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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.

This paper presents a practical optimum design solution of prismatic simply supported prestressed concrete girders that are subjected to uniformly distributed static loads. While assuming linear elastic behaviour and employing working stresses method, the optimum values of two design variables, namely the cross-sectional dimensions and the prestressing force, are formulated as a nonlinear optimization model. Optimum values of the design variables are computed to satisfy either a minimum weight or a minimum cost objective function. To solve the nonlinear programming models, a program named Non-Linear Programming Prestressed Concrete Optimizer (NLPRECO) was coded in MATLAB® programming environment. Some illustrative design problems were solved using NLPRECO and their results are demonstrated herein. Sensitivity analysis of the optimized design variables, to variations in design parameters, was also investigated and the results are presented. Results show substantial improvement over the regular un-optimized solutions and moderate improvement over a linear programming optimization solutions.

Objectives This study aimed to investigate whether DistEn was capable of identifying complexity or irregularity for gait data and whether having low parameter-dependency sensitivity by comparing with the Approximate Entropy (ApEn) and Sample Entropy (SampEn). Material and methods The data were divided into three groups according to gait maturation. Firstly, the mean amplitude histogram, standard deviation (SD), and the power spectrum were calculated for each group. Secondly, ApEn, SampEn, and DistEn algorithms were calculated. Statistical analyses were then performed to compare groups. Results For m=3 with M= 256 and M=512 parameters, DistEn showed a statistically significant difference between in pairwise comparisons between all groups (Pa, Pb, and Pc Conclusion DistEn showed the best performance in capture the complexity changes in gain patterns with growth.

In view of the increasing levels of renewable generation across the world, the operational dynamics of power systems are changing. One of the dimensions of this transformational change is the nature of fault ride through behavior of renewable technologies such as wind and the associated differences compared to conventional generation. This paper investigates the impacts of fault ride through behavior of wind farms on the system stability in detail, using full dynamic model of a low inertia island power system. Recorded PMU measurements are used to establish the variations in wind farm behavior during and after a fault. A detailed sensitivity analysis on the active and reactive power injection behavior of wind farms is carried out to establish the component of fault ride through behavior that is most critical to system stability, using the full model of the Ireland and Northern Ireland Power system (Irish power system). Detailed investigation of the mechanics of a fault in a low inertia, wind generation dominated system is carried out to reveal potential technical scarcities. Finally, mitigations for identified scarcities arising from non-ideal fault ride through behavior of wind farms are presented in terms of Transmission System Operator led novel system services.

Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty. Though, attaining these parameters, for instance total organic carbon (TOC), volatile and residual hydrocarbon (S1 & S2) has a challenge for geologists due to high cost and time consumption. Therefore, addressing this issue has become an interesting subject for many researchers. As a result, on the ground of conventional well logs, vast kinds of methods, for example, back propagation artificial neural network (BPANN), have been introduced to solve this problem. Implementing these kinds of methods bring scientists tremendous amounts of information related to the richness of organic matter in a meantime. However, the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum. Therefore, in the current study to cope with this issue and alleviate the uncertainty, Least Squares Support Vector Machine (LSSVM) and Adaptive-Neuro Fuzzy Inference System (ANFIS) algorithms cooperating with the particle swarm optimization (PSO) were suggested as a suitable method to increase the precision of estimating geochemical factors. The Data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China which consists of geochemical and well logging information. Outputs of this study illustrated that ANFIS-PSO and LSSVM-PSO have a great ability to estimate geochemical parameters. The values of R2 obtained for these two models in order to predict the output parameters of TOC, S1 and S2 are equal to 0.6846 & 0.785, 0.6864 & 0.778, and 0.7343 & 0.8128, respectively. The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model. Also, a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.

This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereafter. In order to find the optimal BPNN parameters, a combination of SSA optimizer and BPNN named (SSA-BPNN) is proposed, so as to enhance prediction accuracy. The proposed method is evaluated against several datasets for the SFP problem. These datasets vary in both size and complexity. The results obtained are evaluated using a variety of performance measures (i.e., the AUC, Confusion Matrix, Sensitivity, Specificity, Accuracy, and Error Rate). The results obtained by SSA-BPNN are better than those obtained by the conventional BPNN over all of the datasets. The proposed method also has the ability to outperform several state-of-the-art methods over the same datasets in respect of most of the aforementioned performance measures. Therefore, the hybridization of SSA with BPNN is a valuable addition to the software engineering issues and can be utilized to achieve higher prediction accuracy for a variety of prediction problems.

