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Parity‐time (PT) and anti‐parity‐time (anti‐PT) symmetries have provided important guiding principles in the research of non‐Hermitian physics. However, realizations of anti‐PT symmetry in photonic systems usually rely on optical nonlinearities and indirect‐coupling approaches. Here, they apply the channel interference principle mediated by synthetic gauge‐flux biasing in open‐cavity systems to construct anti‐PT symmetries. It is shown that a specific π‐flux biasing into a looped‐resonator array can force a frequency degeneracy between pairwise Bloch modes therein. By further coupling the array into two external waveguides with tailored positions of ports, the system near the degeneracy point can be described by an anti‐PT‐symmetric Hamiltonian. When a real gauge‐flux detuning is introduced, the system undergoes a spontaneous transition between anti‐PT and anti‐PT‐broken phases, through which the two extreme cases of complete channel‐drop tunneling and complete tunneling suppression can be switched. Finally, by superimposing a PT‐symmetric term onto the anti‐PT‐symmetric Hamiltonian via applying an imaginary gauge‐flux biasing, extreme channel‐drop amplifying effects can be further realized by exciting the "lasing" mode under the critical‐coupling condition. The work bridges the physical connection between synthetic gauge field and anti‐PT symmetry. This paradigm may also find many applications from optical routing, and switching to buffering and amplifying on a chip–scale platform. [ABSTRACT FROM AUTHOR]

Dynamic localization (DL) refers to a wavefunction confinement effect of electrons in periodic lattices under an ac electric field driving. Recent studies have transplanted DLs from electronic to photonic systems using periodically curved waveguide lattices, where the ac electric field is mimicked by the waveguide's bending curvature. However, due to the severe limitations of bending losses and poor tunability for highly curved, arbitrarily shaped waveguides, present studies mainly focus on DL under the simplest sinusoidal‐wave driving; its extension to an arbitrary‐wave driving remains challenging. Here, by constructing a synthetic temporal lattice with a coupled fiber‐loop circuit, the limitations of spatially curved waveguides are circumvented and generalized DLs are experimentally demonstrated under arbitrary‐wave driving. First, by applying band‐collapse criteria, the general conditions for the driving field's symmetry to achieve DLs are revealed. Then, two representative even‐function driving fields of square‐ and triangle‐waveforms are chosen, and wave‐packet revivals are observed as signatures of DLs. As a counter‐example, the breakdown of DL is also verified using odd‐function driving field of sawtooth‐waveform. The work reveals the conditions for realizing generalized DLs and experimentally verifies them using synthetic lattice schemes. This paradigm may find potential applications in versatile temporal‐waveform reshaping, pulse manipulations for optical communications, and signal processing. [ABSTRACT FROM AUTHOR]

We investigate the topological interface modes of surface plasmon polaritons in a multilayer system composed of graphene waveguide arrays. The topological interface modes emerge when two topologically distinct graphene multilayer arrays are connected. In such multilayer system, the non-trivial topological interface modes and trivial modes coexist. By tuning the configuration of the graphene multilayer arrays, the associated non-trivial interface modes present robust against structural disorder. The total number of topological modes is related to that of graphene layers in a unit cell of the graphene multilayer array. The results provide a new paradigm for topologically protected plasmonics in the graphene multilayer arrays. The study suggests a promising approach to realize light transport and optical switching on a deep-subwavelength scale. [ABSTRACT FROM AUTHOR]

The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • The effect of FLX on ES alkaline dark fermentation was explored for the first time. • FLX over 0.6 mg/Kg inhibited every step associated with biohydrogen production. • High FLX inhibited the release of nutrients from ES anaerobic fermentation. • High content of FLX reduced the activities of key enzymes related to biohydrogen production. Recently, the influence behavior of new pollutants in the environment has been widely concerned. However, the effect of antidepressants widely detected in excess sludge (ES) on biohydrogen production from anaerobic dark fermentation has never been explored. To fill this gap, fluoxetine (FLX), a typical antidepressant, was selected to evaluate its effect on ES mesophilic anaerobic dark biohydrogen fermentation. The results showed that FLX reduced biohydrogen production even at low content (0.1 mg/Kg). The biohydrogen yield was only 12.8 mL/g in the 1.8 mg/Kg (based on total suspended solids) FLX group, decreased by about 34.7%, compared with the control group (without FLX). Further mechanism investigation implied that high levels (more than 0.6 mg/Kg) of FLX reduced every step associated with the biohydrogen production. FLX reduced the concentration of ammonia nitrogen and phosphate in fermentation broth. FLX also had a significant negative effect on enzyme activity in ES dark fermentation. [ABSTRACT FROM AUTHOR]

