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With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion.

The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods.

Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level.

Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Based on the team conflict theory and organizational learning theory, this study aims to discuss the two different types of conflicts of the top management team (TMT) on the different mechanisms of exploratory learning behavior of firms, and, based on the perspective of CEO-TMT (CEO – chief executive officer) interface, the different moderating effects caused by different CEO leadership styles are clarified.

Using the sample of 193 firms’ samples with multi-source data, the authors take an empirical test of the theoretical framework.

The effect of task conflict on exploratory learning behavior was insignificant, and relationship conflict had a positive effect on exploratory learning behavior. However, when CEO’s transformational leadership level was high, or transactional leadership level was low, there existed “bathtub curve” relationship between task conflict and exploratory learning behavior, and the relationship conflict under these conditions strengthened exploratory learning behavior. When CEO’s transactional leadership level was high, or transformational leadership level was low, there existed the inverted U-shaped relationship between task conflict and exploratory learning behavior, and the relationship conflict under such conditions weakened exploratory learning behavior.

First, the authors challenge the assumption of linear mechanism of task conflict, trying to build the mechanism of curve hypothesis, and the nonlinear explanation might be able to integrate the inconsistent results in the existing literature. Second, according to the inconsistent results of relationship conflict in existing literature, this study takes perspective of the CEO-TMT and introduces CEO leadership behavior as a moderating variable to test the moderating effect of CEO leadership and clarifies the boundary conditions of TMT conflicts.

There is increasing desire and need in research of water region detection owing to the unexpected natural disaster that lead to financial, environment and human losses. Surveying of water region and research on its feature is very basic step for many planning, especially for countries like Japan, where tsunami has caused the changes on water region in March, 2011. Essentially, identifying water region from satellite images is one of the grand steps of water resources management for a country. Professional and academic institutions play a vital role in the management of water resources as they are instrumental in research. The objective of this paper is to identify the water region from satellite image. In this paper, the segmentation algorithm based on SOM (self-organizing map) neural network with compression pre-processing by wavelet transform and image smoothing using Gaussian low-pass frequency domain filters are presented.

The purpose of this study is to investigate the utilitarian value (UV), hedonic value (HV) and social value (SV) that make people use a certain type of online media website and how the usage of specific online media website impact the way people perceive online information credibility (OIC). A research model was also proposed to explain the essence of this study.

This study adopted the survey research methodology to empirically test the research model with 873 research participants from the University of Science and Technology of China and Anhui Medical University.

Results from structural equation modeling showed that UV and HV have a significant positive impact on the usage of print news media website (PNMW), usage of broadcast news media website (BNMW) and usage of social networking website (SNW). The SV was also found to have a significant positive impact on the usage of SNWs. The result also indicated that the usage of the PNMW and the usage of the BNMW by online users have a significantly positive impact on high rating of OIC. However, the result showed that the usage of SNW does not have a significant positive impact on the high rating of OIC.

Findings in this study provided substantial contributions toward the advancement of the uses and gratification theoretical framework by unraveling how certain motivational values can influence online media users’ preferences for specific online media websites, as well as showing how specific online media websites affect online users’ perception of OIC.

Interrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.

This paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.

The results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.

Available studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.

The purpose of this paper is to investigate university students’ WeChat usage during the COVID-19 pandemic lockdown in relation to the mediating role of online self-disclosure on their quality of friendship and well-being. A model is proposed to explain how students’ interactions occur during the lockdown and the mediatory role which self-disclosure plays in influencing their socio-psychological markup.

The research model was tested empirically through a survey conducted online with 600 research participants, comprising of university students in China.

Results in structural equation modeling show that WeChat interaction significantly correlates with the quality of friendship, online self-disclosure but not significantly correlates with well-being, but an indirect relationship was found out in the mediation analysis. There is also a significant relationship between online self-disclosure, quality of friendship and well-being. Mediation analysis shows that online self-disclosure mediates the relationship between interactions on WeChat and quality of friendship; it also mediates the relationship between WeChat interaction and well-being. In all, the results achieved in this study will significantly help provide more insights in comprehending the nuances attached to some socio-psychological aspects of WeChat and how its usage affects people during the period of crisis.

Theoretically based investigation of WeChat usage among university students and its relationship with online self-disclosure, quality of friendship and well-being is still quite scarce, thereby underscoring the needs and significance of a theoretically based study in this regard. This study tested the credibility and validity of the proposed model in the context of the recent COVID-19 pandemic lockdown in China, which is one of the first in recent times.

The purpose of this paper is to investigate the impact of e-government usage on citizen engagement during the COVID-19 crisis in China, in relation to the mediating role of how citizens perceive the government. A model was also proposed to explain the relationship between e-government usage during the COVID-19 crisis and the mediating role that different perceptions of government play in influencing citizens level of engagement.

The research model was tested empirically through a survey conducted online with 866 research participants, comprising of Chinese citizens from three large cities, which include Hefei, Shanghai and Nanjing.

The results in structural equation modeling showed that e-government usage has a significant positive influence on citizens' perception about trust in government, government transparency and government reputation but not significant influence on citizens' engagements. However, an indirect relationship was found out in the mediation analysis. There was also a significant relationship between the different perceptions of government. Mediation analysis showed that all the different perceptions of government mediate the relationship between e-government usage and citizens' engagements during the COVID-19 crisis. The single mediation pathways were found to be most effective mediators, identifying citizens' perception about trust in government to be the most effective mediator.

This study filled the gap in literature by examining how e-government usage by Chinese citizens during the COVID-19 crisis helped influence their attitude and behavior. Specifically, this study is one of the first to integrate citizens' usage of e-government and citizens' engagement through the different citizens' perceptions of government such as trust in government, transparency of government and government reputation in a non-liberal country.

The purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.

This research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.

The results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.

The contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

When a vehicle passes through a long highway tunnel, the smoke it discharges accumulates in the tunnel. High smoke concentration has an important influence on the driver’s health and driving safety. The use of numerous jet fans to diffuse the smoke causes excessive energy consumption, so it is of significant practical value to design suitable tunnel ventilation.

The study is based on the continuum hypothesis, incompressible hypothesis, steady flow hypothesis and similar hypothesis of gas in a long highway tunnel. These hypotheses calculate the gas emissions and wind demand in a long highway tunnel given the deployment of the jet fan program.

This program selects each of the two 1120-type jet machine group and sets up 13 groups; each group has an interval of 184.5 m in the end. The analysis of air test results when the tunnel is in operation shows that CO and smoke concentrations meet the design requirements, which can provide reference for a similar engineering design later.

At present, a highway tunnel is recognized at home and abroad by means of clearance of longitudinal ventilation, which is 2,000 m. In view of this, this paper is based on the theory of longitudinal jet ventilation of a highway tunnel, whose length is more than 2,000 m, to calculate the ventilation and apply it to a tunnel’s ventilation design.

The deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).

RSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.

The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.

The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.