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Many mobile devices today are equipped with diversified sensors that enable the acquisition of rich user context (e.g. GPS location, phone activity) for application utilization. With the growing usage of mobile devices in daily life, the problem of conveniently and promptly searching a piece of content that a user has viewed on his/her device before becomes more and more crucial. This paper aims to propose a context‐based query processing framework called UCQP that supports unstructured queries for content search in a user's access history.

Beyond the keywords related to the content properties, a context query in the framework is specified with freeform phrases that describe high‐level mobile contexts of the user at a previous time when the user viewed the searched content.

Experimental results on a prototype system of the framework illustrate its good accuracy and small response time.

To tolerate the incompleteness and inaccuracy in user query texts caused by fading human memory, the authors develop several semantic query parsers that are tailored for different types of contexts using natural language processing and information retrieval techniques. The authors further propose a similarity model to rank the multiple result contents of a query by comparing context entities specified in the query and historical context values associated with each result.

Cross-channel integration improves the operations of multi-channel and omnichannel marketing and increase firms' overall performance. By addressing the extant gaps in current literature, this configurational analysis aims to test the combined effects of organizational, channel and environmental factors on cross-channel integration.

Data were collected from a sample of 180 manufacturers. Necessary condition analysis (NCA) was used to test whether two organizational factors (firm size and IT capability), one environmental factor (environmental dynamism) and two channel factors (channel diversity and proportion of direct channels) were necessary or unnecessary conditions for high cross-channel integration. Fuzzy-set qualitative comparative analysis (fsQCA) was applied to analyze the configurational factors of high vs low cross-channel integration.

First, firm size and IT capability are non-linear and substitute for each other in affecting cross-channel integration in a diversified channel system with a high proportion of direct channels. Second, in a dynamic environment, firms with large size and IT advantage could achieve high cross-channel integration by diversifying channel types or increasing the proportion of direct channels. Third, the effect of channel diversity and proportion of direct channels on cross-channel integration is asymmetric depending on other antecedent conditions.

The authors tested a configurational framework developed from multiple theoretical perspectives. The authors' empirical findings contribute to the literature by providing insights into the mechanisms underlying the formation of high and low cross-channel integration. The results suggest multiple ways for firms to promote cross-channel integration by adjusting channel factors based on configurational conditions.

Five-level rectifiers have received widespread attention because of their excellent performance in high-voltage and high-power applications. Taking a five-level rectifier with only four-IGBT for this study, a sliding mode predictive control (SMPC) algorithm is proposed to solve the problem of poor dynamic performance and poor anti-disturbance ability under the traditional model predictive control with the PI outer loop.

First, mathematical models under the two-phase stationary coordinate system and two-phase synchronous rotating coordinate system are established. Then, the design of the outer-loop sliding mode controller is completed by establishing the sliding mode surface and design approach rate. The design of the inner-loop model predictive controller was completed by discretizing the mathematical model equations. The modulation part uses a space vector modulation technique to generate the PWM wave.

The sliding mode predictive control strategy is compared with the control strategy with a PI outer loop and a model predictive inner loop. The proposed control strategy has a faster dynamic response and stronger anti-interference ability.

For the five-level rectifier, the advantages of fast dynamic influence and parameter insensitivity of sliding mode control are used in the voltage outer loop to replace the traditional PI control, and which is integrated with the model predictive control used in the current inner loop to form a novel control strategy with a faster dynamic response and stronger immunity to disturbances. This novel strategy is called sliding mode predictive control (SMC).

Cotton is one of the most common nature textile fiber that is widely used in clothing, bedding and decorative fields due to its comfort. However, the cellulosic cotton fiber has its own drawbacks. Cotton fiber belongs to flammable material with the limiting oxygen index (LOI) value about 18% that restricts its applications. Cotton fiber is easy to crease during the repeat wearing and laundering process that will influence the wearability. Therefore, it is very important to improve the flame retardancy and anticrease performance of cotton fabric.

In this study, flame retardant and anticrease multifunctional modifications of cotton fabric were conducted by one-step pad–dry–cure process using eco-friendly phytic acid and 1, 2, 3, 4-butanetetracarboxylic acid.

The results of limited oxygen index (LOI) values and vertical burning test indicate that the flame retardancy of modified cotton fabric was greatly improved. The LOI value of modified cotton fabric reached 30.8% when the usage of phytic acid was 12%. The crease recovery angle was over 250° of the modified cotton fabric revealing good anticrease performance.

This research provides a novel feasible cost-effective one-step method for the multifunctional modified cellulosic fiber using eco-friendly chemical agents.

The purpose of this paper is to measure the influence factors of their preferences for urban forest, marginal values of various properties and relative values of different scheme portfolios, thus arriving indirectly at the city residents’ demand for urban forest improvement.

This paper, based on the data from the 2015–2017 field survey questionnaire of city residents over the radius of Beijing’s 5th Ring Road, uses the choice experiment method (CEM) to conduct a study of its residents’ demand for urban forest.

Beijing’s city residents are generally inclined to accept a relatively low payment of urban forest while hoping to access a relatively high urban afforestation coverage with the construction of relatively many city parks, especially focusing on the specialized park management; the marginal values of biodiversity and greenery coverage are far higher than those of greenbelts in quantity and the maximum marginal value of biodiversity remains as high as RMB29.42, indicating that the city residents do not favor much the number of greenbelts over other aspects but they generally hope to achieve a higher greenery coverage and a richer biodiversity.

Generally speaking, what Beijing City needs most is not continuing the increase in the number of greenbelts, but engaging in the rational retrofit of its existing greenbelts and optimizing its urban forest structure.

This paper may provide reference for determining the city residents’ payment criteria for urban forest and will be of equally great significance to developing cities and their urban forest.

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.

MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions.

A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method.

To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods.

This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.