Abstract Previous studies on soil organic carbon content or stock mapping mostly use natural environmental covariates and do not consider the soil management practice factor. However, human activities have become an important influencing factor for soil organic carbon, especially for agricultural soils. Crop species/crop rotations and management practices significantly affect the amount and spatial variation of soil organic carbon in croplands, but have not been considered for mapping soil organic carbon. In this study, we used direct crop rotation information and variables generated using Fourier transform on HJ-1A/1B NDVI time series data to capture the periodic effect of crop rotation, and explored the effectiveness of incorporating such information in predicting topsoil organic carbon content in cropland. A case study applied such method in a largely agricultural area in Anhui province, China. Crop rotation information was obtained through field investigation. Various combinations of predictive environmental variables were experimented for mapping soil organic carbon. The results were validated using field samples. Results showed that the combination of natural environment variables with both crop rotation type and variables derived through Fourier transform yielded the highest accuracy. In addition, only using the Fourier decomposed variables and crop rotation information were able to achieve a similar accuracy with using only soil formative natural environmental variables. This indicates that crop rotation information has comparable predictive power of soil organic carbon as natural environment variables. This study demonstrates the effectiveness of including agricultural practice information in digital soil mapping in agricultural landscapes with differences in crop rotation. Highlights • Variables indicating agricultural practice were derived using Fourier transform from NDVI time series. • Addition of Fourier decomposed variables and crop rotation type improved SOC content mapping. • Crop rotation information has comparable predictive power of SOC as natural environment variables. • Fourier decomposition was demonstrated to be as useful as crop rotation type in predicting SOC. • The study demonstrates the effectiveness of including agricultural practice information in SOC mapping. [ABSTRACT FROM AUTHOR]

Existing sample data are important for digital soil mapping. Different sample points possess different representativeness. The representativeness of samples influences the soil mapping result greatly. However, few study focus on assessing the representativeness of single sample. In this paper, we proposed a method to identify representative samples from existing samples collected from multiple resources. The basic idea of the method was to use clusters of environmental covariates to approximate types of soil variations, and check the occupancy of the existing samples in centroids of environmental clusters. Those samples locating in typical locations or centroids of environmental clusters were considered as representative samples. In this paper, the proposed method was used to discern representative samples in 282 soil samples in Anhui Province, China. SOM content was mapped using a similarity based mapping method. Two cases with different training samples (representative samples, non-representative samples, and training samples including representative and non-representative samples) and validation samples were set to compare the mapping results and accuracies. The results showed that the SOM content maps predicted using representative training samples had generally higher accuracy than the results produced using non-representative samples, and comparative accuracies with the results produced using full training samples. To discern representative samples is helpful for understanding the soil-landscape relationships in an area and the proposed method can be used to design supplementary samples for a better soil mapping result. Mapping results and accuracies showed that different training and validation sample sets impacted the mapping results and accuracies greatly, which indicates that researchers should be cautious when using randomization to obtain training and validation subsets for soil mapping. [ABSTRACT FROM AUTHOR]

The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping. [ABSTRACT FROM AUTHOR]

• We study theoretically and numerically both commensurate and incommensurate AAH graphene lattices and realize the topological edge modes of SPPs and localization transition from extended to all-localized wavefunctions, respectively. • The graphene systems can enable flexible and efficient modulation for constructing AAH lattice, thus circumventing the drawbacks of limited modulation depths in all dielectric AAH waveguide systems. • The topological edge states and localization transitions for graphene SPPs may be utilized for developing plasmonics devices operating in deep subwavelength scale on fully flat and compact platforms. By using rational and irrational on-site modulation in graphene waveguide arrays, we construct both commensurate and incommensurate Aubry-André-Harper (AAH) models and achieve the topological edge states and localization transition for graphene surface plasmon polaritons (SPPs). We show that when the commensurate AAH graphene array is truncated, topological edge states emerge, which are immune to the random perturbation of modulation depth in each individual graphene sheet. While for an incommensurate AAH array, localization transition for SPPs modes have also been achieved when the modulation depth reaches a critical value. Our work points out that graphene waveguide array provides a promising platform for robust light transport and compact localization within a fully deep subwavelength scale. [ABSTRACT FROM AUTHOR]

