The Performance of Fractured Horizontal Well in Tight Gas Reservoir

Horizontal wells have been used to increase reservoir recovery, especially in unconventional reservoirs, and hydraulic fracturing has been applied to further extend the contact with the reservoir to increase the efficiency of development. In the past, many models, analytical or numerical, were developed to describe the flow behavior in horizontal wells with fractures. Source solution is one of the analytical/semi-analytical approaches. To solve fractured well problems, source methods were advanced from point sources to volumetric source, and pressure change inside fractures was considered in the volumetric source method. This study aims at developing a method that can predict horizontal well performance and the model can also be applied to horizontal wells with multiple fractures in complex natural fracture networks. The method solves the problem by superposing a series of slab sources under transient or pseudosteady-state flow conditions. The principle of the method comprises the calculation of semi-analytical response of a rectilinear reservoir with closed outer boundaries. A statistically assigned fracture network is used in the study to represent natural fractures based on the spacing between fractures and fracture geometry. The multiple dominating hydraulic fractures are then added to the natural fracture system to build the physical model of the problem. Each of the hydraulic fractures is connected to the horizontal wellbore, and the natural fractures are connected to the hydraulic fractures through the network description. Each fracture, natural or hydraulically induced, is treated as a series of slab sources. The analytical solution of superposed slab sources provides the base of the approach, and the overall flow from each fracture and the effect between the fractures are modeled by applying superposition principle to all of the fractures. It is assumed that hydraulic fractures are the main fractures that connect with the wellbore and the natural fractures are branching fractures which only connect with the main fractures. The fluid inside of the branch fractures flows into the main fractures, and the fluid of the main fracture from both the reservoir and the branch fractures flows to the wellbore. Predicting well performance in a complex fracture network system is extremely challenged. The statistical nature of natural fracture networks changes the flow characteristic from that of a single linear fracture. Simply using the single fracture model for individual fracture, and then adding the flow from each fracture for the network could introduce significant error. This study provides a semi-analytical approach to estimate well performance in a complex fracture network system

Does Money Laundering on Ethereum Have Traditional Traits?

As the largest blockchain platform that supports smart contracts, Ethereum has developed with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum has fostered the emergence of various illegal activities and money laundering by converting ill-gotten funds to cash. In the traditional money laundering scenario, researchers have uncovered the prevalent traits of money laundering. However, since money laundering on Ethereum is an emerging means, little is known about money laundering on Ethereum. To fill the gap, in this paper, we conduct an in-depth study on Ethereum money laundering networks through the lens of a representative security event on \textit{Upbit Exchange} to explore whether money laundering on Ethereum has traditional traits. Specifically, we construct a money laundering network on Ethereum by crawling the transaction records of \textit{Upbit Hack}. Then, we present five questions based on the traditional traits of money laundering networks. By leveraging network analysis, we characterize the money laundering network on Ethereum and answer these questions. In the end, we summarize the findings of money laundering networks on Ethereum, which lay the groundwork for money laundering detection on Ethereum

Financial Crimes in Web3-empowered Metaverse: Taxonomy, Countermeasures, and Opportunities

At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploit, wash trading, money laundering, and illegal services and shops. To this end, it is especially urgent and critical to summarize and classify the financial security threats on the Web3-empowered metaverse in order to maintain the long-term healthy development of its ecology. In this paper, we first outline the background, foundation, and applications of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy of the security risks and financial crimes that have emerged since the development of the decentralized metaverse. For each financial crime, we focus on three issues: a) existing definitions, b) relevant cases and analysis, and c) existing academic research on this type of crime. Next, from the perspective of academic research and government policy, we summarize the current anti-crime measurements and technologies in the metaverse. Finally, we discuss the opportunities and challenges in behavioral mining and the potential regulation of financial activities in the metaverse. The overview of this paper is expected to help readers better understand the potential security threats in this emerging ecology, and to provide insights and references for financial crime fighting.Comment: 24pages, 6 figures, 140 references, submitted to the Open Journal of the Computer Societ

Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Background and ObjectiveEndometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.Methods and ResultsAn mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein–protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver–operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively.ConclusionThis study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction

Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection

Unsupervised synthetic aperture radar (SAR) image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF) model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF) model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data

Comparison of the efficacy of oral contraceptives and levonorgestrel intrauterine system in intermenstrual bleeding caused by uterine niche

This study aimed to compare the effectiveness of oral contraceptives and a levonorgestrel intrauterine system in treating intermenstrual bleeding due to uterine niche. We retrospectively analyzed 72 patients with intermenstrual bleeding due to uterine niche from January 2017 to December 2021, of whom 41 were treated with oral contraceptives and 31 with a levonorgestrel intrauterine system. Post-treatment follow-ups at 1, 3, and 6 months were conducted to compare the efficiency and adverse effects between the two groups. In the oral contraceptive group, the effectiveness rate was higher than 80% at 1- and 3-months post-treatment and higher than 90% at 6 months. In the levonorgestrel intrauterine system group, the effectiveness rates were 58.06%, 54.84%, and 61.29% at 1, 3, and 6 months of treatment, respectively. Oral contraceptives were more effective than the levonorgestrel intrauterine system in treating intermenstrual bleeding caused by uterine niche (p < 0.05)

Arterial stiffness and obesity as predictors of diabetes: Longitudinal cohort study

BACKGROUND: Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. OBJECTIVE: This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. METHODS: This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. RESULTS: Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. CONCLUSIONS: This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe