Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging

HurriCast: An Automatic Framework Using Machine Learning and Statistical Modeling for Hurricane Forecasting

Hurricanes present major challenges in the U.S. due to their devastating impacts. Mitigating these risks is important, and the insurance industry is central in this effort, using intricate statistical models for risk assessment. However, these models often neglect key temporal and spatial hurricane patterns and are limited by data scarcity. This study introduces a refined approach combining the ARIMA model and K-MEANS to better capture hurricane trends, and an Autoencoder for enhanced hurricane simulations. Our experiments show that this hybrid methodology effectively simulate historical hurricane behaviors while providing detailed projections of potential future trajectories and intensities. Moreover, by leveraging a comprehensive yet selective dataset, our simulations enrich the current understanding of hurricane patterns and offer actionable insights for risk management strategies.Comment: This paper includes 7 pages and 8 figures. And we submitted it up to the SC23 workshop. This is only a preprintin

Prevalence of Neuropsychiatric Symptoms across the Declining Memory Continuum: An Observational Study in a Memory Clinic Setting

Aims: The study aimed to compare the frequency of neuropsychiatric symptoms (NPS) across the declining memory continuum, from normal aging, subjective cognitive impairment (SCI), and mild cognitive impairment (MCI) to Alzheimer’s disease (AD), and to explore the clinical correlates of NPS. Method: In a memory clinic, 157 subjects (46 mild AD patients, 38 MCI individuals, 24 SCI subjects, and 49 normal controls) completed the neurobehavioral assessments with the Neuropsychiatric Inventory (NPI). The clinical significance of each NPI domain was defined as an item score ≥4. Result: Clinically significant depression was more common in the SCI than in the normal control group (p Conclusion: Across the declining memory continuum, the frequency of NPS was highest among mild AD patients. Depression, apathy, and aberrant motor behavior deserve more attention. Presence of apathy might be an independent determinant for mild AD

Chemically induced graphene to diamond transition: a DFT study

The conversion of graphene into diamond is a new way for preparing ultrathin diamond film without pressure. Herein, we investigated the transformation mechanism of surface-hydrogenated bilayer graphene (SHBG) into surface-hydrogenated single-layer diamond (SHSLD) crystal, inserting fifteen kinds of single metal atoms without any pressure, by using the systematical first-principles calculations. Compared with the configuration without metal atom, SHBG can be transformed into SHSLD spontaneously in thermodynamics under the action of single metal atom, and its formation energy can even decrease from 0.82 eV to -5.79 eV under the action of Hf atom. According to our results, the outer electron orbits and atomic radius of metal atom are two important factors that affect the conversion. For the phase transition to occur, the metal atom needs to have enough empty d orbitals, and the radius of the metal atom is in the range of 0.136-0.159 nm. Through further analysis, we find that the p orbitals of carbon atoms and d orbital of metal atom in SHBG will be strongly hybridized, thereby promoting the conversion. The results supply important significance to experimentally prepare diamond without pressure through hydrogenated graphene

TGFBI Gene Mutation Analysis of Clinically Diagnosed Granular Corneal Dystrophy Patients Prior to PTK: A Pilot Study from Eastern China

This study investigated the TGFBI gene mutation types in outpatients clinically diagnosed with granular corneal dystrophy (GCD) prior to phototherapeutic keratectomy (PTK), also calculated the mutation rate of subjects with normal corneas, but positive family history. Clinical GCD outpatients and consanguineous family members were enrolled in this study. Among total 42 subjects: 24 patients from 23 unrelated families had typical signs of GCD on corneas; 5 patients from 5 unrelated families had atypical signs; 13 subjects from 11 unrelated families had no corneal signs but positive family history. Using Avellino gene test kit, the TGFBI mutation detection was performed on DNA samples from all subjects. 36 subjects were detected to carry heterozygous TGFBI gene mutations. Among 24 clinical GCD patients, the proportion of R124H, R555Q, R124L, R555W and R124C were 37.5%, 16.7%, 25.0%, 20.8% and 0%, respectively, and 2 patients had been diagnosed with GCD according to the opacities thriving after LASIK (R124H) and PRK (R555W). The mutation rate of 13 subjects having no signs but positive family history was 69.2%. R124H mutation is the most prominent mutation type among GCD outpatients in Eastern China. It is recommended to conduct gene detection for patients with positive family history prior to refractive surgeries