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic-temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process. [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy membership function is an effective tool to represent relationship between soil and environment for predictive soil mapping. Usually construction of a fuzzy membership function requires knowledge on soil-landscape relationships obtained from local soil experts or from extensive field samples. For areas with no soil survey experts and no extensive soil field observations, a purposive sampling approach could provide the descriptive knowledge on the relationships. However, quantifying this descriptive knowledge in the form of fuzzy membership functions for predictive soil mapping is a challenge. This paper presents a method to construct fuzzy membership functions using descriptive knowledge. Construction of fuzzy membership functions is accomplished based on two types of knowledge: 1) knowledge on typical environmental conditions of each soil type and 2) knowledge on how each soil type corresponds to changes in environmental conditions. These two types of knowledge can be extracted from catenary sequences of soil types and the associated environment information collected at a few field samples through purposive sampling. The proposed method was tested in a watershed located in Heshan farm of Nenjiang County in Heilongjiang Province of China. A set of membership functions were constructed to represent the descriptive knowledge on soil-landscape relationships, which were derived from 22 field samples collected through a purposive sampling approach. A soil subgroup map and an A-horizon soil organic matter content map for the area were generated using these membership functions. Forty five field validation points were collected independently to evaluate the two soil maps. The soil subgroup map achieved 76% of accuracy. The A-horizon soil organic matter content map based on the derived fuzzy membership functions was compared with that derived from a multiple linear regression model. The comparison showed that the soil organic content map based on fuzzy membership functions performed better than the soil map based on the linear regression model. The proposed method could also be used to construction membership functions from descriptive knowledge obtained from other sources. [Copyright &y& Elsevier]

Abstract: In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to identify features. CLNN assumes that in spatial data clustered points and noise can be viewed as two homogenous point processes. The one with the higher intensity is considered as a feature and the one with the lower intensity is treated as noise. As a result, they can be separated according to the difference in intensity between them. With CLNN, points are first classified into feature and noise based on the kth nearest distance (the distance between a point and its kth nearest neighbor) at various values of k. Then, CLNN selects those classifications in which the separated classes (i.e. features and noise) are homogenous Poisson processes and cannot be further divided. Finally, CLNN identifies clustered points by averaging the selected classifications. Evaluation of CLNN using simulated data shows that CLNN reduces the number of false points significantly. The comparison between CLNN, the shared nearest neighbor, the spatial scan and the classification entropy method shows that CLNN produced the fewest false points. A case study using seismic data in southwestern China showed that CLNN is able to identify foreshocks of the Songpan earthquake (M =7.2), which may help to locate the epicenter of the Songpan earthquake. [Copyright &y& Elsevier]

Abstract: Clustered events are usually deemed as feature when several spatial point processes are overlaid in a region. They can be perceived either as a precursor that may induce a major event to come or as offspring triggered by a major event. Hence, the detection of clustered events from point processes may help to predict a forthcoming major event or to study the process caused by a major event. Nevertheless, the locations of existing clustered events alone are not sufficient to identify the area susceptible to a potential major future event or to predict the potential locations of similar future events, so it is desirable to know the shape and the size of the region (the “territory” of feature events) that the feature process occupies. In this paper, the support domain of feature (SDF), the region over which any feature event has the equivalent likelihood to occur, is employed to approximate the “territory” of feature events. A method is developed to delineate the SDF from a region containing spatial point processes. The method consists of three major steps. The first is to construct a discrimination function for separating feature points from noise points. The second is to divide the entire area into a regular mesh of points and then compute a fuzzy membership value for each grid point belonging to the SDF. The final step is to trace the boundary of the SDF. The algorithm was applied to two seismic cases for evaluation, one is the Lingwu earthquake and the other is the Longling earthquakes. Results show that the main earthquakes in both areas as well as most aftershocks triggered by them fell into the estimated SDFs. The case study of Longling shows that the algorithm can deal with a region containing more than two processes. [Copyright &y& Elsevier]

When two spatial point processes are overlaid, the one with the higher rate is shown as clustered points, and the other one with the lower rate is often perceived to be background. Usually, we consider the clustered points as feature and the background as noise. Revealing these point clusters allows us to further examine and understand the spatial point process. Two important aspects in discerning spatial cluster features from a set of points are the removal of noise and the determination of the number of spatial clusters. Until now, few methods were able to deal with these two aspects at the same time in an automated way. In this study, we combine the nearest-neighbour (NN) method and the concept of density-connected to address these two aspects. First, the removal of noise can be achieved using the NN method; then, the number of clusters can be determined by finding the density-connected clusters. The complexity for finding density-connected clusters is reduced in our algorithm. Since the number of clusters depends on the value of k (the k th nearest neighbour), we introduce the concept of lifetime for the number of clusters in order to measure how stable the segmentation results (or number of clusters) are. The number of clusters with the longest lifetime is considered to be the final number of clusters. Finally, a seismic example of the west part of China is used as a case study to examine the validity of our method. In this seismic case study, we discovered three seismic clusters: one as the foreshocks of the Songpan quake ( M = 7.2), and the other two as aftershocks related to the Kangding-Jiulong ( M = 6.2) quake and Daguan quake ( M = 7.1), respectively. Through this case study, we conclude that the approach we proposed is effective in removing noise and determining the number of feature clusters. [ABSTRACT FROM AUTHOR]