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Object detection is a focal point in remote sensing applications. Remote sensing images typically contain a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate feature representation in complex image small object detection tasks. Multiscale detection methods improve the speed and accuracy of detection, while small objects themselves contain limited information, and the features are easily lost following down-sampling. During the detection, the feature images are independent across scales, resulting in a discontinuity at the detection scale. In this article, we propose the multiscale context and enhanced channel attention (MSCCA) model. MSCCA employs PeleeNet as the backbone network. In particular, the feature image channel attention is enhanced and the multiscale context information is fused with multiscale detection methods to improve the characterization ability of the convolutional neural network. The proposed MSCCA method is evaluated on two real datasets. Results show that for 512 × 512 input images, MSCCA was able to achieve 80.4% and 94.4% mAP on the DOTA and NWPU VHR-10, respectively. Meanwhile, the model size of MSCCA is 21% smaller than that of its predecessor. MSCCA can be considered as a practical lightweight oriented object detection model in remote sensing images.

In this study, we report the utilization of different water contents (0–40 vol.%) as cosolvent to ethaline, ChCl:EG (deep eutectic solvent) at the mole ratio of 1:2 to prepare binary oxide of tungsten oxide-molybdenum oxide nanocomposite in an ambient environment, followed by characterization using XRD, FTIR, SEM, TEM, TGA, XPS and UV–vis spectroscopy. The as-prepared composite composed of aggregated particles at low temperature of 60 °C, in the crystallite sizes ranging from ˜48.40 to 71.97 nm, with water cosolvent acting as bad solvent. Further characterizations revealed that the atomic percentage ratios of tungsten and molybdenum components increase with decrease in carbon content as the water content increases, while the morphology changes from cubic to prismatic polyhedral-like structures at low temperature, in addition to retaining ˜73% of its original weight above 700 °C. The UV–vis analysis revealed broad absorption at 239–374 nm with a threshold value of 527 nm and can attain maximum photochromic response in less 60 s on exposure to UV–vis light irradiation.

N,N-dimethylformamide (DMF) is a commonly-used solvent in industry and pharmaceutics for extracting acetylene and fabricating polyacrylonitrile fibers. It is also a starting material for a variety of intermediates such as esters, pyrimidines or chlordimeforms. However, after being used, DMF can be form 5–25% spent liquors (mass fraction) that are difficult to recycle with distillation. From the point of view of energy-efficiency and environment-friendliness, an emergent separation technology, pervaporation, is broadly applied in separation of azeotropic mixtures and organic–organic mixtures, dehydration of aqueous–organic mixtures and removal of trace volatile organic compounds from aqueous solutions. Since the advances in membrane technologies to separate N,N-dimethylformamide solutions have been rarely reviewed before, hence this review mainly discusses the research progress about various membranes in separating N,N-dimethylformamide aqueous solutions. The current state of available membranes in industry and academia, and their potential advantages, limitations and applications are also reviewed.

The mesoporous SBA-15 has long been used as an adsorbent for the removal of toxic metal ions. Achieving secondary utilize of spent adsorbent is undoubtedly an advisible option for environmental pro...

In the presented work, a series of Cr/β zeolites were prepared and used as bifunctional catalysts with Bronsted and Lewis acidity in the conversion of biomass-derived carbohydrates to 5‑hydroxymethylfurfural (HMF). The physicochemical properties of all catalysts were characterized by ICP-AES, N2 physisorption, solid-state 27Al NMR, XRD, SEM-EDX, UV–Vis, XPS, Raman, NH3-TPD and Py-FTIR techniques. It was found that the catalysts with appropriate total acidity, ratio of Lewis to Bronsted acid sites and acid density appear to be beneficial for HMF formation. Under mild reaction conditions, a superior 72% HMF yield with 83% selectivity was achieved from glucose in the H2O/THF biphasic system by using the 0.4-Cr/β zeolite catalyst. Moreover, the results of the stability evaluation showed that the catalytic activity of the 0.4-Cr/β zeolite was not significantly lost after four consecutive cycles, and the framework remained intact. Therefore, the catalytic approach proposed in this study is believed to provide potential prospects for the production of HMF from biomass material refining in the future.

