Opportunities and Challenges of College Mental Health Education from the Perspective of Big Data

This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.

Research on Tax Risk Regulation and Strategic Management in the Context of Big Data

With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.

Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well. Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. Recently, Big data is one of the most important topics in IT industry. Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data. New types of data have different and new challenges also. A lot of researches treat with big data challenges starting from Doug Laney’s landmark paper, during the previous two decades;the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact. The present paper highlights important concepts of Fifty-six Big Data V’s characteristics. This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.

Research on Influencing Factors and Countermeasures of Risk Control of State-Owned Enterprises under the Background of Big Data

This study explores the risk control and response strategies of state-owned enterprises in the context of big data.Global economic uncertainty poses new challenges to state-owned enterprises,necessitating innovative risk management approaches.This article proposes response strategies from four key aspects:establishing a proactive risk management culture,building a foundation in technology and data,conducting big data-driven risk analysis,and implementing predictive analysis and real-time monitoring.State-owned enterprises can foster a proactive risk management culture by cultivating employee risk awareness,demonstrating leadership,and establishing transparency and open communication.Additionally,data integration and analysis,leveraging the latest technology,are crucial factors that can help companies better identify risks and opportunities.

The Big Data Challenge:How to Develop a Winning Strategy

Developing a winning Big Data strategy is challenging because it is as much about getting ahead of the trend and acquiring talent as it is about investing in new technology.To be successful you will need Data Scientists on your team,professionals who are adept with the analytical and visualization tools required to process and recognize patterns in data and who are equally comfortable with business concepts and operations.EMC's Howard Elias recommends three steps to help ensure your organization has the people it needs.

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

The Application of Big data Mining in Risk Warning for Food Safety

Comprehensive evaluation and warning is very important and difficult in food safety. This paper mainly focuses on introducing the application of using big data mining in food safety warning field. At first,we introduce the concept of big data miming and three big data methods. At the same time,we discuss the application of the three big data miming methods in food safety areas. Then we compare these big data miming methods,and propose how to apply Back Propagation Neural Network in food safety risk warning.

HOW BIG DATA MAKES CONSTRUCTION PROJECT RISK INTACT

Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction

The study on systemic risk of rural finance based on macro-micro big data and machine learning

It’s the basic premise of promoting the healthy development of rural finance and strengthen-ing macro-prudential supervision to measure the systemic risk of rural finance accurately.We establish the dynamic factor CAPM and make an all-round and multi-angle quantitative study on the systemic risk of rural finance in China by constructing macro-micro index system and using machine learning to reduce the dimension of high-dimensional data.Our results show that the dynamic factor CAPM of using macro-micro big data can evaluate systemic risk of rural finance more comprehensively and systematically,and machine learning performs well in processing high-dimensional data.In addition,China’s rural financial systemic risk is stable compared with the Shanghai and Shenzhen main markets,but it is also susceptible to macro and micro influ-enced factors.Finally,it is pointed out that the early warning system of rural financial systemic risk could be constructed at macro and micro level,respectively.

Combating emerging financial risks in the big data era:A perspective review

financial services:for example,GPS and Bluetooth inspire location-based services,and search and web technologies motivate online shopping,reviews,and payments.These business services have become more connected than ever,and as a result,financial frauds have become a significant challenge.Therefore,combating financial risks in the big data era requires breaking the borders of traditional data,algorithms,and systems.An increasing number of studies have addressed these challenges and proposed new methods for risk detection,assessment,and forecasting.As a key contribution,we categorize these works in a rational framework:first,we identify the data that can be used to identify risks.We then discuss how big data can be combined with the emerging tools to effectively learn or analyze financial risk.Finally,we highlight the effectiveness of these methods in real-world applications.Furthermore,we stress on the importance of utilizing multi-channel information,graphs,and networks of long-range dependence for the effective identification of financial risks.We conclude our survey with a discussion on the new challenges faced by the financial sector,namely,deep fake technology,adversaries,causal and interpretable inference,privacy protection,and microsimulations.

Atmospheric and ecosystem big data providing key contributions in reaching United Nations’Sustainable Development Goals

Big open data comprising comprehensive,long-term atmospheric and ecosystem in-situ observations will give us tools to meet global grand challenges and to contribute towards sustainable develop-ment.United Nations’Sustainable Development Goals(UN SDGs)provide framework for the process.We present synthesis on how Station for Measuring Earth Surface-Atmosphere Relations(SMEAR)observation network can contribute to UN SDGs.We describe SMEAR II flagship station in Hyytiälä,Finland.With more than 1200 variables measured in an integrated manner,we can under-stand interactions and feedbacks between biosphere and atmo-sphere.This contributes towards understanding impacts of climate change to natural ecosystems and feedbacks from ecosys-tems to climate.The benefits of SMEAR concept are highlighted through outreach project in Eastern Lapland utilizing SMEAR I observations from Värriöresearch station.In contrast to boreal environment,SMEAR concept was also deployed in Beijing.We underline the benefits of comprehensive observations to gain novel insights into complex interactions between densely popu-lated urban environment and atmosphere.Such observations enable work towards solving air quality problems and improve the quality of life inside megacities.The network of comprehensive stations with various measurements will enable science-based deci-sion making and support sustainable development by providing long-term view on spatio-temporal trends on atmospheric compo-sition and ecosystem parameters.

