Individual Calibration Research Articles

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach.

Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

Evaluation of different approaches to applying the standard additions calibration method

The extrapolation approach, traditionally used with standard additions (SA), is compared with the alternative strategies of interpolation, reversed-axis, and normalization. The interpolation approach is based on employing twice the analytical signal recorded for the sample (ysam) to determine an unknown analyte concentration. In the reversed-axis strategy, x- and y-axes are swapped when building the SA calibration plot to facilitate uncertainty estimation. A new strategy, based on signal normalization using ysam, is also described and compared to the other approaches. Results from 3 instrumental methods, 396 sample replicates, 16 analytes, and 2 certified reference materials are included in this study. For most applications, all four SA approaches provide statistically similar trueness and precision. However, extrapolation and reversed-axis provide more consistent values (within narrower ranges) than the other strategies when employing inductively coupled plasma optical emission spectrometry (ICP OES). On the other hand, normalization provides better trueness for the less robust method of microwave-induced plasma OES (MIP OES), as it is capable of minimizing systematic errors associated with different points of the calibration curve. Normalization is particularly useful for quickly processing data, without the need for inspecting each individual calibration plot to identify outlying points. Reversed-axis and normalization are the most adequate approaches for SA applications involving MIP OES and ICP-based methods. In addition to providing similar accuracies to the traditional extrapolation approach, these strategies present the advantage of a simple uncertainty estimation, which can be easily calculated using commonly available software such as Microsoft Excel and R.

Prediction of the Inelastic Behaviour of Radius Segments: Damage-based Nonlinear Micro Finite Element Simulation vs Pistoia Criterion

The Pistoia criterion (PC) is widely used to estimate the failure load of distal radius segments based on linear micro Finite Element (μFE) analyses. The advantage of the PC is that a simple strain-threshold and a tissue volume fraction can be used to predict failure properties. In this study, the PC is compared to materially nonlinear μFE analyses, where the bone tissue is modelled as an elastic, damageable material. The goal was to investigate for which outcomes the PC is sufficient and when a nonlinear (NL) simulation is required. Three types of simulation results were compared: (1) prediction of the failure load, (2) load sharing of cortical and trabecular regions, and (3) distribution of local damaged/overstrained tissue at the maximum sustainable load. The failure load obtained experimentally could be predicted well with both the PC and the NL simulations using linear regression. Although the PC strongly overestimated the failure load, it was sufficient to predict adequately normalized apparent results. An optimised PC (oPC) was proposed which uses experimental data to calibrate the individual volume of overstrained tissue. The main areas of local over-straining predicted by the oPC were the same as estimated by the NL simulation, although the oPC predicted more diffuse regions. However, the oPC relied on an individual calibration requiring the experimental failure load while the NL simulation required no a priori knowledge of the experimental failure load.

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis.

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

A multiplexed ion-exchange membrane-based miRNA (MIX·miR) detection platform for rapid diagnosis of myocardial infarction.

Micro RNAs (miRNAs) have shown great potential as rapid and discriminating biomarkers for acute myocardial infarction (AMI) diagnosis. We have developed a multiplexed ion-exchange membrane-based miRNA (MIX·miR) preconcentration/sensing amplification-free platform for quantifying in parallel a panel of miRNAs, including miR-1, miR-208b, and miR-499, from the same plasma samples from: 1) reference subjects with no evident coronary artery disease (NCAD); 2) subjects with stable coronary artery disease (CAD); and 3) subjects experiencing ST-elevation myocardial infarction (STEMI) prior to (STEMI-pre) and following (STEMI-PCI) percutaneous coronary intervention. The picomolar limit of detection from raw plasma and 3-decade dynamic range of MIX·miR permits detection of the miRNA panel in untreated samples from disease patients and its precise standard curve, provided by large 0.1 to 1 V signals and eliminates individual sensor calibration. The use of molecular concentration feature reduces the assay time to less than 30 minutes and increases the detection sensitivity by bringing all targets close to the sensors. miR-1 was low for NCAD patients but more than one order of magnitude above the normal value for all samples from three categories (CAD, STEMI-pre, and STEMI-PCI) of patients with CAD. In fact, miR-1 expression levels of stable CAD, STEMI-pre and STEMI-PCI are each more than 10-fold higher than the previous class, in that order, well above the 95% confidence level of MIX·miR. Its overexpression estimate is significantly higher than the PCR benchmark. This suggests that, in contrast to protein biomarkers of myocardial injury, miR-1 appears to differentiate ischemia from both reperfusion injury and non-AMI CAD patients. The battery-operated MIX·miR can be a portable and low-cost AMI diagnostic device, particularly useful in settings where cardiac catheterization is not readily available to determine the status of coronary reperfusion.

Cross-Subject Assistance: Inter- and Intra-Subject Maximal Correlation for Enhancing the Performance of SSVEP-Based BCIs.

The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). The proposed method has great potential for developing high-speed BCIs.

Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness. Advanced driver assistance systems that could detect mental states which are associated with hazardous situations, such as drowsiness, are of critical importance. EEG signals are used widely for brain-computer interfaces, as well as mental state recognition. However, these systems are still difficult to design due to very low signal-to-noise ratios and cross-subject disparities, requiring individual calibration cycles. To tackle this research domain, here, we explore drowsiness detection based on EEG signals' spatiotemporal image encoding representations in the form of either recurrence plots or gramian angular fields for deep convolutional neural network (CNN) classification. Results comparing both techniques using a public dataset of 27 subjects show a superior balanced accuracy of up to 75.87% for leave-one-out cross-validation, using both techniques, against works in the literature, demonstrating the possibility to pursue cross-subject zero calibration design.

Multilabel Per-Pixel Quantitation in Mass Spectrometry Imaging.

In quantitative mass spectrometry imaging (MSI), the gold standard adds a single structural homologue of the target compound at a known concentration to the sample. This internal standard enables to map the detected intensity of the target molecule against an external calibration curve. This approach, however, ignores local noise levels and disproportional ion suppression effects, which might depend on the concentration of the target compound. To overcome these issues, we propose a novel approach that applies several isotopically labeled versions, each at a different concentration, to the sample. This allows creating individual internal calibration curves for every MSI pixel. As proof of principle, we have quantified an endogenous peptide of histone H4 by matrix-assisted laser desorption/ionization-Q-MSI (MALDI-Q-MSI), using a mixture of three isotopically labeled versions. The usage of a fourth label allowed us to compare the gold standard to our multilabel approach. We observed substantial heterogeneity in ion suppression across the tissue, which disclosed itself as varying slopes in the per-pixel regression analyses. These slopes were histology-dependent and differed from each other by up to a factor of 4. The results were validated by liquid chromatography–mass spectrometry (LC-MS), exhibiting a high agreement between LC-MS and MALDI-Q-MSI (Pearson correlation r = 0.87). A comparison between the multilabel and single-label approaches revealed a higher accuracy for the multilabel method when the local target compound concentration differed too much from the concentration of the single label. In conclusion, we show that the multilabel approach provides superior quantitation compared to a single-label approach, in case the target compound is inhomogeneously distributed at a wide concentration range in the tissue.

Rapid and nondestructive evaluation of soluble solids content (SSC) and firmness in apple using Vis–NIR spatially resolved spectroscopy

Abstract Visible–near infrared (Vis–NIR) spectroscopy is a rapid and nondestructive method used to characterize organic compounds in postharvest fruit and vegetable assessment. However, developing robust calibration models is a challenge as conventional spectrometers collect only the cumulative effects of light absorption and scattering. In this study, a multifiber-based Vis–NIR spatially resolved spectra measurement system was designed for simultaneous evaluation of soluble solid content (SSC) and firmness in apple. Thirty silica fibers separated into five groups at 1, 2, 3, 4, and 5 mm away from the light illumination point and connected to a cost-effective Vis–NIR hyperspectral imaging camera were used to acquire spectral data with an improved signal-to-noise ratio (S/N) by a two-step signal averaging process (i.e., 30 camera pixels per fiber and six optical fibers per group). Reflectance ratio spectra were then calculated by dividing the diffusely reflected light intensity detected at distance d +△ by that detected at distance d to realize a light reference-free approach. Finally, the useful explanatory variables were selected by competitive adaptive reweighted sampling (CARS) to construct individual calibration models for various regions. The coefficients of determination ( R c a l 2 ) and the root mean square errors (RMSEcal) of the best-performing calibration models were approximately 0.97 and 0.20 % for SSC and 0.96 and 0.37 N for firmness, respectively. Furthermore, the predicted results were 0.92 and 0.35 % for SSC and 0.87 and 0.71 N for firmness. Our method offers low-cost and portable detection of SSC and firmness for postharvest fruit evaluation.

Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers: Model based optimal design of experiments supported by parameter sensitivity analysis

Determination of the optimal design of experiments that enables efficient parametrisation of fuel cell (FC) model with a minimum parametrisation data-set is one of the key prerequisites for minimizing costs and effort of the parametrisation procedure. To efficiently tackle this challenge, the paper present an innovative methodology based on the electrochemical FC model, parameter sensitivity analysis and application of D-optimal design plan. Relying on this consistent methodological basis the paper answers fundamental questions: a) on a minimum required data-set to optimally parametrise the FC model and b) on the impact of reduced space of operational points on identifiability of individual calibration parameters. Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets. In addition, it was shown that increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model.

Содержание двойных связей в полиэтилене

The content of double bonds in high-pressure polyethylene of various brands obtained from Russian and foreign manufacturers was studied. The essence of the method was to build a calibration curve on an infrared Fourier spectrometer for standard substances with a previously known arrangement of the double bond, saturation of double bonds in the initial polyethylene using gaseous bromine, and subsequent assessment of the locations and number of double bonds in the studied brands of high pressure polyethylene. The calibration substances were trans-4-decene with a purity of 96%, 1-decene with a purity of 95%, and 2-methyl-1-heptene with a purity of 98%. To record infrared spectra, calibration substances were dissolved in carbon disulfide with a purity of 99.5%. The spectra of the calibration substances were recorded in the transmission mode. The recording of the spectra of the high-pressure polyethylene samples was carried out in the reflection mode. To calculate the molar adsorption coefficient of individual calibration substances, the areas of the peak absorption of individual substances were allocated. The Peak Separation NETZSCH software was used for this purpose. For all analyzed samples, both brominated and not brominated, the ratio of the areas of absorption peaks was calculated. For each sample of high-pressure polyethylene, the content of trans-vinylidene, vinyl, and vinylidene groups C=C per 1000 carbon atoms was determined. The total number of double bonds was calculated as the sum of all analyzed bonds contained in the polyethylene sample. The accuracy of the analysis method performed by a single laboratory on a single sample is ±10% of double bonds per 1000 carbon atoms. The technique allows one to evaluate such a characteristic of low density polyethylene as the content of double bonds. This characteristic is essential for the efficient development of crosslinkable polyethylene compounds.

Is the Time-Domain Reflectometry (TDR) Technique Suitable for Moisture Content Measurement in Low-Porosity Building Materials?