The present work is aimed to investigate the catalytic activities of various Mn-based MOFs for carbon dioxide coupling with epoxides. The metal organic frameworks, [(CH3NH3][Mn(COOH)3] (MA-MnF), [(CH3CH2NH3][Mn(COOH)3] (EA-MnF), and [C3H5N2][Mn(COOH)3] (Im-MnF) were studied via several characterizations such as XRD, FT-IR, XPS, N2-adsorption, TG-DSC, CO2-adsorption and NH3-TPD. Interestingly, Im-MnF compound was observed to possess the highest catalytic activity among the studied compounds, which is associated not only with the amounts of basic sites, but also related to the nitrogen-containing species. 97.27% conversion of allyl-glycidyl ether (AGE, TOF: 36.78 h−1) and 97.66% selectivity to allyl-glycidyl carbonate (AGC) were obtained under the explored optimized conditions (100 °C, 15 bar, 6 h, 1.0 wt% of AGE). In addition, only a slight downward in catalytic activity was found when the sample was reused twice. Furthermore, coupling reactions of CO2 with various epoxides were also performed, of which, the yield of the cyclic carbonates followed the order: Epichlorohydrin > Allyl glycidyl ether > Styrene oxide > Cyclohexene oxide > Propylene oxide. Finally, a mechanism was proposed, which is in good agreement with the DFT calculation.

It is highly desirable to develop a heterogeneous catalyst that is both efficient and recyclable for the simultaneous acquisition of 5-hydroxymethylfurfural (HMF) and furfural from lignocellulose feedstock. Herein, a series of (x)SnCl2-PTA/β catalysts (x = 10, 15, 20, and 25 wt %) were prepared and characterized using ICP-AES, XRD, SEM, N2 adsorption–desorption, XPS, NH3-TPD, and Py-FTIR techniques. It was found that (20)SnCl2-PTA/β catalyst having a suitable acid density, acid ratio, and texture structure contributed to the furan compound formation from carbohydrate components of wheat straw. Under optimal conditions, up to 71% of furfural and 30% of HMF were simultaneously obtained from wheat straw in a water/tetrahydrofuran biphasic system, while the byproducts formed were identified as soluble C4–C15 molecules and insoluble humins. More importantly, the (20)SnCl2-PTA/β catalyst was still capable of providing moderate furan compounds yields after four cycles, and its catalytic performance could be part...

The pyrolysis of cyanobacteria provides a promising path for the production of nitrogenous compounds. In this work, several catalysts were prepared and applied to the cyanobacteria pyrolysis. The result showed that the synergistic effect between the acidic catalyst and methanol contributes to the formation of bio-oils and nitrogenous compounds. The yield of nitrogenous compounds increased from 46.79% to over 70% with the help of methanol and an acidic catalyst. Pyrolysis over Ni–Al layered double oxide loaded MCM-41 (NiAl-LDO/MCM-41) provided an optimum bio-oil yield of 26.02%, and 25.82% of the nitrogen in the cyanobacteria was transferred into the bio-oil. Nitriles are the main components in nitrogenous compounds and pentadecanenitrile is the major component of nitriles. Although the bio-oil yield upon pyrolysis over Al2O3 loaded MCM-41 (Al2O3/MCM-41) was only 21.18%, the concentration of nitrogenous compounds in the bio-oil was up to 92.59%, and 87.58% of the bio-oil was made of nitriles, whereas pentadecanenitrile accounted for 78.92%. These results gave us a potential way to obtain nitrogenous compounds such as pentadecanenitrile from renewable resources.

Zn2(C9H3O6)(C4H5N2)(C4H6N2)3 (Zn-BTC-2MeIm) was hydrothermally synthesized and employed as a highly efficient catalyst for CO2 coupling with epichlorohydrin (ECH). Various techniques such as XRD, FT-IR, XPS, TG-DTG, NH3/CO2-TPD were employed for characterizing the compound. Interestingly, TG profile illustrated the Zn-BTC-2MeIm was stable above 200 °C. The highest catalytic activity of 98.93% conversion of ECH and 98.32% selectivity to chloropropene carbonate was observed under the optimum conditions (100 °C, 120 mesh, 1000 rpm, 3.0 MPa, 6 h, 0.75 wt.% of ECH). Besides, the recyclability results exhibited Zn-BTC-2MeIm compound could be reused no less than three times with a slight reduction in its catalytic ability. Moreover, coupling results of CO2 with other epoxides showed this compound could efficiently convert various epoxides into cyclic carbonates. Finally, the investigation of the kinetic exhibited the law of CO2 coupling with ECH was coincident with the first order kinetic and the activation energy (Ea) was to be 113.38 kJ/mol.

