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Nano-electro-mechanical (NEMS) systems are compacted to be of dimension less than a micron by MEMS. Extremely high fundamental frequencies can be reached in this size regime while at the same time maintaining extremely elevated mechanical sensitivity (minute strength constant). This powerful mixture of attribute directly translate in to elevated strength sensitivities, ultra-low power operability's, and the capability to cause functional nonlinearities by very diffident controlling powers. Current paper addressed NEMS in this overview and will detail numerous of their stimulating original application. At the threshold for this new domain, however, there is a steep entry fee: modern innovation is key to realizing NEMS ' full potential. Some of the foundations of MEMS ' methods would obviously not usefully fit into the NEMS system. The main problem is the dimensions of the systems relative to the circuits in which they will be embedded, its excessive volume-to-surface ratio and its alternative “characteristic operating range.” These create some of the main challenges currently facing the development of NEMS. The majority of well-known of these is the requirement for: Very sensitive, elevated values of bandwidth dislocation transducer; unprecedented controls of quality of surface and adsorbents; new mode of capable Nano-scale actuations; and new surface as well as bulk Nano-machining approaches that are precise, robust and routinely reproducible.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

Warhead power assessment of the anti-ship missile plays a vital role in determining the optimal design of missile, thus having important strategic research significance. However, in the assessment process, expert’s judgement will directly affect the assessment accuracy. In addition, there are many criteria involved in the missile design alternatives. Some criteria with poor performance may be compensated by other criteria with excellent performance, and then it is impossible to find the truly optimal alternative. Aimed at solving these problems, this paper proposes a synthetical assessment process based on fuzzy hesitant linguistic term set and the Gained and Lost Dominance Score (GLDS) method. In order to improve the assessment accuracy of experts and solve the problem that experts generate different opinions, combined with the advantages of fuzzy hesitant sets and linguistic term sets, the double hierarchy hesitant fuzzy linguistic term sets are used in this paper to improve the accuracy of expert’s judgement. In order to effectively combine expert’s experience with the data of criteria, the evidence theory and entropy weight method are used to transfer the expert’s judgement to the weight. In order to avoid selecting defective alternative of missile design, the GLDS is used to fuse expert information and criteria information. Sensitivity analysis shows that the assessment process has sensitivity to some extent. However, when the fluctuation of expert’s assessment makes the fluctuation of θ in the range of −5% to 5%, the impact on the results is not quite conspicuous. The analysis of calculation result and comparative analysis show that the assessment process proposed in this paper is accurate enough, has great advantage in selecting the current and potential optimal alternative of missile design, and avoids the alternatives with low criteria performance that cannot be compensated by other criteria being selected.

Process-based reactive transport modeling (RTM) integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface environment. RTM is usually conducted through numerical programs based on the first principle of physical processes. However, the calculation for complex chemical reactions in most available programs is an iterative process, where each iteration is in general computationally intensive. A workflow of neural network-based surrogate model as a proxy for process-based reactive transport simulation is established in this study. The workflow includes (1) base case RTM design, (2) development of training experiments, (3) surrogate model construction based on machine learning, (4) surrogate model validation, and (5) prediction with the calibrated model. The training experiments for surrogate modeling are generated and run prior to the predictions using RTM. The results show that the predictions from the surrogate model agree well with those from processes-based RTM but with a significantly reduced computational time. The well-trained surrogate model is especially useful when a large number of realizations are required, such as the sensitivity analysis or model calibration, which can significantly reduce the computational time compared to that required by RTM. The benefits are (1) it automatizes the experimental design during the sensitivity analysis to get sufficient numbers and coverage of the training cases; (2) it parallelizes the calculations of RTM training cases during the sensitivity analysis to reduce the simulation time; (3) it uses the neural network algorithm to rank the sensitivity of the parameters and to search the optimal solution for model calibration.

Dome structures have been used extensively for industrial, residential, and military infrastructure. Therefore, it is necessary to understand the damage risk potential for such structures for blast-resistant design considerations. This paper investigates the effect of blast load variability on the design value and the structural dynamic response. Therefore, the sources of uncertainty in the external blast load on dome structures were discussed firstly. Then based on the probabilistic blast load model for the dome, the rationality of a deterministic mass-increase safety method was assessed. It was found that previous deterministic design method cannot provide a consistent and sound assurance factor or reliability index on the entire dome roof. In addition, it was also proved that the assurance-based load method fails to ensure compliance with structural safety design standards on the dome roof when compared with the reliability-based blast method. A sensitivity analysis on the probabilistic blast load was conducted, and the results indicate that stand-off distance and explosive mass both act as dominant sources to influence the mean and variability of blast load. Therefore, based on the Latin hypercube sampling method, a reliability-based external blast load factor technique was proposed. This technique was further used to estimate structural damage levels of a single-layer reticulated dome under different reliability requirements, associated with a low, medium, and high level of protection grades for a specific explosion scenario, and it indicated that this technique can be useful in the building design to achieve a higher structural anti-explosion capacity. This study herein can serve as a reference for the calculation method of designed blast load.

The performance of turbomachinery is largely affected by the nonuniform boundary conditions caused by the coupling between neighboring parts, such as the inlet distortion and hot streak. Existing works study this problem by comparing the flow fields with uniform and nonuniform boundary conditions, which is cost extensive. In this work a new and efficient method is developed by computing the sensitivities of arbitrary performance metric relative to the boundary conditions and this method quantitatively describes how the disturbance on the boundary surface affects the performance. The adjoint method popular in the design optimization is extended to current field for the efficient computation of the sensitivity. The extra cost is independent of the number of parameters describing the nonuniform distribution. Accuracy of the adjoint boundary sensitivity is validated against analytical results for a converging–diverging nozzle case. Its applications in a representative case are detailed and discussed in this paper. Current method provides a new tool, also direct guides to minimize the negative effect and improve the performance utilizing the adjoint boundary sensitivity.

