Chronostratigraphic palaeo-climate phasing based on southern African wetlands: From the escarpment to the eastern seaboard

This paper aims to identify chronostratigraphic palaeo-climatic boundaries based on proxy indications from mountain- and coastal wetlands in eastern South Africa and Lesotho. Phase boundaries were identified from timing of climate change inferred by proxies, as well as regime shifts in climate variability. Sometimes magnitude and/or frequency of change was also considered. Summarizing the common palaeo-climatic indications suggest the following chronostratigraphic climate phases: 25 to 18 ka, 18 to 15 ka, 15 to 11.5, 11.5 to 8 ka, 8 to 5.5 ka, 5.5 to 2 ka and 2 to 0 ka. The most robust boundaries were identified at 18 ka, 15 ka and 2 ka, i.e. these boundaries were supported by several proxies/sites. The other boundaries were less clearly detected from available proxies/sites and should be regarded tentative. The timing of a climate shift often coincides at coast and mountain sites. However, the climate conditions within each chronostratigraphic phase sometimes vary between coast and inland sites. The 25 to 18 ka phase was cool and dry with strong and frequent storms, followed by the ca. 18 to 15 ka period when conditions were less severe but still generally cool and dry. At ca. 15 to 11.5 ka several proxies infer warmer climate, with less winter rains. During 11.5 to 8 ka a general increase in wetness is inferred, followed by warming over the 8 to 5.5 ka phase. Between 5.5 and 2 ka a successive change towards wetter is indicated, although timing differ between sites. After 2 ka generally a more variable climate is seen, often with high magnitude shifts between dry and wet. The data resolution, i.e. the number of available wetland records, increases with time from very low during glacial times, to highest resolution during late Holocene. Geographically, sites in the mountain region are overrepresented compared to coastal sites. A comparison with coastal lake records suggests a more variable climate at coastal sites compared to mountain sites during mid- and late Holocene, although different proxy resolution and methodology cannot be ruled out as an explanation. A case study compares multiproxy records from Drakensberg (Sekhokong, Ntsikeni) and the coast (Mfabeni), discussing advantages and problems associated with proxy-comparisons within and between sites.

Hi-C Resolution Enhancement with Genome Sequence Data

The increasing interest in chromatin conformation inside the nucleus and the availability of genome-wide experimental data make it possible to develop computational methods that can increase the quality of the data and thus overcome the limitations of high experimental costs. Here we develop a deep-learning approach for increasing Hi-C data resolution by appending additional information about genome sequence. In this approach, we utilize two different deep-learning algorithms: the image-to-image model, which enhances Hi-C resolution by itself, and the sequence-to-image model, which uses additional information about the underlying genome sequence for further resolution improvement. Both models are combined with the simple head model that provides a more accurate enhancement of initial low-resolution Hi-C data. The code is freely available in a GitHub repository: https://github.com/koritsky/DL2021 HI-C

Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling

A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates of monthly flow data and water balance components, namely runoff, groundwater, and water yield. GPM has a better ability for rainfall event detections, especially during high rainfall events or extremes (>40 mm/day). The errors of the satellite products are generally linked to slope, wind, elevation, and evapotranspiration. Hydrologic simulations using SWAT modelling and the three satellite rainfall products show that CHIRPS slightly has the daily best performance, with R2 of 0.59 and 0.62, and NSE = 0.54, and the monthly aggregated improved at a monthly level. The water balance components generated at an annual level, using three satellite products, show that CHIRPS outperformed with a ration closer to one, though with a tendency to overestimate up to 3–4× times the data generated from the rainfall gauges. The findings of this study are beneficial in supporting efforts for improving satellite rainfall products and water resource implications.

Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

Recent attention has been given to mesoscale phenomena across geospace (∼10 s km to 500 km in the ionosphere or ∼0.5 RE to several RE in the magnetosphere), as their contributions to the system global response are important yet remain uncharacterized mostly due to limitations in data resolution and coverage as well as in computational power. As data and models improve, it becomes increasingly valuable to advance understanding of the role of mesoscale phenomena contributions—specifically, in magnetosphere-ionosphere coupling. This paper describes a new method that utilizes the 2D array of Time History of Events and Macroscale Interactions during Substorms (THEMIS) white-light all-sky-imagers (ASI), in conjunction with meridian scanning photometers, to estimate the auroral scale sizes of intense precipitating energy fluxes and the associated Hall conductances. As an example of the technique, we investigated the role of precipitated energy flux and average energy on mesoscales as contrasted to large-scales for two back-to-back substorms, finding that mesoscale aurora contributes up to ∼80% (∼60%) of the total energy flux immediately after onset during the early expansion phase of the first (second) substorm, and continues to contribute ∼30–55% throughout the remainder of the substorm. The average energy estimated from the ASI mosaic field of view also peaked during the initial expansion phase. Using the measured energy flux and tables produced from the Boltzmann Three Constituent (B3C) auroral transport code (Strickland et al., 1976; 1993), we also estimated the 2D Hall conductance and compared it to Poker Flat Incoherent Scatter Radar conductance values, finding good agreement for both discrete and diffuse aurora.

