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This paper aims to propose a system to acquire images automatically for digital reconstruction of heritage artifacts using a six-degree of freedom industrial manipulator.

A virtual environment is created using Robot Studio® software to integrate the trajectory and differential motion of the robot manipulator and the motion of camera while acquiring images. A new area similarity matrix method is proposed to reduce the number of images required for digital reconstruction using Autodesk Recap® software. Real-time experiments have been performed using objects such as minion, ultimaker robot and cube. Evaluation of the digital reconstruction is conducted using the contour area matching method.

The number of images required for reconstruction based on area similarity matrix method is reduced to 63 per cent when compared with the random selection method. Quality parameters such as surface area, volume, number of defect holes, vertices and faces are enhanced for the proposed method.

Digital reconstruction of large-sized heritage artifacts cannot be performed in this setup. But this can be overcome by fixing the manipulator on a mobile platform or overhead crane. This paper does not discuss the reconstruction of partially damaged heritage artifacts, which could be accomplished based on deep learning techniques.

Using this approach, off-the-shelf heritage artifacts and large-scale objects can be reconstructed digitally with a minimum number of images and without compromising the quality of original models.

To the best of the authors’ knowledge, area similarity-based approach in 3D digital reconstruction by coupling the kinematics of an industrial manipulator and camera is proposed for the first time. A fully automated digital reconstruction technology to preserve valuable heritage artifacts has been developed. It also highlights the space constraints of the industrial manipulator in digital reconstruction.

Circular economy denotes future sustainability that allows optimum utilization of resources. In the present era of technology, plenty of innovations are happening across the world, and digital manufacturing is one of such innovations. However, there are several barriers which are impeding adoption of digital manufacturing in circular economy environment. The study explores the barriers of digital manufacturing initiatives in a circular economy and develops a methodological model to prioritize the identified challenges for automotive parts manufacturing industry.

Seven categories of challenges namely process, human resources, financial, collaboration, technological, security and leadership challenges were identified from literature and further validated with subsequent discussions with experts from the industry. The study is conducted in two phases, where in the first phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is used to define the priority and importance of seven categories of challenges. In second phase, the barriers are ranked using a Fuzzy Performance Important Index (FPII), taking into account contextual factors associated with the challenges and linked barriers, to determine the extent to which they impede the adoption of digital manufacturing in the sample automotive parts manufacturing company.

The “risk of data security and information privacy in connection with use of external data and protecting customer data” appeared as the most significant barrier to digital manufacturing in circular economy. Furthermore, technological challenges emerged as the most significant category of challenges followed by financial challenges in adoption of digital manufacturing in circular economy.

Identification of the identified barriers and understanding the interrelationships will lead to easier adoption of digital manufacturing in circular economy.

Despite all the potential benefits of implementing Industry 4.0 technologies in manufacturing industries, the adoption thereof is still in nascent phase with significant challenges yet to be overcome to accelerate the pace of adoption. Hence, this study explores the barriers preventing companies from adopting and benefiting from digital manufacturing initiatives and further develops a methodological model.

The purpose of this paper is to analyze the spatial patterns of different phenomena in the same geographical space. Andresen’s spatial point pattern test computes a global index (the S-index) that informs on the similarity or dissimilarity of spatial patterns. This paper suggests a generalized S-index that allows perfect similarity and dissimilarity in all situations.

The relevance of the generalized S-index is illustrated with police data from the San Francisco Police Department. In all cases, the original S-index, its robust version – which excludes zero-crime areas – and the generalized alternative were computed.

In the first example, the number of crimes greatly exceeds the number of areas and there are no zero-value areas. A key feature of the second example is that most street segments were free of any criminal activity in both patterns. Finally, in the third case, one type of event is considerably rarer than the other. The original S-index is equal to the generalized index (Case 1) or theoretically irrelevant (Cases 2 and 3). Furthermore, the robust index is unnecessary and potentially biased when the number of at least one phenomenon being compared is lower than the number of areas under study. Thus, this study suggests to replace the S-index with its generalized version.

The generalized S-index is relevant for situations when events are relatively rare –as is the case with crime – and the unit of analysis is small but plentiful – such as addresses or street segments.

Heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking and exploration. Exploration missions with heterogeneous robot teams (HeRTs) should acquire a common map for understanding the surroundings better. The purpose of this paper is to provide a unique approach with cooperative use of agents that provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, this method is suitable for real-time applications and autonomous path planning for exploration.

Lidar odometry and mapping and various similarity metrics such as Shannon entropy, Kullback–Leibler divergence, Jeffrey divergence, K divergence, Topsoe divergence, Jensen–Shannon divergence and Jensen divergence are used to construct a common height map of the environment. Furthermore, the authors presented the layering method that provides more accuracy and a better understanding of the common map.

In summary, with the experiments, the authors observed features located beneath the trees or the roofed top areas and above them without any need for global positioning system signal. Additionally, a more effective common map that enables planning trajectories for both vehicles is obtained with the determined similarity metric and the layering method.

In this study, the authors present a unique solution that implements various entropy-based similarity metrics with the aim of constructing common maps of the environment with HeRTs. To create common maps, Shannon entropy–based similarity metrics can be used, as it is the only one that holds the chain rule of conditional probability precisely. Seven distinct similarity metrics are compared, and the most effective one is chosen for getting a more comprehensive and valid common map. Moreover, different from all the studies in literature, the layering method is used to compute the similarities of each local map obtained by a HeRT. This method also provides the accuracy of the merged common map, as robots’ sight of view prevents the same observations of the environment in features such as a roofed area or trees. This novel approach can also be used in global positioning system-denied and closed environments. The results are verified with experiments.

Owing to the large amount of information available on Twitter (a micro blogging service) that is not necessarily true or believable, credibility of news published in such an electronic channel has become an important area for investigation in the field of web credibility. This paper aims to address this issue.

A system was developed to measure the credibility of news content published in Twitter. The system uses two approaches to assign credibility levels (low, high and average) to each tweet. The first approach is based on the similarity between Twitter posts (tweets) and authentic (i.e. verified) news sources. The second approach is based on the similarity with verified news sources in addition to a set of proposed features.

The evaluations of the two approaches showed that assigning credibility levels to Twitter tweets for the first approach has a higher precision and recall. Additional experiments showed that the linking feature has its impact on the second approach results.

The proposed system is experimental; thus further experiments are needed to prove these findings.

This paper contributes to the research on web credibility. It is believed to be the first which provides a proposed system to evaluate the credibility of Twitter news content automatically.

A new and promising approach to document clustering consists of utilizing previously formed clusters of queries to cluster documents. To employ this approach in practice a similarity measure for queries must be available. This requirement does not cause any problem in the case of information retrieval systems in which both the search request formulations and document representations are sets of weighted or unweighted index terms. However, in most operational retrieval systems search request formulations are Boolean combinations of index terms. Research into similarity measures for search request formulations of this type has already been undertaken by the author and reported elsewhere. The present paper provides further results of investigations in this area. The novelty of the approach discussed is the incorporation within the methodology described earlier of a weighting mechanism to indicate the relative importance of particular attributes of a given Boolean search request formulation. A modification suggested is based on the standard probabilistic approach to information retrieval.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.