Special Issue on the Internet of Things (ISCC 2020)

Journal of Signal Processing Systems -     

High-Fat Diet Aggravates Cerebral Infarct,Hemorrhagic Transformation and Neuroinflammation in a Mouse Stroke Model

Background: Stroke in context of type 2 diabetes (T2D) is associated with a poorer outcome than in non-diabetic conditions. We aimed at creating a new reproducible mouse model of stroke in impaired glucose tolerance conditions induced by high-fat diet. Methods: Adult C57BL6 mice were fed for 2 months with either normal diet (ND) or high-fat diet (HFD). We used a model of Middle Cerebral Artery Occlusion (MCAO) for 90 min. Oral Glucose Tolerance Test (OGTT) and Insulin Tolerance Test (ITT) were used to assess pre-diabetic status. Brain infarct volume, hemorrhagic transformation (HT) as well as systemic and cerebral inflammatory markers were evaluated. Results: HFD was associated with an increased body weight and glycemia following OGTT. The HFD group presented a significant increase in brain infarct volume (38.7 (IQR 30–46.7%) vs. 28.45 (IQR 21–30%); p = 0.016) and HT (HFD: 2 (IQR 1–5) vs. ND: 0 (IQR 0–1); p = 0.012) and higher levels of IL-6 and MCP-1 in infarcted hemisphere compared to the ND group. Conclusion: Two months of HFD in adult mice were sufficient to alter the lipid profile and the control of hyperglycemia. These metabolic perturbations were significantly associated with increased infarct volume and hemorrhagic complications.     

Functionalized Dipicolinic Acid Derivatives as TALSPEAK-MME Stripping Agents

ABSTRACT

This work describes the results of an assessment of two derivatives of dipicolinic acid (DPA) as the actinide-selective stripping reagents in the combined HEH[EHP]/Cyanex-923 system, TALSPEAK-MME (mixed monofunctional extractants). One purpose of this investigation is to demonstrate probable advantages derived in phase-transfer kinetics by deploying dipicolinates as preorganized aminopolycarboxylate complexants in TALSPEAK-derived separation systems. The investigation is focused on the behavior of the lanthanides, Am, and selected transition metals (Zr, Mo, Pd, Ru, and Rh) in the extraction, solvent conditioning, and stripping steps. Solvent conditioning with glycine buffer solutions is shown to be effective for the removal of entrained HNO₃ while also managing Pd concentration prior to actinide stripping. The efficacy of the DPA derivative during the actinide-selective strip was assessed, focusing explicitly on the effects of pH and ligand concentration and evaluating the stripping kinetics. These ligands were shown to be highly selective for americium, producing in the TALSPEAK-MME platform separation factors comparable to commonly used actinide selective stripping agents for radiotracer Eu, Am, a trans-lanthanide series and selected transition metals. The stripping kinetics study revealed very rapid phase-transfer reactions, reaching equilibrium in less than 10 s on vigorous agitation. Further work will be required to address the possible contamination of the product by selected transition metals (such as Zr) during the stripping step.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...     

Numerical modeling of wave propagation in anisotropic viscoelastic laminated materials in transient regime: Application to modeling ultrasonic testing of composite structures

Composite laminate structures remain an important family of materials used in cutting-edge industrial areas. Building efficient numerical modeling tools for high-frequency wave propagation in order to represent ultrasonic testing experiments of these materials remains a major challenge. In particular, incorporating attenuation phenomena within anisotropic plies, and thin intermediate isotropic layers between the plies often represent significant obstacles for standard numerical approaches. In our work, we address both issues by proposing a systematic study of the fully discrete propagators associated to the Kelvin-Voigt, Maxwell, and Zener models, and by incorporating effective transmission conditions between plies using the mortar element method. We illustrate the soundness of our approach by proposing intermediate one-dimensional and two-dimensional numerical evidence, and we apply it to a more realistic configuration of a curved laminate composite structure in a three-dimensional setting.     

