Trend Removal Research Articles

Analyzing the impact of climatic conditions on rainfed wheat yield in northwest Iran: a parametric and nonparametric approach

ABSTRACT It is essential to comprehend the relationship between agricultural yields and climatic conditions, especially concerning food security and the possible threats to crop output. Wheat is a crucial agricultural crop that covers a significant amount of rainfed production regions in Iran. This study utilized parametric and nonparametric approaches to assess rainfed wheat yield. The study centered on the Tabriz area in northwest Iran, examining precipitation patterns concerning rainfed wheat cultivation. The study focused on analyzing seasonal and distinct rainfall patterns during the cultivation period, utilizing widely recognized drought metrics such as the standardized precipitation index and the standardized precipitation–evaporation index. The study findings indicate a notable upward trend in rainfed wheat output over the analyzed period. The Mann–Kendall test resulted in a p-value of 0.031, indicating statistical significance for the observed rising trend. We conducted trend removal and normalized rainfed wheat yield figures based on seasonal precipitation to study the data more thoroughly. The second phase of the growing season was particularly notable, spanning from the completion of germination to the beginning of blooming. Instead of analyzing precipitation for the full growing season, concentrating on autumn precipitation or the time from germination to blooming might improve yield forecasts and determinations.

STUDY OF fMRI BRAIN ACTIVITY DURING NON-HANDWRITING AND HANDWRITING TASKS ON ESSENTIAL HAND TREMOR

Essential hand tremor (EHT) is a prevalent neurological condition affecting geriatric populations, yet its underlying mechanisms remain poorly understood. This study aims to investigate the neural substrates associated with motor tasks in EHT patients, illuminating the complex neural activity characterizing this condition. Twenty participants underwent a thorough evaluation to ensure eligibility, excluding factors such as mental illness, drug dependency, or Parkinson’s disease (PD). Functional magnetic resonance imaging (fMRI) was utilized to examine brain activation patterns during non-handwriting tasks (NHWT) and handwriting tasks (HWT). Participants received training to standardize hand and forearm movements for effective fMRI assessment. fMRI data preprocessing included motion correction, filtering, removal of linear trends, normalization to Montreal Neurological Institute (MNI) space, and spatial smoothing. Distinctive patterns of brain activation were observed during motor tasks in individuals with EHT compared to controls. During NHWT, the EHT group showed significantly increased activation in the precentral gyrus, supplementary motor area, thalamus, and posterior cerebellar lobe, highlighting their role in mediating motor challenges in EHT. Similarly, during HWT, the EHT group exhibited heightened activation in the precentral gyrus and supplementary motor areas. In contrast, reduced activation was noted in the caudate nucleus, inferior temporal gyrus, and precuneus during HWT in the EHT group compared to controls. These findings align with previous research on involuntary movement disorders, such as early-stage PD, emphasizing the importance of the caudate nucleus and related regions in EHT. In conclusion, this study sheds light on the intricate neural activity underlying motor tasks in EHT patients. The identified neural regions and their functions offer insights into the neurophysiological basis of EHT-related motor impairments. These findings have the potential to enhance the understanding of EHT beyond its surface-level effects. This study has identified the brain regions involved in motor tasks affected by EHT. This sets a foundation for future research to better understand the complexities of this neurological condition. These discoveries may lead to novel therapeutic interventions tailored to address the unique challenges faced by individuals with EHT, representing a significant milestone in understanding and managing EHT.

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images.

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used.

Blade tip timing for multi-mode identification based on the blade vibration velocity

Blade Tip Timing (BTT) is a widely employed non-contact measurement technique for monitoring turbomachinery blade vibrations. The conventional BTT method typically involves computing the expected time of arrival (TOA) and subtracting it from the actual TOA to derive the time difference, subsequently used to calculate blade-tip displacement. However, calculating the expected TOA introduces computational errors. Moreover, obtaining blade-tip vibration displacement under variable rotational speeds necessitates pre-processing to remove trend terms caused by speed fluctuations. To address these challenges, this study introduces a paradigm shift by replacing the traditional displacement-based blade-tip timing (D-BTT) with velocity-based blade-tip timing (V-BTT). Specifically, this approach utilizes the angular information between adjacent sensors and the measured TOA to extract the blade-tip vibration velocity through a Taylor series expansion, thus eliminating the calculating of the expected TOA and improving the computational efficiency. Building upon this methodology, a compressed sensing model for undersampled vibration velocity is established and solved using the Block-OMP algorithm, enabling vibration parameter identification without relying on prior information. By providing a superior representation of mid-frequency components of blade-tip vibration, V-BTT facilitates precise identification of higher-order vibration parameters in multi-mode blade motion. Finally, the efficacy of the proposed method is confirmed through numerical simulations and experiments involving multi-mode blades. The experimental results reveal that the relative errors of the first two modal frequencies for the five blades identified using vibration velocity are all below 0.5%, affirming the method's accuracy. Compared to the traditional D-BTT method, the V-BTT approach reduces the need for trend removal, eliminates calculation errors associated with the expected TOA, and, most importantly, enhances the accuracy of multi-mode frequency identification.

УСУНЕННЯ ЗАВАД У ДИНАМІЧНИХ СПЕКТРАХ ЗА НАЯВНОСТІ ПОТУЖНОГО СИГНАЛУ. Частина 2. ВУЗЬКОСМУГОВІ ЗАВАДИ УМОВНО-СТАЦІОНАРНОГО ХАРАКТЕРУ

Subject and Purpose. A new method for detecting narrow band interferences is discussed, with the use of an example record of Jovi- an Io-C decametric radio storm as obtained with the UTR-2 array on April 10, 2020. We aimed at developing an efficient and simple algorithm based on a detailed analysis of the effect the radio interference environment and the frequency response of the telescope may have on the efficiency of the interference mitigation procedure. Methods and Methodology. The ‘orthogonal detection’ method proposed for identifying linear interference patterns in dynamic spectra of powerful radio sources has been adapted for application to narrow band interferences of various spectral widths and variable brightness, which often happen to be located close to (or intersect with) the signal of interest. In order to minimize the impact of the telescope’s frequency response, a discretized frequency-scanning technique is used, which permits a steady approximation of the averaged spectrum by low order polynomials, as well as removal of the frequency trend which hinders distinguishing between the low-level interference signals and those coming from the source. Results. An efficient approach to the problem of detecting long lasting, narrowband interferences in dynamical spectra (con- ditionally stationary interferences) is proposed. The algorithm has been tested on the example of a powerful storm of Jovian radio emission. The proposed technique can be especially useful in situations where the signal of interest overlaps with the interference in the time-frequency domain. Conclusions. The new approach to the problem of interference mitigation, based on the development of combined algorithms of signal separation in the time-frequency domain, has been shown to offer an effective method of signal processing. In contrast to the previously used methods based on calculation of statistical moments for amplitudes, the new approach allows avoiding use of the same statistical indicators for both interference detection and subsequent steps of data analysis intended for building physical models and interpreting the observational data.

Deep Learning-Based Segment Trend Removal Approach for Prognostics and Health Management Signals of Rail Vehicles

Deep Learning-Based Segment Trend Removal Approach for Prognostics and Health Management Signals of Rail Vehicles

Operando Electrochemical Impedance Spectroscopy and Its Application to Commercial Li-Ion Batteries

Electrochemical impedance spectroscopy (EIS) is a powerful non-invasive tool for characterising electrochemical systems. Applied to Li-ion batteries, EIS is shown to be an informative indicator for their state-of-health (SoH) [1,2,3]. However, EIS is limited by the constraints of linearity and stationarity, while Li-ion batteries inherently behave in a nonlinear and nonstationary way. Regarding linearity, the voltage over the electrodes is a nonlinear function of the current through the electrodes. Linearity is achieved by applying a small amplitude zero-mean current excitation around an operating point, such that the nonlinear function is quasi linear in this range. Regarding time-variation, the impedances of fully charged and fully discharged cells are different, the same for pristine and aged cells, or cells kept at room temperature and in freezing environments. For Li-ion batteries, this means that EIS experiments should be performed in steady-state when at a specific state-of-charge (SoC) and temperature. The impedance therefore depends on the operating point (temperature and SoC) and the constraints of linearity and stationarity are very restrictive.Lately, we have developed operando to reveal impedance data from measurements not satisfying linearity and stationarity [4,5]. This technique allows to measure the impedance of electrochemical systems along a time-varying trajectory, for instance, while charging or discharging Li-ion batteries [5,6]. For this purpose, a nonzero-mean odd random phase multisine current excitation is used, and the time-varying impedance along the trajectory is estimated from the spectrum of the voltage response. Moreover, nonlinear distortions in the measurements are detected and quantified, the noise level is estimated, and uncertainty bounds are enclosed on the time-varying impedance. For Li-ion batteries most time-variation happens at low frequencies. However, obtaining time-varying impedance data at low frequencies is a challenging problem. This on the one hand due to drift signals, for instance the increasing voltage while charging a battery, hiding the low frequency content, and on the other hand due to low-frequency noise. Operando EIS implements a drift signal suppression such that obtaining impedance data at low frequencies becomes attainable [4].In this paper operando EIS measurements while charging and discharging commercial LG M50 Li-ion batteries are presented, demonstrating that the battery’s impedance while charging, discharging and resting are different from each other. Prospectives are given for utilizing operando EIS data for smart-charging and SOH prognosis applications.[1] Pastor-Fernández, C., Uddin, K., Chouchelamane, G.H., Widanage, W.D. and Marco, J., 2017. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems. Journal of Power Sources, 360, pp.301-318.[2] Zhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U. and Lee, A.A., 2020. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nature communications, 11(1), pp.1-6.[3] Jones, P.K., Stimming, U. and Lee, A.A., 2022. Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nature communications, 13(1), pp.1-9.[4] Hallemans, N., Pintelon, R., Zhu, X., Collet, T., Havigh, M.D., Wouters, B., Revilla, R.I., Claessens, R., Ramharter, K., Hubin, A. and Lataire, J., 2022. Trend Removal in Measurements of Best Linear Time-Varying Approximations—With Application to Operando Electrochemical Impedance Spectroscopy. IEEE Transactions on Instrumentation and Measurement, 71, pp.1-11.[5] Hallemans, N., Widanage, W.D., Zhu, X., Moharana, S., Rashid, M., Hubin, A. and Lataire, J., 2022. Operando electrochemical impedance spectroscopy and its application to commercial Li-ion batteries. Journal of Power Sources, 547, p.232005.[6] Zhu, X., Hallemans, N., Wouters, B., Claessens, R., Lataire, J. and Hubin, A., 2022. Operando odd random phase electrochemical impedance spectroscopy as a promising tool for monitoring lithium-ion batteries during fast charging. Journal of Power Sources, 544, p.231852.

Development of machine learning techniques in corrosion inhibition evaluation of 5-methyl-1 H-benzotriazole on N80 steel in acidic media

In this study, three machine learning models of data analysis based on multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) has been proposed to model the behavior of 5methyl-1 H-benzotriazole (m-BTA) inhibitor on N80 steel in acidic media at different temperatures and inhibitor concentration. Based on experimental observation, the DC trend removal by Polynomial detrending revealed a suitable trend between electrochemical noise (ECN) measurement data and the results obtained from electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization test at m-Factor equals to 3, 4, and 5. The inhibition efficiency (η) of 91% was achieved by using 0.001 M of m-BTA inhibitor. The corrosion rate will increase by increasing the temperature for the blank sample. Still, the m-BTA resulted in corrosion protection for N80 at high temperatures, and ECN resistance (Rn) increases with increased m-BTA concentration. The experimental tests revealed that temperature and m-factor are significant for corrosion inhibition of m-BTA; also, inhibitor concentration is an essential factor. Therefore, these three parameters were chosen as inputs for the Rn of the inhibitor. Removing unnecessary data from the original dataset causes relevant results compatible with experimental observations. The results of the reduced data set showed that R2 for the test of whole and reduced MLR model is 0.41 and 0.69, respectively, which shows that the reduced model is more suitable for modeling the training data than the whole model. The MNR model presents better accuracy than the MLR model, demonstrating that the relation between inputs and output does not follow a linear dependency. Several topologies of the ANN model were constructed, and the number of neurons was changed in one hidden layer. Finally, five neurons in the hidden layer with the Levenberg–Marquardt backpropagation (LMBP) algorithm were selected to develop the optimum ANN model, which has R2 and RMSE values of 0.99 and 17.41.

Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration

Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of heterogeneous data obtained using sensors of various physical nature. In the study of the forest fund, both methods of direct dendrological measurements and methods of remote observation using unmanned aerial vehicles are widely used. Information obtained using these methods must be analyzed in conjunction with hydrometeorological monitoring data.Aim. Investigation of the possibility of automating the monitoring of the well-being of the forest fund based on the integration of ground survey data, remote multispectral measurements and hydrometeorological observations using the mathematical apparatus of nonparametric Bayesian networks.Materials and methods. To assess the long-term joint dynamics of natural and climatic indicators and the radial growth of trees, a modified method of multiscale cross-correlation analysis was used with the removal of the background trend described by the moving average model. Relationships between various indicators were estimated based on the unconditional and conditional nonparametric Spearman correlation coefficients, which were used to reconstruct and parameterize the nonparametric Bayesian network.Results. A multiscale nonparametric Bayesian network was constructed to characterize both unconditional and conditional statistical relationships between parameters obtained from remote sensing, hydroclimatic and dendrological measurements. The proposed model showed a good quality of the plant fund state forecasting. The correlation coefficients between the observed and predicted indicators exceed 0.6, with the correlation coefficient comprising 0.77 when predicting the growth trend of annual tree rings.Conclusion. The proposed nonparametric Bayesian network model reflects the relationship between various factors that affect the forest ecosystem. The Bayesian network can be used to assess risks and improve environmental management planning.

Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls.

Resting-state functional magnetic resonance imaging (rsfMRI) has been proposed as an alternative to task-based fMRI including clinical situations such as preoperative brain tumor planning, due to advantages including ease of performance and time savings. However, one of its drawbacks is the limited ability to accurately lateralize language function. Using the rsfMRI data of healthy controls, we carried out a power spectra analysis on three regions of interest (ROIs): Broca's area (BA) in the frontal cortex for language, hand motor (HM) area in the primary motor cortex, and the primary visual cortex (V1). Spike removal, motion correction, linear trend removal, and spatial smoothing were applied. Spontaneous low-frequency fluctuations (0.01-0.1Hz) were filtered to enable functional integration. BA showed greater power on the left hemisphere relative to the right (p=.0055), while HM (p=.1563) and V1 (p=.4681) were not statistically significant. A novel index, termed the power laterality index (PLI), computed to estimate the degree of power lateralization for each brain region, revealed a statistically significant difference between BA and V1 (p<.00001), where V1 was used as a control since the primary visual cortex does not lateralize. Validation studies used to compare PLI to a laterality index computed using phonemic fluency, a task-based, language fMRI paradigm, demonstrated good correlation. The power spectra for BA revealed left language lateralization, which was not replicated in HM or V1. This work demonstrates the feasibility and validity of an ROI-based power spectra analysis on rsfMRI data for language lateralization.

Использование методов детрендирования для интеллектуальной обработки данных мониторинга строительных процессов

Data processing of monitoring systems of various processes at the stage of construction and operation of buildings requires the development of special tools that belong to the field of artificial intelligence. The trend removal method is one of the ways to preprocess data collected from various sensors and IoT devices that monitor the state of buildings, structures and soil masses during construction and operation. This article analyzes how different approaches to detrending time series affect the performance and accuracy of algorithms for CI computational intelligence models. The analysis compares three approaches: linear detrending, non-linear detrending, and first-order differentiation.&#x0D; Five representative methods are used as CI models: DENFIS dynamic evolving fuzzy neural network, GP Gaussian process, MLP multilayer perceptron, OP-ELM optimally trimmed extremal learning machine, and SVM support vector machine. There are three main conclusions from the experiments performed on the four datasets: 1) detrending does not improve overall performance, 2) the empirical mode decomposition method provides better performance than linear detrending, and 3) first-order differentiation in some cases can be effective and in some cases counterproductive for series with common patterns.

PENDANTSS: PEnalized Norm-Ratios Disentangling Additive Noise, Trend and Sparse Spikes

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties $\ell_p/\ell_q$ with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.

Spatial variability of half-cell potential data from a reinforced concrete structure—a geostatistical analysis

Corrosion in reinforced concrete structures is among the major degradation mechanisms. The quantification and description of the spatial distribution of the corrosion condition within a structure on the basis of condition assessments are important. This study considered half-cell potential mapping data as a widely used technique to detect corrosion in reinforced concrete structures. A four-step workflow was proposed to analyse half-cell potential data with geostatistical techniques, first consisting of trend identification and possible trend removal. The obtained residuals were then subjected to a quantile-quantile transformation. Subsequently, experimental variograms were calculated and fitted with variogram models to estimate the correlation lengths. A case study with data from a road tunnel confirms the applicability of the workflow. It was assumed that the identified trend is primarily a result of the heterogeneity of the exposure conditions within the structure that ranges several metres. The residuals are interpreted as the result of the heterogeneities of material resistances that give rise to spatial variability in corrosion probability on a shorter distance range. The proposed analysis may be utilised for service life modelling (e.g. based on random fields), planning maintenance works, or optimising the grid size for half-cell potential measurements.

Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Abstract This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing.

In recent years, many studies have proposed automatic detection and localization techniques for myocardial infarction (MI) using the 12-lead electrocardiogram (ECG). Most of them applied preprocessing to the ECG signals, e.g., noise removal, trend removal, beat segmentation, and feature selection, followed by model construction and classification based on machine-learning algorithms. The selection and implementation of preprocessing methods require specialized knowledge and experience to handle ECG data. In this paper, we propose an end-to-end convolutional neural network model that detects and localizes MI without such complicated multistep preprocessing. The proposed model executes comprehensive learning for the waveform features of unpreprocessed raw ECG images captured from 12-lead ECG signals. We evaluated the classification performance of the proposed model in two experimental settings: ten-fold cross-validation where ECG images were split randomly, and two-fold cross-validation where ECG images were split into one patient and the other patients. The experimental results demonstrate that the proposed model obtained MI detection accuracies of 99.82% and 93.93% and MI localization accuracies of 99.28% and 69.27% in the first and second settings, respectively. The performance of the proposed method is higher than or comparable to that of existing state-of-the-art methods. Thus, the proposed model is expected to be an effective MI diagnosis tool that can be used in intensive care units and as wearable technology.

The pre-He white dwarfs in eclipsing binaries – IV. WASP 1814+48 with multiperiodic pulsations

ABSTRACT For the EL CVn candidate 1SWASPJ181417.43+481117.0 (WASP 1814+48), we secured the first spectroscopic observations between 2015 April and 2021 March. Using the echelle spectra, the radial velocities (RVs) of the primary star were measured with its atmospheric parameters of Teff, 1 = 7770 ± 130 K and $v$1sin i = 47 ± 6 km s−1. We fitted our single-lined RVs and the TESS light curve simultaneously. From the binary modelling, we determined the following fundamental parameters for each component: M1 = 1.659 ± 0.048 M⊙, R1 = 1.945 ± 0.027 R⊙, and L1 = 12.35 ± 0.90 L⊙ for WASP 1814+48 A, and M2 = 0.172 ± 0.005 M⊙, R2 = 0.194 ± 0.005 R⊙, and L2 = 0.69 ± 0.07 L⊙ for WASP 1814+48 B. The surface gravity of log g2 = 5.098 ± 0.026 obtained from M2 and R2 is concurrent with 5.097 ± 0.025 computed directly from the observable quantities. WASP 1814+48 B is well matched with the 0.176-M⊙ white dwarf (WD) evolutionary model for Z = 0.01. The metallicity and our Galactic kinematics indicate that the program target is a thin-disc star. The whole light residuals after the removal of the binary trend were analysed and found to oscillate at a total of 52 frequencies. Among these, most of the low frequencies below 24 d−1 are aliases and orbital harmonics. The five significant frequencies between 32 and 36 d−1 are the pulsation modes of WASP 1814+48 A located in the δ Sct domain on the zero-age main sequence, and the high frequencies of 128–288 d−1 arise from WASP 1814+48 B in the pre-He WD instability strip. Our results reveal that WASP 1814+48 is the fifth EL CVn star that is composed of a δ Sct-type primary and a pre-ELMV (extremely low-mass pre-He WD variable).

SUPPNet: Neural network for stellar spectrum normalisation

Context. Precise continuum normalisation of merged échelle spectra is a demanding task that is necessary for various detailed spectroscopic analyses. Automatic methods have limited effectiveness due to the variety of features present in the spectra of stars. This complexity often leads to the necessity for manual normalisation which is highly time-consuming. Aims. The aim of this work is to develop a fully automated normalisation tool that works with order-merged spectra and offers flexible manual fine-tuning, if necessary. Methods. The core of the proposed method uses the novel, fully convolutional deep neural network (SUPP Network) that was trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique was applied to deal with possible biases that may arise from training with synthetic spectra and to extend the applicability of the proposed method to features absent in this kind of spectra. Results. The developed normalisation method was tested with high-resolution spectra of stars with spectral types from O to G, and gives a root mean squared (RMS) error over the set of test stars equal to 0.0128 in the spectral range from 3900 Å to 7000 Å and 0.0081 in the range from 4200 Å to 7000 Å. Experiments with synthetic spectra give a RMS of the order of 0.0050. Conclusions. The proposed method leads to results that are comparable to careful manual normalisation. Additionally, this approach is general and can be used in other fields of astronomy where background modelling or trend removal is a part of data processing.

A General Description of Growth Trends

Time series that display periodicity can be described with a Fourier expansion. In a similar vein, a recently developed formalism enables the description of growth patterns with the optimal number of parameters. The method has been applied to the growth of national GDP, population and the COVID-19 pandemic; in all cases, the deviations of long-term growth patterns from purely exponential required no more than two additional parameters, mostly only one. Here, I utilize the new framework to develop a unified formulation for all functions that describe growth deceleration, wherein the growth rate decreases with time. The result offers the prospects for a new general tool for trend removal in time-series analysis.