Time Spectra Research Articles

Specific Emitter Identification Method for Limited Samples via Time–Wavelet Spectrum Consistency

Specific emitter identification (SEI) is a technique that identifies the emitter through physical layer features contained in radio signals, and it is widely used in tasks such as identifying illegal transmitters and authentication. Thanks to the development of deep learning, SEI tasks based on deep learning have achieved significant improvements in recognition performance. However, illegal transmitters often broadcast for short durations and at low frequencies, resulting in very limited available training samples. In such cases, directly training deep learning models may lead to underfitting issues, thereby reducing recognition accuracy. In this paper, to address the issue of traditional methods struggling to classify transmitters when there is a scarcity of emitter samples and limited training data, we propose a method based on TFC-CNN. We propose a TFC-CNN method. Specifically, we first use continuous wavelet transform (CWT) as a data augmentation method to construct time–wavelet spectrum sample pairs. Then, we use complex-valued neural networks (CVNNs) and deep convolutional neural networks (DCNNs) to extract the hidden emitter identity features from the time and wavelet spectrum samples. We train the model using the normalized temperature-scaled cross-entropy (NT-Xent) loss and cross-entropy (CE) loss, ensuring the consistency of feature vector distributions across the two modalities with cosine loss. Finally, we fine-tune the model to achieve SEI tasks with few samples. Experimental results on open-source WiFi datasets and automatic dependent surveillance–broadcast (ADS-B) datasets show that our proposed method outperforms existing state-of-the-art methods. With only 5% of the training samples, the recognition accuracy for the ADS-B test dataset is 84.10%, and for the WiFi test dataset, it is 96.99%.

Sulfonation of IAA in Urtica eliminates its DR5 auxin activity

Key messageN-Sulfonated IAA was discovered as a novel auxin metabolite in Urtica where it is biosynthesized de novo utilizing inorganic sulfate. It showed no auxin activity in DR5::GUS assay, implying possible inactivation/storage mechanism.A novel auxin derivative, N-sulfoindole-3-acetic acid (IAA-N-SO3H, SIAA), was discovered in stinging nettle (Urtica dioica) among 116 sulfonated metabolites putatively identified by a semi-targeted UHPLC–QqTOF-MS analysis of 23 plant/algae/fungi species. These sulfometabolites were detected based on the presence of a neutral loss of sulfur trioxide, as indicated by the m/z difference of 79.9568 Da in the MS2 spectra. The structure of newly discovered SIAA was confirmed by synthesizing its standard and comparing retention time, m/z and MS2 spectrum with those of SIAA found in Urtica. To study its natural occurrence, 73 species in total were further analyzed by UHPLC–QqTOF-MS or targeted UHPLC–MS/MS method with a limit of detection of 244 fmol/g dry weight. However, SIAA was only detected in Urtica at a concentration of 13.906 ± 9.603 nmol/g dry weight. Its concentration was > 30 times higher than that of indole-3-acetic acid (IAA), and the SIAA/IAA ratio was further increased under different light conditions, especially in continuous blue light. In addition to SIAA, structurally similar metabolites, N-sulfoindole-3-lactic acid, 4-(sulfooxy)phenyllactic acid and 4-(sulfooxy)phenylacetic acid, were detected in Urtica for the first time. SIAA was biosynthesized from inorganic sulfate in seedlings, as confirmed by the incorporation of exogenous 34S-ammonium sulfate (1 mM and 10 mM). SIAA exhibited no auxin activity, as demonstrated by both the Arabidopsis DR5::GUS assay and the Arabidopsis phenotype analysis. Sulfonation of IAA may therefore be a mechanism for IAA deactivation and/or storage in Urtica, similar to sulfonation of the jasmonates in Arabidopsis.

