Sampling Frequency Research Articles

Morular Metaplasia of the Endometrium: A Case Report and Literature Review: Care Pathways based on Molecular Biology

Background: Endometrial morular metaplasia, a clinical conundrum from a diagnostic and management angle given its rarity and low oncogenic potential has been linked to endometrial hyperplasia and carcinoma. Case report: A 77-year-old woman with no significant past medical history was found to have an asymptomatic thickened endometrium on pelvic imaging, after presenting with lower abdominal pain, 3 yrs ago. Diagnostic hysteroscopy identified an endometrial polyp within a pyometra. Histopathology showed focal complex hyperplasia without atypia with superimposed morular metaplasia (EMM) amongst negative microbiology. Following conservative management with a Multidisciplinary Team (MDT) overview, as per her choice with 6-monthly follow-up hysteroscopies, endometrial biopsies, and a short use of the Mirena IUS (discontinued due to poor tolerance), histopathology shows resolved hyperplasia with persistent EMM. She is considering a hysterectomy. Discussion: Current evidence suggests that a sub-type of EMM, a likely histological manifestation of beta-catenin (CTNNB1) gene mutation: could be a precursor of endometrial hyperplasia and low-grade endometrioid-endometrial carcinoma sub-type. Though low-grade in nature, the increased recurrence risk raises significant concerns. Management options which include conservative, hormonal, and surgery have been described in the literature. A few cases of premenopausal women have been managed conservatively, with one resulting in a pregnancy. However, due to the lack of a natural history timeline, the optimal frequency of endometrial sampling when uterine-sparing, is unclear, leading to a management conundrum. Conclusion: Management of morular endometrial metaplasia can be difficult but must reflect the woman’s choice with an MDT overview. Immuno-histochemical tools utilizing new molecular biological advances can simplify the diagnostic and prognostication processes, aiding clinical management.

A Method of Reducing Errors Due to Sampling in the Measurement of Electric Power

Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs.

Using a frequency vibration approach, examine the impact of screw rotor clearance on the screw housing in an 11kW compressor

A screw compressor is one of the industrial machines needed for productivity and use in today's production. Industrial machines generally use pneumatic systems with the help of compressed air supplied by a compressor machine. The compressor working system must receive very special attention. Maintenance and prevention of damage to air screw compressor machines is very necessary, especially maintenance and prevention of damage to rotary screw air compressors. Problems that often occur with the shaft will result in reduced air supply flow or unstable air pressure resulting in disruption of the production line. The aim of this research was to discuss the vibration values produced against the rotor clearance from the ideal output on male and female screw compressor rotors using the frequency analysis method, taking the vibration values at the screw compressor housing point using an Accelerometer sensor and a Fast Fourier Transform (FFT) tool. The analyzer rotates at 1500 rpm in the X, Y and Z axes at each predetermined point. Research shows that the clearance is set at 0.03, 0.05 and 0.08. In this research, the high frequency sample values of 1x 25 Hz, 2x 50 Hz, and 3x 75 Hz often appear in each vibration data of 0.03 and 0.05, at the vibration frequency range value of 0.08 none appear. This shows the results that the ideal rotor clearance value is at a distance of 0.08 because there is no frequency graph of 1x, 2x, and 3x rpm at each point and axis on the end housing of the screw rotor compressor

Subcranial Encephalic Temnograph-Shaped Helmet for Brain Stroke Monitoring.

In this contribution, a wearable microwave imaging system for real-time monitoring of brain stroke in the post-acute stage is described and validated. The system exploits multistatic/multifrequency (only 50 frequency samples) data collected via a low-cost and low-complexity architecture. Data are collected by an array of only 16 antennas moved by pneumatic system. Phantoms, built from ABS material and filled with appropriate Triton X-100-based mixtures to mimic the different head human tissues, are employed for the experiments. The microwave system exploits the differential scattering measures and the Incoherent MUSIC algorithm to provide a 3D image of the region under investigation. The shown results, although preliminary, confirm the potential of the proposed microwave system in providing reliable results, including for targets whose evolution is as small as 16 mL in volume.

