Search

Ni-W catalysts find extensive application in catalysis; however, the impact of diverse morphologies on their catalytic activity remains a relatively unexplored area. Using hydrothermal/solvothermal synthesis methods, we successfully synthesized Ni-W catalysts with various morphologies, including sheet-like, sphere-like, rod-like, flower-like, and particulate-like structures. Comprehensive investigations into the influence of morphology on the catalytic activity of these Ni-W catalysts were conducted, employing techniques such as SEM, XRD, XPS, TG-DTG, BET, H2-TPR, and H2-TPD. Remarkably, the flower-like Ni-W catalyst displayed exceptional catalytic properties in ethylbenzene hydrogenation, exhibiting not only high hydrogenation activity but also superior sulfur resistance. Consequently, the flower-like Ni-W catalyst holds great potential for applications in industrial catalysis. [ABSTRACT FROM AUTHOR]

Image steganography is the art of concealing secret information within images to prevent detection. In deep‐learning‐based image steganography, a common practice is to fuse the secret image with the cover image to directly generate the stego image. However, not all features are equally critical for data hiding, and some insignificant ones may lead to the appearance of residual artifacts in the stego image. In this article, a novel network architecture for image steganography with hybrid attention mechanism based on generative adversarial network is introduced. This model consists of three subnetworks: a generator for generate stego images, an extractor for extracting the secret images, and a discriminator to simulate the detection process, which aids the generator in producing more realistic stego images. A specific hybrid attention mechanism (HAM) module is designed that effectively fuses information across channel and spatial domains, facilitating adaptive feature refinement within deep image representations. The experimental results suggest that the HAM module not only enhances the image quality during both the steganography and extraction processes but also improves the model's undetectability. Stego images are mixed with varying levels of noise in the training process, which can further improve robustness. Finally, it is verified that the model outperforms current steganography approaches on three datasets and exhibits good undetectability. [ABSTRACT FROM AUTHOR]

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP. [ABSTRACT FROM AUTHOR]

Feature matching is a core step in multi-source remote sensing image registration approaches based on feature. However, for existing methods, whether traditional classical SIFT algorithm or deep learning-based methods, they essentially rely on generating descriptors from local regions of feature points, which can lead to low matching success rates due to various challenges, including gray-scale changes, content changes, local similarity, and occlusions between images. Inspired by the human approach of finding rough corresponding regions globally and then carefully comparing local regions, and the excellent global attention property of transformers, the proposed feature matching network adopts a coarse-to-fine matching strategy that utilizes both global and local information between images to predict corresponding feature points. Importantly, the network has great flexibility of matching corresponding points for any feature points and can be effectively trained without strong supervised signals of corresponding feature points and only require the true geometric transformation between images. The qualitative experiment illustrate the effectiveness of the proposed network by matching feature points extracted by SIFT or sampled uniformly. In the quantitative experiments, we used feature points extracted by SIFT, SuperPoint, and LoFTR as the keypoints to be matched. We then calculated the mean match success ratio (MSR) and mean reprojection error (MRE) of each method at different thresholds in the test dataset. Additionally, boxplot graphs were plotted to visualize the distributions. By comparing the MSR and MRE values as well as their distributions with other methods, we can conclude that the proposed method consistently outperforms the comparison methods in terms of MSR at different thresholds. Moreover, the MSR of the proposed method remains within a reasonable range compared to the MRE of other methods. [ABSTRACT FROM AUTHOR]

Based on iterated Crank–Nicolson (CN) procedure, an alternative algorithm with perfectly matched layer (PML) formulation is proposed in the body‐of‐revolution (BOR) finite‐difference time‐domain (FDTD) lattice for the simulation of rotational symmetric geometrics. For the nonuniform domain simulation, an alternative subgridding method is employed to during the simulation. The iterated CN procedure improves the efficiency through preventing the calculation of tri‐diagonal matrices. The alternative subgridding method enhances the accuracy in nonuniform domains by the calculation of subregions. Numerical example is carried out for the demonstration of effectiveness including efficiency, accuracy and absorption. Through the results, the proposed scheme shows considerable absorption and accuracy improvement in nonuniform domains. Compared with the other CN schemes, the iterated CN procedure can significantly increase the efficiency with small time steps. In conclusion, the advantages and novelty of the proposed algorithm can be described as follows: (1) The iterated CN procedure is proposed for rotational symmetric geometrics. (2) Absorption boundary condition for iterated CN is proposed in BOR‐FDTD. (3) An alternative subgridding method for iterated CN procedure is proposed in BOR‐FDTD lattice. Thus, the proposed algorithm shows potential in nonuniform rotational symmetric geometrics open region simulation. [ABSTRACT FROM AUTHOR]

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers. [ABSTRACT FROM AUTHOR]

Image registration based on feature is a commonly used approach due to its robustness in complex geometric deformation and larger gray difference. However, in practical application, due to the effect of various noises, occlusions, shadows, gray differences, and even changes of image contents, the corresponding feature point set may be contaminated, which may degrade the accuracy of the transformation model estimate based on Random Sample Consensus (RANSAC). In this work, we proposed a semi-automated method to create the image registration training data, which greatly reduced the workload of labeling and made it possible to train a deep neural network. In addition, for the model estimation based on RANSAC, we determined the process according to a probabilistic perspective and presented a formulation of RANSAC with the learned guidance of hypothesis sampling. At the same time, a deep convolutional neural network of ProbNet was built to generate a sampling probability of corresponding feature points, which were then used to guide the sampling of a minimum set of RANSAC to acquire a more accurate estimation model. To illustrate the effectiveness and advantages of the proposed method, qualitative and quantitative experiments are conducted. In the qualitative experiment, the effectiveness of the proposed method was illustrated by a checkerboard visualization of image pairs before and after being registered by the proposed method. In the quantitative experiment, other three representative and popular methods of vanilla RANSAC, LMeds-RANSAC, and ProSAC-RANSAC were compared, and seven different measures were introduced to comprehensively evaluate the performance of the proposed method. The quantitative experimental result showed that the proposed method had better performance than the other methods. Furthermore, with the integration of the model estimation of the image registration into the deep-learning framework, it was possible to jointly optimize all the processes of image registration via end-to-end learning to further improve the accuracy of image registration. [ABSTRACT FROM AUTHOR]

