Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a...Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness （EWT） and leaf mass per area （LMA）. Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error （RMSE） and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.展开更多
Organic perovskites are promising semiconductor materials for advanced photoelectric applications.Their fluorescence typically shows a negative temperature coefficient due to bandgap change and structural instability....Organic perovskites are promising semiconductor materials for advanced photoelectric applications.Their fluorescence typically shows a negative temperature coefficient due to bandgap change and structural instability.In this study,a novel perovskite-based composite with positive sensitivity to temperature was designed and obtained based on its inverse temperature crystallization,demonstrating good flexibility and solution processability.The supercritical drying method was used to address the limitations of annealing drying in preparing high-performance perovskite.Optimizing the precursor composition proved to be an effective approach for achieving high fluorescence and structural integrity in the perovskite material.This perovskite-based composite exhibited a positive temperature sensitivity of 28.563%℃^(-1)for intensity change and excellent temperature cycling reversibility in the range of 25-40℃in an ambient environment.This made it suitable for use as a smart window with rapid response.Furthermore,the perovskite composite was found to offer temperature-sensing photoluminescence and flexible processability due to its components of perovskite-based compounds and polyethylene oxide.The organic precursor solvent could be a promising candidate for use as ink to print or write on various substrates for optoelectronic devices responding to temperature.展开更多
A kind of international rapid field measurement methods of hydraulic conductivity and it's applications in Sanjiang Plain have been introduced in the paper.
Gravity variation data observed in the process of seismogenesis and occurrences of earthquakes show that the location with the greatest gravity changes does not necessarily coincide with the epicenter. To explain this...Gravity variation data observed in the process of seismogenesis and occurrences of earthquakes show that the location with the greatest gravity changes does not necessarily coincide with the epicenter. To explain this we defined the center of effective mass of stress volume as hypocentroid, and the vertical projection of which on the earths surface as epicentroid. Here we adopt three rotating models, including spheroid, ellipsoid and cylinder, to represent the region of an impending earthquake. Based on the models of gravity variations induced by uniform dilatancy, epicentroids associated with sixteen earthquakes with M>4.0 occurred in 1981~2000 in the Beijing-Tianjin-Tangshan-Zhangjiakou region are determined by means of a proposed least squares iterative inversion method. The results indicate that cylinder model is preferable to the other two, and epicentroids obtained by the cylinder model separate from the epicenters by a range of 0~40 km. Epicentroids are inevitably located within intact tectonic blocks, and usually cluster in groups; while the epicenters are generally located at the terminations of faults or at the intersections of faults. It seems that there exist earthquake-hatching areas in the block among faults. Earthquakes hatch in these areas, but occur around these areas, meanwhile the existence of faults may play an important role in controlling the processes.展开更多
Here,we designed asymmetric(m DS)and symmetrical(d DS)chiral V-shaped molecules by linking one or two dansyl groups to trans-1,2-cyclohexane diamine and investigated the solvent-regulated structural transformation and...Here,we designed asymmetric(m DS)and symmetrical(d DS)chiral V-shaped molecules by linking one or two dansyl groups to trans-1,2-cyclohexane diamine and investigated the solvent-regulated structural transformation and inversed circularly polarized luminescence(CPL)in the self-assemblies.Upon increasing water volume fraction(fw)in the mixed solvent of water/acetonitrile,asymmetric mDS selfassembled into hollow nanospheres and microtubes,while solid nanospheres and solid microplates were corresponding to symmetric d DS.During this transformation process,the emission of m DS and d DS was changed from yellow-green to blue and cyan color,which was ascribed to twisted intramolecular charge transfer(TICT)and locally excited(LE)fluorescence of V-shaped DS molecules.The conformation of N,Ndimethyl groups with respect to naphthalene ring also led to the transformation of structures.These tubular and platelike structures had stronger and reversed CPL signals in comparison with spheroidal structures.The chiral information of DS assembly could be effective transferred to achiral Nile red via co-assembly strategy,which endowed Nile red exhibiting inversed induced CPL signal regulated by water fraction.This work provides a method for achieving a variety of self-assembled structures with adjustable chiroptical properties.展开更多
