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Uranium and plutonium are required to be accounted in spent fuel head-end and major recovery area in pyro-process for safeguards purpose. The possibility of neutron resonance technique, as a non-destructive analysis, was simulated on isotopic fissile analysis for large scale process. Neutron resonance technique has advantage to distinguish uranium from plutonium directly in mixture. Simulation was performed on U235 and Pu239 assay in spent fuel and for scoping examination of assembly type. The resonance energies were determined for U235 and Pu239. The linearity in the neutron transmission was examined for the selected resonance energies. In addition, the limit for detection was examined by changing sample density, thickness and content for actual application. Several factors were proposed for neutron production and the moderated neutron source was simulated for effective and efficient transmission measurement. From the simulation results, neutron resonance technique is promising to analyze U235 and Pu239 for spent fuel assembly. An accurate fissile assay will contribute to an increased safeguards for the pyro-processing system and international credibility on the reuse of fissile materials in the fuel cycle.

The utilization of 120° phase belt toroidal windings (120°-TWs) can bring power density improvement in a tubular permanent magnet linear generator (TPMLG). Unfortunately, the TPMLG with 120°-TWs (120°-TPMLG) usually involve multiple structural parameters, and the interaction or even conflict among those structural parameters would have strong impact on generator performances in the design optimization process. To alleviate this problem, a multi-objective design optimization method is proposed to conduct a multi-objectives optimization, which aims to obtain the high-power density and low-detent force. First, the comprehensive sensitivity analysis method, namely global sensitivity analysis, is adopted to evaluate the sensitivities of the structural parameters on the performances of the 120°-TPMLG. According to the evaluation results, the structural parameters are divided into three levels: non-sensitive level, mild-sensitive level, and strong-sensitive level. Then, to improve the whole design optimization efficiency, the direct-screening method, the surface method, and the genetic algorithm is implemented in the three levels, respectively. And the optimal structural parameters of the 120°-TPMLG are finally determined. To verify the feasibility of the 120°-TPMLG and the effectiveness of the proposed design optimization method, the performances of the optimal 120°-TPMLG are analyzed and compared with the initial one and the same dimensions TPMLG with traditional toroidal windings (T-TPMLG) in detail. It shows that the performances of the optimal 120°-TPMLG meets the design requirements. Moreover, the detent force is largely decreased and the power density is notably improved. The results prove that the proposed multi-objective design optimization method is available for the 120°-TPMLG.

Rate-Distortion Optimization (RDO) is an important step in video coding to achieve the best quality under a certain compression ratio constraint. The traditional RDO assigns equal importance to different color components. However, Human Visual System (HVS) has different sensitivities to different components. In this paper, the color-sensitivity-based combined PSNR (CSPSNR) is utilized as the distortion measurement in the process of RDO, where the characteristics of the color sensitivities of HVS are taken into account. Firstly, the distortion weights of luma and chroma components are derived from the criterion of maximizing CSPSNR. Then Lagrange multiplier and quantization parameter (QP) are adjusted according to the variation of distortion weights among different components. Finally, the CSPSNR-based RDO (CSRDO) adaptively calculates the RD costs of luma and chroma components under different sampling rates to improve the coding efficiency of the whole sequence. Experimental results in H.265/HEVC demonstrate that the proposed method can achieve 3.11% and 3.58% BD-RATE gain for AI and RA configurations in terms of CSPSNR on average.

Condition assessment of Medium Voltage switchgears requires effective, but inexpensive solutions to become a common practice. On the other hand, failure of a switchgear component can cause cascade effects and outage of other apparatus, in addition to significant economic losses due, e.g., to lack of energy availability. A major diagnostic property to evaluate switchgear health and reliability is partial discharges, being a direct cause of failure of organic insulation systems. A new approach to automatic, self-assessment of switchgear reliability based on partial discharge, PD, acquisition and processing, as well as a new type of PD sensor solution are presented in this paper. The sensor is based on the capacitive divider existing in most bushings to indicate the presence or absence of voltage. The innovative automatic software is able to provide automatic noise and PD separation, noise rejection and type of PD-source identification. The embedded bushing sensor has been tested on switchgears in the presence of defective components as cable terminations and bushings. Results show that the sensor, coupled with the automatic software, is able to provide good sensitivity compared to other sensors, and good capability to locate the cabinet and identify the type of source generating partial discharges.

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.