We address a Timoshenko system with memory in the history context and thermoelasticity of type III for heat conduction. Our main goal is to prove its uniform (exponential) stability by illustrating carefully the sensitivity of the heat and history couplings on the Timoshenko system. This investigation contrasts previous insights on the subject and promotes a new perspective with respect to the stability of the thermo-viscoelastic problem carried out, by combining the whole strength of history and thermal effects.

In this article, we investigate some of the fine properties of the value function associated to an optimal control problem in the Wasserstein space of probability measures. Building on new interpolation and linearisation formulas for non-local flows, we prove semiconcavity estimates for the value function, and establish several variants of the so-called sensitivity relations which provide connections between its superdifferential and the adjoint curves stemming from the maximum principle. We subsequently make use of these results to study the propagation of regularity for the value function along optimal trajectories, as well as to investigate sufficient optimality conditions and optimal feedbacks for mean-field optimal control problems., Comment: 55 pages

In recent years, high growth in robotics and automation fields, increased the demand of tactile sensors that are designed to detect various information by physical contact, have gained much importance in the development of biomedicine, social assistance, and industry. Piezoelectric tactile sensors fabricated from polyvinylidene difluoride (PVDF) are used to sense vibrations because of their high sensitivity, mechanical flexibility, multi-technology compatibility, and stability and cheaper cost. The analysis of the accepted design of the sensor as two layers of the PVDF and aluminum electrode in between was performed using COMSOL Multiphysics software. The paper aims to study the electrical properties of sandwich structured tactile sensors and validate as use of piezoelectricity of PVDF in sensor design. The obtained results from the simulation as 0.5 V when 42 N/cm2 stress is applied helps to optimize the development of sensors and allow their adjustments by comparing the analytical results of different output signal changes, eigenfrequency, vibration amplitude, and applied pressure to the film.

In medical science, information technology plays a main role in image processing for medicinal investigation and classification. For this reason “Magnetic Resonance Imaging” (MRI) can be useful today in the medical world which in turn is used to generate a comprehensive picture of the body's soft tissue and bones. The suggested technique utilizes an effective technique for brain tumor segmentation and classification. To segment and classify the brain tumor MR image by Modified fuzzy c means clustering (MFCM) and Artificial Neural Network (ANN). From the input image the proposed method extracts the shape, intensity and texture features. Here the extracted features are optimally selected by means of Hybrid Fruit fly and artificial bee colony (HFFABC). Both the segmentation and classification performance of the implemented technique is estimated in terms of “Accuracy, Sensitivity, Specificity, Ppv, Npv, Fpr And Fnr”. The implementation result shows the efficiency of proposed tumor segmentation and classification technique in segmenting the tissues perfectly from the MRI images of human nervous system. The classification performance of the implemented techniques is evaluated in terms of accuracy, sensitivity and specificity. The implementation results show the sensitivity, specificity & efficiency as 98.1%, 99.8% and 99.59%respectively.

Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from non-diseased individuals. Among various performance metrics for a continuous biomarker, specificity at a controlled sensitivity level (or vice versa) is often desirable for clinical utility since it directly targets where the clinical test is intended to operate. Covariates, such as age, race, and sample collection, could impact the controlled sensitivity level in subpopulations and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. In this paper, we suggest to adopt a parsimonious quantile regression model for the diseased population, locally at the controlled sensitivity level, and assess specificity with covariate-specific control of the sensitivity. Variance estimates are obtained from a sample-based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate-adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer., 28 pages, 2 figures

Seismic related natural hazard would be a major disaster as a result of the recent development and planning of new reservoirs, road networks, and the establishment of new urban settlements in previously uninhabited areas. Producing seismic hazard sensitivity maps could provide very fundamental knowledge to understand the probable occurrence of disasters in a seismic prone area. The aim of this paper was to produce seismic hazard sensitivity maps by integrating the Analytical Hierarchy Process (AHP) and the differential interferometric Synthetic Aperture Radar (DInSAR) methods. For the AHP method, six parameters have been generated and evaluated, such as distance from the fault, Lineament density, Lithology, Soil texture, Proximity of historical earthquake epicenters, and slope. The result of the sensitivity mapping showed that 8% (561.51 km2) of the study area was low-risk, 53% (3801.99 km2) medium, 35 per cent (2526.04 km2) high and 2% (142.82 km2) very high. On the other hand, SLC products from Sentinel-1A data sets were analyzed using a six-month interval master and slave image co-registration using the DInSAR method. A mean annual vertical displacement map showing max subsidence of 9.8 mm/year and uplift of 1.7 mm/year was obtained after processing. The acceptability and accuracy of the resulting seismic hazard sensitivity maps was cross-validated using the Receiver Operating Characteristics (ROC) curve. Validation results showed a strong predictive value with an AUC of 0.848. Therefore, output maps will assist policy and decision makers in identifying natural disaster-prone areas.

