Coherent optical adaptive technique improves the spatial resolution of STED microscopy in thick samples

Stimulated emission depletion(STED) microscopy is one of far-field optical microscopy techniques that can provide sub-diffraction spatial resolution. The spatial resolution of the STED microscopy is determined by the specially engineered beam profile of the depletion beam and its power. However, the beam profile of the depletion beam may be distorted due to aberrations of optical systems and inhomogeneity of a specimen's optical properties, resulting in a compromised spatial resolution. The situation gets deteriorated when thick samples are imaged. In the worst case, the severe distortion of the depletion beam profile may cause complete loss of the superresolution effect no matter how much depletion power is applied to specimens. Previously several adaptive optics approaches have been explored to compensate aberrations of systems and specimens. However, it is difficult to correct the complicated high-order optical aberrations of specimens. In this report, we demonstrate that the complicated distorted wavefront from a thick phantom sample can be measured by using the coherent optical adaptive technique. The full correction can effectively maintain and improve spatial resolution in imaging thick samples.

Touring Pattern of Chinese Classical Gardens Based on VR and Eye Tracking Experiment:A Case Study of Canglang Pavilion Garden in Suzhou City

Classical Chinese gardens,with their intricate interplay of architectural landscapes featuring narrow corridors and expansive courtyards,are designed to surprise and captivate visitors at every turn.Unraveling the connection between visual perception,walking behaviors,and the spatial elements of these garden compounds is essential to grasping the essence of traditional Chinese garden design.This study employs Virtual Reality(VR)and eye-tracking technologies to evaluate the touring patterns within Chinese classical gardens,with a case study focusing on the Canglang Pavilion Garden in Suzhou.A total of 68 participants were enlisted to engage in a VR experiment simulating a visit to a courtyard space within this garden.The study analyzed route choices between 2 distinct spatial types:corridor-architecture and courtyard-wall.Eye movement indicators within the Areas of Interest(AOIs)for frame-of-view and depth-of-view spatial elements were monitored,and visitors’spatial-temporal trajectory maps were documented.This research offers valuable insights into the intricate dynamics between visual attention,spatial elements,and visitor behavior within garden environments.The methodology applied in this study has broad implications for environmental psychology,landscape architecture,and the study of cultural heritage sites,providing a deeper understanding of the interactions and perceptions of visitors within garden spaces.

Effect of land cover on channel form adjustment of headwater streams in a lateritic belt of West Bengal(India)

Present work is exploring the influence of land cover on channel morphology in 34 headwater catchments of the lateritic belt of West Bengal.Non-parametric tests(Mann-Whitney U and Kruskal-Wallis)and multivariate analysis(Principal Component Analysis and Canonical Discriminant Function models)have successfully differentiated the performance of land cover on channel morphology adjustment among the three groups of headwater streams(forested,transitional,and agricultural)on the Kunur River Basin(KRB).Spatial Interpolation Techniques reveal that intense land-use change,particularly forest conversion to agricultural land,is significantly increasing channel widths(269%)and cross-section area(78%),whereas agricultural channels become shallower(40%)than would be predicted from forested streams.Catchments with the dominance of forest and agricultural land are classified as‘C′and‘B′types of streams respectively,as per Rosgen's Stream Classification Model.Finally,the work claimed that transitional stream group is the definitive area to exaggerate the river restoration plan to stabilize the anthropogenic deformation on channel morphology.

New Results on PWARX Model Identification Based on Clustering Approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.