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The “sol–gel method” was used to prepare spherical chitosan-modified bentonite (SCB) hydrogels in this study. The SCB hydrogels were characterized and used as sorbents to remove tetracycline (TC) from aqueous solutions. The adsorbents were characterized by SEM, XRD, FTIR, TG, and BET techniques. Various characterization results showed that the SCB adsorbent had fewer surface pores and a specific surface area that was 96.6% lower than the powder, but the layered mesoporous structure of bentonite remained unchanged. The adsorption process fit to both the Freundlich model and the pseudo-second-order kinetic model showed that it was a non-monolayer chemical adsorption process affected by intra-particle diffusion. The maximum monolayer adsorption capacity determined by the Langmuir model was 39.49 mg/g. Thermodynamic parameters indicated that adsorption was a spontaneous, endothermic, and entropy-increasing process. In addition, solid–liquid separation was easy with the SCB adsorbent, providing important reference information for the synthesis of SCB as a novel and promising adsorbent for the removal of antibiotics from wastewater at the industrial level.

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R2 of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions.

To evaluate the earthquake disaster resistance capability of highway tunnels, it is necessary to study an index system for the earthquake disaster resistance capability of highway tunnels. This paper uses a literature research method. The damage of highway tunnels in 12 earthquake disasters recorded in detail in China and abroad was investigated. According to the types of hazard-beating bodies and damage forms, 17 seismic damage scenes of highway tunnels are classified. According to different damage scenes, the fault tree analysis model is used to identify the damage-causing factors. Combined with the industry standards, norms and research results related to the earthquake resistance of highway tunnels, the evaluation indices are analyzed from three perspectives: engineering geological factors, tunnel structural factors and operation management factors. According to the principle of index system construction, a four-level index system is constructed, which takes the earthquake disaster resistance capability of highway tunnels as the target layer; engineering geological factors, tunnel structural factors and operation management factors as the criterion layer indices; 12 indicators as the index layer and 35 specific indicators. The established index system for the earthquake disaster resistance capability of highway tunnels has strong scientific, rational and application value and can provide a reference for the evaluation of the earthquake disaster resistance capability of highway tunnels and can also provide important support for the safe operation and sustainable development of highway tunnels.

Micromechanical resonators have aroused growing interest as biological and chemical sensors, and microcantilever beams are the main research focus. Recently, a resonant microcantilever with an integrated heater has been applied in on-chip thermogravimetric analysis (TGA). However, there is a strong relationship between the mass sensitivity of a resonant microcantilever and the location of adsorbed masses. Different sampling positions will cause sensitivity differences, which will result in an inaccurate calculation of mass change. Herein, an integrated H-shaped resonant beam with uniform mass sensitivity and temperature distribution is proposed and developed to improve the accuracy of bio/chemical sensing and TGA applications. Experiments verified that the presented resonant beam possesses much better uniformity of sensitivity and temperature distribution compared with resonant microcantilevers. Gas-sensing and TGA experiments utilizing the integrated resonant beam were also carried out and exhibited good measurement accuracy.

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR.

The circular synthetic aperture radar (CSAR) has the ability of all-round continuous observation and high-resolution imaging detection, and can obtain all-round scattering information and higher-resolution images of the observation scene, so as to realize the target information extraction and three-dimensional (3D) contour reconstruction of the observation targets. However, the existing methods are not accurate enough to extract the information of vehicle targets. Through the analysis of the vehicle target scattering model and CSAR image characteristics, this paper proposes a vehicle target information extraction and 3D contour reconstruction method based on multiple different pitch-angle observation CSAR data. The proposed method creatively utilizes the projection relationship of the vehicle in 2D CSAR imaging to reconstruct the 3D contour of the vehicle, without prior information. Firstly, the CSAR data obtained from multiple different pitch-angle observations are fully utilized, and the scattering points of odd-bounce reflection and even-bounce reflection echoes are extracted from the two-dimensional (2D) coherent CSAR images of the vehicle target. Secondly, the basic contour of the vehicle body is extracted from the scattering points of the even-bounce reflected echoes. Then, the geometric projection relationship of the “top–bottom shifting” effect of odd-bounce reflection is used to calculate the height and position information of the scattering points of odd-bounce reflection, so as to extract the multi-layer 3D contour of the vehicle target. Finally, the basic contour and the multi-layer 3D contour of the vehicle are fused to realize high-precision 3D contour reconstruction of the vehicle target. The correctness and effectiveness of the proposed method are verified by using the CVDomes simulation dataset of the American Air Force Research Laboratory (AFRL), and the experimental results show that the proposed method can achieve high-precision information extraction and realize distinct 3D contour reconstruction of the vehicle target.

