Compactness in apartness spaces?

A major problem in the constructive theory of apartness spaces is that of finding a good notion of compactness. Such a notion should (i) reduce to ``complete plus totally bounded\u27\u27 for uniform spaces and (ii) classically be equivalent to the usual Heine-Borel-Lebesgue property for the apartness topology. The constructive counterpart of the smallest uniform structure compatible with a given apartness, while not constructively a uniform structure, offers a possible solution to the compactness-definition problem. That counterpart turns out to be interesting in its own right, and reveals some additional properties of an apartness that may have uses elsewhere in the theory

Diderot, l’Encyclopédie & autres études, sillages de Jacques Proust.

Pour tous les spécialistes de Diderot, pour tous les dix-huitiémistes mais aussi pour tous les universitaires, la figure de Jacques Proust (1926- 2005) conserve une présence singulière. En témoigne ce beau livre préparé par Marie Leca-Tsiomis qui réunit un ensemble d’évocations et d’études variées suscitées par le souvenir du grand professeur et du grand critique. Les cinq années écoulées depuis sa disparition permettent de donner à cet hommage quelque chose qui va plus loin que la piété et l..

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Image Restoration Using One-Dimensional Sobolev Norm Profiles of Noise and Texture

This work is devoted to image restoration (denoising and deblurring) by variational models. As in our prior work [Inverse Probl. Imaging, 3 (2009), pp. 43-68], the image (f) over tilde to be restored is assumed to be the sum of a cartoon component u (a function of bounded variation) and a texture component v (an oscillatory function in a Sobolev space with negative degree of differentiability). In order to separate noise from texture in a blurred noisy textured image, we need to collect some information that helps distinguish noise, especially Gaussian noise, from texture. We know that homogeneous Sobolev spaces of negative differentiability help capture oscillations in images very well; however, these spaces do not directly provide clear distinction between texture and noise, which is also highly oscillatory, especially when the blurring effect is noticeable. Here, we propose a new method for distinguishing noise from texture by considering a family of Sobolev norms corresponding to noise and texture. It turns out that the two Sobolev norm profiles for texture and noise are different, and this enables us to better separate noise from texture during the deblurring process.open0

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes

Influence of B4C and industrial waste fly ash reinforcement particles on the micro structural characteristics and mechanical behavior of aluminium (Al–Mg–Si-T6) hybrid metal matrix composite

The expectations over composite materials have been increased especially in automotive and aerospace applications due to its high strength to weight ratio and good mechanical properties. Here, we aim to fabricate a hybrid composite of high strength and low density for automotive application to suits the above needs. In this investigation, the heat-treated aluminum alloy Al–Mg–Si-T6 was initially reinforced with industrial waste fly ash particles at five different weight fractions of 0%, 5%, 10%, 15% and 20%, respectively by stir casting process. The mechanical properties such as tensile strength, compression strength, hardness and density were tested, and microstructure of the composite was evaluated to explain the mechanical properties evolution. From the results, it was concluded that the composite with 10% fly ash shows enhanced at maximum the properties when compared to others. Then, the Al–Mg–Si-T6 – 5% fly ash was further reinforced with boron carbide particles by using three different fractions of 2.5%, 5% and 7.5%, respectively by stir casting process. The microstructural analysis, Scanning Electron Microscope analysis (SEM) and Energy Dispersive X-ray Spectroscopy analysis (EDS) were carried out for the casted samples to evaluate interfacial bonding, agglomeration, clustering and void formation in the hybrid composite samples. The casted samples were also tested for mechanical properties such as tensile strength, compression strength, hardness and density. It reveals that the optimal combination of 10% reinforcement (5% fly ash and 5% boron carbide) shows 18.7% higher tensile strength, 11.3% higher hardness and 38.6% higher compression when compared with the unreinforced Al–Mg–Si-T6 heat treated alloy. It is expected that the present hybrid metal matrix composites can be adopted for the fabrication of drive shaft in race cars

Transgenic mice overexpressing the extracellular domain of NCAM are impaired in working memory and cortical plasticity

The neural cell adhesion molecule, NCAM, is a pivotal regulator of neural development, with key roles in axonal and dendritic growth and synaptic plasticity. Alterations in NCAM expression or proteolytic cleavage have been linked to human neuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer’s disease, and may contribute to cognitive dysfunction. We have generated mice overexpressing the NCAM extracellular (EC) proteolytic cleavage fragment which has been reported to be increased in schizophrenic versus normal brains. These mice show impaired GABAergic innervation and reduced number of apical dendritic spines on pyramidal neurons in the prefrontal cortex (PFC). Here, these NCAM-EC transgenic mice were subjected to behavioral tasks and electrophysiological measurements to determine the impact of structural abnormalities in the PFC on synaptic and cognitive functions. NCAM-EC mice exhibited impaired working memory in a delayed non-match-to-sample task, which requires PFC function, but showed no differences in anxiety, olfactory abilities, or sociability. Transgenic mice displayed impaired long- and short-term potentiation in the PFC but normal synaptic plasticity in the hippocampus, suggesting that the abnormal synaptic innervation in NCAM-EC mice impairs PFC plasticity and alters working memory. These findings may have implications for cognitive dysfunctions observed in neuropsychiatric disorders

Higher urinary cortisol levels associate with increased cardiovascular risk

There are conflicting data on whether variations of physiologic cortisol levels associated with cardiovascular risk. We hypothesize that prior discordant findings are related to problems associated with varying sample size, techniques for assessing cardiovascular risk and failure to adequately account for environmental factor s. To address these issues, we utilized a large sample size, selected the Framingham risk score to compute cardiovascular risk and performed the study in a highly controlled setting. We had two main objectives: determine whether higher, yet physiologic, cortisol levels associated with increased cardiovascular risk and determine whether caveol in-1 (rs926198) risk allele carriers associated with increased cardiovascular risk. This was a cross-sectional study of 574 non-diabetic individuals who completed a common protocol. Data collection included fasting blood samples, blood pressure measurements and a 24-h urine-free cortisol collection. Five hundred seventeen of these participants also completed caveolin-1 genotyping. Subjects were classified as belonging to either the low-mode or high-mode urine-free cortisol groups, based on the bimodal distribution of urine-free cortisol. In multivariate analysis, Framingham risk score was statistically higher in the high-mode cortisol group (10.22 (mean) ± 0.43 (s.e.m.)) compared to the low-mode cortisol group (7.73 ± 0.34), P < 0.001. Framingham risk score was also statistically higher in n the caveolin-1 risk allele carriers (8.91 ± 0.37) compared to caveolin-1 non-risk allele carriers (7.59 ± 0.48), P = 0.034. Overall, the estimated effect on Framingham risk score of carrying the caveolin-1 risk allele was 1.33 ± 0.61, P = 0.029. Both urinary cortisol and caveolin-1 risk allele status are independent predictors of Framingham risk score