Measuring moisture content in building materials is essential both for professional practice and for research. However, this is a very complex task, especially when long-term minor destructive measurements are desired. The time-domain reflectometry (TDR) technique is commonly used for soil moisture measurements, but its application in construction materials is considered a relatively new method, particularly for low-porosity building materials. The major obstacles to its current use in construction materials are (1) the difficulty of ensuring good contact between the TDR probe and the material, and (2) the lack of appropriate conversion functions between the measured relative permittivity and the moisture content of building materials. This paper intends to contribute to overcoming these difficulties by explaining in detail all the required steps to monitor moisture content in real-scale limestone walls. For that, a device is presented to guarantee the correct installation of the TDR probes on the walls, and a calibration procedure through the gravimetric method is proposed to avoid the use of an unsuitable calibration function developed for soil moisture measurements. In addition, the importance of the individual probe calibration is discussed, as well as TDR advantages and disadvantages for construction materials. The results obtained so far reveal that the TDR technique is suitable to detect moisture content variations in limestone, which is a low-porosity building material.

Hybrid Methodology for Efficient on the Fly (Re)Parametrization of Proton Exchange Membrane Fuel Cells Electrochemical Model for Diagnostics and Control Applications

Prevention and diminution in effects of degradation phenomena under highly dynamic operation of proton exchange membrane fuel cells (PEMFC), while retaining sufficiently high efficiency and performance of the system, are considered significant obstacles toward their wider use in transport applications. Overcoming these obstacles calls for precise on-line monitoring and control tools such as coupled observers, which enable combined performance and service life optimizations. Models used in these applications should feature low computational effort, good extrapolation capabilities and should be easy to parametrize. The first requirement, which is obvious for on-line applications, in frequently in contradiction to the last two, which are heavily intertwined. Good extrapolation capabilities of the model significantly reduce the size of experimental data sets needed for successful parametrization, thus enabling the parametrization on small data sets, while retaining higher accuracy outside of trained variation space. This rationale motivates the use of the computationally-fast reduced-dimensionality electrochemical models e.g. [1, 2], featuring a more profound mechanistic basis; thus, exhibiting better prediction capability of the model.To present significant progress in the aforementioned area, this contribution presents framework of computationally-fast electrochemical models that can be parametrized whether on experimental data from polarization curves or on data obtained by electrochemical impedance spectroscopy (EIS). This hybrid methodology consists of interchangeable thermodynamically consistent reduced-dimensionality electrochemical model for PEMFC [2] and newly developed analytical physical model for impedance spectra with the same set of calibration parameters. The latter is developed by applying electrochemical ansatzes for the cathode and anode reactions derived in [2] on the proposed modelling approach used for calculation of the cathode catalyst layer impedance in the inspiring work of Kulikovsky [3]. Profound modelling basis using the same set of calibration parameters enable enhanced identification of individual calibration parameters that are otherwise harder to be uniquely determined [4] on one hand, and enable parametrization of the model from either of the two separate measurements in time and frequency domains on the other.Enhanced identifiability of calibration parameters using proposed hybrid methodology is especially pronounced in the case of parameters describing anodic and cathodic reaction rates and their activation energies, which are extremely hard to be uniquely determined based on the experimental data consisting of polarization curves only, due to coupling of their information with other calibration parameters. Additionally, proposed hybrid methodology also enables a significant reduction in the measurement time in the case of experimental data obtained by EIS, allowing the low frequency regions, where the transport phenomena could be otherwise characterized, to be omitted due to already uniquely determined calibration parameters from polarization curve measurements. Furthermore, to reduce the necessary amount of experiments for successful parametrization of the model even further, the optimal design of experiments (DoE) is used, based on D-optimal criterion applied onto Fisher information matrix.To assess developed hybrid methodology calibration parameters, obtained by means of minimizing penalty function value with differential evolution algorithm on EIS data, are used for validation against polarization curves measured at the same RuL (remaining useful life). Furthermore, to confirm applicability of the methodology, the same procedure is also used for calibration on the measured polarization curves and validation on the EIS experimental data. Small discrepancy between the values of the root-mean-square errors show plausibility of the proposed hybrid methodology. On the other hand, Fisher information analysis is performed to assess increment in the information about individual calibration parameter when each experimental point from EIS data set is added to experimental space used for parametrization.Results of this joint analysis offer unique insight into the information that data set obtained with the EIS measurements possess about individual calibration parameter in the individual frequency interval and intuitively confirm the results obtained by application of the optimal DoE for a synthetic space of EIS measurements. The latter extremely reduces experimental workload needed for successful calibration of the model in comparison with the full factorial. Innovative hybrid methodology of the measurements in time and frequency domains can thus be used for achieving reduced costs and efforts in initial parametrization and during operation in re-parametrization procedures of the modelling framework for virtual observers.

