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Drones will become part of daily lives similar to smartphones becoming a staple of modern living. Nonetheless, only several past studies investigated the intention to utilise drones for parcel delivery however, the intention to use drones among online shoppers was not fully explored. The study attempts to investigate the factors influencing the intention to use drones for last-mile delivery.

A total of 292 data were gathered via an online survey among online shoppers applying a snowball sampling method. Since the study operationalised the measures as composites, a combination of reflective and formative measurement, and the study focusses on predictive purposes, partial least squares structural equation modelling with SmartPLS 4 was applied to test the model developed based on the stimulus-organism-response model.

The analysis found that all the direct hypotheses were found supported. Moreover, Green support, green desire and pro-environmental behaviour positively and sequentially mediated future orientation and intention, whereas technology anxiety and perceived safety moderated the relationship between pro-environmental behaviour and intention.

The respondents only limit to the online shoppers in Malaysia which based on purposive sampling method, thus the findings cannot be generalized to another countries.

Besides enriching the literature on drone studies, the findings provided practical insights to online platforms and drone operators to develop an effective strategy to encourage online shoppers to shift from conventional delivery to drone delivery.

The study developed a new model for drone delivery studies using the S-O-R model in introducing orientation towards the future and green support as the stimulus, green desire as an organism and pro-environmental behaviour and usage intention as a response. The study introduced multiple sequential mediators, also contributing to the S-O-R model to predict online shoppers' behaviour towards drones as a tool for last-mile delivery. Another important contribution, technology anxiety and perceived safety were confirmed to have a moderation effect for the relationship between pro-environmental behaviour and intention to use drones for last-mile delivery.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Urban air mobility (UAM) development and deployment into future cities is gaining increasing and relevant interest in the past years. This study, a conceptual paper, aims to report the high-level description of the most relevant UAM application use cases (UCs) emerging from the research activities carried out in the ASSURED UAM project.

The UAM application UCs have been obtained from the ASSURED UAM project dedicated activities that have been carried out to, first, develop suitable operational concepts for UAM deployment in the next decades and, then, to further refine and design the most relevant UCs for UAM deployment in the next decades, leading to the public issue of dedicated overall document.

The ASSURED UAM UCs for UAM deployment in the next decades encompass both public (point-to-point, point-to-everywhere, direct medical transport of people) and private (direct last-mile delivery, advanced last-mile delivery, automatic personal aerial transportation) services applications, evolving in incremental way over time according to three considered time horizons (2025, 2030 and 2035), toward progressive integration into metropolitan transport system.

This paper provides final outline of the ASSURED UAM UCs, starting from the analysis of overall identified possible UAM applications, focusing on the description of the six main UCs considered as relevant for the application under the wider societal benefits point of view. The UCs are described in terms of expected operational environment, needed technological enablers and envisaged regulatory implications.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

This paper aims to provide insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services. Also, it aims to provide insights into the potential of digital technologies-based innovations for more inclusive services, broadly.

A conceptual framework is inductively developed by analyzing real-world examples of digital technologies-based innovations for more inclusive healthcare through the lenses of economics of information in digital form and certain characteristics of services.

Concurrent implementation of digital technologies-based healthcare innovations with innovations and/or modifications in service processes can enable greater inclusivity by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services.

Issues relating to inequities in healthcare, as a social problem, are the focus of research at multiple levels (e.g. global, national, regional and local) in several academic disciplines. In relation to the scope of the problems and challenges pertaining to providing quality healthcare to the unserved and underserved segments of society, worldwide, the contribution of the proposed framework to practice is modest. However, by highlighting the promise and potential of digital technologies-based innovations as solutions for alleviating barriers to affordability, accessibility and availability of healthcare services during various stages (prevention, detection, diagnosis, treatment and post-treatment follow-up) with illustrative vignettes and developing a framework, the article offers insights for future research. For instance, in reference to mission-driven social enterprises that operate in the product-market space for inclusive innovations under resource constraints, a resourcefulness-based view of the social enterprise constitutes a potential avenue for theory development and research.

Given the conceptual nature of the article, the implications for practice are limited to cognitive implications. Action implications (instrumental implications or implications for practice) are outside of the scope of the article.

Innovations that are economically viable, environmentally sustainable and socially impactful is one of the important issues of our times.

The proposed framework provides insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers in the context of emerging and less developed country markets and base of the pyramid segments of society in these markets.