In this paper, we study a wide spectrum of dynamical features of a prey–predator model with infection in prey population only. In the model formulation, we consider nonlinear incidence for infection transmission and Holling type II as response function of predator. We analyze the model for local stability and Hopf bifurcation, and the theoretical results are validated and extrapolated by numerical simulations. Also, we perform sensitivity analysis to explore the significance of the crucial parameters influencing the densities of prey and predator populations. Our numerical results show that disease persists in the system even if the basic reproduction number is below unity. The rates of predation on susceptible and infected prey populations are shown to dramatically change the behavior of eco-epidemiological system. We extend our model by considering the fact that the newly infected prey becomes productively infectious after the effective contact between susceptible and infectious preys, and some time lag is involved in this biological process; also the conversion process conceals some time. We analyze our eco-epidemiological model with two time delays (incubation delay and predator’s gestation delay) for stability and bifurcation. From numerical simulations, we observe that both the delay parameters substantially affect the stable configuration of the system.

The paper studied on thermodynamics properties of bionanofluid flow over a stagnation point on a shrinking surface. Natural properties of the stagnation point have facilitated numerous applications in the field of chemistry and biology. A Buongiorno mathematical model describing steady two-dimensional bionanofluid stagnation point flow over a shrinking surface, merged with velocity and thermal slips condition is constructed. Similarity transformation is used to reduce governing partial differential equations into a system of ordinary differential equations. Numerical analysis by shooting technique followed by sensitivity analysis is configured to determine the correlation between input parameters and output response. Relationships or sensitivities of the four parameters of interest namely skin friction coefficient, local Nusselt number, local Sherwood number and local density of motile microorganism with respective velocity, temperature, nanoparticle concentration and microorganism density distributions to the selected input parameters are being focused. Skin friction coefficient promotes with suction signifying positive sensitivity yet demotes with shrinking parameter signifying negative sensitivity. Local Sherwood number is most sensitive to Lewis number whereas local Nusselt number is conditionally sensitive to Lewis number. The sensitivity results would be the comprehensive elementary protocol for the future biomedical device fabrication such as biosensors. There are extensive numerical studies on boundary layer problem however this is the first study discussing on sensitivity of parameters of interest to the input thermophysical parameters using sensitivity analysis under the principle of RSM.

Finger joint motion monitoring is crucial for rehabilitation training and ergonomics. Based on a multilayer dielectric structure, this study proposed flexible and highly sensitive ionic skins (sensors) for the measurement of the bending angles of finger joints. Each ionic skin, consisting silicon rubber films (multiple dielectric layers) and highly stretchable ionic conductors, was attached to the finger joint surface. The capacitance change of the skin was captured and used to measure finger joint bending angles. On this basis, the relationship between angles and capacitance variations was established. The relationship agreed with the experimental results. Furthermore, the effect of number of dielectric layers on sensitivity and linearity was discussed. The experimental results indicated that an increase in the number of dielectric layers improved sensitivity but suppressed linearity. The proposed ionic skins provided higher sensitivity and linearity than other flexible sensors. The five-layer ionic skin was four times more sensitive than existing ionic skins. The results confirmed that the multilayer dielectric structure of our ionic skins substantially enhanced the sensitivity for finger joint bending angle measurements.

This paper describes a design optimization study of the composite egg-shaped submersible pressure hull employing optimization and finite element analysis (FEA) tools as a first attempt to provide an optimized design of the composite egg-shaped pressure hull for manufacturing or further investigations. A total of 15 optimal designs for the composite egg-shaped pressure hull under hydrostatic pressure are obtained in terms of fibers’ angles and the number of layers for 5 lay-up arrangements and 3 unidirectional (UD) composite materials. The optimization process is performed utilizing a genetic algorithm and FEA in ANSYS. The minimization of the buoyancy factor ( B . F ) is selected as the objective for the optimization under constraints on both material failure and buckling strength. Nonlinear buckling analysis is conducted for one optimal design considering both geometric nonlinearity and imperfections. A sensitivity study is also conducted to further investigate the influence of the design variables on the optimal design of the egg-shaped pressure hull.

Solid-state NMR spectroscopy is a powerful technique to study insoluble and non-crystalline proteins and protein complexes at atomic resolution. The development of proton (1H) detection at fast magic-angle spinning (MAS) has considerably increased the analytical capabilities of the technique, enabling the acquisition of 1H-detected fingerprint experiments in few hours. Here an approach based on double-quantum (DQ) 13C spectroscopy, detected on 1H, is proposed for fast MAS regime (> 60 kHz) to perform the sequential assignment of insoluble proteins of small size, without any specific deuteration requirement. By combining two three-dimensional 1H detected experiments correlating a 13C DQ dimension respectively to its intra-residue and sequential 15 N-1H pairs, a sequential walk through DQ (Ca + CO) resonance is obtained. The approach takes advantage of fast MAS to achieve an efficient sensitivity and the addition of a DQ dimension provides spectral features useful for the resonance assignment process.

We measure the 1D Ly α power spectrum P1D from Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ), The Spectral Quasar Absorption Database (SQUAD), and XQ-100 quasars using the optimal quadratic estimator. We combine KODIAQ and SQUAD at the spectrum level, but perfo a separate XQ-100 estimation to control its large resolution corrections in check. Our final analysis measures P1D at scales k < 0.1 s km-1 between redshifts $z$ = 2.0-4.6 using 538 quasars. This sample provides the largest number of high-resolution, high-S/N observations; and combined with the power of optimal estimator it provides exceptional precision at small scales. These small-scale modes (k 0.02 s km-1), unavailable in Sloan Digital Sky Survey and Dark Energy Spectroscopic Instrument analyses, are sensitive to the theal state and reionization history of the intergalactic medium, as well as the nature of dark matter. As an example, a simple Fisher forecast analysis estimates that our results can improve small-scale cut-off sensitivity by more than a factor of 2., NGK and NP were supported by DOE, reference number DE-SC0017660. AFR acknowledges support by FSE funds through the program Ramon y Cajal (RYC-2018-025210) of the Spanish Ministry of Science and Innovation. VI was supported by the Kavli foundation. PN acknowledges financial support from Centre National d’Études Spatiales (CNES).