One key challenge in photocatalytic hydrogen production is how to construct high-performance photocatalyst. Covalent triazine framework (CTF) based polymers as photocatalysts show great application potential because of their good photocatalytic activity, high chemical stability, tunable electronic and optical properties, and easy synthesis process. In this paper, we designed the ternary Z-scheme heterojunction Au@TiO 2 -X%TrTh based on CTF polymer TrTh, TiO 2 and Au nanoparticle, which exhibit higher photocatalytic hydrogen production rate compared with the corresponding binary heterojunction Au@TiO 2 and TiO 2 -12%TrTh. The results of photocatalytic hydrogen production show that the optimized Au@TiO 2 -12%TrTh has a remarkable hydrogen production rate of 4288.54 μmol g−1 h−1, which is about 312.3 times of Au@TiO 2 and 9.1 times of the TiO 2 -12%TrTh. The enhanced hydrogen production activity of the ternary heterojunction comes from the local surface plasmonic resonance effect of Au nanoparticle, lower recombination efficiency of photogenerated electron-holes pairs and Z-scheme electron transfer pathway of Au@TiO 2 -12%TrTh. The work provides a new strategy for designing efficient and practical photocatalyst. [Display omitted] • Ternary Au@TiO 2 -X%TrTh heterojunctions are prepared for hydrogen evolution. • The introduction of polymer TrTh extends the absorption of visible light of the composites. • The Au nanoparticles can accelerate the photoinduced carrier migration. • A Z-Scheme electron flow direction was purposed for the heterojunctions. • The ternary composites show excellent photocatalytic performances. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study was performed to investigate the accuracy of conventional ultrasound (US), contrast-enhanced US (CEUS), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the size of breast cancer. METHODS: In total, 49 breast cancer lesions of 48 patients were included in this study. The inclusion criteria were the performance of total mastectomy or breast-conserving surgery for treatment of breast cancer in our hospital from January 2017 to December 2020 with complete pathological results, as well as the performance of conventional US, CEUS, and DCE-MRI examinations with complete results. The exclusion criteria were non-mass breast cancer shown on conventional US or DCE-MRI, including that found on CEUS with no boundary with surrounding tissues and no confirmed tumor scope; a tumor too large to be completely displayed in the US section, thus affecting the measurement results; the presence of two nodules in the same breast that were too close to each other to be distinguished by any of the three imaging methods; and treatment with preoperative chemotherapy. Preoperative conventional US, CEUS, and DCE-MRI examinations were performed. The postoperative pathological results were taken as the gold standard. The lesion size was represented by its maximum diameter. The accuracy, overestimation, and underestimation rates of conventional US, CEUS, and DCE-MRI were compared. RESULTS: The maximum lesion diameter on US, CEUS, DCE-MRI and pathology were 1.62±0.63 cm (range, 0.6–3.5 cm), 2.05±0.75 cm (range, 1.0–4.0 cm), 1.99±0.74 cm (range, 0.7–4.2 cm) and 1.92±0.83 cm (range, 0.5–4.0 cm), respectively. The lesion size on US was significantly smaller than that of postoperative pathological tissue (P < 0.05). However, there was no significant difference between the CEUS or DCE-MRI results and the pathological results. The underestimation rate of conventional US (55.1%, 27/49) was significantly higher than that of CEUS (20.4%, 10/49) and DCE-MRI (24.5%, 12/49) (P < 0.001 and P = 0.002, respectively). There was no significant difference in the accuracy of CEUS (36.7%, 18/49) and DCE-MRI (34.7%, 17/49) compared with conventional US (26.5%, 13/49); however, the accuracy of both groups tended to be higher than that of conventional US. The overestimation rate of CEUS (42.9%, 21/49) and DCE-MRI (40.8%, 20/49) was significantly higher than that of conventional US (18.4%, 9/49) (P = 0.001 and P = 0.015, respectively). CONCLUSIONS: CEUS and DCE-MRI show similar performance when evaluating the size of breast cancer. However, CEUS is more convenient, has a shorter operation time, and has fewer restrictions on its use. Notably, conventional US is more prone to underestimate the size of lesions, whereas CEUS and DCE-MRI are more prone to overestimate the size. [ABSTRACT FROM AUTHOR]

To efficient simulate symmetric rotationally geophysical problems, implicit Crank–Nicolson (CN) scheme incorporated with approximate decoupling procedure is proposed in the body of revolution (BOR) finite‐difference time‐domain (FDTD) algorithm. For further absorption of large number of low‐frequency waves in bandpass simulation, complex envelope method is incorporated with the perfectly matched layer implementation. To be more specific, the proposed implementation shows advantages in terms of improved efficiency, considerable accuracy, and remarkable absorption. To demonstrate effectiveness and efficiency of the proposed implementation, geophysical numerical examples are carried out in the BOR‐FDTD domain. Through different numerical examples, it can be concluded from simulation results that the proposed implementation can obtain admirable entire performance in bandpass geophysical simulation. In addition, it can also be illustrated that it can keep stable when time step surpasses far beyond the Courant–Friedrichs‐ Levy condition. [ABSTRACT FROM AUTHOR]

