The Big Five Model in Relation to Job Performance:A New Look at Organizational Psychology

The Big Five Theory is often regarded as psychology’s most influential personality theoretical approach.The goal of this study is to examine the role of the Big Five Theory in the workplace,especially which personality qualities are more likely to predict work success.Which traits should companies emphasize throughout the hiring and selection processes?How can businesses use the Big Five personality model to locate employees that are more productive,efficient,and devoted to the organization’s goals?A detailed assessment of existing recent research addresses the aforementioned issues.Following a review of many current articles on the subject,it was established that using this model had a positive influence on individual and group performance,working relationships,manager work performance,and workplace innovation.

The Interdisciplinary Research of Big Data and Wireless Channel: A Cluster-Nuclei Based Channel Model

Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big volume feature,considering the massive antennas,huge bandwidth and versatile application scenarios.This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication,especially for 5th Generation(5G) and beyond.Considering the big data research progress,then a cluster-nuclei based model is proposed,which takes advantages of both the stochastical model and deterministic model.The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects.Combining the channel properties variation principles with antenna size,frequency,mobility and scenario dug from the channel data,the proposed model can be expanded in versatile application to support future mobile research.

Why the Big Bang Model Cannot Describe the Observed Universe Having Pressure and Radiation

It has been recently shown that, since in general relativity (GR), given one time label t, one can choose any other time label t → t*= f(t), the pressure of a homogeneous and isotropic fluid is intrinsically zero (Mitra, Astrophys. Sp. Sc. 333, 351, 2011). Here we explore the physical reasons for the inevitability of this mathematical result. The essential reason is that the Weyl Postulate assumes that the test particles in a homogeneous and isotropic spacetime undergo pure geodesic motion without any collisions amongst themselves. Such an assumed absence of collisions corresponds to the absence of any intrinsic pressure. Accordingly, the “Big Bang Model” (BBM) which assumes that the cosmic fluid is not only continuous but also homogeneous and isotropic intrinsically corresponds to zero pressure and hence zero temperature. It can be seen that this result also follows from the relevant general relativistic first law of thermodynamics (Mitra, Found. Phys. 41, 1454, 2011). Therefore, the ideal BBM cannot describe the physical universe having pressure, temperature and radiation. Consequently, the physical universe may comprise matter distributed in discrete non-continuous lumpy fashion (as observed) rather than in the form of a homogeneous continuous fluid. The intrinsic absence of pressure in the “Big Bang Model” also rules out the concept of a “Dark Energy”.

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by “relaxing” the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.

A New Efficient Obstacle Avoidance Control Method for Cars Based on Big Data and Just-in-Time Modeling

This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.

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。

Big Data in Chinese Government Governance: Analysis of Decision-Making Model Innovation and Practice

The 19th National Congress of the Communist Party of China has put forward higher requirements for Chinese government governance. The government governance has developed to a higher stage. Meanwhile, it faces more challenges, like lack of top-level design and information sharing. To develop a government governance decision-making innovation model, we should make good use of big data to mine in the grassroots government data management network. Both the characteristics of the times and the experience of the practice have proven that big data can empower government governance and promote the construction of a service-oriented government.

Evaluation of Nutrient Components and Nutritive Quality of Larimichthys crocea(Big Yellow Croaker)in Different Aquaculture Models

To ascertain the nutrient components and nutritive quality of the flesh of big yellow croaker in three culture conditions （ traditional cage, offshore cage and cage-free）, basic nutritional components,amino acid,fatty acid and mineral elements were determined. The results indicated that crude protein in flesh of the big yellow croaker in cage-free culture was higher than that in offshore cage and much higher than that in traditional cage （ P 〈0.05）. Crude fat of the croaker cul- tured in the traditional cage was twice as high as that in cage-free culture, while that in the offshore cage was in the middle. Proline content in the cage-free culture was much higher than that in the offshore cage, and also than that in the traditional cage （P 〈 0.05 ）. There was no significant difference in the content of alanine, methionine and tryptophan （ P 〈 0.05）. Contents of other amino acids had no significant difference between the cage-free culture and offshore cage, but were much lower in the traditional cage （P 〈 0.05 ）. Top six fatty acids were 9-Hexadecenoic acid, palmitic acid,9-Octadecenoic acid, Octadecauoic acid, DHA and EPA. The palmitic acid content was the highest in cage-free culture and in traditional cage, 9-Octadeeenoic acid content was the highest in offshore cage. Content of unsaturat- ed fatty acids in cage-free culture, offshore cage and traditional cage was 63.60,66.32,57.67, respectively, and polyunsaturated fatty acid was 29.10, 28.57, and 24.40. Content of DHA in cage-free culture was significantly higher than that in the offshore and traditional cage. Content of zinc had no significant difference in three culture models. Content of phosphorus had no significant difference between that in cage-free culture and offshore cage, was lower in the traditional cage. The cage-free cultured croakers had the highest content of calcium and phosphorus. Content of selenium was about the same between the offshore cage and the traditional cage stocking, higher in the cage-free culture. This research has considerable application value for identifying quality and sources of the big yellow croakers.

