Cubic Model Research Articles

Converting an HLA-B27 flow assay from the BD FACSCanto to the BD FACSLyric.

HLA-B27 is a major histocompatibility complex (MHC) class I antigen which exhibits strong association (90%) with ankylosing spondylitis. HLA-B27 detection in patients by flow cytometry is a widely used clinical test, performed on many different flow cytometer models. We sought to develop and validate a test conversion protocol for the HLA-B27 test performed on the BD FACSCanto to BD's newer FACSLyric flow cytometers. The development and validation experiments were performed using anti-HLA-B27*FITC/CD3*PE antibody-stained whole blood patient specimens. The anti-HLA-B27*FITC logarithmic median fluorescence (LMF) results on the BD FACSCanto were converted to median fluorescence intensity (MFI) values on the BD FACSLyric. Clustering of the HLA-B27 positive and negative values, using a 3rd order polynomial equation, resulted in a conversion of the BD FACSCanto cutoff values, negative (<150 LMF) and positive (≥160 LMF), to negative (<4530 MFI) and positive (≥6950 MFI) on the BD FACSLyric. Accuracy was assessed by comparing the flow results obtained on the BD FACSCanto and BD FACSLyric to a molecular PCR based assay. Additional validation parameters (compensation verification, intra- and inter-assay precision, and instrument comparison) were performed per the recommendations outlined in the Clinical and Laboratory Standards Institute (CLSI) H62 guidelines for validation of flow cytometry assays.

192 Prediction of fecal starch content of fattening cattle using near-infrared spectroscopy and machine learning

Abstract Starch content in cattle feces can be used to predict starch digestibility. Near-infrared spectroscopy (NIRS) can be used to rapidly and non-chemically predict fecal starch content after a calibration model has been consequentially developed. Recent advancements in machine learning have introduced potent methods for developing optimized models. In addition, diverse preprocessing methods, such as scatter correction and smoothing, are available to eliminate irrelevant variance sources. This study aimed to identify optimal spectral preprocessing and regression methods for predicting fecal starch content using NIR spectra. NIR spectra were measured for 196 fecal samples collected from 9 fattening cattle farms. Fecal starch content on a DM basis was measured using enzymatic reactions and colorimetry. The 196 samples were divided into a calibration set (n = 123), a validation set (n = 29) comprising four farms, and an external validation set (n = 44) comprising five farms that differed from the calibration set. Based on the calibration set, two preprocessing methods were selected using the partial least squares (PLS) model. In method 1, the calibration models were developed by a grid search 5-fold cross validation on the 1,152 preprocessed calibration sets, and preprocessing with the lowest root mean square error (RMSE) of cross-validation among these calibration sets was selected. In method 2, the original calibration set (n = 123) was divided into a calibration set (n = 116) and a preprocessing selection set (n = 7) with different neutral detergent fiber and similar starch contents. After preprocessing, calibration models were developed as described in method 1. The preprocessing selection sets were then predicted by the developed models, and preprocessing with the lowest RMSE among these preprocessing selection sets was selected. Using the preprocessing methods selected in methods 1 and 2, the validation and external validation sets were predicted using seven regression models. As a result, the preprocessing selected by method 1 was in the wavelength range of 400–2500 nm, with robust normal variate (RNV) scatter correction and first-order derivative Savitzky-Golay filtering (2nd order polynomial, 2-nm window size). The preprocessing selected by method 2 had a wavelength range of 1500–2500 nm, RNV scatter correction, and 2nd-order derivative Savitzky-Golay filtering (3rd order polynomial, 25-nm window size). Among the seven regression models, the Lasso model was the most accurate for prediction. The Lasso models based on the preprocessing methods selected by methods 1 and 2 had RMSE of external validation = 2.054% of DM and 1.540% of DM, respectively. The feature importance of the Lasso model based on preprocessing method selected by methods 2 was higher at wavelengths of 1775 nm and 2325 nm. In conclusion, seven models were developed and evaluated based on diverse preprocessing methods using NIRS. The Lasso model exhibited the highest accuracy.

