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On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues.

In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.

The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation.

Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range.

The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.

This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

Purpose – Purpose of the research is to make a comprehensive study to elucidate the existing landscape of scientific production of disease outbreaks, pandemics and tourism research. In doing so, authors analyzed scientific production of pandemics and tourism-related studies such as year-wise publications, productive authors, institutes, funding sponsors, thematic areas of research and citation analysis.

Design/methodology/approach – Authors analyzed the research papers indexed in the online Scopus database over 50 years of time starting from 1971 to 2020 by using bibliometrics, and the data are visualized by using data visualization tools like VOSviewer and the Tableau.

Findings – The understanding of disease outbreaks and pandemics in tourism has increased over the years in terms of number of papers, citation, networks and collaborative themes of research.

Research limitations/implications – Data for the study were generated from Scopus online database and limited to English-written journal articles that were produced with search strategy of specific keywords associated with tourism, virus, pandemics and disease outbreak.

Practical implications – Findings of the research provide insights into academia and practitioners on the understanding of disease outbreaks, pandemics and tourism research, coherent development of the concept and understanding the research gap and focussed area of research.

Originality/value – As per authors' understanding, this paper would be one of the first attempts to provide greater understanding of disease outbreaks, pandemics and tourism as a research topic by examining the growth and evolution in an academic context through bibliometric analysis.

Paper type – Review paper.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Bumpless Cu/SiO₂ hybrid bonding, which this paper aims to, is a key technology of three-dimensional (3D) high-density integration to promote the integrated circuits industry’s continuous development, which achieves the stacks of chips vertically connected via through-silicon via. Surface-activated bonding (SAB) and thermal-compression bonding (TCB) are used, but both have some shortcomings. The SAB method is overdemanding in the bonding environment, and the TCB method requires a high temperature to remove copper oxide from surfaces, which increases the thermal budget and grossly damages the fine-pitch device.

In this review, methods to prevent and remove copper oxidation in the whole bonding process for a lower bonding temperature, such as wet treatment, plasma surface activation, nanotwinned copper and the metal passivation layer, are investigated.

The cooperative bonding method combining wet treatment and plasma activation shows outstanding technological superiority without the high cost and additional necessity of copper passivation in manufacture. Cu/SiO₂ hybrid bonding has great potential to effectively enhance the integration density in future 3D packaging for artificial intelligence, the internet of things and other high-density chips.

To achieve heterogeneous bonding at a lower temperature, the SAB method, chemical treatment and the plasma-assisted bonding method (based on TCB) are used, and surface-enhanced measurements such as nanotwinned copper and the metal passivation layer are also applied to prevent surface copper oxide.

Transport is the European Union (EU) sector that produces the second highest amount of greenhouse gas emissions. In its attempts to promote the environmentally sustainable development of transport, the EU has focussed on intermodal transport in particular – but with limited success. It is important to understand how freight transport is selected, which criteria are used and what role environmental sustainability and intermodal transport play in the selection. Therefore, the purpose of this paper is to focus on the role of environmental sustainability and intermodal transport in transport mode decisions. The authors look at this issue from the perspective of logistics service providers (LSPs) and buyers, as they are important stakeholders in guiding this process.

To gain a holistic view of the current state of research, the authors have conducted a systematic literature review of the role of environmental sustainability and intermodal transport in transport mode decisions. The authors have further examined the findings concerning requests for quotations (RfQs), tenders and transport contracts, as these are also linked to decisions on transport choice.

The findings from the literature review include the results of descriptive and structured content analysis of the selected articles. They show that the discussion on environmental sustainability and intermodal transport as a sustainable mode, together with the transport mode selection criteria, RfQs/tenders and transport contracts, is still a rather new and emerging topic in the literature. The main focus related to the selection of transport mode has been on utility and cost efficiency, and only recently have issues such as environmental sustainability and intermodal transport started to gain greater attention. The findings also indicate that the theoretical lenses most typically used have been preference models and total cost theories, although the theoretical base has recently become more diversified.

There is still a need to extend the theoretical and methodological base, which could then lead to innovative theory building and testing. Such diverse application of methodologies will help in understanding how environmental sustainability can be better linked to mode choice decisions.

The findings will be of interest to policy makers and companies opting for environmentally sustainable transport solutions.

If the EU, shippers and LSPs take a more active stance in promoting environmentally sustainable transformation models, this will have long-lasting societal impacts.

It seems that this systematic literature review of the topic is one of the first such attempts in the current body of literature.

Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.

This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.

Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.

Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

The purpose of the study is to map the funding status of COVID-19 research. The various aspects, such as funding ratio, geographical distribution of funded articles, journals publishing funded research and institutions that sponsor the COVID-19 research are studied. To visualize the country collaboration network and research trends/hotspots in the field of COVID-19 funded research, keyword analysis is also performed. The open-access (OA) status of the funded research on COVID-19 is also discussed.

The leading indexing and abstracting database, i.e. Web of Science (WoS), was used to retrieve the funded articles published on the topic COVID-19. The scientometric approach, more particularly “funding acknowledgment analysis (FAA),” was used to study the research funding.

