Duality relation between coherence and path information in the presence of quantum memory

The wave-particle duality demonstrates a competition relation between wave and particle behavior for a particle going through an interferometer. This duality can be formulated as an inequality, which upper bounds the sum of interference visibility and path information. However, if the particle is entangled with a quantum memory, then the bound may decrease. Here, we find the duality relation between coherence and path information for a particle going through a multipath interferometer in the presence of a quantum memory, offering an upper bound on the duality relation which is directly connected with the amount of entanglement between the particle and the quantum memory.Comment: 6 pages, 1 figure, comments are welcom

Lithium chloride promotes neural functional recovery after local cerebral ischemia injury in rats through Wnt signaling pathway activation

Lithium chloride (LiCl) has a significant neuroprotective effect in cerebral ischemia. However, to date, there is a paucity of evidence on the role of LiCl in neural restoration after brain ischemia and the signaling pathways involved remain unclear. Therefore, to address this gap, the middle cerebral artery occlusion (MCAO) rat model was used to simulate human ischemia stroke. Male SD rats were given MCAO for 90 min followed by reperfusion, and Dickkopf-1(DKK1, 5.0 μg/kg) was administered half an hour before MCAO. Rats were then treated with hypodermic injection of LiCl (2.0 mmol/kg) twice a day for one week. After treatment, cognitive impairment was assessed by the Morris Water Maze test. Neurological deficit score, 2,3,5-triphenyl tetrazolium chloride (TTC) staining, brain water content, and histopathology were used to evaluate brain damage. Enzyme-linked immunosorbent assay was used to measure oxidative stress damage and inflammatory cytokines. Apoptosis of the hippocampal neurons was tested by Western blot. The key factors of Wnt signaling pathway in the ischemic penumbra were detected by immunofluorescence (IF) staining and quantitative real-time polymerase chain reaction (qRT-PCR). Current experimental results showed that LiCl treatment significantly improved the impaired spatial learning and memory ability, suppressed oxidative stress, inflammatory reaction, and neuron apoptosis accompanied by attenuating neuronal damage, which subsequently decreased the brain edema, infarct volume and neurological deficit. Furthermore, the treatment of LiCl activated Wnt signaling pathway. Interestingly, the aforementioned effects of LiCl treatment were markedly reversed by administration of DKK1, an inhibitor of Wnt signaling pathway. These results indicate that LiCl exhibits neuroprotective effects in focal cerebral ischemia by Wnt signaling pathway activation, and it might have latent clinical application for the prevention and treatment of ischemic stroke

Total and Parity-Projected Level Densities of Iron-Region Nuclei in the Auxiliary Fields Monte Carlo Shell Model

We use the auxiliary-fields Monte Carlo method for the shell model in the complete $(pf+0g_{9/2})$-shell to calculate level densities. We introduce parity projection techniques which enable us to calculate the parity dependence of the level density. Results are presented for $^{56}$Fe, where the calculated total level density is found to be in remarkable agreement with the experimental level density. The parity-projected densities are well described by a backshifted Bethe formula, but with significant dependence of the single-particle level-density and backshift parameters on parity. We compare our exact results with those of the thermal Hartree-Fock approximation.Comment: 14 pages, 3 Postscript figures included, RevTe

Model migration based on process similarity

In batch process, operation conditions change to meet the requirements of market and customers. For example, in injection molding, the same machine may be used with different molds and materials to make different parts. For each different mold, material, and machine combination, data based modeling process has to be repeated for the development of a quality prediction model. This may involve repetition of a large number of experiments, if common process characteristics are ignored. Obviously, this is inefficient and uneconomical. Despite that operating conditions may be different for different batch processes, certain process behaviors and characteristics are common under these conditions. Effective using and extraction of these common process behaviors and characteristics can allow fewer number of experiments for the development of new process model, resulting in savings of time, cost and efforts. With this as the key objective, process similarity is defined and classified based on process representation. Model migration strategies are proposed for four types of process similarity, scale similarity, family similarity, inclusive similarity and unknown attribute similarity, which involve developing a new process model by taking advantage of an existing base model and process attribute information. For each type of process similarity, an illustrative example is given to demonstrate that the proposed method and results are satisfactory

A full-invariant theorem and some applications

AbstractLet (X,τ1) and (Y,τ2) be two Hausdorff locally convex spaces with continuous duals X′ and Y′, respectively, L(X,Y) be the space of all continuous linear operators from X into Y, K(X,Y) be the space of all compact operators of L(X,Y). Let WOT and UOT be the weak operator topology and uniform operator topology on K(X,Y), respectively. In this paper, we characterize a full-invariant property of K(X,Y); that is, if the sequence space λ has the signed-weak gliding hump property, then each λ-multiplier WOT-convergent series ∑iTi in K(X,Y) must be λ-multiplier convergent with respect to all topologies between WOT and UOT if and only if each continuous linear operator T:(X,τ1)→(λβ,σ(λβ,λ)) is compact. It follows from this result that the converse of Kalton's Orlicz–Pettis theorem is also true

Batch Process Modeling Based on Process Similarity

In the processing industries, operating conditions change to meet the requirements of the market and customers. For example, in injection molding, the same machine may be used with different molds and materials to make different parts. For each different mold, material, and machine combination, data-based process modeling must be repeated for the development of a new prediction model. This may involve the repetition of a large number of experiments, if common process characteristics are ignored. Obviously, this is inefficient and uneconomical. Although operating conditions may be different for different processes, certain process behaviors and characteristics are common under these conditions. The effective use and extraction of these common process behaviors and characteristics can allow fewer experiments for the development of new process model, resulting in a savings of time, cost, and effort. With this as the key objective, a modeling method is proposed for process modeling. This includes information extraction from a base model, a design of experiments (DOE). for the new process, assessment of difference, model migration strategy, and verification for the new model. As an illustrative example, this paper demonstrates model development for new molds to predict the width of an injection-molded part, taking advantage of an existing model

Model Migration with Inclusive Similarity for Development of a New Process Model

in the processing industries, operating conditions change to meet the requirements of the market and customers. Under different operating conditions, data-based process modeling must be repeated for the development of a new process model. Obviously, this is inefficient and uneconomical. Effective use and adaptation of the existing process model can reduce the number of experiments in the development of a new process model, resulting in savings of time, cost, and effort. In this paper, a particular process similarity, inclusive similarity, is discussed in detail. A model migration strategy for processes with this type of similarity is developed to model a new process by taking advantage of existing models and data from the new process. The new model is built by aggregating the existing models using a bagging algorithm. As an illustrated example, the development of a new soft-sensor model for online prediction of melt-flow length for new mold geometry for an injection molding process is demonstrated by taking advantage of existing models for different molds