Third Order Polynomial Bundle Adjustment For Optical Calibration Refinement

We propose a refinement and validity region expansion process of a 3rd order polynomial calibration based on bundle adjustment. The present methodology can be expanded to any model as long as the mapping is a smooth function. Both methods are tested with a tomographic Particle Tracking Velocimetry measurement whose results are compared in terms of the smoothness and length of the retrieved tracks. The refinement method shows a significant improvement in both the associated PDFs. Similarly, the expansion shows a corresponding behaviour to the original calibration even though a higher degree of smoothness of the tracks is achieved.

MODELING OF A PEM FUEL CELL POLARIZATION CURVE BY LOW-ORDER POLYNOMIALS FOR THE OUTPUT POWER CALCULATION ALGORITHMS

The control and energy algorithms that govern the operation of a PEM fuel cell within an energy system must account for the nonlinear phenomena in the cell; it is often captured by the polarization curve. However, the modeling approaches used for simulation studies could be more suitable for the abovementioned algorithms due to their higher computational burden. That leads to the development of simplified approaches. Our study focuses on the algorithms used for the fuel cell output power calculation. Many of them are directed towards the maximum power point operation of the cell. We propose that the fuel cell representation for the algorithms can be used in the low-order polynomial form, thus decreasing the computational load compared to the other approaches. For the maximum power point prediction, we propose using the truncated form of polarization curve input data (ignoring the activation loss region). Our study has demonstrated that based on the same input data and MATLAB curve fitting commands, the 3rd-order polynomial provides the comparable RMSE 3.5…3.9 for the power curve approximation vs. 3.9 for the 5th-order polynomial representation. The value of the maximum PowerPoint is obtained with a 1.3 % relative error with the 2nd-order polynomial using the truncated input data compared to 1.2 % for the 5th-order polynomial representation.

Three data-selection approaches for beam position monitor offline calibration

In this study, we describe the fabrication of an electrostatic beam position monitor (BPM) for beam diagnostic systems. Calibration of the BPM pickup requires both sensitivity and curve fitting owing to manufacturing tolerances following fabrication. To calibrate the BPM, we developed a BPM calibration system that comprises a wire-based test bench and readout electronics. We considered three difference-over-sum (DOS) data selection methods: DOSdiag, DOSaxis, and DOSall, to understand their impact on calibration accuracy. Each method selected specific DOS data, and we compared their characteristic curves. Consequently, we analyzed the calibration results by performing linear, 3rd-order, 5th-order, and two-dimensional (2D) 3rd-order polynomial fitting. Each of the three DOS data selection methods was examined to illustrate their advantages and limitations in terms of calibration accuracy and required amount of data. The results obtained demonstrated that choosing the appropriate DOS data-selection approach could be employed for the BPM offline calibrations, achieving minimized calibration errors and optimized response characteristics with polynomial curve fitting.

Dijkstra algorithm based minimum acceleration/snap quadrotor UAV trajectory planning

This paper focuses on path planning using the Dijkstra/A* algorithm and addresses the trajectory generation problem using the minimum acceleration/snap approach. Firstly, the mathematical model of the optimization problem is formulated, specifically utilizing the Euler-Lagrange equation to derive the necessary conditions for achieving minimum acceleration/snap trajectories, which are represented by either 3rd or 7th order polynomials. The coefficients of these polynomials are determined by imposing appropriate constraints on velocity, acceleration, and higher-order derivatives. Subsequently, a detailed comparison of the performances of the two optimization methods in complex environments is conducted, evaluating differences in position and velocity. Based on the deterministic analysis, conclusive results are obtained.

New experimental measurements of X-ray emissions induced by protons of several tens of MeV on elements with Z=22 to Z=79: Toward quantitative high-energy PIXE

K-shell X-ray production cross-sections measurement campaigns were carried out at the ARRONAX cyclotron with titanium, chromium, copper, molybdenum, silver and gold targets for different energies from 15 to 68 MeV. These measurements, carried out using three detectors (SDD, CdTe and HPGe), complete the database with 66 new values. Our different measurements agree with previously published data and are consistent for all three detectors, confirming the absence of experimental bias. The universal ionisation cross-section representation allows an optimisation of the data using a 3rd-order polynomial function. This function can be used for HE-PIXE quantification for elements with atomic numbers between 22 (Ti) and 79 (Au) in the studied energy region.

Nonlinear Gradient Field Mapping Using a Spherical Grid Phantom for 3 and 7 Tesla MR Imaging Systems Equipped with High-performance Gradient Coils.