Spatially distributed hydrological models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing the engineering decision objectives. Identifying parameters that describe physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using multipliers to adjust the values of spatially distributed parameters to further reduce dimensionality. We evaluate six sensitivity metrics, four of which align with decision objectives and two of which consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results for four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that (1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, (2) evaluating sensitivity metrics at the basin outlet misses many parameters that control streamflows in hillslopes, and (3) for some multipliers, calibrating all parameters in the set being adjusted may be preferable to using the multiplier if parameter sensitivities are significantly different, while for others, calibrating a subset of the parameters may be preferable if they are not all influential. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positives and 6,041 Covid-19 negatives). Samples were clinically labeled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold, and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and a deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world.

Through Silicon Via (TSV) is considered as the near-future solution to realize low-power and high-performance 3D-Integrated Circuits (3D-ICs) and 3D-Network-on-Chips (3D-NoCs). However, the lifetime reliability issue of TSV due to its fault sensitivity and the high operating temperature of 3D-ICs, which also accelerates the fault-rate, is one of the most critical challenges. Meanwhile, most current works focus on detecting and correcting TSV defects after manufacturing without considering high-temperature nodes’ impact on lifetime reliability. Besides, the recovery for defective clusters is also challenging because of costly redundancies. In this work, we present HotCluster: a hotspot-aware self-correction platform for clustering defects in 3D-NoCs to help understand and tackle this problem. We first give a method to predict normalized fault rates and place redundant TSV groups according to each region’s fault rate. In our particular medium fault-rate (normalized to the coolest area), HotCluster reduces about 60% of the redundancies in comparison to the uniformly distributed redundancies while having a higher ratio of router working in a normal state. Furthermore, HotCluster integrates both online (weight-based) and offline (max-flow min-cut offline method) mapping algorithms to help the system correct the faulty TSV clusters. The experimental results show that both the max-flow min-cut offline method and weight-based online mode with a redundancy of 0.25 exhibits less than 1% of routers disabled under 50% defect-rates.

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.

Graphical abstract, Highlights • COVID-19 reveals radiological signatures that can be detected using chest X-rays. • The evaluation of radiological signatures is a time-consuming and error-prone task. • Therefore, there is a need to automate the analysis of chest X-rays. • An automatic analysis of chest X-rays is achieved using deep learning models., The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.

This study investigates a novel interval linear programming based on probabilistic dominance. Firstly, the definition of interval linear programming is briefly reviewed. Then a new interval linear programming model is presented based on probabilistic dominance. The probabilistic dominance index treats the intervals as uniformly distributed variables and the interval inequality relation is further defined by probability. To deal with non-linearity in probabilistic dominance index, sequential quadratic programming is used to solve the problem and the performance measure approach is proposed to overcome the convergence difficulties. The determination and sensitivity analysis of the target performance measure are discussed to assess the sequential quadratic programming algorithm. Meanwhile, the extension of the proposed method to fuzzy interval linear programming is discussed. Furthermore, the proposed method is applied to the design of the plane truss structure with interval parameters. Finally, the effectiveness and rationality of the developed method are demonstrated by two mathematical examples and one interval parametric plane truss structure optimization example.

In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.

Cascading failures are one of the main mechanisms causing widespread blackouts of power networks. Models simulating the behavior of cascading failures are widely used in the literature to understand fault propagation and investigate effective mitigation strategies. However, there is a lack of validated models that address the specific requirements of resilience analysis in power networks and that are computationally fast and converge reliably for very large contingency sizes that may occur under extreme events. This article presents a novel comprehensive ac cascading failure model particularly designed for resilience analysis in power networks. The model is capable to deal with large contingency sizes, it is computationally efficient in large networks and integrates seamlessly with established resilience metrics. It incorporates dynamic phenomena and protection mechanisms using static representations. The model is verified following the recommendations by the IEEE PES working group on cascading failures using internal validation, sensitivity analysis, and comparison to historical outage data. Furthermore, an analysis of the impact of different contingency sizes and the dependency of cascades on network loading level, are given to illustrate some applications of the model and to highlight its capabilities.

