Model-independent study for a quintessence model of dark energy: Analysis and Observational constraints

In this paper, a well-motivated parametrization of the Hubble parameter ($H$% ) is revisited that renders two models of dark energy showing some intriguing features of the late-time accelerating Universe. A general quintessence field is considered as a source of dark energy. We have obtained tighter constraints using recently updated cosmic observational datasets for the considered models. The two models described here show a nice fit to the considered uncorrelated Hubble datasets, Standard candles, Gamma Ray Bursts, Quasars, and uncorrelated Baryonic Acoustic Oscillations datasets. Using the constrained values of the model parameters, we have discussed some features of the late-time accelerating models and obtained the present value of the deceleration parameter ($q_{0}$), the present value of the Hubble parameter ($H_{0}$) and the transition redshift ($z_{t}$) from deceleration to acceleration. The current value of the deceleration parameter for both models is consistent with the Planck 2018 results. The evolution of the geometrical and physical parameters is discussed through graphical representations for both models with some diagnostic analysis. The statistical analysis performed here shows greater results and overall, the outcomes of this investigation are superior to those previously found.Comment: 22 pages, 26 figure

First-principles prediction of redox potentials in transition-metal compounds with LDA+U

First-principles calculations within the Local Density Approximation (LDA) or Generalized Gradient Approximation (GGA), though very successful, are known to underestimate redox potentials, such as those at which lithium intercalates in transition metal compounds. We argue that this inaccuracy is related to the lack of cancellation of electron self-interaction errors in LDA/GGA and can be improved by using the DFT+$U$ method with a self-consistent evaluation of the $U$ parameter. We show that, using this approach, the experimental lithium intercalation voltages of a number of transition metal compounds, including the olivine Li$_{x}$MPO$_{4}$ (M=Mn, Fe Co, Ni), layered Li$_{x}$MO$_{2}$ ($x=$Co, Ni) and spinel-like Li$_{x}$M$_{2}$O$_{4}$ (M=Mn, Co), can be reproduced accurately.Comment: 19 pages, 6 figures, Phys. Rev. B 70, 235121 (2004

From seaweeds to hydrogels: Recent progress in kappa-2 carrageenans

Hybrid carrageenans, also called kappa-2 (K2) or weak kappa, are a class of sulfated polysaccharides with thermo-reversible gelling properties in water and are extracted from a specific family of red seaweeds. K2 are known in the industry for their texturizing properties which are intermediate between those of kappa-carrageenans (K) and iota-carrageenans (I). As such, K2 are gaining industrial interest, as they can replace blends of K and I (K + I) in some niche applications. Over the last decade or so, some progress has been made in unravelling K2's chemical structure. The understanding of K2 gel's structure-rheological properties' relationships has also improved. Such recent progress is reported here, reviewing the literature on gelling K2 published since the last review on the topic. The focus is on the seaweeds used for extracting K2, their block copolymer chemical structures, and how these impact on the gel's formation and rheological properties. The outcome of this review is that additional rheological and structural studies of K2 hydrogels are needed, in particular to understand their viscoelastic behavior under large deformation and to unravel the differences between the texturizing properties of K2 and K + I.This work was supported by the Fundação para a Ciência e Tecnologia (FCT), through the E2B2-PHACAR project, grant number: PTDC/BII-BIO/5626/2020. Additional financial support by the FCT under the framework of Strategic Funding grant: UID/CTM/50025/2020 and grant: CEECINST/00156/2018 are also acknowledged

Phospho-Olivine as Advanced Cathode Material for Lithium Batteries

Nano-sized and micron-sized LiFePO4 electrode materials were prepared by a sol gel and coprecipitation reactions. An improvement of the cycling and rate performances in lithium cells was observed for the carbon coated LiFePO4 materials. The coating process uses a solid/gas-phase reaction which consists of decomposing propylene gas, as carbon source, inside a reactor containing olivine LiFePO4 materials. Optimized LiFePO4 electrode cells, cycled at RT between 3.0 and 4.3 V at a C/10 rate, do not show any sign of capacity fade during the first 50 cycles. Combination of the high volumetric energy density and low cost preparation method makes the micron-sized LiFePO4 olivine an attractive safe cathode for lithium-ion batteries

Ulocuplumab (BMS-936564 / MDX1338): a fully human anti-CXCR4 antibody induces cell death in chronic lymphocytic leukemia mediated through a reactive oxygen species-dependent pathway.

