Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

A Scalable Policy and SNMP Based Network Management Framework

Traditional SNMP-based network management can not deal with the task of managing large-scaled distributed network,while policy-based management is one of the effective solutions in network and distributed systems management.However,cross-vendor hardware compatibility is one of the limitations in policy-based management.Devices existing in current network mostly support SNMP rather than Common Open Policy Service(COPS)protocol.By analyzing traditional network management and policy-based network management,a scalable network management framework is proposed.It is combined with Internet Engineering Task Force(IETF)framework for policy-based management and SNMP-based network management.By interpreting and translating policy decision to SNMP message,policy can be executed in traditional SNMP-based device.

MTNR1B polymorphisms with CDKN2A and MGMT methylation status are associated with poor prognosis of colorectal cancer in Taiwan

BACKGROUND Identifying novel colorectal cancer(CRC)prognostic biomarkers is crucial to helping clinicians make appropriate therapy decisions.Melatonin plays a major role in managing the circadian rhythm and exerts oncostatic effects on different kinds of tumours.AIM To explore the relationship between MTNR1B single-nucleotide polymorphism(SNPs)combined with gene hypermethylation and CRC prognosis.METHODS A total of 94 CRC tumour tissues were investigated.Genotyping for the four MTNR1B SNPs(rs1387153,rs2166706,rs10830963,and rs1447352)was performed using multiplex polymerase chain reaction.The relationships between the MTNR1B SNPs and CRC 5-year overall survival(OS)was assessed by calculating hazard ratios with 95%CIs.RESULTS All SNPs(rs1387153,rs2166706,rs10830963,and rs1447352)were correlated with decreased 5-year OS.In stratified analysis,rs1387153,rs10830963,and rs1447352 risk genotype combined with CDKN2A and MGMT methylation status were associated with 5-year OS.A strong cumulative effect of the four polymorphisms on CRC prognosis was observed.Four haplotypes of MTNR1B SNPs were also associated with the 5-year OS.MTNR1B SNPs combined with CDKN2A and MGMT gene methylation status could be used to predict shorter CRC survival.CONCLUSION The novel genetic biomarkers combined with epigenetic biomarkers may be predictive tool for CRC prognosis and thus could be used to individualise treatment for patients with CRC.

College Basic Development Status Data Management System Based on Data Governance Framework

In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.

A Novel Smart Beta Optimization Based on Probabilistic Forecast

Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.

Policy Optimization Study Based on Evolutionary Learning

In order to achieve an intelligent and automated self-management network,dynamic policy configuration and selection are needed.A certain policy only suits to a certain network environment.If the network environment changes,the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.A policy optimization method based on evolutionary learning was proposed.For different shooting times,the priority of policy with high shooting times is improved,while policy with a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.

植物来源的磷脂酰胆碱可介导抗肺纤维化小RNA(HJT-sRNA-m7)进入哺乳动物细胞

肺纤维化是慢性进行性疾病,死亡率高,临床上除肺移植之外尚无其他明显有效治疗手段.本研究发现,来源于中药大花红景天(Rhodiola crenulata)的小RNA,HJT-sRNA-m7,可以显著降低纤维化因子的表达,在细胞和动物水平上均可改善纤维化症状.此外,本研究组在红景天熬制的汤汁中提取鉴定了一百多种脂质,其中大部分脂质也同时存在于中药蒲公英(Taraxacum mongolicum)、穿心莲(Andrographis paniculata)和金银花(lonicera japonica)中.选取其中两种磷脂酰胆碱PC(18:0/18:2)和PC(16:0/18:2),使之与植物小RNA形成脂质体,发现其可促进HJT-s RNA-m7等小RNA进入人肺和胃肠道细胞,降低纤维化靶基因的表达.实验结果揭示了植物来源小RNA通过脂质复合物进入人体的可能途径,为si RNA的临床应用提供了新的口服递送途径.