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

Guests mediated supramolecule-modified gold nanoparticles network for mimic enzyme application

1434-1441Supramolecules mediated porous metal nanostructures are meaningful materials because of their specific properties and wide range of applications. Here, we describe a general and simple strategy for building Au-networks based on the guest-induced 3D assembly of Au nanoparticles (Au-NPs) resulted in host-guest interaction resolved sulfonatocalix[4]arene (pSC4)-modified Au-NPs aggregate. The diverse guest molecules induced different porous network structures resulting in their different oxidize ability toward glucose. Among three different kinds of guest, hexamethylenediamine-pSC4-Au-NPs have high sensitivity, wide linear range and good stability. By surface characterization and calculating the electrochemical properties of the Au-NPs networks modified glassy carbon electrodes, the giving Au-NPs network reveals good porosity, high surface areas and increased conductance and electron transfer for the electrocatalysis. The synthesized nano-structures afford fast transport of glucose and ensure contact with a larger reaction surface due to high surface area. The fabricated sensor provides a platform for developing a more stable and efficient glucose sensor based on supramolecules mediated Au-NPs networks

An Automatic Glioma Segmentation System Using a Multilevel Attention Pyramid Scene Parsing Network

Background: Due to the significant variances in their shape and size, it is a challenging task to automatically segment gliomas. To improve the performance of glioma segmentation tasks, this paper proposed a multilevel attention pyramid scene parsing network (MLAPSPNet) that aggregates the multiscale context and multilevel features. Methods: First, T1 pre-contrast, T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast sequences of each slice are combined to form the input. Afterwards, image normalization and augmentation techniques are applied to accelerate the training process and avoid overfitting, respectively. Furthermore, the proposed MLAPSPNet that introduces multilevel pyramid pooling modules (PPMs) and attention gates is constructed. Eventually, the proposed network is compared with some existing networks. Results: The dice similarity coefficient (DSC), sensitivity and Jaccard score of the proposed system can reach 0.885, 0.933 and 0.8, respectively. The introduction of multilevel pyramid pooling modules and attention gates can improve the DSC by 0.029 and 0.022, respectively. Moreover, compared with Res-UNet, Dense-UNet, residual channel attention UNet (RCA-UNet), DeepLab V3+ and UNet++, the DSC is improved by 0.032, 0.026, 0.014, 0.041 and 0.011, respectively. Conclusion: The proposed multilevel attention pyramid scene parsing network can achieve stateof-the-art performance, and the introduction of multilevel pyramid pooling modules and attention gates can improve the performance of glioma segmentation tasks

An Automatic Glioma Segmentation System Using a Multilevel Attention Pyramid Scene Parsing Network

Background: Due to the significant variances in their shape and size, it is a challenging task to automatically segment gliomas. To improve the performance of glioma segmentation tasks, this paper proposed a multilevel attention pyramid scene parsing network (MLAPSPNet) that aggregates the multiscale context and multilevel features. Methods: First, T1 pre-contrast, T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast sequences of each slice are combined to form the input. Afterwards, image normalization and augmentation techniques are applied to accelerate the training process and avoid overfitting, respectively. Furthermore, the proposed MLAPSPNet that introduces multilevel pyramid pooling modules (PPMs) and attention gates is constructed. Eventually, the proposed network is compared with some existing networks. Results: The dice similarity coefficient (DSC), sensitivity and Jaccard score of the proposed system can reach 0.885, 0.933 and 0.8, respectively. The introduction of multilevel pyramid pooling modules and attention gates can improve the DSC by 0.029 and 0.022, respectively. Moreover, compared with Res-UNet, Dense-UNet, residual channel attention UNet (RCA-UNet), DeepLab V3+ and UNet++, the DSC is improved by 0.032, 0.026, 0.014, 0.041 and 0.011, respectively. Conclusion: The proposed multilevel attention pyramid scene parsing network can achieve stateof-the-art performance, and the introduction of multilevel pyramid pooling modules and attention gates can improve the performance of glioma segmentation tasks.</p

Rational construction of highly transparent superhydrophobic coatings based on a non-particle, fluorine-free and water-rich system for versatile oil-water separation

Despite the existing effort, fabricating superhydrophobic substrate through an environmental friendly approach remains a great challenge. In the current work, we present a simple approach to fabricate robust superhydrophobic surfaces on different kinds of substrates using phase-separation method. This method uses polydimethylsiloxane (PDMS) as the binder, tetrahydrofuran (THF) as the solvent, and water as nonsolvent. The emulsion prepared under optimum proportion of THF and water was stable and showed no obvious change even after being stored for 6 weeks. The coated substrates exhibit superhydrophobic property with a water contact angle (CA) larger than 155.0°. Moreover, because the solution is rich of water, and the surface modification condition is mild, the PDMS coating enjoys a high degree of transparency. Finally, superhydrophobic cotton and sponge were successfully demonstrated for oil-water and oil-in-water emulsion separation. This facile synthesis method has a potential for a broad range of applications in large-scale fabrication of superhydrophobic surfaces and filtration membranes.Accepted versio

