Lightweight design of an electric bus body structure with analytical target cascading

Lightweight designs of new-energy vehicles can reduce energy consumption,thereby improving driving mileage.In this study,a lightweight design of a newly developed multi-material electric bus body structure is examined in combination with analytical target cascading(ATC).By proposing an ATC-based two-level optimization strategy,the original lightweight design problem is decomposed into the system level and three subsystem levels.The system-level optimization model is related to mass minimization with all the structural modal frequency constraints,while each subsystem-level optimization model is related to the sub-structural performance objective with sub-structure mass constraints.To enhance the interaction between two-level systems,each subsystem-level objective is reformulated as a penalty-based function coordinated with the system-level objective.To guarantee the accuracy of the model-based analysis,a finite element model is validated through experimental modal test.A sequential quadratic programming algorithm is used to address the defined optimization problem for effective convergence.Compared with the initial design,the total mass is reduced by 49 kg,and the torsional stiffness is increased by 17.5%.In addition,the obtained design is also validated through strength analysis.

Parameters matching and optimization of parallel hybrid electric vehicle powertrain

Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

Sequential delivery of PD-1/PD-L1 blockade peptide and IDO inhibitor for immunosuppressive microenvironment remodeling via an MMP-2 responsive dual-targeting liposome

Intelligent responsive drug delivery system opens up new avenues for realizing safer and more effective combination immunotherapy.Herein,a kind of tumor cascade-targeted responsive liposome(NLG919@Lip-pep1)is developed by conjugating polypeptide inhibitor of PD-1 signal pathway(AUNP-12),which is also a targeted peptide that conjugated with liposome carrier through matrix metalloproteinase-2(MMP-2)cleavable peptide(GPLGVRGD).This targeted liposome is prepared through a mature preparation process,and indoleamine-2,3-dioxygenase(IDO)inhibitor NLG919 was encapsulated into it.Moreover,mediated by the enhanced permeability and retention effect(EPR effect)and AUNP-12,NLG919@Lip-pep1 first targets the cells that highly express PD-L1 in tumor tissues.At the same time,the over-expressed MMP-2 in the tumor site triggers the dissociation of AUNP-12,thus realizing the precise block of PD-1 signal pathway,and restoring the activity of T cells.The exposure of secondary targeting moduleⅡVRGDC-NLG919@Lip mediated tumor cells targeting,and further relieved the immunosuppressive microenvironment.Overall,this study offers a potentially appealing paradigm of a high efficiency,low toxicity,and simple intelligent responsive drug delivery system for targeted drug delivery in breast cancer,which can effectively rescue and activate the body's anti-tumor immune response and furthermore achieve effective treatment of metastatic breast cancer.

Peer-to-Peer Trading in Distribution Systems with the Utility's Operation

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing enterprises.Fortunately,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing systems.In this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes.Specifically,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels.Moreover,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play manner.Materials,information,and financial assets are passed through interactive links across the networks.Subsequently,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices.Consequently,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions.The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.This work potentially enables managers and practitioners to implement active perception,active response,self-organization,and self-adaption solutions in discrete manufacturing enterprises.

Distributed Stochastic Security Constrained Unit Commitment for Coordinated Operation of Transmission and Distribution System

With the high penetration of renewable energies in modern power systems,deterministic coordination algorithms are facing two major problems:one is degradation in accuracy if fewer scenarios are utilized for uncertainty evaluation while second is the high computational time if a high number of scenarios are considered for better accuracy・In both cases,the efficiency of the algorithm is degraded・To solve these problems in coupled transmission system and distribution systems(TSDS),probabilistic coordination algorithms are adopted to solve with less effort.In this paper,a TSDS probabilistic coordination model is proposed to solve the coordinated security-constrained unit commitment problem.A mean and standard deviation matching based probabilistic analytical target cascading algorithm has been utilized for evaluation of TSDS coordination problem.Instead of solving each scenario as a separate problem,the proposed algorithm considers a single coordination problem with probabilistic characteristics as shared variables and hence,achieves fast convergence・Different case studies are performed to prove the efficacy of the proposed algorithm.Results verify that the proposed algorithm reduces computational time and resources for large-scale systems.

Risk-aware Distributed Optimal Power Flow in Coordinated Transmission and Distribution System

Active distribution grids cause bi-directional power flow between transmission system(TS)and distribution system(DS),which not only affects the optimal cost but also the secure operation of the power system.This paper proposes a hybrid coordination method to solve the risk-aware distributed optimal power flow(RA-DOPF)problem in coordinated TS and DS.For operation risk evaluation,the weather-based contingencies are considered in both TS and DS.A hybrid coordination method is developed that entails analytical target cascading(ATC)and Benders decomposition(BD).Moreover,the risk-aware optimal power flow(RAOPF)in TS and risk-based security-constrained optimal power flow in DS have been performed using the BD method considering basic optimal power flow as a master problem,whereas N-1 and N-2 contingencies are considered as sub-problems.Different case studies are performed using the IEEE 30-bus system with generation reserves as a TS and a 13-bus system as a DS.The results demonstrate the efficacy of the proposed method.

Gather wisdom to overcome barriers:Well-designed nano-drug delivery systems for treating gliomas

Due to the special physiological and pathological characteristics of gliomas,most therapeutic drugs are prevented from entering the brain.To improve the poor prognosis of existing therapies,researchers have been continuously developing non-invasive methods to overcome barriers to gliomas therapy.Although these strategies can be used clinically to overcome the blood-brain barrier(BBB),the accurate delivery of drugs to the glioma lesions cannot be ensured.Nano-drug delivery systems(NDDS)have been widely used for precise drug delivery.In recent years,researchers have gathered their wisdom to overcome barriers,so many well-designed NDDS have performed prominently in preclinical studies.These meticulous designs mainly include cascade passing through BBB and targeting to glioma lesions,drug release in response to the glioma microenvironment,biomimetic delivery systems based on endogenous cells/extracellular vesicles/protein,and carriers created according to the active ingredients of traditional Chinese medicines.We reviewed these well-designed NDDS in detail.Furthermore,we discussed the current ongoing and completed clinical trials of NDDS for gliomas therapy,and analyzed the challenges and trends faced by clinical translation of these well-designed NDDS.