Recent high-performance gradient coils are fabricated mainly at the expense of spatial linearity. In this study, we measured the spatial nonlinearity of the magnetic field generated by the gradient coils of two MRI systems with high-performance gradient coils. The nonlinearity of the gradient fields was measured using 3D gradient echo sequences and a spherical phantom with a built-in lattice structure. The spatial variation of the gradient field was approximated to the 3rd order polynomials. The coefficients of the polynomials were calculated using the steepest descent method. The geometric distortion of the acquired 3D MR images was corrected using the polynomials and compared with the 3D images corrected using the harmonic functions provided by the MRI venders. As a result, it was found that the nonlinearity correction formulae provided by the vendors were insufficient and needed to be verified or corrected using a geometric phantom such as used in this study.

Physical and aerodynamic properties of date palm pollen grains

The present study was designed to determine the effect of four moisture content levels (4, 5, 6, and 7 %) on the physical and aerodynamic properties of date palm pollen grain (DPP). The physical properties of DPP included pollen length (L), width (w), thickness (T), projected area (Ap), geometric mean diameter (dg), mass (m), sphericity (S), and bulk density (ρp). It was observed that the moisture content did not significantly influence the physical properties of the DPP. The aerodynamic properties of DPP included the terminal velocity (Vt), drag coefficient (Dc), drag force (Df), and Reynolds number (Re). The pollen Reynolds number (Re) is significant at different pollen grain moisture content, and regression models were developed in the form of polynomial and exponential relationships. Also, the 3rd order polynomial relationship was found between Re and Dc. The results showed that the average values of Vt, Dc, Df, and Re were about 0.6 m s-1, (0.38 to 0.45), 1.09E-11 N, and (0.29 to 0.42), respectively. The results of this study will be helpful in the performance of date palm pollination machines.

A graphical derivation and statistical evaluation of simplified polynomials to determine vapour pressure deficit for use in ultra-low power microcontroller applications

The aim of this work has been to derive and statistically evaluate the accuracy of second-order and third-order polynomials to determine vapour pressure deficit (VPD). These polynomials take air temperature and relative humidity measurements to determine VPD without the use of an exponential function, as proposed by F W Murray in 1967. Replacing the exponential function with a 2nd or 3rd order polynomial may be beneficial in ultra-low power microcontroller-based measurement applications where; code size, memory usage and power requirements are critical design drivers. However, oversimplification may impact precision. This work presents alternative 2nd order and 3rd order equations that have been derived from a Murray equation dataset where VPD isothermal datasets were plotted against relative humidity. These linear relationships allow y = mx + c analysis where, (i) ‘c’ can be set to zero with a offset in the relative humidity data, and, (ii) ‘m’ can be derived from a 2nd of 3rd order polynomial where ‘m’ = f(T) and is derived using Excel-based fitting of the gradients from the isothermal datasets. The resulting ‘m’ = f(T) 2nd and 3rd order polynomials presented R 2 values of 0.998 04 and 0.999 98 respectively. A Bland-Altman statistical assessment was performed, where the Murray equation (reference) dataset is plotted against the difference between the reference and polynomial datasets using the same air temperature and relative humidity inputs. The difference datasets presented 2-sigma (95% confidence interval) variances for the 2nd and 3rd order polynomials as <±0.1 kPa and <±0.01 kPa respectively. The 2nd and 3rd order polynomials also resulted in a bias values of <0.0037 kPa and <0.0013 kPa respectively. These results suggest that a 3rd order polynomial equation could be used to determine VPD in ultra-low-power microcontroller measurement applications, with minimal impact on VPD measurement precision.

Linear Complexity Self-Attention With 3rd Order Polynomials.

Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with the number of spatial positions hence making their use impractical in many real case applications. In this work, we analyze these methods through a polynomial lens, and we show that self-attention can be seen as a special case of a 3 rd order polynomial. Within this polynomial framework, we are able to design polynomial operators capable of accessing the same data pattern of non-local and self-attention blocks while reducing the complexity from quadratic to linear. As a result, we propose two modules (Poly-NL and Poly-SA) that can be used as "drop-in" replacements for more-complex non-local and self-attention layers in state-of-the-art CNNs and ViT architectures. Our modules can achieve comparable, if not better, performance across a wide range of computer vision tasks while keeping a complexity equivalent to a standard linear layer.

