The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

The Big Bang as the Creative Force of the Creation of the Universe

The paper considers the mechanism of the Big Bang energy influence on the creation of space-time fields of four structures of the Universe from the 1st type Ether (the Main Field and three spheres of the Relic). It explains how the Big Bang energy leads to the processes of “melting” in these structures, generating emergent properties that are different from their properties before the Big Bang. The key role of the Big Bang in completing the process of formation of 70% of DE is emphasized. It is shown that the Big Bang preceded the emergence of the furcation point, which chose several directions for the creation of cosmic matter—it was the combined efforts of these directions that created the visible worlds. The principle of dynamic equilibrium is considered the main criterion of the space-time field, in contrast to other physical fields, which is a necessary prerequisite for the quantization of the gravitational field. A spin particle is introduced, capable of emitting special particles—spitons, the characteristics of which are associated with the topology of the Mobius strip and determine the spinor properties of gravitational fields. The mechanism of interaction of particles of the 2nd type of Ether with the fields of space-time is described, allowing the creation of matter first and then the materiality of visible worlds. At the same time, the role of the “matter-negotiator” in the creation process of visible worlds of the Universe is especially highlighted. Since the new properties of gravitational fields go beyond Einstein’s standard theory of gravity, it is proposed to build a new theory of space-time that generalizes it and has a clear geometric interpretation. The proposed theory is based on the action built on a full set of invariants of the Ricci tensor. Within the framework of the Poincaré theory, the classification of furcation points is considered. The processes at the furcation point are described by the Gauss-Laplace curve, for which the principle of conservation of probability density is introduced when considering the transition at the furcation point to four different directions of development.

Big-BALB/c小鼠亚系选育前后的抗体制备效率比较研究

目的比较利用BALB/c和Big-BALB/c(简称B-BALB/c)两种亚系小鼠制备鼠痘及鼠肝炎单克隆抗体的效率,为今后通过杂交瘤进行体内诱导单克隆抗体制备时实验动物的选择提供参考。方法从常规繁育的BALB/c小鼠群体中选择4周龄体重超过正常动物5%(后期选种标准为超过10%)的个体进行选种并单独繁育,经10代选育制备B-BALB/c小鼠亚系。选择10~11周龄、雌雄各半的BALB/c小鼠和B-BALB/c小鼠各40只,分别接种用液体石蜡预处理后的鼠痘单克隆抗体杂交瘤细胞株G23或鼠肝炎单克隆抗体杂交瘤细胞株Y15,然后通过体内诱生法获取含单克隆抗体的小鼠腹水,利用间接ELISA法检验抗体效价,按照品系、性别和接种杂交瘤类型对小鼠进行分组,分析腹水产量、抗体效价和抗体产量以评估两种亚系小鼠的抗体制备效率。结果经10代选育后获得的10周龄雄性和雌性B-BALB/c小鼠体重分别比同周龄BALB/c小鼠增加了22.3%和12.8%。抗体制备过程中,B-BALB/c小鼠的耐受力比BALB/c小鼠更好,能够适应二次腹水采集;与BALB/c小鼠相比,B-BALB/c小鼠腹水产量和抗体效价均显著提高,尤以杂交瘤细胞株G23接种组更明显(均P<0.0001)。接种杂交瘤细胞株G23或Y15后,B-BALB/c小鼠的平均抗体产量(14.99×10^(4)U和33.22×10^(4)U)高于BALB/c小鼠(5.33×10^(4)U和19.31×10^(4)U)(均P<0.01)。接种杂交瘤细胞株G23后,B-BALB/c小鼠的平均单位体重的抗体产量(0.55×10^(4)U/g)高于BALB/c小鼠(0.23×10^(4)U/g)(P<0.0001)。接种杂交瘤细胞株Y15后,雌性B-BALB/c和BALB/c小鼠的单位体重抗体产量高于同亚系的雄性B-BALB/c小鼠(均P<0.01)。结论B-BALB/c小鼠在单克隆抗体体内诱导制备中可作为BALB/c小鼠的替代选择,能够达到减少实验动物使用数量、降低人力成本并提高抗体制备效率的目的。

教育大模型的发展现状、创新架构及应用展望

从通用大模型到教育大模型,是人工智能大模型技术深化发展的重要趋势。基于对教育大模型发展现状、典型案例、潜在挑战的分析,文章认为教育大模型是适用于教育场景、具有超大规模参数、融合通用知识和专业知识训练形成的人工智能模型,是大模型技术、知识库技术及各类智能教育技术的集成,能够推动人类学习和机器学习的双向建构,进而提出了应用驱动、共建共享的创新架构和“以学习者为中心”的未来应用场景,旨在建立人工智能大模型与各类数字化教育应用的开放接口,持续训练和完善能够更好地解决教育专业问题的教育场景模型,形成让广大师生常态化使用的智能教育开放模型集群和知识库,在提炼和萃取深度教育知识的同时,破解人工智能教育应用中的风险和挑战。