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

As next-generation pathogen detection methods, CRISPR-Cas-based detection methods can perform single-nucleotide polymorphism (SNP) level detection with high sensitivity and good specificity. They do not require any particular equipment, which opens up new possibilities for the accurate detection and identification of Bacillus anthracis. In this study, we developed a complete detection system for B. anthracis based on Cas12a. We used two chromosomally located SNP targets and two plasmid targets to identify B. anthracis with high accuracy. The CR5 target is completely new. The entire detection process can be completed within 90 min without electrical power and with single-copy level sensitivity. We also developed an unaided-eye visualization system based on G4-DNAzyme for use with our CRISPR-Cas12a detection system. This visualization system has good prospects for deployment in field-based point-of-care detection. We used the antisense nucleic acid CatG4R as the detection probe, which showed stronger resistance to interference from components of the solution. CatG4R can also be designed as an RNA molecule for adaptation to Cas13a detection, thereby broadening the scope of the detection system.

In this paper, we demonstrate the transmission spectral and surrounding refractive index (SRI) sensing performance of long-period fiber gratings (LPFGs) at 2 m waveband. The cladding modes operating at 2 m waveband show higher SRI sensitivity than 1.55 m waveband. The influence of a little off resonance region of grating period, refractive index modulation and duty cycle of gratings are analyzed theoretically. Additionally, the LPFGs of LP08 mode near dispersion turning point operating at 2 m waveband are investigated theoretically and experimentally. Mode splitting brings out with the increasing SRI. After the resonant dip splitting into two dips (dip A and dip A), the SRI sensitivity of dip A are measured to be as high as -8495.2 nm/RIU in the SRI region of 1.445-1.457. The proposed 2-m-waveband LPFG makes it a potential in biochemical and environmental monitoring.

This article presents a 0.975-pJ/bit 56-Gb/s pulse amplitude modulation-4 (PAM-4) receiver using a time-based least significant bit (LSB) decoder in 28-nm CMOS technology. The proposed time-domain decision technique improves the robustness of comparator voltage variations by separating the data and reference paths. If the reference voltage difference is constant regardless of the common-mode voltage shift, the time-domain decoder achieves a low bit error rate (BER). To improve the timing margin of the LSB decoder from the data-dependent jitter, a sample-and-hold (S/H) structure is adopted in both the data and reference paths. The S/H circuits extend the timing margin by converting the input of the comparators to a constant voltage. The number of comparators for data decoding is reduced to two-thirds, and only eight comparators are required for a quarter-rate structure. The number of comparators in the data path, excluding the reference path, is 4, which reduces the loading capacitance. An adaptive threshold voltage calibration was implemented to generate the timing reference pulse. In addition to bathtub graphs, the BER, according to the $ {V_{CM}}$ change of the reference voltages, is plotted to show the sensitivity to the voltage variation.

The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

In this paper, a blind background calibration technique for super-regenerative receivers (SRRs) is presented. The proposed calibration scheme is designed to help SRRs to maintain their high sensitivity and immunity to negative transconductance ( -Gm ) variations under process-voltagetemperature (PVT) variations. Unlike the conventional foreground -Gm variations calibration techniques that require interruption of the receiver input, the proposed calibration technique employs input signal statistics and does not require interruption of the input bit-stream for extraction of the errors. The proposed scheme is based on an adaptive algorithm that compares the probability distribution of the pseudorandom-input (PI) stream and the output of the super-regenerative oscillator (SRO) and forces them to coincide at the end of the calibration. The proposed technique is implemented using a mixed-signal detection circuit and a finite state machine (FSM) that drives an 8-bit successive approximation register (SAR) to adjust the compensation current in the SRO. The simulation results successfully verify the effectiveness and reliability of the proposed calibration technique and show significant improvements in terms of SRR sensitivity under different process corners.

In the calculation of the standardized precipitation index (SPI) index, it is necessary to select a certain period of precipitation samples as the reference climate state, and the SPI obtained by different reference climate states have different size. Therefore, the influence of different reference climate states on the accuracy of SPI calculation is worth analyzing. Based on the monthly precipitation data of 1184 stations in China from 1961–2010, the influence of the selection of the reference climatic state in the calculation of SPI was analyzed. Using 30 consecutive years as the duration of the reference climatic state, 1961–2010 is divided into three periods 1961–1990, 1971–2000, 1981–2010. Taking the SPI obtained from the entire period as the standard value, the spatial distribution of SPI error and the accuracy of SPI classification based on each reference period were analyzed. Then, the resampling method was used to analyze the influence of time-continuous precipitation samples on the size of SPI. The results show that the SPI error of most sites is less than 0.2, and the accuracy of SPI classification is more than 80%. Although the errors of SPI mostly come from extreme drought and extremely wet, this does not affect the accuracy of the recognition of extreme drought and extremely wet. In most regions, it is reliable to calculate SPI based on the precipitation data of continuous 30 years, but the reliability of SPI is relatively low in areas with frequent drought. The results of the resampling analysis and 30-year sliding analysis show that the distribution parameters have noticeable turning characteristics, and the precipitation distribution parameters of nearly 85% stations had noticeable turning point before 1985, which led to the precipitation data of continuous 30 years easily overestimate the dry/wet.

