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This article seeks to highlight the challenges and issues that face merging higher education institutions and also to outline some of the challenges in integrating the quality assurance systems during the pre‐, interim and post‐merger phases in a merged university.

Case studies of merged and merging institutions were critiqued to compare the similarities and differences. A range of issues experienced by two universities that recently merged in South Africa is explored.

It is clear that merging institutions is a thorny issue, and cannot be resolved quickly. The merging of institutions brings with it a whole host of challenges. The current literature review has found that merging higher education institutions globally is invariably unpredictable, as the institutions experience multifaceted complexities.

The lessons learned from this newly merged university can be a very useful resource and guide to those in leadership positions.

This article contributes to the current pool of information and offers practical guidance and assistance to institutions that have merged or are in the process of merging.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

A simultaneous solution to the localization and mapping problem of a graph‐like environment by a swarm of robots requires solutions to task coordination and map merging. The purpose of this paper is to examine the performance of two different map‐merging strategies.

Building a representation of the environment is a key problem in robotics where the problem is known as simultaneous localization and mapping (SLAM). When large groups of robots operate within the environment, the SLAM problem becomes complicated by issues related to coordination of the elements of the swarm and integration of the environmental representations obtained by individual swarm elements. This paper considers these issues within the formalism of a group of simulated robots operating within a graph‐like environment. Starting at a common node, the swarm partitions the unknown edges of the known graph and explores the graph for a pre‐arranged period. The swarm elements then meet at a particular time and location to integrate their partial world models. This process is repeated until the entire world has been mapped. A correctness proof of the algorithm is presented, and different coordination strategies are compared via simulation.

The paper demonstrates that a swarm of identical robots, each equipped with its own marker, and capable of simple sensing and action abilities, can explore and map an unknown graph‐like environment. Moreover, experimental results show that exploration with multiple robots can provide an improvement in exploration effort over a single robot and that this improvement does not scale linearly with the size of the swarm.

The paper represents efforts toward exploration and mapping in a graph‐like world with robot swarms. The paper suggests several extensions and variations including the development of adaptive partitioning and rendezvous schedule strategies to further improve both overall swarm efficiency and individual robot utilization during exploration.

The novelty associated with this paper is the formal extension of the single robot graph‐like exploration of Dudek et al. to robot swarms. The paper here examines fundamental limits to multiple robot SLAM and does this within a topological framework. Results obtained within this topological formalism can be readily transferred to the more traditional metric representation.

The purpose of this paper is to design a new standard instrument arrival called the point merge system (PMS) for converging runways. The PMS enables controllers to handle traffic with no heading instruction, as well as aiming to reduce a controller's frequency occupancy time.

The point merge model was designed for converging runways. Istanbul International Ataturk Airport, which has converging runways, was chosen as an application area for this model. The same 50 traffic arrivals per hour were used both for point merge and vectoring. Implementation was compared using a real time simulation.

The simulation results show that the total average number of instructions is about 33 per cent less and the frequency occupancy is about 37 per cent less for point merge than for vectoring. In addition, in terms of trajectory dispersion, in point merge, traffic is within a narrower triangular area, while in vectoring large traffic dispersion occurs.

The point merge model for converging runways proposed in this paper can be applied by airspace designers and air navigation service providers to perform efficient standard instrument arrival routes.

The PMS has been developed for single and parallel runways; however, in this study, the point merge model is designed for converging runways at Istanbul International Ataturk Airport.

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

The purpose of this paper is to examine the impact of merger and acquisition (M&As) of “second financial restructuring” (SFR) on the productivity growth of commercial banks in Taiwan.

The paper uses the Malmquist productivity change index to evaluate the changes from pre-SFR to SFR period and from pre-SFR to post-SFR period. In addition, the bootstrapping regression method is applied to examine the relationship of SFR policy and productivity change.

Merged banks have improved their productivity and scale efficiency after the M&As program of SFR. In addition, the greater productivity growth of merged banks than non-merged banks is attributed to small-sized and private-voluntary merged banks. Furthermore, the small-sized merged banks have greater productivity growth and scale efficiency improvement than the big-sized merged banks, and the government-mandatory merged banks have lower productivity growth than private-voluntary merged banks after the SFR.

This study has an academic implication for providing additional empirical evidence related to the impact of government M&As policy on bank productivity growth in the developing countries.

The findings on this paper have implications for financial reform policy and banking management on M&As activity, in particular, as they clarify the differential effects of big-sized vs small-sized and government-mandatory vs private-voluntary merged banks.

Understanding the impacts of financial reform is particularly important as the banking industry has become increasingly competitive. This paper contributes to this area by assessing the impact of the M&As policy of SFR on productivity growth and evaluating differential effects of M&As.

The purpose of the paper is twofold: to determine firm characteristics that explain mergers in the banking industry and to predict the likelihood of a merger.

A logit model is used to estimate coefficients. The paper also tests the effect of the Financial Services Modernization Act on market structure and uses a national sample from 1997 to 2003.

Results indicate that profitability (PROF), size (SIZE), asset management (ASSETMGT), and solvency (SOLV) are factors in explaining mergers in the full sample model. Poor ASSETMGT, large target banks, low PROF, and solvent banks represent merged firms in the full sample model. The results also indicate that loan activity is not a factor in explaining mergers. It appears that banks merged before the Financial Services Modernization Act (Pre‐1999 Merger Model) are characterized by poor ASSETMGT, and are solvent. In contrast, the only factor explaining mergers after the Financial Services Modernization Act (Post‐1999 Merger Model) was PROF.

The paper shows that the results for the full sample model and the sub‐sample models are the same except for PROF. This suggests that acquiring banks seek to provide better management, technology, and access to better markets than the smaller merged banks.

The purpose of this paper is to discover and better understand the efficiency of university mergers from the perspective of knowledge production, with the research capability as the point of contact.

In total, 20 colleges and universities directly under the central ministries that merged in 2000 were taken as the research samples. Factor analysis methods are adopted to calculate the public factor scores, which were taken as the basis of the time series analysis and similar comparative analysis.

The results indicated that, by the end of 2005, the reform of universities merger has a positive impact on the universities' knowledge production, but there was a “short‐term prosperity” after the merger happened, especially in the aspect of scientific research funding efficiency and the transformation of knowledge production outcomes.

Few if any mergers are painless and in the literature on mergers it is generally agreed that it can take up to ten years for the wounds to heal and for the new institution forged from previously autonomous identities to operate as a cohesive and well integrated whole.