We present a global and multi-annual space-borne dataset of tropospheric { H2O , δ D } pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) on board the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. ( 2021 b ) . From the independently retrieved H2O and δ D proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset, we generate an optimal estimation product for pairs of H2O and δ D . Here, this standard MUSICA method for deriving { H2O , δ D } pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a { H2O , δ D } pair dataset for the whole period from October 2014 to December 2020 with global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1500 million individually processed observations. The retrievals are most sensitive to variations in { H2O , δ D } pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the { H2O , δ D } pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but higher latitudes also show a considerable amount of reliable data. Exemplary time series over the tropical Atlantic and West Africa are chosen to illustrate the potential of the MUSICA IASI { H2O , δ D } pair data for atmospheric moisture pathway studies. Furthermore, in order to facilitate the application of this rather comprehensive MUSICA IASI { H2O , δ D } pair dataset (referred to as Level-2), we additionally provide the data in a re-gridded and simplified format (Level-3) with focus on the quality-filtered { H2O , δ D } pairs in the free troposphere. A technical documentation for guiding the use of both datasets is attached as the Supplement. Finally, the Level-2 dataset is referenced with the DOI https://doi.org/10.35097/415 ( Diekmann et al. , 2021 a ) and the Level-3 dataset with DOI https://doi.org/10.35097/495 ( Diekmann et al. , 2021 b ) .

In this study the relationship between two families of pairwise associated functions, describing boundary wetting and drying curves, is modelled. This relationship includes both regular and random constituents, and it is represented by a limited dataset of N known pairs of these curves, which pertain to N different soils. Using the dependent domain theory of hysteresis, we show that the regular constituent in this relationship can be described by a linear integral operator that, for a given input measured boundary wetting curve, predicts its associated boundary drying curve. The operator is optimized to reduce the influence of the random scattering of the pairs of curves. The optimization of the operator is performed with respect to the given input measured boundary wetting curve using a representative calibration dataset of k (k≤N) pairs of curves, which are a part of the N available measured pairs. The optimized operator should satisfy two opposite conditions: (i) the best possible accuracy at reproducing the boundary drying curves used for operator optimization, and (ii) the least possible sensitivity with respect to excluding any one pair from the calibration dataset. We also apply the suggested modelling approach for derivation of the inverse operator model that predicts the boundary wetting curves from their associated measured boundary drying curves, with possibility of verification of the predictive reliability. The predictive performance of both operator models is mainly good, and their reliability can be permanently enhanced by updating them with new incoming measured data.

A methodology for calibration and validation of material models using more simultaneous experiments is presented. The procedure contains the selection of suitable parameters for identification using a sensitivity analysis, parameters identification, analysis of usability of the material model, and validation of the material model. Three elastoplastic models are evaluated in this study: the bilinear isotropic hardening model with Von Mises yield condition, the bilinear isotropic hardening model with the Hill anisotropic yield condition, and the nonlinear isotropic hardening model with Hill anisotropic yield condition. Five proportional as well as nonproportional monotonic loading experiments conducted on aluminium alloy 2124-T851 are used for the presentation of the inverse approach application, whereas four of them are the source of identification. The validation of calibrated material models is done based on the strain field measurement realised by the Digital Image Correlation method. The nonlinear isotropic hardening rule together with Hill yield condition brings the best description of stress–strain behaviour of the material under investigation.

In this work, a multi-parameter point sensor based on the combination of Fabry-Perot (FP) and the anti-resonant (AR) reflecting guidance in cascade configuration is proposed and experimentally demonstrated. This structure, based on FP interference and AR reflecting guidance, was fabricated with two different air micro-cavities. The attained experimental results showed different strain and temperature sensitivities for the antiresonance contribution. However, when analyzing the FP interference, only strain sensitivity was observed, demonstrating that this air micro-cavity was also insensitive to temperature variations. This work was supported by the Spanish AEI projects TEC2016-76021-C2 and PID2019-107270RB-C22, FEDER Funds, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 838143, Projects for young researches UPNA 2019, “Caja Navarra” and “la Caixa” foundations under agreement LCF/PR/PR13/51080004 and the grant BGP18/00180 of the “Beatriz Galindo” programme from the Spanish Ministry of Science, Innovation and Universities.

Geoscientific model development : GMD 14(11), 7133-7153 (2021). doi:10.5194/gmd-14-7133-2021, Published by Copernicus, Katlenburg-Lindau

Synchronous optimal pulse width modulation is commonly applied when the ratio between the fundamental frequency and the switching frequency is low. The offline computed pulse patterns are optimized in terms of harmonic distortion in the phase currents. Usually, the underlying optimization problem is based on a simple inductor model representing the harmonic behavior of one phase of a nonsalient machine. This paper proposes an alternative formulation of the optimization problem which generalizes it for salient machine types. An investigation of the optimization results’ sensitivity to operating point dependent parameters is presented.

