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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%). [ABSTRACT FROM AUTHOR]

Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1-R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1-R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring--it is the ideal algorithm for retrieving FSC for the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot). [ABSTRACT FROM AUTHOR]

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013-2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky-Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%-36.4% and 5.3%-29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology. [ABSTRACT FROM AUTHOR]

This paper systematically investigated the topological design of cellular phononic crystals with a maximized gap size between two adjacent bands. Considering that the obtained structures may sustain a certain amount of static loadings, it is desirable to ensure the optimized designs to have a relatively high stiffness. To tackle this issue, we conducted a multiple objective optimization to maximize band gap size and bulk or shear modulus simultaneously with a prescribed volume fraction of solid material so that the resulting structures can be lightweight, as well. In particular, we first conducted the finite element analysis of the phononic band gap crystals and then adapted a very efficient optimization procedure to resolve this problem based on bi-directional evolutionary structure optimization (BESO) algorithm in conjunction with the homogenization method. A number of optimization results for maximizing band gaps with bulk and shear modulus constraints are presented for out-of-plane and in-plane modes. Numerical results showed that the optimized structures are similar to those obtained for composite case, except that additional slim connections are added in the cellular case to support the propagation of shear wave modes and meanwhile to satisfy the prescribed bulk or shear modulus constraints. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of the exact location and time of the initial internal wave for which the generation mechanism of internal waves in the northern South China Sea must be well understood. By analyzing the internal wave field in the northern South China Sea (SCS) simulated using the MIT General Circulation Model (MITgcm) and observations from satellite synthetic aperture radar (SAR) and mooring temperature–salinity–depth (TSD) chains, the source regions and propagation initiation times of internal waves are identified for three typical tidal phases, i.e., the diurnal-tide-dominated phase (DTP), transition tide phase (TTP), and semidiurnal-tide-dominated phase (STP). The generation procedures of Type A and Type B internal waves are discussed in detail with those data. The present study reveals that Type A and Type B waves are generated at the eastern and western ridges, respectively, and both commence their westward propagation at the peak of the eastward tidal flow. The dynamics of lee waves and the resonance effect with double ridges constitute the generation mechanisms of internal waves in the northern SCS. Combined with varying configurations of tidal conditions, topography, and stratification, the generation procedures of Type A and Type B waves in the DTP, TTP, and STP are elucidated with the generation mechanism in a unified and self-consistent way. In short, during DTP, weaker A waves alternate with weaker B waves each day; during TTP, strong A waves and strong B waves appear alternately every day; and there are two weak A waves per day during the STP. The generation mechanism can help in developing future empirical models for generating internal waves using tidal currents, topography, and stratification without requiring complex fluid dynamics calculations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

I Remote Sensing i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. Thanks to the efforts of our reviewers in 2022, the median time to first decision was 20 days and the median time to publication was 44 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Remo Footnotes 1 B Disclaimer/Publisher's Note: b The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Zhao et al. [[11]] used the daily MODIS cloud-free snow cover product to calculate various snow phenology variables, including snow cover area (SCA), snow cover onset (SCS), snow cover melt (SCM) and snow cover day (SCD). This Special Issue contains many research highlights related to the remote sensing of mountain snow cover and vegetation and the relationship between vegetation and snow. Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation. The time of snow accumulation and melting (snow phenology) is closely related to the vegetation growth condition. [Extracted from the article]

