Photonic crystal slabs with maximal chiroptical response empowered by bound states in the continuum

To enhance the strength of chiral light–matter interaction for practical applications,the chirality and quality factors(Q-factors)of current methods need to be strengthened simultaneously.Here,we propose a design of photonic crystal slabs(Ph Cs)supporting chiral bound states in the continuum(BICs)of transverse electric(TE)and transverse magnetic(TM)modes,exhibiting maximal chiroptical responses with high Q-factors and near-unity circular dichroism(CD=0.98).Different from the past,the Ph Cs we employed only have reduced in-plane symmetry and can support simultaneously chiral quasi-BICs(q-BICs)of TE and TM mode with two-dimensional ultra-strong external and internal chirality.Based on the temporal coupled-mode theory,two analytical expressions of CD of chiral q-BICs response are revealed,which are consistent with the simulation results.Furthermore,we elucidate these results within the charge-current multipole expansion framework and demonstrate that the co-excitation of higher-order multipole electric/magnetic modes is responsible for near-perfect CD.Our results may provide more flexible opportunities for various applications requiring high Q-factors and chirality control,such as chiral lasing,chiral sensing,and enantiomer separation.

Support vector machine regression(SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization(RRN)

Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.

Multifrequency superscattering pattern shaping

Multifrequency superscattering is a phenomenon in which the scattering cross section from a subwavelength object simultaneously exceeds the single-channel limit at multiple frequency regimes.Here,we achieve simultaneously,within a graphene-coated subwavelength structure,multifrequency superscattering and superscattering shaping with different engineered scattering patterns.It is shown that multimode degenerate resonances at multiple frequency regimes appearing in a graphene composite structure due to the peculiar dispersion can be employed to resonantly overlap electric and magnetic multipoles of various orders,and,as a result,effective multifrequency superscattering with different engineered angular patterns can be obtained.Moreover,the phenomena of multifrequency superscattering have a high tolerance to material losses and some structural variations.Our work should anticipate extensive applications ranging from emission enhancing,energy harvesting,and antenna design with improved sensitivity and accuracy due to multifrequency operation.

Blockchain-based smart contract for smart payment in construction: A focus on the payment freezing and disbursement cycle

Late payment,and indeed no payment,is a rampant and chronic problem that has plagued the global construction industry for too long.Recent development in blockchain technology,particularly its smart contract,seems to provide a new opportunity to improve this old problem.However,this opportunity is largely unexploited.This study aims to develop a blockchain-based smart contract(BBSC)system for smart payment in the construction industry by focusing on the fundamental cycle of payment freezing(sometimes also synonymously called payment guarantees)and disbursement application.Firstly,a BBSC framework,containing three processes of(a)initiation and configuration,(b)payment freezing,and(c)disbursement application,is developed.Next,based on the framework,the system architecture of the BBSC system,containing three layers of(1)Infrastructure as a Service(IaaS),(2)Blockchain as a Service(BaaS),and(3)Software as a Service(SaaS)is proposed and elaborated.Finally,based on the system architecture,a BBSC prototype system is developed using a real-life modular construction project as a case study.It was found that the prototype system can improve the certainty and efficiency of the progress payment,thereby enabling smart payment in construction transactions.Without advocating radical changes(e.g.,the contractual relationships or the intermediate role of banks in modem construction projects),the prototype can be developed into a real-life BBSC system that can work compatibly with current advancements in the field.Future works are recommended to fine-tune the findings and translate and implement them in real-life applications.