The integration of overhead lines in future MTDC grids leads to a higher fault rate compared to cables. Most of the faults are con-sidered to be non-permanent, so that the line can be recon-nected after dielectric recovery. The auto-reclosing procedure in HVDC systems reveals new challenges since multiple restrikes are possible within a short period of time. This paper proposes an auxiliary circuit for DC Circuit Breakers (DCCB) which enables to use the DCCB internal capacitor for the line pre-energization. A comprehensive description of the switchgear operations during the pre-energization sequence is provided and the fault property identification method is defined. The method is validated by simulations in a MTDC grid and a sensitivity analysis is carried out using EMTP-RV.

Temperature sensors are indispensable in healthcare, human-machine interface, robotics, and other areas. However, facile and rapid fabrication of flexible temperature sensors with good performance are still required. In this work, a fast-response flexible temperature sensors is fabricated in a facile manner on the basis of laser-reduced graphene oxide(GO) for contactless human-machine interface. The combined effects of the GO concentration and scan line spacing of the laser on the sensitivity of the temperature sensor are investigated systematically. The intrinsic correlation among the two critical process parameters, material properties, and sensor sensitivity is revealed. Results show that the temperature sensor achieves the highest sensitivity of 0.37% °C−1 when the GO concentration is 4 mg/mL and the scan line spacing is 0.12 mm. The temperature sensors fabricated using the optimized process parameter also have a fast response time, good linearity, small hysteresis, good repeatability, and stable performance, and are successfully used to monitor human breathing, blowing, and spatiotemporal temperature variation of a curved surface. The sensors can also detect finger proximity. In addition, a 3 × 3 flexible temperature sensor array is fabricated for contactless unlocking of a code lock. The fabricated array reveals the great potential of the sensors developed in this study for contactless human-machine interface.

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.

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (e.g., due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. The trimmed means (TM) estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both treatment groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. In this article, we derive formulae for the bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA, MI and TM estimates under a range of plausible data scenarios, to inform sensitivity analyses. The TM approach is illustrated with simulations and in a sensitivity analysis of the CoBalT RCT of cognitive behavioural therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect. The MI estimates are closer to the null than the CCA estimate, whereas the TM estimate was further from the null. We propose using the TM estimator as a sensitivity analysis for data where it is suspected that extreme outcome values are missing.

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.

The driver’s selection process of parking lot will consider a variety of influencing factors, and consider different influencing factors for different travel purposes. In this paper, the driver’s travel purposes were divided into three categories according to the degree of emergency: emergency, routine and leisure. Four influencing factors of parking lot selection including walking distance, charge, parking index and parking convenience were selected, and ranked according to their sensitivity, and their sensitivity was analyzed by using the BP (back propagation) neural network, which provides a basis for the development of differentiated parking guidance and parking management measures to avoid the uneven parking due to random selection of parking lot and realize the maximum utilization of parking resources.

Актуальність. Поширеність алергічних захворювань збільшується з кожним десятиліттям, що зумовлено кліматичними змінами, глобальним потеплінням, збільшенням тривалості сезону палінації рослин, антропогенним навантаженням на атмосферне повітря.Мета дослідження — вивчити гіперчутливість до основних алергенів у дітей з алергічними захворюваннями за результатами шкірного алерготестування, а також проаналізувати структуру захворювань. Матеріали та методи. Методом прик-тестування було обстежено 228 дітей; оцінку результатів проводили через 25–40 хвилин після постановки тесту. Взаємозв’язки між результатами прик-тестів з різними алергенами вивчалися за допомогою кореляційного аналізу в пакеті прикладної статистики Statistics 6.0. Результати. До пилкових алергенів чутливі 85,5 % дітей, побутових — 54 %, харчових — 21 %, до грибкових — 35 %. Серед пилкових переважає сенситизація до амброзії — 47,8 %, соняшника — 49,5 %, циклахени — 38,5 %; серед побутових — до кліщів роду D. pteronyssinus і D. farinae — 24 %, шерсті кішки 19,7 %, серед грибкових — до Alternaria (23 %). Найбільш часто гіперергічна реакція (діаметр папули ≥ 8 мм) була відзначена до циклахени (44 %), соняшника (46 %), амброзії (50 %), шерсті кішки (42 %), D. farinae (39 %). Встановлено переважно середньої сили вірогідні (р