机器学习方法在盾构隧道工程中的应用研究现状与展望

随着盾构隧道工程信息化水平的提升,隧道掘进设备作业过程监测技术日益完善,记录的工程数据蕴含了掘进设备内部信息及其与外部地层的相互作用关系。机器学习因其数据分析能力强,无需先验的理论公式和专家知识,相较于传统的建模统计分析方法具有更大的应用空间。通过机器学习方法对收集的信息与数据进行深度挖掘并分析其内在联系,有助于提升盾构隧道工程建设的效率和安全保障水平。简述机器学习方法的基本原理,总结和分析机器学习方法在盾构工程中的应用研究状况,综述基于机器学习的盾构设备状态分析、盾构设备性能预测、围岩参数反演、地表变形预测和隧道病害诊断等5个方面的进展,并分析当前研究的不足。最后,分析盾构隧道工程向智能化方向发展需重点攻克的难题。

从技术赋能到整体重塑:公共体育服务数字化治理的模式重构、组织变革及推进机制

数字时代我国公共体育服务从“技术赋能”到“整体重塑”的数字化进程被视为一个重塑多元主体治理关系及其能力的演变历程,对于提高公共体育服务供给效率具有重要意义。运用文献资料法、逻辑分析法等厘清了公共体育服务数字化治理的基本内涵及现存困境,重新审视了其模式重构的核心维度,深入阐释了其组织变革的动因特征,重点讨论了其推进机制的实践要素。研究认为:理念重构、价值重构、方式重构等形成模式重构的核心维度;管理思维转换、运行范式转换、决策方式转换等构成组织变革的动因特征;管理机制、考核机制、协调机制等为推进机制的实践要素。

大地幔楔的两个深部碳循环圈:差异及宜居效应

本文总结评述了西太平洋板块深俯冲及在东亚地幔过渡带滞留和与之相关的晚白垩世和新生代东亚板内玄武岩共同构成的大地幔楔板内深部碳循环圈存在的证据;探讨了大地幔楔板内碳循环圈与岛弧系统碳循环圈在地幔碳酸盐化交代介质、碳酸盐种属、氧化还原反应及碳酸盐化地幔部分熔融发生机制等方面的差异和对显生宙大气氧含量保持稳定及温室效应周期性变化的影响;并指出了定量估计深俯冲碳酸盐歧化反应还原成金刚石而留在地幔过渡带和通过板内玄武质火山返还大气的碳各自所占有比例应是未来需研究的重要课题。

基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一):概念、挑战与研究框架

态势感知是能源互联网的核心任务,为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响,能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴,亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用,提出能源互联网数字孪生系统;进一步,延伸出一种新型数据驱动的态势感知方法论,即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间,继而借助复杂系统理论和大数据分析等工具予以解决,具体涉及孪生体建模、分析和认知等核心环节。此外,该文也讨论元宇宙技术对孪生体互联的增强作用,以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合,对于企业数字化转型、数字城市建设等具备参考价值。

近朱者赤:被纳入碳排放权交易试点的客户能否影响企业ESG表现?

绿色是实现高质量发展最亮的底色。以“双碳”目标为背景,基于企业与客户的二元供应链关系,实证检验被纳入碳排放权交易试点的客户对企业ESG表现的影响。研究表明:被纳入碳排放权交易试点的客户能够显著提升企业ESG表现。对企业ESG表现的分维度研究表明,被纳入碳排放权交易试点的客户对企业E和S维度的表现具有显著的提升作用,而对企业G维度表现的提升作用不显著。被纳入碳排放权交易试点的客户通过倒逼效应提升企业ESG表现。短期内,被纳入碳排放权交易试点的客户倒逼企业“末端治理”;长期内,被纳入碳排放权交易试点的客户倒逼企业“前端预防”。当企业位于低碳试点城市、处于高碳排放行业时,被纳入碳排放权交易试点的客户对企业ESG表现的提升作用更显著。企业ESG表现提升并不能直接增加企业价值,但被纳入碳排放权交易试点的客户能够倒逼企业提高环保投资水平和绿色技术创新水平,进而推动企业ESG表现显著增加企业价值,而且长期更有效。基于被纳入碳排放权交易试点的客户这一新研究视角,揭示了低碳减排与ESG实践的激励相容机制,不仅为深入理解我国碳试点的微观作用效果提供了新思路,而且对推进美丽中国建设、实现经济高质量发展具有重要的实践意义。