The CXCR4 receptor (Chemokine C-X-C motif receptor 4) is highly expressed in different hematological malignancies including chronic lymphocytic leukemia (CLL). The CXCR4 ligand (CXCL12) stimulates CXCR4 promoting cell survival and proliferation, and may contribute to the tropism of leukemia cells towards lymphoid tissues. Therefore, strategies targeting CXCR4 may constitute an effective therapeutic approach for CLL. To address that question, we studied the effect of Ulocuplumab (BMS-936564), a fully human IgG4 anti-CXCR4 antibody, using a stroma--CLL cells co-culture model. We found that Ulocuplumab (BMS-936564) inhibited CXCL12 mediated CXCR4 activation-migration of CLL cells at nanomolar concentrations. This effect was comparable to AMD3100 (Plerixafor--Mozobil), a small molecule CXCR4 inhibitor. However, Ulocuplumab (BMS-936564) but not AMD3100 induced apoptosis in CLL at nanomolar concentrations in the presence or absence of stromal cell support. This pro-apoptotic effect was independent of CLL high-risk prognostic markers, was associated with production of reactive oxygen species and did not require caspase activation. Overall, these findings are evidence that Ulocuplumab (BMS-936564) has biological activity in CLL, highlight the relevance of the CXCR4-CXCL12 pathway as a therapeutic target in CLL, and provide biological rationale for ongoing clinical trials in CLL and other hematological malignancies

Anatomo-topographic and histo-cytological study of dromedary’s spleen in Algeria

Twenty-five spleens of adult, healthy dromedary of the local breed from the region of El Oued, Algeria, were collected at the slaughterhouse in order to carry out research to determine the macroscopic and microscopic structure of spleen in this specie, macroscopic study revealed that the spleen has a rectangular shape with a triangular section, rounded edges, a little bit striated, its surface is smooth in which the aspect of the capsule and the parietal surface is shiny and smooth, the morphometric study was carried out after classifying the sampled spleen in five groups according according to the animal's body weight which increases with age. Our study revealed that the groups show a different value of mass which declines towards a drop of the index, also the indexes of length and width are following a decreasing order. The histological study revealed that the zone occupied by stroma did not exceed 26.81 % of the total components of the capsule which is composed essentially of connective tissue and an inner layer of smooth muscle cells. Vascular and avascular trabeculae extend from the capsule. The immunohistochemistry study made it possible to visualize T lymphocytes of the splenic parenchyma using monoclonal antibodies where a statistical study was carried out to determine the composition of the various compartments of this organ. The localization of immunocompetent cells in the splenic parenchyma has been elucidated with antibodies specific for T lymphocytes. The red pulp occupied a maximum area of the spleen with an average of 68.1% composed of sinusoids venous, the cords extend between the sinuses and the interlobular zone contain many cells: macrophages, plasma cells, red blood cells, white blood cells and platelets

Design and modeling of cylindrical and falt-wound lithium-ion cells for the PNGV application.

In this study, 10-Ah cylindrical and flat-wound cells were designed and studied for use in batteries for the Partnership for a New Generation of Vehicles (PNGV). A low-cost current collection system was devised that results in a low resistance. Heat rejection from flat cells is much better than that from cylindrical cells and is an important safety factor. Very compact, powerful batteries of about 1.5 kW/L can be designed with wound lithium-ion cells

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing

3D Indoor Instance Segmentation in an Open-World

Existing 3D instance segmentation methods typically assume that all semantic classes to be segmented would be available during training and only seen categories are segmented at inference. We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available. To this end, we introduce an open-world 3D indoor instance segmentation method, where an auto-labeling scheme is employed to produce pseudo-labels during training and induce separation to separate known and unknown category labels. We further improve the pseudo-labels quality at inference by adjusting the unknown class probability based on the objectness score distribution. We also introduce carefully curated open-world splits leveraging realistic scenarios based on inherent object distribution, region-based indoor scene exploration and randomness aspect of open-world classes. Extensive experiments reveal the efficacy of the proposed contributions leading to promising open-world 3D instance segmentation performance.Comment: Accepted at NeurIPS 202