A biosensor based on the modified Kretschmann configuration is proposed here. The sensitivity of the conventional prism-based sensor using angular interrogation is low. To enhance the sensor's performance, layers of zinc sulfide (ZnS) and graphene have been deposited over the metal layer. The angular interrogation technique is used to analyze the performance of the sensor. The thickness of the Ag metal has been optimized. The thickness of the Ag metal is taken as 50 nm because minimum reflectance has been achieved. With the combinations of the four layers of ZnS and one graphene layer, the maximum sensitivity attained is 305o/RU. Performance parameters such as detection accuracy, FWHM, and quality factor of the sensor have been evaluated as obtained as 0.33 deg-1, 3.05 deg, 100.7 RIU-1, respectively. The proposed sensor has potential application in the field of biochemical and biological analyte detection.

Deformable Convolutional Networks (DCNs) can mitigate the inherent limited geometric transformation. We reformulate the spatial-wise attention mechanism using DCNs in this paper for semantic segmentation of high-resolution remote sensing (HRRS) images. It combines the sparse spatial sampling strategy and the long-range relationship modeling capability, namely Deformable Attention Module (DAM). Such locality awareness, more adaptable to HRRS image structures, can capture each pixel’s neighbouring structural information. A reasonable Multi-scale Deformable Attention Net (MDANet) is designed for the HRRS image semantic segmentation with a slightly increased computational cost based on the proposed DAM. Specifically, standard convolutional layers in the raw ResNet50 are equipped with a Deformable Attention Module to control sampling over a broader range of feature levels and aggregate multi-scale context information. The experimental results evaluated on Vaihingen and DeepGlobe Land Cover Classification datasets show that the performance accuracy of MDANet is improved by 7.77% and 8.45% compared with the backbone network (ResNet50) in terms of Miou evaluation, respectively. Furthermore, a DAM can perform better than a global spatial attention mechanism with less computation on the 3×64×64 feature map. Additionally, the added ablation studies demonstrate the effectiveness and efficiency of the deformable attention module and multi-scale strategy, respectively. Moreover, the sensitivity of critical hyperparameters is analyzed.

The successful commercialization of wireless power transfer (WPT) technology in mobile phones, laptops, and electric vehicles (EV), requires a safe, reliable, and efficient charging infrastructure. A metal object, such as a coin, a key, or a nail, however, may be inadvertently exposed, by close proximity, to the magnetic fields generated by the WPT coupler, creating a potential safety hazard and affecting the electrical characteristics of the WPT system. In addition, the magnetic fields may produce adverse symptoms, such as dizziness and breathlessness, in living objects near the charging systems. This paper provides a current state of the art review of all available designs for detecting metal and living objects in WPT systems. Working principles and qualitative comparisons in terms of sensitivity, accuracy, and cost of major detection methods are presented to reveal their inherent limitations and available applications. Finally, a number of research topics on foreign object detection (FOD) are suggested to encourage more researchers to make contributions to the development and commercial application of wireless charging systems.

In the field of physical oceanography, the temperature and pressure of seawater are important fundamental parameters. In order to realize the simultaneous measurement of the temperature and pressure in seawater, a reflective optical fiber sensor based on polydimethylsiloxane (PDMS)-sealed optical microfiber coupler combined with a sagnac loop (OMCSL) is proposed. Benefiting from the high thermo-optical coefficient and large elasticity of PDMS, and the large-scale swift field transmission characteristics of optical microfiber coupler (OMC), the sensitivity and structural stability of our sensor is largely improved. The response performance of the sensor is further analyzed by numerical simulation calculations and theoretical modeling. Experimental results show that the temperature and pressure sensitivity of the sensor could reach -2.133 nm/C and 3.416 nm/Mpa, respectively, about one order higher than that of bare fiber OMCSL. Furthermore, via inversely calculating the cross-sensitivity matrix, the temperature and pressure sensing can be demodulated simultaneously, and the average errors of the preliminary experimental settlements are 1.61% and 5.02%, respectively. Due to the merits of compact structure, easy fabrication, high sensitivity, fast response speed and high stability, the basic performance of this dual parametric sensor is comparable to the existing electrical temperature-depth (TD) sensor, which is expected to meet the practical application requirements of marine environmental monitoring and ocean dynamics research.

COVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a newinf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey and Smuggler Optimization (ADSO). The edges which are identified by the ADSO approach is utilized for calculating dissimilarities. An IFCM (Intuitionistic Fuzzy C-Means) clustering approach is applied for computing the similarity of the EA component among the generated edge maps and Ground-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation (SSS) structure is designed using the Randomly Selected Propagation (RP) technique and Inf-Net, which needs only less number of images and unlabelled data. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed using a Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all the advantages of the disease segmentation done using Semi Inf-Net and enhances the execution of multi-class disease labelling. The newly designed SSMCS approach is compared with existing U-Net++, MCS, and Semi-Inf-Net. factors such as MAE (Mean Absolute Error), Structure measure, Specificity (Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-Alignment Measure are considered for evaluation purpose. © 2022 Tech Science Press. All rights reserved.

A supply chain inventory model for decaying goods has been established in this paper, assuming that the decaying items have a maximum life period. Uncertain Lead time is assumed taking into account the effect of learning-forgetting on ordering costs. Constraint on the quality of service is incorporated. Mathematical formulation has been performed for both the manufacturer and the supplier. Numerical examples are given to explain the proposed problem of seeking optimal recovery policies. Finally, a sensitivity analysis was carried out to study the influence of differences in various parameters.

Bubble size distribution (BSD) is a crucial indicator of operating conditions in froth flotation. However, current BSD-based recognition methods have some limitations in the BSD estimation and don't achieve satisfactory results. Therefore, an alternative operating condition recognition method is proposed. Firstly, the temporal cumulative distribution function (TCDF) is designed to represent the BSD on the froth video. Then, the piecewise basis function is established to fit the TCDF. Secondly, the TCDF-Minimum Redundancy Maximum Relevancy (MRMR) feature is generated by MRMR to increase the sensitivity of TCDF features to the operating conditions. Lastly, the proposed TCDF-MRMR feature together with froth velocity and texture features are fed into an AdaBoost-extreme learning machine (ELM) model to recognize the operating conditions. Experiments in a zinc flotation process demonstrate that the TCDF-MRMR feature is more effective than other BSD-based features, and the AdaBoost-ELM shows better performance than traditional single recognition models.

Mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) is widely used due to its flexibility of system topology selection and simplicity. However, the motion compensation (MOCO) of the system is more difficult because the perturbance of mini-UAV platforms is greater and the spatial variance is more complex. In addition, the degrees of freedom of motion error are twice as those of the monostatic SAR. In this article, a novel MOCO algorithm based on a motion sensitivity model is proposed. First, a BiSAR system motion sensitivity model is established to assess the importance of each motion component error. Second, a novel MOCO method is proposed based on the determination of the main motion errors. Third, the proposed algorithm is validated by both simulations and real data sets. The final well-focused image suggests that the variant phase error after compensation with the new algorithm is much smaller than that with the traditional one.

This paper proposed the high sensitive linear and rotational based differential capacitive inertial sensor. The designed sensor consists of a proof of mass, which resides in an air medium — the acceleration measured along with a change of displacement and the differential capacitance. Sensor modeling and simulation have done with CoventorWare software. SOIMUMPS fabrication process has described because it allows pattern on both sides of an SOI wafer. Solid mechanics and electrostatic analysis have applied for measurement of acceleration. The high sensitivity of 981.28fF/g has observed. The simulated results have tabulated. Finally, performance and future scope of the inertial sensor has discussed.

The polar solvent concentration identification or alcohol presence finder or poisons detection and concentration finding are the most needed evaluation methods in crime investigation and hospital cases. This sophisticated method of approach compensates with the usability cost, the complexity of identification, the accuracy of data, instrument inabilities, easy production, and compatibility of all working conditions. Existing analyzers are with less features and less functionality. The accuracy in sensitivity and response time are the two variable in our system got much attention. Our design and developmental approach for the development of polar chemical sensors may overcome the exciting technologies drawback. The alcohol detector, with its specified concentration level representation, will give us broad applicability in all level peoples. Femtosecond laser-assisted machined functional superhydrophobic surfaces are the very innovative and new approach to this proposed device. We are exploring the laser modified superhydrophobic surface with polar solvent chemical sensing ability by contact angle variation incorporated portable device.

The airborne gravimetry measurement can better highlight the anomalous response of deeper resources than ground gravity data, so an integrated survey of airborne and ground gravity data is an effective strategy to obtain the obvious response of shallow and deep mineral resources simultaneously. In order to obtain a more reliable and higher resolution 3-D density distribution model through the joint inversion of airborne and ground gravity data to support deeper source exploration, we propose a high-resolution joint inversion method with a cross-constraint technique. We use structural constraints to establish an objective function for joint inversion of airborne and surface gravity data, and add density-weighting constraints through cross-calculation to make full use of the sensitivity of different data and geological targets. Synthetic model tests prove that the cross-constraint joint inversion method of airborne and ground data improves the model resolution effectively, and can delineate the distribution of deeper targets more clearly compared to the existing joint inversion way. In order to predict the spatial distribution of deep ore-bearing rock bodies in a north Qinling area of Shaanxi province, we obtained regional measured airborne and surface gravity data. High-resolution density joint cross-constraint inversion method was used for real data interpretation, and the distribution feature of possible deep ore-forming rock bodies.

