Search

Flexible humidity sensors (FHSs) with fast response times and durability to high-humidity environments are highly desirable for practical applications. Herein, an FHS based on crosslinked sodium alginate (SA) and MXene was fabricated, which exhibited high sensitivity (impedance varied from 107 to 105 Ω between 10% and 90% RH), good selectivity, prompt response times (response/recover time of 4 s/11 s), high sensing linearity (R2 = 0.992) on a semi-logarithmic scale, relatively small hysteresis (~5% RH), good repeatability, and good resistance to highly humid environments (negligible changes in sensing properties after being placed in 98% RH over 24 h). It is proposed that the formation of the crosslinking structure of SA and the introduction of MXene with good conductivity and a high specific surface area contributed to the high performance of the composite FHS. Moreover, the FHS could promptly differentiate the respiration status, recognize speech, and measure fingertip movement, indicating potential in breath monitoring and non-contact human–machine interactions. This work provides guidance for developing advanced flexible sensors with a wide application scope in wearable electronics.

Surface enhanced fluorescence (SEF) based on noble metal nanoparticles is an effective means to achieve high sensitivity in fluorescence detection. Currently, the physical mechanism behind enhanced fluorescence is not fully understood. This paper measures the fluorescence signals of Dihydroporphyrin f methyl ether (CPD4) under both single-photon and two-photon excitation based on submicrometer silver particles with rough morphologies, achieving enhancement factors of 34 and 45 times, respectively. On this basis, by combining the radiative field characteristics produced by the silver particles, a stimulated radiation model of molecules is established to elucidate the changes in the molecular photophysical process when influenced by silver particles. Moreover, the fluorescence lifetime of the molecules was measured, showing that the presence of silver particles induces an increase in the molecular radiative decay rate, causing the fluorescence lifetime to decay from 3.8 ns to 3 ns. The results indicate that the fluorescence enhancement primarily originates from the submicrometer silver particles’ enhancement effect on the excitation light. Additionally, the fluorescence signal emitted by the molecules couples with the silver particles, causing the local surface plasmon resonances generated by the silver particles to also emit light signals of the same frequency. Under the combined effect, the fluorescence of the molecules is significantly enhanced. The findings provide a theoretical foundation for understanding the fluorescence enhancement mechanism of silver particles, adjusting the enhancement effect, and developing enhanced fluorescence detection devices based on submicrometer silver particles, holding significant practical importance.

Due to its excellent electrical performance, mechanical reliability, and thermal stability, electroplated gold is still the most commonly used material for movable beams in RF MEMS switches. This paper investigates the influence of process conditions on the quality and growth rate of gold electroplating, and the optimized process parameters for the gold electroplating process are obtained. The characterization of the optimized electroplated gold layer shows that it has small surface roughness and excellent thermal stability. With this optimized gold electroplating process, the RF MEMS switches are fabricated and hermetic packaged. In order to obtain the temperature environment adaptability of the packaged switch, the influence of working temperature is studied. The temperature effects on mechanical performance (includes pull-in voltage and lifetime) and RF performance (includes insertion loss and isolation) are revealed.

Multiwave interference plays a crucial role in shaping the spatial variations of internal tides. Based on a combination of in situ mooring and altimeter data, interference of semidiurnal internal tides was investigated in the northern South China Sea. Mooring observations indicate the observed kinetic-to-potential energy ratio and group speed are both relatively lower than the theoretical values of mode-1 semidiurnal internal tides, indicating the presence of partly-standing waves. This is consistent with the altimeter result that the mooring was located at the antinode within the interference pattern formed by the superposition of the westward and southward semidiurnal internal tides from the Luzon Strait and the continental slope of the southern Taiwan Strait. However, the kinetic-to-potential energy ratio and group velocity were notably changed when an anticyclonic eddy passed by the mooring. By employing the ray-tracing method, we identified that mesoscale processes may induce a phase difference in the semidiurnal internal tides between the Luzon Strait and the continental slope of the southern Taiwan Strait. This alteration further leads to changes in the positions of nodes and antinodes within the interference pattern of the semidiurnal internal tides.

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

A contact is the key structure of RF MEMS (Radio Frequency Microelectromechanical System) switches, which has a direct impact on the switch’s electrical and mechanical properties. In this paper, the implementation of highly reliable contacts for direct-contact RF MEMS switches is provided. As a soft metal material, gold has the advantages of low contact resistance, high chemical stability, and mature process preparation, so it is chosen as the metal material for the beam structure as well as the contacts of the switch. However, a Pt film is used in the bottom contact area to enhance the reliability of the contact. Three kinds of contacts with various shapes are fabricated using different processes. Particularly, a circular-shaped contact is obtained by dry/wet combined processes. The detailed fabrication process of the contacts as well as the Pt film on the bottom contact area are given. The experimental test shows that the contact shape has little effect on the RF performance of the switches. However, the circular contact shows better reliability than other contacts and can work well even after 1.2 × 109 cycles.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

Amidst the escalating issue of drug abuse, an urgent need for effective illicit drug detection methods has arisen. This paper introduces a novel optical approach utilizing the Goos–Hänchen Shift (GHS) to explore the possibility of on-site rapid detection of illicit drugs. Delving into the mechanisms, light absorption and attenuation in biological samples are considered through absorption and attenuation coefficients, establishing connections between complex refractive indices, complex dielectric constants, and GHS. A self-assembled GHS detection system measured GHS values across various samples: ultrapure water, serum, methamphetamine, serum–methamphetamine, heroin, and serum–heroin. These experiments unveiled substantial GHS variations among the samples. Refractive indices for serum, serum–methamphetamine, and serum–heroin samples were computed using GHS values and sample extinction coefficients, highlighting GHS’s remarkable sensitivity to refractive index variations as a high-sensitivity refractive index sensing technology. The correlation between the dielectric constant and GHS was explored, yielding refractive indices for pure solutes—serum, methamphetamine, and heroin—of 1.66300, 1.51300, and 1.62300, respectively. Notably, the dielectric constants for these solutes were 2.76557, 2.28917, and 2.63413, emphasizing the dielectric constant’s discriminative potential in identifying illicit drugs. In conclusion, these findings suggest that GHS holds promise for distinguishing various illicit drug types, charting an innovative path for illicit drug detection.

Recent research has indicated that metal nanoparticles, known for their unique optical properties, can enhance the spectral response of photovoltaic modules. Since most nanoparticles demonstrate enhancement effects within a specific wavelength range, broadening the spectral response of photoelectric devices is critical for their application in imaging, energy harvesting, and optical communication. In this study, we applied flower-like silver particles to achieve this broadband enhancement. The optical absorption of photovoltaic modules, featuring an amorphous Si p-i-n structure, was improved across a broad wavelength range of 400~2000 nm by integrating these flower-like silver particles, resulting in an approximately tenfold increase in peak spectral responsivity. The theoretical investigation further elaborates that the enhancement originates from the near-field effects of silver particles due to the interaction of different parts of the flower-like silver particles. Through these studies, we demonstrate that utilizing the flower-like silver particles with roughness surface can achieve the spectral response of the photoelectric device enhanced in broadband range, which can improve the utilization efficiency of optical energy for the applications of sensing, imaging, optical communication, and energy harvesting.

Internal solitary waves evolving with time in shallow water are known to affect sound propagation significantly. Unlike prior work studying the acoustic effects of individual internal-wave properties separately, this paper elucidates and evaluates the influence of a complete evolution process of internal waves on acoustic fields both theoretically and by the coupled ocean-acoustic simulation. Two evolving wave properties considered here are shape deformations including the variations of wave amplitudes and widths and packet dispersion manifested as the increasing wavelength (i.e., the distance between successive solitons). The acoustic modal intensity expressed by the Dyson series solution is reformulated to explicitly reveal the modulation effects induced by the deformation and dispersion of internal waves. Dispersion leads to modal interference and causes the intensity envelope to oscillate with the varying wavelength. Deformation modulates intensity in a non-oscillatory manner that is less predictable due to the complexity of amplitude and width variations. In the environment reconstructed from the field observations of internal waves in the South China Sea, the modal intensity simulated by the parabolic-equation model exhibits pronounced modulation effects, where the modal interference due to dispersion dominates the intensity-envelope shape, and deformation affects the extremum positions of envelopes.

Due to the dense distribution of tomato fruit with similar morphologies and colors, it is difficult to recognize the maturity stages when the tomato fruit is harvested. In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed based on improved YOLOv5 to recognize the four types of different tomato maturity stages: mature green, breaker, pink, and red. Tomato maturity datasets were established using tomato fruit images collected at different maturing stages in the greenhouse. The small-target detection performance of the model was improved by Mosaic data enhancement. Focus and Cross Stage Partial Network (CSPNet) were adopted to improve the speed of network training and reasoning. The Efficient IoU (EIoU) loss was used to replace the Complete IoU (CIoU) loss to optimize the regression process of the prediction box. Finally, the improved algorithm was compared with the original YOLOv5 algorithm on the tomato maturity dataset. The experiment results show that the YOLOv5s-tomato reaches a precision of 95.58% and the mean Average Precision (mAP) is 97.42%; they are improved by 0.11% and 0.66%, respectively, compared with the original YOLOv5s model. The per-image detection speed is 9.2 ms, and the size is 23.9 MB. The proposed YOLOv5s-tomato can effectively solve the problem of low recognition accuracy for occluded and small-target tomatoes, and it also can meet the accuracy and speed requirements of tomato maturity recognition in greenhouses, making it suitable for deployment on mobile agricultural devices to provide technical support for the precise operation of tomato-picking machines.

The laser powder bed fusion (LPBF) method, more commonly known as selective laser melting (SLM), is one of the most common metal additive manufacturing (AM) processes. It is a layer-by-layer fabrication process where each powder layer is melted and fused by a laser beam, which traverses over the designated part geometry cross-section, as defined by a sliced CAD model. The LPBF process is being popularly used to manufacture end products of intricate geometry for various industries, such as the automobile, aerospace, defence, and biomedical industries. In designing parts, the topology optimisation (TO) technique can be effectively employed to optimise the distribution of material throughout the part and obtain the minimum volume/weight without compromising the mechanical performance of the component. This study focusses on the design optimisation and validation of the optimisation approaches used for LPBF-printed AlSi12 metal parts. The mechanical performance of three different topologically optimised lattice beams, viz. 1 × 1, 8 × 3, and 12 × 3, printed using the LPBF process, was investigated. When the beams were tested in bending, it was found that these TO LPBF-printed beams behaved differently when compared to the LPBF-printed solid beam. The 1 × 1 lattice beam performed better than the other two lattice beams due to the lower number of links where premature failure was delayed. The 1 × 1 lattice beam exhibited a load-bearing capacity of 17 ± 2 kN, whereas the 8 × 3 and 12 × 3 lattice beams showed load capacities of 13 ± 1 kN and 10 ± 1 kN, respectively. This mechanical behaviour was modelled and simulated by using a finite element analysis, and it was found that the LPBF-printed material property was affected by the design elements present in the beam. It was also found that each topology-optimised beam fits a different material model when compared to the SLM-printed solid beam. Therefore, a new material model or simulation technique needs to be developed to overcome this issue.

Imidacloprid is one of the most commonly used insecticides for managing pests, thus, improving the quality and yield of vegetables. The abuse/misuse of imidacloprid contaminates the environment and threatens human health. To reduce the risk, a colorimetric enzyme-linked immunoassay assay (Co-ELISA) and chemiluminescence enzyme-linked immunoassay assay (Cl-ELISA) were established to detect imidacloprid residues in vegetables. The linear range of Co-ELISA ranged between 1.56 μg/L and 200 μg/L with a limit of detection (LOD) of 1.56 μg/L. The values for Cl-ELISA were 0.19 μg/L to 25 μg/L with an LOD of 0.19 μg/L, which are lower than those of Co-ELISA. Fortifying Chinese cabbage, cucumber, and zucchini with imidacloprid at 10, 50, and 100 μg/L yielded recoveries between 81.7 and 117.6% for Co-ELISA and at 5, 10, and 20 µg/L yielded recoveries range from 69.7 to 120.6% for Cl-ELISA. These results indicate that Cl-ELISA has a high sensitivity and a rapid detection time, saving cost (antigen and antibody concentrations) and serving as a more efficient model for the rapid detection of imidacloprid residue.

This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community’s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community.

At present, the power system has the characteristics of mutual independence but interconnection, and the interconnection between the various subsystems brings certain challenges to the distributed computing of the power grid. In addition, a substantial amount of naturally uncertain renewable resources are incorporated into the power system, which will impose volatile dynamics on the grid. In this paper, an alternating direction multiplier method (ADMM) is proposed for the power system with real-time renewables to tackle the online optimal power flow (OPF) problem. Due to the adoption of the Lagrangian duality, the proposed distributed ADMM scheme utilizes consensus ADMM to solve the dual OPF problem, which only discloses boundary coupling via the Lagrangian multiplier and further reduces the amount of information communication. Given the natural uncertainty of distributed energy resources (DER), the algorithm avoids the double-loop implementation or the uncertainty of traditional distributed methods of using the boundary information as equality constraints caused by dynamic DER. It is thus capable of providing a provable performance guarantee and is inherently developed to cope with the dynamic OPF problem with renewables in an online fashion. Taking the IEEE 30-bus system as a test feeder, the simulation results verify the efficiency and robustness of the proposed algorithms in solving both the static and dynamic OPF problems; in addition, the online method can effectively avoid the violent fluctuations of the conventional generator output copying with renewables rapid variation in comparison with the offline algorithms.

In this study, a novel composite material prepared by using deep eutectic solvent (tetrabutylammonium chloride-dodecanol, DES5) functionalized magnetic MWCNTs−ZIF−8 (MM/ZIF−8@DES5) was employed as an adsorbent for the magnetic solid-phase extraction of six pyrethroids from tea drinks. The characterization results show that MM/ZIF−8@DES5 possessed sufficient specific surface area and superparamagnetism, which could facilitate the rapid enrichment of pyrethroids from tea drink samples. The results of the optimization experiment indicated that DES5, which comprised tetrabutylammonium chloride and 1-dodecanol, was selected for the next experiment and that the adsorption properties of MM/ZIF−8@DES5 were higher than those of MM/ZIF−8 and M-MWCNTs. The validation results show that the method has a wide linear range (0.5–400 μg L−1, R2 ≥ 0.9905), low LOD (0.08–0.33 μg L−1), and good precision (intra-day RSD ≤ 5.6%, inter-day RSD ≤ 8.6%). The method was successfully applied to the determination of pyrethroids in three tea drink samples.

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products.

Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation [...]

To validate the accuracy of snow depth products retrieved from passive microwave remote sensing data with a high confidence level, the verification method based on points of ground observation is subject to great uncertainty, due to the scale effect. Thus, it is necessary to use a point-to-surface scale transformation method to obtain the relative ground truth at the remote sensing pixel scale. In this study, by using the snow depth ground observations at different observation scales, the upscaling methods are conducted based on simple average (SA), geostatistical, Bayes maximum entropy (BME), and random forest (RF) algorithms. In addition, the cross-validation of the leave-one-out method is employed to validate the upscaling results. The results show that the SA algorithm is seriously inadequate for estimating snow depth variation in space, and is only suitable for regions with relatively flat terrain and small variation of snow depth. The BME algorithm can introduce prior knowledge and perform kernel smoothing on observed data, and the upscaling result is superior to geostatistical and RF algorithms, especially when the observed data is insufficient, and outliers appear. The results of the study are expected to provide a reference for developing a point-to-surface upscaling method based on snow depth ground observations, and to further solve the uncertainties caused by scale effects in snow depth and other land surface parameter inversion and validation, by using remote sensing data.

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season.

Season snow cover plays an important role in vegetation growth in alpine regions. In this study, we analyzed the spatial and temporal variations in seasonal snow cover and the start of the growing season (SOS) of alpine grasslands and preliminarily studied the mechanism by which snow cover affects SOS changes by modifying the soil temperature (ST) and soil moisture (SM) in spring. The results showed that significant interannual trends in the SOS, snow end date (SED), snow cover days (SCD), ST, and SM existed over the Tibetan Plateau (TP) in China from 2000 to 2020. The SOS advanced by 2.0 d/10 a over the TP over this period. Moreover, the SOS showed advancing trends in the eastern and central parts of the TP and a delayed trend in the west. The SED and SCD exhibited an advancing trend and a decreasing trend in high-elevation areas, respectively, and the opposite trends in low-elevation areas. The ST showed a decreasing trend in low-elevation areas and an increasing trend in high-elevation areas. The SM tended to increase in most areas. The effects of the seasonal snow cover on the ST and SM indirectly influenced the SOS of alpine grasslands. The delayed SEDs and more SCD observed herein could provide increasingly wet soil conditions optimal for the advancement of the SOS, while less snow and shorter snow seasons could delay the SOS of alpine grasslands on the TP.

Soil moisture content (SMC) is a significant factor affecting crop growth and development. However, SMC estimation, based on synthetic aperture radar (SAR), is influenced by a variety of surface parameters, such as vegetation cover and surface roughness. As a result, determining the SMC across agricultural areas (e.g., wheat fields) remotely (i.e., without ground measurement) is difficult to achieve. In this study, a model-based polarization decomposition method was used to decompose the original SAR signal into different scattering components that represented different scattering mechanisms. The different volume scattering models were applied, and then the results were compared in order to remove the scattering contribution from vegetation canopy, and extract the surface scattering components related to the soil moisture. Finally, by combining extensively used surface scattering models (e.g., CIEM and Dubois), and a method of roughness parameters optimization, a lookup table was developed to estimate the soil moisture during the wheat growth period. When CIEM is applied, the R2 and RMSE of the SMC are 0.534, 5.62 vol.%, and for the Dubois model, 0.634, 5.16 vol.%, respectively, which indicates that this approach provides good estimation performance for measuring soil moisture during the wheat growing season.

In the early morning of 21 April 2021 local time, the Indonesian Navy submarine KRI nanggala-402 crashed in the Bali Sea (BS). As internal solitary waves (ISWs) are a great threat to submarine navigation, this paper analyzes the characteristics of ISWs in the BS by surveying satellite remote sensing images collected from 12–21 April 2021. The satellite images revealed active ISWs in the BS near the submarine wreck site with crest lengths approaching 200 km. Originating from the Lombok Strait (LS), the waves travelled northwestward across the BS deep basin, passed through the submarine wreck site, and shoaled onto the continental shelf west of the Kangean Islands, during which process, the propagation speed reached 2.69 m/s in the deep basin and 0.71 m/s in the shallow water. Based on the satellite images, the wave amplitude near the wreck site was reconstructed to be 41 m, and the reconstructed underwater wave structure showed a maximum vertical velocity of 10 cm/s. Satellite images also demonstrated the near-source evidence of ISWs near the Nusa Penida sill of the LS, and their generation were estimated to be related to the southward tidal current troughs.

