbred cows within high concentrate input systems has received little attention. This experiment was designed to examine the performance of Holstein-Friesian (HF) and Jersey x Holstein-Friesian (J x HF) cows within a high concentrate input total confinement system (CON) and a medium concentrate input grazing system (GRZ). Eighty spring-calving dairy cows were used in a 2 (cow genotype) x 2 (milk production system) factorial design experiment. The experiment commenced when cows calved and encompassed a full lactation. With GRZ, cows were offered diets containing grass silage and concentrates [70:30 dry matter (DM) ratio] until turnout, grazed grass plus 1.0 kg of concentrate/day during a 199-d grazing period, and grass silage and concentrates (75:25 DM ratio) following rehousing and until drying-off. With CON, cows were confined throughout the lactation and offered diets containing grass silage and concentrates (DM ratio; 40:60, 50:50, 40:40, and 75:25 during d 1 to 100, 101 to 200, 201 to 250, and 251 until drying-off, respectively). Full-lactation concentrate DM intakes were 791 and 2,905 kg/cow for systems GRZ and CON, respectively. Although HF cows had a higher lactation milk yield than J x HF cows, the latter produced milk with a higher fat and protein content, so that solids-corrected milk yield (SCM) was unaffected by genotype. Somatic cell score was higher with the J x HF cows. Throughout lactation, HF cows were on average 37 kg heavier than J x HF cows, whereas the J x HF cows had a higher body condition score. Within each system, food intake did not differ between genotypes, whereas full-lactation yields of milk, fat plus protein, and SCM were higher with CON than with GRZ. A significant genotype x environment interaction was observed for milk yield, and a trend was found for an interaction with SCM. Crossbred cows on CON gained more body condition than HF cows, and overall pregnancy rate was unaffected by either genotype or management system. In summary, milk and SCM yields were higher with CON than with GRZ, whereas genotype had no effect on SCM. However, HF cows exhibited a greater milk yield response and a trend toward a greater SCM yield response with increasing concentrate levels compared with the crossbred cows.

Portfolio of Compositions investigating ensemble improvisation, distributed and real-time scoring, and multi-nodal input and output

This output is a collection of compositions which explore issues of ensemble improvisation, ensemble management and orchestration, real-time and distributed scoring, multi-nodal inputs and outputs, and animated and graphic notation. Compositions include: Activities I; tutti, duet, trio, solo, quartet; Lewitt Notations I; Webwork I; and Sometimes I feel the space between people (voices) in terms of tempos. These compositions are presented in computer animated scores which are synchronized through the network and subject to real-time modification and control. They can be performed by ensembles distributed over large physical spaces connected by the network. The scores for these compositions include software which displays the animations to the performers, software to structure and disseminate score events, and triggering software that allows the control of a performance to be distributed. Scores can also include live electronics which are coordinated with graphic events.

A single microphone capillary-based system for measuring the complex input impedance of musical wind instruments

Capillary-based systems for measuring the input impedance of musical wind instruments were first developed in the mid-20th century and remain in widespread use today. In this paper, the basic principles and assumptions underpinning the design of such systems are examined. Inexpensive modifications to a capillary-based impedance measurement set-up made possible due to advances in computing and data acquisition technology are discussed. The modified set-up is able to measure both impedance magnitude and impedance phase even though it only contains one microphone. In addition, a method of calibration is described that results in a significant improvement in accuracy when measuring high impedance objects on the modified capillary-based system. The method involves carrying out calibration measurements on two different objects whose impedances are well-known theoretically. The benefits of performing two calibration measurements (as opposed to the one calibration measurement that has been traditionally used) are demonstrated experimentally through input impedance measurements on two test objects and a Boosey and Hawkes oboe. © S. Hirzel Verlag · EAA.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Low-Power Process-Variation Tolerant Arithmetic Units Using Input-Based Elastic Clocking

In this paper we propose a design methodology for low-power high-performance, process-variation tolerant architecture for arithmetic units. The novelty of our approach lies in the fact that possible delay failures due to process variations and/or voltage scaling are predicted in advance and addressed by employing an elastic clocking technique. The prediction mechanism exploits the dependence of delay of arithmetic units upon input data patterns and identifies specific inputs that activate the critical path. Under iso-yield conditions, the proposed design operates at a lower scaled down Vdd without any performance degradation, while it ensures a superlative yield under a design style employing nominal supply and transistor threshold voltage. Simulation results show power savings of upto 29%, energy per computation savings of upto 25.5% and yield enhancement of upto 11.1% compared to the conventional adders and multipliers implemented in the 70nm BPTM technology. We incorporated the proposed modules in the execution unit of a five stage DLX pipeline to measure performance using SPEC2000 benchmarks [9]. Maximum area and throughput penalty obtained were 10% and 3% respectively.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.