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 this study, a sensitivity analysis was performed for an integrated SESMR process, and an optimization approach was formulated by developing an artificial neural network-based optimization (ANN-based optimization). The process comprised a cyclic fluidized bed (CFB), pressure swing adsorption (PSA), compressor, dehydrator, and other units. The PSA variables considerably affected product quality, while the CFB variables mainly contributed to other performance parameters. From the data analysis and domain knowledge, three main objectives and five main variables were selected for the process optimization. Thereafter, the ANN models were integrated with the economic model to formulate a SESMR-driven model for optimization. At the optimum conditions, the cost (1.7 $/kg) of the H2 (+99.99% purity) with 90.3% CO2 capture from the integrated SESMR process was 15% reduction compared to that of the SMR process, which agreed well with the US Department of Energy prediction (15–20%). These results suggest that the integrated SESMR process is valuable for the production of blue H2, and the ANN-based optimization is very effective for a complex integrated process.

This paper presents a fast phase-locking and amplitude detection scheme for single-phase applications. The proposed scheme employs a pseudo-rectification process in order to generate even harmonics from the grid signal. The DC-offset component present in the grid signal increases the sensitivity of the pseudo-rectification process, and the design of the pre-filtering stage must, therefore, be completely focused on the rejection of DC-offset and the even-harmonics. The application of a half-cycle pre-filter would consequently appear to be a highly attractive option as regards improving the dynamic response time and even harmonic rejection abilities. However, the elimination of the DC-offset component and the estimation of phase and amplitude information is still a very challenging task. The aforementioned issues are addressed by employing a half-cycle comb filter and a half-cycle non-adaptive Lyapunov orthogonal signal generator. The estimation of single-phase grid parameters is thereby made simpler by employing an improved open-loop frequency estimation, which is capable of providing accurate frequency deviation information with good precision. The research shown herein also reveals that the estimation of the amplitude and phase information differs slightly from the usual mathematical notations owing to the pseudo-rectification method adopted in the proposed work. Note that the pre-filtering stage is non-adaptive in nature, which may lead to steady-state errors in the amplitude and phase information under off-nominal frequency conditions. An online mathematical correction approach dependent on frequency deviation is, therefore, employed in order to obtain the error-free amplitude and phase information. Finally, the robustness of the current proposal is validated by experimentally comparing several known grid parameter estimation schemes using a real-time controller.

In this work, we firstly used a new two-dimensional (2D) semiconductor material Bi2O2Se for optical-fiber SPR sensor sensitization, proposed and validated a U-shaped optical-fiber SPR sensor with Au film- Bi2O2Se Sandwich structure. The finite element simulation results illustrate that the electric field strength on the surface is 2.08 times higher than that of the pure Au film, which excites a stronger SPR phenomenon thus leading to a high sensitivity of the sensor. The effect of the Radius on the sensor performance was investigated, and the optimal structural parameters were derived to fabricate the sensing probes. The RI sensitivity of the presented sensor is 6827.41 nm/RIU, 1.98 times superior to that of the pure Au film U-shaped optical SPR sensor. The sensor is insensitive to temperature fluctuations and has good repetitive and environmental stability. The proposed sensor has good potential for future applications in biomass detection, which also indicates that Bi2O2Se has good prospects for future applications in optoelectronic devices.

It has been demonstrated theoretically and experimentally that the Vehicle-to-Grid (V2G) enabled electric vehicle (EV) charger is of a reactive power compensation ability with a battery or capacitor connected. To exploit the aggregated reactive power V2G abilities of massively dispersed EV chargers, a distributed model predictive control (DMPC) strategy applying to both balanced and unbalanced distribution networks (DNs) is proposed to integrate them into real-time DN voltage regulation. Firstly, based on the instantaneous power theory and voltage sensitivity matrices, a voltage regulation model considering the reactive response of EV chargers is established without violating the normal EV active charging demands. Then, a completely distributed framework is achieved by DMPC, in which prediction information and calculation results are shared in a Peer-to-Peer (P2P) way to realize the asynchronous broadcast. The proposed model and techniques are validated by numerical results obtained from the IEEE European low voltage test feeder system. The case studies indicate that the proposed DMPC is robust to communication latency (CML) and works effectively in both balanced and unbalanced DNs without any control center, which is a significant advantage for the promotion of real-time reactive power V2G in DNs with irregular user integration and relatively poor communication infrastructure.

