Intraday volatility analysis of CSI 300 index futures: a dependent functional data method

This study introduces a new volatility model based on dependent functional data to investigate the intraday volatility characteristics of CSI 300 in the context of high-frequency data. The volatility curve is fitted and reconstructed using three methods: functional principal component analysis, Newey-West kernel, and truncationfree Bartlett kernel. We adopt a functional time series approach for short-term dynamic forecasting. The empirical results show that the proposed dependent functional volatility estimation model based on the long-term covariance of the truncated Bartlett kernel can accurately capture the intraday volatility trajectory and outperforms other models in terms of forecast accuracy and profitability. This study improves the volatility-related research methodology, which is conducive to discovering the price formation mechanism of the stock index futures market and improving risk management capabilities

GraphGPT: Graph Learning with Generative Pre-trained Transformers

We introduce \textit{GraphGPT}, a novel model for Graph learning by self-supervised Generative Pre-training Transformers. Our model transforms each graph or sampled subgraph into a sequence of tokens representing the node, edge and attributes reversibly using the Eulerian path first. Then we feed the tokens into a standard transformer decoder and pre-train it with the next-token-prediction (NTP) task. Lastly, we fine-tune the GraphGPT model with the supervised tasks. This intuitive, yet effective model achieves superior or close results to the state-of-the-art methods for the graph-, edge- and node-level tasks on the large scale molecular dataset PCQM4Mv2, the protein-protein association dataset ogbl-ppa and the ogbn-proteins dataset from the Open Graph Benchmark (OGB). Furthermore, the generative pre-training enables us to train GraphGPT up to 400M+ parameters with consistently increasing performance, which is beyond the capability of GNNs and previous graph transformers. The source code and pre-trained checkpoints will be released soon\footnote{\url{https://github.com/alibaba/graph-gpt}} to pave the way for the graph foundation model research, and also to assist the scientific discovery in pharmaceutical, chemistry, material and bio-informatics domains, etc.Comment: 9 page

Evaluating the Sealing Effectiveness of a Caprock-Fault System for CO 2

An effective sealing system is crucial for CO2-EOR storage, and these sealing systems are typically composed of the caprocks and faults that surround a reservoir. Therefore, the sealing effectiveness of a caprock-fault system must be evaluated at various stages of CO2-EOR storage projects. This paper presents a new evaluation framework that considers specific site characteristics and a case study on the sealing effectiveness of the caprock-fault system in the Shengli Oilfield. The proposed method is a weighted ranking system where a set of 17 indicators has been developed for the assessment and ranking of the G89 block in terms of their sealing ability for CO2 sequestration. Additional indicators are involved in the method, such as the newly proposed parameter, frontier displacement work which reflects the influence of formation pressure, displacement pressure resistance, and caprock thickness. The new approach considers the sealing mechanisms of caprocks and faults as well as the configuration relationships between them. The method was used to evaluate the sealing effectiveness of the G89 block that has a considerable number of faults and good sealing ability of caprock in the Shengli Oilfield

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem

An Improved Animal Migration Optimization Algorithm for Clustering Analysis

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems is k-means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of the k-means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of the k-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem

Leptomeningeal enhancement of myelin oligodendrocyte glycoprotein antibody-associated encephalitis: uncovering novel markers on contrast-enhanced fluid-attenuated inversion recovery images

BackgroundMyelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a newly defined autoimmune inflammatory demyelinating central nervous system (CNS) disease characterized by antibodies against MOG. Leptomeningeal enhancement (LME) on contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) images has been reported in patients with other diseases and interpreted as a biomarker of inflammation. This study retrospectively analyzed the prevalence and distribution of LME on CE-FLAIR images in children with MOG antibody-associated encephalitis (MOG-E). The corresponding magnetic resonance imaging (MRI) features and clinical manifestations are also presented.MethodsThe brain MRI images (native and CE-FLAIR) and clinical manifestations of 78 children with MOG-E between January 2018 and December 2021 were analyzed. Secondary analyses evaluated the relationship between LME, clinical manifestations, and other MRI measures.ResultsForty-four children were included, and the median age at the first onset was 70.5 months. The prodromal symptoms were fever, headache, emesis, and blurred vision, which could be progressively accompanied by convulsions, decreased level of consciousness, and dyskinesia. MOG-E showed multiple and asymmetric lesions in the brain by MRI, with varying sizes and blurred edges. These lesions were hyperintense on the T2-weighted and FLAIR images and slightly hypointense or hypointense on the T1-weighted images. The most common sites involved were juxtacortical white matter (81.8%) and cortical gray matter (59.1%). Periventricular/juxtaventricular white matter lesions (18.2%) were relatively rare. On CE-FLAIR images, 24 (54.5%) children showed LME located on the cerebral surface. LME was an early feature of MOG-E (P = 0.002), and cases without LME were more likely to involve the brainstem (P = 0.041).ConclusionLME on CE-FLAIR images may be a novel early marker among patients with MOG-E. The inclusion of CE-FLAIR images in MRI protocols for children with suspected MOG-E at an early stage may be useful for the diagnosis of this disease

MDM2 promotes cancer cell survival through regulating the expression of HIF-1α and pVHL in retinoblastoma

Hypoxia is an important tumor feature and hypoxia-inducible factor 1 (HIF-1) is a master regulator of cell response to hypoxia. Mouse double minute 2 homolog (MDM2) promotes cancer cell survival in retinoblastoma (RB), with the underlying mechanism remaining elusive. In this study, we investigated the role of MDM2 and its relation to HIF-1α in RB. Expression analysis on primary human RB samples showed that MDM2 expression was positively correlated with that of HIF-1α while negatively correlated with von Hippel-Lindau protein (pVHL), the regulator of HIF-1α. In agreement, RB cells with MDM2 overexpression showed increased expression of HIF-1α and decreased expression of pVHL, while cells with MDM2 siRNA knockdown or MDM2-specific inhibitor showed the opposite effect under hypoxia. Further immuno-precipitation analysis revealed that MDM2 could directly interact with pVHL and promotes its ubiquitination and degradation, which consequently led to the increase of HIF-1α. Inhibition of MDM2 and/or HIF-1α with specific inhibitors induced RB cell death and decreased the stem cell properties of primary RB cells. Taken together, our study has shown that MDM2 promotes RB survival through regulating the expression of pVHL and HIF-1α, and targeting MDM2 and/or HIF-1α represents a potential effective approach for RB treatment

AaABF3, an Abscisic Acid–Responsive Transcription Factor, Positively Regulates Artemisinin Biosynthesis in Artemisia annua

Artemisinin is well known for its irreplaceable curative effect on the devastating parasitic disease, Malaria. This sesquiterpenoid is specifically produced in Chinese traditional herbal plant Artemisia annua. Earlier studies have shown that phytohormone abscisic acid (ABA) plays an important role in increasing the artemisinin content, but how ABA regulates artemisinin biosynthesis is still poorly understood. In this study, we identified that AaABF3 encoded an ABRE (ABA-responsive elements) binding factor. qRT-PCR analysis showed that AaABF3 was induced by ABA and expressed much higher in trichomes where artemisinin is synthesized and accumulated. To further investigate the mechanism of AaABF3 regulating the artemisinin biosynthesis, we carried out dual-luciferase analysis, yeast one-hybrid assay and electrophoretic mobility shift assay. The results revealed that AaABF3 could directly bind to the promoter of ALDH1 gene, which is a key gene in artemisinin biosynthesis, and activate the expression of ALDH1. Functional analysis revealed that overexpression of AaABF3 in A. annua enhanced the production of artemisinin, while RNA interference of AaABF3 resulted in decreased artemisinin content. Taken together, our results demonstrated that AaABF3 played an important role in ABA-regulated artemisinin biosynthesis through direct regulation of artemisinin biosynthesis gene, ALDH1