Internal solitary waves in the South China Sea have attracted attention because of their large amplitude and high rate of occurrence. Internal solitary waves have a substantial influence on underwater sound propagation and ambient noise. However, there have seldom been reports on the noise they cause. In this paper, we conducted an internal solitary waves cooperative observation experiment in the South China Sea in 2016. We analyzed the temperature, flow velocity and noise changes induced by internal solitary waves. The power spectra of noise generated by internal solitary waves at frequencies below 100 Hz was almost 20 dB higher than ambient noise. The observed low-frequency noise had uniform harmonics. Combined with the changes of flow velocity, we interpreted the low frequency noise as flow noise induced by vortex-induced vibration of internal solitary waves flowing past the cable mooring system. The noise spectra were related to the position of the cable where the hydrophone was mounted. The closer they were to the middle of the cable, the greater the vibration amplitude, and the stronger the noise. This study provided a passive acoustic monitoring and warning method for high marine currents.

Polyoxymethylene dimethyl ether (PODE) is a low-viscosity oxygenated fuel that can improve the volatility of blended fuels. In this work, the macroscopic and microscopic spray characteristics of diesel-PODE3-4 under different ambient temperatures and injection pressures (IP) are studied. The studied blends consisted of pure diesel (P0), two diesel blend fuels of 20% (P20) and 50% (P50) by volume fraction of PODE3-4. The Mie scattering and Schlieren imaging techniques are used in the experiment. The results show that with the increase in IP, the vapor phase penetration distance and the average cone angle of the three fuels increased, and the Sauter mean diameter (SMD) of the three fuels decreased. When the ambient temperature increased, the vapor phase projection area and the average vapor phase cone angle of P20 and P50 increased, and the SMD decreased, but the vapor phase projection area of pure diesel did not change significantly. The results indicate that the blended fuel with PODE3-4 has better spray characteristics than P0 at low temperature, and the SMD hierarchy between the three fuels is P0 > P20 > P50. Through the visualization experiment, it is helpful to further understand the evaporation characteristics of different fuel properties and develop appropriate alternative diesel fuel.

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.

Excessive bleeding induces a high risk of death and is a leading cause of deaths that result from traffic accidents and military conflict. In this paper, we developed a novel porous chitosan–CaCO3 (CS–CaCO3) composite material and investigated its hemostatic properties and wound healing performance. The CS–CaCO3 composites material was prepared via a wet-granulation method. Granulation increases the infiltrating ability of the CS–CaCO3 composites material. The improved water absorption ability was enhanced to 460% for the CS–CaCO3 composites material compared to the CaCO3 or chitosan with only one single component. The coagulation studies in vivo illustrated that the blood clotting time was greatly reduced from 31 s for CaCO3 to 16 s for the CS–CaCO3 composite material. According to the results of the wound healing experiments in rats, it was found that the CS–CaCO3 composite material can promote wound healing. The CS–CaCO3 composite material could accelerate wound healing to a rate of 9 days, compared with 12 days for the CaCO3. The hemostatic activity, biocompatibility, and low cost of CS–CaCO3 composite material make it a potential agent for effective hemostatic and wound healing materials.

The roof–column structural system is utilized for many engineering and architectural applications due to its structural efficiency. However, it typically requires column locations to be predetermined, and involves a tedious trial-and-error adjusting process to fulfil both engineering and architectural requirements. Finding efficient column distributions with the aid of computational methods, such as structural optimization, is an ongoing challenge. Existing methods are limited, with continuum methods involving the generation of undesired complex shapes, and discrete methods involving a time-consuming process for optimizing columns’ spatial order. This paper presents a new optimization method to design the distribution of a given number of vertical supporting columns under a roof structure. A computational algorithm was developed on the basis of the optimality-criterion (OC) method to preserve and removed candidate columns pre-embedded with design requirements. Three substrategies are presented to improve optimizer performance. The effectiveness of the new method was validated with a range of roof–column structural models. Treating column locations as design variables provides opportunities to significantly improve structural performance.

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

In this paper, multi-physical models of bending characteristics, including the static, dynamic and microwave models, are firstly proposed for the double-clamped beam switch based on flexible substrate. Both simulated and experimental verification have been carried out to prove that the changing regularity of the driving voltage and time of the switch is inversely proportional with the increase in the bending curvature of the flexible substrate. The microwave performance of the switch at the ON state is found to get worse with the increase in the bending curvature. The measured results indicate that when the bending curvature increases from 0 m−1 to 28.6 m−1, the measured driving voltage decreases from 90.0 V to 72.6 V with the error of 5.9% compared with the calculated results. The measured driving time decreases from 52.4 μs to 35.6 μs with the error of 16.7% compared with the calculated results. When the substrate bending curvature increases from 0 m−1 to 28.6 m−1, the measured reflection loss S11 of the switch gradually deteriorates from −27.1 dB to −22.0 dB with the error of 1.3 dB corresponding to the calculated results at 10 GHz. All the simulated and experimental results are consistent with the theoretical calculated results.

Dengue, chikungunya and Zika viruses share similar disease features, rendering them difficult to distinguish clinically. Incapacitating arthralgia/arthritis is a specific manifestation associated with chikungunya virus infection. However, the profile of arthralgia/arthritis in Zika virus (ZIKV) cases has not been well characterized. Articles were extracted from PubMed and Scopus databases reporting original data from patients with arthralgia/arthritis, according to the Cochrane Collaboration. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 137 articles reporting ZIKV-associated joint symptoms were reviewed. Arthralgia was more frequently reported (n = 124 from case studies, n = 1779 from population-based studies) than arthritis (n = 7 and n = 121, respectively). Arthralgia was resolved in 90% of these. We found no evidence of chronic rheumatic manifestations following ZIKV infection.

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

Snow plays an important role in meteorological, hydrological and ecological processes, and snow phenology variation is critical for improved understanding of climate feedback on snow cover. The main purpose of the study is to explore spatial-temporal changes and variabilities of the extent, timing and duration, as well as phenology of seasonal snow cover across the large part of Eurasia from 2000 through 2016 using a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-free snow product produced in this study. The results indicate that there are no significant positive or negative interannual trends of snow cover extent (SCE) from 2000 to 2016, but there are large seasonal differences. SCE shows a significant negative trend in spring (p = 0.01) and a positive trend in winter. The stable snow cover areas accounting for 78.8% of the large part of Eurasia, are mainly located north of latitude 45° N and in the mountainous areas. In this stable area, the number of snow-covered days is significantly increasing (p < 0.05) in 6.4% of the region and decreasing in 9.1% of the region, with the decreasing areas being mainly located in high altitude mountain areas and the increasing area occurring mainly in the ephemeral snow cover areas of northeastern and southern China. In central Siberia, Pamir and the Tibetan Plateau, the snow onset date tends to be delayed while the end date is becoming earlier from 2000 to 2016. While in the relatively low altitude plain areas, such as the West Siberian Plain and the Eastern European Plain region, the snow onset date is tending to advance, the end date tends to be delayed, but the increase is not significant.

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D.

With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher resolution) products, but are also characterized by high snow and overall classification accuracies (~85% and ~98%, respectively), substantially greater than those of the existing daily snow-cover products for all sky conditions and very similar to, or even slightly greater than, those of the daily MODIS products for clear-sky conditions. Using the snow-cover products, we analyzed the snow cover dynamics over the Tibetan Plateau and determined that the maximum number of snow-covered days (SCD) in a year followed a decreasing tendency from 2003 to 2010, with a decrease in snow-covered area (SCA) equivalent to 55.3% of the total Tibetan Plateau area. There is also a slightly increasing tendency in the maximum snow cover area (SCA), and a slightly decreasing tendency in the persistent snow cover area (i.e., pixels of SCD > 350 days) was observed for the 8-year period, which was characterized by increases in temperature (0.09 °C/year) and in precipitation (0.26 mm/year). This suggests that, on the Tibetan Plateau, changes in temperature and precipitation exert a considerable influence on the regional SCD and SCA, as well as the distribution of persistent snow cover.

A simple, sensitive and effective magnetic solid-phase extraction (MSPE) technique was developed for the extraction of pyrethroid pesticides from environmental water samples, followed by gas chromatography tandem triple quadrupole mass spectrometry determination. An adsorbent of magnetic zeolitic imidazolate framework-8@deep eutectic solvent (M-ZIF-8@DES) was prepared using deep eutectic solvent coated on the surface of M-ZIF-8. The features of M-ZIF-8@DES were confirmed by material characterizations, and the results indicated that M-ZIF-8@DES has a good magnetism (61.3 emu g−1), a decent surface area (96.83 m2 g−1) and pore volume (0.292 mL g−1). Single factor experiments were carried out to investigate the effect of different conditions on the performance of MSPE. Under the optimal conditions, the developed method performs good linearity (R2 ≥ 0.9916) in the concentration range of 1−500 μg L−1. The limits of detection were in the range of 0.05−0.21 μg L−1 (signal/noise = 3/1). The intraday relative standard deviation (RSD) and interday RSD were less than 9.40%. Finally, the proposed technique was applied for the determination of pyrethroid pesticides in environmental water samples. This work shows the potential of DES-modified metal-organic frameworks for different sample pretreatment techniques.

