Examining retweeting behavior on social networking sites from the perspective of self-presentation.

On social networking sites, people can express themselves in a variety of ways such as creating personalized profiles, commenting on some topics, sharing their experiences and thoughts. Among these technology-enabled features, retweeting other-sourced tweet is a powerful way for users to present themselves. We examine users' retweeting behavior from the perspective of online identity and self-presentation. The empirical results based on a panel dataset crawled from Twitter reveal that, people are prone to retweet topics they are interested in and familiar with, in order to convey a consistent and clear online identity. In addition, we also examine which user groups exhibit a stronger propensity for a clear online identity, considering the practical value of these users to both social media platforms and marketers. By integrating self-presentation theory with social influence theory and social cognitive theory, we propose and confirm that users with higher value in online self-presentation efficacy and users who are more involved with the social media platform have a stronger than average propensity to maintain a consistent online identity, and thus are more likely to retweet familiar topics. These users are characterized by (1) owning a larger number of followers, (2) authoring longer and more original tweets than average, (3) being active in retweeting other-sourced posts. This study contributes to our understanding of SNS users' retweeting behavior and adds to the emerging line of research on online identity. It also provides insights on how microblogging service providers and enterprises can promote people's retweeting behavior.

Public Health Risk Evaluation through Mathematical Optimization in the Process of PPPs.

The public sector is becoming increasingly appealing. In the context of declining public money to support health studies and public health interventions, public-private partnerships with entities (including government agencies and scientific research institutes) are becoming increasingly important. When forming this type of cooperation, the participants highlight synergies between the private partners and the public's missions or goals. The tasks of private and public sector actors, on the other hand, frequently diverge significantly. The integrity and honesty of public officials, institutions, trust, and faith in those individuals and institutions may all be jeopardized by these collaborations. In this study, we use the institutional corruption framework to highlight systemic concerns raised by PPPs affiliated with the governments of one of South Asia's countries. Overall analytical frameworks for such collaborations tend to downplay or disregard these systemic impacts and their ethical implications, as we argue. We offer some guidelines for public sector stakeholders that want to think about PPPs in a more systemic and analytical way. Partnership as a default paradigm for engagement with the private sector needs to be reconsidered by public sector participants. They also need to be more vocal about which goals they can and cannot fulfill, given the limitations of public financing resources.

Optimization of Multi-Objective Mobile Emergency Material Allocation for Sudden Disasters.

The mobile emergency system is a new emergency mode that provides a solution to deal with increasingly frequent sudden disasters by reasonably allocating mobile emergency facilities and optimizing the allocation of mobile emergency materials. We consider mobile emergency cost and mobile emergency time as two objective functions. This paper establishes a multi-objective mobile emergency material allocation model, and transforms the multi-objective. We choose the emergency material transportation path for coding, and apply the hybrid leapfrog algorithm for material allocation to obtain the optimal solution. Finally, the feasibility of the model is verified by taking Zhengzhou urban area under the "21.7" severe rainstorm and flood disaster in Henan Province. The result analyses show that the model can correspond to each stage of mobile emergency material allocation based on the value of cost preference, and put forward suggestions on the location of mobile emergency facilities and the amount of material allocation.

A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events.

This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-022-07276-5.

The dynamic causality in sporadic bursts between CO2 emission allowance prices and clean energy index.

This study examines the dynamic causality between the carbon emission market and the clean energy market, using an information flow-based, quantitative Liang causality analysis which is firmly grounded on physics and derived from first principles. The dynamic causal relationships between European Union Allowance (EUA) prices and clean energy index allow us to explore whether the causality in return or in variance from CO2 emission allowances to the clean energy index is time-varying. The results show that the causal relationships in return and in variance between EUA and Clean Energy Index (CEI) are drastically time-varying. For the causality in return, a significant unidirectional long-term and stable causality from CEI to EUA is identified after March 2020. For that in variance, a bidirectional causality is found after March 2020, but values after 2020 are opposite to those in return. It seems when fluctuations in the clean energy market are low, the clean energy market has a weak causal effect on the carbon emission market but when volatility in the clean energy market is increasing, causalities between the two markets are significantly strengthened. These results obtained through this rigorous causality analysis can serve as a reference for academics, market participants, and policymakers to understand the underlying links between EUA prices and clean energy index.

How to Manage Red Alert in Emergency and Disaster Unit in the Hospital? Evidence From London.