Neutron stars in low mass X-ray binaries are hypothesised to emit continuous gravitational waves that may be detectable by ground-based observatories. The torque balance model predicts that a higher accretion rate produces larger-amplitude gravitational waves, hence low mass X-ray binaries with high X-ray flux are promising targets for gravitational wave searches. The detection of X-ray pulsations would identify the spin frequency of these neutron stars, and thereby improve the sensitivity of continuous gravitational-wave searches by reducing the volume of the search parameter space. We perform a semi-coherent search for pulsations in the two low mass X-ray binaries Scorpius X-1 and Cygnus X-2 using X-ray data from the \textit{ Rossi X-ray Timing Explorer} Proportional Counter Array. We find no clear evidence for pulsations, and obtain upper limits (at $90\%$ confidence) on the fractional pulse amplitude, with the most stringent being $0.034\%$ for Scorpius X-1 and $0.23\%$ for Cygnus X-2. These upper limits improve upon those of Vaughan et al. (1994) by factors of $\sim 8.2$ and $\sim 1.6$ respectively., 10 pages, 11 figures

A low-cost device for eddy current non-destructive testing made of off-the-shelf components is described. The system consists of a dual H-bridge stepper motor driver, a coil wound in-house on an additively manufactured support, a tunneling magnetoresistance sensor, and a data generation/acquisition module. The system is compact, and it can flexibly generate rectangular pulses, square wave bursts and arbitrary binary excitation waveforms. Among these, in this paper pseudo-noise binary signals were used within a pulse-compression measurement protocol to increase the SNR and the inspection sensitivity with respect to pulsed excitation. A benchmark sample was analyzed, and all the defects were correctly located, demonstrating a good detection capability of the overall hardware-software solution proposed. It was also found that pulse-compression output signals can be further processed as standard pulsed eddy current data. Features can be extracted from them for defect classification purposes and the lift-off invariant point can be used for imaging. These results were achieved by assembling a low-cost handy device, which can be further improved in portability and performances using different off-the-shelf components and by integrating it with a single-board PC, paving the way for future developments in this field.

Quantitative calibration of analytes using chemical ionization mass spectrometers (CIMSs) has been hindered by the lack of commercially available standards of atmospheric oxidation products. To accurately calibrate analytes without standards, techniques have been recently developed to log-linearly correlate analyte sensitivity with instrument operating conditions. However, there is an inherent bias when applying log-linear calibration relationships that is typically ignored. In this study, we examine the bias in a log-linear-based calibration curve based on prior mathematical work. We quantify the potential bias within the context of a CIMS-relevant relationship between analyte sensitivity and instrument voltage differentials. Uncertainty in three parameters has the potential to contribute to the bias, specifically the inherent extent to which the nominal relationship can capture true sensitivity, the slope of the relationship, and the voltage differential below which maximum sensitivity is achieved. Using a prior published case study, we estimate an average bias of 30 %, with 1 order of magnitude for less sensitive compounds in some circumstances. A parameter-explicit solution is proposed in this work for completely removing the inherent bias generated in the log-linear calibration relationships. A simplified correction method is also suggested for cases where a comprehensive bias correction is not possible due to unknown uncertainties of calibration parameters, which is shown to eliminate the bias on average but not for each individual compound. Alfred P. Sloan FoundationAlfred P. Sloan Foundation [P-2018-11129] Published version This research has been supported by the Alfred P. Sloan Foundation (grant no. P-2018-11129).

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected NMR methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to perform virtual decoupling by recording multiple spectra and taking linear combinations. Here, we propose an alternative method of virtual decoupling using deep neural networks, which only requires a single spectrum and gives a significant boost in resolution while reducing the minimum effective phase cycles of the experiments by at least a factor of 2. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra, 13C-13C correlation spectra of protein side chains, and 13Cα-detected protein 13Cα-13CO spectra where two large homonuclear couplings are present. The deep neural network approach effectively decouples spectra with a high degree of flexibility, including in cases where existing methods fail, and facilitates the use of simpler pulse sequences.

This paper reviews a novel method for integrated topology and packaging optimization in lightweight systems design. Presented as the component-existence model, this approach introduces a new class of packaging design variable coupled with traditional material pseudo-densities in a standard topology workflow. This method adopts a modified interpolation scheme compatible with standard finite element mesh discretizations, and through development of so-called design fields and variable mapping functions, applies gradient-based optimization and analytical sensitivity expressions for driving design updates. The resulting framework can consider multiple overlapping components, enhances effective component mobility in the design domain, and features numerical efficiency comparable to standard topology-only problems. This method is demonstrated in six numerical case studies and compared against solutions from full factorial analysis, with resulting integrated designs approaching or exceeding benchmark configurations within ±5%.

In this work we investigate the effect of the symmetry in a 2-D arrays of gold nanoparticle on the sensitivity to the refractive index change. We demonstrate a generalized result that an asymmetric periodic arrangement of metallic nanoparticle leads to a higher sensitivity than a regular square of nanoparticle. Further decreasing the symmetry of the system by using asymmetric nanoparticle (nanorods, triangle) rather than symmetric nanoparticle (nanocylinder) will further improve this sensitivity. Finally, we suggest that such asymmetric nanostructure could operate as a SERS and LSPR plasmon based sensor by changing the polarization of the incident light.

It is crucial to predict the worst surface accuracy of deployable mesh reflectors considering uncertainties at the design stage. The traditional method is computationally expensive because a form-finding process which needs many iterations is required in each Monte Carlo simulation. A quick method is adopted to rapidly compute the worst surface accuracy of deployable mesh reflectors. The sensitivity relationships between nodal coordinate deviations, cable force deviations and cable length errors are primarily derived. Instead of the time-consuming form-finding process, the derived sensitivity relationships are utilized to carry out the Monte Carlo simulations in the quick method. Ultimately, both the symmetric and asymmetric AstroMesh reflectors are numerically analyzed. The results show that the derived sensitivity relationships are effective, and the quick method can predict the worst surface accuracy as precise as the traditional method but with far less time consumption.

