Entanglement increase from local interaction in the absence of initial quantum correlation in the environment and between the system and the environment

We consider a bipartite quantum system S=AB such that the part A is isolated from the environment E and only the part B interacts with E. Under such circumstances, entanglement of the system may experience decreases and increases, during the evolution of the system. Here, we show that the entanglement of the system can exceed its initial value, under such local interaction, even though, at the initial moment, there is no entanglement in the environment and the system and the environment are only classically correlated. The case which is studied in this paper possesses another interesting feature too: The reduced dynamics of the system can be modeled as a completely positive map. In addition, we introduce the concept of inaccessible entanglement to explain why entanglement can exceed its initial value, under local interactions, in open quantum systems.Comment: 8 pages, 1 figure. References are added. The title is change

Efficient Algorithm for Distinction Mild Cognitive Impairment from Alzheimer’s Disease Based on Specific View FCM White Matter Segmentation and Ensemble Learning

Purpose: Alzheimer's Disease (AD) is in the dementia group and is one of the most prevalent neurodegenerative disorders. Between existing characteristics, White Matter (WM) is a known marker for AD tracking, and WM segmentation in MRI based on clustering can be used to decrease the volume of data. Many algorithms have been developed to predict AD, but most concentrate on the distinction of AD from Cognitive Normal (CN). In this study, we provided a new, simple, and efficient methodology for classifying patients into AD and MCI patients and evaluated the effect of the view dimension of Fuzzy C Means (FCM) in prediction with ensemble classifiers. Materials and Methods: We proposed our methodology in three steps; first, segmentation of WM from T1 MRI with FCM according to two specific viewpoints (3D and 2D). In the second, two groups of features are extracted: approximate coefficients of Discrete Wavelet Transform (DWT) and statistical (mean, variance, skewness) features. In the final step, an ensemble classifier that is constructed with three classifiers, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Discriminant Analysis (LDA), was used. Results: The proposed method has been evaluated by using 1280 slices (samples) from 64 patients with MCI (32) and AD (32) of the ADNI dataset. The best performance is for the 3D viewpoint, and the accuracy, precision, and f1-score achieved from the methodology are 94.22%, 94.45%, and 94.21%, respectively, by using a ten-fold Cross-Validation (CV) strategy. Conclusion: The experimental evaluation shows that WM segmentation increases the performance of the ensemble classifier, and moreover the 3D view FCM is better than the 2D view. According to the results, the proposed methodology has comparable performance for the detection of MCI from AD. The low computational cost algorithm and the three classifiers for generalization can be used in practical application by physicians in pre-clinical

Protein-based structures for food applications: from macro to nanoscale

Novel food structures' development through handling of macroscopic and microscopic properties of bio-based materials (e.g., size, shape, and texture) is receiving a lot of attention since it allows controlling or changing structures' functionality. Proteins are among the most abundant and employed biomaterials in food technology. They are excellent candidates for creating novel food structures due to their nutritional value, biodegradability, biocompatibility, generally recognized as safe (GRAS) status and molecular characteristics. Additionally, the exploitation of proteins' gelation and aggregation properties can be used to encapsulate bioactive compounds inside their network and produce consistent delivery systems at macro-, micro-, and nanoscale. Consequently, bioactive compounds which are exposed to harsh storage and processing conditions and digestion environment may be protected and their bioavailability could be enhanced. In this review, a range of functional and structural properties of proteins which can be explored to develop macro-, micro-, and nanostructures with numerous promising food applications was discussed. Also, this review points out the relevance of scale on these structures' properties, allowing appropriate tailoring of protein-based systems such as hydrogels and micro- or nanocapsules to be used as bioactive compounds delivery systems. Finally, the behavior of these systems in the gastrointestinal tract (GIT) and the impact on bioactive compound bioavailability are thoroughly discussed.JM and AP acknowledge the Portuguese Foundation for Science and Technology (FCT) for their fellowships (SFRH/BPD/89992/2012 and SFRH/BPD/101181/2014). This work was supported by Portuguese FCT under the scope of the Project PTDC/AGR-TEC/5215/2014, of the strategic funding of UID/BIO/04469 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

Regularity for systems and the angle condition.

In this dissertation we consider weak solutions of the System A(u) + B(u) = 0 on a bounded domain W⊂ Rn where B(u) is a perturbation of critical growth, i.e. its growth exponent p equals the integrability exponent of the Sobolev space for which A(u) is a coercive elliptic operator. Under certain structure conditions we get a higher integrability result for 1 < p < n and a regularity result for p > 2