Calibration Data Research Articles

Modelling across Multiple Scales to Design Biopolymer Membranes for Sustainable Gas Separations: 2-Multiscale Approach

The majority of materials used for membrane-based separation of gas mixtures are non-renewable and non-biodegradable, and the assessment of alternative bio-based polymers requires expensive and time-consuming experimental campaigns. This effort can be reduced by adopting suitable modelling approaches. In this series of works, we propose various modelling approaches to assess the CO2/CH4 separation performance of eight different copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV) using a limited amount of experimental data for model calibration. In part 1, we adopted a fully atomistic approach based on Molecular Dynamics (MD), while, in this work, we propose a multiscale methodology where a molecular description of the polymers is bridged to a macroscopic prediction of its gas sorption behaviour. PHBV structures were simulated using MD to obtain pressure–volume–temperature data, which were used to parametrise the Sanchez–Lacombe Equation of State. This, in turn, allows for the evaluation of the CO2 and CH4 solubility in the copolymers at various pressures and compositions with little computational effort, enabling the estimate of the sorption-based selectivity. The gas separation performance obtained with this multiscale technique was compared to results obtained with a fully atomistic model and experimental data. The solubility–selectivity for the CO2/CH4 mixture is in reasonable agreement between the two models and the experimental data. The multiscale method presented is a time-efficient alternative to fully atomistic methods and detailed experimental campaigns and can accelerate the introduction of renewable materials in different applications.

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

Development of mKate3/HaloTag7 (JFX650) and CFP/YFP Dual-Fluorescence (or Förster) Resonance Energy Transfer Pairs for Visualizing Dual-Molecular Activity.

Although several imaging strategies for dual fluorescence (or Förster) resonance energy transfer (FRET) biosensors have been reported, their implementation is challenging because of the limited performance of fluorescent proteins and the spectral overlap of FRET biosensors. These processes often require additional data calibration to eliminate artifacts. Many CFP/YFP FRET biosensors have been developed. In this study, we introduced the mKate3/HT7(JFX650) FRET pair, which effectively formed two pairs of FRET pairs for dual-FRET imaging when combined with the CFP/YFP FRET pair. The FRET donor mKate3 exhibited higher brightness than its predecessor mKate. The FRET acceptor, HT7(JFX650), is a HaloTag7 protein covalently conjugated with a far-red JFX650-THL ligand. The pair comprising mKate3 and HT7(JFX650) represents an excellent FRET dyad, exhibiting a high FRET efficiency ratio. To use the FRET pair for dual FRET biosensor imaging, we constructed PKA and K+ biosensors based on the mKate3/HT7(JFX650) FRET pair. These biosensors can be used along with CFP/YFP biosensors to simultaneously detect the responses of intracellular PKA/Src, PKA/Ca2+, and K+/Ca2+ under different stimuli. The findings revealed that dual FRET biosensors, which are based on the combination of CFP/YFP and mKate3/HT7 (JFX650), exhibit adequate compatibility and can be used to visualize multiple molecular activities in a live cell.

An empirical isochrone archive for nearby open clusters

Context. The ages of star clusters and co-moving stellar groups contain essential information about the Milky Way. Their special properties and placement throughout the galactic disk make them excellent tracers of galactic structure and key components to unlocking its star formation history. Yet, even though the importance of stellar population ages has been widely recognized, their determination remains a challenging task often associated with highly model-dependent and uncertain results. Aims. We propose a new approach to this long-standing problem, which relies on empirical isochrones of known clusters extracted from high-quality observational data. These purely observation-based data products open up the possibility of relative age determination, free of stellar evolution model assumptions. Methods. For the derivation of the empirical isochrones, we used a combination of the statistical analysis tool principal component analysis for preprocessing and the supervised machine learning method support vector regression for curve extraction. To improve the statistical reliability of our result, we defined the empirical isochrone of a color-magnitude diagram (CMD) of a cluster as the median calculated from a set of nboot = 1000 curves derived from bootstrapped data. The algorithm requires no physical priors, is computationally fast, and can easily be generalized over a large range of CMD combinations and evolutionary stages of clusters. Results. We provide empirical isochrones in all Gaia DR2 and DR3 color combinations for 83 nearby clusters (d < 500 pc), which cover an estimated age range of 7 Myr to 3 Gyr. In doing so, we pave the way for a relative comparison between individual stellar populations based on an age-scaling ladder of empirical isochrones of known clusters. Furthermore, due to the exceptional precision of the available observational data, we report accurate lower main sequence empirical isochrones for many clusters in our sample, which are of special interest as this region is known to be especially complex to model. We validate our method and results by comparing the extracted empirical isochrones to cluster ages in the literature. We also investigate the added information that empirical isochrones covering the lower main sequence can provide on case studies of the IC 4665 cluster and the Meingast 1 stream. Conclusions. The archive of empirical isochrones offers a novel approach to validating age estimates and can be used as an age-scaling ladder or age brackets for new populations and serve as calibration data for further constraining stellar evolution models.

Ensemble strategies exploration for the calibration data optimized spatial filters based SSVEP recognition algorithms

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) exhibit high information transfer rate (ITR) characteristics. However, the electroencephalogram (EEG) data encoding SSVEP patterns has a low signal-to-noise ratio, leading to limitations in applications. To enhance the application of SSVEP-BCIs, various SSVEP recognition algorithms have been developed by utilizing prior knowledge based on the calibration data optimized spatial filters (CDOSF). Yet, the process of combining spatial filters from multiple stimuli remains unexplored, resulting in performance bottlenecks for SSVEP recognition and application scenarios. In this study, we provide an overview of four popular CDOSF-based SSVEP recognition algorithms and introduce three ensemble strategies to extend these recognition algorithms. For comparison purposes, we selected three benchmark SSVEP datasets and conducted comparative experiments on the three ensemble strategies across the four recognition algorithms. The experimental results demonstrate that the ensemble strategies significantly enhance SSVEP recognition accuracy and ITR. Ablation studies also confirm that ensemble strategies possess characteristics of requiring less calibration data, fewer EEG channels, and insensitivity to parameters. The ensemble strategies extended CDOSF-based recognition algorithms offer a novel choice for constructing SSVEP-BCIs.

OS-SSVEP: One-shot SSVEP classification

It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject. Specifically, CSDuDoFN uses multi-reference least-squares transformation (MLST) to map data from both the source subjects and the target subject into the domain of sine-cosine templates, thereby reducing cross-subject domain gap and benefiting transfer learning. In addition, CSDuDoFN is fed with both transformed and original data, with an adequate fusion of their features occurring at different network layers. To capitalize on the calibration trial of the target subject, OS-SSVEP utilizes source aliasing matrix estimation (SAME)-based data augmentation to incorporate into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the SSVEP-based brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.

Global calibration method for multi-view-based vibration measurement of large structures

A multi-view global calibration method is proposed to overcome the limitations of overlapping regions in multi-view structural vibration measurement. This method establishes a mapping relationship from image points to experimental model points at the outset of vibration measurement, enabling a one-time global calibration of multi-view vibration response data. Furthermore, an image point detection method based on shape-function matching is designed to improve global calibration accuracy. Experimental results demonstrate that the proposed method excels in point detection and global calibration. Finally, the feasibility of this method is validated through vibration experiments on large aircraft fuselage structures.

Early Mission Calibration Performance of NOAA-21 VIIRS Reflective Solar Bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD’s reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands’ radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%.

Hydrogen isotopic ratio by residual gas analysis during the JET DT campaigns

Abstract The analysis of hydrogen isotope content is crucial for understanding the operation of fusion devices. Hydrogen isotopic analysis in the core and plasma edge is conducted through neutron and spectroscopic diagnostics. In the case of exhaust gas, mass spectrometry is employed using residual gas analyzers (RGAs), along with optical gas analysis (OGA) utilizing an optical Penning gauge. The use of traditional quadrupole mass spectrometers for RGA encounters challenges during discharges with tritium gas due to signal overlap of different hydrogen molecules at the same mass number. This paper introduces a novel technique to address this issue through a cross-related analysis of RGA data with OGA results. Another consideration in mass spectrometry analysis is the instrument’s varying sensitivity concerning the gas mass number. The paper includes gas calibration data results for all the quadrupoles used in the JET gas analysis. Hydrogen isotopic ratios are calculated from RGA detected currents using simple formulas. The results of this procedure are presented for selected DT JET discharges in relation to the JET pulse time, and they are compared with corresponding optical data. Time-averaged hydrogen isotopic ratio values are computed for numerous discharges during the DT and T campaigns.

A General On-Orbit Absolute Radiometric Calibration Method Compatible with Multiple Imaging Conditions

On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration.

10Boron-film-based gas detectors at ESS

The development of detectors for the European Spallation Source is an important parallel element to the effort put into the design and construction of the neutron source and instruments. The 10Boron-film-based detector developments that started over a decade ago as basic detector concepts have now reached technical maturity after intense prototyping work and numerous testing campaigns. Several of the ESS beamlines that will soon enter the commissioning phase started welcoming the detector systems built with the 10B-film converter technology. The real-size demonstrators evolved in the last 2–3 years into a diverse suite of gas proportional counters for use in diffraction, reflectometry and small-angle scattering studies in spite of the operational complexity posed by the requirements for large-area coverage, low material budget and robustness for operation in the high-flux and high-radiation environment of ESS. The detectors are now being shipped to ESS or undergoing the last performance and calibration tests before being handed over to the instrument teams for installation in the host beamlines. A common feature of these detector systems is the very large number of readout channels that they are instrumented with in order to fulfill the demanding requirements for sensitivity, spatial resolution and count-rate capability. Across all of the detector types that will operate at ESS, the integration, testing and commissioning of the read-out technologies and software tools for data reduction, calibration and analysis are the focus of the detector and integration teams. These will yield a wealth of knowledge about their operation as well as initial results on the in-situ performance, a very important asset to ensure rapid commissioning of the detectors when neutrons from the ESS source become available. In this paper we will give an overview of the 10B-film-based detector technologies included in the ESS detector suite that are currently facing the transition from the production phase to installation and integration with the other beamline components, which comes with its own specific challenges of both organizational and technical nature.

Quantitative x-ray scattering of free molecules

Abstract Advances in X-ray free electron lasers have made ultrafast scattering a powerful method for investigating molecular reaction kinetics and dynamics. Accurate measurement of the ground-state, static scattering signals of the reacting molecules is pivotal for these pump-probe X-ray scattering experiments as they are the cornerstone for interpreting the observed structural dynamics. This article presents a data calibration procedure, designed for gas-phase X-ray scattering experiments conducted at the Linac Coherent Light Source X-ray Free-Electron Laser at SLAC National Accelerator Laboratory, that makes it possible to derive a quantitative dependence of the scattering signal on the scattering vector. A self-calibration algorithm that optimizes the detector position without reference to a computed pattern is introduced. Angle-of-scattering corrections that account for several small experimental non-idealities are reported. Their implementation leads to near quantitative agreement with theoretical scattering patterns calculated with ab-initio methods as illustrated for two X-ray photon energies and several molecular test systems.

Sensory and organoleptic assessment from the byproducts of mamoncillo or quenepa Melicoccus bijugatus Jacq: Alternative food processing and morpho-physiological characterization of the fruit

This research aims to analyze the morpho-physiological characteristics of ripe fruit and assess the feasibility and acceptability of agro-industrial byproducts derived from mamoncillo or quenepa (Melicoccus bijugatus Jacq) fruit. In addition to this, it was considered to be the optimal harvest point, which begins at 69% of the Maturity Index. In addition, evaluations of the chemical and morphological characteristics were carried out. To gather analytical scales and calibrator data, the two ripeness phases were analyzed and compared, using the maturity index to establish the optimal harvest point. Ripe fruits have increased shell, juice, pulp, seed, length, width, and thickness than unripe fruits, with 69% maturity being best. Further, it is possible to use the pulp of Melicoccus bijugatus to make syrup, jam, or jelly among other things. For this reason, the hedonic test was conducted with panelists who had not received any prior training in order to ascertain the level of acceptance of these innovative products. The analyses showed that the highest rank given through the panelists by the hedonic test and ranks of taste was "P-2 Pattern-2" with an X = 55.5 (Rank) which is a substitute pattern "Lychee in syrup", the second-highest was X = 52 which is "T-1 Treatment i.e. Melicoccus bijugatus Jelly"' followed by "T-2 Treatment i.e. M. bijugatus in syrup" with X = 51 Rank. According to the findings of the statistical analysis, the various agro-industrial byproducts derived from Melicoccus bijugatus were positively received.

Development of an affordable light emitting diode spectrophotometer paired with a Python program for calibration and linearity testing and the measurement of uranium(VI).

Uranium (U) is a radiologically and chemically toxic element that occurs naturally in water, soil, and rock at generally low levels. However, anthropogenic uranium can also leach into groundwater sources due to mining, ore refining, and improper nuclear waste management. Over the last few decades, various methods for measuring uranium have emerged; however, most of these techniques require skilled scientists to run samples on expensive instrumentation for detection or require the pretreatment of samples in complex procedures. In this work, a Schiff base ligand (P1) is used to develop a simple spectrophotometric method for measuring the concentration of uranium (VI) with an accurate and affordable light-emitting diode (LED) spectrophotometer. A test for a higher-order polynomial relationship was used to objectively determine the calibration data's linearity. This test was done with a Python program on a Raspberry Pi computer that captured the spectrophotometer's calibration and sample measurement data.

393 The role of on-animal sensors in understanding feed intake, behavior, and performance of grazing livestock

Abstract Intake by grazing animals is difficult to reliably measure in the field, particularly in more diverse grazing systems where pastures vary greatly across time and space in resource availability, which in turn leads to greater variation in animal time budgets and intake patterns. Advances in remote livestock monitoring (e.g., on-animal sensors, walk -over-weighing), and advanced analytics such as machine learning algorithms can contribute to the development of robust, adaptable tools for better understanding of animal performance and behavior in grazing systems. Many existing tools for predicting livestock production require estimates of feed intake, and while estimates based on forage sward characteristics have been developed and used for temperate, relatively homogenous pastures, they are less well defined for more diverse rangelands. These diverse pastures are more difficult to characterize due to their greater variation in quantity and quality; consequently, animal selectivity is greater in more variable pastures. However, such feedback loops between pasture diversity and animal behavior may not yet be adequately represented in biophysical models. On-animal sensors such as accelerometers and GPS devices can be used to develop new ways of assessing relative intake by providing accurate estimates of animal location and behavior profiles, such as the use of machine learning algorithms to detect how much time is spent grazing, walking, ruminating, or resting. Measurements of animal behavior could then be combined with other animal-based methods such as walk-over-weighing and fecal NIR to improve estimates of intake and production for a wide range of extensive grazing environments. Sensor-derived measurements of animal movement could also be used to improve estimates of the energetic costs of grazing, thereby improving estimates of energy available for growth. As an example, data on recent activity and paddock use from on-animal sensors could be entered into a model to estimate current feeding requirements, then combined with weather and pasture quality forecasts. The ensuing baseline could then be used to explore the potential variability of these projections and generate a range for possible expected performance over the next month or season. This approach could be repeated over time, and animal performance and behavior measures used to update model estimates and improve forward projections. These methods could also be used in conjunction with estimates of animal energy requirements to provide real-time prediction of relative feed intake, which would provide a step-change in modelling livestock production in extensive grazing systems. For these approaches to be successful, additional calibration data are needed across a wide variety of environments and species, and the tools must be cost-effective to implement. In summary, on-animal measurements provide an adaptable, data-driven approach that could be integrated with existing tools to improve our understanding of feed intake, behavior, and performance of grazing animals.