To realize the handedness controllable circularly polarized luminescence(CPL) system remains challenging. Herein, the solvent-mediated CPL inversion and amplification systems were successfully constructed by camptothe...To realize the handedness controllable circularly polarized luminescence(CPL) system remains challenging. Herein, the solvent-mediated CPL inversion and amplification systems were successfully constructed by camptothecin derivative(CPT-A). Due to the planar structure of N,N-dimethylformamide, it could coassemble with CPT-A, resulting in the alteration of g_(lum) from –0.0082 to +0.0085 by increasing water content. While in the non-planar solvent(hexafluoroisopropanol), the g_(lum) was amplified to 0.034 with the increase in water content. Moreover, the CPT-A could react with the glutathione, resulting in the anticancer drug CPT to make it more toxic to the cancer cells. Overall, the handedness controllable CPL systems were realized by tuning the supramolecular self-assembly of a prodrug.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we...This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of me...Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.展开更多
Delineating sweet spots is critical for the exploration and production of oil and gas in deep and tight sand reservoirs.The lack of advanced and reliable methods makes this a challenge for geologists and geophysicists...Delineating sweet spots is critical for the exploration and production of oil and gas in deep and tight sand reservoirs.The lack of advanced and reliable methods makes this a challenge for geologists and geophysicists.This study introduces,for the first time,an integrated workflow that combines pre-stack seismic inversion with rock physics modeling to predict reservoir porosity and shale volume(V-shale)for sweet spot identification in tight sand reservoirs.A new elastic parameter,the density calculation index(DCI),is introduced which links acoustic and shear impedance for seismic density inversion,thereby addressing the long-standing problem of poor density inversion accuracy.A novel combined Sun–Walsh rock physics model,developed as part of this study,significantly improves V-shale evaluation from seismic data.The proposed three-step seismic inversion approach includes:(1)deriving acoustic and shear impedance from angle-stack seismic data using model-based inversion;(2)calculating density using shear impedance constrained by DCI,followed by porosity estimation from the density–porosity relation;and(3)evaluating V-shale using theα-parameter derived from the Sun–Walsh model and pre-stack inversion results.This integrated workflow provides an effective tool for building accurate 3D reservoir models,and is especially applicable to deep,low-porosity,tight sand reservoirs worldwide.展开更多
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral mole...Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.展开更多
Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which req...Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.展开更多
A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion e...A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion efficiency.The results indicate that the computational efficiency of each inversion can be improved by approximately a factor of three by using the proposed MG solver.First,the accuracy of the MG solver is validated through a test on a synthetic model.Next,the numerical performance of the inversion algorithm is evaluated using this model.Finally,the inversion algorithm is applied to a field EM data collected at the Beiya gold polymetallic ore district.A 3D resistivity model is obtained,and the formation process of the metal ore is analyzed.展开更多
Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode...Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.展开更多
Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO...Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.展开更多
To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous na...To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous narrow-channel boundaries and subdivision of multi-stage superimposed sandbodies.Taking the Upper Cretaceous continental sandstone in the Sazhong Oilfield of the Daqing Placanticline as an example,a technical system integrating OVT high-resolution processing,multi-attribute fusion,and varible-scale inversion was developed to establish a complete workflow from seismic processing to reservoir prediction and remaining oil recovery.The following results are obtained.First,the Offset Vector Tile(OVT)seismic processing technology is extended,for the first time,from fracture imaging to sandbody prediction,in order to address the weak seismic responses from boundaries of narrow and thin sandbodies.A geology-oriented OVT partitioning method is developed to significantly improve the imaging accuracy,enabling identification of channel sandbodies as narrow as 50 m.Second,an amplitude-coherence dual-attribute fusion method is proposed for predicting narrow channel boundaries between wells.Constrained by a sedimentary unit-level sequence chronostratigraphic framework,this method accurately delineates 800-2000 m long subaqueous distributary channels with bifurcation-convergence features.Third,considering the superimposition of multi-stage channels,a three-level variable-scale stratigraphic model(sandstone groups,sublayers,sedimentary units)is constructed to overcome single-scale modeling limitations,successfully characterizing key sedimentary features like meandering river“cut-offs”through 3D seismic inversion.Based on these advances,a direct link between seismic prediction and remaining oil recovery is established.The horizontal wells deployed using narrow-channel predictions encountered oil-bearing sandstones in the horizontal section by 97%,and achieved initial daily production of 12.5 t per well.Precise identification of individual channel boundaries within 17 composite sandbodies guided recovery processes in 135 wells,yielding an average daily increase of 2.8 t per well and a cumulative increase of 13.6×10^(4)t.展开更多
Band inversion induced by spin–orbit coupling in topological semimetals typically generates light charge carriers with high Fermi velocities,which are highly desirable for low-dissipation and coherent quantum transpo...Band inversion induced by spin–orbit coupling in topological semimetals typically generates light charge carriers with high Fermi velocities,which are highly desirable for low-dissipation and coherent quantum transport in topological devices.The presence of these carriers in real materials strongly depends on the Fermi-level position.2M-WSe_(2),with its topological and van der Waals nature,serves as an ideal platform for studying quantum transport in two-dimensional systems,despite the fact that interlayer coupling suppresses the formation of light carriers.In this study,we solvothermally intercalate 1,3-diaminopropane molecules into the interlayer space of 2M-WSe_(2);these molecules effectively adapt to the electronic structure by eliminating interlayer coupling.Simultaneously,slight electron doping via charge transfer results in a small Fermi pocket with an extremely light effective mass,0.04–0.06 me,as revealed by quantum oscillation measurements.This study demonstrates that molecular intercalation is an effective approach for engineering van der Waals topological materials to achieve specific quantum transport properties.展开更多
基金supported by the West Light Talents Cultivation Program of Chinese Academy of Sciences (XBBS 200801)the National Natural Science Foundation of China (40801146)the JSPS Project (21403001)
文摘Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness （EWT） and leaf mass per area （LMA）. Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error （RMSE） and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.
基金the financial support from the National Natural Science Foundation of China(No.61904005,52103010 and 52003200)Guangdong Provincial Department of Education Featured Innovation Project(No.2021KTSCX138)+4 种基金Jiangmen Key Project of Research for Basic and Basic Application(No.2021030102800007443 and 2021030102790006114)the Science Foundation for Young Research Group of Wuyi University(No.2020AL021,2019AL019,and 2020AL016)Wuyi University-Hong Kong/Macao Joint Research Funds(No.2021WGALH05)Youth Innovation Talent Project for the Universities of Guangdong(No.2020KQNCX089)Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110897)
文摘Organic perovskites are promising semiconductor materials for advanced photoelectric applications.Their fluorescence typically shows a negative temperature coefficient due to bandgap change and structural instability.In this study,a novel perovskite-based composite with positive sensitivity to temperature was designed and obtained based on its inverse temperature crystallization,demonstrating good flexibility and solution processability.The supercritical drying method was used to address the limitations of annealing drying in preparing high-performance perovskite.Optimizing the precursor composition proved to be an effective approach for achieving high fluorescence and structural integrity in the perovskite material.This perovskite-based composite exhibited a positive temperature sensitivity of 28.563%℃^(-1)for intensity change and excellent temperature cycling reversibility in the range of 25-40℃in an ambient environment.This made it suitable for use as a smart window with rapid response.Furthermore,the perovskite composite was found to offer temperature-sensing photoluminescence and flexible processability due to its components of perovskite-based compounds and polyethylene oxide.The organic precursor solvent could be a promising candidate for use as ink to print or write on various substrates for optoelectronic devices responding to temperature.
文摘A kind of international rapid field measurement methods of hydraulic conductivity and it's applications in Sanjiang Plain have been introduced in the paper.
基金State Natural Science Foundation of China (49774224)Joint Seismological Science Foundation of China (102019).
文摘Gravity variation data observed in the process of seismogenesis and occurrences of earthquakes show that the location with the greatest gravity changes does not necessarily coincide with the epicenter. To explain this we defined the center of effective mass of stress volume as hypocentroid, and the vertical projection of which on the earths surface as epicentroid. Here we adopt three rotating models, including spheroid, ellipsoid and cylinder, to represent the region of an impending earthquake. Based on the models of gravity variations induced by uniform dilatancy, epicentroids associated with sixteen earthquakes with M>4.0 occurred in 1981~2000 in the Beijing-Tianjin-Tangshan-Zhangjiakou region are determined by means of a proposed least squares iterative inversion method. The results indicate that cylinder model is preferable to the other two, and epicentroids obtained by the cylinder model separate from the epicenters by a range of 0~40 km. Epicentroids are inevitably located within intact tectonic blocks, and usually cluster in groups; while the epicenters are generally located at the terminations of faults or at the intersections of faults. It seems that there exist earthquake-hatching areas in the block among faults. Earthquakes hatch in these areas, but occur around these areas, meanwhile the existence of faults may play an important role in controlling the processes.
