The energy expenditure of 2 Holstein cow strains in an organic grazing system

Until recently, measurements of energy expenditure (EE; herein defined as heat production) in respiration chambers did not account for the extra energy requirements of grazing dairy cows on pasture. As energy is first limiting in most pasture-based milk production systems, its efficient use is important. Therefore, the aim of the present study was to compare EE, which can be affected by differences in body weight (BW), body composition, grazing behavior, physical activity, and milk production level, in 2 Holstein cow strains. Twelve Swiss Holstein-Friesian (HCH; 616 kg of BW) and 12 New Zealand Holstein-Friesian (HNZ; 570 kg of BW) cows in the third stage of lactation were paired according to their stage of lactation and kept in a rotational, full-time grazing system without concentrate supplementation. After adaption, the daily milk yield, grass intake using the alkane double-indicator technique, nutrient digestibility, physical activity, and grazing behavior recorded by an automatic jaw movement recorder were investigated over 7d. Using the (13)C bicarbonate dilution technique in combination with an automatic blood sampling system, EE based on measured carbon dioxide production was determined in 1 cow pair per day between 0800 to 1400 h. The HCH were heavier and had a lower body condition score compared with HNZ, but the difference in BW was smaller compared with former studies. Milk production, grass intake, and nutrient digestibility did not differ between the 2 cow strains, but HCH grazed for a longer time during the 6-h measurement period and performed more grazing mastication compared with the HNZ. No difference was found between the 2 cow strains with regard to EE (291 ± 15.6 kJ) per kilogram of metabolic BW, mainly due to a high between-animal variation in EE. As efficiency and energy use are important in sustainable, pasture-based, organic milk production systems, the determining factors for EE, such as methodology, genetics, physical activity, grazing behavior, and pasture quality, should be investigated and quantified in more detail in future studies.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

Real-World Energy Measurements of a Wireless Mesh Network

Over the past several years the topics of energy consumption and energy harvesting have gained significant importance as a means for improved operation of wireless sensor and mesh networks. Energy-awareness of operation is especially relevant for application scenarios from the domain of environmental monitoring in hard to access areas. In this work we reflect upon our experiences with a real-world deployment of a wireless mesh network. In particular, a comprehensive study on energy measurements collected over several weeks during the summer and the winter period in a network deployment in the Swiss Alps is presented. Energy performance is monitored and analysed for three system components, namely, mesh node, battery and solar panel module. Our findings cover a number of aspects of energy consumption, including the amount of load consumed by a mesh node, the amount of load harvested by a solar panel module, and the dependencies between these two. With our work we aim to shed some light on energy-aware network operation and to help both users and developers in the planning and deployment of a new wireless (mesh) network for environmental research.

A physical approach on flood risk vulnerability of buildings

The design of efficient hydrological risk mitigation strategies and their subsequent implementation relies on a careful vulnerability analysis of the elements exposed. Recently, extensive research efforts were undertaken to develop and refine empirical relationships linking the structural vulnerability of buildings to the impact forces of the hazard processes. These empirical vulnerability functions allow estimating the expected direct losses as a result of the hazard scenario based on spatially explicit representation of the process patterns and the elements at risk classified into defined typological categories. However, due to the underlying empiricism of such vulnerability functions, the physics of the damage-generating mechanisms for a well-defined element at risk with its peculiar geometry and structural characteristics remain unveiled, and, as such, the applicability of the empirical approach for planning hazard-proof residential buildings is limited. Therefore, we propose a conceptual assessment scheme to close this gap. This assessment scheme encompasses distinct analytical steps: modelling (a) the process intensity, (b) the impact on the element at risk exposed and (c) the physical response of the building envelope. Furthermore, these results provide the input data for the subsequent damage evaluation and economic damage valuation. This dynamic assessment supports all relevant planning activities with respect to a minimisation of losses, and can be implemented in the operational risk assessment procedure.

A new histology scoring system for the assessment of the quality of human cartilage repair: ICRS II

A reliable and reproducible method is needed to assess cartilage repair.

Adsorption of Components of the Plasma Kinin-forming System on the Surface of Porphyromonas gingivalis Involves Gingipains as the Major Docking Platforms

Enhanced production of proinflammatory bradykinin-related peptides, the kinins, has been suggested to contribute to the pathogenesis of periodontitis, a common inflammatory disease of human gingival tissues. In this report, we describe a plausible mechanism of activation of the kinin-generating system, also known as the contact system or kininogen-kallikrein-kinin system, by the adsorption of its plasma-derived components such as high-molecular-mass kininogen (HK), prekallikrein (PK), and Hageman factor (FXII) to the cell surface of periodontal pathogen Porphyromonas gingivalis. The adsorption characteristics of mutant strains deficient in selected proteins of the cell envelope suggested that the surface-associated cysteine proteinases, gingipains, bearing hemagglutinin/adhesin domains (RgpA and Kgp) serve as the major platforms for HK and FXII adhesion. These interactions were confirmed by direct binding tests using microplate-immobilized gingipains and biotinylated contact factors. Other bacterial cell surface components such as fimbriae and lipopolysaccharide were also found to contribute to the binding of contact factors, particularly PK. Analysis of kinin release in plasma upon contact with P. gingivalis showed that the bacterial surface-dependent mechanism is complementary to the previously described kinin generation system dependent on HK and PK proteolytic activation by the gingipains. We also found that several P. gingivalis clinical isolates differed in the relative significance of these two mechanisms of kinin production. Taken together, these data show the importance of this specific type of bacterial surface-host homeostatic system interaction in periodontal infections.

VMC++ validation for photon beams in the energy range of 20-1000 keV

In high energy teletherapy, VMC++ is known to be a very accurate and efficient Monte Carlo (MC) code. In principle, the MC method is also a powerful dose calculation tool in other areas in radiation oncology, e.g., brachytherapy or orthovoltage radiotherapy. However, VMC++ is not validated for the low-energy range of such applications. This work aims in the validation of the VMC++ MC code for photon beams in the energy range between 20 and 1000 keV.

A robotic system to train activities of daily living in a virtual environment

In the past decade, several arm rehabilitation robots have been developed to assist neurological patients during therapy. Early devices were limited in their number of degrees of freedom and range of motion, whereas newer robots such as the ARMin robot can support the entire arm. Often, these devices are combined with virtual environments to integrate motivating game-like scenarios. Several studies have shown a positive effect of game-playing on therapy outcome by increasing motivation. In addition, we assume that practicing highly functional movements can further enhance therapy outcome by facilitating the transfer of motor abilities acquired in therapy to daily life. Therefore, we present a rehabilitation system that enables the training of activities of daily living (ADL) with the support of an assistive robot. Important ADL tasks have been identified and implemented in a virtual environment. A patient-cooperative control strategy with adaptable freedom in timing and space was developed to assist the patient during the task. The technical feasibility and usability of the system was evaluated with seven healthy subjects and three chronic stroke patients.