Time Series Of Measurements Research Articles

Comparison of methods measuring electrical conductivity in coastal aquifers

Coastal aquifers, the transition zone between freshwater and saltwater, show large salinity contrasts in the subsurface. Salinity is a key parameter to understand coastal groundwater flow dynamics and consequently also geochemical and microbial processes. For mapping porewater salinity, a variety of methods exists, mainly using electrical conductivity as a proxy. We investigate methods including hydrological/geochemical (well sampling, fluid logger) as well as geophysical method (direct push, geoelectrics) utilising measurements near the high-water line of a high-energy beach at the North Sea island of Spiekeroog. We compare the methods, discuss their benefits and limitations and assess their spatial and temporal resolution. One key to enable a comparison is the estimation of formation factors transforming bulk conductivity measured by geophysical tools in to fluid conductivities obtained from direct measurements. We derive depth-dependent formation factors derived from time-series measurements of fluid loggers and a vertical electrode installation. Using these formation factors, the vertical electrode chain proves to provide reliable salinities at high spatial and temporal dimension. Direct-push profiling data provide the highest vertical resolution. However, a careful calibration is needed to allow for salinity quantification. On the other hand, electrical resistivity tomography (ERT) exhibits the lowest spatial resolution, but can image two-dimensional salinity distributions. We found ERT to fit very well to all other methods, but the data analysis should be aimed at salinities instead of bulk conductivities, i.e. including formation factors and temperature models into the inversion process.

Archival records housed at USTUR support radium dial worker dosimetry.

The American radium dial worker (RDW) cohort of over 3,200 persons is being revisited as part of the Million Person Study (MPS) to include a modern approach to RDW dosimetry. An exceptional source of data and contextualization in this project is an extensive collection of electronic records (requiring 43 gigabytes (GB) of storage) digitized from existing microfilm and microfiche housed at the United States Transuranium and Uranium Registries (USTUR). Although the type, extent, and quality (e.g., legibility) of record(s) varies between individuals, the remarkable occupational, medical and demographic data include in vivo radiation measurements (e.g., radon breath, whole body counts), autopsy results, medical records (including copies of radiographs), interviews over the years, and correspondence. Of particular dosimetric interest are the details of radiation measurements. For example, there are some instances where hand-written and transcribed values are both available, along with notes providing context for why a particular measurement in a time series of measurements was chosen to assign an intake, or if there were concerns about a particular measurement. Born prior to 1935, RDW have nearly all passed away. Thus, the updated dosimetry, especially for the bone, will allow the correlation of lifetime cumulative dose with radiation risk. Here we review typical information available in this collection of historical records, highlighting some interesting finds, and discuss the relevance to current and ongoing work related to updating the dosimetry of the RDW in the Million Person Study, including providing an example of the usefulness of information contained in these records.

The variability of Betelgeuse explained by surface convection

Betelgeuse is a red supergiant (RSG) that is known to vary semi-regularly on both short and long timescales. The origin of the short period of Betelgeuse has often been associated with radial pulsations, but could also be due to the convection motions present at the surface of RSGs. We investigate the link between surface activity and the variability of the star. Linear polarization in Betelgeuse is a proxy of convection that is unrelated to pulsations. Using ten years of spectropolarimetric data of Betelgeuse, we looked for periodicities in the least-squares deconvolution profiles of Stokes $I$, $Q$, $U$ and the total linear polarization using Lomb-Scargle periodograms. We find periods in linear polarization signals that are similar to those in photometric variability. The 400 d period is too close to a peak of the window function of our data, but the two periods of 330 d and 200 d are present in the periodogram of Stokes $Q$ and $U$, showing that the variability of Betelgeuse can be interpreted as being due to surface convection. Since the linear polarization in the spectrum of Betelgeuse is not known to vary with pulsations, but is linked to surface convection, and since similar periods are found in the time series of photometric measurements and spectropolarimetry, we conclude that the photometric variability is due to the surface convective structures, and not to any pulsation phenomenon.

Research on Monitoring the Speed of Glacier Terminus Movement Based on the Time-Series Interferometry of a Ground-Based Radar System

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion.