Exploring New and Promising Genetic Biomarkers for Evaluating Traumatic Brain Injuries: A Case-Control Study

Traumatic brain injury (TBI) is a common cause of morbidity and death in all age groups, with an estimated 50 million people having brain injury due to trauma each year. Accurate blood-based biomarkers are needed to assist with diagnosis of patients across the spectrum of time and severity. Our objectives were to explore the diagnostic precision of time- and severity- related four blood-based biomarkers: AKT3, GSK-3β, hsa-miR-16-5p, and MALAT-1 for TBI for the purpose of diagnosis, prognosis, and follow-up. 40 samples were recruited as the following: 30 TBI patients and 10 healthy volunteers as controls with matched age and sex. They were divided according to the Glasgow Coma Scale into mild (mTBI), moderate (modTBI), and severe(sTBI) TBI. Blood samples were withdrawn at entry, and after 5 and 30 days, RT-PCR was used for measuring the expression level. The results showed upregulated expression levels of AKT3, hsa-miR-16-5p and significantly downregulated expression levels of GSK-3β in TBI patients compared to controls at all timings measured. mTBI patients showed a higher expression level of hsa-miR-16-5p compared with modTBI, and sTBI patients. MALAT-1 level showed a significant increase in severe cases only. We concluded that AKT3, hsa-miR-16-5p, and GSK-3β are excellent diagnostic biomarkers in TBI patients at initial assessment, as well as at 5 and 30 days following the injury. Moreover, MALAT-1 had good diagnostic value in sTBI patients, and its prognostic value extends to 30 days. GSK-3β was an excellent biomarker for detecting mTBI.

Polyketides and alkaloids from the fungus Aspergillus Fumigatus YB4-17 and ent-Fumiquinazoline J induce apoptosis, paraptosis in human hepatoma HepG2 cells

Hepatocellular carcinoma (HCC) is one of the most common malignancies. The currently available clinical drugs for HCC frequently cause serious side effects and the treatment outcomes are unsatisfactory. It is urgent to develop effective drugs with high selectivity and low adverse effects for HCC. Metabolites produced by microorganisms have shown great potential in the development of therapeutic agents for HCC. In our study, the EtOAc extract of the strain Aspergillus fumigatus YB4-17 exhibited significant cytotoxicity towards the HCC HepG2 cells at 10 μg/mL. Various column chromatographic separations of the extract afforded seven polyketides (1–7), including a new diphenyl ether derivative (1), along with fourteen known alkaloids (8–21). The structure elucidation was conducted via NMR spectroscopic data and MS data analysis. The absolute configuration of compound 11 was confirmed by comparing experimental and calculated electronic circular dichroism spectrum for the first time. The biological evaluation of these metabolites revealed that compound 11 selectively inhibited the proliferation of HCC HepG2 cells with negligible toxicity to normal cells. Mechanism study indicated that compound 11 induced apoptosis and paraptosis in HepG2 cells, providing a novel therapeutic perspective for the treatment of hepatocellular carcinoma.

Screen Time and Autism Spectrum Disorder: A Comprehensive Systematic Review of Risk, Usage, and Addiction.

The relationship between autism spectrum disorder (ASD) and screen time (ST) has been extensively studied; but the evidence remains inconsistent, and a comprehensive quantitative synthesis of this association is lacking. This study aims to provide a robust quantitative assessment of the relationship between ASD and ST by consolidating and analyzing available evidence to offer a more precise understanding of this complex association. This systematic review followed PRISMA 2020 statement, and applied a quality assessment tool for quantitative studies to identity best available evidence. A comprehensive search was conducted in PubMed, Web of Science, Embase, and Scopus, covering literature published from January 1, 2006 to June 12, 2024. In total, 30 studies involving 356,666 participants met the inclusion criteria. The findings revealed a significant association between screen exposure in preschool children and the development of ASD, with a longer duration of screen exposure correlating with an increased likelihood of being diagnosed with autism. Furthermore, autistic people tend to have longer daily screen use and are at a higher risk of screen addiction compared to non-autistic individuals. These findings emphasize the importance of limiting ST for preschoolers and autistic people. However, the level of evidence supporting these conclusions is very low. Future studies should focus on controlling for confounding factors, using more objective measures, and further investigate the relationship between screen engagement styles (active or passive), screen use patterns (screen devices and content), and ASD.