A novel nitrogen-riched composite (PPYTZ/C) was firstly prepared by chemical polymerization, and then the Cu-PPYTZ/C complex was obtained by immersing the PPYTZ/C modified electrode in copper sulphate solution. For comparing the performance of the two catalysts, physical and electrochemical measurements were adopted. Wherein, physical measurements include scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). And, the electrochemical measurements include cyclic voltammetry (CV), rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE), and then the above measurement results were compared with commercial Pt/C. The CV and RDE results proved that Cu-PPYTZ/C catalyst exhibits excellent performance than that of PPYTZ/C in term of ORR activities and stability. In addition, the RRDE results also indicated that Cu-PPYTZ/C is comparable to the commercialization Pt/C in term of electron transfer numbers. In conclusion, Cu-PPYTZ/C is the potential candidate for oxygen reduction reaction catalysts, which is attribute to the role of CNx-M in Cu-PPYTZ/C catalyst as the catalytic group. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data. Results: In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively. Conclusions: The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional. [ABSTRACT FROM AUTHOR]

The development of highly active hydrodesulfurization (HDS) catalysts is still of great importance in hydroprocessing of the heavy residue oils in industry. Herein, hierarchically structured alumina hollow microspheres with high specific surface area were successfully prepared via a citric-acid-modulated hydrothermal method. With different dosages of citric acid applied, alumina microspheres were assembled with different specific surface areas, pore volumes and acidity. After the loading of the MoNi active components, a series of HDS catalysts were characterized systematically by various relevant techniques; and their catalytic activity and selectivity towards hydrodesulfurization of dibenzothiophene (DBT) were evaluated and compared. It is revealed that, the catalytic efficiency of the catalyst highly depends on the factors including the specific surface area and the acidity, the sulfidity and the dispersion of the active metal components. On this basis, we have established a facile method for preparation of hierarchically structured alumina supports with desirable physicochemical properties and high HDS catalytic efficiency. This work could also provide theoretical guidance for rational design of highly active HDS catalysts. [ABSTRACT FROM AUTHOR]

The design of hierarchically porous catalytic supports is considered as one of the most efficient strategies to achieve efficient mass transport and thus high catalytic performance in heterogeneous catalysis. As one of the most important catalytic supports, facile synthesis of γ-Al 2 O 3 with hierarchical porosities is of great industrial importance. In this paper, nest-like hollow γ-AlOOH microspheres constructed from numerous hierarchically organized nanowires were synthesized via a template-free simple hydrothermal approach, from which hierarchically porous γ-Al 2 O 3 microspheres with average macropores of 900 nm, mesopores of 20 nm and a pore volume of 0.93 cm 3 /g were obtained readily. Due to their structural merits, such nest-like γ-Al 2 O 3 microspheres could serve as excellent catalytic support. As a demonstration, a MoNi/γ-Al 2 O 3 supported catalyst was prepared, which was applied for hydrodemetallization (HDM) catalysis. Compared with the catalyst prepared with the commercial γ-Al 2 O 3 , our catalyst exhibits superior catalytic performance and longer life due to enhanced diffusion of the reactants. [ABSTRACT FROM AUTHOR]

Hierarchical SAPO-11 was successfully synthesized without a secondary template by changing the hydrothermal crystallization temperature. SAPO-11 with a larger external surface area, more acidity, and a large number of external active sites was constructed through a novel two-step crystallization strategy. Both precrystallization step and the low-temperature crystallization step were confirmed to be indispensable conditions for constructing hierarchical SAPO-11. After loading Pt nanoparticles by incipient wetness impregnation, the heptane hydroisomerizations based on 0.5 wt% Pt/C-SAPO-11-200 and 0.5 wt% Pt/T-SAPO-11-120 catalysts were carried out in a fixed-bed at a total pressure of 1.5 MPa with a H/n-heptane volumetric of 400 and WHSV = 10 h. A conversion level of 75.7% of 0.5 wt% Pt/T-SAPO-11-120 catalyst was reached, which was higher than that of 0.5 wt% Pt/C-SAPO-11-200 catalyst (64.3%) at 300 °C. The enhanced performance achieved was attributed to the improved effect of the support, in terms of not only better mass transport and higher number of exposure active sites but also improved dispersity and decrease in the size of Pt particles. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Co N C catalyst had been developed using an inexpensive and N -rich precursor. • The covalent triazine-based framework provided high density and evenly distributed M N C sites. • The HAT-CN-Co/C-800 catalyst showed the highest ORR activity close to Pt/C. The preparation of non-noble metal catalysts with performance in the oxygen reduction reaction (ORR) comparable or superior to platinum-based catalysts remains challenging. In the present study, hexaazatriphenylenehexacarbonitrile (HAT-CN), which is rich in both cyano groups and pyrazine units, was used as the precursor and then cyclotrimerized into a covalent triazine-based framework with a macrocyclic structure containing abundant complexation sites for transitional metal ions. Subsequently, a series of non-platinum Co N C type catalysts (HAT-CN-Co/C) were prepared, and then pyrolyzed at different temperatures (700 °C, 800 °C, and 900 °C). The pyrolysis treatment endowed the catalysts with activated Co-N x sites, large pore sizes and high specific surface areas, which can promote oxygen transfer and accelerate the ORR catalytic process. It is found that the catalyst (HAT-CN-Co/C-800) obtained from pyrolysis at 800 °C possessed the best ORR activity and followed the four- electrons transfer pathway; the onset potential of ORR was determined as 0.972 V vs reversible hydrogen electrode (RHE), very close to that of Pt/C (0.993 V vs RHE), and the half-wave potential (0.895 V vs RHE) was significantly higher than that of Pt/C (0.859 V vs RHE). Furthermore, the HAT-CN-Co/C-800 catalyst showed good durability (83.2 % current retention after 28000 s) and significant methanol tolerance with good application potential as a substitute for the platinum-based ORR catalyst in fuel cell. [ABSTRACT FROM AUTHOR]

