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The information behavior of National Science and Technology Library (NSTL) electronic resources users in China has not been researched extensively. This paper aims to help producers and providers collect information on user behavior and develop more electronic resources.

The study investigates NSTL users' behavior from seven “211 projects” universities in Wuhan, a city in central China. The questionnaire includes questions about respondents' basic information (i.e. educational level, discipline, etc.) and their information service requirements. Correlations among users' educational level, academic discipline, retrieval method, and literature use, among other variables, were analyzed.

The results show that most NSTL users are graduate students and young staff members. The number of users who use advanced retrieval and choose the criterion “literature's citation” to judge the literature's value increases slightly with the improvement of the educational level. There is higher demand for literature written in English in the disciplines of natural science, medicine, and engineering, and a certain proportion of demand for materials written in Japanese in the disciplines of medicine and engineering.

The findings suggest that electronic resource producers should offer more foreign literature and that providers should improve the quality of services.

The paper provides suggestions for further improvement of the NSTL to fulfil the information needs and requirements of users.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

Although consumers are viewed as one of the important target groups of new product preannouncements (NPPs), little existing literature focuses on the NPP's consequences from consumer perspective. To fill up this research gap, this paper explores how a NPP signal influences consumer purchase intention and how its influences vary across consumers.

Based on a scenario‐based survey with different new cellular phone preannouncement contexts, this paper examines impacts of brand, prior vaporware history, and innovativeness conveyed by NPP signals, as well as consumer characteristics, on purchase intentions. A logit regression and a hierarchical Bayesian Logit regression are applied to test effects of NPP signal and consumer factors, respectively.

The empirical results show that consumers may mainly rely on brand and prior vaporware history to decide whether to purchase this new product after it is launched. They are more likely to purchase a preannounced new product with strong brand, or from a company without prior vaporware. The results also demonstrate that the brand and vaporware impacts on purchase intention are moderated by consumer product knowledge, NPP experience, and risk attitude.

Following the competitive signal interpretation process model in signaling theory, this paper first provides and empirically examines an overall framework of NPP impacts on purchase intentions from the consumer perspective, which may contribute to the preannouncement literature. The findings also provide useful insights to help companies to make right NPP decisions.

The purpose of this paper is to draw attention to the significant lower completion rate of mergers and acquisitions (M&As) by firms from emerging economies (EEs) (China in particular) compared with firms from advanced economies, and identify the country- and industry-level factors that affect the completion of cross-border M&As by Chinese firms.

This study explores the effects of economic, cultural and institutional distances and target firms in technology- and knowledge-intensive industries on the completion of cross-border M&As by Chinese firms. It also examines the interplay between distance factors and technology- and knowledge-intensive industries on cross-border M&A completion. This study adopts a quantitative approach and is based on a sample of 768 announced cross-border M&A deals by firms in China between 2000 and 2015.

The results indicate that economic distance increases the likelihood of the completion of cross-border M&As when the target is in a more developed economy than China, but decreases when the target is in a less developed economy. Cultural and institutional distances have a significant, negative impact on the completion of cross-border M&As. In addition, target technology-intensive industries have a significant direct negative effect on cross-border M&A completion and moderate the relationship between the distance factors and the likelihood of cross-border M&A completion.

The results reveal factors that affect the completion of cross-border M&As by emerging market firms (EMFs). Further research, however, is needed to discover how distance factors affect how EMFs find, evaluate and negotiate international bids. To broaden the scope of the research, data for firms from other EEs would also be required.

The study expands the literature that considers the effects of major distances on cross-border M&A completion. In addition, the importance of defining and measuring distances in the context of cross-border M&As is highlighted. Finally, the study expands knowledge on how cross-border M&As affect the internationalization strategies of EMFs by conceptualizing and testing how target industries affect cross-border M&A completion.

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

Social media technology has changed donors' decision-making process in online philanthropy compared with traditional charity. How do IT affordances support donor perceptions and motivations in charitable crowdfunding? The purpose of this study is to explore how the five sub-dimensions of charitable crowdfunding IT affordances (i.e. visibility, association, meta-voicing, trading and security) afford initiators and platforms in motivating donors to support charitable crowdfunding projects.

This paper uses a quantitative research approach. An online survey was conducted to collect research data from WeChat users who had experienced charitable crowdfunding. A sample of 344 valid responses were received and analyzed.

The results show that four of the five IT affordances facilitate donors' perceptions (perceived emotions and trust) and motivations (intrinsic motivations and extrinsic motivations), and thereby increase behavioral intention on supporting charitable crowdfunding projects.

This study advances the affordances and online charity literature by examining the antecedents and outcome of perceptions and motivations that determining behavioral intention in more detail. The authors’ findings not only benefit researchers in explaining how technology helps donors perceiving projects and motivating them to donate online, but also assists practitioners in developing better charitable crowdfunding management strategy.

The purpose of this paper was to prepare a fluorinated acrylate resin, which would be synthesised via solution polymerisation of fluorinated monomer, acrylate monomers and other functional monomers. Relevant characterisation and application studies were also carried out. Fluorinated polymers are expected to be adopted in specific coatings to afford outstanding advantages, such as high chemical and photochemical resistance, low surface tension and low refractive index. At present, fluorinated cathodic electrodeposition (CED) coatings are attracting the attention that they deserve and seldom reported.

Cationic fluorinated acrylic resin was successfully prepared by solution polymerisation of dodecafluoroheptyl methacrylate, butyl acrylate, methyl methacrylate, dimethylaminoethyl methacrylate and methacrylic acid initiated by 2,2,-azo-bis-iso-butyronitrile in a solvent of butyl cellosolve. The resultant resin was neutralised with acetic acid. The CED coatings are prepared when moderate amounts of blocked isocyanate and distilled water were added into the resultant resin.

The hydrophobicity of the film is improved when the fluorinated monomer is introduced to co-polymerise with other monomers. The optimum conditions of preparing the resin are as follows: the amount of azodiisobutyronitrile is controlled with the range of 3.0 and 4.0 per cent; the amine value of the resin is 70 mg KOH/g; the hydroxyl value of resin and mole ratio of hydroxyl to isocyanate is 60 mg KOH/g and 1.0/1.0, respectively; the degree of neutralisation of the resin is within the range of 35 and 40 per cent.

The cationic fluorinated acrylic resin can be used to be the binder of CED coatings, which can be applied to electrodeposition finishing for high demand of exterior decorative and weather resistance, such as hardware, accessories, office furniture and so on.

The cationic fluorinated acrylic resin was successfully prepared by solution polymerisation. The hydrophobicity of the film is improved.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Wireless sensor networks, due to their potentially wide application perspectives, may proliferate in future. Two major stumbling blocks are the dynamic variance of the network caused by both the capacity constraint of sensor nodes and uncertainties of wireless links, and secure routing in the special security sensitive environment. Therefore, adaptable and defendable routing mechanism is in urgent need for the deployment of sensor networks. This paper aims to propose a feedback‐based secure routing protocol (FBSR).

Feedback from the neighboring nodes serves as the dynamic information of the current network, with which sensor nodes make forwarding decisions in a secure and energy aware manner. Feedback message is included in the MAC layer acknowledgement frame to avoid network congestion, and it is authenticated with the proposed Keyed One Way Hash Chain (Keyed‐OWHC) to avoid feedback fabrication. FBSR's resilience to node compromise is enhanced by statistic efforts accomplished by the base station.

Both mathematical analysis and simulation results show that FBSR is not only reliable but also energy efficient.

The paper introduces a novel routing scheme for wireless sensor networks.