Qualitative and quantitative analysis of chemical components of Dracocephalum moldavica based on UPLC-Q-TOF-MS/MS and UPLC

Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS) was used to rapidly identify the chemical components in Dracocephalum moldavica, and UPLC was employed to determine the content of its main components. MS analysis was performed using an electrospray ionization(ESI) source and data were collected in the negative ion mode. By comparing the retention time and mass spectra of reference compounds, and using a self-built compound database and the PubChem database, 68 compounds were identified from D. moldavica, including 36 flavonoids, 22 phenylpropanoids, 4 phenols, and 6 other compounds. On this basis, a UPLC quantitative method was established to simultaneously determine 8 main components, i.e., luteolin-7-O-glucuronide, apigenin-7-O-glucuronide, rosmarinic acid, diosmetin-7-O-glucuronide, tilianin, acacetin-7-O-glucuronide, acacetin-7-O-(6″-O-malonyl)-glucoside, and acacetin. A Waters ACQUITY BEH C_(18) column(2.1 mm × 100 mm, 1.7 μm) was used, with acetonitrile and a water solution containing 0.1% formic acid and 0.1% phosphoric acid as the mobile phase for gradient elution. The detection wavelength was set at 330 nm, with a flow rate of 0.4 mL·min~(-1), and the column temperature was maintained at 35 ℃. The 8 components demonstrated good linearity(r≥0.999 9) over a wide mass concentration range(50 or 100 times). The average recovery rate ranged from 97.5% to 105.1%, and the relative standard deviations(RSDs) were 0.90% to 3.4%(n= 6), indicating that the method was simple, accurate, and reliable. In 17 batches of D. moldavica samples, the content of these 8 components ranged from 0.405 to 2.10, 0.063 to 0.342, 0.446 to 2.43, 0.415 to 1.47, 1.57 to 4.34, 0.173 to 0.386, 1.00 to 5.40, and 0.069 to 0.207 mg·g~(-1), respectively. These results indicate significant differences in the internal quality of the samples, highlighting the need for strict quality control to ensure their pharmacodynamic efficacy. This study provides a scientific basis for the rapid discovery of pharmacodynamic substances, comprehensive quality control, and the formulation or revision of quality standards for D. moldavica.

Comparison of meshing characteristics and dynamic behavior of hypoid gear drive under different load conditions

The study of loaded meshing characteristics and dynamic behavior is significant for evaluating the stability of hypoid gear under actual working conditions. Loaded tooth contact analysis (LTCA) of a hypoid gear drive under different loads is carried out. The equivalent meshing force, loaded transmission error (LTE), comprehensive elastic deformation, and time-varying meshing stiffness (TVMS) are investigated. Results show that the LTE and TVMS are sensitive to load, and the LTE and TVMS curves show obvious asymmetric as the load increases. Under low load conditions, the actual contact ratio increases significantly with the increase of load, but tends to be flat when the load reaches a certain range. The obtained TVMS and LTE are fitted by higher Fourier series and introduced into an 8-DOF dynamic model. By comparing the time history diagram, phase diagram, Poincaré section, and spectrum, the dynamic characteristics of the system under different loads are contrasted. On low load condition, the system may exhibit chaotic motion in certain meshing frequency bands and damping ratio intervals. While the tooth backlash increases to a critical value, the system may change to chaotic motion from periodic motion. The results provided a basis for improving the meshing performance of hypoid gear.

Model for the Shape of the Relaxation Spectrum in Glass Formers.