Effects of agriculture and urbanization on the geochemistry of the Scioto River, Ohio

Despite efforts to reduce nonpoint source contamination of water resources, agriculture and urbanization remain primary sources that impair surface water quality. The Scioto River in Ohio, USA has headwaters in land dominated by primarily row-crop agriculture, then flows through metropolitan Columbus, the 14th largest city in the U.S. The river serves as a major drinking water source for the city and is dammed to form two reservoirs. The Scioto River was sampled for major ions, nutrients, and select trace elements weekly to monthly at five locations along changing agricultural and urban land use. Dissolved concentrations of many analytes were highest at the most upstream agricultural location, decreased to downtown Columbus, and increased at the location furthest downstream in agricultural land. The same trend was observed in discharge-weighted yields. Because much of the study area is underlain by carbonate bedrock, correlations with Ca2+ were likely to indicate a primarily geogenic source of the solute. Elements with moderate to strong positive correlations with Ca2+ included SO42−, Ni2+, Rb+, Mo6+, and Ba2+. Inorganic N species, emanating primarily from an agricultural source, had negative relationships with Ca2+. Concentration-discharge relationships revealed chemostatic to weak dilution of analytes positively correlated with Ca2+, suggesting a geogenic source of these elements that was weakly diluted with increasing discharge. Cu2+ showed a positive concentration-discharge slope, suggesting an anthropogenic input, but a defined source was not identified. Dams on the Scioto River that form drinking water reservoirs dampened chemical correlations and concentration-discharge relationships. Both agricultural and urban sources influence the river's chemistry based on hydroclimatic conditions and land use. Higher frequency sampling of the Scioto River during high flows allowed for a more nuanced understanding of sources of solutes to the river compared to previous work. Continued water quality monitoring is imperative to maintain and protect water quality resources, especially as urban populations are predicted to grow significantly in the next century.

Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution.

In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the synchronization differences, we propose a pulse compression shape-based algorithm to measure the entire delay parameter of the ADC/DAC chain, which achieves sub-sampling resolution by mapping the shape of the discrete pulse compression peak to the signal propagation delay. Moreover, owing to the matched filtering in the pulse compression process, the algorithm exhibits good noise performance and is suitable for wireless scenarios. Experiments verified that the algorithm can achieve precise measurements with sub-sampling resolution in scenarios where the signal-to-noise ratio (SNR) is greater than -10 dB.

Class-Balanced Regularization for Long-Tailed Recognition

Long-tailed recognition performs poorly on minority classes. The extremely imbalanced distribution of classifier weight norms leads to a decision boundary biased toward majority classes. To address this issue, we propose Class-Balanced Regularization to balance the distribution of classifier weight norms so that the model can make more balanced and reasonable classification decisions. In detail, CBR separately adjusts the regularization factors based on L2 regularization to be correlated with the class sample frequency positively, rather than using a fixed regularization factor. CBR trains balanced classifiers by increasing the L2 norm penalty for majority classes and reducing the penalty for minority classes. Since CBR is mainly used for classification adjustment instead of feature extraction, we adopt a two-stage training algorithm. In the first stage, the network with the traditional empirical risk minimization is trained, and in the second stage, CBR for classifier adjustment is applied. To validate the effectiveness of CBR, we perform extensive experiments on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. The results demonstrate that CBR significantly improves performance by effectively balancing the distribution of classifier weight norms.

Hybrid BOFDA/BOCDA system for distributed static and dynamic strain measurements.