Optical receivers (ORXs) with integrated CMOS electronics enable compact, low-power solutions for 400-G Ethernet and co-packaged optics. In this article, we present a 100-Gb/s PAM-4 ORX with TIA and sampler integrated into a single 28-nm CMOS IC. ORX sensitivity is optimized using a low noise, sub-Nyquist bandwidth TIA followed by a mixed signal sampler that includes 2-tap FFE and 2-tap DFE. A distributed current-integrating summer helps meet feedback latency requirements of 50-Gbaud direct-feedback PAM-4 DFE. Measurements characterizing the CMOS linear TIA indicate ~23-GHz trans-impedance (ZT) bandwidth (BW) with-2.5 μA $_{{rms}}$ input-referred noise. Optical measurement results at 100 Gb/s show that -8.9-dBm sensitivity is achieved at 2.4e-4 BER with 3.9-pJ/bit energy efficiency.

Better reliability of power supply is assured with the inculcation of DGs in a distribution network. Smart sensors and latest grid communication protocols have played a crucial role in the development of intelligent MGs. Conventional protection schemes do not provide reliable performance when implemented in MGs. This paper proposes an approach which requires root mean square value of one cycle three phase voltage and current measurements during fault. These data are treated as inputs for developing a fault isolation and locator module. This module is supposed to be available at central protection system, and is designed using ML based techniques viz. GPR for fault location prediction and SVM for fault identification. Effectiveness of the proposed methodology is evaluated by considering practical grid scenarios with load variation and different DG penetration level. Further, the robustness of the proposed model is assessed by performing sensitivity analysis with consideration of variation in line parameters and load as well as effect of DG correlation. A 7-bus meshed AC MG test system consisting of three DGs and two grid sources is modelled in SIMULINK platform, and is used to demonstrate the proposed module. Data analytics tools of MATLAB 2020a has been explored to develop a machine learning-based fault isolation and location module for MGs. The proposed scheme has also been validated with real time MG data obtained from OPAL-RT real time simulator OP-4510. The accuracy in predicted results proves that the proposed scheme is pertinent for real time practical applications.

In this paper, a new architecture for developing plasmonic bio-sensors is proposed in which sensing is achieved through optical beam steering. The proposed structure consists of an array of nano-antennas that generate an outcoming optical beam whose direction varies when the material under test or its volume changes. This mechanism of sensing eliminates the requirement for complex instruments such as optical spectrum analyzers. For realization of the proposed bio-sensor, both 1-D and 2-D configurations for the nano-antenna array are designed and numerically studied. The full wave numerical simulation results show that the designed bio-sensor provides a very high sensitivity of per unit refractive index, and also the output light has an enough intensity to be observed by a naked eye. The final results show that although both versions have the same sensitivity, the 2-D structure can project the results with much higher intensity. It is also theoretically shown that the performance of the biosensor will be subject to the size of the array, and therefore, a practical large-scale version of the numerically studied structure would significantly outperform the simulated structure.

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 the present work, the hydrodynamics behavior of a 3D small-scale cold flow modeling of a gas-solid tapered fluidized bed is evaluated with the Eulerian-Eulerian two-fluid modeling (TFM) approach. The sensitivity of different modeling parameters, including lift and virtual mass forces, viscous force, gas-solid drag force, granular temperature, granular viscosity, granular pressure, particle-wall specularity coefficient, particle-particle restitution coefficient, and particle-wall restitution coefficient, are systematically investigated to optimize the performance of the present numerical model. The results from different sensitivity analyses reveal that the gas-particle drag force is a more important force to resolve the correct time-averaged axial and radial solids holdup profiles of the tapered fluidized bed than the other investigated parameters. The highest accuracy possible from simulations is achieved from the Gibilaro drag model followed by the Arastoopour and EMMS/bubbling models. In contrast, all other drag models over-predict the gas-particle interaction force. The assessment of the lift and virtual mass forces, viscous force, granular temperature, granular viscosity, granular pressure, and particle-wall restitution coefficient results reveal that these parameters are less critical for the numerical simulations of the tapered fluidized bed than the gas-solid drag force. However, the assessment of particle-wall specularity coefficient and particle-particle restitution coefficient reveals that adjusting these parameters in the numerical simulations is helpful to achieve a better agreement between the experimental data and model results. For the higher gas flow rate, the current work shows that a better agreement can be achieved by tuning the parameter values in the standard sub-models than the previously published TFM studies. Meanwhile, for the lower gas flow rate, the accuracy of the present work is slightly lower than the previously published work but acceptable with the experimental measurements. The current work has an advantage over the previously published work in terms of lower computational effort by using the simplified geometry and mean particle diameter assumptions.