数据驱动教育数字化转型的信任机制——教育大数据全生命周期隐私增强模型的构建与典型应用场景分析

教育大数据正在成为驱动国家教育数字化转型战略行动的核心资源,带来了巨大的教育价值,但教育大数据的采集和分析产生了隐私侵害风险、信任危机等严峻问题,阻碍了数据的共享、开发和利用。如何增强教育大数据在开放共享、共建共用中的隐私保护和信任关系,成为制约数据驱动教育数字化转型的关键难题。对此,文章首先采用文献分析法,剖析了教育大数据隐私保护的研究现状与关键问题。随后,文章从应用场景角度设计了教育大数据全生命周期隐私增强框架,并基于此框架从技术实现角度构建了满足不同实体、不同阶段隐私需求的教育大数据全生命周期隐私增强模型及其应用流程。最后,文章介绍了该模型的典型应用场景,涉及学分与资质认证、教育协同与治理、可信评教、个性化学习服务等多个方面。文章的研究能够厘清教育大数据开放共享时的隐私保护关键问题和技术思路,可为实现教育数据要素化全生命周期的隐私保护、建立健全数据驱动教育数字化转型的信任机制提供参考。

气象基础信息一张图设计和实现

气象基础信息一张图作为气象数据资源与行业间数据融合的应用示范,通过构建时空联动的气象大数据应用基础,解决多源气象数据融合应用服务问题。气象基础信息一张图基于高分辨率气象网格数据空间化处理、长序列海量网格数据任意时空范围检索以及气象数据与三维地形融合服务3项关键技术,实现多源异构时空数据的统一接入、处理、存储、管理和服务。气象基础信息一张图整合基础地理、自然资源、生态环境、社会经济、气象五大数据资源,面向不同气象应用场景,通过动态组合构建专题服务,实现多源数据快速服务和高效应用。气象基础信息一张图为2022年北京冬奥会及冬残奥会的圆满举办等国家重大活动、2021年郑州“7·20”特大暴雨气象预报复盘服务以及气象数据的社会化服务共享提供应用服务保障,在服务“生命安全、生产发展、生活富裕、生态良好”方面提供有效支撑。

“人工智能+大数据”在医院服务流程管理中的应用与实践

智慧医疗和数字化转型是医疗领域发展的方向。以吉林大学中日联谊医院实践为例,探讨了“人工智能+大数据”在医院精细化服务流程管理中的应用,着重分析其在提升医疗服务水平和满足患者需求方面的潜在优势。通过智慧医疗路径设计和构建精细化智慧管理服务流程的框架,为医院数字化转型提供思路和方法。

数字普惠金融服务小微企业融资研究--以全国首个小微企业数字征信实验区为例

数字普惠金融将数字技术与普惠金融深度融合,为纾解小微企业融资困境提供了可行的新路。根据对全国首个小微企业数字征信实验区的案例剖析和区内588家小微企业的问卷调查结果,小微企业融资行为符合普惠金融“短、小、频、急”的典型特征,融资方式以信用贷款为主,资金用途多样,但是信息和信用不对称制约其融资可得性。实验区以服务小微企业为中心,构建综合金融服务体系、全国首个区块链数字征信平台、金融科技创新监管试点“三位一体”的运行模式,充分发挥了数字普惠金融的赋能效应。但是,要复制、推广实验区的建设经验,仍面临一些制约因素,因此要完善相关法律法规,稳步扩大金融科技创新监管试点,深化数字技术和数据要素融合应用,探索构建全国互联互通的数字征信平台和数字普惠金融“元宇宙”,以期在更大范围更好地服务小微企业融资和实体经济高质量发展。

基于大数据技术的服装智能制造创新模式

为进一步提高服装行业的智能化水平,在对目前服装行业面临的智能制造建设任务分析的基础上,探讨大数据技术在数字经济时代助力服装行业转型升级实现智能化过程中所发挥的关键作用。通过梳理大数据技术在服装流行趋势智能预测、数字化设计、智慧工厂生产、个性化营销4个方面的现有研究,提出由大数据技术驱动的服装智能制造创新模式,探讨从预测、设计、生产到销售整个闭环的智能化制造流程,以及基于大数据技术的服装智能制造发展趋势。

大数据驱动的企业与用户互动创新:理论框架与前沿课题

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