Mining the Relationship between Emoji Usage Patterns and Personality

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social Media (ICWSM) 201

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

open access articleIn this study, a three-step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large-scale rotatingmachinery in the presence of scarce failure data. In the feature extraction step, eight time-domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendabilitymetrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals-based method. In the prognostic step, graymodel is used in combinationwith empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected froman operational industrial centrifugal pump and a compressor

Research on ordered charge and discharge of cluster electric vehicle based on index selection

The basic characteristics of electric vehicles are important basis for studying the behavior of electric vehicles. According to the basic characteristics of electric vehicles, this paper establishes an electric vehicle convergence model and its control strategy with demand-side response. Taking into account the demand for electric vehicles, electric vehicle aggregators and power companies, reducing the cost of control, while reducing the impact on electric vehicles. Based on the real-time state of charge, the conditions of electric vehicle in the network and other factors to build the assessment model of the scheduling potential, and then put forward the demand response indicators of electric vehicles, and give the corresponding aggregation strategy. considering the multiple constraints , such as the cost constraints of electric vehicles participating in grid regulation, the charging requirements of electric vehicle owners, and the battery consumption of electric vehicles, a control strategy model is proposed for electric vehicles participating in demand response of power systems. The simulation test shows that the aggregation strategy can not only meet the travel needs of electric vehicle owners, but also reduce the impact on the electric vehicle caused by frequent switching of charge and discharge status. In addition, it can also reduce the cost of grid regulation

An adaptive synchroextracting transform for the analysis of noise contaminated multi-component non-stationary signals

The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under vary-ing speed or/and load conditions. However, the time frequency representation (TFR) of a signal pro-duced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to im-prove the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals

Controlled growth of atomically thin transition metal dichalcogenides via chemical vapor deposition method

Two-dimensional (2D) transition metal dichalcogenides (TMDC) have attracted great research interest due to their potential application in electronics, optoelectronics, electrocatalysis, and so on. To satisfy expectations, high-quality materials with designed structures are highly desired through the controlled growth of TMDC. Chemical vapor deposition (CVD) offers facile control in synthesizing 2D TMDC as well as a high degree of freedom for tuning their structures and properties. In this review, we elaborate on recent advances in CVD techniques for synthesizing atomically thin TMDC. The novel techniques for achieving continuous uniform 2D films are provided along with insights into the growth mechanisms. Moreover, approaches toward high-quality materials by growing large single crystals and oriented domains are thoroughly summarized. The strategies for controlling the crystal thickness, phase, and doping condition are also discussed. Finally, we address the challenges in the field and prospective research directions

Recolonization of marginal coral reef flats in response to recent sea-level rise

In an era of global change and rising sea levels, the capacity for inshore reefs to survive is increasingly unclear. We report on recent colonization of an inshore reef-flat environment at Sanya Bay, northern South China Sea, in shallow, muddy, eutrophic, and turbid conditions, which are widely viewed as marginal for sustained coral growth. U-Th dating of fossil Acropora substrate indicated that the reef flat has existed in a dormant state since ~5,400\ua0years BP, with no vertical space available to accommodate coral expansion. Our surveys revealed that populations of free-living Porites compressa have recolonized the reef flat through asexual fragmentation, covering 13.9\ua0±\ua01.3% of reef-flat substrates. Age-frequency analysis indicated that the majority (86%) of P.\ua0compressa colonies were less than 30\ua0years old. Analysis of long-term sea-level data indicated that recent recolonization of the reef flat occurred in response to a sea-level rise of 16.2\ua0±\ua00.6\ua0cm over the past 30\ua0years (1987–2016). Modern sea-level rise at Sanya Bay appears to have turned on reef growth which has existed in a senescent turned off state for over five millennia. The asexual life history strategy of P.\ua0compressa colonies, which involves forming free-living colonies (coralliths), allows them to overcome turbid environmental conditions that are otherwise adverse to sexual recruitment. Our results provide novel insight into the response of marginal habitats to sea-level rise, and suggest that coral cover on degraded coral reef flats could increase under future sea-level rise, albeit with assemblages dominated by a few well-adapted species