Full Time Series Research Articles

Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies. The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector. This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations. The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.

Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.

Thermal error prediction of ball screws in full-time series using working condition data based on a mechanism and data hybrid-driven model

Thermal error prediction of ball screws in full-time series using working condition data based on a mechanism and data hybrid-driven model

ROME/REA: Three-year, Tri-color Timeseries Photometry of the Galactic Bulge

The Robotic Observations of Microlensing Events/Reactive Event Assessment Survey was a Key Project at Las Cumbres Observatory (hereafter LCO) which continuously monitored 20 selected fields (3.76 sq.deg) in the Galactic Bulge throughout their seasonal visibility window over a three-year period, between 2017 March and 2020 March. Observations were made in three optical passbands (SDSS −g′ , −r′ , −i′ ), and LCO’s multi-site telescope network enabled the survey to achieve a typical cadence of ∼10 hr in i′ and ∼15 hr in g′ and r′ . In addition, intervals of higher cadence (<1 hr) data were obtained during monitoring of key microlensing events within the fields. This paper describes the Difference Image Analysis data reduction pipeline developed to process these data, and the process for combining the photometry from LCO’s three observing sites in the Southern Hemisphere. The full timeseries photometry for all ∼8 million stars, down to a limiting magnitude of i ∼ 18 mag is provided in the data release accompanying this paper, and samples of the data are presented for exemplar microlensing events, illustrating how the tri-band data are used to derive constraints on the microlensing source star parameters, a necessary step in determining the physical properties of the lensing object. The timeseries data also enables a wealth of additional science, for example in characterizing long-timescale stellar variability, and a few examples of the data for known variables are presented.

Mapping Annual Tidal Flat Loss and Gain in the Micro-Tidal Area Integrating Dual Full-Time Series Spectral Indices

Tracking long-term tidal flat dynamics is crucial for coastal restoration decision making. Accurately capturing the loss and gain of tidal flats due to human-induced disturbances is challenging in the micro-tidal areas. In this study, we developed an automated method for mapping the annual tidal flat changes in the micro-tidal areas under intense human activities, by integrating spectral harmonization, time series segmentation from dual spectral indices, and the tide-independent hierarchical classification strategy. Our method has two key novelties. First, we adopt flexible temporal segments for each pixel based on the dual full-time series spectral indices, instead of solely using a fixed period window, to help obtain more reliable inundation frequency features. Second, a tide-independent hierarchical classification strategy based on the inundation features and the Otsu algorithm capture the tidal flat changes well. Our method performed well in Guangdong, Hong Kong, and Macao (GHKM), a typical area with micro-tidal range and intense human activities, with overall accuracies of 89% and 92% for conversion types and turning years, respectively. The tidal flats in GHKM decreased by 24% from 1986 to 2021, resulting from the loss of 504.45 km2, partially offset by an accretion of 179.88 km2. Further, 70.9% of the total loss was in the Great Bay Area, concentrated in 1991–1998 and 2001–2016. The historical trajectories of tidal flat loss were driven by various policies implemented by the national, provincial, and local governments. Our method is promising for extension to other micro-tidal areas to provide more scientific support for coastal resource management and restoration.

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data.

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry

Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.

Regime change in northwest Atlantic sea surface temperatures revealed using a quantile approach

Trends in sea surface temperatures (SST) of the US Northeast Continental Shelf and the adjacent Slope Sea were analyzed using a quantile approach. SST through the period 1982–2021 were analyzed for change in annual mean and range. In addition, the mean of each 25% quantile of the data by year was calculated. Using the full time series, the climatological SSTs at each quantile was determined, which in turn were used with the annual data to determine the number of days temperature fell within the limits of each climatological quantile. The time series of the data parameters were summarized by grid location and for a series of representative index areas. The index areas were analyzed for evidence of change points or regime shifts using the STARS (Sequential T-test Analysis of Regime Shifts) algorithm. The widely reported SST warming in this region was reinforced with our analysis; our findings also revealed new aspects of the change of thermal conditions in important ways. Mean annual temperature trended at approximately 0.36 °C per decade; however, warm quantile SSTs increased by more than 1.25 °C per decade in the Slope Sea, representing a five degree increase over the study period. The change point analysis suggested the change in thermal regime began as early as 2005 or 2007 with the study region fully enveloped by regime change by 2012. A previously identified change point in 1999 appears to be related to the duration of warming as opposed to SST levels. Mean SST alone does not capture the nature of high temperature exposure for living marine species in the study area; these regions may be experiencing a month or more of stressfully higher SST. The thermal effects derived using the quantile approach suggest different types of thermal exposures compared to heat wave analyses, especially related to cumulative exposure.