This research gave an overview of coordinated hospital planning issues. In these issues, patients desire an arrangement for different source types, ideally as quickly as time permits. This field of context has just picked up academic interest, despite its reality since 1995. The way may discover a clarification for the above aspect that managing the hospital sources is regularly performed separately without taking a bigger picture. Therefore, it is particularly valid if the sources are situated in different departments. Another subsequent clarification may be related to the notoriety of the patient flow context. Hence, patients shouldn't be planned in these issues to be queued for another source or leave the system in case of their satisfaction of solicitation for the services at a particular source. The primary contribution of the present research is assisting present and new scholars via enumeration for every progression of the study of accessible decisions in the present context. Such means could be represented by major references for scientists to discover such studies endeavors tailored to their respective requirements. This principle removes the message: scientists ought to consistently coordinate their decisions concerning the setting, the capacity, and the approaches, as not all blends are conceivable.

The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test.

Combined with the B-P (breakpoint) test and VAR-DCC-GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures.

Robust optimization approach to emergency mobile facility routing.

Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people's lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing model is proposed to maximize the total served demand by the available mobile facilities. Based on the uninterruptible feature of emergency services, the model abstracts emergency events act as a combination of multiple uncertain variables. To overcome the computational difficulty, a robust optimization approach and genetic algorithm are employed to obtain solutions. Illustrative examples show that it provides an effective method for solving the emergency mobile facility routing problem, and that the risk factor and penalty factor of the model can further guide decision-making.

Explaining Local Residents' Attitudes toward Shale Gas Exploitation: The Mediating Roles of Risk and Benefit Perceptions.

Using Fishbein's multi-attribute model, this paper proposes that the impact of socio-demographic and psychosocial factors on local residents' overall attitude toward shale gas exploitation (SGE) is mediated by their risk and benefit perceptions. The proposition has been validated with the generalized structural equation modeling approach with a cross-sectional dataset of 825 residents from China's Fuling shale gas field. Results indicate that the influence of benefit perception on residents' overall attitude outweighs that of risk perception. Moreover, residents' perceived fairness, affective feeling, and trust in regulatory agencies have positive influences on their overall attitude, primarily via their risk and benefit perceptions, in decreasing order of influences. Finally, we also find that residents' attitudes have been significantly influenced by their socio-demographic factors, including age, residential area, and political ideology. Thus, our study extends the literature with theoretical and empirical models by exploring the influences factors of local residents' attitudes toward SGE, and results from our empirical survey provide insight into policy design to promote the acceptance of SGE.

A consensus-based decision model for assessing the health systems.

Many countries and international organisations have been developing health system performance assessment frameworks and indicators to support healthcare management and inform public health policy. Effectiveness, accessibility, safety and patient-centeredness were four dimensions that were most commonly measured. This paper develops a new consensus-based decision model to assess the health systems, in which different stakeholders of healthcare systems are identified by different decision approaches, i.e., the coefficient variation approach, the Shannon entropy approach and the distance-based approach, respectively. The consensus result is obtained by minimizing the total deviation from the ideal point. A numerical illustration with simulated data is presented to show the effectiveness of our model.

An Analysis of China's Onshore and Offshore Exchange Rates-Adjusted Thermal Optimal Path Approach Based on Pruning and Path Segmentation.

The study of the lead-lag relationship between the Hong Kong offshore Renminbi (CNH) spot market and onshore (CNY) spot market is of great importance for its wide application in market risk management. In this paper, we study the correlation between the CNH and CNY spot markets in the contexts of daily closing price change and the 2011-2016 Bid-Ask spread (BAS). We test the existence of causality relation between CNH/CNY pairwise change and BAS by using the conventional method of vector auto-regression (VAR) model in the observation period. Furthermore, we detect the local lead-lag dependence relationships between CNH/CNY pairwise change and BAS by using a non-parametric approach-adjusted Thermal Optimal Path (TOP) method. Through introducing a Pruning and Path segmentation algorithm, we address the problem of computation infeasibility that may be encountered in application of the existing TOP method for the detection of lead-lag relationship between two time series with long time duration. Theoretical analyses and simulation results are presented to verify validity of adjusted TOP method in the setting of big time-series data set. This study also provides some interesting findings: (1) the offshore CNH market is informationally integrated with the onshore CNY market from two aspects of closing price change over two consecutive single days and BAS used as a proxy for market liquidity; (2) Local dependency between the two markets changes with economic conditions changing, which would facilitate both investor and policy maker decision making.

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.

Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.