[Display omitted] • S-scheme polymer heterojunctions are built based on two linear conjugated polymers. • N-Py1/Py2 was obtained by a ultrasonic assisted NMP exfoliation process of Py1/Py2. • N-Py1/Py2 shows a significantly accelerated charge carrier separation and transport. • N-Py1/Py2 shows a high HER rate of 25.6 mmol g-1 h−1 without any co-catalyst. • The exfoliation process transforms the "bulk to bulk" to "segment to segment" contact-mode. In this work, two donor–acceptor linear conjugated polymers were designed and synthesized based on thianthrene-5,5,10,10-tetraoxide (TTO) as the acceptor unit, benzo[1,2- b :4,5- b ']dithiophene derivative (Py1) and thiophene (Py2) as the donor units, respectively. The Py1/Py2 composite was prepared by physical ball milling of the two polymers in a mixture, which was further treated with a N -methyl-2-pyrrolidone (NMP)-assisted sonication treatment, and the obtained catalyst was named N-Py1/Py2. Compared with the single polymer or Py1/Py2, the FTIR characteristic peaks of O=S=O have a red shift for N-Py1/Py2, accompanied by a profound change in morphology. Furthermore, N-Py1/Py2 has a broader light response and more efficient separation and transport of charge carriers, and as a result it exhibits a higher photocatalytic hydrogen evolution rate (26.5 mmol g-1 h−1) without the involvement of any co-catalyst than Py1/Py2 catalyst (3.56 mmol g-1 h−1). The underlying mechanism for the enhanced photocatalytic activity by the sonication treatment in NMP is discussed based both on experimental and theoretical calculation data. [ABSTRACT FROM AUTHOR]

Improving the soil quality of cultivated land generated by the Gully Land Consolidation Project could promote sustainable economic and ecological development in the hilly gully area of the Loess Plateau, China. A pot experiment with maize crop was conducted to assess the effects of different organic amendments and chemical fertilizer reduction on soil fertility and the microbial community in newly cultivated land, of which the amount of chemical fertilizer applied was half of the local conventional maize field (90 kg ha−1 of CO(NH 2) 2 , 45 kg ha−1 of P 2 O 5 , and 60 kg ha−1 of K 2 O). The experiment involved six treatments: bare soil (CK), only maize (M), maize + chemical fertilizer (MF), maize + chemical fertilizer + organic fertilizer (MFO), maize + chemical fertilizer + biochar (MFB), and maize + chemical fertilizer + polyacrylamide (MFP). The results are as follows: (1) compared with the MF treatment, the MFO treatment exhibited the best effect on improving soil physicochemical properties, such as electrical conductivity (EC), soil organic carbon (SOC), nitrate nitrogen (NO 3 −-N), total nitrogen (TN), available phosphorus (AP) and available potassium (AK), followed by MFB and MFP. (2) The MFO treatment significantly (P < 0.05) improved the Species richness and Shannon indices of the bacteria, but its effects on fungal diversity index are not significant. (3) The MFO treatment increased the relative abundance of Betaproteobacteria, Alphaproteobacteria, Gammaproteobacteria and Gemmatimonadetes and decreased the relative abundance of Sordariomycetes and Agaricomycetes, and it also increased the functional abundance of Amino acid metabolism, Membrane transport, Xenobiotics biodegradation and metabolism, Undefined Saprotrophs and Arbuscular Mycorrhizal Fungi. (4) The TN and EC were the main driving factors of bacterial community structural variation, while TN and NO 3 −-N were the main factors for variation of fungal community structure. (5) The fresh weight and dry weight of maize increased significantly (P < 0.05) under all amendment treatments, among which the MFO treatment had the greatest effect. In conclusion, organic fertilizer application is the most effective measure for rapidly improving soil fertility and microbial community structure. Our findings have valuable implications for agricultural land productivity, food security and long-term sustainability of the environment. [Display omitted] • Different organic amendments were investigated in newly cultivated land. • Chemical fertilizer reduction and organic fertilizer could improve soil quality and microbial community structure effectively. • TN and EC are the main driving factors of bacterial community structural variation. [ABSTRACT FROM AUTHOR]

The automatic and accurate diagnosis of thyroid nodules in ultrasound images is of great significance to reduce the workload and radiologists' misdiagnosis rate. Although deep learning has shown strong image classification performance, the inherent limitations of medical images small dataset and time-consuming access to lesion annotations, leaving this work still facing challenges. In our study, a multi-task cascade deep learning model (MCDLM) was proposed, which integrates radiologists' various domain knowledge (DK) and uses multimodal ultrasound images for automatic diagnosis of thyroid nodules. Specifically, we transfer the knowledge learned by U-net from the source domain to the target domain under the guidance of radiologist' marks to obtain more accurate nodules' segmentation results. We then quantify the nodules' ultrasound features (UF) as conditions to assist the dual-path semi-supervised conditional generative adversarial network (DScGAN) in generating higher quality images obtaining more powerful discriminators. After that, we concatenate DScGAN learning's image representation to train a supervised support vector machine (S3VM) for thyroid nodule classification. The experiment results on ultrasound images of 1030 patients suggest that the MCDLM model can achieve almost the same classification performance as the fully supervised learning (an accuracy of 90.01% and an AUC of 91.07%) using only about 35% of the full labeled dataset, which saves a lot of time and effort compared to traditional methods. • Incorporating domain knowledge into both segmentation and classification task. • Using the radiologist's marks to guide the training process of U-net. • Using domain knowledge to aid the training of generative confrontation network. • Semi-supervised classification methods can alleviate the limitations of data labels. • The proposed model is potentially integrated into the thyroid ultrasound system. [ABSTRACT FROM AUTHOR]

Li2O2, as the cathodic discharge product of aprotic Li-O2 batteries, is difficult to electrochemically decompose. Transition-metal oxides (TMOs) have been proven to play a critical role in promoting the formation and decomposition of Li2O2. Herein, a NiO/CNT catalyst was prepared by anchoring a NiO nanosheet on the surface of CNT. When using the NiO/CNT as a cathode catalyst, the Li-O2 battery had a lower overpotential of 1.2 V and could operate 81 cycles with a limited specific capacity of 1000 mA h g−1 at a current density of 100 mA g−1. In comparison, with CNT as a cathodic catalyst, the battery could achieve an overpotential of 1.64 V and a cycling stability of 66 cycles. The introduction of NiO effectively accelerated the generation and decomposition rate of Li2O2, further improving the battery performance. SEM and XRD characterizations confirmed that a Li2O2 film formed during the discharge process and could be fully electrochemical decomposed in the charge process. The internal network and nanoporous structure of the NiO/CNT catalyst could provide more oxygen diffusion channels and accelerate the decomposition rate of Li2O2. These merits led to the Li-O2 battery's better performance. [ABSTRACT FROM AUTHOR]

Image hiding is a task that hides secret images into cover images. The purposes of image hiding are to ensure the secret images are invisible to the human and the secret images can be recovered. The current state‐of‐the‐art steganography methods run the risk of secret information leakage. A safe image hiding network (SIHNet) is presented to reduce the leakage of secret information. Based on some phenomena of image hiding methods which use invertible neural network, a reversible secret image processing (SIP) module is proposed to make the secret images suitable for hiding and make the stego images leak less secret information. Besides, a reversible lost information hiding (LIH) module is used to hide the lost information into the cover images, thus the method can recover the secret images better than the method that uses random noise to replace the lost information. Experimental results show that SIHNet outperforms other state‐of‐the‐art methods on the PSNR and SSIM values of the recovered secret images and the stego images. Besides, residual images of other state‐of‐the‐art methods all contain information about secret images while residual images of SIHNet leak almost no secret information. Thus the method can prevent the listener of transmission channel from obtaining the information of the secret image through the residual image, which means SIHNet performs better in security than other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of mobile edge–cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and user mobility. Current research has partially addressed aspects of service migration and resource allocation, yet it often falls short in thoroughly examining the nuanced interdependencies between migration strategies and resource allocation, the consequential impacts of migration delays, and the intricacies of handling incomplete tasks during migration. This study advances the discourse by introducing a sophisticated framework optimized through a deep reinforcement learning (DRL) strategy, underpinned by a Markov decision process (MDP) that dynamically adapts service migration and resource allocation strategies. This refined approach facilitates continuous system monitoring, adept decision making, and iterative policy refinement, significantly enhancing operational efficiency and reducing response times in MECC environments. By meticulously addressing these previously overlooked complexities, our research not only fills critical gaps in the literature but also enhances the practical deployment of edge computing technologies, contributing profoundly to both theoretical insights and practical implementations in contemporary digital ecosystems. [ABSTRACT FROM AUTHOR]

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy. [ABSTRACT FROM AUTHOR]

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer's expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network. [ABSTRACT FROM AUTHOR]

Development of non-precious metal electrocatalyst that can efficiently catalyze the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER) simultaneously, is a great challenge. Herein, density functional theory (DFT) calculations are first conducted to explore the beneficial effect of NiSe 2 /NiS heterostructure on OER. Based on this, a series of nickel selenosulfides constructed by different amounts of NiS and NiSe 2 are synthesized. Results indicate that Ni 4 –Se 2 –S 2 with the NiS to NiSe 2 ratio of 5.82/2.47 displays outstanding electrocatalytic activity for OER/HER, with overpotentials of 228 mV and 155 mV at 10 mA cm−2, respectively. Moreover, a two-electrode electrolyzer using Ni 4 –Se 2 –S 2 as the anode and cathode exhibits a lower voltage than RuO 2 ||Pt/C. Moderate ratio of NiS to NiSe 2 achieves a balance between conductivity and surface area, which endows Ni 4 –Se 2 –S 2 the best catalytic performances. Our results pave a way for the optimization of heterostructured catalysts. [Display omitted] • NiSe 2 /NiS heterostructures were one-pot hydrothermally synthesized. • HER and OER bifunctional catalytic activity are obtained. • DFT reveals reduced energy barrier and lower d-band center in the heterostructure. • Ni 4 –Se 2 –S 2 shows promising electrocatalytic performances in overall water splitting. [ABSTRACT FROM AUTHOR]

Metalloviologens, as emerging electron-transfer photochromic compounds, have shown intriguing properties such as radiochromism, photochromism and photoconductance. However, only a limited number of them have been reported so far. Exploration of new metalloviologens is strongly desired. Herein, we report a new solvothermally synthesized metalloviologen complex [CdCl2(ND)2]n (1, ND = 1,5-naphthalenes) that exhibits photochromic and intrinsic white light emission properties. Density functional theory calculation results reveal that the photochromism could be assigned to photoinduced electron transfer from chlorine atoms to ND molecules. The photoinduced charge-separated states are heat/air stable, attributed to the delocalization of ND and strong intermolecular π–π interactions. Besides, complex 1 consistently emits intrinsic white light when excited with 340–370 nm UV light, achieving high color rendering index (CRI) values (82.54–94.04). By adjusting the excitation wavelength, both "warm" and "cold" white light emission can be produced, making it suitable for the application of a white light emitting diode (WLED). Thus, this work demonstrates that the ND-based metalloviologen is not only helpful in producing photochromism, but also beneficial for creating white-light emission. [ABSTRACT FROM AUTHOR]

Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT. [ABSTRACT FROM AUTHOR]

A hollow ZSM-5 nano-structure with a shell thickness of ca. 40 nm is successfully formed in 40 min via ammonia leaching under mild conditions. It is supposed to be crucial to control the release of hydroxyl ions for intact hollow structure construction. Management of the dissolution procedure enables visualization of the internal architecture, providing fundamental knowledge on the primary subunits and potential diffusion barriers of MFI-type single nanocrystals. The dissolution process can be extended to prepare hollow zeolite encapsulated catalysts with better performance in shape-selective hydrogenation reactions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two triazine-based CPPs were constructed with thiophene- or phenyl-linkage units. • The optimal sample presented an ultrahigh HER performance of 21.65 mmol g−1 h−1. • Excellent HER performance was obtained under full spectrum without co-catalyst. • The thiophene-linkage units played a vital role to promote HER performance. Conjugated porous polymers (CPPs) have been widely reported as promising photocatalysts. However, the realization of powerful photocatalytic hydrogen production performance still benefits from the rational design of molecular frameworks and the appropriate choice of building monomers. Herein, we synthesized two novel conjugated porous polymers (CPPs) by copolymerizing pyrene and 1,3,5-triazine building blocks. It is found that minor structural changes in the peripheral groups of the triazine units can greatly affect the photocatalytic activity of the polymers. Compared with the phenyl-linkage unit, the thiophene-linkage unit can give CPP a wider absorption range of visible light, a narrower band gap, a higher transmission and separation efficiency of photo-generated carriers (electrons/holes), and a better interface contact with the photocatalytic reaction solution. The catalyst containing thiophene-triazine (ThPy-CPP) has an efficient photocatalytic hydrogen evolution rate of 21.65 and 16.69 mmol g−1h−1 under full-arc spectrum and visible light without the addition of a Pt co-catalyst, respectively, much better than the one containing phenyl-triazine (PhPy-CPP, only 5.73 and 3.48 mmol g−1h−1). This study provides a promising direction to design and construct highly efficient, cost-effective CPP-based photocatalysts, for exploring the application of noble metal-free catalysts in photocatalytic hydrogen evolution. [ABSTRACT FROM AUTHOR]

Electrochemical carbon dioxide reduction reaction (CO2RR) using clean electric energy provides a sustainable route to generate highly‐valuable chemicals and fuels, which is beneficial for realizing the carbon‐neutral cycle. Up to now, achieving a narrow product distribution and highly targeted product selectivity over Cu‐based electrocatalysts is still a big challenge. Herein, sulfur modification on different crystal planes of cuprous oxide (Cu2O) is demonstrated, results in an improvement for formate generation to different degrees. Experimental results and density functional theory (DFT) calculations reveal that sulfur species modified on the surface of Cu2O (100) facet effectively lower the formation energy of key intermediate *OCOH for formate generation compared with Cu2O (111) facet. As a consequence, the modification of p‐Block elements over Cu‐based electrocatalysts is an effective strategy to optimize the adsorption energy of key intermediate during CO2RR, leading to a highly selective product. [ABSTRACT FROM AUTHOR]

Massive and diverse remote sensing data provide opportunities for data-driven tasks in the real world, but also present challenges in terms of data processing and analysis, especially pixel-level image interpretation. However, the existing shallow-learning and deep-learning segmentation methods, bounded by their technical bottlenecks, cannot properly balance accuracy and efficiency, and are thus hardly scalable to the practice scenarios of remote sensing in a successful way. Instead of following the time-consuming deep stacks of local operations as most state-of-the-art segmentation networks, we propose a novel segmentation model with the encoder–decoder structure, dubbed XANet, which leverages the more computationally economical attention mechanism to boost performance. Two novel attention modules in XANet are proposed to strengthen the encoder and decoder, respectively, namely the Attention Recalibration Module (ARM) and Attention Fusion Module (AFM). Unlike current attention modules, which only focus on elevating the feature representation power, and regard the spatial and channel enhancement of a feature map as two independent steps, ARM gathers element-wise semantic descriptors coupling spatial and channel information to directly generate a 3D attention map for feature enhancement, and AFM innovatively utilizes the cross-attention mechanism for the sufficient spatial and channel fusion of multi-scale features. Extensive experiments were conducted on ISPRS and GID datasets to comprehensively analyze XANet and explore the effects of ARM and AFM. Furthermore, the results demonstrate that XANet surpasses other state-of-the-art segmentation methods in both model performance and efficiency, as ARM yields a superior improvement versus existing attention modules with a competitive computational overhead, and AFM achieves the complementary advantages of multi-level features under the sufficient consideration of efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two pair of analogous conjugated polymers containing dibenzo[ b , d ]thiophene sulfone are synthesized. • The polymers containing dual pyridinic N atoms have much higher HPR than their analogues. • The dual pyridinic N atoms and sulfone units are equally effective HER active sites. • The dual pyridinic N atoms perform a parallel HER role to that of the sulfone unit. • The experimental results are consistent with the theoretical calculation results. Research a new type of semiconductor photocatalyst and apply it to photocatalytic water splitting to produce hydrogen. This is the most important topic in today's society. Therefore, we report four linear organic polymers PH-1, PH-2, PN-1 and PN-2, which are formed by the condensation of dibenzothiophene- S, S -dioxide units with a series of derivatives of benzene respectively and used for photocatalytic hydrogen evolution. What's more, we also conducted a series of detailed characterization experiments on the four polymers, such as UV–vis, XPS, XRD, etc., to explore their molecular structure and optical properties. Compared with the polymer PH-1 and PH-2, the polymer PN-1 and PN-2 containing nitrogen element can better adjust the structure, reduce the band gap value, increase the specific surface area, and accelerate the separation of electrons and holes, PN-2 had the highest hydrogen evolution rate of 6658 μmol g−1 h−1 among four polymers without Pt as the co-catalyst under full arc irradiation. This work not only synthesized the four photocatalysts, but also opened up a new way for the conversion of solar energy into chemical fuels. [ABSTRACT FROM AUTHOR]

By incorporating higher order formulation and one‐step leapfrog alternating direction implicit (ADI) procedure, unconditionally stable perfectly matched layer implementation is proposed for terminating frequency‐dependent left‐handed material in finite‐difference time‐domain lattice. To be more specific, frequency‐dependent left‐handed material can be calculated by the piecewise linear recursive convolution method. The proposed scheme shows advantages of higher order formulation and one‐step leapfrog ADI procedure in terms of enhancing absorbing performance, improving computational efficiency and simulating open region problems. The effectiveness is demonstrated through the computational domain with dipole antenna model above the left‐handed material slab. From results, it can be concluded that the proposed formulation can obtain considerable absorption, outstanding efficiency, and improved entire performance. Meanwhile, the proposed scheme also keeps its stability when time step far exceeds the Courant‐Friedrich‐Levy condition. [ABSTRACT FROM AUTHOR]

