Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1-minute and 5-minute resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data. When predicting the SYM-H indices (2 hours in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.Comment: 28 pages, 8 figure

Candidate Eclipsing Binary Systems with a ? Scuti Star in Northern TESS Field

The existence of pulsating stars in eclipsing binaries has been known for decades. These types of objects are extremely valuable systems for astronomical studies as they exhibit both eclipse and pulsation variations. The eclipsing binaries are the only way to directly measure the mass and radius of stars with a good accuracy (<= 1%), while the pulsations are a unique way to probe the stellar interior via oscillation frequencies. There are different types of pulsating stars existing in eclipsing binaries. One of them is the delta Scuti variables. Currently, the known number of delta Scuti stars in eclipsing binaries is around 90 according to the latest catalog of these variables. An increasing number of these kinds of variables is important to understand the stellar structure, evolution and the effect of binarity on the pulsations. Therefore, in this study, we focus on discovering new eclipsing binaries with delta Scuti component(s). We searched within the northern Transiting Exoplanet Survey Satellite (TESS) field with a visual inspection by following some criteria such as light curve shape, the existence of pulsation like variations in the out-of-eclipse light curve and the T (eff) values of the targets. As a result of these criteria, we discovered some targets. The orbital variations were first removed from the TESS light curves and frequency analysis was performed on the residuals. The luminosity, and absolute and bolometric magnitudes of the targets were calculated as well. To find how much of these parameters represent the primary (more luminous) binary component, we also computed the flux density ratio of the systems by utilizing the area of the eclipses. In addition, the positions of the systems in the H-R diagram were examined considering the flux density ratios. As a consequence of the investigation, we defined 38 candidate delta Scuti stars and also one Maia variable in eclipsing binary systems.Scientific and Technological Research Council (TUBITAK) [120F330]; NASA Explorer ProgramThis work has been supported in part by the Scientific and Technological Research Council (TUBITAK) under Grant No. 120F330. The TESS data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). F.K.A. thanks Prof. Gerald Handler for showing how to clean binarity with a phenomenological fit. Funding for the TESS mission is provided by the NASA Explorer Program. This work has made use of data from the European Space Agency (ESA) mission Gaia (http://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa.int/web/gaia/dpac/consortium).Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the SIMBAD database, operated at CDS, Strasbourq, France

Halloysite nanotube-enhanced polyacrylonitrile ultrafiltration membranes: fabrication, characterization, and performance evaluation

This research focuses on the production and characterization of pristine polyacrylonitrile (PAN) as well as halloysite nanotube (HNT)-doped PAN ultrafiltration (UF) membranes via the phase inversion technique. Membranes containing 0.1, 0.5, and 1% wt HNT in 16% wt PAN are fabricated, and their chemical compositions are examined using Fourier transform infrared (FTIR) spectroscopy. Scanning electron microscopy (SEM) is utilized to characterize the membranes’ surface and cross-sectional morphologies. Atomic force microscopy (AFM) is employed to assess the roughness of the PAN/HNT membrane. Thermal characterization is conducted using thermal gravimetric analysis (TGA) and differential thermal analysis (DTA), while contact angle and water content measurements reveal the hydrophilic/hydrophobic properties. The pure water flux (PWF) performance of the porous UF water filtration membranes is evaluated at 3 bar, with porosity and mean pore size calculations. The iron (Fe), manganese (Mn), and total organic carbon (TOC) removal efficiencies of PAN/HNT membranes from dam water are examined, and the surfaces of fouled membranes are investigated by using SEM post-treatment. Mechanical characterization encompasses tensile testing, the Mori–Tanaka homogenization approach, and finite element analysis. The findings offer valuable insights into the impact of HNT doping on PAN membrane characteristics and performance, which will inform future membrane development initiatives

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of sociohydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001)

Hippocampal pyramidal cells: the reemergence of cortical lamination

The increasing resolution of tract-tracing studies has led to the definition of segments along the transverse axis of the hippocampal pyramidal cell layer, which may represent functionally defined elements. This review will summarize evidence for a morphological and functional differentiation of pyramidal cells along the radial (deep to superficial) axis of the cell layer. In many species, deep and superficial sublayers can be identified histologically throughout large parts of the septotemporal extent of the hippocampus. Neurons in these sublayers are generated during different periods of development. During development, deep and superficial cells express genes (Sox5, SatB2) that also specify the phenotypes of superficial and deep cells in the neocortex. Deep and superficial cells differ neurochemically (e.g. calbindin and zinc) and in their adult gene expression patterns. These markers also distinguish sublayers in the septal hippocampus, where they are not readily apparent histologically in rat or mouse. Deep and superficial pyramidal cells differ in septal, striatal, and neocortical efferent connections. Distributions of deep and superficial pyramidal cell dendrites and studies in reeler or sparsely GFP-expressing mice indicate that this also applies to afferent pathways. Histological, neurochemical, and connective differences between deep and superficial neurons may correlate with (patho-) physiological phenomena specific to pyramidal cells at different radial locations. We feel that an appreciation of radial subdivisions in the pyramidal cell layer reminiscent of lamination in other cortical areas may be critical in the interpretation of studies of hippocampal anatomy and function