108 research outputs found
Short-range order and precipitation in Fe-rich Fe-Cr alloys: Atomistic off-lattice Monte Carlo simulations
Short-range order (SRO) in Fe-rich Fe-Cr alloys is investigated by means of
atomistic off-lattice Monte Carlo simulations in the semi-grand canonical
ensemble using classical interatomic potentials. The SRO parameter defined by
Cowley [Phys. Rev. B 77, 669 (1950)] is used to quantify the degree of
ordering. In agreement with experiments a strong ordering tendency in the Cr
distribution at low Cr concentrations (~< 5%) is observed, as manifested in
negative values of the SRO parameters. For intermediate Cr concentrations (5%
~< c_Cr ~< 15%) the SRO parameter for the alpha-phase goes through a minimum,
but at the solubility limit the alpha-phase still displays a rather strong SRO.
In thermodynamic equilibrium for concentrations within the two-phase region the
SRO parameter measured over the entire sample therefore comprises the
contributions from both the alpha and alpha-prime phases. If both of these
contributions are taken into account, it is possible to quantitatively
reproduce the experimental results and interpret their physical implications.
It is thereby shown that the inversion of the SRO observed experimentally is
due to the formation of stable (supercritical) alpha-prime precipitates. It is
not related to the loss of SRO in the alpha-phase or to the presence of
unstable (subcritical) Cr precipitates in the alpha-phase.Comment: 9 pages, 8 figure
Effect of alpha-7 nicotinic acetylcholine receptor activation on beta-amyloid induced recognition memory impairment. Possible role of neurovascular function
Fall Classification by Machine Learning Using Mobile Phones
Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls
Adenosine A1 receptor: Functional receptor-receptor interactions in the brain
Over the past decade, many lines of investigation have shown that receptor-mediated signaling exhibits greater diversity than previously appreciated. Signal diversity arises from numerous factors, which include the formation of receptor dimers and interplay between different receptors. Using adenosine A1 receptors as a paradigm of G protein-coupled receptors, this review focuses on how receptor-receptor interactions may contribute to regulation of the synaptic transmission within the central nervous system. The interactions with metabotropic dopamine, adenosine A2A, A3, neuropeptide Y, and purinergic P2Y1 receptors will be described in the first part. The second part deals with interactions between A1Rs and ionotropic receptors, especially GABAA, NMDA, and P2X receptors as well as ATP-sensitive K+ channels. Finally, the review will discuss new approaches towards treating neurological disorders
What scans we will read: imaging instrumentation trends in clinical oncology
Oncological diseases account for a significant portion of the burden on public healthcare systems with associated
costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific
morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-
invasively, so as to provide referring oncologists with essential information to support therapy management
decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards
integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/
CT), advanced MRI, optical or ultrasound imaging.
This perspective paper highlights a number of key technological and methodological advances in imaging
instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as
the hardware-based combination of complementary anatomical and molecular imaging. These include novel
detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system
developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing
methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging
in oncology patient management we introduce imaging methods with well-defined clinical applications and
potential for clinical translation. For each modality, we report first on the status quo and point to perceived
technological and methodological advances in a subsequent status go section. Considering the breadth and
dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the
majority of them being imaging experts with a background in physics and engineering, believe imaging methods
will be in a few years from now.
Overall, methodological and technological medical imaging advances are geared towards increased image contrast,
the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall
examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is
complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To
this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis,
including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor
phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-
dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and
analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts
with a domain knowledge that will need to go beyond that of plain imaging
Service quality assessment of a referral hospital in Southern Iran with SERVQUAL technique: patients’ perspective
Disposal cost sharing and bargaining for coordination and profit division in a three-echelon supply chain
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