We present a distributed optical fiber sensor based on a hybrid Brillouin optical frequency/correlation-domain analysis (BOFDA/BOCDA) configuration for both static and dynamic strain measurements. Distributed static strain (or temperature) measurements are realized using the conventional BOFDA method, i.e., acquiring the baseband transfer function of the fiber through a vector network analyzer (VNA). With little modifications, the same setup can perform dynamic, position-selective measurements synthesizing a correlation peak through a frequency modulation of the laser source while operating the VNA at a single modulation frequency. Experimental tests, carried out at a sampling frequency up to 40 Hz and a spatial resolution of ≈5 cm, demonstrate the validity of the proposed approach.

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

Analysis of irregularly sampled stream temperature time series: challenges and solutions

Water temperature is a key physical indicator of stream health, and as such, is commonly measured as part of long-term river health monitoring programs. However, analysis of long-term stream water temperature time series can be challenging, due to typically low frequencies of sampling combined with common characteristics of data collection from streams – such as observations unevenly spaced across seasons, changes to routine sampling frequencies, or improvements in the accuracy of measurements over time – known as sampling artifacts. While there are many models regularly used to estimate trends and summary statistics in long-term stream temperature datasets, there is limited understanding of the impact that commonly encountered sampling artifacts have on the accuracy and uncertainty of their estimates. This study constructed Monte-Carlo simulations to examine the influence that common sampling artifacts and the choice of analysis model can have on trend and mean estimates from long-term stream temperature time series covering tropical, temperate and cold climates. We found that, if not appropriately accounted for during analysis, sampling artifacts may obscure true trends or summary statistics, such as site means, and lead to inaccurate or misleading estimates. However, models that included components to account for seasonal variation within the model structure could estimate trends and means with high confidence, in the presence of almost all of the sampling artifacts commonly found in long-term stream temperature datasets. Structural biases in the time-of-day of sampling, such as always sampling in the morning, or where the start and end of the record are sampled at different times of day, could make estimates highly inaccurate and uncertain, and should be avoided in data collection strategies. This work aims to facilitate the analysis of historical stream temperature datasets with confidence, through identifying models that perform reliably in the presence of common sampling artifacts. The findings will enable further global insights into stream temperature, support management decisions based on accurate analysis and assist the design of future stream sampling programs using cost-effective, low-frequency sampling strategies.

Steadystate: A MATLAB-based routine for determining steady-state friction conditions in the framework of rate-and-state friction analysis

Reliable determination of rate-and-state friction (RSF) parameters depends on achieving steady-state (SS) friction conditions before and after experimental velocity-stepping friction tests. This operation, through nonlinear least squares fitting, is commonly preceded by the removal of any overall slip weakening/hardening after friction velocity steps (VSs) through a sufficiently large window of slip displacement at SS ( = linear detrend). However, to date, the identification of SS and thus the correct linear detrend is dependent on the user, which potentially results in differing RSF outputs from the same data set. Here, we demonstrate that large errors in the determination of the fitted RSF parameters can result if SS conditions are not reached before and after VSs. Such errors can be particularly relevant for materials characterized by long evolution of frictional resistance with slip, such as clay-rich gouge layers, in which identifying SS after VSs is not always obvious. To this end, we propose a methodology to accurately and consistently identify where SS is achieved after VSs. This methodology is coded into a new MATLAB-based routine, steadystate. We show the key features of the methodology, as well as how to use steadystate and read its output. We also illustrate the broad applicability of the approach to friction data with different noise levels and sampling frequencies referenced to slip velocity, by reviewing observations from synthetic data sets and specific examples of experiments from different laboratories involving various sheared materials.

Females present reduced minimum toe clearance during walking as compared to males in active older adults.