Polyetheramine (PEA) is a swelling inhibitor used to address engineering challenges arising from the interaction between montmorillonite (Mt) and water. This study comprehensively investigates the adsorption characteristics of PEA on three representative expansive clay samples: Na-Mt, Ca-Mt, and engineered expansive soil. Additionally, the desorption of exchangeable ions is examined. The findings reveal that a two-stage adsorption kinetic model and a pseudo-second-order kinetic model can properly describe the adsorption kinetics of PEA on expansive clays. PEA exhibits a strong capacity for ion exchange with sodium ions, while the exchange capacity for calcium ions is limited. Both protonated and non-protonated PEA contribute to rapid adsorption processes. The adsorption isotherms are well-fitted by the Langmuir and Freundlich models, with the Langmuir model being reasonable. At lower equilibrium concentrations, a higher proportion of the adsorption amount is attributed to ion exchange compared to higher equilibrium concentrations. Ion exchange emerges as the primary factor contributing to the adsorption of PEA on Na-Mt, whereas the adsorption of PEA on Ca-Mt and expansive soil is primarily attributed to physical adsorption by non-protonated PEA. X-ray diffraction results reveal significant intercalation effects of PEA as they penetrate the interlayer space and hinder interlayer ion hydration. Fourier transform infrared spectrum results demonstrate that the adsorption of PEA minimally impacts the framework of Mt structural units but primarily reduces the adsorbed water content. Clay-PEA composites exhibit a decreased affinity for water. Zeta potential experiments indicate that the adsorption of PEA significantly diminishes the surface potential of clay-PEA composite particles, effectively inhibiting their hydration dispersion.

Ship classification using the synthetic aperture radar (SAR) images has a significant role in remote sensing applications. Aiming at the problems of excessive model parameters numbers and high energy consumption in the traditional deep learning methods for the SAR ship classification, this paper provides an energy-efficient SAR ship classification paradigm that combines spiking neural networks (SNNs) with Siamese network architecture, for the first time in the field of SAR ship classification, which is called the Siam-SpikingShipCLSNet. It combines the advantage of SNNs in energy consumption and the advantage of the idea in performances that use the Siamese neuron network to fuse the features from dual-polarized SAR images. Additionally, we migrated the feature fusion strategy from CNN-based Siamese neural networks to the SNN domain and analyzed the effects of various spiking feature fusion methods on the Siamese SNN. Finally, an end-to-end error backpropagation optimization method based on the surrogate gradient has been adopted to train this model. Experimental results tested on the OpenSARShip2.0 dataset have demonstrated the correctness and effectiveness of the proposed SAR ship classification strategy, which has the advantages of the higher accuracy, fewer parameters and lower energy consumption compared with the mainstream deep learning method of the SAR ship classification.

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Silicon microneedle (Si-MN) systems are a promising strategy for transdermal drug delivery due to their minimal invasiveness and ease of processing and application. Traditional Si-MN arrays are usually fabricated by using micro-electro-mechanical system (MEMS) processes, which are expensive and not suitable for large-scale manufacturing and applications. In addition, Si-MNs have a smooth surface, making it difficult for them to achieve high-dose drug delivery. Herein, we demonstrate a solid strategy to prepare a novel black silicon microneedle (BSi-MN) patch with ultra-hydrophilic surfaces for high drug loading. The proposed strategy consists of a simple fabrication of plain Si-MNs and a subsequent fabrication of black silicon nanowires. First, plain Si-MNs were prepared via a simple method consisting of laser patterning and alkaline etching. The nanowire structures were then prepared on the surfaces of the plain Si-MNs to form the BSi-MNs through Ag-catalyzed chemical etching. The effects of preparation parameters, including Ag+ and HF concentrations during Ag nanoparticle deposition and [HF/(HF + H2O2)] ratio during Ag-catalyzed chemical etching, on the morphology and properties of the BSi-MNs were investigated in detail. The results show that the final prepared BSi-MN patches exhibit an excellent drug loading capability, more than twice that of plain Si-MN patches with the same area, while maintaining comparable mechanical properties for practical skin piercing applications. Moreover, the BSi-MNs exhibit a certain antimicrobial activity that is expected to prevent bacterial growth and disinfect the affected area when applied to the skin.