Gafftopsail Catfish in Texas estuaries: Population trends and ecosystem implications

AbstractObjectiveGiven the limited information on population dynamics of Gafftopsail Catfish Bagre marinus in the western Gulf of Mexico, the objectives of this study were to determine the status of this species in Texas estuaries by assessing trends in relative abundance, length, and habitat preferences from fishery‐independent trawl (juvenile) and gill‐net (adult) data, as well as angler harvest trends from fishery‐dependent creel surveys collected in all Texas estuaries.MethodsFishery‐independent and ‐dependent monitoring data were collected by the Texas Parks and Wildlife Department in all major bay systems of the Texas coast from 1983 to 2022. Linear trend analysis was used to assess trends across the full time series and all major bay systems for both juvenile and adult specimens and for total recreational harvest. Boosted regression tree analysis was used to assess habitat preference for both juveniles and adults.ResultBoth juvenile and adult catch rates consistently increased in most Texas estuaries, with the most extreme increases occurring in central Texas coast estuaries beginning in the early 2000s. During the same time frame, mean lengths of adult specimens significantly decreased in many of the same estuaries. Habitat suitability analysis suggested that Gafftopsail Catfish were primarily influenced by salinity, though they still exist across a very broad range of salinity conditions. Angler catch rates showed some localized patterns of increasing or decreasing trends, though they generally did not follow fishery‐independent trends for most estuaries.ConclusionThese trends interpreted in the context of the known trophic role of Gafftopsail Catfish imply the potential for larger ecosystem‐level effects on both prey organisms and competitors. A more thorough awareness of these trends and potential interactions should help fisheries managers operate in an ecosystem‐based context when investigating population dynamics and recommending management strategies.

Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets

Structural change tests aim to identify evidence of a structural break or change in the underlying generating process of a time series. The BDS test has its origins in chaos theory and seeks to test, using the correlation integral, the hypothesis that a time series is generated by an identically and independently distributed (IID) stochastic process over time. The BDS test is already widely used as a powerful tool for testing the hypothesis of white noise in the residuals of time series models. In this paper, we illustrate how the BDS test can be implemented also in a recursive manner to evaluate the hypothesis of structural change in a time series, taking advantage of its ability to test the IID hypothesis. We apply the BDS test repeatedly, starting with a sub-sample of the original time series and incrementally increasing the number of observations until it is applied to the full sample time series. A structural change in the unknown underlying generator model is detected when a change in the trend shown by this recursively computed BDS statistic is detected. The strength of this recursive BDS test lies in the fact that it does not require making any assumptions about the underlying time series generator model. We ilustrate the power and potential of this recursive BDS test through an application to real economic data. In this sense, we apply the test to assess the structural changes caused by the COVID-19 pandemic in international financial markets. Using daily data from the world’s top stock indices, we have detected strong and statistically significant evidence of two major structural changes during the period from June 2018 to June 2022. The first occurred in March 2020, coinciding with the onset of economic restrictions in the main Western countries as a result of the pandemic. The second occurred towards the end of August 2020, with the end of the main economic restrictions and the beginning of a new post-pandemic economic scenario. This methodology to test for structural changes in a time series is easy to implement and can detect changes in any system or process behind the time series even when this generating system is not known, and without the need to specify or estimate any a priori generating model. In this sense, the recursive BDS test could be incorporated as an initial preliminary step to any exercise of time series modeling. If a structural change is detected in a time series, rather than estimating a single predictive model for the full-sample time series, efforts should be made to estimate different predictive models, one for the time before and one for the time after the detected structural change.

Ground reaction forces in baseball pitching: temporal associations with pitch velocity among high-velocity pitchers

ABSTRACT How baseball pitchers interact with the ground is an important aspect of pitching technique and performance. Previous studies on ground reaction forces in baseball pitching have largely been limited to pitchers at the youth or adolescent level, with only a few studies examining higher velocity pitchers. Additionally, previous studies have limited their analyses to only peak kinetic values, neglecting any temporal importance of when these peak values occur. Therefore, our purpose was to provide normative ground reaction force values and examine the associations between pitch velocity and ground reaction forces in high-velocity pitchers. We retrospectively extracted pitch velocities as well as rear and lead leg ground reaction force data from internal databases for 105 high-velocity pitchers. We analysed the associations between the full ground reaction force time series and pitch velocity using statistical parametric mapping regression. Regression analysis revealed pitch velocity significantly predicted lead leg braking ground reaction force from approximately 27% to 35% of the period between front foot contact and ball release. These data reinforce the importance of effective braking forces for achieving maximal pitch velocities. Additionally, our observed peak ground reaction force values were considerably higher than those previously reported.

The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980

AbstractThe loss of multiyear sea ice (MYI) in the Arctic Ocean is a significant change that affects all facets of the Arctic environment. Using a Lagrangian ice age product, we examine MYI loss and quantify the annual MYI area budget from 1980 to 2021 as the balance of export, melt, and replenishment. Overall, MYI area declined at 72,500 km2/yr; however, a majority of the loss occurred during two stepwise reductions that interrupt an otherwise balanced budget and resulted in the northward contraction of the MYI pack. First, in 1989, a change in atmospheric forcing led to a +56% anomaly in MYI export through Fram Strait. The second occurred from 2006 to 2008 with anomalously high melt (+25%) and export (+23%) coupled with low replenishment (−8%). In terms of trends, melt has increased since 1989, particularly in the Beaufort Sea, export has decreased since 2008 due to reduced MYI coverage north of Fram Strait, and replenishment has increased over the full time series due to a negative feedback that promotes seasonal ice survival at higher latitudes exposed by MYI loss. However, retention of older MYI has significantly declined, transitioning the MYI pack toward younger MYI that is less resilient than previously anticipated and could soon elicit another stepwise reduction. We speculate that future MYI loss will be driven by increased melt and reduced replenishment, both of which are enhanced with continued warming and will one day render the Arctic Ocean free of MYI, a change that will coincide with a seasonally ice‐free Arctic Ocean.

Machine learning assisted representative period selection as input to modelling of field degradation in photovoltaic modules

Sustainable and affordable solar energy production is critically dependent on the ability of photovoltaic (PV) modules to perform reliably in the outdoor environment over several decades. A suite of accelerated stress tests is central in the efforts to qualify the reliability of new module architectures and materials, which are evolving at a rapid pace. Complimentary to these, mathematical modelling of PV module degradation phenomena constitutes a useful set of tools that has become increasingly relevant in recent years. For certain degradation phenomena, modelling is best solved by numerical methods such as using the finite element modelling framework. These numerical methods can be computationally expensive, thus the input of longer timeseries of outdoor weather data can come at a high computational cost and require significant model simplifications. To alleviate this problem, we here present the use of an unsupervised machine learning algorithm to select representative periods of historical outdoor weather data (or derivatives thereof). We exemplify the approach by selecting representative daily cell temperature profiles, instead of using the full timeseries, as input to modelling of thermomechanical fatigue in soldered ribbon cell interconnects. We explore how a subset of representative daily temperature profiles can reproduce key characteristics of the overall distribution of a multi-year dataset, as well as differences between datasets for sites in different climates. Such representative datasets could be used as input to complex PV module models and drastically reduce the computational costs.

Association between Opioid Dependence and Scale Free Fractal Brain Activity: An EEG Study

Self-similarities at different time scales embedded within a self-organizing neural manifold are well recognized. In this study, we hypothesize that the Hurst fractal dimension (HFD) of the scalp electroencephalographic (EEG) signal reveals statistical differences between chronic pain and opioid use. We test this hypothesis by using EEG resting state signals acquired from a total of 23 human subjects: 14 with chronic pain, 9 with chronic pain taking opioid medications, 5 with chronic pain and not taking opioid medications, and 9 healthy controls. Using the multifractal analysis algorithm, the HFD for full spectrum EEG and EEG frequency band time series was computed for all groups. Our results indicate the HFD varies spatially and temporally across all groups and is of lower magnitude in patients not taking opioids as compared to those taking opioids and healthy controls. A global decrease in HFD was observed with changes in gamma and beta power in the chronic pain group compared to controls and when paired to subject handedness and sex. Our results show the loss of complexity representative of brain wide dysfunction and reduced neural processing can be used as an EEG biomarker for chronic pain and subsequent opioid use.

