PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.Comment: 21 pages, submitted preprint to Elsevier Expert Systems with Application

Numerical analysis of a time discretized method for nonlinear filtering problem with L\'evy process observations

In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is a unnormalized probability density function of the filter solution. Then we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis

Techniques and graft materials for repairing peripheral nerve defects

Peripheral nerve defects refer to damage or destruction occurring in the peripheral nervous system, typically affecting the limbs and face. The current primary approaches to address peripheral nerve defects involve the utilization of autologous nerve transplants or the transplantation of artificial material. Nevertheless, these methods possess certain limitations, such as inadequate availability of donor nerve or unsatisfactory regenerative outcomes post-transplantation. Biomaterials have been extensively studied as an alternative approach to promote the repair of peripheral neve defects. These biomaterials include both natural and synthetic materials. Natural materials consist of collagen, chitosan, and silk, while synthetic materials consist of polyurethane, polylactic acid, and polycaprolactone. Recently, several new neural repair technologies have also been developed, such as nerve regeneration bridging technology, electrical stimulation technology, and stem cell therapy technology. Overall, biomaterials and new neural repair technologies provide new methods and opportunities for repairing peripheral nerve defects. However, these methods still require further research and development to enhance their effectiveness and feasibility

Imaging diagnosis in peripheral nerve injury

Peripheral nerve injuries (PNIs) can be caused by various factors, ranging from penetrating injury to compression, stretch and ischemia, and can result in a range of clinical manifestations. Therapeutic interventions can vary depending on the severity, site, and cause of the injury. Imaging plays a crucial role in the precise orientation and planning of surgical interventions, as well as in monitoring the progression of the injury and evaluating treatment outcomes. PNIs can be categorized based on severity into neurapraxia, axonotmesis, and neurotmesis. While PNIs are more common in upper limbs, the localization of the injured site can be challenging. Currently, a variety of imaging modalities including ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI) and positron emission tomography (PET) have been applied in detection and diagnosis of PNIs, and the imaging efficiency and accuracy many vary based on the nature of injuries and severity. This article provides an overview of the causes, severity, and clinical manifestations of PNIs and highlights the role of imaging in their management

Controlled synthesis and enhanced toluene-sensing properties of mesoporous NixCo1-xFe2O4 nanostructured microspheres with tunable composite

The mesoporous NixCo1-xFe2O4 (x = 0, 0.33, 0.50, 0.67) nanostructured microspheres with tunable composite were successfully controllable synthesized by a simple solvothermal method. The results of XRD, XPS and FT-IR demonstrated that the Ni, taking the place of Co, were successfully doped into CoFe2O4. The gas sensing results demonstrated that the Ni0.33Co0.67Fe2O4 nanostructured microspheres showed higher gas-sensing response and better selectivity to harmful toluene than that of other samples. Moreover, the influences of Ni-doping amount on the structure, morphology and gas-sensing property of NixCo1-xFe2O4 were investigated. The enhanced gas-sensing properties of gas sensor based on Ni0.33Co0.67Fe2O4 to toluene may be due to appropriate Ni-doping into cobalt ferrite which resulted in the increased concentration of oxygen vacancies, large specific surface area (77.2m(2) g(-1)) and suitable catalytic activity. Therefore, the NixCo1-xFe2O4 will be a promising candidate for gas-sensing material for detecting toluene

Co3O4 /N-doped RGO nanocomposites derived from MOFs and their highly enhanced gas sensing performance

Co3O4/N-doped reduced graphene oxide (N-RGO) nanocomposite with mesoporous structure was fabricated by using metal organic frameworks (MOFs) as both template and precursor growing on RGO sheets. Porous Co3O4 cubes were assembled and grown on the surface of N-RGO layers, while N was doped into RGO to form N-RGO in situ synthesis route. The effect of RGO initial concentration on structure, component and gas-sensing properties of Co3O4/N-RGO nanocomposite was studied. The gas-sensing result demonstrated that the sensor based-on Co3O4/N-RGO-0.5 (the mass of RGO was 0.5 mg) nanocomposite possessed better gas-sensing performances to ethanol, such as higher response, faster response-recovery time and lower working temperature than that of other samples. The enhanced gas-sensing properties of sensor based-on Co3O4/N-RGO-0.5 nanocomposite to ethanol could be attributed to increasing of specific surface area, coupling effect between Co3O4 and nitrogen doped RGO as well as the existence of N-doping RGO which improved electron transferring of material in sensing process. Co3O4/N-RGO-0.5 nanocomposite has been proved to be a promising gas-sensing material for detecting ethanol at a low temperature

