Aerial refueling scheduling of multi-receiver and multi-tanker under spatial-temporal constraints for forest firefighting

Forest fires pose a significant threat to human life and property,so the utilization of unmanned aircraft systems provides new ways for forest firefighting.Given the constrained load capacities of these aircraft,aerial refueling becomes crucial to extend their operational time and range.In order to address the complexities of firefighting missions involving multi-receiver and multi-tanker deployed from various airports,first,a fuel consumption calculation model for aerial refueling scheduling is established based on the receiver path.Then,two distinct methods,including an integrated one and a decomposed one,are designed to address the challenges of establishing refueling airspace and allocating tasks for tankers.Both methods aim to optimize total fuel consumption of the receivers and tankers within the aerial refueling scheduling framework.The optimization problem is established as nonlinear optimization models along with restrictions.The integrated method seamlessly combines refueling rendezvous point scheduling and tanker task allocation into unified process.It has a complete solution space and excels in optimizing total fuel consumption.The decomposed method,through the separation of rendezvous point scheduling and task allocation,achieves a reduced computational complexity.However,this comes at the cost of sacrificing optimality by excluding specific feasible solutions.Finally,numerical simulations are carried out to verify the feasibility and effectiveness of the proposed methods.These simulations yield insights crucial for the practical engineering application of both the integrated and decomposed methods in real-world scenarios.This comprehensive approach aims to enhance the efficiency of forest firefighting operations,mitigating the risks posed by forest fires to human life and property.

Progress in modeling and control of probe-and-drogue autonomous aerial refueling

By refueling aircraft while they are in flight,aerial refueling is an efficient technique to extend their endurance and range.Autonomous Aerial Refueling(AAR)is anticipated to be used to complete aerial refueling for unmanned aircraft.There are three aerial refueling methods:the Probe-and-Drogue Refueling(PDR)refueling method,the flying-boom refueling method,and the boom-drogue-adapter refueling method.The paper considers the PDR approach,the most challenging of the three,because the flexible hose-drogue assembly has fast dynamics and is susceptible to various kinds of winds,which makes the probe docking with the drogue difficult.PDR is divided into four phases,namely the rendezvous phase,joining phase,refueling phase,and reform phase,with the refueling phase being the most crucial.The controller design faces the greatest challenge during the docking control of the refueling phase since it calls for a high level of safety,precision,and efficiency.As a result,the modeling and control issues encountered during the refueling phase are typical and difficult.The fundamental idea of AAR is presented in the paper first,after which the characteristics and requirements of AAR are outlined.The progress in modeling and control techniques for the AAR’s refueling phase is then systematically reviewed.Finally,potential future work for high safety,precision,and efficiency requirements is examined and suggested.

Advances in synthetic biology-based drug delivery systems for disease treatment

With the increasing demand for personalized and precise treatment,the rapid advancement of synthetic biology technology has inevitably led to the development of nanobiology-based drug delivery systems.Synthetic biology-based drug delivery systems are being increasingly used in the treatment of various diseases.On one hand,synthetic biology technology enables the clever combination of chassis cells,bacteria,and their derivatives with nanomaterials,forming nano-artificial hybrid systems.These systems effectively integrate the functions of both materials,leading to further breakthroughs and optimization of biological functions.On the other hand,synthetic biology strategies guide the self-assembly of modular nanocomponents with biocatalytic or intelligent response functions,resulting in the mimicry of living cell features such as compartmentalization of enzymatic reactions and responsiveness to external stimuli.This provides novel design ideas for the construction of artificial cells.This paper aims to explore the construction and application of biogenic drug delivery systems based on whole cells,cell membrane-encapsulated nanoparticles,exosomes,bacteria,bacterial outer membrane vesicles and artificial cells,taking into account recent advances in this field.The advantages and limitations of current synthetic biology-based nanodrug delivery systems for clinical translation are discussed,and the future prospects of nanotechnology for intelligent drug diagnostic and therapeutic systems are envisioned.

Docking control for probe-drogue refueling: An additive-state-decomposition-based output feedback iterative learning control method

Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control(ILC) is adopted in this paper.First, Additive State Decomposition(ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of Non Minimum Phase(NMP) by separating these features into two subsystems(a primary system and a secondary system).After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant(LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation.The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system.Furthermore, to compensate for the receiverindependent uncertainties, a correction action is proposed by using the terminal docking error,which can lead to a smaller docking error at the docking moment.Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.

Discovery of potential biomarkers for human atherosclerotic abdominal aortic aneurysm through untargeted metabolomics and transcriptomics

Abdominal aortic aneurysm(AAA)and atherosclerosis(AS)have considerable similarities in clinical risk factors and molecular pathogenesis.The aim of our study was to investigate the differences between AAA and AS from the perspective of metabolomics,and to explore the potential mechanisms of differential metabolites via integration analysis with transcriptomics.Plasma samples from 32 AAA and 32 AS patients were applied to characterize the metabolite profiles using untargeted liquid chromatography-mass spectrometry(LC-MS).A total of 18 remarkably different metabolites were identified,and a combination of seven metabolites could potentially serve as a biomarker to distinguish AAA and AS,with an area under the curve(AUC)of0.93.Subsequently,we analyzed both the metabolomics and transcriptomics data and found that seven metabolites,especially 2’-deoxy-D-ribose(2 d DR),were significantly correlated with differentially expressed genes.In conclusion,our study presents a comprehensive landscape of plasma metabolites in AAA and AS patients,and provides a research direction for pathogenetic mechanisms in atherosclerotic AAA.