Monitoring the large-scale magnetic field of AD Leo with SPIRou, ESPaDOnS, and Narval

Context. One clear manifestation of dynamo action on the Sun is the 22-yr magnetic cycle, exhibiting a polarity reversal and a periodic conversion between poloidal and toroidal fields. For M dwarfs, several authors claim evidence of activity cycles from photometry and analyses of spectroscopic indices, but no clear polarity reversal has been identified from spectropolarimetric observations. These stars are excellent laboratories to investigate dynamo-powered magnetic fields under different stellar interior conditions, that is partly or fully convective. Aims. Our aim is to monitor the evolution of the large-scale field of AD Leo, which has shown hints of a secular evolution from past dedicated spectropolarimetric campaigns. This is of central interest to inform distinct dynamo theories, contextualise the evolution of the solar magnetic field, and explain the variety of magnetic field geometries observed in the past. Methods. We analysed near-infrared spectropolarimetric observations of the active M dwarf AD Leo taken with SPIRou between 2019 and 2020 and archival optical data collected with ESPaDOnS and Narval between 2006 and 2019. We searched for long-term variability in the longitudinal field, the width of unpolarised Stokes profiles, the unsigned magnetic flux derived from Zeeman broadening, and the geometry of the large-scale magnetic field using both Zeeman-Doppler imaging and principal component analysis. Results. We found evidence of a long-term evolution of the magnetic field, featuring a decrease in axisymmetry (from 99% to 60%). This is accompanied by a weakening of the longitudinal field (−300 to −50 G) and a correlated increase in the unsigned magnetic flux (2.8–3.6 kG). Likewise, the width of the mean profile computed with selected near-infrared lines manifests a long-term evolution corresponding to field strength changes over the full time series, but does not exhibit modulation with the stellar rotation of AD Leo in individual epochs. Conclusions. The large-scale magnetic field of AD Leo manifested first hints of a polarity reversal in late 2020 in the form of a substantially increased dipole obliquity, while the topology remained predominantly poloidal and dipolar for 14 yr. This suggests that low-mass M dwarfs with a dipole-dominated magnetic field can undergo magnetic cycles.

Mars Global Surveyor's Mars Orbiter Camera (MOC) Wide‐Angle Images (1999–2006): 2. Data Investigation Into North Polar Hood Formation, Broad Brightness Changes in Acidalia, and Seasonal Frost in Hellas

AbstractMars Orbiter Camera Wide Angle images were taken from late 1999 through most of 2006, in both red and blue, providing nearly daily, nearly global coverage of Mars for approximately four Mars Years. Significant work has been published from those data, but there did not exist in a public archive a uniform, full time series of the processed data for easier analysis. The companion work to this (Robbins, 2023a, https://doi.org/10.1029/2022EA002673) presents that processing and quality control of the data, producing the full time series in both colors, in equirectangular and polar stereographic projections, in mosaics that have been binned into four different time intervals (daily, ΔLs = 2°, 5°, and 10° [≈4 through 19 days]) and as approximately true‐color composites. The multidimensional data set can be mined for surface and atmospheric variations on Mars. To demonstrate agreement with past work, several well‐known trends are demonstrated in a review‐style narrative format in Supporting Information S1, including changes after a global dust storm, seasonal frost variations at the poles, and surface variations. The data are analyzed further in the main text to demonstrate new qualitative and quantitative analyses of atmospheric and surface features: the North Polar Hood can form earlier than previously demonstrated, Acidalia has significant seasonal brightness variations that can be quantified, and the seasonal frost deposition and sublimation of Hellas can be quantified and shown to vary from year‐to‐year. These different investigations represent a small slice of what can be done now that these data are made available and easily accessible.

A Multi-Objective Time – Series Optimization for Optimum Planning Design of Integrative Power System with the Effects of Multi-Dimensional Sources of Uncertainty

Time series aggregation (TSA) procedures are used to simplify data entry and enhance solution efficiency. This study suggested a two-step multi-objective optimization framework for TSA strategy determination. TSA methodologies for optimum MES design and operation were used to create the multi-energy system’s preliminary design. In the second step, the system’s total annual cost, as well as loss of load probability (LSLP), were used as objectives, or the optimal combination of the several objectives was found optimal TSA strategy for the MES’s structure and performance. The ideal energy-storage MES design proved the method’s efficacy. TSA techniques were compared and analyzed for architectural schemes & system performance variations. The findings show that the initial full-time series data may be used to verify the viability of the best MES design schemes and quantify the departure of outcomes from alternative TSA procedures. LSLP decrease raises expenses. The TAC of the framework improves from $9.79 × 107 to $1.56 × 108 when the LSLP reduces to 7.90% to zero in the numerous grids method (TG1) with 730-time steps (TS). To fulfill energy system demand, the minimal LSLP approach must be chosen. The best TSA tactics for MES design may be chosen based on unique needs.