In order to solve the shortcomings of traditional TLDs (tuned liquid dampers), such as their narrow frequency bands and their occupation of large amounts of space, based on deep-water theory, a new type of TLD incorporating Karmen vortex street theory is proposed and named as KV-TLD. A shaking table experimental test is conducted to fully analyze its vibration damping characteristics and its effectiveness in terms of vibration control is confirmed. Through comparison with a traditional TLD, the superiority of KV-TLD is verified. By analyzing parameters such as the mass ratio and the frequency ratio, it is found that a larger mass ratio means a better damping effect. When the frequency ratio is close to 1.0, the damping effect is better, while the vibration damping advantage of KV-TLD is much lower. Good performance in terms of installation, a wide frequency band, and damping effect means that KV-TLD is superior in terms of actual engineering application possibilities, and can also act as a good reference for subsequent engineering projects.

Rheumatoid arthritis (RA) is an autoimmune polyarthritis in which synovial fibroblasts (SFs) play a major role in cartilage and bone destruction through tumor−like proliferation, migration, and invasion. Circular RNAs (circRNAs) have emerged as vital regulators for tumor progression. However, the regulatory role, clinical significance, and underlying mechanisms of circRNAs in RASF tumor−like growth and metastasis remain largely unknown. Differentially expressed circRNAs in synovium samples from patients with RA and patients with joint trauma were identified via RNA sequencing. Subsequently, in vitro and in vivo experiments were performed to investigate the functional roles of circCDKN2B−AS_006 in RASF proliferation, migration, and invasion. CircCDKN2B−AS_006 was upregulated in synovium samples from patients with RA and promoted the tumor-like proliferation, migration, and invasion of RASFs. Mechanistically, circCDKN2B−AS_006 was shown to regulate the expression of runt−related transcription factor 1 (RUNX1) by sponging miR-1258, influencing the Wnt/β−catenin signaling pathway, and promoting the epithelial−to−mesenchymal transition (EMT) in RASFs. Moreover, in the collagen−induced arthritis (CIA) mouse model, intra−articular injection of lentivirus−shcircCDKN2B−AS_006 was capable of alleviating the severity of arthritis and inhibiting the aggressive behaviors of SFs. Furthermore, the correlation analysis results revealed that the circCDKN2B−AS_006/miR−1258/RUNX1 axis in the synovium was correlated with the clinical indicators of RA patients. CircCDKN2B−AS_006 promoted the proliferation, migration, and invasion of RASFs by modulating the miR−1258/RUNX1 axis.

Groundwater-surface water interactions play a crucial role in hydrologic cycles, especially in arid and semi-arid basins. There is a growing interest in developing integrated hydrologic models to describe groundwater-surface water interactions and the associated processes. In this study, an integrated process-based hydrologic model, ParFlow, was tested and utilized to quantify the hydrologic responses, such as changes in surface runoff and surface/subsurface storage. We progressively conducted a complexity-increasing series of benchmarking cases to assess the performance of ParFlow in simulating overland flow and integrated groundwater-surface water exchange. Meanwhile, the overall performance and the computational efficiency were quantitatively assessed using modified Taylor diagrams. Based on the benchmarking cases, two case studies in the Heihe River Basin were performed for further validation and to diagnose the hydrologic responses under disturbance, named the Bajajihu (BJH) and Dayekou (DYK) cases, respectively. Both cases were 2D transects configured with in-situ measurements in the mid- and downstream of the Heihe River Basin. In the BJH case, simulated soil moisture by ParFlow was shown to be comparable with in-situ observations in general, with Pearson’s correlation coefficient (R) > 0.93 and root mean square difference (RMSD) < 0.007. In the DYK case, seven scenarios driven by remote sensing and reanalysis data were utilized to study hydrological responses influenced by natural physical processes (i.e., precipitation) and groundwater exploitations (i.e., pumping) that are critical to surface and subsurface storage. Results show that subsurface storage is sensitive to groundwater exploitation before an obvious stationary point. Moreover, a correlation analysis was additionally provided demonstrating the impacts of different factors on subsurface storage timeseries. It was found that pumping influences subsurface storage remarkably, especially under short-term but large-volume pumping rates. The study is expected to provide a powerful tool and insightful guidance in understanding hydrological processes’ effects in arid and semi-arid basins.

