Failure strength of thin-walled cylindrical GFRP composite shell against static internal and external pressure for various volumetric fiber fraction

A study on a Circular cylindrical thin-walled shell failure made of GRP composite subjected to static internal and external pressure was carried out. The results were acquired using analytical and FEM simulation approaches for various volumetric fiber fractions. Fiber breakage, matrix breakage, interlaminate shear deformation, delamination shear deformation and micro buckling failure were investigated employing maximum failure criteria against internal and external pressure. One-ply cylindrical shell with fiber angle orientation of 0 degree was modeled in ABAQUS finite element simulation and the result was varied using analytical approaches. Moreover, the pressure fluctuations for various volumetric fiber fraction were quadratic according to plotted graphs. Meanwhile, MATLAB software was used for theoretical analysis. The comparison of two approaches was proved to be accurate. Subsequently, failure strength of various laminated GFRP cylindrical shell with different fiber angle orientations at each ply was studied for diverse volumetric fiber fraction factors. Stacking sequence, fiber angle orientations were mainly effective on failure strength

SmartFog: Training the Fog for the energy-saving analytics of Smart-Meter data

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively

Deepfogsim: A toolbox for execution and performance evaluation of the inference phase of conditional deep neural networks with early exits atop distributed fog platforms

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox

Learning-in-the-Fog (LiFo): Deep learning meets Fog Computing for the minimum-energy distributed early-exit of inference in delay-critical IoT realms

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-Alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-Time. Motivated by this consideration, this article focuses on the optimized design and performance validation of {L} earning-{i} ext{n}-The-Fo g (LiFo), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-Tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this article are threefold, namely: (i) we design the main building blocks and supporting services of the LiFo architecture by explicitly accounting for the multiple constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an adaptive algorithm for the minimum-energy distributed joint allocation and reconfiguration of the available computing-plus-networking resources of the LiFo platform. Interestingly enough, the designed algorithm is capable to self-detect (typically, unpredictable) environmental changes and quickly self-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an Information Centric-based networking architecture, which enables the LiFo platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay LiFo performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-The-Art benchmark solutions that do not rely on the Fog support

An accuracy vs. complexity comparison of deep learning architectures for the detection of covid-19 disease

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%

Efficient creation of dipolar coupled nitrogen-vacancy spin qubits in diamond

Coherently coupled pairs or multimers of nitrogen-vacancy defect electron spins in diamond have many promising applications especially in quantum information processing (QIP) but also in nanoscale sensing applications. Scalable registers of spin qubits are essential to the progress of QIP. Ion implantation is the only known technique able to produce defect pairs close enough to allow spin coupling via dipolar interaction. Although several competing methods have been proposed to increase the resulting resolution of ion implantation, the reliable creation of working registers is still to be demonstrated. The current limitation are residual radiation-induced defects, resulting in degraded qubit performance as trade-off for positioning accuracy. Here we present an optimized estimation of nanomask implantation parameters that are most likely to produce interacting qubits under standard conditions. We apply our findings to a well-established technique, namely masks written in electron-beam lithography, to create coupled defect pairs with a reasonable probability. Furthermore, we investigate the scaling behavior and necessary improvements to efficiently engineer interacting spin architectures

Genetically engineered fusion of allergen and viral-like particle induces a more effective allergen-specific immune response than a combination of them

Abstract: Chimeric virus-like particles (VLPs) were developed as a candidate for allergen-specific immunotherapy. In this study, hepatitis B core antigen (HBcAg) that genetically fused to Chenopodium album polcalcin (Che a 3)–derived peptide was expressed in E. coli BL21, purified, and VLP formation was evaluated using native agarose gel electrophoresis (NAGE) and transmission electron microscopy (TEM). Chimeric HBc VLPs were characterized in terms of their reactivity to IgE, the induction of blocking IgG and allergen-specific IgE, basophil-activating capacity, and Th1-type immune responses. Results from IgE reactivity and basophil activation test showed that chimeric HBc VLPs lack IgE-binding capacity and basophil degranulation activity. Although chimeric HBc VLPs induced the highest level of efficient polcalcin-specific IgG antibody in comparison to those induced by recombinant Che a 3 (rChe a 3) mixed either with HBc VLPs or alum, they triggered the lowest level of polcalcin-specific IgE in mice following immunization. Furthermore, in comparison to the other antigens, chimeric HBc VLPs produced a polcalcin-specific Th1 cell response. Taken together, genetically fusion of allergen derivatives to HBc VLPs, in comparison to a mix of them, may be a more effective way to induce appropriate immune responses in allergen-specific immunotherapy. Key points: • The insertion of allergen-derived peptide into major insertion region (MIR) of hepatitis B virus core (HBc) antigen resulted in nanoparticles displaying allergen-derived peptide upon its expression in prokaryotic host. • The resultant VLPs (chimeric HBc VLPs) did not exhibit IgE reactivity with allergic patients’ sera and were not able to degranulate basophils. • Chimeric HBc VLPs dramatically improved protective IgG antibody response compared with those induced by allergen mixed either with HBc VLPs or alum. • Chimeric HBc VLPs induced Th1 responses that were counterparts of Th2 responses (allergic). • Chimeric HBc VLPs increased IgG2a/ IgG1 ratio and the level of IFN-γ compared to those induced by allergen mixed with either HBc VLPs or alum. [Figure not available: see fulltext.

Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics

Crohn's Disease and Early Exposure to Domestic Refrigeration

Environmental risk factors playing a causative role in Crohn's Disease (CD) remain largely unknown. Recently, it has been suggested that refrigerated food could be involved in disease development. We thus conducted a pilot case control study to explore the association of CD with the exposure to domestic refrigeration in childhood.Using a standard questionnaire we interviewed 199 CD cases and 207 age-matched patients with irritable bowel syndrome (IBS) as controls. Cases and controls were followed by the same gastroenterologists of tertiary referral clinics in Tehran, Iran. The questionnaire focused on the date of the first acquisition of home refrigerator and freezer. Data were analysed by a multivariate logistic model. The current age was in average 34 years in CD cases and the percentage of females in the case and control groups were respectively 48.3% and 63.7%. Patients were exposed earlier than controls to the refrigerator (X2 = 9.9, df = 3, P = 0.04) and refrigerator exposure at birth was found to be a risk factor for CD (OR = 2.08 (95% CI: 1.01-4.29), P = 0.05). Comparable results were obtained looking for the exposure to freezer at home. Finally, among the other recorded items reflecting the hygiene and comfort at home, we also found personal television, car and washing machine associated with CD.This study supports the opinion that CD is associated with exposure to domestic refrigeration, among other household factors, during childhood