Double Sampling Research Articles

Bioinspired soft-hard interfaces fabricated by multi-material additive manufacturing: A fracture mechanics investigation using essential work of fracture

This study employs the Essential Work of Fracture (EWF) concept to evaluate the interfacial fracture toughness of bioinspired interfaces between soft and hard polymer phases. The experimental framework utilizes a model material system with Polylactic Acid (PLA) as the hard phase and Thermoplastic Polyurethane (TPU) as the soft phase. Employing the Fused Filament Fabrication (FFF) technique, bioinspired sutural interfaces are created, characterized by interpenetrating soft and hard protrusions. The modulation of a critical parameter in the FFF process enables the variation of interpenetration length of protrusions at the interface, thereby achieving a spectrum of interfacial strength and toughness. The determination of essential work of fracture from double edge notch tension samples, with varied ligament sizes, allows for the specific EWF measurement. This specific EWF is found to align with the initiation value of plane strain interfacial fracture toughness obtained through the Double Cantilever Beam (DCB) test. Therefore, the proposed approach asserts the elimination of the need for complex interfacial fracture tests, such as DCB. A noteworthy discovery is the establishment of a correlation between specific plastic energy dissipation and protrusion length. This correlation suggests an increased plastic dissipation with longer protrusions within the investigated bioinspired interface. Due to this heightened plastic energy dissipation, the shape of the interfacial nominal stress-displacement curves demonstrates substantial dependence on interface morphology, shifting from a triangular to a trapezoidal shape as protrusion length increases. These findings offer valuable insights into the mechanics of bioinspired interfaces, presenting a more efficient and nuanced approach to characterizing their fracture properties.

Online sequential analysis of volatile and semivolatile organic compounds in water matrices by double robotic sample preparations and dual-channel mono and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry system

The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges, we introduce a novel approach utilizing a dual-channel mono (1D) and comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS) system, integrated with a robotic pretreatment platform, for online monitoring of both volatile organic compounds (VOCs) and semivolatile organic compounds (SVOCs) in water matrices. Employing the robotic platform, we establish a suite of online liquid–liquid extraction (LLE) pretreatment processes for water samples, marking the first instance of such procedures. Leveraging the automatic headspace (HS) module, dual robotic preparations of HS and LLE are sequentially executed to extract VOCs and SVOCs from water matrices. The GC × GC–TOFMS system is distinguished by its dual-channel analytical column configuration, facilitating sequential analysis of VOCs in GC–TOFMS mode and SVOCs in GC × GC–TOFMS mode. Quantitative detection of 55 target VOCs and 104 SVOCs is achieved in a water sample using the instrument system. Our method demonstrates excellent correlation coefficients ranging from 0.990 to 1.000, method detection limits ranging from 0.08 to 4.78 μg L–1, relative standard deviations below 19.3 %, and recovery rates ranging from 50.0 % to 124.0 %. To validate the online monitoring capabilities of our system, we assess target SVOCs at three different concentration levels over a 3-day period. Most compounds exhibit recovery rates ranging from 70.0 % to 130.0 %. Furthermore, we apply our method to analyze a real water sample, successfully identifying over 100 target and nontarget VOCs/SVOCs, including alcohols, aldehydes, ketones, acids, esters, and phenols. These results highlight the efficacy of the proposed analysis system, capable of conducting two distinct analyses in automatic sequence, thereby enhancing the efficiency and accuracy of organic compound analysis in water matrices.

Calibrated EWMA estimators for time-scaled surveys with diverse applications

Recently, several memory-type mean estimators (including ratio, product, and logarithmic) have been developed. These estimators rely on exponentially weighted moving average (EWMA), which incorporate both historical and present sample data. In this article, we propose EWMA type calibrated estimators under single and double stratified random sampling (StRS). Because calibration method enhances the estimates by modifying the stratification weight, taking advantage of supplementary information. To evaluate the performance of estimators, various real-world time-scaled data sets pertaining to stock market and weather are taken into account. Additionally, we also conduct a simulation study using a bivariate symmetric data set. The numerical results show the superiority of proposed estimators (y¯TM,y¯TaM) over the adapted ones (y¯PM,y¯PaM).

Data reduction strategy in the PandaX-4T experiment

The PandaX-4T experiment is designed for multiple purposes, including searches for solar neutrinos, weakly interacting massive particles, and rare double beta decays of xenon isotopes. The experiment produces a huge amount of raw data that needs to be stored for related physical analyses in a wide energy range. With the upgrading of the PandaX-4T experiment, the doubled sampling rate resulted in a larger data size, which challenges both the cost and the data processing speed. To address this issue, we propose a data reduction strategy by removing the noise tail of large signals and downsampling the remaining parts of them. This strategy reduces the requirement for storage by 65% while increasing data processing speed. The influences on physical analyses on different topics at different energy regions are negligible.

The Effect of a Proposed Training Program on Some Elements of Physical Fitness among Students of the Athletics Training Course at Yarmouk University

The study aimed to identify the effect of a proposed training program on some elements of physical fitness among students of the athletics training course at Yarmouk University. The researchers used the experimental approach, and the study sample consisted of twenty (N= 20) students at Yarmouk University during the first semester of the academic year 2019-2020, who were purposively chosen. The samples were divided into two equal groups: experimental and control groups, with (10) students in each group. To analyze the results, means, standard deviation, Pearson correlation coefficient, paired samples t Test for double samples, and independent samples t Test were used. The results showed statistically significant differences related to the effect of the training program in relation to all study variables between the pre- and post- measurements of the two groups, in favor of the post measurement. The results further showed statistically significant differences among the post measurements in relation to all the study variables between the experimental and control groups in favor of the experimental group. The researchers recommended that the training program should be generalized to the athletics coaches in the Jordanian universities.

Patterns of Cross-Peaks in Two-Dimensional Correlation Spectra to Probe Intermolecular Interactions Described by Two Reversible Reactions.

Two-dimensional correlation spectroscopy is used to investigate the intermolecular interaction between two substances dissolved in the same solutions, where the intermolecular interaction is described by two reversible reactions producing two supramolecular aggregates. The severe overlappings expected among the characteristic peaks of the original solute and aggregates make conventional one-dimensional spectra difficult to accurately reflect the physiochemical nature of the intermolecular interaction. The double asynchronous orthogonal sample design (DAOSD) approach is utilized to analyze the simulated data for proof-of-principle demonstration. The patterns of cross-peaks are much more complex compared with the intermolecular interaction described by only a single reaction. Four major groups of cross-peaks with characteristic patterns observed in the pair of DAOSD asynchronous spectra are systematically analyzed and classified. Further analysis of the spectral feature of the cross-peaks of the DAOSD asynchronous spectra is helpful to exact additional information concerning the variation of the peak position and peak width of the aggregates compared with those of the original solute. The result is important to reveal the physicochemical nature of intermolecular interaction between the solutes (e.g., changes in conformation, dynamical behavior, etc.). The pattern of cross-peaks in the corresponding 2D asynchronous spectra may become rather complex when the peak position, peak width, and peak intensity of two supramolecular aggregates change simultaneously. Further work using artificial intelligence techniques to interpret the complex cross-peaks is still being carried out.

Closed-form expressions of the run-length distribution of the nonparametric double sampling precedence monitoring scheme

AbstractA significant challenge in statistical process monitoring (SPM) is to find exact and closed-form expressions (CFEs) (i.e. formed with constants, variables and a finite set of essential functions connected by arithmetic operations and function composition) for the run-length properties such as the average run-length ($$ARL$$ ARL ), the standard deviation of the run-length ($$SDRL$$ SDRL ), and the percentiles of the run-length ($$PRL$$ PRL ) of nonparametric monitoring schemes. Most of the properties of these schemes are usually evaluated using simulation techniques. Although simulation techniques are helpful when the expression for the run-length is complicated, their shortfall is that they require a high number of replications to reach reasonably accurate answers. Consequently, they take too much computational time compared to other methods, such as the Markov chain method or integration techniques, and even with many replications, the results are always affected by simulation error and may result in an inaccurate estimation. In this paper, closed-form expressions of the run-length properties for the nonparametric double sampling precedence monitoring scheme are derived and used to evaluate its ability to detect shifts in the location parameter. The computational times of the run-length properties for the CFE and the simulation approach are compared under different scenarios. It is found that the proposed approach requires less computational time compared to the simulation approach. Moreover, once derived, CFEs have the added advantage of ease of implementation, cutting off on complex convergence techniques. CFE's can also easily be built into mathematical software for ease of computation and may be recalled for further work.

A 180 fps WVGA CIS with 3.2e− TRN and dual-readout modes

In order to broaden the application scenarios and reduce the noise performance for global shutter CMOS image sensors (CISs), this paper proposes a low-noise and dual-readout-modes global shutter WVGA CIS. By implementing buried channel nMOS and pMOS without body effect as the source followers of 8-transistors active pixel (8TAP), the noise performance of the sensor is optimized. Moreover, based on correlated double sampling and correlated multiple sampling, two timing schemes are configured in the same readout circuit to achieve high frame rate and low noise readout modes. The layout of 8TAP is also optimized to improve the pixel fill factor and quantum efficiency. Measurement results show that the sensor achieves a maximum frame rate of 180 fps and a minimum temporal random noise (TRN) of less than 3.2e−. The proposed dual-mode readout method can be used as an extended functionality for CIS products.