Innovative safety monitoring system based on fiber optic sensors technology compatible with 4-20mA standard

In this contribution an innovative, full analog, fiber optic sensors (FOS) interrogator is designed which, being fully compatible with the 4-20 mA standard of the Programmable Logic Controller (PLC), enable the integration of the FOS technology in safety framework, such as the Detector Safety System (DSS) of the LHC Experiments. It is composed by a full analog electrical circuitry, capable to directly transduce the signal coming from the arrayed waveguide grating (AWG), into a monotonic electrical current in the range of 4-20mA. In a first experimental analysis, a temperature of 50°C was detected, exhibiting an output trend which can be fitted with a 3rd order polynomial equation over the whole range. Furthermore, in a reduced range of 20°C, the trend behaves linearly. The proposed system has the potential to be fully integrated in the DSS of the LHC experiments. Indeed, a validation on field is foreseen in the framework of the Compact Muon Solenoid experiment DSS.

Coastal tides measurement in Indonesia using GNSS-Reflectometry

The important role of coastal areas in human life and their vulnerability to natural hazards such as sea level rise, make continuous monitoring of coastal ecosystem rehabilitation concerning the local sea level is urgently required. Conventional tide observation using a tide gauge sensor measures a local sea level relative to the land. Unfortunately, the effects of deformation and land subsidence around tidal stations make local tide observations irrelevant to measure absolute sea level rise. Currently, an alternative method is being developed accordingly. The method utilizes Global Navigation Satellite Systems (GNSS) signal, known as GNSS-Reflectometry (GNSS-R), to observe the tide. This research aimed to observe the sea level from the reflection of the GNSS signal. Tidal observation using GNSS-R was carried out in four Geospatial Information Agency’s tidal stations, i.e., Morotai, Piru, Tehoru, and Pasangkayu. The tide was determined based on the dominant frequency of a multipath signal, which is obtained by separating the composite signal from the direct one using 3rd order polynomial fitting. The combination of GPS and GLONASS at the L1 and L2 frequencies resulted in tide observations with an RMSE value of 18 cm. The result is 90% correlated with tidal observation using tide gauge sensor. The residual of main tidal constituent vector in both semidiurnal and diurnal components ranged from 0.01 to 0.02 m. This means that the tide prediction generated from the GNSS-R observation is statistically similar to the prediction generated from the tide gauge sensor.

Efektivitas Penggunaan Fly Ash Batubara sebagai Koagulan dan Adsorben dalam Menurunkan Chemical Oxygen Demand (COD) dan Total Suspendedd Solid (TSS) Limbah Cair Industri Pulp and Paper

The content of Al and Fe in coal fly ash has underpinned several studies on the use of coal fly ash as a coagulant and adsorbent. Coal fly ash coagulant was activated with H2SO4. While coal fly ash adsorbent is activated with HCl. This study aims to compare the effectiveness of the coagulant or as adsorbent, to determine the optimum concentration of coagulant/ adsorbent addition, and to determine the effectiveness of fly ash as a coagulant/ adsorbent in reducing COD and TSS in Pulp and Paper wastewater. This research used 2 treatments, activating coal fly ash with 2M H2SO4 and 4M HCl, and then comparing the effect of fly ash coagulants and adsorbents in reducing COD and TSS wastewater. The results showed that coagulant reduced COD by 53.23% and TSS by 60.1%, more effective than adsorbent which only reduced COD by 6.38%, and increased TSS value by 7.2%. However, it has not yet below the quality standards. Numerical analysis using 3rd order polynomials shows that doses of 41,000-42,000 ppm of coagulant can reduce COD below the quality standard. This concludes is coal fly ash coagulant is effective in reducing COD and TSS of pulp and paper wastewater, with the optimal dose to reduce COD below the quality standard, is 41,000 - 42,000 ppm.

Exploring EEG spectral and temporal dynamics underlying a hand grasp movement.

For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

Real-Time Classification of EMG Myo Armband Data Using Support Vector Machine

ObjectivesThis study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results. Material and methods:EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition. Results14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set. ConclusionThe greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures.