AIM: To assess the feasibility and efficiency of contrast-enhanced ultrasound (CEUS) real-time guided fine needle aspiration (FNA) for sentinel lymph node (SLN) of breast cancer. MATERIALS AND METHODS: This retrospective study reviewed 21 breast cancer patients who scheduled for surgical resection performed CEUS real-time guided SLN-FNA and intraoperative SLN biopsy (SLNB). The success rate of CEUS real-time guided SLN-FNA was analyzed. The FNA diagnostic efficiency of SLN metastasis was analyzed compared to SLNB. RESULTS: Twenty-six SLNs were detected by intradermal CEUS whereas 130 SLNs were detected by SLNB. The median SLNs detected by intradermal CEUS (n = 1) and by SLNB (n = 5) was significantly difference (p < 0.001). All 26 CE-SLNs of 21 patients were successfully performed intradermal CEUS dual image real-time guided SLN-FNA including 5 SLNs of 4 patients which were difficult to distinguish in conventional ultrasound. Compared to SLNB, FNA found 2 of 5 cases of SLN metastasis, the diagnosis sensitivity, specificity, positive predictive value, negative predictive value, false negative rate, false positive rate and Yoden index were 40%, 100%, 100%, 84.2%, 60%, 0%and 40%, respectively. CONCLUSION: SLN-FNA real-time guided by dual CEUS image mode was technically feasible. Patients with a positive SLN-FNA should be advised to ALND without intraoperative SLNB according to Chinese surgeon and patients' conservatism attitude. But a negative SLN-FNA did not obviate the need of conventional SLNB because of the high false negative rate. [ABSTRACT FROM AUTHOR]

A Rainwater harvesting, concentrating Infiltration with multi-holed pipe and Mulching (RIM) system was designed to sustain soil moisture at a proper level in rain-fed orchards in the loess hilly and gully region of china. The RIM system induced a large increase in the spatial extent of wetted areas in the whole soil layers. • The RIM system induced a large increase in the spatial extent of wetted areas in the whole soil layers. • Precipitation was primary driver factor for SMC in the upper soil layer (0−60 cm). • The RIM system can efficiently improve the apple yield and the water use efficiency. Rainwater is a primary water source for the hilly and gully region of the Loess Plateau where an improved efficiency of rainwater utilization is crucial for sustainable agricultural development. A new system, comprised of rainwater harvesting, concentrating infiltration with multi-holed pipe and mulching (RIM), was designed to sustain soil moisture at a proper level in rain-fed orchards in the hilly and gully loess region of China. This study monitored changes in soil moisture and distribution over soil profiles using four treatments: (a) RIM1 with two rainwater harvesting areas, two infiltration pipes and film mulching, (b) RIM2 with one rainwater harvesting area, one infiltration pipe and film mulching, (c) film mulching (FM) only and (d) traditional apple orchard as a control treatment (CK) as a baseline in this experiment. The results showed that mean soil moisture content (SMC) in a range of soil layer between 0 cm and 300 cm for RIM1 and RIM2 treatments increased by 43.01% and 34.78% in 2018 and 30.55% and 26.41% in 2019, respectively. Dividing the soil vertical profile into three layers, i.e., an easy-changing layer (0−60 cm), an increasing or decreasing layer (60−180 cm), and a relatively steady layer (180−300 cm), this study examined the vertical changes of soil moisture. The RIM1 and RIM2 treatments induced a large increase in the size and depth of wetted areas over the whole soil layers. The soil moisture was replenished to a horizontal distance of 1.5 m and 2.5 m apart from the trunk within the soil layer between 60 cm and 300 cm under the RIM1 treatment while only the horizontal distance of 1.5 m apart from the trunk under the RIM2 treatment. While the seasonal variation of SMC in the easy-changing soil layer (0−60 cm) corresponded to precipitation consistently, the SMC in the deep soil layers (60−300 cm) was not sensitive to precipitation. As a result, the treatments of RIM1, RIM2 and FM improved apple yield and crop water productivity compared with those under the control treatment. In particular, the RIM1 treatment performed the best among the treatments investigated in this study. The results highlighted the efficiency of the RIM system in enhancing the soil moisture, which may be useful to improve an orchard production for rain-fed orchards in the loess hilly and gully region of China. [ABSTRACT FROM AUTHOR]

Abstract The room temperature deep eutectic solvent (DES) is demonstrated as an ideal precursor to prepare self-hybridized graphitic carbon nitride (g-C 3 N 4) for the first time, for its special components, randomly reorganized molecules and extensive hydrogen bonds. In this work, by adjusting the proportion of urea and ammonium thiocyanate in the precursor DES, the self-hybridized coralloid g-C 3 N 4 , stacked by a mass of double-layer g-C 3 N 4 /g-C 3 N 4 nanocages, were prepared without any templates. Compared to conventional g-C 3 N 4 prepared using melamine, urea and AT as precursors, the improved charge transfer between the interfaces of the self-hybridized g-C 3 N 4 has been demonstrated, which is attributed to the well-matched electronic band structures of the two g-C 3 N 4 species, the abundant 2D/2D interfacial contact and increased photocatalytic active sites. As expected, it exhibits highly enhanced photocatalytic activity, which is among the best g-C3N4 photocatalysts without doping any other elements. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Nano-sized SAPO-11 zeolite was successfully fabricated by a seed-induced quasi-solvothermal conversion (SIQSC) protocol without a secondary porogen that combined an in situ inoculating seed method (SI) and a quasi-solvothermal assisted conversion (QSC) process. The aggregation of SAPO-11 nanocrystals (20–30 nm) to give larger particles with an approximate 200 nm average size creates a lower acidity, larger surface area and higher inter-crystal mesoporosity than those in micro-sized SAPO-11 crystals. Importantly, the nano-sized SAPO-11 shows better performance in the dehydration of methanol to dimethyl ether (DME) than Al 2 O 3 and micro-sized SAPO-11. [ABSTRACT FROM AUTHOR]