A patient with tumor necrosis factor receptor-associated periodic syndrome misdiagnosed as Kawasaki disease: A case report and literature review

This article reports a case of tumor necrosis factor receptor-associated periodic syndrome (TRAPS) misdiagnosed as Kawasaki disease and summarizes the clinical features and therapeutic progress of TRAPS and the relationship between its clinical manifestations and gene mutations. We retrospectively analyzed a patient with tumor necrosis factor receptor superfamily member 1A (TNFRSF1A) -mutated auto-inflammatory disease who was misdiagnosed with Kawasaki disease in another hospital. The clinical features and therapeutic progress of TRAPS were analyzed by combining clinical features and gene reports of this case and literature review. TRAPS onset occurred in a female pediatric patient at the age of 4 months. The child and in his father at the age of 6 years, both of whom manifested periodic fever, and recurrent rash, as well as elevated leukocytes, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) during episodes but normal between episodes. This child carried a heterozygous mutation in TNFRSF1A located in the region 6442923–6442931 on chromosome 12. The nucleic acid alteration was: c.298 (exon3) _c.306 (exon3) 291 delCTCAGCTGC, resulting in a 3 amino acid deletion p.L100_C 102del 292 (p.Leu100_Cys102del) (NM_001065). After etanercept treatment, the symptoms of fever and rash disappeared, and the levels of ESR, CRP, interleukin (IL)-1, IL-6, and TNF-α levels were normal. Subsequently, no liver, kidney, or cardiac amyloidosis and severe etanercept-related adverse events were observed at 1-year follow-up. TRAPS pathogenesis is associated with TNFRSF1A mutation, which is characterized by periodic episodes of fever, mostly accompanied by recurrent rashes, periorbital edema, abdominal pain, and serious complications of organ amyloidosis. Moreover, etanercept can effectively alleviate the clinical symptoms and high inflammation level of TRAPS

Co-solvent induced self-roughness superhydrophobic coatings with self-healing property for versatile oil-water separation

Despite of the extensive effort made to construct a superhydrophobic surface in labs, achieving a short processing time and via a sustainable production route remains a challenge for practical applications. Here, with tetrahydrofuran and n-hexane as co-solvent, we demonstrate that roughness can be induced on polydimethylsiloxane (PDMS) coatings to achieve superhydrophobic coatings on different types of substrates including woven fabrics, non-woven fabrics, and melamine sponge. The sample constructed without adding particles exhibited excellent performance for versatile oil-water separation of mixtures of heavy oil and water, light oil and water, as well as oil-water emulsion. Due to the good solubility of the PDMS in the co-solvent, the dipping solution exhibited a long-time stability. Moreover, the abundant CH3 provided by the self-roughness PDMS coating helped the substrates recover its superhydrophobic property even after destroyed by plasma for 10 times. We believe that this extremely easy dipping-curing method would open up a new direction for fabricating a series of self-roughed superhydrophobic surface with self-healing property. Besides, the developed strategy is fast and easily scalable for industrial applications.Accepted versio

A self-roughened and biodegradable superhydrophobic coating with UV shielding, solar-induced self-healing and versatile oil-water separation ability

Traditional superhydrophobic coatings prepared from non-degradable materials tend to do harm to the environment throughout the fabrication process as well as after being discarded. Great efforts have been devoted to exploring more environmentally friendly approaches and materials to settle this problem. Here we report an eco-friendly strategy based on aqueous systems to construct superhydrophobic coatings on various fabrics. Fabrics were first coated with polydopamine (PDA) and then modified with the stearic acid emulsion to introduce the desired surface morphology and energy. The as-prepared fabrics achieved robust superhydrophobicity with a contact angle (CA) about 162.0° and sliding angle (SA) about 7.8°. Moreover, due to the UV-absorbing and the photo-thermal conversion ability of PDA, the modified fabrics exhibited excellent UV shielding and solar-induced self-healing properties. The as-prepared fabrics also possessed high efficiency oil–water separation properties. Without the usage of harmful organic solvents and the addition of micro/nano-particles, this biodegradable superhydrophobic fabric exhibited a clear advantage of being environmentally friendly over conventional coatings. Furthermore, the facile and low-cost fabrication process makes its large-scale production easy.Accepted versionThe authors thank the National Natural Science Foundation of China (21501127; 51502185), Natural Science Foundation of Jiangsu Province of China (BK20140400), and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (15KJB430025). We also acknowledge the funds from the project of the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)