Contact-free infrared OD measurement for online monitoring of parallel stirred-tank bioreactors up to high cell densities

Miniaturized stirred-tank bioreactor systems provide a scalable platform for high-throughput bioprocess development. Online measurement of process variables is a major demand to enable efficient process monitoring and control in parallel operated bioreactors. One miniaturized laser light source and two photodiodes were placed around a cylindrical disposable bioreactor made of polystyrene for individual and contact-free measurement of optical density (OD). One photodiode was positioned at an angle of 28° to the laser for measuring the scattered light, a second photodiode was positioned face to face with the laser for measuring the transmitted light. Miniaturized lasers with wavelengths of 650 nm (orange), 780 nm (NIR) and 850 nm (IR) were evaluated. The best results were achieved with a laser emitting at 850 nm. Both signals (scattered and transmitted light) were influenced by the stirrer speed (usually constant) and the microorganisms under study. After individual calibration, online OD monitoring of pH-controlled fed-batch processes was successfully shown with Escherichia coli, Corynebacterium glutamicum, and Trichosporon oleaginosus. Online OD measurements based on the transmitted light signals showed low standard deviation at low cell densities, whereas scattered light signals were more accurate at higher cell densities. Correlations were reliable up to cell dry weight concentrations of 46 g L−1 in stirred-tank bioreactors on a milliliter scale.

Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter- and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.

Hybrid Methodology for Efficient on the Fly (Re)Parametrization of Proton Exchange Membrane Fuel Cells Electrochemical Model for Diagnostics and Control Applications

Successful parametrization and re-parametrization of the models used in PEMFC observer applications is instrumental to assure ideal control of the system. To increase ease of parametrisation and accuracy of parameter determination, this paper presents framework of twin analytical ansatzes for modelling PEMFC response in time and frequency domains sharing same set of calibration parameters. Owing to thermodynamically consistent modelling basis of the twin models, which are based on electrochemical model with state-of-the-art extrapolation capabilities and replication of the experimental data with one set of calibration parameters, they are valid in all current density regions. Furthermore, unique sharing of the calibration parameters enables unprecedented enrichment of the dataset that can be used to determine values of model’s calibration parameters with higher certainty or enhances identification of individual calibration parameters that are otherwise harder to be uniquely determined. Additionally, proposed hybrid methodology also enables a significant reduction in the measurement time and enable re-parametrization on the fly.

Ion species discrimination method by linear energy transfer measurement in Fujifilm BAS-SR imaging plate.

We have developed a novel discrimination methodology to identify ions in multispecies beams with similar charge-to-mass ratios, but different atomic numbers. After an initial separation by charge-to-mass ratios using co-linear electric and magnetic fields, individual ions can be discriminated by considering the linear energy transfer of ions irradiating a stimulable phosphor plate (Fujifilm imaging plate) by comparison with the Monte Carlo calculation. We apply the method to energetic multispecies laser-driven ion beams and use it to identify silver ions produced by the interaction between a high contrast, high intensity laser pulse; and a sub-micrometer silver foil target. We also show that this method can be used to calibrate the imaging plate for arbitrary ion species in the range of Z ≥ 6 with dE/dx > 0.1 MeV/μm without requiring individual calibration.

A predictive combustion model for one-dimensional gasoline engine simulation

A new predictive combustion model for a one-dimensional computational fluid dynamics tool in the multibody dynamics processes of gasoline engines was developed and validated. The model consists of (1) a turbulent burning velocity model featuring a flame radius–based transitional function, steady burning velocity that considers local quenching using the Karlovitz number and laminarization by turbulent Reynolds number, as well as turbulent flame thickness and its quenching model near the liner wall, and (2) a knock model featuring auto-ignition by the Livengood–Wu integration and ignition delay time obtained using a full-kinetic model. The proposed model and previous models were verified under a wide range of operating conditions using engines with widely different specifications. Good agreement was only obtained for combustion characteristics by the proposed model without requiring individual calibration of model constants. The model was also evaluated for utilization after prototyping. Improved accuracy, especially of ignition timing, was obtained after further calibration using a small amount of engine data. It was confirmed that the proposed model is highly accurate at the early stage of the engine development process, and is also applicable for engine calibration models that require higher accuracy.

Comparison of nine heart rate-based models to predict work metabolism of Forest workers

Predicted work metabolism (WM) from 9 heart rate (HR)-based models were compared to measured WM obtained during work in 39 forest workers. Using measured (i.e. raw) HR in these models can overestimate actual WM since the HR increase associated with body heat accumulation is non-metabolic. Hence, accuracy of WM prediction was assessed on all possible combinations of models using raw HR and corrected HR (thermal component removed) and with five different estimates of maximum work capacity (MWC) for the models that require it as an input. The 50 model combinations produced a wide range of WM estimates. Three models using individual calibration produced the lowest RMSE and narrowest LoA with corrected HR (rRMSE ≤13%; LoA [rBias <5% ± 25%]). One of the models that requires neither determination of the thermal component nor individual calibration performed very well (rRMSE = 18%; LoA [rBias = 1% ± 36%]). Practitioner Summary: These results provide a better understanding of the accuracy of various HR-based work metabolism (WM) estimation models. This information should prove particularly useful to ergonomics professionals wishing to select a method that provides accurate estimation of WM from HR measurements during work in varied thermal environments. Abbreviations: BMI: body mass index; HR: heart rate (beats per min); HR99: HR value exceeded during 99% of the duration of the HR recording period; HRcorr: heart rate without thermal pulses; HRraw: measured heart rate; HRres: heart rate reserve; HRrest: heart rate at rest; LoA: limits of agreement; Mrest: resting metabolism; MWC: maximum work capacity; RMSE: root mean square error; VO2: oxygen consumption; VO2 max: maximum oxygen consumption; WM: work metabolism

The first attempt on fabrication of a nano-biosensing platform and exploiting first-order advantage from impedimetric data: Application to simultaneous biosensing of doxorubicin, daunorubicin and idarubicin

In this work, for the first time, we have developed a novel and very interesting electroanalytical methodology assisted by first-order multivariate calibration (MVC) for simultaneous determination of doxorubicin (DX), daunorubicin (DN) and idarubicin (ID) as three chemotherapeutic drugs at simulated physiological conditions. A sever overlapping was observed among signals of the three drugs which hindered us for simultaneous determination of them by conventional electroanalytical techniques. Therefore, we had to assist our method by chemometric approaches to develop a novel method for simultaneous determination of DX, DN and ID. Among the existing electroanalytical methods, electrochemical impedance spectroscopy (EIS) due to its high sensitivity was chosen. After individual calibration of the three drugs with the EIS data, a set of calibration samples was designed which was used to develop several first-order MVC models by partial least squares (PLS), continuum power regression (CPR), radial basis function-partial least squares (RBF-PLS), RBF-artificial neural network (RBF-ANN) and least squares-support vector machines (LS-SVM) as linear and non-linear chemometric algorithms. Then, performance of the developed MVC models in predicting concentrations of DX, DN and ID in synthetic samples was compared to choose the best model for the analysis of real samples. Our records confirmed more superiority of RBF-PLS algorithm than the other developed models which motivated us to choose it for the analysis of real samples. Fortunately, the results of the RBF-PLS in the analysis of real samples towards simultaneous determination DX, DN and ID was acceptable.