How motivations of SNSs use and offline social trust affect college students' self-disclosure on SNSs: An investigation in China

Social Networking Sites (SNSs) have been proliferating and growing in popularity worldwide throughout the past few years, which have received significant interest from researchers. Previous literatures on Internet suggest that offline social trust influences online perceptions and behaviors, and there is linkage between trust and self-disclosure in face-to-face context. Adopting the Uses and Gratifications perspective as the theoretical foundation, this exploratory study aimed to address the roles that motivations of SNSs use and offline social trust play in predicting levels of self-disclosure on SNSs. Taking 640 snowballing sampling on Renren.com, the study found that there was an instrumental orientation of SNSs use among China's college students. Social interaction, self-image building and information seeking were three major motivations when college students use SNSs. As expected, the results also indicated that motivations of SNS use and offline social trust play a more important role in predicting self-disclosure on SNSs than demographics. This exploratory study gives an empirical insight in the influence of motivations of SNSs use and offline social trust on self-disclosure online. --Social Networking Sites,Motivations,Self-disclosure,Offline Social Trust

Evaluating the Cost of a Lapse in Life Insurance and its Implications on Developing a Policyholder Retention Strategy for a Company

In the insurance industry, many companies focus on policyholder retention as one of their key tools to retain premiums collected from customers. Customers may lapse after their purchases. Insurance companies will not receive premiums and have to pay out surrender benefits which makes it costly for an insurer. Indeed, understanding the cost of a lapse is important to retain policyholders. This paper focuses on the cost of a lapse in life insurance, and its implications on developing policyholder retention strategies. The first part of the paper summarizes the general background of life insurance, lapses, and conservation strategies. The second part introduces the modeling and simulations of three types of life insurance policies. The economic gain of a life insurance policy is defined as the accumulated value (AV) of past premiums plus the present value (PV) of future premiums until death/lapse less the PV of future benefits and less the AV of acquisition costs at issue. Next, the paper proceeds to analyze the cost of a lapse of a policy and quantify it as the difference between the economic gain of a policy at 0% lapse rate and that at 10% lapse rate. Based on the cost of a lapse, which is the same as the gain from conservation strategies, the insurance company will be able to rank its policies and prioritize which policies to focus on. Recommendations on developing conservation strategies to retain policyholders are discussed in the following section from both an actuarial perspective and a business perspective

How motivations of SNSs use and offline social trust affect college students' self-disclosure on SNSs: An investigation in China

Social Networking Sites (SNSs) have been proliferating and growing in popularity worldwide throughout the past few years, which have received significant interest from researchers. Previous literatures on Internet suggest that offline social trust influences online perceptions and behaviors, and there is linkage between trust and self-disclosure in face-to-face context. Adopting the Uses and Gratifications perspective as the theoretical foundation, this exploratory study aimed to address the roles that motivations of SNSs use and offline social trust play in predicting levels of self-disclosure on SNSs. Taking 640 snowballing sampling on Renren.com, the study found that there was an instrumental orientation of SNSs use among China's college students. Social interaction, self-image building and information seeking were three major motivations when college students use SNSs. As expected, the results also indicated that motivations of SNS use and offline social trust play a more important role in predicting self-disclosure on SNSs than demographics. This exploratory study gives an empirical insight in the influence of motivations of SNSs use and offline social trust on self-disclosure online

