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This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan.

The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis.

Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases.

It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases.

The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

The return on investment (ROI) model is a tool used to measure the financial benefits and costs of an investment, in this case, the investment in digital library resources. By applying this model to the AAU digital library resources, the study seeks to determine whether these resources are providing sufficient value for the investment made in them.

The proposed ROI model has two distinct phases and utilizes two different sets of data to calculate the return on investment for a database subscription. In Phase I, the ROI is calculated based on the total number of downloads of full-text articles from the database during the academic year 2019–2020. This information is used to determine the financial returns of the database subscription costs. In Phase II, the ROI is calculated by examining the citations drawn from the Scopus database on a sample of 30 funded research projects for the College of Engineering during the year 2019. These data are used to determine the impact of the database subscription on research output and its contribution to the success of the College of Engineering's research projects. The two phases of the proposed ROI model aim to provide a comprehensive understanding of the value of the database subscription and its impact on both financial returns and research output.

The findings of the study indicated different results between Phase 1 and Phase 2 of the study. The positive ROI in Phase 1 suggests that the investment in online databases has a good return for the AAU, as they are gaining almost a dollar for every dollar spent. However, the negative ROI in Phase 2 is concerning. It suggests that the investment in the IEEE database is not generating a positive return for the AAU and may even be costing the institution money. Overall, these findings highlight the importance of measuring ROI in academic libraries, particularly in Arab countries where resources may be limited. By understanding the impact of library investments on institutional outcomes, libraries can make informed decisions about where to allocate their resources and how to optimize their services to best serve their communities.

The findings of the current study were based on data collected from a specific sample, therefore, the findings may not be generalized to other academic libraries. A similar study with larger and more diverse samples can help to validate and extend the results of this study.

The findings of the study provide evidence that the proposed ROI model can be effectively applied in Arab countries and academic libraries in the Arab world, this could encourage more institutions in the region to adopt this model for evaluating their investments and projects. The study may also guide how to adapt the model to the specific cultural and organizational contexts of Arab countries.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

Operation and maintenance (O&M) processes projects such as identification, assessment, planning and execution, embody a variety of standards such as technical (method of statement), environmental, economic (campus development) and social (health and wellbeing). Because these standards have proven to be challenging to integrate, local governments are increasingly experimenting with social innovation (SI) as a bottom-up form of standard integration. This study aims to apply the concept of SI to the O&M processes of facilities management at polytechnics in Malaysia to identify problems with conventional working practices in this area and to recommend potential solutions.

The paper reviews evidence that conventional working methods generate significant problems related to paper-based forms, improper database management and flawed decision-making processes. Because of the lack knowledge about different ways of how standard integration is achieved, the comparison of three polytechnic institutions which are Rensselaer Polytechnic Institute (RPI) and Southern Polytechnic College of Engineering and Engineering Technology (SPCEET) in USA as well as Seberang Perai Polytechnic, Pulau Pinang (PSP) in Malaysia shares the ambition to realise standard integration of O&M through SI.

The findings reveal that SI leads to four ways of standard integration: computerised maintenance management system, online customer complaint, electronic form and relational database. Application of the concept of SI reveals the need for more sophisticated management solutions in the O&M processes of facilities management.

These standard integration arrangements unfortunately seem to mainly contribute to greater alignment between standard rather than true standard integration. The concept of SI will guide future improvements and developments in maintenance management systems to fulfil requirements in this area.

This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching.

The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020.

Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances.

The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.

The aim of the article is to determine research areas and to recognize the current direction in the development of maturity models, to indicate the key areas of organizational maturity models (OMMs) development and their classification as well as to pinpoint research gaps and areas of potential development of OMMs in the context of scientific research and the needs of management practitioners.

The research was conducted using the literature review method, bibliometric analysis and visual mappings.

The empirical classification developed in this paper identified 12 categories based on management areas, constituting the criteria for classifying OMMs models, where OMMs are being developed: Information Technology, Project Management, Business Management and Strategy, Human Resource, Ergonomics, Health and Safety Management, Industry 4.0 concept, Knowledge Management, Process Management, Performance Management, Quality Management, Supply Chain Management, Risk Management and Innovation Management.

The main limitation is the analysis in the scope of topic OMMs including solely the Scopus and Thompson Reuters Web of Science database. Another shortcoming is conducting data analysis and classification based on the abstracts of the selected articles.

This work is a starting point to prospect trends for future revolving around the OMMs crossing different databases.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

This study aims to identify the development, trends and research gaps related to start-ups during the COVID-19 pandemic. Furthermore, this research identified future research directions.

This research is a bibliometric study using 192 Scopus-indexed articles with the assistance of the VOSviewer. The selected articles discussed start-ups during the COVID-19 pandemic and were published between 2020 and 2022. The analyses included initial statistics on publication trend, distribution of research field, most productive journals, most productive authors, most cited article, geographic distribution, multinational collaboration and science.

Between 2020 and 2022, the literature on start-ups during the COVID-19 pandemic has increased. Most articles are in Business, Management and Accounting, Economics, Econometrics and Finance and Social Sciences. Regarding geographic distribution and multinational collaboration, the USA, India and the UK led the numbers. The UK was also the forerunner in research collaboration. Based on the science mapping results, COVID-19, fintech, entrepreneurship and innovation were the most simultaneously used keywords. Furthermore, this study also identified that there is a lack of study regarding consumer behavior, employee behavior and quality management of start-ups during the COVID-19 pandemic.

This research also has several limitations. First, this research only used the Scopus database. Even though Scopus has advantages compared to other databases, combining the Scopus database with other databases will be able to provide more comprehensive data. However, there is a risk associated with combining two or more databases, namely, duplication. When using more than one database, researchers must ensure no repetition of the articles used because it will affect the analysis results. Future research can consider combining the Scopus database with other databases. Second, this bibliometric study covered the publication of start-ups during the COVID-19 pandemic. The COVID-19 pandemic is still ongoing. This condition indicated that several research on start-ups during COVID-19 are still being performed and will be published. Thus, future bibliometric study start-ups during COVID-19 were needed to update the trend of the research on start-ups during COVID-19.

The bibliometric study provided at least two main practical implications that can be used to support the growth of start-ups during the COVID-19 pandemic. First, this research has mapped the existing literature on start-ups during the COVID-19 pandemic. This information can be used as input for start-up practitioners and policymakers when they need scientific reference and evidence in developing a policy and/or a management action/program needed during the COVID-19 pandemic. Second, this research highlighted several clusters of keywords that are attracted many researchers. These keywords can be viewed as important issues that are needed to be monitored by start-up practitioners and policymakers during the COVID-19 pandemic.

Start-ups have an important role in economic development. It is well known that start-ups can boost productivity and economic growth. As start-ups tend to use technology, it also has an important role in disrupting traditional economic practices. Start-ups may also offer a more convenient life. Furthermore, the availability of start-ups may also increase the quality of life of the people that are serviced by the start-ups.

To the best of the authors’ knowledge, this is the first bibliometric study that discussed start-ups during the pandemic. It provided a comprehensive literature review process to identify research gaps that allowed future researchers to develop studies related to start-ups.

In 2019, FIU-the Netherlands celebrated its 25th anniversary. This study takes the occasion to reflect on the role of the FIU in financial surveillance and to describe its core practices of collecting, analysing and disseminating financial intelligence.

Because FIU practices are often secret and its transaction data classified as state secrets, the FIU’s daily operational activities remain obscure. Drawing on interviews, public reports and an online training course, this study encircles secrecy and offers a fine-grained analysis of the FIU's core activities.

The article finds that the FIU plays a pivotal role in financial surveillance because it can operate at various intersections. An FIU operates at the intersection of finance and security, in between the public and private sector and at the national and international domain. This pivotal role makes the FIU indispensable in the surveillance of payment systems and spending behavior.

The article poses that the desirability and effectiveness of financial surveillance has to date not received sufficient consideration, while it affects (the privacy of) anyone with a bank account. The article asks: is it ethically justifiable that transaction information is declared suspect, investigated, and shared nationally and internationally, without the individual or entity concerned officially being notified and legally named a suspect?

This case-study is not only relevant for the study of finance/security, AML/CFT and financial surveillance, but also to policy makers and the broader public who merit an understanding of how their financial behaviour is being surveilled.