Polymer-Modified Fertilizers for Mitigating Strawberry Root Burn

Polymer-modified fertilizers (PMFs) with prolonged nutrient release present a promising solution to address the challenges associated with conventional fertilization practices, particularly for sensitive crops such as strawberries. This study investigates the effectiveness of biodegradable PMFs in maintaining nutrient availability at optimal levels while minimizing root burn and nutrient losses. In a factorial field experiment, we obtaineda total of 3780 sets of parallel measured time series for soil EC, moisture, and temperature as well as two sets of harvest data to evaluate the impact of varying concentrations of polyvinyl alcohol (PVA) on the nutrient release rates from complex NPK fertilizer and monoammonium phosphate. Results indicate that polymer modifications significantly slow down nutrient release, leading to optimal salt levels and maximizing yield while remaining low enough to prevent the risk of root burn (EC of soil solution below 1 mS/cm). Consequently, the application of PMFs enhances strawberry yield surplus (on average 2.8 times in the second harvest) by ensuring a steady supply of nutrients throughout the growing season without inducing stress, which reduces the yield by nearly half. This research provides valuable insights into the development of more effective fertilization strategies for strawberry cultivation and other sensitive crops using PMFs.

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

Dual-Approach Co-expression Analysis Framework (D-CAF) Enables Identification of Novel Circadian Regulation From Multi-Omic Timeseries Data.

The circadian clock is a central driver of many biological and behavioral processes, regulating the levels of many genes and proteins, termed clock controlled genes and proteins (CCGs/CCPs), to impart biological timing at the molecular level. While transcriptomic and proteomic data has been analyzed to find potential CCGs and CCPs, multi-omic modeling of circadian data, which has the potential to enhance the understanding of circadian control of biological timing, remains relatively rare due to several methodological hurdles. To address this gap, a Dual-approach Co-expression Analysis Framework (D-CAF) was created to perform perturbation-robust co-expression analysis on time-series measurements of both transcripts and proteins. Applying this D-CAF framework to previously gathered transcriptomic and proteomic data from mouse macrophages gathered over circadian time, we identified small, highly significant clusters of oscillating transcripts and proteins in the unweighted similarity matrices and larger, less significant clusters of of oscillating transcripts and proteins using the weighted similarity network. Functional enrichment analysis of these clusters identified novel immunological response pathways that appear to be under circadian control. Overall, our findings suggest that D-CAF is a tool that can be used by the circadian community to integrate multi-omic circadian data to improve our understanding of the mechanisms of circadian regulation of molecular processes.

Linked Data-Driven, Physics-Based Modeling of Pumping-Induced Subsidence with Application to Bangkok, Thailand.

Research into land subsidence caused by groundwater withdrawal is hindered by the availability of measured heads, subsidence, and forcings. In this paper, a parsimonious, linked data-driven and physics-based approach is introduced to simulate pumping-induced subsidence; the approach is intended to be applied at observation well nests. Time series analysis using response functions is applied to simulate heads in aquifers. The heads in the clay layers are simulated with a one-dimensional diffusion model, using the heads in the aquifers as boundary conditions. Finally, simulated heads in the layers are used to model land subsidence. The developed approach is applied to the city of Bangkok, Thailand, where relatively short time series of head and subsidence measurements are available at or near 23 well nests; an estimate of basin-wide pumping is available for a longer period. Despite the data scarcity, data-driven time series models at observation wells successfully simulate groundwater dynamics in aquifers with an average root mean square error (RMSE) of 2.8 m, relative to an average total range of 21 m. Simulated subsidence matches sparse (and sometimes very noisy) land subsidence measurements reasonably well with an average RMSE of 1.6 cm/year, relative to an average total range of 5.4 cm/year. Performance is not good at eight out of 23 locations, most likely because basin-wide pumping is not representative of localized pumping. Overall, this study demonstrates the potential of a parsimonious, linked data-driven, and physics-based approach to model pumping-induced subsidence in areas with limited data.

Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

Correlation Fuzzy measure of multivariate time series for signature recognition.

Distinguishing different time series, which is determinant or stochastic, is an important task in signal processing. In this work, a correlation measure constructs Correlation Fuzzy Entropy (CFE) to discriminate Chaos and stochastic series. It can be employed to distinguish chaotic signals from ARIMA series with different noises. With specific embedding dimensions, we implemented the CFE features by analyzing two available online signature databases MCYT-100 and SVC2004. The accurate rates of the CFE-based models exceed 99.3%.

