How To Reorganize Social Network For Better Knowledge Contribution During Mobile Collaboration? A Study Based On Anti-Social Behavioral Perspective

Mobile collaboration is an emerging kind of collaboration that adopts mobile devices (i.e., laptops, PDAs, and smart phones) and social media software to improve the efficiency and productivity of collaboration. However, many collaborative teams suffer from an anti-social behavior called social loafing. Social loafing will hinder knowledge exchange within the team and further influence team performance and project outcomes. Moreover, the state of an individual’s social loafing is unobservable and changes overtime, making it difficult to be identified in real time. Therefore, our research aims to investigate the evolution of social loafing and its impact on knowledge contribution in the mobile collaboration context. We propose a machine learning model to infer individuals’ unobserved and evolving social loafing state from the series of task behaviors (quantity and quality of the contributed knowledge). Also, we explore how one’s centrality in a social network affects his/her knowledge contribution behavior when he/she is in different social loafing states. We conduct an empirical study and the results show that individuals with high or low social loafing state are very ‘sticky’ to maintain the previous state and the centrality in the network only positively influences individuals in medium social loafing state. In conclusion, our research adopts a machine leaning method to infer the evolution of individuals’ social loafing and provides a comprehensive understanding of knowledge contribution in team work

Does the policy of rural land rights confirmation promote the transfer of farmland in China?

Land tenure security and land transfer markets are once again a topmost priority in the policy development agenda because of their expected outcomes in terms of equity and efficiency in the rural sector of China. The policy of rural land rights confirmation has been implemented since 2010 to enhance land tenure security and the transferability of farmland. However, only a few studies have been conducted on the effect of rural land rights confirmation on farmland transfer. Therefore, we use household-level survey data from 48 villages across Tianjin City and Shandong Province to explore whether rural land rights confirmation promotes the transfer of farmlands. Our empirical results show that rural land rights confirmation has significant and positive effects on the likelihood and amount of transfer-out land at the 5% significance level, but the effect on transfer-in farmland is insignificant. The results of the study have several policy implications. For instance, the agricultural comparative advantage should be improved through various agricultural subsidy policies. Moreover, the intermediary service network for farmland transfer should be established, and strengthening the non-farm employment skills and improving the non-agricultural employment market are necessary for the rural labour force

NB-IoT Uplink Synchronization by Change Point Detection of Phase Series in NTNs

Non-Terrestrial Networks (NTNs) are widely recognized as a potential solution to achieve ubiquitous connections of Narrow Bandwidth Internet of Things (NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is the uplink synchronization of Narrowband Physical Random Access procedure which refers to the estimation of time of arrival (ToA) and carrier frequency offset (CFO). Due to the large propagation delay and Doppler shift in NTNs, traditional estimation methods for Terrestrial Networks (TNs) can not be applied in NTNs directly. In this context, we design a two stage ToA and CFO estimation scheme including coarse estimation and fine estimation based on abrupt change point detection (CPD) of phase series with machine learning. Our method achieves high estimation accuracy of ToA and CFO under the low signal-noise ratio (SNR) and large Doppler shift conditions and extends the estimation range without enhancing Random Access preambles

Multi-Objective Feature Selection With Missing Data in Classification

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study

Measurement of Stimulated Raman Side-Scattering Predominance and Energetic Importance in the Compression Stage of the Double-Cone Ignition Approach to Inertial Confinement Fusion

Due to its particular geometry, stimulated Raman side-scattering (SRSS) drives scattered light emission at non-conventional directions, leading to scarce and complex experimental observations. Experimental campaigns at the SG-II UP facility have measured the scattered light driven by SRSS over a wide range of angles, showing an emission at large polar angles, sensitive to the plasma profile and laser polarization. Furthermore, direct comparison with back-scattering measurement has evidenced SRSS as the dominant Raman scattering process in the compression stage, leading to the scattering loss of about 5\% of the total laser energy. The predominance of SRSS was confirmed by 2D particle-in-cell simulations, and its angular spread has been corroborated by ray-tracing simulations. The main implication is that a complete characterization of the SRS instability and an accurate measurement of the energy losses require the collection of the scattered light in a broad range of directions. Otherwise, spatially limited measurement could lead to an underestimation of the energetic importance of stimulated Raman scattering

Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy

Purpose: To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods: Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results: The resulting R (2) (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ(2) test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (-1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (-0.67Gy ± 2.17Gy) and lower integral dose (-0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (-1.13 ± 2.34). Conclusions: The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites