青海省东乘公麻金矿床Ⅵ号矿体原生晕特征及深部找矿预测

东乘公麻金矿床是青海省巴颜喀拉造山带北缘Au-Sb成矿带上新发现的中型金矿床,由于受“生态保护第一,尊重群众意愿”政策影响,勘查工作主要以深部勘查为主,本文选择该矿床有代表性的VI号矿体,研究其构造原生晕轴向分带序列。研究结果表明：VI号矿体原生晕分带序列为：（Au、Sb、As）→（Bi、Pb）→（Ag、Cu、Zn）;分带序列呈现“反分带”趋势,地球化学参数“反转”,前、尾晕共存,预测VI号矿体深部延伸较好或有隐伏矿体存在的可能性,圈定了成矿靶区,以期为矿山下一步探矿工作提供科学依据。     

Prediction of stress levels in the workplace using surrounding stress

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification.     

MTGCN: A multi-task approach for node classification and link prediction in graph data

Both node classification and link prediction are popular topics of supervised learning on the graph data, but previous works seldom integrate them together to capture their complementary information. In this paper, we propose a Multi-Task and Multi-Graph Convolutional Network (MTGCN) to jointly conduct node classification and link prediction in a unified framework. Specifically, MTGCN consists of multiple multi-task learning so that each multi-task learning learns the complementary information between node classification and link prediction. In particular, each multi-task learning uses different inputs to output representations of the graph data. Moreover, the parameters of one multi-task learning initialize the parameters of the other multi-task learning, so that the useful information in the former multi-task learning can be propagated to the other multi-task learning. As a result, the information is augmented to guarantee the quality of representations by exploring the complex constructure inherent in the graph data. Experimental results on six datasets show that our MTGCN outperforms the comparison methods in terms of both node classification and link prediction.     

中国智能网联汽车产业人才需求预测研究

为科学预测智能网联汽车（ICV）人才需求,采用定性与定量研究相结合的方法,确定ICV产业人才的结构,明确以研发技术人才为预测对象,构建分层级多指标的ICV人才需求预测模型,基于情景分析,得出未来5年ICV产业人才的需求量,并分析ICV产业不同类型研发技术人才以及不同业务模块人才的需求差异。     

我国2008-2011年16种体育核心期刊载文特征研究

以“题要分类法”研究了2008年版16种中文体育核心期刊2008-2011年载文（共15582篇）的特征 ,把载文归纳成十大类型,简述了文章的意义并对其最终归属进行了预测研究。分类结果显示：不定式类 型文章最多,焦点争议类型文章最少,预测未来类型的文章比较薄弱。建议各期刊社尝试拟照十大类型进 行栏目选择设置,突出办刊亮点,既可使作者可根据自己的专业特点确定投稿方向,克服机动栏目变化大 的困难,同时可为读者及体育科研人员提供快速查找帮助。     

世界女子跳马技术发展回顾及展望简

通过对世界女子跳马技术的发展进行整理和分析,旨在梳理女子跳马技术发展的轨迹,以期探究未来女子跳马技术发展趋势.结果表明：女子跳马技术发展经历了三个发展时期;由于夺冠权重发生变更,当前女子跳马难度技术发展减缓;未来女子跳马踺子上板类技术依然为发展主流,但塚原类和前手翻类动作将会被更多运动员选择,高难度技术动作仍是攻克的目标,而规则的完善,使难与美达到前所未有的统一.     

预测2015年高考数列考点

高考一直是大家关注的焦点,而数列也是高考的必考的重要知识点。2014年的高考已过,2015年高考成为着重点。本论文主要以以往各届高考真题为依据,探讨预测2015年数列知识点的试卷分布和试题特点。     

公众要素预报实习中温度预报技术探讨

温度预报是公众要素预报的难点,为了更好地让学生完成这部分实习内容,本文在分析目前气象部门温度预报方法的基础上,对授课重点逐一剖析,力求在有限实习时间内,使学生全面、准确地掌握温度预报技术的要领,提高学生温度预报准确率,从而达到优化实习内容和实习方式的目的。     

第8-12届全运会女子七项全能运动员成绩分析及预测模型的建立

主要运用文献资料法、数理统计法对我国女子七项全能运动员的成绩进行了数据统计及关联度分析。结果表明:女子七项全能总成绩中影响最大的单项成绩是100m栏,其次是铅球和跳远,然后是200m,相对影响较小的是800m和跳高,而总分与各类项目的关联中,速度类项目与总分的关联度最高,其次是投掷类项目而对于耐力类和跳跃类项目尤其是跳跃类项目相比较而言关联度最低。基于灰色建模理论的灰色预测法,建立我国女子七项全能的灰色GM(1,1)预测模型,为我国女子七项全能运动的发展趋势进行预测提供理论性依据。     

江苏高校龙狮运动进入社区体育的前景探析

龙狮运动有着悠久的历史渊源和广泛的群众基础,具有较强的健身性、观赏性、娱乐性和竞技性。社区体育是我国体育事业的重要组成部分,直接关系到占全国人口绝大多数成年人的身心健康、体格健美与幸福生活指数。加速高校龙狮运动发展就必须加速其社会化进程,让龙狮运动进入社区。调查显示,江苏省社区体育中鲜有龙狮运动的身影,本文从普及全民健身的角度,对我国城市社区体育的现状及存在的问题进行分析,论述了高校龙狮运动与社区体育互动发展是必要可行的。认为二者能够取长补短、优势互补,实现"双赢"局面,并提出了将高校龙狮运动与社区体育有机结合的新思路。