Non-rare earth doped oxide phosphors with far-red emission have become one of the hot spots of current research due to their low price and excellent physicochemical stability as the red component in white light-emitting diodes (W-LEDs) and plant growth. Herein, we report novel Mn4+-doped La2CaSnO6 and La2MgSnO6 phosphors by high-temperature solid-phase synthesis and analyzed their crystal structures by XRD and Rietveld refinement. Their excitation spectra consist of two distinct excitation bands with the dominant excitation range from 250 to 450 nm, indicating that they possess strong absorption of near-ultraviolet light. Their emission is located around 693 and 708 nm, respectively, and can be absorbed by the photosensitive pigments Pr and Pfr, proving their great potential for plant growth. Finally, the prepared samples were coated with 365 nm UV chips to fabricate far-red LEDs and W-LEDs with low correlation color temperature (CCT = 4958 K/5275 K) and high color rendering index (Ra = 96.4/96.6). Our results indicate that La2CaSnO6:Mn4+ and La2MgSnO6:Mn4+ red phosphors could be used as candidate materials for W-LED lighting and plant growth.

Hepatocellular carcinoma (HCC) is one of the cancers with the highest morbidity and mortality in the world. However, clinical progress in the treatment of HCC has not shown a satisfactory therapeutic effect. Here, we have developed a novel strategy to treat HCC with an adenovirus (Ad)-based vaccine, which contains a specific antigen glypican-3 (GPC3) and an immunostimulatory cytokine IL-12. In the subcutaneous tumor model, Ad-IL-12/GPC3 vaccine was injected into muscles three times to evaluate its therapeutic effect. Compared with the control immunization group, the Ad-IL-12/GPC3 immunization group showed a significant tumor growth inhibition effect, which was confirmed by the reduced tumor volume and the increased tumor inhibition. Ad-IL-12/GPC3 co-immunization promoted the induction and maturation of CD11c+ or CD8+CD11c+ DCs and increased the number of tumor-infiltrating CD8+ T cells. Furthermore, in the Ad-IL-12/GPC3 group, the proliferation of CD8+ T cells, the induction of multifunctional CD8+ T cells, and CTL activity were significantly increased. Interestingly, the deletion of CD8+ T cells abolished tumor growth inhibition by Ad-IL-12/GPC3 treatment, suggesting that CD8+ T cell immune responses were required to eliminate the tumor. Likewise, Ad-IL-12/GPC3 vaccine also effectively inhibited lung tumor growth or metastasis by enhancing CD8+ DCs-mediated multifunctional CD8+ T cell immune responses in the lung metastasis model. Therefore, these results indicate that IL-12 combined with Ad-GPC3 vaccine co-immunization might provide a promising therapeutic strategy for HCC patients.

Recently, weakly supervised object detection (WSOD) with image-level annotation has attracted great attention in the field of computer vision. The problem is often formulated as multiple instance learning in the existing studies, which are often trapped by discriminative object parts and fail to localize the object boundary precisely. In this work, we alleviate this problem by exploiting contextual information that may potentially increase object localization accuracy. Specifically, we propose novel context proposal mining strategies and a Symmetry Context Module to leverage surrounding contextual information of precomputed region proposals. Both naive and Gaussian-based context proposal mining methods are adopted to yield informative context proposals symmetrically surrounding region proposals. Then mined context proposals are fed into our Symmetry Context Module to encourage the model to select proposals that contain the whole object, rather than the most discriminative object parts. Experimental results show that the mean Average Precision (mAP) of the proposed method achieves 52.4% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-art methods and demonstrating its effectiveness for weakly supervised object detection.

Accurate and efficient reconstruction of hidden geological structures under the surface is the main task of high-resolution Velocity Model Building (VMB). The most commonly used methods in practice are Tomography and Full Waveform Inversion (FWI), which rely heavily on the initial model. Recently, deep learning types of methods have received widespread attention and have performed well in many tasks such as image segmentation and classification. Therefore, it is of great significance to introduce deep learning algorithms into the VMB procedure to accelerate the production cycle, especially for the velocity anomalies detection, which is crucial for a high-resolution initial model. In this paper, a modified U-Net framework is proposed and applied directly on the seismic shot gathers to identify anomalies in the early stage of VMB, which can provide a suitable initial guess for the following large-scale VMB procedures such as FWI. The numerical examples show the power of the proposed method on synthetic data.