High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properly, but also consumes as little computing time and space as possible to locate the optima. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.

For online early fault detection of rolling bearings in non-stop scenarios, one of the main concerns is the model bias caused by the distribution shift between offline and online working conditions. Under such concern, how to improve the feature sensitivity to early faults and the robustness of detection model has become a key challenge of improving the effectiveness of online detection. To solve this problem, a new online early fault detection method is proposed in this paper based on a strategy of deep transfer learning. First, a new robust state assessment method is presented. By introducing priori degradation information in the anomaly detection process of the isolated forest algorithm, this method can accurately assess the normal state and early fault state under noise interference. Second, a new deep domain adaptation algorithm is proposed. The algorithm uses the results of state assessment as output labels, and designs a deep domain adaptation neural network for joint adversarial training at feature level and model level simultaneously. Then a domain-invariant feature representation can be extracted from the data of different working conditions, and an online detection model can then be constructed. Comparative experiments are run on two bearing datasets IEEE PHM Challenge 2012 and XJTU-SY, and the results verifies the effectiveness of the proposed method in false alarm number and detection location.

Frequency competition is critical for a full-service airline in gaining market share, and adopting a proper strategy can improve an airline’s profits. This study proposes a new equilibrium programming model with flow balance to address frequency competition on airports network with time slot constraints. We first show that a pure-strategy Nash equilibrium may not always exist, and thus forming a pure strategy profile in frequency competition among airlines may naturally lead to deviation from current frequency. Therefore, we formulate the problem as a programming model with a mixed-strategy Nash equilibrium. To avoid shocks from dramatic frequency changes across the network, airlines tend to fine-tune frequencies on select segments during each adjustment. We propose a procedure to generate a computationally tractable amount of representative strategies from a finite set of feasible strategies to demonstrate mixed-strategy Nash equilibrium. We conduct an empirical analysis using an example in which industry profitability increased by as much as 7.89%. We then extend the model to formulate frequency competition among metal-neutral alliances. The results show that forming metal-neutral alliances can improve total industry profits by 10.59%. In particular, a sensitivity analysis with real data on the tolerance of flow imbalance demonstrates that deducting the potential costs due to the relaxation of flow balance between congested airports may earn additional total industry profits in a frequency competition.

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.

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach.

Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet.

This paper presents a chaos-opposition-enhanced slime mould algorithm (CO-SMA) to minimize energy (COE) cost for the wind turbines on high-altitude sites. The COE model is established based on rotor radius, rated power, and hub height needed to achieve an optimal design model. In this context, an improved variant of SMA, named CO-SMA, is proposed based on a chaotic search strategy (CSS) and crossover-opposition strategy (COS) to cope with the potential weaknesses classical SMA while dealing with nonlinear tasks. First, the COS is introduced to enhance the diversity of solutions and thus improves the exploratory tendencies. The CSS is incorporated into the basic SMA to improve the exploitative abilities and thus avoids the premature convergence dilemma. The proposed CO-SMA is validated on the design of wind turbines with high-altitude sites. Furthermore, the sensitivity analysis based on the Taguchi method is developed to exhibit the impact of the COE model’s optimized parameters. The influence of uncertainty based on the fuzziness scheme of wind resource statistics is also explored to depict a real scheme for the changes that occurred by seasonal time, atmospheric conditions, and topographic conditions. The proposed CO-SMA is compared with the PSO, WOA, GWO, MDWA, and SMA, where the COE values are recorded as 0.052408, 0.052462, 0.052435, 0.052409, 0.052413, and 0.052915, respectively. Furthermore, the proposed CO-SMA records the faster convergence than the others. On the other hand, the Taguchi method reveals that the rated power is the most significant parameter on the COE model. Also, the impact of the fuzziness scheme on COE is exhibited, where the increasing interval of vagueness can increase the value of COE.

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.