Chestnut-like CoFe2O4@SiO2@In2O3 nanocomposite microspheres with enhanced acetone sensing property

Chestnut-like CoFe2O4@SiO2@In2O3 nanocomposite microspheres with hierarchical structures were successfully synthesized by an in situ hydrothermal growth of In2O3 nanowires covering on the CoFe2O4@SiO2 microspheres. The resultant nanocomposite microspheres possessed an average size of 480 nm, while the In2O3 nanowires exhibited a relatively small diameter of 5 nm. Especially, the unique hierarchical structures showed a large pore size of about 18 nm and large specific surface area of 54 m(2) g(-1). The gas sensing results demonstrated that the chestnut-like CoFe2O4@SiO2@In2O3 nanocomposite micro spheres exhibited superior sensitivity and fast response speed to low-ppm-level acetone. That superior property might be attributed to the unique hierarchical structure of chestnut-like CoFe2O4@SiO2@In2O3 nanocomposite microspheres, which could be promising gas-sensing materials. (C) 2017 Published by Elsevier B.V.</p

Ultrahigh surface density of Co-N₂C single-atom-sites for boosting photocatalytic CO₂ reduction to methanol

Cobalt species as active sites for photocatalytic reduction of CO$_{2}$ to valuable products such as methanol have received increasing attention, however, it remains a huge challenge to achieve the high activity. Herein, a pyrolysis-induced-vaporization strategy was successfully employed to fabricate Co/g-C$_{3}$N$_{4}$ single-atom catalysts (Co/g-C$_{3}$N$_{4}$ SACs) with surface Co atom loading up to 24.6 wt%. Systematic investigation of Co/g-C$_{3}$N$_{4}$ SACs formation process disclosed that concentrated-H$_{2}$SO$_{4}$ exfoliation of g-C$_{3}$N$_{4}$ nanosheets (g-C$_{3}$N$_{4}$ NSs) as the substrate followed by a two-step calcination process is essential to achieve ultrahigh metal loading. It was found that the ultrahigh-density of Co single-atom sites were anchored on the g-C$_{3}$N$_{4}$ substrate surface and coordinated with two nitrogen and one carbon atoms (Co-N$_{2}$C). These single dispersed Co-N$_{2}$C sites on the g-C$_{3}$N$_{4}$ surface were found to act not only as electron gathering centers but also as the sites of CO$_{2}$ adsorption and activation, subsequently, boosting the photocatalytic methanol generation during light irradiation. As a result, the methanol formation rate at 4 h (941.9 μmol g$^{-1}$) over Co/g-C$_{3}$N$_{4}$-0.2 SAC with 24.6 wt% surface Co loading was13.4 and 2.2 times higher than those of g-C$_{3}$N$_{4}$ (17.7 μmol g$^{-1}$) and aggregated CoOx/g-C$_{3}$N$_{4}$-0.2 (423.9 μmol g$^{-1}$), respectively. Simultaneously, H$_{2}$ (18.9 μmol g$^{-1}$ h$^{-1}$), CO (2.9 μmol g$^{-1}$ h$^{-1}$), CH$_{4}$ (3.4 μmol g$^{-1}$ h$^{-1}$), C$_{2}$H$_{4}$ (1.1 μmol g$^{-1}$ h$^{-1}$), C$_{3}$H$_{6}$ (1.4 μmol g$^{-1}$ h$^{-1}$), and CH$_{3}$OCH$_{3}$ (3.3 μmol g$^{-1}$ h$^{-1}$) products were detected over Co/g-C$_{3}$N$_{4}$-0.2 SAC. Besides, the photocatalytic activity of the Co/g-C$_{3}$N$_{4}$-0.2 SAC for the reduction of CO$_{2}$ to methanol was stable within 12-cycle experiments (~48 h). This work paves a strategy to boost the photoreduction CO$_{2}$ activity via loading ultrahigh surface density single atomically dispersed cobalt active sites