The low-earth-orbit synthetic aperture radar (SAR) can achieve enhanced remote-sensing capabilities by exploiting the large-scale and long-duration beam coverage of a geosynchronous (GEO) SAR illuminator. Different bistatic imaging modes can be implemented by the steering of an antenna beam onboard the LEO receiver, such as high-resolution sliding-spotlight mode. In this paper, the accurate focusing of GEO-LEO bistatic SAR (GEO-LEO BiSAR) in sliding-spotlight mode is investigated. First, the two major problems of the accurate bistatic range model, i.e., curved trajectory within long integration time and ‘stop-and-go’ assumption error, for sliding-spotlight GEO-LEO BiSAR are analyzed. Then, a novel bistatic range model based on equivalent circular orbit trajectory is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode. Based on the proposed range model, a frequency-domain imaging method is put forward. First, a modified two-step preprocessing method is implemented to remove the Doppler aliasing caused by azimuth variance of Doppler centroid and beam steering. Then, an azimuth trajectory scaling is formulated to remove the azimuth variance of motion parameters due to curved trajectory. A modified frequency-domain imaging method is derived to eliminate the 2-D spatial variance and achieve accurate focusing of the echo data. Finally, imaging results and analysis on both simulated data and real data from an equivalent BiSAR experiment validate the effectiveness of the proposed method.

Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, ρ neighbor voting and ρ neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors’ voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.

In order to make full use of the advantages of PD (particle damper) and TLD (tuned liquid damper) technologies, a new kind of damping system combining these two already-existing dampers is proposed and was named as PDAL (tuned particle damper with additional liquid). A shaking table test of a steel frame structure with a PDAL system is conducted here for the purpose of vibration control analysis. The results of the test demonstrate well the reliability and effectiveness of the PDAL system under various seismic waves. Seismic responses (mainly acceleration value) are investigated thoroughly for parameter analysis based on the experimental data, and some suggestions are proposed for future designs, including the necessity for parameter optimization and awareness of the dynamic characteristic changes that might occur in actual structures if attached with a PDAL system. This paper constitutes a preliminary study for the PDAL system, and it can serve as a baseline and conceptual reference for future investigations.

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.

This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm.

Lymphoid enhancer-binding factor 1 (LEF1) is a key transcription factor mediating the Wnt signaling pathway. LEF1 is a regulator that is closely associated with tumor malignancy and is usually upregulated in cancers, including colonic adenocarcinoma. The underlying molecular mechanisms of LEF1 regulation for colonic adenocarcinoma progression remain unknown. To explore it, the LEF1 expression in caco2 cells was inhibited using an shRNA approach. The results showed that downregulation of LEF1 inhibited the malignancy and motility associated microstructures, such as polymerization of F-actin, β-tubulin, and Lamin B1 in caco2 cells. LEF1 inhibition suppressed the expression of epithelial/endothelial-mesenchymal transition (EMT) relevant genes. Overall, the current results demonstrated that LEF1 plays a pivotal role in maintaining the malignancy of colonic adenocarcinoma by remodeling motility correlated microstructures and suppressing the expression of EMT-relevant genes. Our study provided evidence of the roles LEF1 played in colonic adenocarcinoma progression, and suggest LEF1 as a potential target for colonic adenocarcinoma therapy.

Iron pyrite is a cheap, stable, non-toxic, and earth-abundant material that has great potential in the field of photovoltaics. Electrochemical deposition is a low-cost method, which is also suitable for large-scale preparation of iron pyrite solar cells. In this work, we prepared iron pyrite films by electrochemical deposition with thiourea and explored the effect of sulfurization on the synthesis of high-quality iron pyrite films. Upon sulfurization, the amorphous precursor film becomes crystallized iron pyrite film. Optical and electrical characterization show that its band gap is 0.89 eV, and it is an n type semiconductor with a carrier concentration of 3.01 × 1019 cm−3. The corresponding photovoltaic device shows light response. This work suggests that sulfurization is essential in the electrochemical preparation for fabricating pure iron pyrite films, and therefore for low-cost and large-scale production of iron pyrite solar cells.