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

It is generally acknowledged that soil erosion has become one of the greatest global threats to the human−environment system. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely used for soil erosion estimation, the algorithm for calculating soil erodibility factor (K) in this equation remains limited, particularly in the context of China, which features highly diverse soil types. In order to address the problem, a modified algorithm describing the piecewise function of gravel content and relative soil erosion was used for the first time to modify the soil erodibility factor, because it has been proven that gravel content has an important effect on soil erosion. The Chaohu Lake Basin (CLB) in East China was used as an example to assess whether our proposal can improve the accuracy of soil erodibility calculation and soil erosion estimation compared with measured data. Results show that (1) taking gravel content into account helps to improve the calculation of soil erodibility and soil erosion estimation due to its protection to topsoil; (2) the overall soil erosion in the CLB was low (1.78 Mg·ha−1·year−1) the majority of which was slight erosion (accounting for 85.6%) and no extremely severe erosion; and (3) inappropriate land use such as steep slope reclamation and excessive vegetation destruction are the main reasons for soil erosion of the CLB. Our study will contribute to decision-makers to develop soil and water conservation policies.

As persistent organic pollutants, dichlorodiphenyltrichloroethanes (DDTs) and their metabolites pose considerable risks to human health and the environment. Therefore, monitoring DDTs in the environment is essential. Here, we developed a green, simple, and effective magnetic solid-phase extraction (MSPE) method coupled with gas chromatography tandem triple-quadrupole mass spectrometry to determine the DDT content of environmental water samples. A magnetic ionic liquid (IL) adsorbent was developed based on a modified magnetic multiwalled carbon nanotube/zeolitic imidazolate framework-8 (MM/ZIF-8/IL), synthesized by immobilizing the IL on the surface of MM/ZIF-8. We confirmed successful synthesis of MM/ZIF-8/IL by material characterization, and our results suggested that the MM/ZIF-8/IL had a high Brunauer−Emmett−Teller surface area (159.9 m2 g−1), good thermostability (−1). Several experimental conditions affecting the MSPE efficiency were optimized. Under the best conditions, good detection linearity was achieved (0.5−500 µg L−1) with determination coefficients ranging from 0.9927 to 0.9971. The lower limits of detection (0.0016−0.0072 µg L−1) also had good precision, having an intraday relative standard deviation (RSD) ≤ 6.5% and an interday RSD ≤ 8.9%. Finally, we used the as-developed method to determine DDT levels in environmental water samples.

Molecular imprinting technology (MIT), also known as molecular template technology, is a new technology involving material chemistry, polymer chemistry, biochemistry, and other multi-disciplinary approaches. This technology is used to realize the unique recognition ability of three-dimensional crosslinked polymers, called the molecularly imprinted polymers (MIPs). MIPs demonstrate a wide range of applicability, good plasticity, stability, and high selectivity, and their internal recognition sites can be selectively combined with template molecules to achieve selective recognition. A molecularly imprinted fluorescence sensor (MIFs) incorporates fluorescent materials (fluorescein or fluorescent nanoparticles) into a molecularly imprinted polymer synthesis system and transforms the binding sites between target molecules and molecularly imprinted materials into readable fluorescence signals. This sensor demonstrates the advantages of high sensitivity and selectivity of fluorescence detection. Molecularly imprinted materials demonstrate considerable research significance and broad application prospects. They are a research hotspot in the field of food and environment safety sensing analysis. In this study, the progress in the construction and application of MIFs was reviewed with emphasis on the preparation principle, detection methods, and molecular recognition mechanism. The applications of MIFs in food and environment safety detection in recent years were summarized, and the research trends and development prospects of MIFs were discussed.

Recent conflict along the border of Bangladesh and Myanmar has amplified a food security crisis and access to the region remains challenging. Moderate-resolution satellite remote sensing offers an approach to complement more traditional food insecurity hot spot assessment across Rakhine, Myanmar; however, conflict creates unique signals that are not agroclimatologically driven and need to be considered. Time series radar and optical data cubes were built and used to assess for deviations across space and time for rice paddy production areas based on established techniques. Ultimately, the Sentinel-1 radar was more helpful compared to fused Landsat-7 and -8 and Sentinel-2 data cubes that were substantially impacted by cloud cover during key growth stages. Anecdotal reporting, very high resolution (VHR) imagery, and expert knowledge were used to support operational analyses routines in an attempt to characterize rice into failed, abandoned, and cultivated classes across 2016 to 2018 seasons. Accuracy assessment using co-timed VHR showed overall accuracy (%) of 86.5, 87.5, and 91.0 for 2016, 2017, and 2018, respectively. Nearly one-third of rice production was characterized as failed or abandoned in any given year. Qualitative analyses showed paddy failure was often adjacent to conflict events. The moderate-resolution imagery and automated routines offer complementing metrics that can be used to help guide food security assessments. In regions where climate change, migration, and conflict coincide, decision support tools will need to evolve and continue to integrate human perspectives.

Due to their unique optical properties, narrow size distributions, and good biological affinity, gold nanoparticles have been widely applied in sensing analysis, catalytic, environmental monitoring, and disease therapy. The color of a gold nanoparticle solution and its maximum characteristic absorption wavelength will change with the particle size and inter-particle spacing. These properties are often used in the detection of hazardous chemicals, such as pesticide residues, heavy metals, banned additives, and biotoxins, in food. Because the gold nanoparticles-colorimetric sensing strategy is simple, quick, and sensitive, this method has extensive applications in real-time on-site monitoring and rapid testing of food quality and safety. Herein, we review the preparation methods, functional modification, photochemical properties, and applications of gold nanoparticle sensors in rapid testing. In addition, we elaborate on the colorimetric sensing mechanisms. Finally, we discuss the advantages and disadvantages of colorimetric sensors based on gold nanoparticles, and directions for future development.

Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R2 as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R2 due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection.

Crop type inventory and within season estimates at moderate (85%) accuracies and the ability to provide insight on crop location and extent within the crop season. HLS fused with S1 had, at times, a higher accuracy (5–10% relative overall accuracy and kappa increases) within season although the combination of fused data was minimal at times, crop dependent, and the accuracies tended to converge by harvest. In cloud prone regions and certain temporal periods, S1 performed well overall. The growth in the availability of time dense moderate resolution data streams and different sensitivities of optical and radar data provide a mechanism for within season crop mapping and area estimates that can help improve food security.

Monitoring of low levels of chlorsulfuron in environmental water samples is important. Although several detection methods have been developed, they all have some drawbacks, such as being time-consuming, requiring expensive instruments and experienced operators, and consuming large volumes of organic solvents. There is an urgent need for a simple, rapid, and inexpensive detection method for chlorsulfuron. Herein, such a method was developed using anti-aggregation of gold nanoparticles (AuNPs) in the presence of acetamiprid in agricultural irrigation water samples. Aggregation of the AuNPs was induced by acetamiprid, and this produced a distinct color change from Bordeaux red to blue. However, the strong hydrogen bonding interaction between chlorsulfuron and acetamiprid could inhibit AuNP aggregation. The effect of chlorsulfuron on the anti-agglomeration behavior of AuNPs was monitored by ultraviolet–visiblespectroscopy (UV-Vis) and the naked eye over a concentration range 0.1–100 mg/L. The detection limit for chlorsulfuron was 0.025 mg/L (signal-to-noise ratio of three). This colorimetric method was successfully applied to the determination of chlorsulfuron in spiked tap water and agricultural irrigation water with satisfactory recoveries (76.3%–94.2%).

Magnetic solid-phase extraction is an effective and convenient sample pretreatment technique that has received considerable interest in recent years. A lot of research indicated that magnetic nanocarbon-material-based composites have good application prospects as adsorbents for magnetic solid-phase extraction of pesticides. Herein, a novel zeolitic imidazolate framework based on magnetic multiwalled carbon nanotubes (M-M-ZIF-67) has been prepared as an adsorbent for magnetic solid-phase extraction of nine organochlorine pesticides from agricultural irrigation water samples. The obtained M-M-ZIF-67 material possessed porous surfaces and super-paramagnetism due to the utilization of magnetic multiwalled carbon nanotubes as the magnetic kernel and support. To evaluate the extraction performance of the M-M-ZIF-67, the main parameters that affected the extraction efficiency were researched. Under the optimal conditions, a good linearity for the nine organochlorine pesticides was achieved with the determination coefficients (R2) higher than 0.9916. The limits of detection (signal/noise = 3:1) were in the range 0.07–1.03 µg L−1. The recoveries of all analytes for the method at spiked levels of 10 and 100 µg L−1 were 74.9–116.3% and 75.1–112.7%, respectively. The developed M-M-ZIF-67 based magnetic solid-phase extraction method has a potential application prospect for the monitoring of trace level of organochlorine pesticides in environmental water samples.

Various highly sensitive and selective analytical methods have been used to monitor metsulfuron-methyl residue in the environment. However, these methods involve costly instruments and complex, time-consuming operations performed in laboratories. Here, a rapid, convenient, and sensitive colorimetric sensor based on anti-aggregation of gold nanoparticles (AuNPs) is demonstrated for the rapid detection of metsulfuron-methyl in agricultural irrigation water. The AuNPs could be induced to aggregate in the presence of melamine and exhibited a distinct color change from wine-red to blue. The aggregation was suppressed by a strong hydrogen-bonding interaction between metsulfuron-methyl and melamine. The differences of the absorbance at 523 nm (ΔA523) and the color change was linearly related to metsulfuron-methyl concentration over the range 0.1–100 mg/L, as observed visually and by UV-vis (Ultraviolet-visible) spectrometry. The detection limit of the sensor was as low as 0.05 mg/L (signal/noise = 3), and was used to determine metsulfuron-methyl in spiked water and in agricultural irrigation water samples. Recoveries were in the range of 71.2–100.4%, suggesting that the colorimetric sensor was suitable for the determination of metsulfuron-methyl in agricultural water samples.