基金financial support from the National Key R&D Program of China(No.2021YFA1200301)the National Natural Science Foundation of China(Nos.21890734,92156018,and 21972150)CAS Project for Young Scientists in Basic Research(No.YSBR-027)。
文摘Here,we designed asymmetric(m DS)and symmetrical(d DS)chiral V-shaped molecules by linking one or two dansyl groups to trans-1,2-cyclohexane diamine and investigated the solvent-regulated structural transformation and inversed circularly polarized luminescence(CPL)in the self-assemblies.Upon increasing water volume fraction(fw)in the mixed solvent of water/acetonitrile,asymmetric mDS selfassembled into hollow nanospheres and microtubes,while solid nanospheres and solid microplates were corresponding to symmetric d DS.During this transformation process,the emission of m DS and d DS was changed from yellow-green to blue and cyan color,which was ascribed to twisted intramolecular charge transfer(TICT)and locally excited(LE)fluorescence of V-shaped DS molecules.The conformation of N,Ndimethyl groups with respect to naphthalene ring also led to the transformation of structures.These tubular and platelike structures had stronger and reversed CPL signals in comparison with spheroidal structures.The chiral information of DS assembly could be effective transferred to achiral Nile red via co-assembly strategy,which endowed Nile red exhibiting inversed induced CPL signal regulated by water fraction.This work provides a method for achieving a variety of self-assembled structures with adjustable chiroptical properties.
基金the National Natural Science Foundation of China (No. 22101280)Wenzhou Medical University (No. KYYW201901)+1 种基金University of Chinese Academy of Science (Nos. WIBEZD201700103 and WIUCASQD2020005)Zhejiang Provincial Natural Science Foundation (No. LQ20B020009) for financial support。
文摘To realize the handedness controllable circularly polarized luminescence(CPL) system remains challenging. Herein, the solvent-mediated CPL inversion and amplification systems were successfully constructed by camptothecin derivative(CPT-A). Due to the planar structure of N,N-dimethylformamide, it could coassemble with CPT-A, resulting in the alteration of g_(lum) from –0.0082 to +0.0085 by increasing water content. While in the non-planar solvent(hexafluoroisopropanol), the g_(lum) was amplified to 0.034 with the increase in water content. Moreover, the CPT-A could react with the glutathione, resulting in the anticancer drug CPT to make it more toxic to the cancer cells. Overall, the handedness controllable CPL systems were realized by tuning the supramolecular self-assembly of a prodrug.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
文摘This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金financially supported by the National Natural Science Foundation of China(No.52363007)。
文摘Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.
文摘Delineating sweet spots is critical for the exploration and production of oil and gas in deep and tight sand reservoirs.The lack of advanced and reliable methods makes this a challenge for geologists and geophysicists.This study introduces,for the first time,an integrated workflow that combines pre-stack seismic inversion with rock physics modeling to predict reservoir porosity and shale volume(V-shale)for sweet spot identification in tight sand reservoirs.A new elastic parameter,the density calculation index(DCI),is introduced which links acoustic and shear impedance for seismic density inversion,thereby addressing the long-standing problem of poor density inversion accuracy.A novel combined Sun–Walsh rock physics model,developed as part of this study,significantly improves V-shale evaluation from seismic data.The proposed three-step seismic inversion approach includes:(1)deriving acoustic and shear impedance from angle-stack seismic data using model-based inversion;(2)calculating density using shear impedance constrained by DCI,followed by porosity estimation from the density–porosity relation;and(3)evaluating V-shale using theα-parameter derived from the Sun–Walsh model and pre-stack inversion results.This integrated workflow provides an effective tool for building accurate 3D reservoir models,and is especially applicable to deep,low-porosity,tight sand reservoirs worldwide.
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金financially supported by the National Natural Science Foundation of China (Nos.22171165 and 22371170)Natural Science Foundation of Shandong Province (No.ZR2022MB080)Scientific and Technological Frontiers in Project of Henan Province(No.242102110192)。
文摘Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.
基金supported in part by the National Science Foundation under GrantsDMS 2436630 and 2436629.
文摘Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.