Traditionally the broadness of the spectrum of the relaxation times observed in glass-forming materials has been rationalized by local heterogeneity, where a variety of atomistic environments leads to spectrum of single-exponential relaxation responses. However, the assumption of heterogeneity can break down when tested against the shape of the relaxation spectrum. An alternative homogeneous scenario assumes that the relaxation is inherently multiexponential. A recently developed switchback model [Medvedev, G. A. Phys. Rev. E 2023, 107 (3), 034122] naturally results in a multiexponential wedge-like spectrum that is consistent with the dielectric relaxation, light scattering, and the NMR data. As a particular case the switchback model allows for the spectrum to become single-exponential; under the heterogeneous scenario this would require the heterogeneities to completely vanish, which is hard to justify. Using data from photobleaching experiments and molecular dynamic simulations, it is shown that the relaxation spectrum may become single-exponential under large anisotropic deformation. This is interpreted as an argument in favor of the homogeneous scenario and specifically the switchback model for the relaxation of the glass formers.

Study on the Influence of Wind Load on the Safety of Magnetic Adsorption Wall-Climbing Inspection Robot for Gantry Crane

The maintenance of the surface of steel structures is crucial for ensuring the quality of shipbuilding cranes. Various types of wall-climbing robots have been proposed for inspecting diverse structures, including ships and offshore installations. Given that these robots often operate in outdoor environments, their performance is significantly influenced by wind conditions. Consequently, understanding the impact of wind loads on these robots is essential for developing structurally sound designs. In this study, SolidWorks software was utilized to model both the wall-climbing robot and crane, while numerical simulations were conducted to investigate the aerodynamic performance of the magnetic wall-climbing inspection robot under wind load. Subsequently, a MATLAB program was developed to simulate both the time history and spectrum of wind speed affecting the wall-climbing inspection robot. The resulting wind speed time-history curve was analyzed using a time-history analysis method to simulate wind pressure effects. Finally, modal analysis was performed to determine the natural frequency and vibration modes of the frame in order to ensure dynamic stability for the robot. The analysis revealed that wind pressure predominantly concentrates on the front section of the vehicle body, with significant eddy currents observed on its windward side, leeward side, and top surface. Following optimization efforts on the robot’s structure resulted in a reduction in vortex formation; consequently, compared to pre-optimization conditions during pulsating wind simulations, there was a 99.19% decrease in induced vibration displacement within the optimized inspection robot body. Modal analysis indicated substantial differences between the first six non-rigid natural frequencies of this vehicle body and those associated with its servo motor frequencies—indicating no risk of resonance occurring. This study employs finite element analysis techniques to assess stability under varying wind loads while verifying structural safety for this wall-climbing inspection robot.

Screen Time Before 2 Years of Age and Risk of Autism at 12 Years of Age

This cohort study evaluates the association between screen time and autism spectrum disorder by considering socioeconomic factors as instrumental variables.

Remote sensing of wave-orbital velocities in the surfzone

Wave-orbital velocities are estimated with particle image velocimetry (PIV) applied to rapid sequences of images of the surfzone surface obtained with a low-cost camera mounted on an amphibious tripod. Time series and spectra of the remotely sensed cross-shore wave-orbital velocities are converted to the depth of colocated acoustic Doppler velocimeters (ADVs), using linear finite depth theory. These converted velocities are similar to the velocities measured in situ (mean nRMSE for time series = 16% and for spectra = 10%). Small discrepancies between depth-attenuated surface and in situ currents may be owing to errors in the surface velocity measurements, uncertainties in the water depth, the vertical elevation of the ADVs, and the neglect of nonlinear effects when using linear finite depth theory. These results show the potential to obtain spatially dense estimates of wave velocities using optical near-field remote methods during field campaigns and continuous monitoring operations.

Measuring the OAM spectrum of a fractional helical beam in a single shot

We propose and experimentally demonstrate a new technique, to our knowledge, to precisely measure the orbital angular momentum (OAM) spectrum of the fractional helical beam in a single shot. This is realized using a single-path interferometer scheme combined with space division multiplexing and polarization phase-shifting. Such a combination enables the single-shot recording of multiple phase-shifted interferograms, which leads to extracting the phase profile of the incident fractional helical beam. Furthermore, by adopting an orthogonal projection method, this measured phase is utilized to evaluate the corresponding OAM spectrum. To test the efficacy, a set of simulations and experiments for different fractional helical beams is demonstrated. The proposed method shows enormous potential to characterize the OAM spectrum in real time.

Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion

In order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this study proposes a method to identify the damage width of steel wire rope for multi-channel fusion of a Hall sensor array. Firstly, the Hall sensor array is used to capture the magnetic flux leakage data of steel wire rope; then, continuous wavelet transform is used to decompose the original data, and moving average filtering is used to denoise each component; the denoised components are merged and converted into a time spectrum, and the time spectrum is classified by ResNet50 image classification model to realize the detection of wire rope damage width. According to the dataset used in this study, the results show that the proposed method performs best in the mainstream noise reduction model; detection accuracy for the width of damage in steel wire ropes is 97%, which proves that the proposed method is effective and feasible.

Statistically sound identification of compounds by low-resolution GC-MS: Identification of tear agents in tear gas sprays

Gas-chromatography hyphenated with low-resolution mass spectrometry is a very flexible tool for the cost-effective identification and quantification of volatile compounds in complex matrices. In some analytical fields, criteria for the agreement between retention time and mass spectra of the analyte in calibrators and samples are defined based on the general understanding of the performance of these parameters. However, since this harmonisation is not based on experimental performance observed for specific GC-MS conditions and analyte it leads to false identifications. This research proposes a novel and robust tool for defining statistically sound criteria for the identification of compounds by GC-MS and LC-MS using experimental data. The Monte Carlo Method (MCM) simulation of the correlated abundance of characteristic ions of analyte mass spectrum allows simulating the abundance ratio difference of the analyte in a calibrator and sample used for statistically sound identifications. The Cholesky decomposition of the covariance matrix of ion abundances for MCM simulations allows the reliable use of many ion abundance ratios in identifications. The developed methodology was implemented in a user-friendly Excel spreadsheet and applied to the identification of tear gas agents in tear gas sprays. Criteria defined by SANTE for identifying pesticide residues in foodstuffs were compared with the developed tool. The cross-validation of computational and SANTE tools allowed concluding that the statistical control of retention time and mass spectra performs according to the defined confidence level. On the other hand, the SANTE criteria can produce up to 92 % false identifications for being too strict considering signal dispersion.

Optimising the acquisition conditions of high information quality low-field NMR signals based on a cutting-edge approach applying information theory and Taguchi's experimental designs – Virgin olive oil as an application example

BackgroundDeveloping a new spectrometric analytical method based on a fingerprinting approach requires optimisation of the experimental stage, particularly with novel instruments like benchtop low-field NMR spectrometers. To ensure high-quality LF-NMR spectra before developing the multivariate model, an experimental design to optimise instrument conditions is essential. However, difficult-to-control factors may be critical for optimisation. Taguchi methodology addresses these factors to obtain a system robust to variation. This study uses the Taguchi methodology to optimise instrument settings for acquiring high-quality 1H and 13C LF-NMR signals in a short time from virgin olive oil (VOO). ResultsTwo experimental trials (for 1H and 13C signals, respectively) were carried out and analysed to find an optimal and robust combination of instrument settings against changes in two difficult-to-control factors: ambient temperature and small deviations of the NMR tube volume (700 ± 50 μL). The responses to be optimised, run time and spectral information quality, were analysed separately and jointly, as some factors showed opposite behaviour in the effect on the responses. Multiple response analysis based on suitable desirability functions yielded a combination of factors resulting in desirability values above 0.8 for 1H LF-NMR signals and almost 1.0 for 13C LF-NMR signals.In addition, a novel approach to assess the information quality of an analytical signal was proposed, addressing a major challenge in analytical chemistry. By applying information theory and calculating information entropy, this approach demonstrated its potential for selecting the highest quality (i.e. most informative) analytical signals. SignificanceThe acquisition instrument conditions of LF-NMR were successfully optimised using Taguchi methodology to acquire highly informative 1H and 13C spectra in a minimum run time. The importance lies in the future development of non-targeted analytical applications for VOO quality control. In addition, the innovative use of information entropy to a priori assess the signal quality represents a significant advance and proposes a solution to a long-standing challenge in analytical chemistry.

