994 research outputs found
Dissipative quantum metrology in manybody systems of identical particles
Estimation of physical parameters is a must in almost any part of science and
technology. The enhancement of the performances in this task, e.g., beating the
standard classical shot-noise limit, using available physical resources is a
major goal in metrology. Quantum metrology in closed systems has indicated that
entanglement in such systems may be a useful resource. However, it is not yet
fully understood whether in open quantum systems such enhancements may still
show up. Here, we consider a dissipative (open) quantum system of identical
particles in which a parameter of the open dynamics itself is to be estimated.
We employ a recently-developed dissipative quantum metrology framework, and
investigate whether the entanglement produced in the course of the dissipative
dynamics may help the estimation task. Specifically, we show that even in a
Markovian dynamics, in which states become less distinguishable in time, at
small enough times entanglement generated by the dynamics may offer some
advantage over the classical shot-noise limit.Comment: 9 pages, 2 figure
Correlations in quantum thermodynamics: Heat, work, and entropy production
We provide a characterization of energy in the form of exchanged heat and
work between two interacting constituents of a closed, bipartite, correlated
quantum system. By defining a binding energy we derive a consistent quantum
formulation of the first law of thermodynamics, in which the role of
correlations becomes evident, and this formulation reduces to the standard
classical picture in relevant systems. We next discuss the emergence of the
second law of thermodynamics under certain---but fairly general---conditions
such as the Markovian assumption. We illustrate the role of correlations and
interactions in thermodynamics through two examples.Comment: 16 page
A new family of matrix product states with Dzyaloshinski-Moriya interactions
We define a new family of matrix product states which are exact ground states
of spin 1/2 Hamiltonians on one dimensional lattices. This class of
Hamiltonians contain both Heisenberg and Dzyaloshinskii-Moriya interactions but
at specified and not arbitrary couplings. We also compute in closed forms the
one and two-point functions and the explicit form of the ground state. The
degeneracy structure of the ground state is also discussed.Comment: 15 pages, 1 figur
Review paper: Experimental models of absence epilepsy
Introduction: Absence epilepsy is a brief non-convulsive seizure associated with sudden abruptness in consciousness. Because of the unpredictable occurrence of absence seizures and the ethical issues of human investigation on the pathogenesis and drug assessment, researchers tend to study animal models. This paper aims to review the advantages and disadvantages of several animal models of nonconvulsive induced seizure. Methods: The articles that were published since 1990 were assessed. The publications that used genetic animals were analyzed, too. Besides, we reviewed possible application methods of each model, clinical types of seizures induced, purposed mechanism of epileptogenesis, their validity, and relevance to the absence epileptic patients. Results: The number of studies that used genetic models of absence epilepsy from years of 2000 was noticeably more than pharmacological models. Genetic animal models have a close correlation of electroencephalogram features and epileptic behaviors to the human condition. Conclusion: The validity of genetic models of absence epilepsy would motivate the researchers to focus on genetic modes in their studies. As there are some differences in the pathophysiology of absence epilepsy between animal models and humans, the development of new animal models is necessary to understand better the epileptogenic process and, or discover novel therapies for this disorder. © 2020 Iran University of Medical Sciences. All rights reserved
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The balanced scorecard as a strategic management tool in hospital pharmacies: an experimental study
Purpose: A balanced scorecard (BSC) is an applied tool for implementing strategic management in various organizations. Implementing strategic management using the BSC approach has not received much attention in pharmacy departments. This study aims to provide a model for the strategic management of pharmacy departments using the BSC framework.
Design/methodology/approach: This experimental study was conducted from 2015 to 2018 in a 300-bed hospital and regional healthcare centers affiliated with the Petroleum Industry Health Organization in Tehran province, Iran. After carefully reviewing the organization's mission and vision, the strategic objectives were determined via the internal matrix and the external matrix (IE matrix), and the strengths–weaknesses–opportunities–threats matrix (SWOT matrix) were examined. Then, six BSC measures and interventions were identified, and each was examined from the perspectives of finance, patient satisfaction, internal processes and learning/growth. Finally, the proposed strategy was evaluated.
Findings: Results showed significant increases in patient satisfaction and gross profit. The observed increase range, from 0.09 to 0.29, indicates more effective operational management for optimal resource utilization. In addition, the pharmacy department was able to save US 442,899 during the two years of our strategic management plan by implementing the standard mechanism for returning unused medications to the pharmacy department after patients were discharged from various treatment units.
Originality/value: This study is among the first studies to demonstrate the simultaneous development, implementation and evaluation of the proposed strategy using the BSC in a pharmacy department in a public healthcare center. The BSC application improved the optimal use of resources and reduced costs while increasing patient satisfaction. It appears that the application of such an intervention may be as valuable to public pharmacies as it is to other private centers
EVALUATION OF DIFFERENT PARAMETERS FOR PLANT CLASSIFICATION BY PRE-TRAINED DEEP LEARNING MODELS WITH BIGEARTHNET DATASET
Vegetation monitoring and mapping are essential for a diverse range of environmental problems such as forest management, food resources, and climate change assessment. Several methods have been developed to classify different vegetation types based on remote sensing (RS) data. Land use classification has been revolutionized with the advent of neural networks. Various vegetation types were classified using multispectral Sentinel-2 satellite images due to their high spatial resolution and spectral information. Deep Convolutional Neural Network is considered a promising method for classifying remote sensing images with high spatial resolution due to its powerful feature extraction capabilities. However, large labeled datasets are required for better classification performance, so we have used pre-trained ResNet networks with 152 layers, 50 layers, and 101 layers trained on Big Earth Net (BEN). In order to obtain the best network performance and evaluate the sensitivity of the parameters in this study, we have performed two experiments: 1) the effect of different patch sizes and 2) increasing the number of images. The results demonstrate that ResNet 152 shows the highest accuracy with patches of 120 × 120 pixels, with an accuracy of 76.62%, and ResNet 50 is the best with an accuracy of 76.2% since the process of this network does not take much time
Exact symmetry breaking ground states for quantum spin chains
We introduce a family of spin-1/2 quantum chains, and show that their exact
ground states break the rotational and translational symmetries of the original
Hamiltonian. We also show how one can use projection to construct a spin-3/2
quantum chain with nearest neighbor interaction, whose exact ground states
break the rotational symmetry of the Hamiltonian. Correlation functions of both
models are determined in closed form. Although we confine ourselves to
examples, the method can easily be adapted to encompass more general models.Comment: 4 pages, RevTex. 4 figures, minor changes, new reference
The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in cortical neural circuits
Most of the recent work in psychedelic neuroscience has been done using
non-invasive neuroimaging, with data recorded from the brains of adult
volunteers under the influence of a variety of drugs. While this data provides
holistic insights into the effects of psychedelics on whole-brain dynamics, the
effects of psychedelics on the meso-scale dynamics of cortical circuits remains
much less explored. Here, we report the effects of the serotonergic psychedelic
N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of
in vitro organotypic cultures made from rat cortical tissue. Three hours of
spontaneous activity were recorded: an hour of pre-drug control, and hour of
exposure to 10M DPT solution, and a final hour of washout, once again
under control conditions. We found that DPT reversibly alters information
dynamics in multiple ways: first, the DPT condition was associated with higher
entropy of spontaneous firing activity and reduced the amount of time
information was stored in individual neurons. Second, DPT also reduced the
reversibility of neural activity, increasing the entropy produced and
suggesting a drive away from equilibrium. Third, DPT altered the structure of
neuronal circuits, decreasing the overall information flow coming into each
neuron, but increasing the number of weak connections, creating a dynamic that
combines elements of integration and disintegration. Finally, DPT decreased the
higher-order statistical synergy present in sets of three neurons.
Collectively, these results paint a complex picture of how psychedelics
regulate information processing in meso-scale cortical tissue. Implications for
existing hypotheses of psychedelic action, such as the Entropic Brain
Hypothesis, are discussed.Comment: 19 pages, 2 figure
Chemical composition, free-radical-scavenging and insecticidal activities of the aerial parts of Stachys byzantina
Stachys byzantina K. Koch. is an Iranian endemic species of the genus Stachys L., which comprises about 300 species, and is one of the largest genera of the family Lamiaceae. A combination of solid phase extraction (SPE) and high pressure liquid chromatography (HPLC) of the methanolic extract of the aerial parts of S. byzantina afforded three phenylethanoids, 2'-O-arabinosyl verbascoside (1), verbascoside (2), aeschynanthoside C (3) and three flavones apigenin 7-O-glucoside (4), apigenin 7-O-(6-p-coumaroyl)-glucoside (5) and apigenin (6). The structures of these compounds were determined by spectroscopic methods. Free-radical-scavenging and insecticidal properties of the crude extracts, the fractions and the isolated compounds were assessed.
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