Fault rupture in Baribis Fault possibly related to the 1847 major earthquake event in the Cirebon area

Baribis Fault is a recently identified active fault known to have thrust movement which located along the northern part of the West Java area. This E-W striking fault runs across high-populated areas, including Cirebon, Indramayu, Sumedang, and Subang area (with a probability of continuing to Jakarta and Banten areas). The last major historical earthquake occurred on November 16th, 1847 around the fault line with a radius of shaking area up to 400 km. The available high-resolution Digital Elevation Model from Geospatial Information Agency, called DEMNAS, has about 7.5-m grid data resolution but still not adequate to be used for identifying fault ruptures of this event. Hence, we conducted an Unmanned Aerial Vehicle (UAV) 3D Photogrammetry survey flown in the lower latitude (~100-m high) in the suspected sites. This study identified clear fault scarp associated with stream-valley offsets indicating strike-slip movement in the Ujung Jaya subdistrict, Sumedang. The trace of fault rupture has a 5±1-meter sinistral offset. This sharp fault deformation feature is possibly related to the 1847 earthquake in this area. This fact is different from regional morphology, which shows that the Baribis Fault is a thrust. Further study is necessary to get more detailed and precise information.

The influence of refinement strategies on the wavefunctions derived from an experiment

The synergy between theory and experiment found in X-ray wavefunction refinement (XWR) makes it one of the most compelling techniques available for chemical physics. The foremost benefit of XWR – obtaining wavefunctions constrained to experimental data – is at the same time its Achilles heel, because of the dependence of the results on the quality of both empirical and theoretical data. The purpose of this work is to answer the following: What is the effect of the refinement strategy and manipulation of input data on the physical properties obtained from XWR? With that in mind, cutoffs based on data resolution and F/σ(F) ratios were applied for both steps of XWR, the Hirshfeld atom refinement (HAR) and the X-ray constrained wavefunction fitting (XCW), for four selected systems: sulfur dioxide, urea, carbamazepine and oxalic acid. The effects of changing the weighting scheme or the method to transform σ(F 2) to σ(F) were also analysed. The results show that while HAR always reaches the same result, XCW is extremely sensitive to crystallographic data manipulation. This is a result of the variability of the experimental uncertainties for different resolution shells, and of not having proper standard uncertainties. Therefore, the use of distinct constraints for each resolution interval in XCW is proposed to fix this instability.

A New Real-time Lossless Data Compression Algorithm for ECG and PPG Signals

<i>Objective:</i> Data compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signal is proposed, maintaining all dominant features. <i>Methods: </i>The single channel data are first quantized using optimal quantization level, so that lesser number of bits are needed to represent it, maintaining low quantization error. Then second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. <i>Results:</i> This algorithm was tested on various single lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. <i>Discussion:</i> This algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. <i>Significance:</i> A comparative study of proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

A New Real-time Lossless Data Compression Algorithm for ECG and PPG Signals

<i>Objective:</i> Data compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signal is proposed, maintaining all dominant features. <i>Methods: </i>The single channel data are first quantized using optimal quantization level, so that lesser number of bits are needed to represent it, maintaining low quantization error. Then second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. <i>Results:</i> This algorithm was tested on various single lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. <i>Discussion:</i> This algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. <i>Significance:</i> A comparative study of proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Development of an image processing method for wake meandering studies and its application on data sets from scanning wind lidar and large-eddy simulation

Wake meandering studies require knowledge of the instantaneous wake shape and its evolution. Scanning lidar data are used to identify the wake pattern behind offshore wind turbines but do not immediately reveal the wake shape. The precise detection of the wake shape and centerline helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with the distance from the rotor and require good data resolution for an accurate fit. The thresholding methods usually imply a fixed value or manual selection of a threshold, which hinders the wake detection on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake detection method, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The image processing method is developed to reduce dependency on the supplementary reference data such as wind speed and direction. We show that the centerline found with the image processing is in a good agreement with the manually detected centerline and the Gaussian fit method. We also discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.