Facebook’s emotional consequences: Why Facebook causes a decrease in mood and why people still use it

Facebook is the world’s most popular online social network and used by more than one billion people. In three studies, we explored the hypothesis that Facebook activity negatively affects people’s emotional state. A first study shows that the longer people are active on Facebook, the more negative is their mood afterwards. The second study provides causal evidence for this effect by showing that Facebook activity leads to a deterioration of mood compared to two different control conditions. Furthermore, it was demonstrated that this effect is mediated by a feeling of not having done anything meaningful. With such negative outcomes for its users, the question arises as to why so many people continue to use Facebook on a daily basis. A third study suggests that this may be because people commit an affective forecasting error in that they expect to feel better after using Facebook, whereas, in fact, they feel worse.     

A triparametric family of three-step optimal eighth-order methods for solving nonlinear equations

A new triparametric family of three-step optimal eighth-order iterative methods free from second derivatives are proposed in this paper, to find a simple root of nonlinear equations. Convergence analysis as well as numerical experiments confirms the eighth-order convergence and asymptotic error constants.     

煤炭采制样过程中误差来源与分析

煤质分析中80%误差来源于采样,而16%来源于制样,因此采取有代表性煤样,并将其制成无偏差的分析煤样,是煤质分析中取得可靠数据的重要环节。通过对煤炭采制样过程中部分误差来源进行分析,并通过数据进行比较,提出相应的解决方案,以减少煤质分析过程中的误差和纠纷。     

浮筒液位计误差分析及处理

本文简单介绍了浮筒液位计的分类、测量原理及安装规范,对其各种测量误差的来源作出了详细的分析,并针对各种测量误差的特性,指出了它们的作用方式、影响范围及程度,在此基础上提出相应的抑制及消除方法。     

Half supervised coefficient regularization for regression learning with unbounded sampling

In this study, we investigate the consistency of half supervised coefficient regularization learning with indefinite kernels. In our setting, the hypothesis space and learning algorithms are based on two different groups of input data which are drawn i.i.d. according to an unknown probability measure ρ _X . The only conditions imposed on the kernel function are the continuity and boundedness instead of a Mercer kernel and the output data are not asked to be bounded uniformly. By a mild assumption of unbounded output data and a refined integral operator technique, the generalization error is decomposed into hypothesis error, sample error and approximation error. By estimating these three parts, we deduce satisfactory learning rates with proper choice of the regularization parameter.     

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.     

Vehicle speed measurement: Cosine error correction

Although laser devices are commonly used to measure distances and vehicle speed in traffic safety-related studies, they suffer from a drawback known as the cosine effect. Furthermore, such traffic safety studies analyze both vehicle speed and a large amount of geographical data. As such, the development of efficient and cost-effective techniques for correcting the cosine error and managing the data involved in vehicle speed studies are needed. This paper presents an algorithm for correcting cosine error based on a Geographic Information System (GIS) that makes use of vehicle speed measurements and Global Positioning System (GPS) coordinates. An experiment using more than 350 vehicle speeds was conducted to test the algorithm, and the case study shows that controlling the quality of laser-measured speeds in order to suppress low quality data or correct measurement errors due to the cosine effect is very useful.     

核电厂中事件发生机理与人因管理

分析了核电厂中事件发生的机理,阐述了人因失误发生的四要素及相互之间的关系,探讨了核电厂人因失误产生的特点,阐明了在核电厂中人因培训的原则是全员培训,其实施方法是根据工作人员不同的工作特点,将工作人员分为执行层、技术层、管理层,针对不同层次的工作人员开发不同的人因工具。