ABSTRACT
To meet the growing demand for sustainable development and ecofriendliness, hydrogels based on biopolymers have attracted widespread attention for developing flexible pressure sensors. Natural globular proteins exhibit great potential for developing biobased pressure sensors owing to their advantages of high water solubility, easy gelation, biocompatibility, and low production cost. However, realizing globular protein hydrogel-based sensors with interfacial and bulk toughness for pressure sensing and use in wearable devices remains a challenge. This study focuses on developing a high-performance flexible pressure sensor based on a biobased protein hydrogel. Consequently, a flexible protein/polyacrylamide (PAM) hydrogel with a featured double-network (DN) structure linked covalently with hydrogen bonds was first synthesized via a one-pot method based on natural ovalbumin (OVA). The unique DN structure of the as-synthesized OVA/PAM hydrogel affords excellent mechanical performance, flexibility, and adhesion properties. The mechanical properties of the DN hydrogel were enhanced after further cross-linking with Fe3+ and treatment with glycerol. Subsequently, the flexible pressure sensor was constructed by sandwiching a microstructured OVA/PAM dielectric layer between two flexible silver nanowire electrodes. The obtained sensor exhibits a high sensitivity of 2.9 kPa-1 and a short response time of 18 ms, ensuring the ability to monitor physiological signals. Based on its excellent performance, the fabricated sensor was used for monitoring the signals obtained using practical applications such as wrist bending, finger knocking, stretching, international Morse code, and pressure distribution. Particularly, we implemented a contactless delivery system using the fabricated OVA-based pressure sensors linked to unmanned vehicles and global positioning systems, providing a solution for low-risk commodity distribution during Coronavirus disease 2019 (COVID-19).
ABSTRACT
The unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability. IEEE
ABSTRACT
To meet the growing demand for sustainable development and ecofriendliness, hydrogels based on biopolymers have attracted widespread attention for developing flexible pressure sensors. Natural globular proteins exhibit great potential for developing biobased pressure sensors owing to their advantages of high water solubility, easy gelation, biocompatibility, and low production cost. However, realizing globular protein hydrogel-based sensors with interfacial and bulk toughness for pressure sensing and use in wearable devices remains a challenge. This study focuses on developing a high-performance flexible pressure sensor based on a biobased protein hydrogel. Consequently, a flexible protein/polyacrylamide (PAM) hydrogel with a featured double-network (DN) structure linked covalently with hydrogen bonds was first synthesized via a one-pot method based on natural ovalbumin (OVA). The unique DN structure of the as-synthesized OVA/PAM hydrogel affords excellent mechanical performance, flexibility, and adhesion properties. The mechanical properties of the DN hydrogel were enhanced after further cross-linking with Fe3+ and treatment with glycerol. Subsequently, the flexible pressure sensor was constructed by sandwiching a microstructured OVA/PAM dielectric layer between two flexible silver nanowire electrodes. The obtained sensor exhibits a high sensitivity of 2.9 kPa-1 and a short response time of 18 ms, ensuring the ability to monitor physiological signals. Based on its excellent performance, the fabricated sensor was used for monitoring the signals obtained using practical applications such as wrist bending, finger knocking, stretching, international Morse code, and pressure distribution. Particularly, we implemented a contactless delivery system using the fabricated OVA-based pressure sensors linked to unmanned vehicles and global positioning systems, providing a solution for low-risk commodity distribution during Coronavirus disease 2019 (COVID-19). © 2023 American Chemical Society.
ABSTRACT
The spread of the COVID-19 pandemic has resulted in the launch of contactless delivery services. This research integrates resource matching, service quality evaluation, and perceived value theories to explore the factors that promote the use contactless delivery services. The data was obtained through questionnaire surveys, and research hypotheses were verified through the structural equation modelling approach. With the exception of convenience, the results show that privacy, reliability, security, and flexibility have a significantly positive effect on consumers' intention to use "contactless” delivery services through consumers' perceived value. This study contributes to the literature by introducing theoretical frameworks from various paradigms and enriches the academic research on existing theoretical structure models. It also helps optimize resource allocation and realize the social environment related to coexisting with the COVID-19 pandemic. © 2023 Elsevier Ltd
ABSTRACT
The development of a community express delivery system in the context of COVID-19 reduces the risk of virus infection for people in the community who go out to pick up their deliveries, which highlights the benefits of "contactless delivery”. The system is developed using a separate development method for the front-end and back-end. The mobile end in the front end uses the WeChat applet, and the Web end uses the Vue framework for development. The back-end server is developed in Java and designed with a MySQL database. This system contributes to the community residents' courier delivery challenges. © 2022 SPIE.
ABSTRACT
The development of a community express delivery system in the context of COVID-19 reduces the risk of virus infection for people in the community who go out to pick up their deliveries, which highlights the benefits of "contactless delivery”. The system is developed using a separate development method for the front-end and back-end. The mobile end in the front end uses the WeChat applet, and the Web end uses the Vue framework for development. The back-end server is developed in Java and designed with a MySQL database. This system contributes to the community residents' courier delivery challenges. © 2022 SPIE.
ABSTRACT
The spread of the COVID-19 pandemic has resulted in the launch of contactless delivery services. This research integrates resource matching, service quality evaluation, and perceived value theories to explore the factors that promote the use contactless delivery services. The data was obtained through questionnaire surveys, and research hypotheses were verified through the structural equation modelling approach. With the exception of convenience, the results show that privacy, reliability, security, and flexibility have a significantly positive effect on consumers' intention to use "contactless” delivery services through consumers' perceived value. This study contributes to the literature by introducing theoretical frameworks from various paradigms and enriches the academic research on existing theoretical structure models. It also helps optimize resource allocation and realize the social environment related to coexisting with the COVID-19 pandemic.
ABSTRACT
This study aims to identify the factors associated with the adoption of drone delivery in Medellin, Colombia, in the context of the COVID-19 pandemic. For that purpose, it implemented the Diffusion of Innovation (DOI) theory and the Technology Acceptance Model (TAM), which have constructs that complement each other to determine the decision to accept a given technology. A survey was administered to 121 participants in order to validate the model proposed here, which is based on variables that reflect the perceived attributes and risks of this innovation and individuals' characteristics. The results indicate that the factors Performance Risk, Compatibility, Personal Innovativeness, and Relative Advantage of Environmental Friendliness have the greatest influence on Intention to Use Drone Delivery (mediated by Attitude Towards Drone Delivery). This paper offers relevant information for the academic community and delivery companies because few other studies have investigated this topic. Additionally, the proposed technology adoption model can be a benchmark for other emerging economies in similar social, economic, and technological conditions.
ABSTRACT
The spread of COVID-19 outbreak has promoted truck-drone delivery from trials to commercial applications in end-to-end contactless solutions. To fully integrate truck-drone delivery in contactless solutions, we introduce the robust traveling salesman problem with a drone, in which a drone makes deliveries and returns to the truck that is moving on its route under uncertainty. The challenge is to find, for each customer location in truck-drone routing, an assignment to minimize the expected makespan. Apart from the complexity of this problem, the risk of synchronization failure associated with uncertain travel time should be also considered. The problem is first formulated as a robust model, and a novel efficient frontier heuristic is proposed to solve this model. By coupling the implicit adaptive weighting with epsilon-constraint methods, the heuristic generates a series of scalarized single-objective problems, where the goal is to minimize expected makespan under the constraint of synchronization risk. The experiment results show that the robust (near-)optimal solutions offer a considerable reduction in risk, yet only hint at a small increase in makespan. The heuristic in the present study is effective to construct approximations of Pareto frontier and allows for assignment decisions in a priori or a posteriori manner. © 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies.
ABSTRACT
Plastic pollution control has been on top of the political agenda in China. In January 2020, China announced a phased ban on the production and usage of various types of single-use plastics as a solution to environmental pollution problems. However, the outbreak of COVID-19 seems to be a new obstacle to the ban on single-use plastic products. To basically satisfied the daily necessities and contain the spread of SARS-CoV-2 under the background of the regular epidemic prevention and control in China, online ordering, contactless delivery and wearing mask have become an important and feasible way of daily life. However, the unrestrained use of disposable plastic bags, lunch boxes and masks within the nationwide quarantine leads to hundreds of millions of plastics wastes every day. The potential environmental pollution caused by the use of disposable plastic products during the pandemic should arouse social concern. The Chinese government should manage environmental protection in parallel with anti-pandemic endeavors as the situation of the pandemic evolves.
Subject(s)
COVID-19 , Pandemics , China/epidemiology , Humans , Plastics , SARS-CoV-2ABSTRACT
Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.