基金financially supported by the National Science and Technology Major Project,China(No.2024ZD1002100)the National Natural Science Foundation of China(Nos.42330801,42474112,42504062)+1 种基金the China Postdoctoral Science Foundation(No.2024M761704)Shuimu Tsinghua Scholar Program of Tsinghua University,China(No.2024SM114)。
文摘A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion efficiency.The results indicate that the computational efficiency of each inversion can be improved by approximately a factor of three by using the proposed MG solver.First,the accuracy of the MG solver is validated through a test on a synthetic model.Next,the numerical performance of the inversion algorithm is evaluated using this model.Finally,the inversion algorithm is applied to a field EM data collected at the Beiya gold polymetallic ore district.A 3D resistivity model is obtained,and the formation process of the metal ore is analyzed.
基金supported by the National Science and Technology Major Project(2022ZD0119901)the National Natural Science Foundation of China under Grant(U2141234,62463004 and U24A20260)+1 种基金the Hainan Province Science and Technology Special Fund(ZDYF2024GXJS003)the Scientific Research Fund of Hainan University(KYQD(ZR)23025).
文摘Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.
基金supported by the FNRS-FRFC,the Walloon Region,and the University of Namur(Conventions No.2.5020.11,GEQ U.G006.15,1610468,RW/GEQ2016 et U.G011.22)funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement n°101034383。
文摘Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.
基金Supported by the China National Science and Technology Major Project(2025ZD1407000)PetroChina Science and Technology Major Project(2023ZZ22)。
文摘To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous narrow-channel boundaries and subdivision of multi-stage superimposed sandbodies.Taking the Upper Cretaceous continental sandstone in the Sazhong Oilfield of the Daqing Placanticline as an example,a technical system integrating OVT high-resolution processing,multi-attribute fusion,and varible-scale inversion was developed to establish a complete workflow from seismic processing to reservoir prediction and remaining oil recovery.The following results are obtained.First,the Offset Vector Tile(OVT)seismic processing technology is extended,for the first time,from fracture imaging to sandbody prediction,in order to address the weak seismic responses from boundaries of narrow and thin sandbodies.A geology-oriented OVT partitioning method is developed to significantly improve the imaging accuracy,enabling identification of channel sandbodies as narrow as 50 m.Second,an amplitude-coherence dual-attribute fusion method is proposed for predicting narrow channel boundaries between wells.Constrained by a sedimentary unit-level sequence chronostratigraphic framework,this method accurately delineates 800-2000 m long subaqueous distributary channels with bifurcation-convergence features.Third,considering the superimposition of multi-stage channels,a three-level variable-scale stratigraphic model(sandstone groups,sublayers,sedimentary units)is constructed to overcome single-scale modeling limitations,successfully characterizing key sedimentary features like meandering river“cut-offs”through 3D seismic inversion.Based on these advances,a direct link between seismic prediction and remaining oil recovery is established.The horizontal wells deployed using narrow-channel predictions encountered oil-bearing sandstones in the horizontal section by 97%,and achieved initial daily production of 12.5 t per well.Precise identification of individual channel boundaries within 17 composite sandbodies guided recovery processes in 135 wells,yielding an average daily increase of 2.8 t per well and a cumulative increase of 13.6×10^(4)t.
基金supported by the National Key Research and Development Program of China (Grant No.2023YFA1406301)the National Natural Science Foundation of China (Grant Nos.52250308 and 52525205)。
文摘Band inversion induced by spin–orbit coupling in topological semimetals typically generates light charge carriers with high Fermi velocities,which are highly desirable for low-dissipation and coherent quantum transport in topological devices.The presence of these carriers in real materials strongly depends on the Fermi-level position.2M-WSe_(2),with its topological and van der Waals nature,serves as an ideal platform for studying quantum transport in two-dimensional systems,despite the fact that interlayer coupling suppresses the formation of light carriers.In this study,we solvothermally intercalate 1,3-diaminopropane molecules into the interlayer space of 2M-WSe_(2);these molecules effectively adapt to the electronic structure by eliminating interlayer coupling.Simultaneously,slight electron doping via charge transfer results in a small Fermi pocket with an extremely light effective mass,0.04–0.06 me,as revealed by quantum oscillation measurements.This study demonstrates that molecular intercalation is an effective approach for engineering van der Waals topological materials to achieve specific quantum transport properties.