Simple Summary: Squamous cell carcinoma (SCC) arising either de novo or from benign inverted papilloma (IP) is the most common histological subtype of sinonasal malignancies. Owing to the rarity of the disease, the limited comparative cohort studies provide inconsistent results. Given that none of the studies have well-balanced baseline characteristics and few studies provide adjusted hazard ratios (HRs), it is difficult to draw cogent conclusions. Further studies to identify the difference in prognosis may provide evidence for risk stratification and clinical decision-making. Our study had three main strengths. First, we used the largest patient data set to investigate the differences in clinical characteristics between IP-SCC and DN-SCC. Second, we used propensity score weighting to control for confounders and minimize bias. Third, we compared the annual failure hazards for local failure and distant metastasis between IP-SCC and DN-SCC, and provided a basis for individualized follow-up strategies. Background: Sinonasal squamous cell carcinoma (SNSCC) can arise as either inverted papilloma–associated SCC (IP-SCC) or as de novo SCC (DN-SCC). It is controversial as to whether survival differences between IP-SCC and DN-SCC exist. Methods: Between January 2000 and December 2016, 234 patients with SNSCC were analyzed retrospectively, including 68 with IP-SCC and 166 with DN-SCC. Propensity score matching (PSM) was performed to balance baseline characteristics. The Kaplan–Meier method and Cox proportional hazard model were used to determine risk factors on survival outcomes. Results: The median follow-up time was 98.4 months. Before PSM, lymph node metastasis was noted to be lower in patients with IP-SCC. After PSM, the 5-year DFS, DSS and OS between IP-SCC and DN-SCC were 43.0% vs. 44.5% (p = 0.701), 49.2% vs. 56.2% (p = 0.753), and 48.2% vs. 52.9% (p = 0.978). The annual hazards of local failure, respectively, peaked at 28.4% and 27.8% for IP-SCC and DN-SCC within 12 months after treatment. Afterward, the hazards gradually decreased and the hazard for IP-SCC was always higher before approaching null. Conclusions: This study provides novel evidence to support the clinical utility of improved distinction between IP-SCC and DN-SCC. Further studies are necessary to validate these findings before considering escalation of IP-SCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Yellow River basin (YRB) has played an important role in the forming of Chinese civilization. Located in the upper reaches of the YRB and the southeastern edge of the Qinghai–Tibet Plateau (QTP), the Gannan Plateau (GP), which consists of mainly alpine and mountain ecosystems, is one of the most important water conservation areas for the Yellow River and recharges 6.59 billion cubic meters of water to the Yellow River each year, accounting for 11.4% of the total runoff of the Yellow River. In the past 30 years, due to climate change and intense human activities, the GP is facing increasing challenges in maintaining its ecosystem integrity and security. Vegetation is a central component of the terrestrial ecosystem and is also key to maintaining ecosystem functioning and services. To form sound ecological restoration projects for the GP and the upper reaches of the YRB in general, this study assesses the trend in FVC (Fractional Vegetation Cover) and its drivers across the GP by integrating high-resolution satellite remote sensing images and meteorological data from 2000 to 2020. Results showed that the mean value of FVC for the entire GP between 2000 and 2020 was 89.26%. Aridity was found to be the main factor that determined the spatial distribution of FVC, while ecosystem type exhibited the secondary effect with forests having the highest FVC within each aridity class. From 2000 to 2020, the FVC in 84.11% of the study area did not exhibit significant change, though 10.32% of the study area still experienced a significant increase in FVC. A multi-factor analysis revealed that precipitation surpassed temperature as the main driver for the FVC trend in semi-arid and semi-humid areas, while this pattern was reversed in humid areas. A further residual analysis indicated that human activities only played a minor role in determining the FVC trend in most naturally vegetated areas of the study area, except for semi-arid crops where a significant positive role of human influences on the FVC trend was observed. The findings highlight the fact that aridity and vegetation types interact to explain the relative sensitivity of alpine and mountain ecosystems to climate trends and human influences. Results from this study provide an observational basis for better understanding and pattern prediction of ecosystem functioning and services in the GP under future climate change, which is key to the success of the national strategy that aims to preserve ecosystem integrity and promote high-quality development over the entire YRB. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000-2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America. [ABSTRACT FROM AUTHOR]

To improve the osteoconductivity of apatite cement (AC) for reconstruction of bone defects after oral maxillofacial surgery, we previously fabricated AC containing atelocollagen (AC(ate)). In the present study, we examined the initial attachment, proliferation and differentiation of mouse osteoblastic cells (MC3T3-E1 cells) on the surface of conventional AC (c-AC), AC(ate) and a plastic cell dish. The number of osteoblastic cells showing initial attachment to AC(ate) was greater than those attached to c-AC and similar to the number attached to the plastic cell wells. We also found that osteoblastic cells were well spread and increased their number on AC(ate) in comparison with c-AC and the wells without specimens, while the amount of procollagen type I carboxy-terminal peptide (PIPC) produced in osteoblastic cells after three days on AC(ate) was greater as compared to the others. There was no significant difference in regard to alkaline phosphatase (ALP) activity and osteocalcin production by osteoblastic cells among the three surface types after three and six days. However, after 12 days, ALP activity and the produced osteocalcin were greater with AC(ate). In conclusion, AC(ate) may be a useful material with high osteoconductivity for reconstruction of bone defects after oral maxillofacial surgery. [ABSTRACT FROM AUTHOR]

The development of artificial surfaces which can regulate or trigger specific functions of living cells, and which are capable of inducing in vivo-like cell behaviors under in vitro conditions has been a long-sought goal over the past twenty years. In this work, an alternative, facile and cost-efficient method for mass-producible cellular templates is presented. The proposed methodology consists of a cost-efficient, two-step, all-wet technique capable of producing ZnO-based nanostructures on predefined patterns on a variety of substrates. ZnO--apart from the fact that it is a biocompatible material--was chosen because of its multifunctional nature which has rendered it a versatile material employed in a wide range of applications. Si, Si3N4, emulated microelectrode arrays and conventional glass cover slips were patterned at the micrometer scale and the patterns were filled with ZnO nanostructures. Using HeLa cells, we demonstrated that the fabricated nanotopographical features could promote guided cellular adhesion on the pre-defined micron-scale patterns only through nanomechanical cues without the need for further surface activation or modification. The basic steps of the micro/nanofabrication are presented and the results from the cell adhesion experiments are discussed, showing the potential of the suggested methodology for creating low-cost templates for engineered cellular networks. [ABSTRACT FROM AUTHOR]

The mechanical behavior of aluminum hexagonal honeycombs subjected to out-of-plane dynamic indentation and compression loads has been investigated numerically using ANSYS/LS-DYNA in this paper. The finite element (FE) models have been verified by previous experimental results in terms of deformation pattern, stress-strain curve, and energy dissipation. The verified FE models have then been used in comprehensive numerical analysis of different aluminum honeycombs. Plateau stress, σpl, and dissipated energy (EI for indentation and EC for compression) have been calculated at different strain rates ranging from 10² to 104 s-1. The effects of strain rate and t/l ratio on the plateau stress, dissipated energy, and tearing energy have been discussed. An empirical formula is proposed to describe the relationship between the tearing energy per unit fracture area, relative density, and strain rate for honeycombs. Moreover, it has been found that a generic formula can be used to describe the relationship between tearing energy per unit fracture area and relative density for both aluminum honeycombs and foams. [ABSTRACT FROM AUTHOR]

The static and dynamic mechanical behaviour of cellular materials can be designed by the architecture of the underlying unit cell. In this paper, the phononic band structure of 2D and 3D cellular structures is investigated. It is shown how the geometry of the unit cell influences the band structure and eventually leads to full band gaps. The mechanism leading to full band gaps is elucidated. Based on this knowledge, a 3D cellular structure with a broad full band gap is identified. Furthermore, the dependence of the width of the gap on the geometry parameters of the unit cell is presented. [ABSTRACT FROM AUTHOR]

Designing scaffolds made from natural polymers may be highly attractive for tissue engineering strategies. We sought to produce and characterize chitosan-coated collagen membranes and to assess their efficacy in promoting chondrocyte adhesion, growth, and cytokine secretion. Porous collagen membranes were placed in chitosan solutions then crosslinked with glutaraldehyde vapor. Fourier transform infrared (FTIR) analyses showed elevated absorption at 1655 cm-1 of the carbon--nitrogen (N=C) bonds formed by the reaction between the (NH2) of the chitosan and the (C=O) of the glutaraldehyde. A significant peak in the amide II region revealed a significant deacetylation of the chitosan. Scanning electron microscopy (SEM) images of the chitosan-coated membranes exhibited surface variations, with pore size ranging from 20 to 50 μm. X-ray photoelectron spectroscopy (XPS) revealed a decreased C--C groups and an increased C--N/C--O groups due to the reaction between the carbon from the collagen and the NH2 from the chitosan. Increased rigidity of these membranes was also observed when comparing the chitosan-coated and uncoated membranes at dried conditions. However, under wet conditions, the chitosan coated collagen membranes showed lower rigidity as compared to dried conditions. Of great interest, the glutaraldehyde-crosslinked chitosan-coated collagen membranes promoted chondrocyte adhesion, growth, and interleukin (IL)-6 secretion. Overall results confirm the feasibility of using designed chitosan-coated collagen membranes in future applications, such as cartilage repair. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein. [ABSTRACT FROM AUTHOR]

Aircraft panel assembly mainly includes the pre-joining process and the riveting process. In addition, the traditional pre-joining process is mainly executed by bolts, which has problems such as the large tightening torque, inconvenient bilateral tightening, heavy workload, and inconvenient loading and unloading. To solve the above-mentioned problems, a research of new temporary fastener is performed deeply from three levels of quick installation, labor-saving, and reversible ability. This involves (a) employing the lever mechanism and the rapid expansion anchor to implement the rapid clamping and disassembly of working processes by labor-saving; (b) integrating the adjusting spring to overcome the tolerances of parts; and (c) building up the space-cross slide rails to provide the axial clamping forces and the reversible forces. The application of designed fasteners was employed into the production of aircraft panel, and the error between theoretical and experimental values was less than 10%. Besides this, the result showed the good effect in panel clamping and the reliable processes of loading and unloading installation, and will greatly reduce the complexity of pre-joining process, the difficulty of installation, and the comprehensive cost. [ABSTRACT FROM AUTHOR]