The electrochemical carbon dioxide reduction reaction (CO2RR) to C2 chemicals has received great attention. Here, we report the cuprous oxide (Cu2O) nanocubes cooperated with silver (Ag) nanoparticles via the replacement reaction for a synergetic CO2RR. The Cu2O-Ag tandem catalyst exhibits an impressive Faradaic efficiency (FE) of 72.85% for C2 products with a partial current density of 243.32 mA·cm−2. The electrochemical experiments and density functional theory (DFT) calculations reveal that the introduction of Ag improves the intermediate CO concentration on the catalyst surface and meanwhile reduces the C-C coupling reaction barrier energy, which is favorable for the synthesis of C2 products.

Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.

The in-plane biaxial specimen can well reflect the complex stress state of sheet metal. However, there is no standard for small-sized specimens used in the low-power biaxial fatigue testing machine. The main goal of this paper is to apply the finite element method and orthogonal experiment method to design the cruciform specimen, considering the influence of three main parameters including the diameter of the central semispherical thinning area, the minimum thickness of the center and the arm thickness of the specimen. According to the central strain dispersion coefficient and the strain concentration coefficient proposed in this paper, we ensured that the distribution of strain in the gauge area is uniform and the strain it at its maximum value at the same time. The optimized specimen is verified by a biaxial fatigue test with the digital image correlation (DIC) technique. It is found that the fatigue crack appears in the center region, which proves that the optimized specimen can be effectively used for biaxial fatigue test.

Weakly supervised and semi-supervised semantic segmentation has been widely used in the field of computer vision. Since it does not require groundtruth or it only needs a small number of groundtruths for training. Recently, some works use pseudo groundtruths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the pseudo groundtruths in this paper, and then we train the network based on a modified U-net model with the generated pseudo groundtruths, finally we utilize a small amount of groundtruths to fine tune the model. Extensive experiments on the challenging RIM-ONE and DRISHTI-GS benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE and DRISHTI-GS databases.

The biaxial tensile testing of cruciform specimens is an effective way to create complex loading, and is a feasible experimental method for studying the subsequent yield behavior. However, relevant knowledge gaps still exist in the geometric design of miniaturized cruciform specimens which are applicable to test machines with maximum load less than 5000 N. The present work outlines the systematic investigations of the optimal design of the miniaturized cruciform specimen of a commercial pure titanium TA2 for biaxial tensile testing. Finite element modeling (FEM) coupled with the orthogonal design is employed to explore the influence of various geometric parameters, i.e., the thickness of the central gauge region, the width, the length, and the number of the slit, and the radius of the inner chamfer, on the stress distribution of the central gauge region. The optimal geometric design of the miniaturized cruciform specimen is successfully obtained, simultaneously considering the stress uniformity in the central gauge region and economic factors. The full-field strain distributions are also determined via the digital image correction (DIC) technique, which confirm the accuracy of the results achieved from FEM. This work provides a complete and reliable procedure for optimizing the geometry of miniaturized cruciform specimens, whose application can be expanded to other metals in the future.

The hardness, toughness, wear resistance, and fatigue behavior of materials can be improved through a deep cryogenic treatment (DCT). During this treatment, low temperatures (−100 °C to −196 °C) are maintained and then increased to room or higher. In this work, an indirect-extrusion plate of 7A99 ultra-high strength aluminum alloy was subjected to a T6 (peak aging) treatment and a T6-DCT treatment. The influence of the T6-DCT treatment on the mechanical properties, grain morphologies, precipitates, and atom−cluster distribution was investigated via tensile testing, electron backscatter diffraction, transmission electron microscopy, and three-dimensional atom probe analysis. The tensile strength (maximum: 705 deep cryogenic treatment), yield strength (maximum: 678 MPa), and elongation (maximum: 14%) of the T6-DCT-treated alloy were higher than those of the T6-treated alloy. Moreover, the T6-DCT treatment resulted in (i) grain size refinement and increased uniformity of the microstructure (homogeneous distribution of η’-MgZn2- and η-phase precipitates), and (ii) reduced segregation degree of Zn, Mg, and Cu atoms in the matrix (fraction of small atom clusters (sizes: 10−20 nm, 20−50 nm) increased, fraction of large clusters (size: >1,000 nm) decreased). Therefore, DCT can refine the precipitates and increase the uniformity of the precipitate distribution, thereby improving the strength and plasticity of the alloy.