Conservation of the Charge in Signal from Drift Tube Ion Mobility Spectrometers.

Quantitative information obtained from drift tube detectors used in ion mobility spectrometry is contained in the area of peaks forming the drift time spectrum. The area of all peaks corresponds to the total charge of ions entering the drift section of the spectrometer. It was found that this charge is not conserved when the ion composition changes. This work is devoted to studying the causes of this phenomenon. Experimental research consisted of recording drift time spectra for 2-pentanone and n-heptanone, at various analyte concentrations and different opening times of the shutter grid. Measurements of the total ion current were also performed in static mode with an open grid. The research results indicated that the reasons for the lack of ion charge conservation in the drift time spectrum are ion recombination, mutual repulsion, and mobility-dependent transmission of ions through the shutter grid. The explanation of the relationships obtained experimentally was based on a simple theoretical model, which considered the phenomenon of ion transport along the reaction section and the penetration of ions through the shutter. The developed model provides a good description of the measurement results and allows the estimation of ion currents and ion concentrations in the reaction section upstream of the grid. This information is important for proper quantitative analysis as well as when the ion mobility spectrometer is used in quantitative studies of chemical ionization processes.

Diffusion and mobility measurements for propane gas with a nanodosimetric detector

For the operation of nanodosimetric detectors with propane gas, it is essential to know the gas properties at room temperature, such as drift velocity, ion mobility, longitudinal and transversal diffusion coefficients. For propane gas there is only limited experimental data available for the ion mobility and transverse diffusion coefficients. In this work, the drift velocities and ion mobilities for propane were obtained over the reduced electric field range E/N from 24 to 212 Td (1 Td = 10−17 Vcm2) based on arrival time measurements. The reduced ion mobility K0 for propane was determined to be (0.71±0.09) cm2/Vs. The longitudinal diffusion coefficients were determined from the widths of the experimental arrival time spectra and found to be NDL = (3.7 ± 0.5)⋅1019 1/ms in the low field limit below 30 Td.The results presented in this work extend the range of available experimental data for propane, as well as introduce first experimental measurements of the longitudinal diffusion coefficient of propane.

Direct Identification of the Continuous Relaxation Time and Frequency Spectra of Viscoelastic Materials

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined.

Punishment is slower than cooperation or defection in online network games

Punishment serves as a balancing force that dissuades people from acting selfishly, which complements cooperation as an essential characteristic for the prosperity of human societies. Past studies using economic games with two options (cooperation and defection) reported that cooperation decisions are generally faster than defection decisions and that time pressure possibly induces human players to be more intuitive and thus cooperative. However, it is unclear where punishment decisions sit on this time spectrum. Therefore, we recruited human players and implemented two series of online network games with cooperation, defection, and punishment options. First, we find that punishment decisions are slower than cooperation or defection decisions across both game series. Second, we find that imposing experimental time pressure on in-game decisions neither reduces nor increases the frequency of punishment decisions, suggesting that time pressure may not directly interact with the mechanisms that drive players to choose to punish.

Short baseline neutrino anomalies at stopped pion experiments

Stopped-pion experiments that measure coherent elastic neutrino-nucleus scattering (CEνNS) are sensitive to sterile neutrinos via disappearance. Using timing and energy spectra to perform flavor decomposition, we show that the delayed electron neutrino component provides an independent test of short-baseline anomalies that hint at ~ eV-mass sterile neutrinos. Dedicated experiments will be sensitive to nearly the entire sterile neutrino parameter space consistent with short-baseline data.