A double sampling plan for truncated life tests under two-parameter Lindley distribution

A double sampling plan for truncated life tests under two-parameter Lindley distribution

Ratio estimator for double sampling procedure with non-response: An empirical study

This study proposes a ratio-type estimator for population mean estimation using auxiliary variables with double sampling in the presence of non-response. The study provides expressions for the constant, bias, and mean square errors (MSE) of the proposed estimator and compares it with ten existing estimators. The study employed the secondary source of data collection to evaluate the efficiency of the proposed and existing estimators by analyzing five natural populations from three different sources. The performance of ten (10) estimators was considered in this study. The findings suggest that the proposed estimator and the H estimator provide more accurate and precise estimates of the population mean using an auxiliary variable. Additionally, the study found significant differences amongst the mean values of the constant and bias for the different estimators. A Dunn Kruskal-Wallis multiple comparison tests with the Bonferroni method was performed to ascertain the pair of estimators that contributed to the significant difference observed. When estimating the population means using an auxiliary variable, the proposed estimator outperformed other existing estimators that were taken into consideration in the study

Design and implementation of a fully differential 2nd‐order CIFF DSM with the synthetic techniques of novel CDS chopper‐integrator and DER using additive‐error capacitors

SummaryCompared to the independent application of chopper stabilization or correlated double sampling (CDS), this paper proposed a 2nd‐order cascade of integrators with feedforward delta–sigma modulator (DSM) utilizing an innovatively synthetic technique of chopper‐integrator within CDS at 1st stage for reducing the flicker noise and DC offset cancelation from about 9.5 mV to −4.4 μV ± 115.6 μV (δ). On the regard of further enhancing the noise suppression and mitigating the nonlinearities of capacitor mismatch, instead of applying the dynamic elements randomization (DER) to the nonlinear correction of DAC under multi‐bits quantizer, here the operation of 3‐bits DER with scrambler has been intelligently employed on the 2nd integrators' sampling capacitors to gain the 2nd noise‐shaping characteristic in spite of 1‐bit quantizer. The noise floor could be theoretically scaled down by −16.1 dB at input frequency of 10 kHz within the limited bandwidth of 100 kHz. The prototype DSM was manufactured in 180‐nm BCD process with an active area of 0.2625 mm2 and achieves the SNDR‐based Schreier figure‐of‐merit (FoMS) of 144 dB and Walden figure‐of‐merit (FoMW) of 25.96 pJ/conv under a 3.3 V supply at 10 kHz working frequency.

Study on the grain evolution of austenitic steel for nuclear power during hot working based on large area EBSD mapping

To address the issue of non-standard grain sizes and difficult control of uniformity in the manufacturing of large, complex-shaped nuclear power pipelines. In this paper, double wedge-shaped samples of X2 CrNiMo 18.12 (CN) austenitic stainless steel were hot deformed at 1000 °C, 1100 °C, and 1180 °C to obtain deformation samples with a gradient strain, and the deformation sample at 1180 °C was subjected to solid solution treatment at 1060 °C × 4 h (water quenching; the measured cooling rate of water cooling was approximately 4400 °C/min). Large-area electron backscattered diffraction mapping technology was used to characterize the 80 mm × 5 mm full-area range of samples. The initiation sequence of various dynamic recrystallization mechanisms and the grain evolution under continuous effective strain conditions at a hot deformation temperature of 1000–1180 °C were investigated. The influence of the hot deformation process parameters on the grains after solid solution treatment was examined, the process interval for the generation of severe mixed grains was determined. The results show that the recrystallization behavior of X2 CrNiMo 18.12 (CN) austenitic stainless steel is mainly determined by a combination of recrystallization mechanisms dominated by discontinuous dynamic recrystallization, supplemented by continuous dynamic recrystallization, twinning-induced dynamic recrystallization, and geometrical dynamic recrystallization during the hot deformation process at 1000–1180 °C. The effective strain intervals of the solid solution sample that produces severely mixed grains are in the range of 0.11–0.22 mm/mm, with the probability of producing severely mixed grains with an effective strain in the range of 0.13–0.14 mm/mm reaching 74%.