In this study, shear resistance of ultra-high-performance fibre-reinforced concrete (UHPFRCs) containing different types (smooth, hooked and twisted fibre), fibre aspect ratios (l/d = 100, 95 and 65) and volume contents (0.0, 0.5 and 1.5 vol.%) of steel fibre was investigated using a new shear test method. Shear resistance of UHPFRCs strongly depended on the fibre type, fibre aspect ratio and fibre volume content. The twisted fibre exhibited the highest resistance in terms of shear strength, shear strain capacity and shear peak toughness, the smooth was the next and the hooked fibres produced the lowest response. The bigger the aspect ratio of fibre added to the mixture, the higher the shear resistance of UHPFRCs can be observed. Besides, the shear resistance of UHPFRCs was significantly enhanced with the increase of fibre volume content. The compressive strength and flowability of all mixtures were also checked. The compressive strength increased from 184 MPa for ultra-high-performance concrete to 210 MPa for UHPFRCs, whereas the flowability of mixtures varied from 203 mm to 250 mm. Finally, a theoretical model to predict the shear resistance of UHPFRC beams without the stirrup bar was proposed and verified.

An ordinary differential equation (ODE) model of the working of the thyroid system for euthyroidism has been presented. As clinical data for thyroid hormones is relatively scarce, such modelling offers potential benefits over wet lab procedures. Genetic algorithms developed for determining of parameters of the ODE system using the available data have been presented and evaluated. This approach enables subject specific parameter estimation towards characterisation of individual thyroid operation. Initially, a simple steady state model was used. Later a cosinor model for the circadian variation of thyroid hormones was used to obtain more reliable results, as indicated through sensitivity analysis in conjunction with other statistical methods. Our parameter determination method has been tested on groups of patients with similar observed values of thyroid stimulating hormone (TSH), free T3 and free T4 (identified through clustering) to determine their parameter values jointly. This approach appears to produce parameter sets with lower variation than parameters determined independently, thus leading to better parameter determination.

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.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss-Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

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.

Sensitivity analysis and multiparametric programming in optimization modeling study variations of optimal value and solutions in the presence of uncertain input parameters. In this paper, we consider simultaneous variations in the inputs of the objective and constraint (jointly called the RIM parameters), where the uncertainty is represented as a multivariate probability distribution. We introduce a tolerance approach based on principal component analysis, which obtains a tolerance region that is suited to the given distribution and can be considered a confidence set for the random input parameters. Since a tolerance region may contain parameters with different optimal bases, we extend the tolerance approach to the case where multiple optimal bases cover the tolerance region, by studying theoretical properties of critical regions (defined as the set of input parameters having the same optimal basis). We also propose a computational algorithm to find critical regions covering a given tolerance region in the RIM parameter space. Our theoretical results on geometric properties of critical regions contribute to the existing theory of parametric programming with an emphasis on the case where RIM parameters vary jointly, and provide deeper geometric understanding of critical regions. We evaluate the proposed framework using a series of experiments for sensitivity analysis, for model predictive control of an inventory management problem, and for large optimization problem instances.

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted.

This paper addresses the problem of fault detection observer design for positive two-dimensional (2D) systems in the Roesser model. Firstly, an analytical method is presented to compute the exact value of l − index for positive 2D systems, where l − index measures the sensitivity to faults. Necessary and sufficient conditions are developed under which the l 1 -gain from noise to residuals is not larger than a prescribed level γ and the l 1 -gain from faults to residuals is not less than a prescribed level β . Secondly, based on above results, necessary and sufficient conditions for the existence of mixed l 1 / l − fault detection observer are derived such that the effect of disturbance on the residual output is minimized and the effect of faults on the residual output is maximized. Thirdly, an iterative algorithm is proposed to solve the convex optimization conditions. Finally, an example is provided to show the effectiveness and correctness of the theoretical results.

Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag (OOB) predictions, Gini changes as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analysed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production feature importance and correlations by three various quantitative indicators including the unbiased feature importance (FI), gain improvement FI and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.

This paper presents the simulation of material flows in the large-scale charging chute of Fos-Sur-Mer sinter cooler plant by using three-dimensional Discrete Element Method (DEM). As a result of granular flows in the chute, segregation patterns are formed at the charging exit. The sinter material is modelled as dry particles and their size distribution in the simulation is larger than in the reality to keep the simulation in a reasonable run time. Suitable calibration tests are performed to specify the DEM contact parameters in order to replicate the behavior observed during experiments with the virtual sinter particles. The DEM model is found to be in good agreement with a segregation measurement. A sensitivity analysis using different particle sizes shows that the segregation patterns are not affected by the different sizes used for fine particles. Furthermore, the filling ratio in the charging system is varied in the model and shows that a high filling ratio leads to less segregation when exiting the charging chute. The DEM model is used to analyze the granular flows in the charging chute, understand the material behavior and optimize the sinter cooling efficiency.