Physical decline due to aging has been associated with the risk of falls. Minimum toe clearance (MTC) is a gait parameter that might play a role in the mechanism of tripping and falling. However, it is unclear if there are any sex-related effects regarding MTC as people age. The present study investigated if there are sex-related differences in MTC in older active adults. Twenty-three females and 23 males (F: 65.5 ± 4.8 yrs; M: 61.9 ± 5.2 yrs) walked on a treadmill at a preferred walking speed, while kinematic data were obtained at a sampling frequency of 100 Hz and up-sampled to 120 and 240Hz. MTC was calculated from the kinematics data and evaluated concerning its magnitude (i.e., MTC and MTC/leg length), the time between left/right MTC (i.e., T-MTC), amount of variability (i.e., CV and CVm), and temporal structure of variability i.e., the complexity of the time series (i.e., MTC α, T-MTC α). No sex effects were found for MTC/leg length, for the amount of variability (i.e., CV and CVm), and for the complexity of the time series (MTC α, T-MTC α). However, females exhibited significantly lower MTC and T-MTC after adjusting for walking speed, mass, and age as covariates. The reduced MTC in females suggests a potential sex-related disparity in the risk of tripping and falling among active older adults.

The NANOGrav 12.5-Year Data Set: Dispersion Measure Misestimations with Varying Bandwidths

Noise characterization for pulsar-timing applications accounts for interstellar dispersion by assuming a known frequency dependence of the delay it introduces in the times of arrival (TOAs). However, calculations of this delay suffer from misestimations due to other chromatic effects in the observations. The precision in modeling dispersion is dependent on the observed bandwidth. In this work, we calculate the offsets in infinite-frequency TOAs due to misestimations in the modeling of dispersion when using varying bandwidths at the Green Bank Telescope. We use a set of broadband observations of PSR J1643−1224, a pulsar with unusual chromatic timing behavior. We artificially restricted these observations to a narrowband frequency range, then used both the broad- and narrowband data sets to calculate residuals with a timing model that does not account for time variations in the dispersion. By fitting the resulting residuals to a dispersion model and comparing the fits, we quantify the error introduced in the timing parameters due to using a reduced frequency range. Moreover, by calculating the autocovariance function of the parameters, we obtained a characteristic timescale over which the dispersion misestimates are correlated. For PSR J1643−1224, which has one of the highest dispersion measures (DM) in the NANOGrav pulsar timing array, we find that the infinite-frequency TOAs suffer from a systematic offset of ∼22 μs due to incomplete frequency sampling, with correlations over about one month. For lower-DM pulsars, the offset is ∼7 μs. This error quantification can be used to provide more robust noise modeling in the NANOGrav data, thereby increasing the sensitivity and improving the parameter estimation in gravitational wave searches.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

Research on the countermeasures of the single-phase-to-earth faults in flexibly grounded distribution networks

In a neutral, flexible, grounded distribution network, the phenomenon that a single-phase ground fault with high impedance connected in parallel with a low resistance can result in a relatively lower zero-sequence current flowing through the faulty feeder. This zero-sequence current can make it a challenge to use zero-sequence protection schemes to accurately identify and effectively protect the faulty feeder. In terms of the aforementioned challenge, based on the zero-sequence equivalent circuit of a single-phase-to-earth fault, this paper conducts an investigation on the phase difference between the zero-sequence current and the non-fault phase line voltage at power frequency in flexible grounding distribution network with different grounded conditions, both before and after the introduction of a parallel low-resistor connection. Subsequently, a criterion for selecting the faulty feeder is developed based on the observed phase difference characteristics following the implementation of the parallel low-resistor connection. Furthermore, a disposal strategy for single-phase-to-earth faults is formulated, which takes fault resistance estimation into account. To validate the proposed method, a simulation model of a flexible grounding distribution network is implemented with PSCAD/EMTDC. The effectiveness and reliability of this method in multiple scenes, including the initial fault conditions, sampling frequency, and distributed generation (DG), are proved through simulation results.

Analysis of EEG Signals Between Motor Imaginary Tasks and Rest Condition for Biometric Application

Biometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while spectral entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), decision tree and Naive Bayes, were employed for the classification task. The Welch method combined with the support vector machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the development of robust and reliable biometric systems in cybersecurity.

Microwave Dielectric Response of Bovine Milk as Pregnancy Detection Tool in Dairy Cows.

