A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation

Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: “For allacceptable deviations (deviations which are inside tolerances),there existsa gap configurationsuch asthe assembly requirements and the behavior constraints are verified” & “For allacceptable deviations (deviations which are inside tolerances), andfor alladmissible gap configurations, the assembly and functional requirements and the behavior constraints are verified”. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism.     

A personalized measure of thermal comfort for building controls

A personalized measure for thermal comfort has been applied for use in combination with smart controls for building automation. Using data from a field study, we first show the superiority of personalized measures for thermal comfort compared to standard non-adaptive methods. Based on this knowledge we describe a methodology, using logistic regression techniques, to convert user votes to a probability of comfort. We also describe the interface used to collect the votes. We show that, for a given subject, our thermal profile converges against the probabilities found in the field study. As a case study we implemented the measure in a control algorithm to control the shading devices. The results clarify the mode of action and also show the effectiveness of the method.     

Terahertz Real-Time Imaging Uncooled Arrays Based on Antenna-Coupled Bolometers or FET Developed at CEA-Leti

Journal of Infrared, Millimeter, and Terahertz Waves - Sensitive and large-format terahertz focal plane arrays (FPAs) integrated in compact and hand-held cameras that deliver real-time terahertz...     

Time‐space PGD for the rapid solution of 3D nonlinear parametrized problems in the many‐query context

This work deals with the question of the resolution of nonlinear problems for many different configurations in order to build a ‘virtual chart’ of solutions. The targeted problems are three‐dimensional structures driven by Chaboche‐type elastic‐viscoplastic constitutive laws. In this context, parametric analysis can lead to highly expensive computations when using a direct treatment. As an alternative, we present a technique based on the use of the time‐space proper generalized decomposition in the framework of the LATIN method. To speed up the calculations in the parametrized context, we use the fact that at each iteration of the LATIN method, an approximation over the entire time‐space domain is available. Then, a global reduced‐order basis is generated, reused and eventually enriched, by treating, one‐by‐one, all the various parameter sets. The novelty of the current paper is to develop a strategy that uses the reduced‐order basis for any new set of parameters as an initialization for the iterative procedure. The reduced‐order basis, which has been built for a set of parameters, is reused to build a first approximation of the solution for another set of parameters. An error indicator allows adding new functions to the basis only if necessary. The gain of this strategy for studying the influence of material or loading variability reaches the order of 25 in the industrial examples that are presented. Copyright © 2015 John Wiley & Sons, Ltd.     

Molecular Heterojunctions of Oligo(phenylene ethynylene)s with Linear to Cruciform Framework

Electrical transport properties of molecular junctions are fundamentally affected by the energy alignment between molecular frontier orbitals (highest occupied molecular orbital (HOMO) or lowest unoccupied molecular orbital (LUMO)) and Fermi level (or work function) of electrode metals. Dithiafulvene (DTF) is used as substituent group to the oligo(phenylene ethynylene) (OPE) molecular wires and different molecular structures based on OPE3 backbone (with linear to cruciform framework) are achieved, with viable molecular orbitals and HOMO–LUMO energy gaps. OPE3, OPE3–DTF, and OPE3–tetrathiafulvalene (TTF) can form good self‐assembled monolayers (SAMs) on Au substrates. Molecular heterojunctions based on these SAMs are investigated using conducting probe–atomic force microscopy with different tips (Ag, Au, and Pt) and Fermi levels. The calibrated conductance values follow the sequence OPE3–TTF > OPE3–DTF > OPE3 irrespective of the tip metal. Rectification properties (or diode behavior) are observed in case of the Ag tip for which the work function is furthest from the HOMO levels of the OPE3s. Quantum chemical calculations of the transmission qualitatively agree with the experimental data and reproduce the substituent effect of DTF. Zero‐bias conductance, and symmetric or asymmetric couplings to the electrodes are investigated. The results indicate that improved fidelity of molecular transport measurements may be achieved by systematic studies of homologues series of molecular wires applying several different metal electrodes.     

High Spatial Resolution Imaging of Endogenous Hydrogen Peroxide in Living Cells by Solid‐State Fluorescence

Herein, we describe selective imaging of hydrogen peroxide using a precipitating dye conjugated to a boronic acid‐based immolative linker. We achieved visualization of endogenous hydrogen peroxide in phagosomes by solid‐state two‐photon fluorescence imaging in living cells with exceptionally high spatial resolution.     

Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera

Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient’s serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients’ treatment.     

Opioids and Sepsis: Elucidating the Role of the Microbiome and microRNA-146

Sepsis has recently been defined as life-threatening organ dysfunction caused by the dysregulated host response to an ongoing or suspected infection. To date, sepsis continues to be a leading cause of morbidity and mortality amongst hospitalized patients. Many risk factors contribute to development of sepsis, including pain-relieving drugs like opioids, which are frequently prescribed post-operatively. In light of the opioid crisis, understanding the interactions between opioid use and the development of sepsis has become extremely relevant, as opioid use is associated with increased risk of infection. Given that the intestinal tract is a major site of origin of sepsis-causing microbes, there has been an increasing focus on how alterations in the gut microbiome may predispose towards sepsis and mediate immune dysregulation. MicroRNAs, in particular, have emerged as key modulators of the inflammatory response during sepsis by tempering the immune response, thereby mediating the interaction between host and microbiome. In this review, we elucidate contributing roles of microRNA 146 in modulating sepsis pathogenesis and end with a discussion of therapeutic targeting of the gut microbiome in controlling immune dysregulation in sepsis.