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This chapter discusses the contributions and challenges involving regulatory technology (regtech) in financial services. It explores the salient areas where regtech can and should focus, observing existing and forthcoming industry, technology, and legal developments. This chapter outlines regtech use cases to clarify the shaping of that industry sector. It draws on developments in industry and academia, where significant research sets the tone and direction of technological solutions and regulatory drivers. A brief critical account of the benefits and challenges in regtech is offered. This chapter presents potential future directions, focusing on the salient areas of environmental, social, and governance (ESG), cryptocurrency, and decentralized compliance.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This study aims to examine academic staff’s engagement with sustainable development goals (SDGs) in higher education institutions.

The triangulation, convergence model of the mixed methods research design was adopted as the strategy for inquiry. A total of 56 questionnaires and 25 interviews were used to collect the data, and this was buttressed by document review and use of secondary data obtained from Scival.

The results show moderate levels of engagement of academic staff with the SDGs. However, SDGs familiarisation is not correlated with the rate of localisation. The lack of funding deflated political will by university management, demotivated academia and shrinking government support are the leading impediments to SDGs localisation.

The results could be improved by using a larger sample size equally distributed across disciplines. Triangulation of academics’ views with those of students and non-academic staff could have improved the understanding of other dynamics involved in the localisation of SDGs by university teaching staff.

The results point towards the need for a university-based framework that interweaves national, institutional, thematic, structural and personal aspects into the SDGs implementation matrix. The underlying determinants of successful localisation of SDGs by academia need to be addressed through a bottom-up approach.

To the best of the authors’ knowledge, this paper is the first attempt in Zimbabwe to exclusively look at University teaching staff’s engagement with SDGs.

Saudi Arabia (SA) is amongst the few countries with a significant foreign workforce who are employed in the higher education sector. More specifically, 39% of SA's academic staff members are foreign nationals and 63% of that proportion occupy professorial positions. Drawing from a workforce localisation perspective, the study was framed as an exploration of equity and social justice amongst Saudi nationals and foreign nationals in a university work setting. The authors employ the lens of how human resource development (HRD) opportunities are administered.

Following the choice of an exploratory qualitative study, the authors employed a multi-case study approach where each of the six universities represented a unit of analysis.

The authors found that nationality differences influenced access to HRD opportunities. These differences are reinforced by practices associated with procedural processes, managerial discretion and selective restrictions in accessing HRD opportunities.

The findings have both practical and social implications, specifically for the SA government's strategic vision of developing local human capabilities.

The workforce localisation agenda within the higher education sector has both a compounding effect on local human capital and supports SA's 2030 Vision and human capital target. Nonetheless, perceived inequity and injustice in accessing HRD opportunities by foreign nationals potentially undermine morale, academic quality standards and research performance, which impacts the development of future human capital and the ‘Saudization’ goals.

This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

This study investigates the mediating role of wage and workforce adjustments, along with the moderating influence of collective bargaining system and employees’ localization, in elucidating the relationship between the COVID-19 shock and workplace employee relations (ER) tension.

Survey data from 1,483 enterprises across 21 prefectural cities in China’s Guangdong Province are collected. The hypotheses are tested by logistic regression.

The study reveals a positive correlation between the COVID-19 shock and workplace ER tension across crisis-hit enterprises, irrespective of their size or industrial sector. Wage reduction and mass layoffs emerge as significant mediators, while the collective bargaining system (CBS) and employees’ localization act as moderators.

The measurement of ER is limited in a single-item scale. Representation of China is also limited since the study exclusively focuses on Guangdong province. The study offers some contributions that firm-level data reveal the pathway through which COVID-19 creates ER tension.

On the one hand, the authors recommend the establishment of an effective communication system between employers and employees. On the other hand, managers should consider the role of informal institutions. Furthermore, the authors suggest implementing tailored strategies at the enterprise level.

Intense external shocks result in widespread layoffs and increased wage reductions within workplaces, and under such circumstances, formal or informal institutions may be insufficient to alleviate ER tension. In this case, the state authorities – including governments and other public agencies or bodies – are necessary to intervene in to organize tripartite dialogue.

While numerous emerging studies on COVID-19 explore how different countries manage industrial relations tension at the national level, few focus on ER at workplace level, particularly in developing countries. Understanding how workplace ER evolve during external shocks and identifying institutional measures to mitigate their negative impact is crucial for future crisis management.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization.

This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments.

Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment.

This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.

This study analyses the socioeconomic impact of COVID-19 on government accountability regarding the employment of both national and migrant workforces by bringing evidence from an emerging market. In doing so, this study addresses if/how the government discharged its accountability to the public during this recent global health crisis, which started in late 2019, with its effects still being felt today.

This study is based on a close reading of the relevant news media (local and international), published research and official reports, as well as ten conversations with business managers to analyse the socioeconomic impact of COVID-19 on government accountability in the Kingdom of Saudi Arabia (KSA). This study draws on insights from public choice theory in trying to understand why some governments take an economic perspective while exercising accountability to their population during the pandemic.

It was found that COVID-19 led the government to pursue plans for the localization of the professions and increase employment rates among nationals vs. foreigners or migrant workers. The crisis was exploited by the government to achieve macro socio-political and economic goals, demonstrating its accountability to citizens, rather than foreign workers. This shift shows that difficult and exceptional circumstances can present opportunities for policymakers in emerging markets to achieve national policy and political aims.

This study enhances the author’s understanding of accountability during crises (i.e. crises-induced accountability) in emerging markets. The analyses presented enrich the crisis management literature by highlighting the implicit actions of national leaders that affect the lives and well-being of their constituents, especially vulnerable groups.