The most reliable methods for pregnancy diagnosis in dairy herds include rectal palpation, ultrasound examination, and evaluation of plasma progesterone concentrations. However, these methods are expensive, labor-intensive, and invasive. Thus, there is a need to develop a practical, non-invasive, cost-effective method that can be implemented on the farm to detect pregnancy. This study suggests employing microwave dielectric spectroscopy (MDS, 0.5-40 GHz) as a method to evaluate reproduction events in dairy cows. The approach involves the integration of MDS data with information on milk solids to detect pregnancy and identify early embryonic loss in dairy cows. To test the ability to predict pregnancy according to these measurements, milk samples were collected from (i) pregnant and non-pregnant randomly selected cows, (ii) weekly from selected cows (n = 12) before insemination until a positive pregnancy test, and (iii) daily from selected cows (n = 10) prior to insemination until a positive pregnancy test. The results indicated that the dielectric strength of Δε and the relaxation time, τ, exhibited reduced variability in the case of a positive pregnancy diagnosis. Using principal component analysis (PCA), a clear distinction between pregnancy and nonpregnancy status was observed, with improved differentiation upon a higher sampling frequency. Additionally, a neural network machine learning technique was employed to develop a prediction algorithm with an accuracy of 73%. These findings demonstrate that MDS can be used to detect changes in milk upon pregnancy. The developed machine learning provides a broad classification that could be further enhanced with additional data.

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

Dual-channel air-pulse optical coherence elastography for frequency-response analysis

Microliter air-pulse optical coherence elastography (OCE) has recently been proposed for the characterization of soft-tissue biomechanics using transient, sub-nanometer to micrometer-scale natural frequency oscillations. However, previous studies have not been able to provide real-time air-pulse monitoring during OCE natural frequency measurement, which could lead to inaccurate measurement results due to the unknown excitation spectrum. To address this issue, we introduce a dual-channel air-pulse OCE method, with one channel stimulating the sample and the other being simultaneously measured with a pressure sensor. This allows for more accurate natural frequency characterization using the frequency response function, as proven by a comprehensive comparison under different conditions with a diverse range of excitation spectra (from broad to narrow, clean to noisy) as well as a diverse set of sample response spectra. We also demonstrate the capability of the frequency-response analysis in distinguishing samples with different stiffness levels: the dominant natural frequencies increased with agar concentrations (181–359 Hz, concentrations: 1–2%, and maximum displacements: 0.12–0.47 µm) and intraocular pressures (IOPs) for the silicone cornea (333–412 Hz, IOP: 5–40 mmHg, and maximum displacements: 0.41–0.52 µm) under a 200 Pa stimulation pressure. These frequencies remained consistent across different air-pulse durations (3 ms to 35 ms). The dual-channel OCE approach that uses transient, low-pressure stimulation and high-resolution imaging holds the potential to advance our understanding of sample frequency responses, especially when investigating delicate tissues such as the human cornea in vivo.

Broadband metasurfaces for steering flexural waves in thin plates: A topology optimization approach

Flexural vibrations in thin plates are prevalent in practical engineering applications, and significant efforts have been dedicated to controlling these vibrations in recent years. However, steering of broadband flexural vibrations/waves in thin plates remains challenging. In this study, we propose a topology optimization-based technique to design broadband metasurfaces to steer flexural waves in plate-like structures. Broadband performance is achieved by formulating fitness functions that incorporate results at uniformly distributed sampling frequencies within the target frequency range. As an illustration, two refractive metasurfaces with a relative bandwidth of 20% and 60% are designed and characterized numerically. The first metasurface is further fabricated using 3D printing and experimentally tested under two different signal excitations. Both numerical simulations and experiments validate the efficacy of our designs. In addition, we conduct a comparative analysis with two counterparts designed through experience-based methods, highlighting the evident advantages of employing topology optimization for broadband metasurface design. This work proposes a topology optimization-based approach for designing broadband elastic metasurfaces.