B-4. A review on feeding system for deer production(Abstracts of the International Symposium on Recent Advances in Animal Science(IS-RAAS),Joint meeting of 2^<nd> IS-AS and 3^<rd> IS-IFS)

Deer farmingvelvet productionfeed sourcemedicinal her

Black Hole Feedback On The First Galaxies

We study how the first galaxies were assembled under feedback from the accretion onto a central black hole (BH) that is left behind by the first generation of metal-free stars through self-consistent, cosmological simulations. X-ray radiation from the accretion of gas onto BH remnants of Population III (Pop III) stars, or from high-mass X-ray binaries (HMXBs), again involving Pop III stars, influences the mode of second generation star formation. We track the evolution of the black hole accretion rate and the associated X-ray feedback starting with the death of the Pop III progenitor star inside a minihalo and following the subsequent evolution of the black hole as the minihalo grows to become an atomically cooling galaxy. We find that X-ray photoionization heating from a stellar-mass BH is able to quench further star formation in the host halo at all times before the halo enters the atomic cooling phase. X-ray radiation from a HMXB, assuming a luminosity close to the Eddington value, exerts an even stronger, and more diverse, feedback on star formation. It photoheats the gas inside the host halo, but also promotes the formation of molecular hydrogen and cooling of gas in the intergalactic medium and in nearby minihalos, leading to a net increase in the number of stars formed at early times. Our simulations further show that the radiative feedback from the first BHs may strongly suppress early BH growth, thus constraining models for the formation of supermassive BHs.Astronom

A systematic review of what factors attract and retain nurses in aged and dementia care

Aim. To present evidence-based factors for the recruitment and retention of licensed nurses caring for older people and persons with dementia. Background. The international nurse shortage crisis is intensified in the aged and dementia care sector. Strategies to address this crisis rely on qualitative, quasi-experimental, anecdotal and unsubstantiated literature. Design. Systematic literature review. Method. Search terms 'nurse''nurses''nursing''clinical supervision''staff''staffing''staff mix''staff levels''recruitment''retention''aged care''gerontology''gerontological''dementia care''residential''nursing home,' were used in all possible combinations and applied in a wide range of relevant academic databases, with secondary hand searches of selected bibliographies. Results. Two hundred and twenty-six papers were retrieved and scanned, with 105 chosen for closer examination that were relevant to recruitment and retention strategies for dementia and aged care nursing. Twenty-five of the papers chosen for review were rated at level 2++ to 3, according to the guidelines of the National Institute for Health and Clinical Excellence (The NICE Guidelines Manual, National Institute for Health and Clinical Excellence, London). The 25 critically reviewed papers are organised as promising strategies for (1) nurse recruitment and (2) nurse retention. Conclusions. The intrinsic rewards of the caring role attract nurses to dementia and aged care. Essential strategies linking recruitment with retention are: careful selection of student nurse clinical placements and their ongoing supervision and education, training for skills, leadership and teamwork for new and existing nurses, increased staffing levels, pay parity across different health settings and family friendly policies. Relevance to clinical practice. A family-friendly, learning environment that values and nurtures its nursing staff, in the same way as nurses are expected to value and care for their patients and residents, is critical in ensuring their retention in dementia and aged care. © 2010 Blackwell Publishing Ltd

Potential and efficiency of statistical learning closely intertwined with individuals’ executive functions: A mathematical modeling study

Statistical learning (SL) is essential in enabling humans to extract probabilistic regularities from the world. The ability to accomplish ultimate learning performance with training (i.e., the potential of learning) has been known to be dissociated with performance improvement per amount of learning time (i.e., the efficiency of learning). Here, we quantified the potential and efficiency of SL separately through mathematical modeling and scrutinized how they were affected by various executive functions. Our results showed that a high potential of SL was associated with poor inhibition and good visuo-spatial working memory, whereas high efficiency of SL was closely related to good inhibition and good set-shifting. We unveiled the distinct characteristics of SL in relation to potential and efficiency and their interaction with executive functions

Long-range supercurrents through a chiral non-collinear antiferromagnet in lateral Josephson junctions

The proximity-coupling of a chiral non-collinear antiferromagnet (AFM)1,2,3,4,5 with a singlet superconductor allows spin-unpolarized singlet Cooper pairs to be converted into spin-polarized triplet pairs6,7,8, thereby enabling non-dissipative, long-range spin correlations9,10,11,12,13,14. The mechanism of this conversion derives from fictitious magnetic fields that are created by a non-zero Berry phase15 in AFMs with non-collinear atomic-scale spin arrangements1,2,3,4,5. Here we report long-ranged lateral Josephson supercurrents through an epitaxial thin film of the triangular chiral AFM Mn3Ge (refs. 3,4,5). The Josephson supercurrents in this chiral AFM decay by approximately one to two orders of magnitude slower than would be expected for singlet pair correlations9,10,11,12,13,14 and their response to an external magnetic field reflects a clear spatial quantum interference. Given the long-range supercurrents present in both single- and mixed-phase Mn3Ge, but absent in a collinear AFM IrMn16, our results pave a way for the topological generation of spin-polarized triplet pairs6,7,8 via Berry phase engineering15 of the chiral AFMs

X-ray magnetic circular dichroism characterization of GaN/Ga1-xMnxN digital ferromagnetic heterostructure

We have investigated the magnetic properties of a GaN/Ga1-xMnxN (x = 0.1) digital ferromagnetic heterostructure (DFH) showing ferromagnetic behavior using soft x-ray absorption spectroscopy (XAS) and x-ray magnetic circular dichroism (XMCD). The Mn L2,3-edge XAS spectra were similar to those of Ga1-xMnxN random alloy thin films, indicating a substitutional doping of high concentration Mn into GaN. From the XMCD measurements, it was revealed that paramagnetic and ferromagnetic Mn atoms coexisted in the Ga1-xMnxN digital layers. The ferromagnetic moment per Mn atom estimated from XMCD agreed well with that estimated from SQUID measurements. From these results, we conclude that the ferromagnetic behavior of the GaN/Ga1-xMnxN DFH sample arises only from substitutional Mn2+ ions in the Ga1-xMnxN digital layers and not from ferromagnetic precipitates. Subtle differences were also found from the XMCD spectra between the electronic states of the ferromagnetic and paramagnetic Mn2+ ions.Comment: 12 pages, 8 figure

Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility