Dielectric properties assessment of honey by using non-destructive dielectric spectroscopy

<p>In this research the dielectric constant of three types of Malaysian honey has been investigated using a non-destructive measurement technique. The objective of this research is to assess the dielectric constant of the three types of honey in Malaysia using a non-destructive measurement technique known as an open-ended coaxial probe in the frequency range from 100 MHz to 10 GHz frequency. Analysis on the effect water concentration in honey on the dielectric constant and the effect of temperature on dielectric constant of honey has been conducted. The three types of honey that have been chosen to be investigated in this project are stingless bee honey, wild honey and commercial (organic) honey and together their water adulterated samples. For this research, the probe had been set up by setting a range of frequency from 100 MHz to 10 GHz and needs to be calibrated with three calibration methods namely open, short and reference water. From the result it was found that the higher the temperature of the honey and the higher percentage of water content in the honey, the dielectric constant is increased. The dielectric constants of all honeys decreased with increasing frequency in the measured frequency range and increased with increase percentage of water content and temperature.</p>

Small Blubber Samples (50 mg) Sufficient for Analyses of 10 Stress and Reproductive Steroid Hormones in Gray and Fin Whales via Liquid Chromatography Mass Spectrometry

Information on stress, reproductive fitness, and health is difficult to obtain in wild cetaceans but critical for conservation and management. The goal of this study was to develop a methodology requiring minimal blubber mass for analysis of reproductive and stress steroid hormones and, hence, suitable for cetacean biopsies. Blubber biopsies and samples were collected from free-ranging and stranded gray and fin whales. Steroid hormones were extracted from blubber samples as small as 50 mg using liquid-liquid extraction methodology developed to handle the high fat content of blubber. Samples were analyzed via liquid chromatography with tandem mass spectrometry for 10 hormones: aldosterone, androstenedione, cortisol, cortisone, corticosterone, 17β-estradiol, estrone, 17α-hydroxyprogesterone, progesterone, and testosterone. As part of the optimization, homogenization via bead beating and blade dispersion were compared, and the former found superior. To investigate optimal yet minimal tissue mass required, hormone panels were compared among paired 50, 150, and 400 mg samples, the latter two being commonly reported masses for hormone blubber analysis. Results indicated that 50 mg of blubber was suitable and sometimes superior. Additionally, significant differences in precision values were observed between species, possibly stemming from differences in blubber composition, and relevant to homogenization technique selection and calibration methods that use blubber matrix matches obtained from a species other than the study species. Based on recovery and precision values, our methodology was accurate and precise in the measurement of spiked known quantities for all 10 hormones, confirming the methodology capabilities in 50 mg blubber mass in both species. Altogether, and in our specific sample sets, all endogenous hormones, except corticosterone, were identified above the detection limit in 50 mg gray whale blubber samples while all endogenous hormones, except aldosterone, cortisone, estrone, and progesterone, were detected in 50 mg fin whale blubber samples. We present a robust methodology for the analysis of multiple reproductive and stress steroid hormones in minimal masses of cetacean blubber compatible with small biopsies. Finally, we identified statistically significant differences in corticosteroid concentrations between stranded and free ranging animals.

Graph-optimisation-based self-calibration method for IMU/odometer using preintegration theory

Determination of calibration parameters is essential for the fusion performance of an inertial measurement unit (IMU) and odometer integrated navigation system. Traditional calibration methods are commonly based on the filter frame, which limits the improvement of the calibration accuracy. This paper proposes a graph-optimisation-based self-calibration method for the IMU/odometer using preintegration theory. Different from existing preintegrations, the complete IMU/odometer preintegration model is derived, which takes into consideration the effects of the scale factor of the odometer, and misalignments in the attitude and position between the IMU and odometer. Then the calibration is implemented by the graph-optimisation method. The KITTI dataset and field experimental tests are carried out to evaluate the effectiveness of the proposed method. The results illustrate that the proposed method outperforms the filter-based calibration method. Meanwhile, the performance of the proposed IMU/odometer preintegration model is optimal compared with the traditional preintegration models.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

Two Reliable Calibration Methods for accurate in-situ U-Pb dating of Scheelite

Scheelite is an important metal mineral in W-related hydrothermal deposits and can be utilized as a reliable geochronometer to directly date the timing of mineralization. Up to now, two previous...

Tweezepy: A Python package for calibrating forces in single-molecule video-tracking experiments

Single-molecule force spectroscopy (SMFS) instruments (e.g., magnetic and optical tweezers) often use video tracking to measure the three-dimensional position of micron-scale beads under an applied force. The force in these experiments is calibrated by comparing the bead trajectory to a thermal motion-based model with the drag coefficient, γ, and trap spring constant, κ, as parameters. Estimating accurate parameters is complicated by systematic biases from spectral distortions, the camera exposure time, parasitic noise, and least-squares fitting methods. However, while robust calibration methods exist that correct for these biases, they are not always used because they can be complex to implement computationally. To address this barrier, we present Tweezepy: a Python package for calibrating forces in SMFS video-tracking experiments. Tweezepy uses maximum likelihood estimation (MLE) to estimate parameters and their uncertainties from a single bead trajectory via the power spectral density (PSD) and Allan variance (AV). It is well-documented, fast, easy to use, and accounts for most common sources of biases in SMFS video-tracking experiments. Here, we provide a comprehensive overview of Tweezepy’s calibration scheme, including a review of the theory underlying thermal motion-based parameter estimates, a discussion of the PSD, AV, and MLE, and an explanation of their implementation.

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.

Calibrating Variable-Value Population Ethics

Variable-Value axiologies propose solutions to the challenges of population ethics. These views avoid Parfit’s Repugnant Conclusion, while satisfying some weak instances of the Mere Addition principle (for example, at small population sizes). We apply calibration methods to Variable-Value views while assuming: first, some very weak instances of Mere Addition, and, second, some plausible empirical assumptions about the size and welfare of the intertemporal world population. We find that Variable-Value views imply conclusions that should seem repugnant to anyone who opposes Total Utilitarianism due to the Repugnant Conclusion. So, any wish to avoid repugnant conclusions is not a good reason to choose a Variable-Value view. More broadly, these calibrations teach us something about the effort to avoid the Repugnant Conclusion. Our results join a recent literature arguing that prior efforts to avoid the Repugnant Conclusion hinge on inessential features of the formalization of repugnance. Some of this effort may therefore be misplaced.