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The purpose of this paper is to assess the water resources vulnerability (WRV) rationally in Haihe River Basin (HRB) using set pair analysis (SPA) theory.

An improved intelligent set pair analysis (IISPA) model is established, in which intelligent SPA theory is introduced and the weights are determined by use of the maximum entropy principle and the improved analytic hierarchy process method. The index systems and criteria of WRV assessment in terms of water cycle, socio-economy, and ecological environment are established based on the analysis of sensibility and adaptability.

The authors apply IISPA to the WRV assessment of seven administrative divisions in HRB. Results show IISPA can fully take advantage of certain and uncertain information compared with fuzzy assessment and topsis assessment models. For present situation, Shanxi, Shandong, Tianjing, Inner Mongolia, Hebei are higher, Henan and Beijing are the middle vulnerability. But Henan will become worse under climate change scenario II and IV.

The analysis results may be affected by the limited climate change data.

It is helpful for further research to the complexity analysis of water resources system.

This paper will have an important impact on water resources sustainable utilization.

This is the first time to utilize IISPA method to analyze the WRV of seven administrative divisions in HRB. This paper provides an important theoretical support for water resources management.

The purpose of this paper is to reduce the computational burden and improve the precision of the parameter optimization in the convection-diffusion equation, a new algorithm, the refined gray-encoded evolution algorithm (RGEA), is proposed.

In the new algorithm, the differential evolution algorithm (DEA) is introduced to refine the solutions and to improve the search efficiency in the evolution process; the rapid cycle operation is also introduced to accelerate the convergence rate. The authors apply this algorithm to parameter optimization in convection-diffusion equations.

Two cases for parameter optimization in convection-diffusion equations are studied by using the new algorithm. The results indicate that the sum of absolute errors by the RGEA decreases from 74.14 to 99.29 percent and from 99.32 to 99.98 percent, respectively, compared to those by the gray-encoded genetic algorithm (GGA) and the DEA. And the RGEA has a faster convergent speed than does the GGA or DEA.

A more complete convergence analysis of the method is under investigation. The authors will also explore the possibility of adapting the method to identify the initial condition and boundary condition in high-dimension convection-diffusion equations.

This paper will have an important impact on the applications of the parameter optimization in the field of environmental flow analysis.

This paper will have an important significance for a sustainable social development.

The authors establish a new RGEA algorithm for parameter optimization in solving convection-diffusion equations. The application results make a valuable contribution to the parameter optimization in the field of environmental flow analysis.

The purpose of this paper is to accurately simulate and predict the daily extreme temperature in Beijing Reservoir and the monthly extreme temperature in Tianjin Reservoir using wavelet refined rank set pair analysis (WRRSPA).

The new method, called WRRSPA, which combines wavelet analysis and refined rank set pair analysis (RRSPA), was proposed for use in this study because of the non-linear and multi-time scale characteristics of the temperature series. The model includes the advantages of the multi-resolution feature of wavelet analysis and the non-parametric data-driven prediction from refined rank set air analysis.

Based on the daily extreme temperature of Beijing Reservoir, the predictions of the last 18 days reveal that WRRSPA is more appropriate because the percentage of the relative errors that are smaller than 10 percent increased from 78 percent by Back Propagation (BP) and 78 percent by RRSPA to 100 percent by WRRSPA in Beijing Reservoir. In addition, WRRSPA has lower values of root mean squared error (RMSE) and mean absolute error (MAE) and a higher coefficient of efficiency (modified coefficient of efficiency (MCE)). The last 12 monthly extreme temperature predictions of Tianjin Reservoir demonstrate that WRRSPA produces prediction results: the percentage of relative errors that are smaller than 10 percent are improved from 34 percent by BP and 58 percent by RRSPA to 67 percent by WRRSPA. In addition, WRRSPA also has lower values of RMSE and MAE and a higher coefficient of efficiency (MCE).

The analysis results ignore the physical processes and may be affected by the limited observation data. In addition, the WRRSPA method is still in its early stages of application and must be further tested.

The results of the study are helpful for the study of the complex features and accurate prediction of temperature series.

This paper contributes to further the process of research of climate change.

This study represents the first use of the WRRSPA method to analyze the multi-scale characteristics and forecast the future values of the extreme temperature series from Beijing Reservoir and Tianjin Reservoir. This paper provides an important theoretical support for extreme temperature prediction.

A good customer experience means meeting the customer expectation. Thus, unexpected customer experience is usually a good point to initiate improvement or innovation for product or service design. Attempting to enhance the customer experience in the customer journey, this study aims to demonstrate a customer journey centred service design approach to receive the design requirements based on customers' needs and to use a systematic approach to generate solutions.

A holistic service design method named 3E model was proposed. It integrates customer experience journey map (CXJM), the theory of inventive problem solving (TRIZ) and service assembly and service replacement mechanism into three design stages. In stage 1, CXJM is enhanced with emotional range analysis to identify the customer pain points as well as customers' requirements (CRs) in exhibition, tourism and hotel sectors for initializing service design. Stage 2 investigates the specific design requirements (DRs) of the smart exhibition system and the contradictions. Then, the innovative principles were analyzed. In Stage 3, expected exhibition service system was designed.

The new service system which named the smart expo system based on information and communication technology (ICT) is proposed. It consists of “Tourism Link assists”, “i-Kaohsiung hotel service center”, “Smart AEC” and “O2O e-tickets”.

The proposed 3E model builds a systematic and coherent design method for the smart exhibition service area. It provides the linkage and action-oriented guidance from customer pain points, service parameters, innovative principles to solutions.

The purpose of this study is to investigate the mechanism of shared leadership on team members’ innovative behavior.

Paired questionnaires were collected from 89 scientific research teams in the Beijing-Tianjin-Hebei region of China at two-time points with a time lag of 4 months. Then multilevel structural equation model method was applied to analyze the multiple mediating effects.

This study finds that: the form of shared leadership in scientific research teams of universities; shared leadership has a positive impact on team members’ innovative behavior; there are multiple mediations in the relationship including synchronization and sequence of creative self-efficacy and achievement motivation.

According to the “stimulus-organism-response” model, this paper has constructed a multi-level theoretical model that shared leadership influences individual innovation behavior and reveals the “black box” from the perspective of psychological mechanism. It not only verifies that “can-do” shapes “willing to do” but also makes up for the gap of an empirical test of the effectiveness of shared leadership in scientific research teams of universities. Besides, the formal scale of shared leadership in the Chinese situation is revised, which can provide a reference for future empirical research on shared leadership. The research conclusions provide new ideas for improving the management of scientific research teams in universities.

Although recent studies have increased attention on the effects of related and unrelated variety on innovation, a Chinese test has until now been missing from the literature. This paper aims to investigate how related and unrelated variety affect regional innovation in Chinese provinces. In particular, emphasis was placed on differentiating the analysis for the industry and services sectors at a detailed sectoral level.

This paper’s sample is composed of 30 provinces in China from 2003 to 2016. Feasible generalized least squares was used to estimate the effects of related and unrelated variety on regional innovation.

The results show that related variety in all sectors promotes regional innovation, whereas unrelated variety in all sectors does not play a role. In-depth analyses were performed by comparing the industry and services sectors. Only related variety in the industry sector and unrelated variety in the services sector promote regional innovation, whereas unrelated variety in the industry sector exerts a negative effect. After dividing the country into eastern, central and western regions, different findings appear in the sub-samples.

This study contributes to the literature on evolutionary economic geography and innovation by exploring how related and unrelated variety promote regional innovation in a developing country context (China). It also sheds light on the sectoral and regional differences in the influence of related and unrelated variety on regional innovation.

This study aims to analyze the paths and mechanisms of firms’ sustainable high growth. Firms’ high growth is susceptible to interruption, stagnation or reversal. Thus, how firms can achieve sustainable high growth is an important topic that requires urgent discussion and has significant implications for sustainable economic development and employment.

This study applies a longitudinal case study approach to portray the process by which Jiashu orchestrated digital elements with traditional resources to continuously fulfill their user demands and ultimately achieve sustainable high growth.

This study reveals three resource orchestration strategies: trust-oriented, demand-oriented and efficiency-oriented. These strategies are adopted in an organization’s startup, expansion and maturity periods, respectively. By dynamically integrating and orchestrating digital elements with traditional resources, firms implement a growth strategy with expanding and stacking dimensions, leading to sustainable high growth. The replicability and connectivity resulting from orchestrating digital elements and traditional resources encourage firms to expand their dimensions of growth and achieve sustainable high growth in multiple dimensions.

This study conducts a preliminary exploration of the relationship between the integration of digital and traditional elements and the sustainable high growth of enterprises. A more stable theoretical relationship between the two requires further multi-case studies and empirical analysis for substantiation.

This study first clarifies the concept of sustainable high growth and reveals a unique nonlinear path characterized by growth with expanding and stacking dimensions. The findings contribute to deepening the theories of sustainable high growth and resource orchestration in the digital economy era and offer practical implications for the sustainable high-growth practices of firms.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

Information encountering is the serendipitous acquisition of information that requires low or no involvement and expectation of users. The purpose of this paper is to model the explicit process and the implicit factors of online information encountering, i.e. how and why it occurs.

The critical incident technique was adopted to collect qualitative data from 16 interview participants. They contributed 27 true incidents of online information encountering which were used to identify the key phases of the encountering process. They also commented on the factors that they thought had an influence on the chance of the occurrence of encountering.

The macro-process of information encountering is composed of three phases. First, browsing, searching, or social interaction provides the context for encountering; second, the encountering occurrence consists of three steps – noticing the stimuli, examining the content, and acquiring interesting or useful content; and third, the information encountered will be explored further, saved, used, or shared. The 14 influencing factors of information encountering obtained divide into three clusters. User-related factors include sensitivity, emotions, expertise, attitudes, intentionality, curiosity, activity diversity; information-related factors include type, relevance, quality, visibility, and sources; and environment-related factors include time limits and interface usability.

This study engenders useful implications for designing information encountering experience. The changeable nature of some influencing factors suggests that encountering can be elicited through the purposive design of encountering support features or even encountering systems, and the macro-process depicts the natural occurring mechanisms of encountering for the design to follow.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.