Design of a charge-sensitive amplifier for MEMS piezoelectric hydrophone

Hydrophone is an important part of sonar system and convert acoustic signal into electrical signal. However, the electrical signal generated by the hydrophone is relatively weak and cannot be directly used for underwater detection and communication. Therefore, it is necessary to preprocess the generated electrical signal. In this paper, a switched capacitor charge-sensitive amplifier for MEMS piezoelectric hydrophone is designed. In the structure, two single-channel charge amplifiers are used for time-division multiplexing. The noise sampling and charge amplification are performed separately in the same half cycle to achieve the effect of alternating output, thereby increasing the sampling rate and solving the problem of discontinuous output of the switched capacitor circuit. In the circuit design, virtual switch and two-phase non-overlapping clock are adopted to reduce the error caused by charge injection and clock feedthrough effect of CMOS switch. What’s more, the correlated double sampling technique is used to reduce the offset and low frequency noise of the circuit. The gain of the charge-sensitive amplifier designed in this study is 26 dB, the bandwidth is 0.8Hz∼17.6kHz, the equivalent input noise power spectral density is 115.6 nV/Hz at 0.1Hz, and the charge sensitivity is 2 mV / pC. The fitting line between the input charge and the amplitude of the output curve is similar to the theory. The simulation results show that the charge-sensitive amplifier proposed in this study meets the design requirements of the MEMS piezoelectric hydrophone readout circuit.

Optimized design and characterization of HYLITE, a charge-integration readout chip of XFEL

HYLITE (High dYnamic range free electron Laser Imaging deTEctor) is a charge-integration pixel readout chip designed for SHINE (Shanghai HIgh repetition rate XFEL aNd Extreme light facility). The target specifications for the full-size HYLITE chip include a pixel matrix of 128 × 128 pixels and a frame rate of 10 kHz. In order to meet these specifications, two small-scale chips, designated HYLITE0.1 and HYLITE0.2, were fabricated and underwent comprehensive testing. Some issues were discovered during tests including the noise performance and the error of output ADU (Analog Digital Units) codes.The HYLITE200S represents the third prototype chip developed to address the above issues. The CDS (Correlated Double Sampling) circuits and glitch-clear clock structures are integrated in pixels. Furthermore, to meet the data output rate requirements of the full-size chip, a high-speed data interface has been designed. Test results show that the signal-to-noise ratio is improved to 9.31 for a 12 keV single photon, and the ADU error is fixed. By integrating a phase-locked loop and a balanced encoding logic, the data interface can work steadily at a speed of 3.125 Gbps.

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

This study introduces the concept of inverse exponentiation in formulating calibration weights in stratified double sampling and proposes a more improved calibration estimator based on Koyuncu and Kadilar (2014) calibration estimator. The variance of the proposed logarithmic calibration estimator has been derived under large sample approximation. Calibration asymptotic optimum estimator and its approximate variance estimator are derived for the proposed logarithmic calibration estimator. Results of empirical study showed that the proposed logarithmic calibration estimator performs better than the Koyuncu and Kadilar (2014) calibration estimator with appreciable gains in efficiency. Also, simulation study for the comparison of the proposed logarithmic estimator with a Global estimator [Generalized Regression (GREG) estimator ] proved the robustness of the proposed logarithmic calibration estimator and by extension the efficacy of inverse exponentiation in calibration weightings. Analysis and evaluation are presented. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 91-102

A chain ratio-type exponential estimator for population mean in double sampling

In this paper we have proposed an efficient ratio-type exponential estimator for estimating the population mean of the study variable, by incorporating two auxiliary variables in twophase (double) sampling. The bias and the mean square error of the proposed estimator have been obtained up to the first order of approximation. The newly proposed estimator offers more precision in comparison to other competing estimators, theoretically as well as empirically, by considering a known value of some population parameter.

Selection of Quick Switching Double Sampling System with MAPD AND MAAOQ Using Weighted Poisson Distribution

Selection of Quick Switching Double Sampling System with MAPD AND MAAOQ Using Weighted Poisson Distribution

Interval prediction of PV power based on double sample entropy framework and integrated bidomain decomposition deep learning

AbstractA method for photovoltaic (PV) power interval prediction based on double sample entropy framework and integrated bidomain decomposition deep learning is proposed to address the volatility, randomness, intermittency and timeliness of PV power prediction. By using the integrated bidomain decomposition method under the framework of double sample entropy, the PV power time series is decomposed into several components with similar complexity and the deep learning network is used to learn and predict the results of each component reconstruction to obtain point predictions. The quantile regression method is then used based on the point prediction to obtain interval predictions. The actual calculation examples show that the RMSE and CPRS of the proposed method are reduced by an average of 45.56% and 31.42%, respectively, compared with other integrated bidomain decomposition deep learning methods that are not in the double sample entropy framework and single decomposition deep learning methods.

Research on Parkinson's disease recognition algorithm based on sample enhancement

Parkinson's disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson's disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson's disease recognition. The validation experiments of the effectiveness of this paper's algorithm are carried out on the Parkinson's disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson's disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson's disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson's disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.

FCDS-DETR: detection transformer based on feature correction and double sampling

FCDS-DETR: detection transformer based on feature correction and double sampling