A field study on ice melting and breakup in a boreal lake, Pääjärvi, in Finland

Abstract. Lake ice melting and breakup form a fast, nonlinear process with important mechanical, chemical, and biological consequences. The process is difficult to study in the field due to safety issues, and therefore only little is known about its details. In the present work, the field data were collected on foot, by hydrocopter, and by boat for a full time series of the evolution of ice thickness, structure, and geochemistry through the melting period. The observations were made in lake Pääjärvi in 2018 (pilot study) and 2022. In 2022, the maximum thickness of ice was 55 cm with 60 % snow ice, and in 40 d the ice melted by 33 cm from the surface and 22 cm from the bottom while the porosity increased from less than 5 % to 40 %–50 % at breakup. In 2018, the snow-ice layer was thin, and bottom and internal melting dominated the ice decay. The mean melting rates were 1.31 cm d−1 in 2022 and 1.55 cm d−1 in 2018. In 2022 the electrical conductivity (EC) of ice was 11.4 ± 5.79 µS cm−1, which is 1 order of magnitude lower than in the lake water, and ice pH was 6.44 ± 0.28, which is lower by 0.4 than in water. The pH and EC of ice and water decreased during the ice decay except for slight increases in ice due to flushing by lake water. Chlorophyll a was less than 0.5 µg L−1 in porous ice, approximately one-third of that in the lake water. The results are important for understanding the process of ice decay with consequences for lake ecology, further development of numerical lake ice models, and modeling the safety of ice cover and ice loads.

Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment

Alzheimer's Disease (AZD) is a neurodegenerative disease for which there is now no known effective treatment. Mild cognitive impairment (MCI) is considered a precursor to AZD and affects cognitive abilities. Patients with MCI have the potential to recover cognitive health, can remain mildly cognitively impaired indefinitely or eventually progress to AZD. Identifying imaging-based predictive biomarkers for disease progression in patients presenting with evidence of very mild/questionable MCI (qMCI) can play an important role in triggering early dementia intervention. Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly studied in brain disorder diseases. In this work, employing a recent developed a time-attention long short-term memory (TA-LSTM) network to classify multivariate time series data. A gradient-based interpretation framework, transiently-realized event classifier activation map (TEAM) is introduced to localize the group-defining “activated” time intervals over the full time series and generate the class difference map. To test the trustworthiness of TEAM, we did a simulation study to validate the model interpretative power of TEAM. We then applied this simulation-validated framework to a well-trained TA-LSTM model which predicts the progression or recovery from questionable/mild cognitive impairment (qMCI) subjects after three years from windowless wavelet-based dFNC (WWdFNC). The FNC class difference map points to potentially important predictive dynamic biomarkers. Moreover, the more highly time-solved dFNC (WWdFNC) achieves better performance in both TA-LSTM and a multivariate CNN model than dFNC based on windowed correlations between timeseries, suggesting that better temporally resolved measures can enhance the model's performance.

Ground Displacements in NY Using Persistent Scatterer Interferometric Synthetic Aperture Radar and Comparison of X- and C-Band Data

In this study, we investigated the quality of Interferometric Synthetic Aperture Radar (InSAR) data to measure surface displacements in upstate New York, an area with dense vegetation, snowy winters, and strong seasonal signals. We used data from the German Space Agency’s TerraSAR-X and TanDEM-X satellites (X-band, 3.1 cm radar wavelength) as well as the European Space Agency’s Sentinel-1 satellite (C-band, 5.6 cm radar wavelength); both datasets covered a ~3-year time period from 2018 to 2021. Using persistent scatterer interferometry (PSI), we were able to observe several deforming features in the region with sub-centimeter/year deformation rates. We also examined a version of the X-band data that we spatially averaged to the same pixel size as the Sentinel-1 imagery in order to separate out the effects of wavelength and pixel size on PSI accuracy and coverage. Overall, the largest number of stable PS points was found in the full-resolution X-band data, which was followed by the C-band data and then by the downsampled X-band data. Our analysis also included a subset of snow-free imagery so that we could assess the effect that snow-covered images had on the distribution and accuracy of PS points and the resulting time series. This analysis revealed that PS populations increased by 50–60% for the snow-free data when compared with analyses using the full datasets. The average deformation rates inferred from the time series generated using only snow-free images were nearly identical to those estimated from the full time series. We assessed the accuracy of the inferred rates through comparisons between the results of different datasets and with limited ground survey data. We found that all of the inferred deformation rates from each of the datasets agreed with in situ measurements in an area of known ground subsidence above an underground salt mine in Lansing, NY. The S1 datasets, however, had higher levels of noise.