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

The promulgation of Internet-of-Things technologies requires higher energy efficiency than the past. Approximate computing is a promising computation paradigm in the post-Moore era. It seeks a subtle balance between computation accuracy and many other metrics, especially power consumption. Thereinto, approximate adders are important because addition is the essential operation in most applications. In this article, we propose a runtime accuracy reconfigurable adder with the accurate mode and 16 approximate modes with different accuracy. The statistical error model of the adder is built on two typical dataflow graphs: the adder chain and adder tree. Then, we introduce two methods to compensate for the accuracy loss based on the error model: Input Gating and Dataflow Reorganization. Our proposed adder achieves higher configuration flexibility with much less area overhead. The experiment results show our methods can make average output error reduce up to 61% without energy cost.

This present work aimed to investigate the influence of CO2 on HCl removal using calcined Ca&ndash, Mg&ndash, Al layered double hydroxides (CaMgAl-LDHs) at medium&ndash, high temperature (400&ndash, 800 &deg, C) in a fixed-bed reactor. It was revealed that a moderate CO2 concentration (~6%) in the flue gas of the municipal solid-waste incinerators could reduce the HCl capacity of the CaMgAl-layered double oxides (CaMgAl-LDOs). The highest capacity for HCl removal was observed over the CaMgAl-LDOs at 600 &deg, C. However, sintering was also detected when the reaction temperature was below the calcination temperature (600 &deg, C). Moreover, the decreasing HCl adsorption capacity of CaMgAl-LDOs was attributed to the existence of CO2 in the flue gas, which could efficiently inhibit the decomposition of carbonates as well as the conversion into metal chloride during the HCl removal process.

Gain redshift in high peak power Ti:sapphire laser systems can result in narrowband spectral output and hence lengthen the compressed pulse duration. In order to realize broadband spectral output in 10 PW-class Ti:sapphire lasers, the influence on gain redshift induced by spectral pre-shaping, gain distribution of cascaded amplifiers and Extraction During Pumping (EDP) technique have been investigated. The theoretical and experimental results show that the redshift of output spectrum is sensitive to the spectral pre-shaping and the gain distribution of cascaded amplifiers, while insensitive to the pumping scheme with or without EDP. Moreover, the output spectrum from our future 10 PW Ti:sapphire laser is theoretically analyzed based on the investigations above, which indicates that a Fourier-transform limited (FTL) pulse duration of 21 fs can be achieved just by optimizing the spectral pre-shaping and gain distribution in 10 PW-class Ti:sapphire lasers.

The present work aimed to investigate the law of the conversion of glycerol with diethyl carbonate (DEC) over the highly efficient Ce–NiO catalyst prepared by co-precipitation method. The thermodynamic and kinetics parameters of this reaction for glycerol carbonate synthesis were studied. The reaction equilibrium constants were investigated experimentally in the temperature region of 338–358 K, and the standard molar properties (at 298 K) were obtained (△H0= 115.71 kJ mol−1, △G0= 13.523 kJ mol−1, △S0= 0.343 kJ mol−1). Effects of various experimental conditions including stirring speed, reaction temperature, catalyst amount, reactant mole ratio on the reaction kinetics were also studied. Different order kinetic equations were used to simulate law of the transesterification process. The result shows a three-order equation of glycerol (two orders) and DEC (one order) was more appropriate to interpret the law of glycerol transesterification with diethyl carbonate. The activation energy of this reaction was obtained with 87.90 kJ mol−1. Finally, the microstructure and physicochemical properties of the Ce–NiO samples were studied by XRD, SEM, TEM, and CO2-TPD. One-pot catalytic conversion of glycerol into glycerol carbonate over Ce–NiO catalyst.

Interest on anomaly detection for hyperspectral images has increasingly grown during the last decades due to the diversity of applications that benefit from this technique. However, the high computational cost inherent to this detection procedure seriously limits its processing efficiency, especially for onboard application scenarios. In this paper, a novel spectral and spatial approximate computing approach, named SSAC is proposed for onboard anomaly detection from hyperspectral images. To efficiently design the proposed approach, two preliminary aspects have been deeply analyzed in this work. First, data correlation in hyperspectral images in both spectral and spatial dimensions has been analyzed. The high data correlation in both spectral and spatial dimensions is considered to be one of the cornerstones of the SSAC approach. Second, the error resilience of a popular hyperspectral anomaly detection algorithm in both data level and algorithm level has been analyzed, which is considered to be another cornerstone of the SSAC approach. Based on the outcomes of this analysis, the processing of spectrally and spatially degraded images has been employed for reducing computation complexity in onboard hyperspectral anomaly detection scenarios in this work. Performance assessment tools such as ROC curves, Cost curves, and computing times have been used for evaluating the computing accuracy and efficiency of our proposal. The results obtained with a nonlinear anomaly detector for hyperspectral imagery, such as the well-known kernel RX-algorithm, show that the proposed SSAC approach greatly improves anomaly detection efficiency compared to the traditional method with negligible degeneration in accuracy. This is an important achievement to meet the restrictions of onboard hyperspectral anomaly detection scenarios.

A series of Ce1-xCdxO mixed oxide catalysts prepared by co-precipitation method were tested for synthesis of glycerol carbonate (GC) from glycerol and diethyl carbonate (DEC). The microstructural and physicochemical properties of the bimetal catalysts were characterized by XRD, SEM, TEM, XPS, CO2-TPD, N2-adsorption, TG-DSC and 1H NMR. The results show that the spherical particles of CdO were uniformly dispersed on the surface of lamellar CeO2. Besides, a noticeable increase was observed in the moderate basic sites concentration, because more oxygen atoms were adsorbed on the bimetal sample surface which was attributed to Ce4+ doped into CdO lattice. The Ce0.7Cd0.3O sample annealed at 500 °C was observed with the highest catalytic activity. 96.84% of glycerol conversion and 100% selectivity to GC were obtained over this catalyst under the optimized conditions (5 wt.% catalyst of glycerol, 90 °C, 180 min, glycerol/DEC molar ratio of 1:3). Besides, it was found that the conversion of glycerol carbonate was synergistically accelerated by the moderate and strong basic sites.

As important glycerol derivatives, glycerol carbonate (GC) and glycidol (GD) have attracted increasing attention in recent years. In the present work, a series of new solid base catalysts were developed to catalyze the conversion of glycerol to afford GC and GD. These catalysts were prepared by loading KF on the porous La-Zr solid base catalyst and characterized using a series of methods. A large number of various basic sites were generated upon loading KF onto the La-Zr-600 support. The weak basic sites were assigned to the surface hydroxyl groups produced during the formation of LaOF, while the strong and super basic sites were related to the Lewis base produced due to the interaction between KF and the La-Zr-600 support. A glycerol conversion of 91.77% and a GC selectivity of 99% were obtained over 0.3KF/La-Zr, which displayed the best catalytic performance under the optimal reaction conditions. The excellent activity of these catalysts was attributed to the presence of the strong and super basic sites, which favor the transesterification of glycerol with dimethyl carbonate. The production of GD from GC decarbonylation was unfeasible at low temperature using the newly developed catalysts. A plausible reaction mechanism has been proposed based on the experimental results and characterization.