如何理解,如何行动,如何成为?——人工智能时代教师专业发展的反思

人工智能时代的教师专业发展,其核心内涵是在“如何理解”“如何行动”“如何成为”三方面为教师提供有效支持。本文从智能时代教师职能职责和能力素养重构的角度构建了理解框架,从智能时代教师角色定位和教学方式重构的角度构建了行动框架,从智能时代教师发展路径重构的角度构建了设计框架。文章最后提出,“如何理解”的关键是深刻理解智能技术赋能教师的“留白”与“创新”;“如何行动”的关键是教师要在立德树人、技术治理与结构性创新上发挥“人在回路”的作用;“如何成为”的关键是要以大系统观发展教师的大视野、大思维和统整性大能力。

大数据与计算模型

当前,人工智能持续升温,大语言模型吸引了众多人士的关注,并在全球范围内掀起了一股热潮。人工智能的成功本质上不是大算力“出奇迹”,而是改变了计算模型。首先,肯定了数据对于人工智能的基础性作用,指出合成数据将是未来数据的主要来源。然后,回顾了计算模型的发展历程,重点介绍了神经网络模型与图灵模型的历史性竞争;指出了大模型的重要标志是机器涌现智能,强调大模型的本质是“压缩”;分析了大模型产生“幻觉”的原因。最后,呼吁科技界在智能化科研中要重视大科学模型。

World-Universe Model—Alternative to Big Bang Model

This manuscript provides a comparison of the Hypersphere World-Universe Model (WUM) with the prevailing Big Bang Model (BBM) of the Standard Cosmology. The performed analysis of BBM shows that the Four Pillars of the Standard Cosmology are model-dependent and not strong enough to support the model. The angular momentum problem is one of the most critical problems in BBM. Standard Cosmology cannot explain how Galaxies and Extra Solar systems obtained their substantial orbital and rotational angular momenta, and why the orbital momentum of Jupiter is considerably larger than the rotational momentum of the Sun. WUM is the only cosmological model in existence that is consistent with the Law of Conservation of Angular Momentum. To be consistent with this Fundamental Law, WUM discusses in detail the Beginning of the World. The Model introduces Dark Epoch (spanning from the Beginning of the World for 0.4 billion years) when only Dark Matter Particles (DMPs) existed, and Luminous Epoch (ever since for 13.8 billion years). Big Bang discussed in Standard Cosmology is, in our view, transition from Dark Epoch to Luminous Epoch due to Rotational Fission of Overspinning Dark Matter (DM) Supercluster’s Cores. WUM envisions Matter carried from the Universe into the World from the fourth spatial dimension by DMPs. Ordinary Matter is a byproduct of DM annihilation. WUM solves a number of physical problems in contemporary Cosmology and Astrophysics through DMPs and their interactions: Angular Momentum problem in birth and subsequent evolution of Galaxies and Extrasolar systems—how do they obtain it;Fermi Bubbles—two large structures in gamma-rays and X-rays above and below Galactic center;Diversity of Gravitationally-Rounded Objects in Solar system;some problems in Solar and Geophysics [1]. WUM reveals Inter-Connectivity of Primary Cosmological Parameters and calculates their values, which are in good agreement with the latest results of their measurements.

基于大数据的高校学生心理危机智能预警模型构建

目的本研究基于大学生心理健康测评数据,综合学生基本信息及日常行为数据,利用大数据、人工智能技术,探索建立大学生心理危机智能预警模型。方法采用整群抽样法,选取某高校部分在校大学生作为测试样本;采取大数据技术,分析提取影响大学生心理健康问题的特征量;采取神经网络技术,构建大学生心理危机预警模型。结果1)成功提取大学生心理健康问题的有效特征量,其中心理健康症状特征因子4个,分别是强迫、人际关系敏感、抑郁等3个单症状因子,1个多症状因子;学生基本信息特征量4个,分别是母亲教养方式、父亲教养方式、家庭经济条件、有无心理治疗(咨询)史;学生日常行为特征量2个,分别是学生学业情况和出勤情况。2)实现特征量归一化处理,通过数据对比分析及特征量的影响大小,分别对3个方面的10个特征量赋予权重并进行归一化处理。3)完成大学生心理危机预警模型构建。结论本研究提取了影响大学生心理健康问题的主要特征,建立了大学生心理危机预警指标体系,并结合心理危机预警等级,利用神经网络技术,搭建了大学生心理危机预警模型,为及时有效干预大学生心理危机提供了新的解决方案。

