Search results

The purpose of this article is for three teacher educators to reflect on their use of mapping and mapping-adjacent activities in university courses vis-à-vis the development of their own critical praxis toward spatial justice. The authors focus on how the centering of geospatial literacies through spatial justice issues impacts the development of criticality for preservice teachers and their teacher educators.

The paper opted for collaborative reflections about our teacher educator praxis through self-study and critical friends. Three teacher educators wrote vignettes about their experiences with place-based mapping approaches in teacher education coursework.

The paper suggests that mapping activities (broadly defined) create space(s) for courageous conversations on difficult topics (e.g. race and social-economic status). These spaces are not only between teacher and student but also can be extended to teacher educators by focusing on critical and collaborative self-study.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to use critical and collaborative reflection to inform their own praxis.

The paper shares pedagogical approaches and reflections for highlighting geospatial literacies and critical place consciousness within teacher education.

This has significance as there is a relative dearth of literature detailing how critical teacher educators can learn with and from each other when working to focus place-based learning in the context of teacher preparation.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.

This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.

The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.

This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This study aims to examine the application of Wardley mapping on developing and implementing an information technology (IT) strategy in a small-scale business. This case study illustrates the potency of multitier analysis using Wardley mapping technique to explore capability gaps and areas to focus on before undertaking IT projects.

This study uses a single case of enterprise resource planning implementation and subsequent abandonment in a small-scale manufacturing company.

This study demonstrates that multitier analysis using Wardley mapping enables a comprehensive examination of organizational capabilities, including a granular analysis of issues that must be managed for successful implementation of an IT project.

While the findings of a single case study offer limited generalizability, this study sets out the approach that can be extended in more complex settings.

The approach to understanding the organizational capabilities required to execute IT projects, or any change management project, offers significant benefits to managers and leaders.

The authors make two distinct contributions: The Wardley mapping technique has emerged as a new tool for understanding business models, particularly in government projects notable in the UK. This study extends its application to the small-scale manufacturing sector. The second contribution is elaborating on multitier analysis, which allows a granular analysis of issues that can impair the IT project implementation, including an assessment of the organizational capabilities required to deliver a successful project.

Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop.

In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching.

Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization.

The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

The purpose of this paper is to provide an overview of the trends and challenges relating to research into the human aspects of ransomware.

A systematic mapping study was carried out to investigate the trends in studies into the human aspects of ransomware, identify challenges encountered by researchers and propose directions for future research. For each of the identified papers from this study, the authors mapped the year of publication, the type of paper, research strategy and data generation method, types of participants included, theories incorporated and lastly, the authors mapped the challenges encountered by the researchers.

Fifty-nine papers published between 2006 and 2022 are included in the study. The findings indicate that literature on the human aspects of ransomware was scarce prior to 2016. The most-used participant groups in this area are students and cybersecurity professionals, and most studies rely on a survey strategy using the questionnaire to collect data. In addition, many papers did not use theories for their research, but from those that did, game theory was used most often. Furthermore, the most reported challenge is that being hit with ransomware is a sensitive topic, which results in individuals and organisations being reluctant to share their experiences.

This mapping study reveals that the body of literature in the area of human aspects of ransomware has increased over the past couple of years. The findings highlight that being transparent about ransomware attacks, when possible, can help others. Moreover, senior management plays an important role in shaping the information security culture of an organisation, whether to have a culture of transparency or of secrecy.

This study is the first of its kind of systematic mapping studies contributing to the body of knowledge on the human aspects of ransomware.

The purpose of this study is to investigate the integrated application of business process modeling and notation (BPMN) and value stream mapping (VSM) to improve knowledge work performance and productivity in police services. In order to explore the application of the hybrid BPMN-VSM approach in police services, this study uses the department of digital crime investigation (DCI) in one Norwegian police district as a case study.

Service process identification was the next step after selecting an appropriate organizational unit for the case study. BPMN-VSM-based current state mapping, including time and waste analyses, was used to determine cycle and lead time and identify value-adding and nonvalue-adding activities. Subsequently, improvement opportunities were identified, and the current state process was re-designed and constructed through future state mapping.

The study results indicate a 44.4% and 83.0% reduction in process cycle and lead time, respectively. This promising result suggests that the hybrid BPMN-VSM approach can support the visualization of bottlenecks and possible causes of increased lead times, followed by the systematic identification and proposals of avenues for future improvement and innovation to remedy the discovered inefficiencies in a complex knowledge-work environment.

This study focused on one department in a Norwegian police district. However, the experience gained can support researchers and practitioners in understanding lean implementation through an integrated BPMN and VSM model, offering a unique insight into the ability to investigate complex systems.

Complex knowledge work processes generally characterize police services due to a high number of activities, resources and stakeholder involvement. Implementing lean thinking in this context is significantly challenging, and the literature on this topic is limited. This study addresses the applicability of the hybrid BPMN-VSM approach in police services with an original public sector case study in Norway.

Compliance risk management in the banking sector is crucial because of its multifaceted nature and its potential repercussions on reputation and financial stability. This study aims to present a systematic approach to mapping compliance risks, leveraging the risk self-control assessment (RSCA) method. Integrating quantitative and qualitative assessment techniques and drawing on human expertise and industry best practices.

The authors followed a methodology that involves conducting semistructured interviews with banking compliance professionals and analyzing professional frameworks to gather pertinent information. This comprehensive approach allows to obtain a holistic view of the risks involved and to refine the risk analysis process.

A step-by-step guide to map compliance risk within banks is proposed. The authors offer a rigorous process to enhance compliance risk management practices within the banking sector, providing actionable insights to effectively navigate regulatory complexities and safeguard financial stability.

The insights from this paper will guide compliance officers and risk managers to better assess and monitor compliance risk within their organizations.

To the best of the authors’ knowledge, this may be the first thorough paper that tackles the topic of mapping compliance risk in banks, from identifying areas of risk to corrective measures after drawing up the risk map, using the RSCA approach.

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.