Liquid phase and one-step reaction for furfural (FFR) hydrogenated to tetrahydrofurfuryl alcohol (THFA) was conducted by employing noble metal-absence and toxicity-free, bimetallic Ni-Co catalysts. The bimetallic Ni-Co catalysts with different molar ratio of Ni to Co and two monometallic catalysts (Ni, Co) were synthesized by conventional incipient-wetness impregnation method with short channeled SBA-15 as support. Among these catalysts, highly selective hydrogenation of FFR to THFA was attained over Ni-Co/SBA-15 catalysts. The impact of molar ratio of Ni/Co was studied with a fixed Ni loading (10 wt %) in bare support. The samples were characterized by N2 sorption, XRD, XPS, H2-TPR, SEM, TEM and CO chemisorption. The results confirmed that the reducibility of Ni-catalysts can be improved by incorporating Co metal and revealed that appropriate amount of spinel NiCo2O4 oxides is favorable to uniform and high dispersion of Ni0 and CoO over short channeled SBA-15, which would be benefit for the catalytic performance. For catalytic activity investigation, other four mesoporous SiO2 materials (MCM-41, MCM-4, MCF-2 and common SBA-15) were employed as supports to investigate the effect on the hydrogenation of FFR and the reaction parameters including the reaction temperature, initial of H2 pressure, reaction time and the reusability of the catalysts were also studied. 100% of FFR conversion and 92.1% of THFA selectivity were achieved at 90 °C, 50 bar of initial H2 and 2.0 h over catalyst Ni-Co/SBA-15 when the molar ratio of Ni to Co was 0.67.

Hierarchical H[Sn, Al]ZSM-5 zeolites were successfully prepared by hydrothermal method with directly incorporating Sn species into framework of HZSM-5 zeolites followed by alkali treatment. The prepared samples were characterized by XRD, BET, SEM-EDX, TEM, NH 3 -TPD, FT-IR and Py-IR. The results indicate that Sn species were successfully incorporated into HZSM-5 zeolites. The catalytic performances of all prepared zeolites were evaluated by glycerol aromatization. The highest catalytic performance of GTA synthesis was observed with H[Sn, Al]ZSM-5 treated with 0.3 M NaOH (32.1% carbon yield of BTX aromatics and 13 h catalyst lifetime), which was prior to the pure HZSM-5 (17.8% carbon yield of BTX aromatics and 3 h catalyst lifetime). The remarkably improved performance in catalytic activity and stability of H[Sn, Al]ZSM-5/0.3AT zeolitize could be attributed to synergy effect between Sn species doped into HZSM-5 zeolite and the generated micro-mesoporous structure by alkali treatment.

[(CH3)2NH2][M3(BTC)(HCOO)4(H2O)].H2O (M–BTC, M = Mn, Ni, Co) were prepared under hydrothermal conditions and used as highly efficient catalysts for cycloaddition of CO2 with epichlorohydrin (ECH). The microstructure and physicochemical properties of the compounds were determined by PXRD, FT-IR, XPS, N2-adsorption, TG–DSC, NH3–TPD and CO2–TPD. 98.01% conversion of ECH and 96.05% selectivity to chloropropene carbonate was obtained over the Mn–BTC under the optimized reaction conditions (105 °C, 3.0 MPa, 9 h, 1.5 wt.% of ECH). Besides, the recyclability result exhibited the Mn–BTC compound can be utilized as least three times with a slight reduction in its catalytic ability. In addition, cycloaddition of CO2 with other epoxides and DFT calculation were also performed. The result exhibited the yield followed the order: ECH > 1, 2-epoxybutane > propene oxide > Allyl glycidyl ether, which was mainly determined by the energy of reaction.

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.

Object detection based on remote sensing imagery has become increasingly popular over the past few years. Unlike natural images taken by humans or surveillance cameras, the scale of remote sensing images is large, which requires the training and inference procedure to be on a cutting image. However, objects appearing in remote sensing imagery are often sparsely distributed and the labels for each class are imbalanced. This results in unstable training and inference. In this paper, we analyze the training characteristics of the remote sensing images and propose the fusion of the aggregated-mosaic training method, with the assigned-stitch augmentation and auto-target-duplication. In particular, based on the ground truth and mosaic image size, the assigned-stitch augmentation enhances each training sample with an appropriate account of objects, facilitating the smooth training procedure. Hard to detect objects, or those in classes with rare samples, are randomly selected and duplicated by the auto-target-duplication, which solves the sample imbalance or classes with insufficient results. Thus, the training process is able to focus on weak classes. We employ VEDAI and NWPU VHR-10, remote sensing datasets with sparse objects, to verify the proposed method. The YOLOv5 adopts the Mosaic as the augmentation method and is one of state-of-the-art detectors, so we choose Mosaic (YOLOv5) as the baseline. Results demonstrate that our method outperforms Mosaic (YOLOv5) by 2.72% and 5.44% on 512 × 512 and 1024 × 1024 resolution imagery, respectively. Moreover, the proposed method outperforms Mosaic (YOLOv5) by 5.48% under the NWPU VHR-10 dataset.