Sound event detection of passing trains using convolutional recurrent neural network (CRNN)

Acoustic measurements near railway infrastructure typically involve precise identification and labelling of train passages in time series of noise measurements. The labelling of events is done manually by an acoustician, by cross-referencing several sources of information, such as train timetable data, audio data, images and/or videos. In this paper, we present a method which automatically detects and labels the passage of trains in front of a microphone using a convolutional recurrent neural network (CRNN). The advantage of this type of model is that it can be trained using existing data (time series of LAeq,1s and 1/3 octave band with a good labelling of passing trains); which has been widely available for a number of years. The results are promising if limited to binary classification but would require further development if the aim included the recognition and categorisation of various train types.

Using cross-recurrence quantification analysis to compute similarity measures for time series of unequal length with applications to sleep stage analysis

Comparing time series of unequal length requires data processing procedures that may introduce biases. This article describes, validates, and applies Cross-Recurrence Quantification Analysis (CRQA) to detect and quantify correlation and coupling among time series of unequal length without prior data processing. We illustrate and validate this application using continuous and discrete data from a model system (study 1). Then we use the method to re-analyze the Sleep Heart Health Study (SHHS), a rare large dataset comprising detailed physiological sleep measurements acquired by in-home polysomnography. We investigate whether recurrence patterns of ultradian NREM/REM sleep cycles (USC) predict mortality (study 2). CRQA exhibits better performance compared with traditional approaches that require trimming, stretching or compression to bring two time series to the same length. Application to the SHHS indicates that recurrence patterns linked to stability of USCs are associated with all-cause mortality even after controlling for other sleep parameters, health, and sociodemographics. We suggest that CRQA is a useful tool for analyzing categorical time series, where the underlying structure of the data is unlikely to result in matching data points—such as ultradian sleep cycles.

Simultaneous spectral illumination of microplates for high-throughput optogenetics and photobiology.

The biophysical characterization and engineering of optogenetic tools and photobiological systems has been hampered by the lack of efficient methods for spectral illumination of microplates for high-throughput analysis ofaction spectra. Current methods to determine action spectraonly allow the sequential spectral illumination of individual wells. Here we present the open-source RainbowCap-system, which combines LEDs and optical filters in a standard 96-well microplate format for simultaneous and spectrally defined illumination. The RainbowCap provides equal photon flux for each wavelength, with the output of the LEDs narrowed by optical bandpass filters. We validatedthe RainbowCap for photoactivatable G protein-coupled receptors (opto-GPCRs) and enzymes for the control of intracellular downstream signaling. The simultaneous, spectrally defined illumination provides minimal interruption during time-series measurements, while resolving 10 nm differences in the action spectra of optogenetic proteins under identical experimental conditions. The RainbowCap is also suitable for studying the spectral dependence of light-regulated gene expression in bacteria, which requires illumination over several hours. In summary, the RainbowCap provides high-throughput spectral illumination of microplates, while its modular, customizable design allows easy adaptation to a wide range of optogenetic and photobiological applications.

A QR Code for the Brain: A dynamical systems framework for computing neurophysiological biomarkers.

Neural circuits are often considered the bridge connecting genetic causes and behavior. Whereas prenatal neural circuits are believed to be derived from a combination of genetic and intrinsic activity, postnatal circuits are largely influenced by exogenous activity and experience. A dynamical neuroelectric field maintained by neural activity is proposed as the fundamental information processing substrate of cognitive function. Time series measurements of the neuroelectric field can be collected by scalp sensors and used to mathematically quantify the essential dynamical features of the neuroelectric field by constructing a digital twin of the dynamical system phase space. The multiscale nonlinear values that result can be organized into tensor data structures, from which latent features can be extracted using tensor factorization. These latent features can be mapped to behavioral constructs to derive digital biomarkers. This computational framework provides a robust method for incorporating neurodynamical measures into neuropsychiatric biomarker discovery.

Simultaneous measurement of temperature and C2H4 concentration in hydrocarbon flames using interference-immune differential absorption spectroscopy at 3.3 μm

In diagnostic applications based on tunable diode laser absorption spectroscopy, the measurement of target substances can be influenced by factors such as background thermal radiation in the combustion environment, extinction caused by solid or liquid particles, and other interfering absorptions. In this work, we developed a differential absorption diagnostic technique based on wavelength pairs, utilizing an interband cascade laser near 3.3 μm to simultaneously measure temperature and C2H4 concentration in hydrocarbon flames. Based on a detailed study of the C2H4 spectrum in this region and considering the optimal standard for spectral lines, two wavelength pairs were selected. The temperature is determined by the ratio of the absorption cross-sections of two wavelength pairs, and the C2H4 concentration is inferred based on the wavelength pair with higher differential absorption. In the initial stage, the system’s accuracy was verified in high-temperature static conditions (T = 300–800 K, P = 1 atm), and continuous time series measurements demonstrated the system’s stability. The limit of detection achieved by Allan-Werle variance analysis is 2.5 ppm at the optimal average time of 100 s. Subsequently, measurements were taken in a hydrocarbon flame. The obtained results indicate an average deviation of 1.021 % between the measured temperature in the flame and the reference value, with a standard deviation of 1.381 % for concentration measurement. All the measurements show that the system can be potentially applied to combustion diagnosis.

Natural and shipping underwater sound distribution in the Northern Adriatic Sea basin and possible application on target areas

The underwater sound distribution generated by natural sources, shipping and trawling activities has been computed by the Quonops© modelling webservice for the Northern Adriatic Sea (NAS) during 2020, a year characterized by the COVID-19 lockdown restrictions. Modelling has been calibrated by using a year-long time series of field measurements covering the domain of interest. Sound levels (50th percentile) ranged between 75 and 90 dB re 1μPa for all the considered frequencies (63 Hz, 125 Hz, 250 Hz third octave bands). Noisier NAS areas match with the shipping lanes and the distribution of trawling activity. Pressure sound indices based on masking effect were computed for two Ecologically/Biologically Significant Marine Areas (EBSA) located in the NAS. Results indicated a significant contribution of vessel and fishery-generated noise to the local soundscape and provide a basis for addressing NAS underwater noise pollution, with special reference to the Marine Spatial Planning approach.

Firms’ cost structure and stock return volatility

PurposeThis paper proposes a framework to identify a pattern in the relationship between firms’ cost structure (i.e. fixed versus variable) and their volatility in stock returns.Design/methodology/approachOur empirical analysis is based on a panel data regression where we use an extended sample period and a time-series regression-based elasticity measure of operating leverage.FindingsWe document significantly higher systematic risk among firms with large fixed costs, a conclusion which confirms theoretical predictions of earlier studies. In new findings, we document high firm-specific risk and high stock return volatility among firms with a fixed cost structure.Originality/valueThe paper fills a gap in the literature by examining the effect of cost structure using various operating leverage measures and other control measures for firm characteristics on idiosyncratic risk. Studies that seek to explain firms’ systematic risks are numerous; conversely, there are relatively fewer studies on the determinants of firms’ specific risks.

PSInet: a new global water potential network.

Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI-for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants' physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Controls on Water-Column Respiration Rates in a Coastal Plain Estuary: Insights from Long-Term Time-Series Measurements

Controls on Water-Column Respiration Rates in a Coastal Plain Estuary: Insights from Long-Term Time-Series Measurements

Nitrate trend reversal in Dutch dual-permeability chalk springs, evaluated by tritium-based groundwater travel time distributions

Historical use of fertilizer and manure on farmlands is known to have a lasting impact on ecosystems and water resources, but few studies assess the legacy of nitrate pollution on groundwater and surface water after farming applications were reduced. We studied the response of nitrate in spring water to a reduction of nitrogen fertilizer applications in agriculture realized since the mid-1980s. We assessed the travel time distribution of groundwater based on a time series of tritium measurements for 90 springs and small brooks that drain a dual porosity chalk aquifer. The travel time distributions were constrained using the tritium data in combination with time series of nitrate concentrations, applying a shape-free travel time distribution model. A clear trend reversal of nitrate concentrations was observed and simulated for springs with a large fraction of young water (< 30 years old) whereas the nitrate response in springs with relatively older water was attenuated and delayed. We conclude that obtaining a time series of tritium data helps to constrain age distributions of water that is discharged from dual permeability aquifers. The fraction of water aged <30 years was a meaningful parameter to distinguish between different types of springs. Nitrate trends in springs that drain large fractions of young water (> 0.6) show higher peak concentrations, shorter lag-time between leaching and outflow peaks and steeper declines after trend reversal, relative to trends in springs which are dominantly fed by older groundwater. The study thus shows that the nitrate legacy of groundwater systems is strongly determined by the range of their travel time distributions, and trend reversal in receiving springs and surface waters may appear within 10 to 15 years after measures to reduce nitrate losses from farming.