Detecting outliers in real-time is increasingly important for many real-world applications such as detecting abnormal heart activity, intrusions to systems, spams or abnormal credit card transactions. However, detecting outliers in data streams rises many challenges such as high-dimensionality, dynamic data distribution and unpredictable relationships. Our simulations demonstrate that some advanced solutions still show drawbacks. In this paper, first, we improve the capacity to detect outliers of both micro-clusters based algorithms (MCOD) and distance-based algorithms (Abstract-C and Exact-Storm) known for their performance. This is by adding a layer called LiCS that classifies online the K-nearest-neighbors (Knn) of each node based on their evolutionary status. This layer aggregates the results and uses a count threshold to better classify nodes. Experiments on SpamBase datasets confirmed that our technique enhances the accuracy and the precision of such algorithm and helps to reduce the unclassified nodes.Second, we propose a hybrid solution based on iterative majority voting and our LiCS. Experiments on real data proves that it outperforms discussed algorithms in terms of accuracy, precision and sensitivity in detecting outliers. It also minimizes the issue of unclassified instances and consolidate the different outputs of algorithms.

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 developing countries, the management of chronic diseases in the elderly is challenging because of limited medical resources and a large population. Hospitals cannot quickly provide treatment for chronic diseases for the elderly. This article discusses a mobile-medical-based system that uses effective data decision-making and wireless network communication to assist the elderly in implementing remote medical services and proposes a deep learning model-combined sparse autoencoder (CSAE) in the decision-making module. The model uses sparse autoencoders to process patient detection data and monitoring data collaboratively to achieve multisource data association and fusion. The prediction probability of diseases is obtained through deep neural networks and a classifier, and the best disease prediction sequence is output to realize the prediction and early warning of chronic diseases. In the experimental part, we compare CSAE with the other four machine learning classification algorithms in terms of accuracy, sensitivity, and specificity. The results prove that CSAE is superior to other algorithms in the single-disease prediction, with the accuracy and specificity exceeding 85%, and the sensitivity not lower than 75%. When predicting multiple diseases, our proposed algorithm's performance is the most stable, with an accuracy higher than other algorithms by more than 10%.

Detection of the liquefied natural gas (LNG) leakage attracts increasing attention for preventing environments and governments from severe pollution and economic loss. Existing frameworks take advantage of stationary surveillance thermal cameras to detect the LNG leakage, which comprises background subtraction and leakage classification. However, these methods are limited in rural areas due to the lack of sensitivity and accuracy. In this article, a generalized framework, i.e., tensor-based leakage detection (TBLD), is proposed to detect LNG leakage in the rural area from surveillance thermal cameras. First, the proposed TBLD takes advantage of tensor factorization to fuse thermal image and corresponding gradient maps for improving sensitivity. Additionally, a finite-state-machine is designed to maintain leakage foreground along with the video streaming. The experiments demonstrate the robust performance of TBLD in the background subtraction stage. Second, multiple classification techniques are explored in the leakage classification stage. The results suggest that the TBLD can accurately detect the LNG leakage by applying 50 layers of residual networks (ResNet50). Finally, compared with contemporary frameworks, the TBLD has consistently improved performance concerning the different distances of LNG leakage. The experimental results demonstrate the effectiveness of the proposed TBLD, which also shows the great potential of TBLD in future industrial applications.

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.

The complexity of linguistic distribution assessments increases the difficulty for the decision makers dealing with them. Recently, stochastic dominance has been varied to be a useful tool to compare two stochastic variables. Inspired by this, in this paper we dedicate to utilizing the stochastic dominance to compare the linguistic distribution assessments and further discuss the consensus reaching issue in GDM with linguistic distribution assessments. First, we introduce three types of individuals’ semantic sensitivity. Based on this, we define the linguistic stochastic dominances respectively under different semantic sensitivity contexts, and then provide several desirable properties. Then, we design a consensus reaching resolution framework based on linguistic stochastic dominance (CRRF-LSD). Finally, a case study is provided to show the application value of the CRRF-LSD, and two comparison analyses are further conducted to show the advantages of the linguistic stochastic dominance and the CRRF-LSD. The comparison results show that the proposed linguistic stochastic dominances method has clear advantages over several classical existing methods in comparing two linguistic distribution assessments. Meanwhile, the comparison results show that only the CRRF-LSD method takes the PIS and semantic sensitivity into account, which is helpful to determine more accurate individual ranking results.