Correlation between natural and artificial aging in particleboards

Abstract The main objective of this work was to establish a correlation between natural aging with artificial aging in medium density particleboards produced with polyurethane resin of castor oil and Pinus sp. For such correlation, climatological parameters at São Carlos city, São Paulo state Brazil were used. The established correlation was that 20 cycles would be proportional to the same degradation of two months of exposure under natural aging. The relation between physical and mechanical properties with the effects provided by number of months (M) on the natural aging with number of cycles (Nc) on the artificial aging was obtained with cubical polynomial regression models. In general, for physical properties, 20 Nc represents 7 to 8 months and for mechanical properties, 11 to 12 months, with exception to internal bonding, with 8 months of natural aging. The equations which best represent the relation between Nc and M were thickness swelling at 2h and water absorption at 2 h and 24 h, with R² above 93%.

다중 파라메트릭 변환곡선 기반 선수 선형 변환기법 연구

In this paper, we propose a systematic hull form variation technique which automatically satisfies the displacement constraint and guarantees a high level of fairness. This method is possible through multiple parameter correction curves. The present method is to improve the hull form variation method based on parametric modification function and consists of two sub-categories: SAC variation and section lines modification. For SAC variation, the utilization of two B-Spline curves satisfying GC¹ condition led to the satisfaction of displacement constraint and high level of fairness at the same time. Section lines modification methods involves in using two fuctions: the first is the waterplane modification function combining two cubic splines. the other function is the sectional area modification function consisting of 2nd order polynomial over the DLWL(Design Load Waterline) and 3rd order polynomial below the DLWL, This function enables not only the fundamental U-V section shape variation but also systematically modified section lines. The present method is expected to be more useful in the hull form optimization process using CFD compared to the existing method.

Диаграммы производительности судовых вакуумных рыбонасосных установок

The article presents the analysis of performance diagrams of marine vacuum fish-pumping units based on Ocean Master water-ring compressor machines published in the domain by Samson Pumps. The diagrams represent the dependence of the flow rate of the water-fish mixture on the given level difference at different values of the rotor speed for two stages of the plant operation. These experimental dependences are well approximated by 3rd-order polynomials over the entire range of acceptable argument values. The calculation of the values of the adjusted determination indices showed that at a rotor speed of 20 s-1 or more the degree of the polynomial can be reduced to the second power. Contour graphs of the performance of vacuum fish pumping units at each stage of their operation were constructed. The argument values for a given feed can be determined from these graphs. They allowed to set the scope of arguments in which the unit would not work. The formula for the performance of vacuum fish pumping units for the full cycle of operation is obtained on the basis of regression models. The maximal performance of the investigated vacuum fish-pumping units indicated in the technical documentation refers to the individual stages of their operation. The performance for a full cycle of work in real conditions can be 5-10 % of the maximum. The obtained analytical expressions can be used in the practical application of performance diagrams of vacuum fish-pumping units, taking into account the hydraulic head losses in the pipeline.

Accurate Assessment Model for Solving Resistance-Temperature Equations of Platinum Thermometers in Range from -40 °C up to 660 °C

The missions of the thermal metrology laboratory are to maintain, disseminate, develop and realize the International Temperature Scale 1990 (ITS-90). One of the services that the laboratory has introduced is the routine calibration to all industrial sectors that covers almost all fields inside and outside Egypt. This work compares different interpolating equations of set of industrial Platinum Resistance Thermometers (IPRTs) in the temperature range from -40 °C up to 660 °C that convert the resistance of thermometer sensing element to temperature and find the optimum method in which range applicable. The used analytical methods are Callendar–van Dusen (CVD) equation and the (4th -5th) order polynomials of resistance as a function of temperature. The study splits the ranges of investigation into lower subranges, test the significant standard error and residual for each case, and give advice to the lower level calibration laboratories that does not own primary standard about the best method used. From the results it is found that the standard fit error (SFE) for 4th order polynomial on average = 30 % compared to the CVD equation, =50 % compared to the ITS-90 deviation function and = 73% compared to the 3rd order polynomial. It is preferable to used 5th order polynomial than CVD to decrease 1 point in the calibration and reduce the cost of the calibration with nearly the same accuracy if the mathematical model is applied. The study gives advice to the end user who wants to use CVD equation by selecting the best two-calibration points above 0 °C which are 100 °C and 400 °C.