Dependences in Strategy Logic

Strategy Logic (SL) is a very expressive temporal logic for specifying and verifying properties of multi-agent systems: in SL, one can quantify over strategies, assign them to agents, and express LTL properties of the resulting plays. Such a powerful framework has two drawbacks: first, model checking SL has non-elementary complexity; second, the exact semantics of SL is rather intricate, and may not correspond to what is expected. In this paper, we focus on strategy dependences in SL, by tracking how existentially-quantified strategies in a formula may (or may not) depend on other strategies selected in the formula, revisiting the approach of [Mogavero et al., Reasoning about strategies: On the model-checking problem, 2014]. We explain why elementary dependences, as defined by Mogavero et al., do not exactly capture the intended concept of behavioral strategies. We address this discrepancy by introducing timeline dependences, and exhibit a large fragment of SL for which model checking can be performed in 2-EXPTIME under this new semantics.     

Natural food mastication capability in preschool children according to their oral condition: A preliminary study

This study investigated, for the first time, the masticatory capability of preschool children using natural foods, and the impact of an early oral health alteration (early childhood caries: ECC) on the granulometry of ready-to-swallow food boluses. Thirteen children with ECC were compared to 13 preschool children with a healthy oral condition. Oral health criteria and NOT-S scores (Nordic Orofacial dysfunction Test-Screening) were recorded. For each child, number of masticatory cycles (Nc), chewing time (Ti), and frequency (Fq = Nc/Ti) were recorded during mastication of raw carrot (CAR), cheese (CHS) and breakfast cereals (CER) samples. Food boluses were collected by stopping children at their food-dependent individual swallowing threshold (Nc), and the median food bolus particle size value (D50) was calculated. Correlations were sought between oral health and masticatory criteria. In the ECC group, mean Fq values were significantly decreased for all three foods (p ≤ .001) and mean D50 values were significantly increased (p ≤ .001) compared to the control group (i.e., D50 CAR = 4,384 μm ± 929 vs. 2,960 μm ± 627). These alterations were related to the extent of ECC. The NOT-S mean global score was significantly increased in children with ECC (2.62 ± 1.37 vs. 1 ± 0.91 in the control group, p ≤ .01), due to “Mastication and swallowing” domain impairment. This study gives granulometric normative values for three foods in preschool children and shows the impact of ECC on D50 values. The progression of children's masticatory capability after dental treatment, and the impact of such modifications of sensory input on future eating habits should be explored.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Evaluation of dynamic modulus measurement for C/C-SiC composites at different temperatures

The determination of elastic properties at application temperature is fundamental for the design of fibre reinforced ceramic composite components. An attractive method to characterize the flexural modulus at room and high temperature under specific atmosphere is the nondestructive Resonant Frequency Damping Analysis (RFDA). The objective of this paper was to evaluate and validate the modulus measurement via RFDA for orthotropic C/C-SiC composites at the application temperature. At room temperature flexural moduli of C/C-SiC with 0/90° reinforcement were measured under quasi-static 4-point bending loads and compared with dynamic moduli measured via RFDA longitudinally to fibre direction. The dynamic modulus of C/C-SiC was then measured via RFDA up to 1250°C under flowing inert gas and showed an increase with temperature which fitted with literature values. The measured fundamental frequencies were finally compared to those resulting from numerical modal analyses. Dynamic and quasi-static flexural moduli are comparable and the numerical analyses proved that bending modes are correctly modeled by means of dynamic modulus measured via RFDA. The nondestructive RFDA as well as the numerical modeling approach are suitable for evaluation of C/C-SiC and may be transferred to other fibre reinforced ceramic composite materials.     

A pipeline structure for the block QR update in digital signal processing

The Journal of Supercomputing - There exist problems in the field of digital signal processing, such as filtering of acoustic signals that require processing a large amount of data in real time....