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This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

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.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

This paper analyses the establishment and evolution of a public oversight body (POB) – the Egyptian Audit Oversight Unit (AOU) – and its implications for local auditing firms and practices.

Primary data were gathered from 34 semi-structured interviews (including follow-up ones) between 2014 and 2020. Secondary data was obtained through publicly available documents and internal memos. Drawing on Debord's (1967) Society of the Spectacle, the insights focus on the POB's conception, materialisation and evolution in a context characterised by weak regulatory structures.

Through a series of acts, the findings reveal how the AOU first accepted the image of “international best practice” oversight (the “metaphorical”), followed by the construction of the local structure and décor replicating a United States (US) style POB archetype (the “transformational”) by primarily relying on visible processes/procedures. Yet, these mechanisms emphasised the spectacular nature of oversight, with little improvement for practice and limiting itself to “cracking down” on smaller local firms. A final stage (the “performative”) reveals how the AOU seeks to expand its activities beyond its original mandate without challenging the image-driven nature of its oversight.

The paper offers two key contributions. First, it reveals how actors, through a combination of symbolic and tangible measures, create a new performative reality of public oversight. Second, it advocates Debord's “spectacle” to complement other theoretical lenses, with a view to illuminating the materialisation stages that bridge the gap between proclaimed oversight policies and actual practices (including conscious and unconscious omissions) within a given political economy context.

This study aims to examine the differential impact of ride-hailing services (RHS) on private and commercial vehicle ownership from five metropolitan cities in India.

Using vehicle ownership data from five metropolitan cities over period 1991 to 2020, a panel corrected standard errors model was estimated to model the association between RHS and vehicle ownership.

The results indicate that advent of RHS has led to a significant reduction in private vehicle ownership rates and a corresponding increase in addition of intermediate public transport. The net effects of RHS on road congestion and pollution levels need to be studied in detail.

The findings of this study can potentially assist policymakers and mobility planners in efforts to decarbonise and decongest urban transport.

This study sets precedence in analysing the impact of RHS on private and commercial vehicle independently. Further, to the best of the author’s knowledge, this is the first study to examine this association for the city of Delhi and Kolkata.

Introduction: The Udaan Scheme was implemented in response to enduring conflict, economic downturn, and employment scarcity. Under the Rangarajan Committee, the scheme aimed to address unemployment in a selected region through skill development programmes. Based on practical experience, Udaan aimed to build a competitive workforce for India and the global economy.

Purpose: The purpose of the study is to evaluate the success ratio of the Udaan Scheme in addressing the employment challenges faced by the youth.

Need of the study: The chapter highlights the potential of the scheme to be a part of a resilient industry for job employability in politically disturbed areas.

Methodology: The compiled data were analysed using a spreadsheet collected from online sources, providing information on the number of registrations for the skill development programme between March 2012 and May 2018, that is, the programme’s implementation in the pre-UT era, mainly sourced from the Udaan Impact Assessment Report and the Review of the Udaan Scheme in Jammu and Kashmir (J&K).

Findings: The programme, which provided professional training and increased the job-securing capacity of youth, has had a dismal success rate despite the government’s investment of Rs 246 crore. The initiative has employed less than 10,000 individuals, or at most 10% of the target population, falling short of its claimed goals.

Significance of the study in the global market: The scheme addresses unemployment and career development for educated youth, enhancing India’s economic growth and global competitiveness. By providing skill development and exposure to the corporate sector, it empowers youth and attracts international business opportunities. It aligns with global efforts to bridge the skills gap and showcases India’s commitment to human capital development in a conflict-driven state.

This paper aims to examine the factors associated with a household business entrepreneur’s decisions to formalise the firm at a multidimensions level.

The data set is a panel of 2,336 SMEs and household businesses from Vietnamese SME surveys during the 2005–2015 period.

This study elucidates how firm-level resources, entrepreneur characteristics and costs of doing business influence an entrepreneur’s decision to enter, the speed and the degree of formality.

This study provides insight into the origins of an entrepreneur’s decisions to the multidimensions of business formality through the lenses of the resource-based view, entrepreneurship and institution theories.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Road transport is an important sector of the economy, however, its negative impacts on the environment, human health and sustainability of potential economic growth are significant. Transport externalities tend to be neglected within the market process. The damage generated through significant externalities of road transport in Czechia was identified, based on unique data processing, to reach almost 3.5 billion € annually. This chapter presents an overview of internalization taxes and fees, their current rates and generated receipts. If excise duty is disregarded as fundamentally unsuitable for the internalization of externalities, then the receipts from other applied taxes and fees, particularly the road tax, seem to be insufficient. Although economic growth is encouraged, its form is not sustainable in view of the rising phase of the Kuznets curve and the related irreversible environmental impacts.