Multifunction integrated lithium niobate photonic chip for photon pairs generation and manipulation.

We report on a unique photonic quantum source chip highly integrating four-stage photonic elements in a lithium niobate (LN) waveguide circuit platform, where an aperiodically poled LN (APPLN) electro-optic (EO) polarization mode converter (PMC) is sandwiched between two identical type-0 PPLN spontaneous parametric down-converters (SPDCs), followed by an EO phase controller (PC). These core nonlinear optic and EO building blocks on the chip are systematically characterized stage by stage to show its high performance as an integrated quantum source. The APPLN EO PMC, optimally constructed by a genetic algorithm, is characterized to have a broad bandwidth (>13 nm), benefiting an efficient control of broadband type-0 SPDC photon pairs featuring a short correlation time. We demonstrate an efficient conversion of the |VV> photon-pair state generated from the first PPLN SPDC stage to the |HH> state through the APPLN EO PMC stage over its operating bandwidth, a broadband or broadly tunable polarization-entangled state can thus be possibly produced via the superposition of the |VV> state generated from the other PPLN SPDC on the third stage of the chip. Such a state can be further manipulated into two of the Bell states if the relative phases between the two polarization states can be properly modulated through the EO PC on the fourth stage of the chip. Such a multifunction integrated quantum photonic source chip can be of high value to developing a compact, efficient, and high-speed quantum information processor.

Broadband tunable electro-optic switch/power divider as potential building blocks in integrated lithium niobate photonics.

We demonstrate an electro-optic (EO) switch or in general, an EO controllable power divider based on a periodically poled lithium niobate (PPLN) polarization mode converter (PMC) and a five-waveguide adiabatic coupler integrated on a Ti:LN photonic circuit chip. In this integrated photonic circuit (IPC) device, the PPLN works as an EO controllable polarization rotator (and therefore a PMC), while the adiabatic coupler functions as a broadband polarization beam splitter (PBS). The 1-cm long PPLN EO PMC of the IPC device is characterized to have a half-wave (or switching) voltage of Vπ∼20 V and a conversion bandwidth of ∼2.6 nm. The splitting ratios of the adiabatic coupler PBS in the IPC device are >99% for both polarization modes over a broad spectral range from 1500-1640 nm. The EO mode of the implemented IPC device is activated when the PPLN EO PMC section is driven by an external voltage; the characterized EO switching/power division behavior of the device is in good agreement with the theoretical fit. The tunability of the EO IPC device in the 100-nm experimental spectral range is also demonstrated via the temperature tuning. The featured broad tunability and high integrability of the EO device presented in this study facilitates it to be an advantageous building block for realizing an on-chip photonic system.

Simultaneous electro-optic spectral switching and Q switching in a dual-wavelength Nd:YVO4 laser based on an aperiodic optical superlattice lithium niobate.

We report the demonstration of an electro-optic (EO) switchable dual-wavelength (1064- and 1342-nm) Nd:YVO4 laser based on an aperiodically poled lithium niobate (APPLN) chip whose domain structure is designed using aperiodic optical superlattice (AOS) technology. The APPLN works as a wavelength-dependent EO polarization-state controller in the polarization-dependent laser gain system to enable switching among multiple laser spectra simply by voltage control. When the APPLN device is driven by a voltage-pulse train modulating between a VHQ (in which target laser lines obtain gain) and a VLQ (in which laser lines are gain suppressed), the unique laser system can produce Q-switched laser pulses at dual wavelengths 1064 and 1342 nm, single wavelength 1064 nm, and single wavelength 1342 nm, as well as their non-phase-matched sum-frequency and second-harmonic generations at VHQ = 0, 267, and 895 V, respectively. A laser can benefit from such a novel, to the best of our knowledge, simultaneous EO spectral switching and Q switching mechanisms to increase its processing speed and multiplexity for versatile applications.

A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping.

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.

Electro-optically spectrum switchable, multiwavelength optical parametric oscillators based on aperiodically poled lithium niobate.

We report the first fast switchable multiwavelength optical parametric oscillator based on aperiodic optical superlattice technology. The constructed aperiodically poled lithium niobate (APPLN) integrates the functionalities of two quasi-phase-matching devices on a chip to work simultaneously as an electro-optic (EO) switchable notch-like filter and a multiline optical parametric downconverter. When such an APPLN is built in a 1064-nm-pumped optical resonator system, we achieve the oscillation of dual signals at 1540 and 1550 nm, for a single signal at 1540 nm, and a single signal at 1550 nm in the system when the 3-cm-long APPLN is driven by 0 V, 354 V, and 805 V, respectively. The switching among the three signal spectra is operationally simple and electro-optically fast. The electro-optically switched signals also feature enhanced power spectral density due to the unique EO gain-spectrum filtering mechanism employed in this work.

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010⁻2018.

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

The lunar nodal cycle controls mangrove canopy cover on the Australian continent.

Long-phase (interannual) tidal cycles have been shown to influence coastal flooding and sedimentation, but their role in shaping the extent and condition of tidal wetlands has received little attention. Here, we show that the 18.61-year lunar nodal cycle, popularly termed the "lunar wobble," is a dominant control over the expansion and contraction of mangrove canopy cover over much of the Australian continent. Furthermore, the contrasting phasing of the 18.61-year lunar nodal cycle between diurnal and semidiurnal tidal settings has mediated the severity of drought impacts in northern bioregions. Long-phase tidal cycles regulate maximum tide heights, are an important control over mangrove canopy cover, and may influence mangrove ecosystem services including forest productivity and carbon sequestration at regional scales.

Design of a High-Efficiency Multilayer Dielectric Diffraction Grating with Enhanced Laser Damage Threshold.

Diffraction gratings are becoming increasingly widespread in optical applications, notably in lasers. This study presents the work on the characterization and evaluation of Multilayer Dielectric Diffraction Gratings (MDG) based on the finite element method using Comsol MultiPhysics software. The optimal multilayer dielectric diffraction grating structure using a rectangular three-layer structure consisting of an aluminum oxide Al2O3 layer sandwiched between two silicon dioxide SiO2 layers on a multilayer dielectric mirror is simulated. Results show that this MDG for non-polarized lasers at 1064 nm with a significantly enhanced -1st diffraction efficiency of 97.4%, reaching 98.3% for transverse-electric (TE) polarization and 96.3% for transverse-magnetic (TM) polarization. This design is also preferable in terms of the laser damage threshold (LDT) because most of the maximum electric field is spread across the high LDT material SiO2 for TE polarization and scattered outside the grating for TM polarization. This function allows the system to perform better and be more stable than normal diffraction grating under a high-intensity laser.

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion.

Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean - square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.

Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods.

The main objective of the present study was to provide a novel methodological approach for flash flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative flash-flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for flash flood susceptibility mapping.

Adsorption of Polyelectrolyte onto Nanosilica Synthesized from Rice Husk: Characteristics, Mechanisms, and Application for Antibiotic Removal.

Adsorption of the polyelectrolyte polydiallyldimethylammonium chloride (PDADMAC) onto nanosilica (SiO2) fabricated from rice husk was studied in this work. Nanosilica was characterized by X-ray diffraction, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Adsorption of PDADMAC onto SiO2 increased with increasing pH because the negative charge of SiO2 is higher at high pH. Adsorption isotherms of PDADMAC onto silica at different KCl concentrations were fitted well by a two-step adsorption model. Adsorption mechanisms of PDADMAC onto SiO2 are discussed on the basis of surface charge change, evaluation by ζ potential, surface modification by FTIR measurements, and the adsorption isotherm. The application of PDADMAC adsorption onto SiO2 to remove amoxicillin antibiotic (AMX) was also studied. Experimental conditions such as contact time, pH, and adsorbent dosage for removal of AMX using SiO2 modified with PDADMAC were systematically optimized and found to be 180 min, pH 10, and 10 mg/mL, respectively. The removal efficiency of AMX using PDADMAC-modified SiO2 increased significantly from 19.1% to 92.3% under optimum adsorptive conditions. We indicate that PDADMAC-modified SiO2 rice husk is a novel adsorbent for removal of antibiotics from aqueous solution.