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

The piezoelectric dij coefficient is often regarded in materials science as the most important figure of merit of piezoelectric performance. For many applications, the piezoelectric gij coefficient which correlates to voltage output and sensitivity of a piezoelectric material can be considered of equal or increased importance, however is often an overlooked parameter in materials science literature. The aim of this review is to highlight the importance of this parameter and to provide insight into the mechanisms that drive a high piezoelectric voltage coefficient in single crystal, polycrystalline, and composite form. For bulk ceramics, special attention is given to tetragonal systems due to the availability of electrical and crystallographic data allowing for a clear structure-property relation. Orthorhombic and rhombohedral systems are mentioned and specific cases highlighted, however investigating structure-property relations is difficult due to the lack of crystallographic datasets. Composite materials have been the forefront of high gij piezoelectric materials over the decades and are therefore also considered in both ceramic-matrix and polymer-matrix form. An overview of applications in medical, energy, fishing and defence industries where a high gij is desirable are considered and the scientific and commercial considerations that must be made for the transition from laboratory to industry are discussed from the perspective of integrating new piezoelectric materials into sonar devices.

International audience; Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in the non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution. While existing deterministic solutions reach their limit in handling complex data, emerging techniques such as deep learning-based algorithms have shown a promising potential to overcome these limitations. This paper proposes a deep neural network that paves the way to make the complex features of coda waves more handleable to reliably exploit coda waves in several applications even in a changing or unstable environment. Specifically, designed a Coda-Convolutional Neural Network that is able to identify coda waves with 95.65% precision in a silicon chaotic cavity including 5 emitters and 16 receivers using ultra high frequency ultrasonic coda waves.

International audience; Diffusion-ordered NMR spectroscopy (DOSY NMR) is a widely used method for the analysis of mixtures. It can be used to separate the spectra of a mixture's components, and to analyse interactions. The classic implementation of DOSY experiments, based on an incrementation of the diffusion-encoding gradient area, requires several minutes or more to collect a 2D data set. Spatially encoded (SPEN) DOSY makes it possible to collect a complete data set in less than one second, by spatial parallelisation of the effective gradient area. While several short descriptions of SPEN DOSY experiments have been reported, a thorough characterisation of its features and its practical use is missing, and this hinders the use of the method. Here we present the unusual principles and implementation of the SPEN DOSY experiment, an understanding of which is useful to make optimal use of the method. The encoding and acquisition steps are described, and the parameter relations that govern the setup of SPEN DOSY experiments are discussed. The influence of key parameters, including on sensitivity, is illustrated experimentally on mixtures of small molecules. This study should be useful for the setup of SPEN DOSY experiments, which is particularly useful for systems that evolve in time.

In this paper we consider a chemotaxis system coupling with the incompressible Euler equations in spatial dimension two, which describing the dynamics of chemotaxis in the inviscid fluid. We establish the regular solutions globally in time under some assumptions on the chemotactic sensitivity.

Analyzing the impacts of model parameters on model outputs is an important but challenging topic for scientific research involving simulation models. Global Sensitivity Analysis (SA) has been recently employed by many transportation researchers for such task, but a proper SA is still not a common practice. In particular, many modelers simply assume that all parameters are uncorrelated in the SA. However, this assumption is often unrealistic for traffic simulation models, in which many parameters are actually correlated, leading to wrong conclusions. In this paper, a sampling-based approach is provided for the SA of correlated parameters. It uses Gaussian copula to link the marginal distributions of individual parameters with their global distributions and correlations, and utilizes the extended Sobol’ formula to estimate the variance-based sensitivity indexes in a Monte Carlo framework. Its application is illustrated using two different car-following models: the Intelligent Driver Model (IDM) and the Wiedemann-74 (W74) model. Results show that this method is able to accurately quantify the sensitivity of all model parameters. As a general method, this approach can be transferred like a standard quantitative SA tool to any traffic model or complex model in the wider scientific community, especially when correlated parameters exist.

A topology optimization method for steady-state flows of incompressible fluids which is capable of imposing accurate boundary conditions along the solid walls of the sought fluid paths is presented. In each topology optimization cycle, body-conforming Cartesian meshes are generated around shapes of any complexity by tracing the fluid-solid interfaces and a cut-cell flow solver is implemented together with its adjoint. Sensitivity derivatives are computed along the fluid-solid interfaces via the continuous adjoint method. Changes in topology are caused by expressing the computed sensitivity derivatives w.r.t. an auxiliary background material distribution, that helps updating the fluid-solid interfaces. The proposed method performance is assessed on three 2D benchmark examples and a 3D case. Two out of the three 2D examples are also solved using a porosity-based topology optimization approach in which impermeable regions are penalized by a Brinkman term and useful conclusions are drawn. For a fair comparison, designs optimized using the porosity-based method are re-evaluated after extracting fluid-solid interfaces from the computed porosity fields.

There is a gap to be filled in the literature regarding uncertainties that may exist in nanobeams’ material and geometric properties, such as the nonlocal parameters, Young's modulus, Poisson ratio, density, and beam's length, width, and height. Considering the fact there are so many input parameters that can affect the response of the system, parametric studies that only focus on single parameters may limit the usefulness of the obtained output. Thus, in this effort, local and global sensitivity analysis and uncertainty quantification techniques are applied to determine the most influential parameters on the acoustic dispersion curves and the linear response of nanobeams which are modeled utilizing Euler–Bernoulli beam theory and the general nonlocal theory. The sensitivity analysis and uncertainty quantification methods include expanded parametric studies around an ideal or baseline system configuration, the Morris method elementary effects, Sobol’ sensitivity indices, Pearson correlation coefficients, and output distribution curves. Various input probability distributions are used for the input parameters uncertainties and the effects of all parameters on the ideal configurations are investigated for both acoustic dispersions and first natural frequency. For this study, there is great agreement in input parameter ranking between the methods. The introduced methodologies can be used for many other complex dynamical systems to show the limits of applicability of each method and determine the most influential input parameters and dominant output configurations.