miR-124 Regulates the Phase of Drosophila Circadian Locomotor Behavior

Animals use circadian rhythms to anticipate daily environmental changes. Circadian clocks have a profound effect on behavior. In Drosophila, for example, brain pacemaker neurons dictate that flies are mostly active at dawn and dusk. miRNAs are small, regulatory RNAs ( approximately 22 nt) that play important roles in posttranscriptional regulation. Here, we identify miR-124 as an important regulator of Drosophila circadian locomotor rhythms. Under constant darkness, flies lacking miR-124 (miR-124(KO)) have a dramatically advanced circadian behavior phase. However, whereas a phase defect is usually caused by a change in the period of the circadian pacemaker, this is not the case in miR-124(KO) flies. Moreover, the phase of the circadian pacemaker in the clock neurons that control rhythmic locomotion is not altered either. Therefore, miR-124 modulates the output of circadian clock neurons rather than controlling their molecular pacemaker. Circadian phase is also advanced under temperature cycles, but a light/dark cycle partially corrects the defects in miR-124(KO) flies. Indeed, miR-124(KO) shows a normal evening phase under the latter conditions, but morning behavioral activity is suppressed. In summary, miR-124 controls diurnal activity and determines the phase of circadian locomotor behavior without affecting circadian pacemaker function. It thus provides a potent entry point to elucidate the mechanisms by which the phase of circadian behavior is determined. SIGNIFICANCE STATEMENT: In animals, molecular circadian clocks control the timing of behavioral activities to optimize them with the day/night cycle. This is critical for their fitness and survival. The mechanisms by which the phase of circadian behaviors is determined downstream of the molecular pacemakers are not yet well understood. Recent studies indicate that miRNAs are important regulators of circadian outputs. We found that miR-124 shapes diurnal behavioral activity and has a striking impact on the phase of circadian locomotor behavior. Surprisingly, the period and phase of the neural circadian pacemakers driving locomotor rhythms are unaffected. Therefore, miR-124 is a critical modulator of the circadian output pathways that control circadian behavioral rhythms

A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy

Binary Nonlinearization for AKNS-KN Coupling System

The AKNS-KN coupling system is obtained on the base of zero curvature equation by enlarging the spectral equation. Under the Bargmann symmetry constraint, the AKNS-KN coupling system is decomposed into two integrable Hamiltonian systems with the corresponding variables x, tn and the finite dimensional Hamiltonian systems are Liouville integrable

Electronic and magnetic properties of Lu and LuH2_2

Clarifying the electronic and magnetic properties of lutetium, lutetium dihydride, and lutetium oxide is very helpful to understand the emergent phenomena in lutetium-based compounds (such as room-temperature superconductivity). However, this kind of study is still scarce at present. Here, we report on the electronic and magnetic properties of lutetium metals, lutetium dihydride powders, and lutetium oxide powders. Crystal structures and chemical compositions of these samples were characterized by X-ray diffraction and X-ray photoemission spectroscopy, respectively. Electrical transport measurements show that the resistance of lutetium has a linear behavior depending on temperature, whereas the resistance of lutetium dihydride powders is independent of temperature. More interestingly, paramagnetism-ferromagnetism-spin glass transitions were observed at near 240 and 200 K, respectively, in lutetium metals. Our work uncovered the complex magnetic properties of Lu-based compounds

The two-way knowledge interaction interface between humans and neural networks

Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the "language" for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN

Invariant Solutions and Conservation Laws of the (2 + 1)-Dimensional Boussinesq Equation

Invariant solutions and conservation laws of the (2 + 1)-dimensional Boussinesq equation are studied. The Lie symmetry approach is used to obtain the invariant solutions. Conservation laws for the underlying equation are derived by utilizing the new conservation theorem and the partial Lagrange approach

Information Fields Navigation with Piece-Wise Polynomial Approximation for High-Performance OFDM in WSNs

Since Wireless sensor networks (WSNs) are dramatically being arranged in mission-critical applications,it changes into necessary that we consider application requirements in Internet of Things. We try to use WSNs to assist information query and navigation within a practical parking spaces environment. Integrated with high-performance OFDM by piece-wise polynomial approximation, we present a new method that is based on a diffusion equation and a position equation to accomplish the navigation process conveniently and efficiently. From the point of view of theoretical analysis, our jobs hold the lower constraint condition and several inappropriate navigation can be amended. Information diffusion and potential field are introduced to reach the goal of accurate navigation and gradient descent method is applied in the algorithm. Formula derivations and simulations manifest that the method facilitates the solution of typical sensor network configuration information navigation. Concurrently, we also treat channel estimation and ICI mitigation for very high mobility OFDM systems, and the communication is between a BS and mobile target at a terrible scenario. The scheme proposed here combines the piece-wise polynomial expansion to approximate timevariations of multipath channels. Two near symbols are applied to estimate the first-and second-order parameters. So as to improve the estimation accuracy and mitigate the ICI caused by pilot-aided estimation, the multipath channel parameters were reestimated in timedomain employing the decided OFDM symbol. Simulation results show that this method would improve system performance in a complex environment