Evaluating dedicated and shared storage policies in robot-based compact storage and retrieval systems

Robot-based compact storage and retrieval systems (RCSRS) have seen many implementations over the last few years. In such a system, the inventory items are stored in bins, organized in a grid. In each cell of the grid, a certain number of bins are stored on top of each other. Robots with transport and lifting capabilities move on the grid roof to transport bins between manual workstations and storage stacks. We estimate performance and evaluate storage policies of RCSRS, considering both dedicated and shared storage policies coupled with random and zoned storage stacks. Semi-open queueing networks (SOQNs) are built to estimate the system performance, which can handle both immediate and delayed reshuffling processes. We approximate the models by reduced SOQNs with two load-dependent service nodes and use the Matrix-Geometric Method (MGM) to solve them. Both simulations and a real case are used to validate the analytical models. Assuming a given number of stored products, our models can be used to optimize not only the length to width ratio of the system, but also the stack height, depending on the storage strategy used. For a given inventory and optimal system configuration, we demonstrate that the dedicated storage policy outperforms the shared storage policy in terms of dual command throughput time. However, from a cost perspective, wit

Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving

Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via communications. Despite advances in previous approaches, challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity. Our novelties lie in three aspects. We first design a transmission delay minimization method, which can construct the communication graph and minimize the transmission delay according to different channel information state. We then propose an adaptive data reconstruction mechanism, which can dynamically adjust the rate-distortion trade-off to enhance perception efficiency. Moreover, it minimizes the temporal redundancy during data transmissions. Finally, we conceive a domain alignment scheme to align the data distribution from different vehicles, which can mitigate the domain gap between different vehicles and improve the performance of the target task. Comprehensive experiments demonstrate the effectiveness of our method in comparison to the existing state-of-the-art works.Comment: 6 pages, 6 figure

Fullerenol inhibits tendinopathy by alleviating inflammation

Tendinopathy is a common disease in orthopaedics, seriously affecting tendon functions. However, the effects of non-surgical treatment on tendinopathy are not satisfactory and surgical treatments possibly impair the function of tendons. Biomaterial fullerenol has been proved to show good anti-inflammatory effects on various inflammatory diseases. For in vitro experiments, primary rat tendon cells (TCs) were treated by interleukin-1 beta (IL-1β) combined with aqueous fullerenol (5, 1, 0.3 μg/mL). Then inflammatory factors, tendon-related markers, migration and signaling pathways were detected. For in vivo experiments, rat tendinopathy model was constructed by local injection of collagenase into Achilles tendons of rats and fullerenol (0.5, 1 mg/mL) was locally injected 7 days after collagenase injection. Inflammatory factors and tendon-related markers were also investigated. Fullerenol with good water-solubility showed excellent biocompatibility with TCs. Fullerenol could increase expression of tendon-related factors (Collagen I and tenascin C) and decrease expression of inflammatory factors (matrix metalloproteinases-3, MMP-3, and MMP-13) and reactive oxygen species (ROS) level. Simultaneously, fullerenol slowed the migration of TCs and inhibited activation of Mitogen-activated protein kinase (MAPK) signaling pathway. Fullerenol also attenuated tendinopathy in vivo, including reduction of fiber disorders, decrease of inflammatory factors and increase of tendon markers. In summary, fullerenol is a promising biomaterial that can be used to treat tendinopathy

Highly precision carbon dioxide acoustic wave sensor with minimized humidity interference

Extensive applications of carbon dioxide (CO2) in various fields, such as food industry, agricultural production, medical and pharmacological industries, have caused a great demand for high-performance CO2 sensors. However, most existing CO2 sensors suffer from poor performance in a wet environment and often cannot work accurately in a high humidity condition. In this study, a quartz crystal resonator (QCR) coated with a uniform layer of reduced graphene oxide (RGO) is proposed to detect both the concentrations of CO2 and water molecules simultaneously, which can be used to significantly minimize the humidity interference. Unlike the other common gas sensors, the RGO-based CO2 QCR sensor can be operated in different humidity levels and the concentration of CO2 can be quantified precisely and effectively. Moreover, it has a fast response (~0.4 s), which is also suitable for respiration monitoring. Our results showed that before and after a volunteer did a low-intensity exercise, the sensor could detect the differences of concentrations of CO2 in the exhaled breath (i.e., 4.50% and 5.15%, respectively)

A comprehensive review on COVID-19: What we know and how to treat against the novel coronavirus

COVID-19 emerged in Wuhan, China, at the end of 2019 and then soon evolved into a global pandemic. The novel coronavirus inducing this pandemic is under extensive study held by researchers all over the world. We give out a comprehensive review of what we have known about this novel coronavirus, including the pathogenesis. Passive immunity, different strategies, and targets for vaccine development and antiviral drugs are introduced as therapeutic strategies. At last, many other properties of SARS-Cov-2 are discussed

Supply chain choice with financial constraints on the internet : Drop shipping vs. traditional channel

International audienceWhile some existing literature on e-commerce supply chains neglects financial constraints, this study analyzes channel choice on the internet between drop shipping and traditional channel in a supply chain including a wholesaler W and multiple financially constrained online retailers. Drop shipping is a new order fulfillment practice on the internet, wherein retailers focus on customer acquisition while forwarding orders to wholesalers. A traditional channel is another mode of operation that is widely used on the internet, where retailers own and handle inventory, while wholesalers extend credit conditioned on retailers’ financial status. To understand the impact of a retailer’s financial constraints on her supply chain structure on the internet, we formulate profit-maximizing inventory models for both wholesaler W and retailer R. By analyzing and comparing their expected payoffs under different channels, we find that the supply chain structure on the internet first depends on the critical fractile in a newsvendor setting, and subsequently it depends on R’s preference and capital level if the critical fractile of R is much larger than that of W. Furthermore, we discuss the choice of channel on the internet under different trade credit policies and find that W always provides two channel options conditionally under partial trade credit but may offer only one supply chain structure under full trade credit. Finally, we provide management insights by identifying the decision zones for different supply chain structures under full or partial trade credit on the internet.<br/

Neuroadaptive Dynamic Surface Asymptotic Tracking Control of a VTOL Aircraft with Unknown Dynamics and External Disturbances

This work studies the asymptotic tracking control problem of a vertical take-off and landing (VTOL) aircraft with unknown dynamics and external disturbances. The unknown nonlinear dynamics of the VTOL aircraft are approximated via the introduction of radial basis function neural networks. Then, the weight update laws are designed. Furthermore, the parameter update control laws are presented to deal with the errors generated during the approximation process and the external disturbances of the aircraft system. Moreover, first-order filters are introduced to avoid repeated differentiation of the designed virtual control laws, thereby effectively eliminating the “complexity explosion” problem caused by traditional backstepping control. Based on the application of the neural network control method, dynamic surface control technique, weight update laws and parameter update control laws, neuroadaptive dynamic surface control laws for the aircraft system are finally proposed. Theoretical analysis shows that the proposed control law can ensure that the aircraft system asymptotically tracks the reference trajectories and the tracking errors can converge to a small neighborhood of zero by choosing the appropriate designed parameters. Finally, simulation examples are provided to verify the effectiveness of proposed control laws

Heterogeneous wafer bonding technology and thin-film transfer technology-enabling platform for the next generation applications beyond 5g

10.3390/mi12080946Micromachines12894

Optimal decisions in reducing loss rate of returnable transport items

International audienceReturnable transport items (RTI) have been commonly used due to their advantages in reducing packaging waste and improving supply chain sustainability. However, RTI loss will result in a mismatch between the products flow and the RTI supply, which will affect operational continuity. This paper studies a RTI supply chain consisting of a single manufacturer and a single retailer, assuming that the retailer can reduce RTI loss by investing in staff training. We first develop an inventory model to minimize the total cost of the system. Then we analyze the optimal decision of the closed-loop supply chain of RTI. We compare the case where the retailer invests in reducing RTI loss and the case where the retailer does not. The results show that the supply chain will be more efficient if the retailer invests in reducing RTI loss, and the total cost of the system will be reduced. However, the retailer will invest only when its cost decreases. Thereby, we consider side payments as an available incentive to stimulate the retailer to invest. Moreover, we build an asymmetric Nash bargaining model considering investment cost sharing to coordinate the RTI supply chain. We compare the total optimal cost of the manufacturer and the retailer. The results show that the system can be coordinated, and the optimal total costs of both the manufacturer and the retailer are reduced.<br/

Assignment rules in robotic mobile fulfilment systems for online retailers

International audienc