Banking with blockchain-ed big data

Blockchain is disrupting the banking industry and contributing to the increased big data in banking.However,there exists a gap in research and development into blockchain-ed big data in banking from an academic perspective,and this gap is expected to have a significant negative impact on the adoption and development of blockchain technology for banking.In hope of motivating more active engagement by academics,researchers and bankers alike,we present the most comprehensive review of the impact of blockchain in banking to date by summarizing the opportunities and challenges from a bankers perspective.In addition,we also discuss the impact that big data from blockchain will have on banking data analytics in future and show the increasing importance of filtering and signal extraction for the banking industry.Whilst there is evidence of selected banks adopting blockchain technology in isolation or small groups,we find the need for extensive research and development into several aspects of banking with blockchain to overcome the challenges which are currently hindering its adoption in banking across the globe.

Artificial intelligence-assisted psychosis risk screening in adolescents:Practices and challenges

Artificial intelligence-based technologies are gradually being applied to psychiatric research and practice.This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents.In terms of the practice of psychosis risk screening,the application of two artificial intelligence-assisted screening methods,chatbot and large-scale social media data analysis,is summarized in detail.Regarding the challenges of psychiatric risk screening,ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence,which must comply with the four biomedical ethical principles of respect for autonomy,nonmaleficence,beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings.By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens,we propose that assuming they meet ethical requirements,there are three directions worth considering in the future development of artificial intelligenceassisted psychosis risk screening in adolescents as follows:nonperceptual realtime artificial intelligence-assisted screening,further reducing the cost of artificial intelligence-assisted screening,and improving the ease of use of artificial intelligence-assisted screening techniques and tools.

Financial risk assessment model based on big data

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low.In order to solve this problem,a financial risk assessment model based on big data is proposed.In this method,the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment.The market-to-book ratio,asset–liability ratio,cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment.On this basis,the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification,and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment.Based on the intervention factors of financial market participants,quantitative regression analysis is performed,and according to the economic game theory,big data analysis and prediction of financial risk assessment are performed through the regression analysis method.Then the big data fusion and clustering algorithms are adopted for financial risk assessment.The simulation results show that this method can provide a relatively high accuracy in financial risk assessment,and has relatively strong adaptive evaluation capability to the risk coefficient,so it has a good application value in the prevention and control of risk factors in financial systems.

Big Earth Data for quantitative measurement of community resilience:current challenges,progresses and future directions

Quantitative assessment of community resilience can provide support for hazard mitigation,disaster risk reduction,disaster relief,and long-term sustainable development.Traditional resilience assessment tools are mostly theory-driven and lack empirical validation,which impedes scientific understanding of community resilience and practical decision-making of resilience improvement.In the advent of the Big Data Era,the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand,measure,and promote community resilience.This article provides a comprehensive review of the definitions of community resilience,along with the traditional and emerging data and methods of quantitative resilience measurement.The theoretical bases,modeling principles,advantages,and disadvantages of the methods are discussed.Finally,we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience.This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.

大数据下企业供应链风险管理与竞争情报融合模型构建--以华为公司为例

[研究目的]国际经贸对抗环境下,中美贸易摩擦将持续升级,国家层面的技术创新竞争使得企业面临的供应链风险大幅提升。同时,大数据改变了供应链风险管理模式,而竞争情报可以从理论、技术和方法等方面提升大数据下企业供应链风险管理能力,因此大数据下企业供应链风险管理与竞争情报的融合研究具有重要意义。[研究方法]采用文献分析、案例研究等方法,分析供应链风险管理与竞争情报的异同点,继而论证大数据下供应链风险管理与竞争情报融合的可行性和必要性,再辅之以标杆企业华为的供应链中断风险事件验证,最终构建大数据下华为供应链风险管理与竞争情报融合模型,并分析运行维护此模型的保障要素。[研究结论]研究表明,基于战略、组织、制度、技术和资源五要素的共同保障,大数据下供应链风险管理与竞争情报的融合能够实现供应链风险管理的全面性、及时性和有效性。

大数据背景下造纸企业经营风险识别与管理优化研究

近年来,大数据技术的快速发展颠覆了造纸企业传统的风险控制模式,大数据技术在企业经营管理中实现降本增效的趋势越发明显。基于此,简述了造纸企业经营风险的种类与控制现状,探究了大数据时代给造纸企业经营风险控制带来的积极效应与消极影响,结合某些造纸企业经营实例进行经营风险的识别与分析,并基于大数据背景提出造纸企业经营风险识别与管理优化策略,以期建立科学、有效的经营风险预防与管控体系,提升造纸企业经营管理水平。

大数据税收征管对企业风险承担的影响研究

当前我国企业的风险承担处于较低水平,这给企业的可持续发展和国家的长期繁荣带来了极大的挑战。借助“金税三期”工程这一准自然实验,采用多期双重差分法研究了大数据税收征管对企业风险承担的影响及作用机制,研究表明:大数据税收征管对企业风险承担具有显著促进作用。机制检验发现,大数据税收征管通过抑制代理行为、缓解融资约束、增进税负公平、优化营商环境来促进企业风险承担。进一步的分析表明,大数据税收征管对非国有企业和有产业政策扶持企业风险承担的促进作用更大。