A total of 5,546 publications of varied nature have been published on COVID-19, of which 1,760 are funded, thus indicating a funding ratio of 32%. China is the leading producer of funded research (760, 43.182%) on COVID-19 followed by the USA (482, 27.386%), England (179, 10.17%), Italy (119, 6.761%), Germany (107, 6.08%) and Canada (107, 6.08%). China is also in lead in terms of the funding ratio (60.94%). However, the funding ratio of the USA (31.54%) is at 11th rank behind Canada (40.68%), Germany (34.18%) and England (35.87%). The USA occupies a central position in the collaboration network having the highest score of articles with other countries (n = 489), with the USA–China collaboration ranking first (n = 123). National Natural Science Foundation of China (NSFC) is the largest source of funding for COVID-19 research, supporting 342 (19.432%) publications, followed by the United States Department of Health Human Services (DHHS) and National Institute of Health (NIH), USA with 211 (11.989%) and 200 (11.364%) publications, respectively. However, China's National Key Research and Development Program achieves the highest citation impact (80.24) for its funded publications. Journal of Medical Virology, Science of the Total Environment and EuroSurveillance are the three most prolific journals publishing 63 (3.58%), 35 (1.989%) and 32 (1.818%), respectively, of the sponsored research articles on the COVID-19. A total of 3,138 institutions produce funded articles with Huazhong University of Science Technology and Wuhan University from China at the forefront publishing 92 (5.227%) and 83 (4.716%) publications, respectively. The funded research on COVID-19 is largely available in OA mode (1,674, 95.11%) and mainly through the Green and Bronze routes. The keyword clustering reveals that the articles mainly focus on the impact, structure and clinical characteristics of the virus.

The study's main limitation is that the results are based on the publications indexed by WoS, which has limited coverage compared to other databases. Moreover, all the funding agencies do not require or authors miss to acknowledge funding sources in their publications, which ultimately undermines the number of funded publications. The research publications on COVID-19 are also proliferating; thus, the study's findings shall be valid for a minimum period.

The funding of research on the COVID-19 is highly essential to accelerate innovative research and help countries fight against the global pandemic. The study's findings reflect the efforts made by nations and institutions to remove the financial and accessibility hurdles. It not only underscores the lead of the USA in the research on COVID-19, but also shows China as a forerunner in sponsoring the research, thus, helping to know the contribution of nations toward understanding the dynamics of pandemic and controlling it. The study will help healthcare practitioners and policymakers recognize the areas that remain the focus of sponsored research on COVID-19 and other left-out areas that need to be taken up and thus may help in policy formulation. It further highlights the impact of prolific funding agencies so that efforts may be initiated to increase the impact and thereby the returns of investment. The study can help to map the scientific structure of COVID-19 through the lens of funded research and recognize core inclinations of its development. Overall, a comprehensive analysis has been performed to present the detailed characteristics of sponsored research on emerging area of COVID-19, and it is informative, useful and one of its kind on the theme.

The study explores the funding support of research on COVID-19 and its other aspects, along with the mode of availability.

The purpose of this study is to analyze the share of coronavirus publications and its citation-based indicators in various journal impact factor quartiles to discover their relationship and analyze the advantages of Q1 publications.

Bibliometric analyses of world coronavirus research publications (articles and reviews) indexed in Web of Science database over 20 years among four journal quartiles were performed.

The publication and citation shares in various journal quartiles were decreased in the following order: Q1 > Q2 > Q3 > Q4. World coronavirus publications/citations share in Q1 journals were on average 1.78/4.18, 2.75/7.90 and 5.07/27.79 times greater than Q2, Q3 and Q4 publications, respectively. Moreover, similar patterns were obtained for various research performance dimensions: impact, excellence, corporate interest and funding indicators. These indicators of Q1 publications were much better than the corresponding values for world overall and infectious disease literature. Thus, there was a clear research performance advantage of Q1 coronavirus publications.

To the best of the authors’ knowledge, this is the first study analyzing the journal impact factor quartiles and its impact on coronavirus research performance. The results/findings of this study are useful for many stakeholders to enhance the research influence by considering journal impact factor quartiles especially Q1 journals.

This study aims to determine whether financial statement users suffered a significant loss of information when, in November 2007, the SEC dropped the requirement for foreign private issuers using International Financial Reporting Standards (“IFRS firms”) to reconcile their financial statements to US generally accepted accounting principles (GAAP).

The study investigates whether analyst forecast errors and forecast dispersion increased for IFRS firms to a greater extent than for US GAAP firms after the Securities and Exchange Commission (SEC) dropped the reconciliation requirement. Using a treatment group comprised of IFRS firms and a matched sample of US GAAP firms, this study uses regression analyses to compare forecast errors and dispersion for the last fiscal year the reconciliation was available and the first fiscal year during which the reconciliation was unavailable to analysts.

The study finds evidence that forecast errors for IFRS firms exhibited no systematic change after the reconciliation was no longer available for analysts covering those firms. Thus, it does not appear that dropping the reconciliation requirement was associated with a change in forecast accuracy. However, the study does find evidence of increased dispersion in the IFRS firms’ forecasts relative to their US GAAP counterparts after the reconciliation requirement was dropped.

These findings have implications for evaluating the Securities and Exchange Commission’s 2007 decision to eliminate the reconciliation for IFRS firms. Specifically, the Securities and Exchange Commission’s decision does not appear to have significantly altered analysts’ information environments.

This paper contributes to the understanding of how a group of sophisticated financial statement users adapt to different sets of accounting standards.

Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.

Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.

The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.

The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.

Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.

The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.