A series of Ce-NiO catalysts prepared by co-precipitation method were tested for glycerol transesterification with diethyl carbonate (DEC) to produce glycerol carbonate (GC). The microstructural and physicochemical properties of the catalysts were determined by XRD, XPS, SEM, EDX, CO 2 -TPD, N 2 -adsorption, FT-IR, and TG-DTG. The results showed that the particle size (100–350 nm) of the Ce-NiO was smaller than that of pure NiO (400–900 nm), suggesting addition of Ce in the catalyst synthesis could lessen the aggregation or facilitate the dispersion of NiO particles. The highest catalyst activity was observed over the Ce-NiO catalyst with Ce/Ni atomic ratio of 0.2 and calcined at 400 °C. 94.14% of glycerol conversion and 90.95% selectivity to GC were obtained over this catalyst under the optimum conditions (5 wt.% catalyst of glycerol, 85 °C, 8 h, glycerol/DEC ratio of 1:3). This catalyst can be reused up to three cycles with a slight decrease in its catalytic activity. It was found the well dispersed NiO particles and the strong basic sites derived from the substitution of Ce 4+ by Ni 2+ in the CeO 2 lattices synergistically promoted the glycerol conversion.

A series of efficient and low-cost Li/ZnO catalysts were prepared by a simple impregnation method and investigated for the synthesis of glycerol carbonate (GC) from the transesterification of glycerol with dimethyl carbonate (DMC). The Li/ZnO catalysts were characterized using XRD, SEM, FT-IR, TG-DSC, TPD and XPS. It was found that the basicity of the catalysts highly depended on the Li loading and calcination temperature. The weak and moderate basic sites on the catalyst surfaces originated from the ZnO and Li + interaction. The strong basic sites were attributed to the substitution of Zn 2+ by Li + in the ZnO lattice, which led to straining of Zn-O bonds and the formation of [Li + O − ] species. It was the strong basic sites rather than the weak and moderate basic sites that catalyzed the transesterification of glycerol with DMC. The highest catalytic activity was observed over the ZnO loaded with 1 wt.% LiNO 3 and calcined at 500 °C. Glycerol conversion of 97.40% and GC yield of 95.84% were obtained over this catalyst at 95 °C in 4 h.

Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.

Two dimensional carbon nitride (2D CN) has attracted extensive attention due to its potential application in the field of photocatalysis. However, the poor electron-hole separation efficiency and low charge carrier migration speed confine the photocatalytic performance of 2D CN. In this work all organic van der Waals heterojunction (vdWH) catalyst (2D CN-COF) was prepared from 2D CN and triazine-based covalent organic framework (COF-TD) to efficiently enhance the catalytic activity of 2D CN. Characterization and simulation results verified the successful preparation of the catalyst. The heterojunction was originated from the intense interface van der Waals force interaction between the molecules of 2D CN and COF-TD. Furthermore, the intense π-π interaction between 2D CN and COF-TD led to the morphology transformation of COF-TD from sphere of isolated sample to elliptic sheet in 2D CN-COF. The intimate interface contact of vdWH has led to reduce bandgap, which remarkably enlarged the visible light responding region of the samples. Meanwhile, the charge carrier recombination was significantly reduced because of the formation of vdWH. The evolution rate of CO over 2D CN-COF was 9.2 times and 3.3 times higher than that of 2D CN and COF-TD, respectively. Moreover, the reaction was performed in the absence of any solvent and sacrificial agent, matching well with the idea of sustainable development. This work not only provides a new and simple strategy to fabricate 2D CN based all organic heterojunction catalyst for efficient photoreduction of CO2 to CO and CH4, but also serves as a reference for other researchers to design organic heterojunction, which is based on 2D CN and COFs.