The aim of this study is the assessment of desertification risk for a typical Mediterranean island, in the frame of climate change and the application of good agricultural practices. Based on the MEDALUS Environmentally Sensitive Area Index (ESAI) approach, the sensitivity in desertification is estimated by employing 15 quantitative parameters divided in to four main quality indices: climate, vegetation, soil, and management quality. The methodology applied for a baseline scenario (current conditions), two future climate change scenarios (RCP 4.5 and RCP 8.5) and a soil quality improvement scenario. According to the results, more than 13% of the island's area is characterized as critically sensitive to desertification in the current conditions. This percentage will increase in the future under both the RCP 4.5 and the RCP 8.5 climate scenarios, where the critical areas will rise above to 15%. By applying the soil quality improvement scenario simultaneously with the climate change scenarios, a slight mitigation of desertification risk in the future could be achieved. The methodology developed in this study may be used to assess desertification process under various climate, soil, and land use management scenarios in regions of the Mediterranean Sea. HIGHLIGHTS This study investigates, using GIS, the effects of all the above approaches on the sensitivity of desertification risk for the Mediterranean island of Crete (Greece) for the period 2031–2060. This study also highlights the results of the comparison of the future desertification scenarios under the application of GAPs and the maintenance of the traditional-conventional agricultural practices.

The market for space solar power (SSP) is developing. This analysis utilizes a case study of SSP for remote mine operations to determine its economic feasibility. We employ a discounted cash flow (DCF) evaluation and then assess the sensitivity of the DCF model to changes in its underlying assumptions. The results indicate that there are scenarios for SSP where it is considered a viable power alternative. Further, a discussion of the assumed manufacturing and transportation costs decreases to expand the market is given.

One of the main fields of application of personal sound zones (PSZs) is the car industry. PSZ systems are usually designed assuming that the acoustic environment is not changing. However, due to variability in the car cabin's acoustic conditions, it is necessary to design a system capable of adapting to these variations. This paper introduces the use of the filtered-x least-mean-squares algorithm to adapt a PSZ system in a car cabin when the seat positions change. The article focuses on four aspects. First, the effect of the initial coefficients is investigated to help with the convergence of the adaptive algorithm. Second, a study is made on the definition of the desired pressure and its effect on the reproduction. Third, the system's sensitivity to external noise is also evaluated. Last, the possibility to reduce the number of microphones for the adaptive system is considered. The method's performance is evaluated using a two headrest system installed in a car cabin and when moving the seats to different positions. It is shown that the system is capable of maintaining the performance in the different seat positions.

We present the serendipitous discovery of a galaxy group in the XMM-LSS field with MIGHTEE Early Science observations. 20 galaxies are detected in H i in this z ∼0.044 group, with a 3σ column density sensitivity of NHI = 1.6 × 1020 cm-2. This group has not been previously identified, despite residing in a well-studied extragalactic legacy field. We present spatially resolved H i total intensity and velocity maps for each of the objects which reveal environmental influence through disturbed morphologies. The group has a dynamical mass of log10(Mdyn/M⊙) = 12.32, and is unusually gas-rich, with an H i-to-stellar mass ratio of log10(f∗HI) = -0.2, which is 0.7 dex greater than expected. The group's high H i content, spatial, velocity, and identified galaxy type distributions strongly suggest that it is in the early stages of its assembly. The discovery of this galaxy group is an example of the importance of mapping spatially resolved H i in a wide range of environments, including galaxy groups. This scientific goal has been dramatically enhanced by the high sensitivity, large field-of-view, and wide instantaneous bandwidth of the MeerKAT telescope. © 2021 The Author(s)., The research of SR and RPD is supported by the South African Research Chairs Initiative (grant ID 77948) of the Department of Science and Innovation and National Research Foundation. SR, RPD, and MJJ acknowledge the financial assistance of the South African Radio Astronomy Observatory (SARAO) towards this research (www.ska.ac.za). The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IMPU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. AAP acknowledges the support of the STFC consolidated grant ST/S000488/1. WM is supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation. RB acknowledges support from the Glasstone Foundation and from an STFC Ernest Rutherford Fellowship [grant number ST/T003596/1]. NM acknowledges support from the Bundesministerium für Bildung und Forschung (BMBF) award 05A20WM4. MJJ acknowledges support from the UK Science and Technology Facilities Council [ST/S000488/1 and ST/N000919/1]. MT and MJJ acknowledge support from the Oxford Hintze Centre for Astrophysical Surveys which are funded through generous support from the Hintze Family Charitable Foundation. LVM acknowledges financial support from the grants AYA2015-65973-C3-1-R and RTI2018-096228-B-C31 (MINECO/FEDER, UE), as well as from the State Agency for Research of the Spanish Ministry of Science, Innovation and Universities through the ‘Center of Excellence Severo Ochoa’ award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This research made use of Astropy,4 a community-developed core PYTHON package for Astronomy. MG and JC acknowledge financial support from the Inter-University Institute for Data Intensive Astronomy (IDIA).

