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This paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.

Using survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.

The findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.

This research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.

Firms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.

The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability during the warranty period. By extending the proposed warranty to the consumer's post-warranty maintenance model, besides the authors investigate two kinds of random maintenance policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating depreciation expense depending on working time, the cost rate is constructed for each random maintenance policy and some special cases are provided by discussing parameters in cost rates. Finally, sensitivities on both the proposed warranty and random maintenance policies are analyzed in numerical experiments.

The working cycle of products can be monitored by advanced sensors and measuring technologies. By monitoring the working cycle, manufacturers can design warranty policies to ensure product reliability performance and consumers can model the post-warranty maintenance to sustain the post-warranty reliability. In this article, the authors design a limited number of random working cycles as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability performance during the warranty period. By extending a proposed warranty to the consumer's post-warranty maintenance model, the authors investigate two kinds of random replacement policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating a depreciation expense depending on working time, the cost rate is constructed for each random replacement and some special cases are provided by discussing parameters in the cost rate. Finally, sensitivities to both the proposed warranties and random replacements are analyzed in numerical experiments.

It is shown that the manufacturer can control the warranty cost by limiting number of random working cycle. For the consumer, when the number of random working cycle is designed as a greater warranty limit, the cost rate can be reduced while the post-warranty period can't be lengthened.

The contribution of this article can be highlighted in two key aspects: (1) the authors investigate early warranties to ensure reliability performance of the product which executes successively projects at random working cycles; (2) by integrating random working cycles into the post-warranty period, the authors is the first to investigate random maintenance policy to sustain the post-warranty reliability from the consumer's perspective, which seldom appears in the existing literature.

The purpose of this paper is to study the mechanism of efficient sputum excretion from the distal lung by using a tessellationally distal lung model with alveolar pores.

First, a two-dimensional tessellational composite structure of the bronchus, alveoli and alveolar pores (Kohn pore) is constructed with the tessellational splitting and fusion of regular hexagonal elements. Then, the level set method is used to study the effects of alveolar pores and their sizes, expiratory cycles and respiratory intensity.

The existence of alveolar pores is the prerequisite for sputum excretion, and there is an optimal size of alveolar pores for sputum excretion. Strong asymmetric respiration can break the reversibility of the flow at a low Reynolds number and causes significant net displacement of sputum. The expiratory cycle is negatively correlated with the net displacement of sputum. The respiratory intensity is positively correlated with the net displacement of sputum.

This research is helpful for understanding the complex sputum excretion process in diseases, such as pneumonia, and developing corresponding adjuvant therapy.

This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability.

Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively.

The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms.

The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety.

Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.

In this study, suggestions on the protection and development of marine resources, sea area use and management, improvement of laws and regulations, monitoring of marine resources and impact assessment of marine environment are put forward.

Literature research method.

A major factor contributing to the decline in fishery resources is excessive fishing. At present, there are many problems to be solved in the exploitation and utilization of marine mineral resources in China. The pollution problem of marine tourism resources is becoming increasingly serious. Overmining of coastal sea resources has led to planning and management failures.

This paper discusses the current situation of marine resource exploitation and protection in China and analyzes the reasons for excessive resource exploitation from three aspects of marine concept, laws and regulations and marine management.

This study, based on social exchange theory, aims to explore the association between appreciative leadership and employees' helping behaviors by investigating the mediation role of emotional reactions (pride, anxiety), and choosing organizational trust as a boundary condition between appreciative leadership and helping behaviors.

A total sample of 285 reliable questionnaires were collected in three time lags from employees working in the Pakistani education and banking sectors. PROCESS macro using SPSS and AMOS are employed for data analyses of the proposed model.

The findings reveal that appreciative leadership has positive impacts on employees' helping behaviors and emotional reactions (pride, anxiety) mediate the relationship of appreciative leadership and employees' helping behaviors. In addition, the results show that high organizational trust strengthens the positive relationship between appreciative leadership and employees' helping behaviors.

This research has provided empirical proof between the relationship of appreciative leadership and helping behaviors and the findings are of great significance for managers, employees, and organizations. The study proposes that leaders should have appreciative behavior while treating their subordinates. Moreover, it is revealed that the role of organizational trust should be given more attention and importance because it is a factor moderating the employees' helping behaviors.

The present study, among the first empirical efforts investigating the relationship between appreciative leadership and helping behaviors, organizational trust as a moderator, enriches the existing academic literature of and provides worthy insight into the research on appreciative leadership and helping behaviors.

Library Hi Tech is one of the most influential journals that publish leading research in library and information science (LIS). The present study aims to understand the scholarly communication in Library Hi Tech by profiling its historic footprint, emerging trends and knowledge diffusion.

A total of 3,131 bibliographic records between 1995 and 2018 were collected from the Web of Science. Text mining, graph analysis and data visualization were used to analyze subject category assignment, domain-level citation trends, co-occurrence of keywords, keyword bursts, networks of document co-citation and landmark articles.

Findings indicated that published research in the journal was largely influenced by the psychology, education and social domain as a unidisciplinary discipline. Knowledge of the journal has been disseminated into multiple domains such as LIS, computer science and education. Dominant thematic concentrations were also identified: (1) library services in academic libraries and related to digital libraries, (2) adoption of new information technologies and (3) information-seeking behavior in these contexts. Additionally, the journal has exhibited an increased research emphasis on mixed-method user-centered studies and investigations into libraries' use of new media.

This study provides a promising approach to understand scientific trends and the intellectual growth of journals. It also helps Library Hi Tech to become more self-explanatory with a detailed bibliometric profile and to identify future directions in editorship and readership. Finally, researchers in the community can better position their studies within the emerging trends and current challenges of the journal.

Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.

The literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.

The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.

The drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.