The irtQ R package: a user-friendly tool for item response theorybased test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package (http://CRAN.R-project.org/), which simplifies IRTbased analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.

The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data. Concretely, RCC reconstructs training samples using probabilistically mixed eigenvector matrices derived from covariance matrices across source domains. This process manipulates the channel correlation of training samples, implicitly creating novel synthesized domains. By increasing the diversity of source domains, RCC aims to enhance the domain generalizability of the pre-trained model. The effectiveness of RCC is validated through subject-independent and subject-adaptive classification experiments. The results of subject-independent classification demonstrate that RCC significantly improves the classification performance of the pre-trained model on unseen target subjects. Moreover, when compared to the fine-tuning process using the RCC-absent pre-trained model, the fine-tuning process using the RCC-enhanced pre-trained model yields significantly improved performance in the subject-adaptive classification. RCC proves to enhance the performance of transfer learning by optimizing the utilization of the source domain data. The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.

PEMANFAATAN DATA HUJAN BERBASIS SATELIT CHIRPS UNTUK PEMETAAN HUJAN RANCANGAN DI KABUPATEN LOMBOK UTARA

The design rainfall value is a critical parameter in flood analysis and water structure design. In general, the design rainfall calculation uses measured rainfall data. However, sometimes it is difficult to find complete rainfall data from evenly distributed rainfall stations and in an extended measurement range. This study aims to test the use of CHIRPS satellite-based rainfall data as an alternative solution for providing data in areas with difficulty with rainfall data. The analysis includes data accuracy and calibration evaluation, followed by design rainfall analysis with frequency analysis and mapping using spatial data processing software. The accuracy test results obtained an average deviation value of the original CHIRPS that did not meet the average NSE value of 0.346. Furthermore, the data was calibrated using the regression method, and corrected data was obtained with an NSE of 0.446 with an interpretation of the meeting. A rainfall analysis was conducted for corrected CHIRPS with ARR (Automatic Rainfall Recorder) measurement as a control. The results obtained the average percentage deviation for design rainfall with a return period of 2, 5, 10, 25, 50, and 100 years using corrected CHIRPS ranging from 18 - 28% lower than design rainfall using ARR. The design rainfall isohyet map using spatial data processing software for the North Lombok Region shows that high design rainfall occurs in the eastern part of North Lombok, namely around the Santong and Sambik Bangkol stations, while design rainfall with low values is in the western part of North Lombok, namely around the Tanjung and Gunungsari stations.

Predicting cognitive load with EEG using Riemannian geometry-based features

Objective. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.Approach. Performance is evaluated by using the Minimum Distance to Riemannian Mean model trained on CL classification. The baseline performance is established using spatial covariance matrices of the signal as features. Various novel features are explored and analyzed in depth, including spatial covariance and correlation matrices computed on the EEG signal and its first-order derivative. Furthermore, each RPA step effect on the performance is investigated, and the generalization performance of RPA is compared against a few different generalization methods.Main results. Performances are greatly improved by using the spatial covariance matrix of the first-order derivative of the signal as features. Furthermore, this work highlights both the importance and efficiency of RPA for CL prediction: it achieves good generalizability with little amounts of calibration data and largely outperforms all the comparison methods.Significance. CL prediction using RPA for generalizability across subjects is an approach worth exploring further, especially for real-world applications where calibration time is limited. Furthermore, the feature exploration uncovers new, promising features that can be used and further experimented within any Riemannian geometry setting.