Imidazolium ionic liquids functionalized UiO-66-NH2 MOFs were synthesized by two different approach, further applied as efficient catalysts for the catalytic conversion of CO2 into cyclic carbonates. The functionalized UiO-66-NH2 were investigated using various characterization technologies including XRD, NMR, FT-IR, XPS, TG-DTG, TEM, N2-adsorption, and NH3-TPD. The octahedron structure of UiO-66-NH2 becomes rough after UiO-66-NH2 interacted with 1-(4-carboxybutyl)-3-methylimidazolium bromide via amidation, while some small grains were formed around UiO-66-NH2 species when UiO-66-NH2 was functionalized by 1-(3-sulfopropyl)-3-methylimidazolium bromide ([SPMIM]Br) via the H2N– and HSO3– interaction. Moreover, when [SPMIM]Br was grafted onto the surface of UiO-66-NH2, a higher catalytic activity was found to exhibit 98.12% conversion of epichlorohydrin (ECH) and 98.23% selectivity to chloropropene carbonate (CPC) under optimized conditions (Initial pressure 2.5 MPa, 1.2 wt% catalyst of ECH, 95 °C, reaction time 8 h). No significant deactivation of the catalyst was observed after the [SPMIM]Br functionalized UiO-66-NH2 was reused three times. Furthermore, several additional coupling reactions were investigated with other epoxides as substrates.

Chitosan was prepared by N-deacetylation of squid pens β-chitin, and N-carboxyethylated chitosan (N-CECS) with different degrees of substitution (DS) were synthesized. DS values of N-CECS derivatives calculated by (1)H nuclear magnetic resonance (NMR) spectroscopy were 0.60, 1.02 and 1.46, respectively. The adsorption capacity of Cu(II) by N-CECS correlated well with the DS and pH ranging from 3.2 to 5.8. The maximum Cu(II) adsorption capacity (qm) of all three N-CECS at pH 5.4 was 207.5mg g(-1), which was 1.4-fold higher than that of chitosan. The adsorption equilibrium process was better described by the Langmuir than Freundlich isotherm model. Adsorption of Cu(II) ion onto N-CECS followed a pseudo-second order mechanism with chemisorption as the rate-limiting step. In a ternary adsorption system, the adsorption capacity of Cu(II) by N-CECS also presented high values, and qm for Cu(II), Cd(II), and Pb(II) were 150.2, 28.8, and 187.9mg g(-1), respectively.

A framework based on color feature extraction of social media photos and correlation analysis with air quality parameters is proposed to monitor environmental change. More specifically, photos of the Beijing Olympic Park from Panoramio website have been analyzed as a case study. The aerosol optical depth data at 500 nm wavelength (AOD 500) obtained from sun-photometer observation network station has been used as reference. Results show a proof of concept that social media photos have an interesting potential for air pollution estimate and remote sensing parameter validation with a low cost.

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.

We have theoretically and experimentally investigated the evolution of the temporal contrast in a 10-PW-level Ti:sapphire laser in the Shanghai Superintense Ultrafast Laser Facility (SULF). The effects induced by the grism pair, spectral shaping filter, and increase in gain on the temporal contrast were investigated. First, it was found that the energy loss of clean seed pulses in the grism pair is a major factor in contrast degradation. Because of the low transmission efficiency of the grism pair (~10%), the temporal contrast is degraded by one order of magnitude. Second, the spectral shaping filter in the regenerative amplifier degrades the temporal contrast by increasing the intracavity loss. Finally, as the amplified spontaneous emission pedestal experiences gain more than the main pulse in Ti:sapphire amplifiers, particularly during saturated amplification, the temporal contrast will further deteriorate as the gain increases in multi-stage Ti:sapphire amplifiers. In addition, the effect on the temporal contrast induced by the extraction during pumping technique in large-aperture Ti:sapphire amplifiers has been considered. According to the investigations described above, the design of the SULF can be further improved. It is predicted that a temporal contrast of over 10−11 can be achieved at a peak power of 10 PW following the improvements. The investigations conducted in this study can provide guidelines for improving the temporal contrast in ultrahigh-peak-power Ti:sapphire lasers.