Selection of natural fibers in composites for usage in aircraft or spacecraft cabin interior requires determining the performance scores for various criteria specified by material requirements. Because of the widespread available alternatives and their various criteria, it becomes a Multi criteria Decision Making (MCDM) problem and requires statistical methods to carry out the selection process. However, the selection process of the most suitable natural fiber can provide different results depending on their assigned weightages and sensitivity to the variation of that weightages. The main contribution of this study lies in analyzing the sensitivity of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for subjective weightage method: Analytic Hierarchy Process (AHP) and objective weightage method: CRiteria Importance Through Inter-criteria Correlation (CRITIC) for the selection of the best natural fiber among twelve alternatives by eliminating the rank reversal problem. This study will assist the researchers in this field to focus on developing the best possible natural fiber reinforced polymer composites for applications in the aerospace cabin interior.

Validation of satellite albedo products is an essential step because their quantitative application lie in their ability to record the real state of the earth surface. Upscaling in situ measurements to the corresponding pixel scale is necessary due to the spatial scale mismatch between in situ and satellite measurements. Machine learning-based models have been increasingly used for upscaling because they can yield more reliable results than traditional methods. Nevertheless, the main controlling factors on upscaled results have rarely been discussed. This article explores the control factors that bring uncertainties to the upscaled results based on machine learning models. Three machine learning models, including random forest (RF), k-nearest neighbor (KNN), and Cubist models, were selected to upscale single site in situ-based albedo to the coarse pixel scale. The upscaled results were carefully assessed through comparison with pixel scale albedo reference. The results indicate that the accuracy of upscaled results depends on the machine learning models, the inclusion of key variables related to albedo, the dataset selection of these variables, the amount of training data, and the sensitivity of machine learning models to these factors. Despite the dependence on control factors, the machine learning-based upscaling methods generally have excellent applicability across different spatial scales and over other untrained areas. Therefore, they open the door to generating a time series of globally, spatially continuous distributed reference datasets with sufficient length, consistency, and continuity to adequately fulfill the requirement of a comprehensive validation.

Kolari, Pynnonen, and Tuncez rely on simulation outcomes to criticize the normalization of firm characteristics employed by Bessembinder and Zhang (2013) to assess returns after major corporate events. However, their simulation outcomes simply verify that a non‐linear normalization is inappropriate if the true relation is linear. The relation between log returns and firm characteristics is unknown, but is unlikely to be linear, as the distribution of firm characteristics is strongly skewed. Here, we report on bootstrap simulations that show our methods provide unbiased estimates with appropriate statistical size and high power to detect abnormal returns when implemented in actual data. Kolari, Pynnonen, and Tuncez also provide empirical estimates that comprise useful sensitivity tests. Their results verify that firm characteristics are useful in assessing whether returns to event firms are abnormal, largely confirm our conclusions with regard to SEOs, M&As, and dividend increases, but show that conclusions regarding IPOs depend on implementation choices.

In this work, sensitivity, optimal control, and cost-effectiveness of several intervention strategies of filariasis are discussed. We study the intervention strategies that are related to bednet use, insecticide, and the combination of bed-net use and insecticide. We use Pontryagin’s maximum principle to characterize the optimal controls. The Average Cost-Effectiveness Ratio (ACER) and Infection Averted Ratio (IAR) are used to identify the most cost-effective strategy. We also determine the basic reproduction number and investigate the sensitivity of the basic reproduction number on the parameters that are related to bed-net use and insecticide. Based on the ACER values, the most cost-effective strategy to control filariasis is insecticide intervention. On the other hand, the IAR values indicates that bed-net use intervention is the most cost-effective strategy. Furthermore, it is also the most effective strategy to eliminate filariasis. The sensitivity analysis results show that the control parameter related to bed net use and treatment have a central role in reducing the basic reproduction number and filariasis spread.

Wireless symbiotic communication is an emerging green technology for Internet of Things (IoT), where batteryless devices harvest energy from ambient radio frequency (RF) signals and use backscatter technology for communications. One practical but often neglected constraint for the symbiotic communications is circuit sensitivity, below which the passive devices cannot harvest energy. In this paper, we focus on the capacity analysis under the constraint of sensitivity. Specifically, we first build up the mathematical model for the wireless symbiotic communication system, then derive its channel capacity considering the sensitivity constraint, and compare the capacity performance with the existing symbiotic communication systems without sensitivity constraint and also the traditional point-to-point systems. Simulations are then provided to corroborate our proposed studies.