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

The swelling properties of expansive soil at different depths were investigated by the laboratory model test. It was experimentally found that the swelling deformation of different expansive soil layers was obviously different under unidirectional seepage. Pore solution collection boxes were designed to collect the pore solution of different soil layer interfaces during the saturation process. Cation concentration tests were then used to analyze the migration regulation of cations in these soil layers. Results showed that the divalent cations in the surface soil layer migrated downward along with water molecules and aggregated around clay particles to form the electrical environment in expansive soil. Additionally, swelling tests were performed to compare with their initial values to research the variation of swelling behavior of expansive soil at different depths after completion of the model test. It was observed that the swelling potential of the surface expansive soil layer at a depth of 0–30 mm was released while that of the middle and bottom ones at depths of 90–120 mm and 180–210 mm was inhibited. An analysis on the above results was used to clarify the mechanism controlling the swelling difference of different expansive soil layers. This swelling difference may be explained by the joint effect of two factors that can be treated as an electric charge effect, namely (i) the modification effect, (ii) the electrical environment.

We examine the 2002 Yakutsk wildfire event and simulate the impacts of smoke aerosols on local radiative energy budget, using the WRF-Chem-SMOKE model. When comparing satellite retrievals (the Surface Radiation Budget (SRB) dataset) with model simulations, we found that the agreement is generally good, except for the regions where the model predicts too few clouds or SRB misclassifies strong smoke plumes as clouds. We also found that the smoke-induced changes in upward shortwave fluxes at top of atmosphere (TOA) vary under different burning and meteorological conditions. In the first period of the fire season (9–12 August), smoke particles cause a warming effect around 3 W/m2, mainly through functioning as ice nuclei, which deplete the cloud water amount in the frontal system. At the beginning of the second period of the fire season (19–20 August), large amounts of pre-existing smoke particles cause a strong cooling effect of −8 W/m2. This is offset by the warming effect caused by relatively small amounts of cloud condensation nuclei increases, which promotes the rain formation and depletes the cloud water amount. After the cloud decks are well mixed with smoke plumes (21–22 August), the first indirect and direct effects of smoke together lead to a cooling of −10 W/m2. These results highlight the importance of meso-scale modeling efforts in estimating the smoke-induced changes in the radiative energy budget over high latitudes.

Direct propylene epoxidation using Au-based catalysts is an important gas-phase reaction and is clearly a promising route for the future industrial production of propylene oxide (PO). For instance, gold nanoparticles or clusters that consist of a small number of atoms demonstrate unique and even unexpected properties, since the high ratio of surface to bulk atoms can provide new reaction pathways with lower activation barriers. Support materials can have a remarkable effect on Au nanoparticles or clusters due to charge transfer. Moreover, Au (or Au-based alloy, such as Au–Pd) can be loaded on supports to form active interfacial sites (or multiple interfaces). Model studies are needed to help probe the underlying mechanistic aspects and identify key factors controlling the activity and selectivity. The current theoretical/computational progress on this system is reviewed with respect to the molecular- and catalyst-level aspects (e.g., first-principles calculations and kinetic modeling) of propylene epoxidation over Au-based catalysts. This includes an analysis of H2 and O2 adsorption, H2O2 (OOH) species formation, epoxidation of propylene into PO, as well as possible byproduct formation. These studies have provided a better understanding of the nature of the active centers and the dominant reaction mechanisms, and thus, could potentially be used to design novel catalysts with improved efficiency.

This Special Issue (SI) of Applied Sciences on Development and Application of NonlinearDissipative Devices in Structural Vibration Control contains papers that focus on the developmentand application of innovative nonlinear dissipative systems that mitigate the potentially catastrophiceffects of extreme loading by incorporating new materials or effective mechanical control technologies [...]

Tuned mass damper (TMD) has a wide application in the human-induced vibration control of pedestrian bridges and its parameters have great influence on the control effects, hence it should be well designed. A new optimization method for a TMD system is proposed in this paper, based on the artificial fish swarm algorithm (AFSA), and the primary structural damping is taken into consideration. The optimization goal is to minimize the maximum dynamic amplification factor of the primary structure under external harmonic excitations. As a result, the optimized TMD has a smaller maximum dynamic amplification factor and better robustness. The optimum TMD parameters for a damped primary structure with different damping ratios and different TMD mass ratios are summarized in a table for simple, practical design, and the fitting equation is also provided. The TMD configuration optimized by the proposed method was shown to be superior to that optimized by other classical optimization methods. Finally, the application of an optimized TMD based on AFSA for a pedestrian bridge is proposed as a case study. The results show that the TMD designed based on AFSA has a smaller maximum dynamic amplification factor than the TMD designed based on the classic Den Hartog method and the TMD designed based on the Ioi Toshihiro method, and the optimized TMD has a good effect in controlling human-induced vibrations at different frequencies.