大数据牵引背景下的服装设计开发模式

在大数据牵引背景下,随着数据量呈爆炸式增长和大数据应用的不断深化,大数据资源以数据要素的形式牵引着传统服装设计模式的改变。通过对服装设计相关的大数据进行分类甄别,探讨大数据在多方面赋能服装设计协同价值创造,多维度比较传统服装设计开发与以大数据牵引的服装设计开发模式,以大数据赋能服装精准设计,构建基于大数据引导下的服装设计框架;将该框架应用于MZ品牌的开发案例中,从商品企划、设计开发和预售下单3个方面论述大数据在服装设计开发中的实践应用。大数据时代设计模式的演变将更加聚焦于设计师与海量数据之间的相互作用,应用大数据牵引服装设计开发模式的迭代,可实现更高精准度和更快反应能力的服装设计开发。

“助”与“替”:生成式AI对学术研究的双重效应

生成式AI应用于学术研究,揭开了知识生产的新篇章,它可以通过“助”读、“助”知、“助”写和“助”思来助力人的学术研究,自动化一些准备性或辅助性的工作,减轻人的认知负荷,使人将主要精力集中学术创新的关键环节,提高学术知识生产的效率。但对于生成式AI辅助功能的过度使用,可能导致“替”读、“替”知、“替”写、“替”思等负面效应,带来人的学术能力多向度退化。如何“助而不替”地合理使用生成式AI,是智能时代人机合作进行学术知识生产的新课题。

人工智能和社会科学研究

人工智能属于通用目的技术,不仅能够引发产业变革,而且能够催生新科学研究范式。随着以生成式人工智能为代表的通用人工智能的发展,人工智能的应用成为社会科学研究范式创新的前沿。数据的高维度、编程语言的强结构性和长程关联性、以参数形式表征的隐式知识库和预训练的结构重整带来新知识和模式发现,为社会科学研究创造出新的发展空间。同时,数据隐私、价值观和研究结论的“黑箱”化带来了人工智能在社会科学应用中的新挑战。如何把大模型的知识发现和基于研究者实践经验的探索式研究相结合,形成人机共生演进的知识生产方式,是社会科学研究中人工智能应用的方向。

面向数据密集型科研的科学数据管理模式研究

数据密集型科研范式下,科学研究中产生越来越多的科学数据,科学数据管理变得尤为重要。在梳理国内外相关研究的基础上,剖析数据密集型科研环境下对科学数据管理的新要求,结合数据生命周期理论分析科学数据管理各个阶段任务,构建相应的科学数据管理模式,提出多方主体实践的对策建议,以推动大数据时代的科学数据管理与共享。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

大数据背景下工商管理学生数据分析能力素质模型的构建与应用

在大数据时代背景下,企业对工商管理人才的数据分析能力要求提升,这也对高校现有工商管理学生的培养方式提出了挑战。基于访谈、岗位分析和理论分析,工商管理学生应具备数据分析知识和技能、数据思维和数据态度,据此构建数据分析能力素质模型,并建立相应的测量模型和指标体系,包括数据获取能力、数据预处理能力、数据分析能力、数据应用能力、数据思维和数据态度六个方面,运用数据分析能力素质模型能够为高校培养工商管理学生数据分析能力提供理论支持与实践建议。

融合大数据的校园公权力监督机制研究与模型构建

通过运用大数据技术,构建了一种校园公权力监督模型(CPASM),旨在提升教育资源配置的透明度和公平性,以及优化教学与行政决策。利用案例分析法,聚焦于A学院计算机应用技术专业,探讨了大数据在财务管理、教学质量评估和行政决策过程中的应用。CPASM模型结合时间序列分析和外部因素分析,应用自回归积分滑动平均(ARIMA)模型处理数据,同时将校园公权力动态变化纳入考量。CPASM模型的拟合与验证结果表明,其预测的均方误差(MSE)、均方根误差(RMSE)较低,确定系数(R^(2))接近1,准确地描绘了财务趋势,有助于管理层进行财务规划和资源配置。

基于用户性格和语义-结构特征的文本评论情感分类方法

由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments,BF_Bi GAC).依据大五人格模型能够有效表达用户性格的优势,通过计算不同维度性格得分,从评论文本中获取用户性格特征.利用双向门控循环单元(Bidirectional Gated Recurrent Unit,Bi GRU)和卷积神经网络(Convolutional Neural Network,CNN)可以有效提取文本上下文语义特征和局部结构特征的优势,提出一种基于Bi GRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响,引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合,以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明,BF_Bi GAC在分类准确率(Accuracy)和加权macro F_(1)值(F_(w))上均获得较好表现,相对于拼接Bi GRU、CNN的情感分类方法(Sentiment Classification Method Concatenating Bi GRU and CNN,Bi G-RU_CNN)在Accuracy值上分别提升0.020、0.012、0.017及0.011,相对于拼接CNN、Bi GRU的情感分类方法(Sentiment Classification Method Concatenating CNN and Bi GRU,Conv Bi LSTM)F_(w)值上分别提升0.022、0.013、0.028及0.023;相对于预训练模型BERT和Ro BERTa,BF_Bi GAC在保证分类精度的情况下获得了较高的运行效率.