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The purpose of this paper is to explain the effects of the customer‐supplier relationship and of automatic forecast data communication and registration on the perceived information quality of forecasts.

A conceptual model and three hypotheses are derived. The empirical analysis is based on survey data from 219 Swedish manufacturing companies.

Findings show that the customer‐supplier relationship and automatic data communication and registration have significant impact on the perceived quality of forecast information received from a downstream customer in the supply chain. The reliability and timeliness of the forecast information are affected to about the same extent by both the relationship type and the data communication and registration strategy. Credibility is correlated with the relationship type, while the completeness, validity and conciseness of the received forecast are operative issues depending mainly on the communication strategy.

Using single informants, focal customers and some single‐item constructs in research design.

The paper explains how various dimensions of forecast information quality are affected by different factors, thus guiding how to differentiate information quality improvement work in diverse situations.

Detailed empirical studies of supply chain information exchange, especially focusing on explaining causes of high‐quality information exchange, are lacking in the literature and demanded in industry.

Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

The fusion of aerial imagery and LiDAR point clouds are considered as one of the most promising approaches for many fields, such as 3D city reconstruction and tree detection. The purpose of this paper is to achieve reliable registering LiDAR data and aerial images without orientation parameters based on a progressive optimizing process.

First, combination of edges and their corners is extracted and considered as registration primitives; then search conjugate primitives globally with a suitable buffer of each edge; after that, a progressive algorithm is adopted to optimize the registration; finally, error analysis and data fusion are carried out.

After a progressive optimum algorithm, the number and the distribution of the matched pairs are sufficient for generation of reliable and accurate orientation parameters. The results show RMS of residual errors gets close to one DSM cell, which is equal to or even better than that in other literatures.

The method proposed in the paper is feasible and effective to generate reliable and accurate registering results.

Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

This paper aims to clarify the potential to use data on doctors and fitness to practise (FTP) cases held by the UK General Medical Council (GMC) for wider regulatory purposes, such as identifying risk factors. The paper aims to concentrate on how data are shaped by the GMC's functions and organisational concerns, and by the configuration and use of their electronic database.

The GMC provided samples of their data, access to documentation surrounding the configuration and use of the database, and meetings with staff able to provide background on the database, GMC procedures, and the GMC as an organisation.

The FTP database is designed to process cases within complex legal rules, and to provide for accountability. The database and its use are adapted to these purposes. Attempts to use it for other purposes are likely to find it difficult to use, the scope and quality of data uneven and some codes unsuitable. The register data are very narrow in scope. While combining register and FTP data to identify risk factors is by itself of limited value, the database can contribute to closer study of risks to patient safety from poorly performing doctors.

The research was exploratory. It provides initial insights and the basis for further research.

The data have potential policy use for the GMC, but it is essential to understand the limitations.

The paper examines previously unanalysed influences on the GMC's data. It also develops new angles on questions in the regulation literature about organisational risks and the creation of risk data.

While efforts to mainstream collection of ethnicity in routine health datasets have gathered pace since the Race Relations (Amendment) Act 2000, relatively little attention has focused on the capture of migrants, and data on asylum seekers and refugees are even more sparse. There is negligible coverage in the key datasets for primary and secondary care, and only a few of the new contract datasets to support the National Service Frameworks accord importance to the migrant population. Some of the communicable disease data collections record country of birth, but its incompleteness is a drawback. Given the growing size of the non‐UK‐born population and the accumulating evidence on the health and health care needs of the migrant population, country of birth merits a place alongside other access variables such as age, sex, ethnicity, socioeconomic status and place of residence in routine data collection. While record linkage studies offer some scope, and there is currently a focus on obtaining improved migration statistics in the forthcoming census and other data flows, health and social care sources remain a neglected area.

The paper outlines a new approach for positioning a patient on the treatment table for radiation therapy sessions. The vision approach utilizes lasers and cameras for positioning and has several advantages over the conventional methods.

The positioning is accomplished by comparison of a set of computed tomography (CT) contours (acquired from the patient) with a set of corresponding contours acquired by a 3D vision system from the same region of the patient's body. The overall positioning error calculated by the iterative closest point algorithm is used to reorient the treatment table. Various issues related to the acquisition and generation of the 3D spatial data are discussed.

Positioning is accurate and can detect small movement in the patient's position.

Testing was done on a cast of a human torso and additional testing is required on in a hospital environment to fully test the efficiency of the approach.

The method merges data readily available from standard CT imaging systems and 3D imaging systems. Therefore, the additional hardware requirements are minimal. The system integrates well with existing hardware, software and treatment practices.

The method introduces a new approach to patient positioning employing a combination of sensor technologies. The approach is accurate, reliable, consumes less time and most importantly prevents the use of X‐rays for patient positioning.

The Sendai Framework for Disaster Risk Reduction 2015–2030 calls for a reduction in disaster mortality, yet measuring mortality remains a challenge due to varying definitions of disaster mortality, the quality, availability and diversity of data sources, generating mortality estimates, and how mortality data are interpreted.

This paper uses five case studies to provide details around some of the complexities involved with measuring disaster mortality and to demonstrate the clear need for accurate disaster mortality data.

The findings highlight the benefits of combining multiple data sources for accurate mortality estimates, access to interoperable and readily available global, national, regional and local data sets, and creating standardized definitions for direct and indirect mortality for easier attribution of causes of death.

Countries should find a method of measuring mortality that works for them and their resources, and for the hazards they face. Combining accurate mortality data and estimates and leadership at all levels can inform policy and actions to reduce disaster mortality, and ultimately strengthen disaster risk reduction in countries for all citizens.

The Norwegian Ministry of Environment has undertaken to build and implement a meta information system for environmental information/data. The objective is to increase availability of cross sector information and efficiency in use of environmental information/data. The system is to be accessible via Internet, but based on SQL‐database. Further the same system may be used locally by the governmental institutions and centrally at national level. Access will be public and free of charge, software will be free of charge. The metadata will be characterized according to European standards; amongst others, by use of the multilingual environmental thesaurus (GEMET) and a standard for mandatory field entries. Interoperability with other systems is assured by used of standard communication protocols (Z.39.50). To define objectives at a high precision level and to have a development guide, a technical “Systems Requirements” were defined. Development is done with users, and by means of prototypes, laboratory and field tests.

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.