It is well known that the Faraday rotation (FR) is obviously embedded in spaceborne polarimetric synthetic aperture radar (PolSAR) data at L-band and lower frequencies. By model inversion, some widely used FR angle estimators have been proposed for compensation and provide a new field in high-resolution ionospheric soundings. However, as an integrated product of electron density and the parallel component of the magnetic field, FR angle measurements/observations demonstrate the ability to characterize horizontal ionosphere. In order to make a general study of ionospheric structure, this paper reconstructs the electron density distribution based on a modified two-dimensional computerized ionospheric tomography (CIT) technique, where the FR angles, rather than the total electron content (TEC), are regarded as the input. By using the full-pol (full polarimetric) data of Phase Array L-band Synthetic Aperture Radar (PALSAR) on board Advanced Land Observing Satellite (ALOS), International Reference Ionosphere (IRI) and International Geomagnetic Reference Field (IGRF) models, numerical simulations corresponding to different FR estimators and SAR scenes are made to validate the proposed technique. In simulations, the imaging of kilometer-scale ionospheric disturbances, a spatial scale that is rarely detectable by CIT using GPS, is presented. In addition, the ionospheric reconstruction using SAR polarimetric information does not require strong point targets within a SAR scene, which is necessary for CIT using SAR imaging information. Finally, the effects of system errors including noise, channel imbalance and crosstalk on the reconstruction results are also analyzed to show the applicability of CIT based on spaceborne full-pol SAR data.

In order to study the base pounding effects of base-isolated structure under earthquake excitations, a base pounding theoretical model with a linear spring-gap element is proposed. A finite element analysis program is used in numerical simulation of seismic response of based-isolated structure when considering base pounding. The effects of the structure pounding against adjacent structures are studied, and the seismic response of a base-isolated structure with lead-rubber bearing and a base-isolated structure with friction pendulum isolation bearing are analyzed. The results indicate that: the model offers much flexibility to analyze base pounding effects. There is a most clearance unfavorable width between adjacent structures. The structural response increases with pounding. Significant amplification of the story shear-force, velocity, and acceleration were observed. Increasing the number of stories in a building leads to an initial increase in impact force, followed by a decrease in such force. As a result, it is necessary to consider base pounding in the seismic design of base-isolated structures.

Polyurethane-steel sandwich structure, which creatively uses the polyurethane-steel sandwich composite as a structural material, is proposed to strengthen the impact resistance of buildings under debris flow. The impact resistance of polyurethane-steel sandwich structure under debris flow is investigated by a series of impact loading tests, compared with that of traditional steel frame structures. Additionally, further discussions regarding the hidden mechanism are performed. During the whole impact process, as for steel frame structure, the impacted column appeared obvious local deformation both at its column base and on the impact surface, leading to remarkable decrease of its impact resistance; while the stress and strain of polyurethane-steel sandwich structure develops more uniformly and distribute further in the whole structure, maintaining excellent integrity and impact transmission capability. The impact loading tests confirm that polyurethane-steel sandwich structure possesses superior impact resistance under debris flow. This is of great practical significance for the prevention and reduction of geological disasters.

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

Simulating the impact of debris flows on structures and exploring the feasibility of applying energy dissipation devices or shock isolators to reduce the damage caused by debris flows can make great contribution to the design of disaster prevention structures. In this paper, we propose a new type of device, a lateral damping buffer, to reduce the vulnerability of building structures to debris flows. This lateral damping buffer has two mechanisms of damage mitigation: when debris flows impact on a building, it acts as a buffer, and when the structure vibrates due to the impact, it acts as a shock absorber, which can reduce the maximum acceleration response and subsequent vibration respectively. To study the effectiveness of such a lateral damping buffer, an impact test is conducted, which mainly involves a lateral damping buffer attached to a two-degree-of-freedom structure under a simulated debris flow load. To enable the numerical study, the equation of motion of the structure along with the lateral damping buffer is derived. A subsequent parametric study is performed to optimize the lateral damping buffer. Finally, a practical design procedure is also provided.