We propose a program at B-factories of inclusive, multi-track displaced vertex searches, which are expected to be low background and give excellent sensitivity to non-minimal hidden sectors. Multi-particle hidden sectors often include long-lived particles (LLPs) which result from approximate symmetries, and we classify the possible decays of GeV-scale LLPs in an effective field theory framework. Considering several LLP production modes, including dark photons and dark Higgs bosons, we study the sensitivity of LLP searches with different number of displaced vertices per event and track requirements per displaced vertex, showing that inclusive searches can have sensitivity to a large range of hidden sector models that are otherwise unconstrained by current or planned searches., 25+16 pages, 17 figures. v2: typos corrected, references updated, submitted to JHEP

This paper describes the realization and characterization of $${\text{La}}_{0.8}{\text{Pb}}_{0.1}{\text{Ca}}_{0.1}{\text{Fe}}_{1-x}{\text{Co}}_{x}{\text{O}}_{3}$$ gas-based sensors. $${\text{La}}_{0.8}{\text{Pb}}_{0.1}{\text{Ca}}_{0.1}{\text{Fe}}_{1-x}{\text{Co}}_{x}{\text{O}}_{3}$$ $$(0.00\le x\le 0.20)$$ thin films are prepared using the drop-coating method over a $${\text{Si}}/{\text{SiO}}_{2}$$ substrate with inter-digitated $${\text{Pt}}$$ -electrodes. The sensors, thus, produced exhibit a good sensitivity to ozone at low concentration. The substitution of iron atoms by cobalt ones increases the sensor responses “S,” from 1.716 for $$x = 0.00$$ to 6.584 to $$x = 0.20$$ for an ozone concentration equal to $$400\,{\text{ppb}}$$ . In addition, the substitution of iron by cobalt induces a decrease in the sensor-operating temperature from $$270\,^\circ{\text{C}}$$ for the parent compound $$(x = 0.00)$$ to $$170\,^\circ{\text{C}}$$ for $$x = 0.20$$ .

In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287–304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin’s concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters. We perform a sensitivity analysis of different prior specifications for the mixtures of finite mixture model, i.e., the mixture model where a prior on the number of components is included. We use an extensive set of different prior specifications in a full factorial design and assess their impact on the estimated number of clusters for the Galaxy data set. Results highlight the interaction effects of the prior specifications and provide insights into which prior specifications are recommended to obtain a sparse clustering solution. A simulation study with artificial data provides further empirical evidence to support the recommendations. A clear understanding of the impact of the prior specifications removes restraints preventing the use of Bayesian methods due to the complexity of selecting suitable priors. Also, the regularizing properties of the priors may be intentionally exploited to obtain a suitable clustering solution meeting prior expectations and needs of the application.

International audience; Probing the transition dipoles responsible for the luminescence of a nano-emitter is essential to understand its physical properties, its interactions with its environment and its potential applications. Various methods in photoluminescence microscopy, based on polarimetry or Fourier imaging, have been developed to measure an emitter's dipole properties : number of radiating dipoles, oscillator strength ratio between them, their orientation etc. In this paper, we model the most used of these protocols and show that their sensitivity depends crucially on the experimental conditions : substrate material, presence of another lower or upper layer, objective numerical aperture. We develop guidelines to optimize the measurement sensitivity by tailoring the experimental conditions, depending on the type of protocol used and the dipole property to be measured.

Objective: Falls are often accompanied by huge social costs, and fall risk assessment is essential to protect the elderly from serious injuries and reduce financial burdens. The standard timed up and go (TUG) balance assessment test focuses on the total walking time and scenarios without environmental changes, which is flawed in providing rich information related to falls and evaluating the gait adaptability in response to environmental changes. Therefore, a fall risk assessment system that relies on a variable environment is actually needed. Methods: We have constructed an environment-adaptive TUG (EATUG) test system with three terrain surfaces (levels/obstacles/stairs). One hundred and three elderly from Shenzhen Luohu Hospital is recruited to participate in the experiment. The wearable inertial sensors attached to the two shanks are used to acquire data, and the gait parameters that may be related to falls are extracted and quantified. Results: Most of the parameters have significant differences between the high-risk group and the low-risk group (e.g., peak power, maximum radius, double support, etc., p < 0.001). In addition, the average sensitivity and specificity of fall risk prediction are 85.7% and 92.9%, while the average accuracy is 9.52% higher than the standard TUG test. Conclusion: The EATUG test system can provide richer gait characteristics and fall-related information, which is a good improvement on the drawbacks of the standard TUG test. Significance: The proposed test system is expected to replace the standard TUG test and be used for fall screening of high-risk elderly in the community to reduce the occurrence of falls.

Due to the poor conversion efficiency and signal-to-noise ratio (SNR) of Electromagnetic Acoustic Transducer (EMAT) testing, the defect detection sensitivity is limited, which restricts the extensive industrial applications. A finite element model for the testing process of a meander-coil EMAT was established that considers the simplified excitation and detection circuits for the EMAT. Based on this model, the effect of the connection methods (parallel or series) of the coils in the generating and receiving EMATs on their generating and receiving efficiency was investigated, and the simulation results were validated experimentally. Subsequently, the pulse compression technique with a 13-bit Barker code was used for the EMAT detection, and improvements of the SNR and range resolution were established through numerical simulation and experimental measurement. The results show that compared with the traditional EMAT design, which comprises single-layer transmitting and receiving coils, the conversion efficiency of the optimized EMAT with two excitation and receiving coils in the parallel connection can be improved by 52.8%. With the application of the Barker-coded pulse compression to detect the ultrasonic A-scan signal with no synchronous average, the SNR of the defect echo can be improved by 9.5 dB compared with the A-scan signal with 128 synchronous averages.