The rapid charging of lithium ion battery cells while minimizing degradation is a key challenge in battery management. Offline optimal control frameworks employing physics-based models provide a first-principles approach to this problem that provides critical insights and benchmarks for real-time oriented online optimization algorithms. In this letter, we approach the optimal control problem of minimizing both charging time and degradation of a NMC battery using the single shooting optimization framework, employing a coupled electrochemical-thermal-aging model. The aging mechanism used captures growth of the SEI layer, solvent diffusion. The effectiveness of the single shooting method using a piecewise polynomial control and the low sensitivity of the method to chosen numerical parameters in the multi-objective problem is illustrated. The tradeoff between charging time and degradation is investigated numerically.

Evolutionary node shifting is an effective approach to the shape optimization of spatial structures for excellent mechanical performance. In this paper, a novel shape optimization algorithm is proposed, which is applicable to complex gridshell structures of irregular geometries and non-uniform grids. With the objective of maximizing the structural stiffness, the nodal coordinates are iteratively updated according to the sensitivity information. The perturbation displacements referring to the inverse hanging method are applied to the initial flat model. By analysis, we found that the irregular grids give rise to non-smooth gradient fields, which results in jagged surfaces. To normalize the non-smooth gradient fields and to prevent the optimization process from falling into local optima, a double filter scheme is introduced in the process of optimization. A variety of examples are presented to demonstrate that the proposed algorithm can effectively solve shape optimization problems of general gridshell structures.

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.

Traditional ELISA, long the workhorse for specific target protein detection using microplate wells, is nearing its fundamen-tal limit of sensitivity. New opportunities in healthcare call for in vitro diagnostic tests with ultra-high sensitivity. Magnetic bead-based ELISA formats have been developed that can reach unprecedented sensitivities order of magnitude better than are allowed for by the rate constants for a single ligand-receptor interaction. However, these ultra-high sensitivity assays are highly vulnerable to a host of confounding factors, including nonspecific binding from background molecules and loss of low-abundance target to tube walls and during wash steps. Moreover, optimization of workflow is often time-consuming and expensive. In this work, we present a simulation tool that allows users to graphically define arbitrary binding assays, includ-ing fully reversible first-order binding kinetics, timed addition of extra components, and timed wash steps. The tool is freely available as a user-friendly webapp. The framework is lightweight and fast, allowing for inexpensive simulation and visuali-zation of arbitrarily complex assay schemes, including but not limited to digital immunoassays, DNA hybridization, and enzyme kinetics, for validation and optimization of assay designs without requiring any programming knowledge from the user. We demonstrate some of these capabilities and provide practical guidance on assay simulation design.

High Dynamic Range (HDR) technology offers a higher visual quality compared to its Standard Dynamic Range (SDR) counterpart, as it tries to imitate the way our eyes perceive brightness and color information. Converting SDR content to HDR format - using inverse Tone Mapping Operators (iTMOs) - to take advantage of the superior visual quality offered by HDR displays, is an attractive proposition to SDR content owners and real-time broadcasters. In this paper, we propose a novel content adaptive iTMO that works in the perceptual domain to model the sensitivity of the human eye to brightness changes in different areas of a scene. To preserve the overall visual impression, our proposed iTMO utilizes an entropy-based brightness segmentation, which also makes our method content adaptive. In addition, we propose a novel perception-based color adjustment method that can maintain the color accuracy between input SDR and generated HDR frames. By performing the color adjustment in the perceptual domain, our iTMO prevents hue shifts and generates HDR colors that closely follow their SDR counterparts. Our subjective evaluations indicate that our proposed method outperforms other state-of-the-art methods by an average of 81% in terms of visual quality, and 76% in terms of how closely the HDR colors match their SDR counterparts. In addition to subjective evaluations, we also performed objective evaluations using the HDR-VDP 2.2 and PU-SSIM metrics and concluded that, on average, our proposed iTMO outperforms the state-of-the-art methods in terms of these two metrics.