The hierarchically structured zeolite with high surface area and a large number of mesoporosities has attracted tremendous attention. Particularly, the morphology and structural tailoring of a diverse mesoporous zeolite with a single template is regarded as a crucial step toward the realization of an environmentally friendly synthesis. Herein, crystalline silicoaluminophosphate (SAPO-5) of the AFI framework type with a bimodal pore system was hydrothermally synthesized using di-n-butylamine (DBA) as a single template. The structural and catalytic properties of the hierarchical SAPO-5 were extensively characterized and compared to those of a conventional SAPO-5 using triethylamine (TEA) as a template. After loading Pt nanoparticles by incipient wetness impregnation, due to the promoting effect of hierarchical SAPO-5 in which the reactant and product were rapidly contacted with the acidity sites and metal sites, the 0.5 wt% Pt/SAPO-5-1.0DBA catalyst exhibits an enhanced hydroisomerization performance, with heptane conversion of 76.1% and iso-C7 yields of 48.4%, compared to a conventional 0.5 wt% Pt/SAPO-5-1.0TEA catalyst. The crystallization evolution under a series of hydrothermal times was also studied here. More importantly, this green, sustainable and economical strategy will extend the application of the synthesis of a silicoaluminophosphate-based zeolite. [ABSTRACT FROM AUTHOR]

The development of bifunctional electrocatalysts to reduce the polarization loss is essential to enhance the performance of Zn-air batteries. The combination of metal-organic frameworks (MOFs) with metal or metallic compounds provides a way to integrate their merits and effectively improve their electrocatalytic performances. However, previous synthesis of MOFs/metal oxides usually involves high-temperature calcination, which makes it difficult to control their morphologies. In this work, through a ligand-insufficient and high temperature controlled solvothermal reaction, innovative NiFe-MOF/NiFe 2 O 4 hollow nanospheres were one-pot prepared without using any shape-controlled agent or calcination process. The as-obtained NiFe-MOF/NiFe 2 O 4 shows better ORR and OER performance than pure NiFe-MOF and NiFe 2 O 4 , suggesting a synergetic effect between NiFe-MOF and NiFe 2 O 4. The maximum power density of the liquid Zn-air battery assembled using NiFe-MOF/NiFe 2 O 4 as the air electrode catalyst is 158.4 mW cm−2 at 246.1 mA cm−2. This work offers fresh thoughts into designing highly efficient MOF-derived electrocatalysts. [Display omitted] • NiFe-MOF/NiFe 2 O 4 hollow spheres were one pot solvothermally synthesized. • Lower amount of ligands and high solvothermal temperature are essential in preparation. • ORR and OER bifunctional catalytic activity are obtained. • NiFe-MOF/NiFe 2 O 4 shows promising performances in Zn-air battery. [ABSTRACT FROM AUTHOR]

Hydroisomerization of long-chain n -alkanes is an indispensable process in the upgrading of fuel oil and lubricants to meet the state of art regulations. In this work, a novel core-shell structural material comprising USY zeolite core and mesoporous aluminosilicate (MSA) shell, denoted as USY@MSA, was fabricated by virtue of a desilication and recrystallization approach. Comprehensive characterizations demonstrate that the USY@MSA composite exhibits a hierarchical pore architecture and a gradient acidity. Due to the confinement effect of the mesoporous aluminosilicate shell, highly dispersed platinum particles were selectively deposited on the shell, generating a nanoscale distance with acidic sites of USY zeolite core. In the hydroisomerization of n -hexadecane, the Pt/USY@MSA composite catalyst outperforms the conventional Pt/USY and desilicated Pt/USY-ds catalysts, effectively suppressing the cracking side reactions and producing higher selectivity to branched isomers. [Display omitted] • Core-shell USY@MSA was designed and fabricated via desilication and recrystallization processes. • The USY@MSA feathered hierarchical architecture and gradient acidity. • Pt nanoparticles selectively deposited on the mesoporous MSA shell. • The favorable metal-acidity balance and hierarchical architecture contributed to remarkable hydroisomerization performance. [ABSTRACT FROM AUTHOR]

A new kind of random ternary D-A type conjugated copolymers were designed employing [1,2,5]thiadiazolo[3,4-g]quinoxaline (TQ) units as the acceptor, carbazole and cyclopentadithiophene units as the donors. In the experiments, two polymers named PTCD-1 and PTCD-2 were obtained by changing feed ratios of the three units. It was found that the two polymers possessed double-doping feature, and they exhibited multiple color changes of yellow/green/transparent gray in the p-type doping process and single color changes of yellow/red in the n-type doping process. In the electrochromic switching, PTCD-1 and PTCD-2 displayed low optical band gaps as 1.29 eV and 1.27 eV, respectively. They also presented favorable solubility, kinetic properties and thermal stability. In particular, both polymers had short response time (lowest as 0.18 s) and high coloring efficiency (up to 657.1 cm2 C−1). The favorable electrochromic behaviors especially the multicolor and double-doping properties made the two polymers worth in-depth attention and investigation. [Display omitted] • Two D-A type copolymers containing thiadiazoloquinoxaline, carbazole and cyclopentadithiophene units were synthesized. • The polymers displayed multichromism and double-doping feature. • The polymers showed desirable electrochromic properties with low band gaps. [ABSTRACT FROM AUTHOR]

As a new type of electrode material, transition metal silicates anode has drawn increasing attention owing to high theoretical capacity, sufficient reserves and nontoxicity. Herein, a unique flower-shaped cobalt silicate was fabricated via an alkali-assisted hydrothermal synthesis method, which feathered large specific surface area and abundant meso-pores. Serving as an anode material for lithium ion batteries, the hierarchical CoSiO 2 material exhibited excellent cycling stability (1111 mAh g−1 at 0.1 A g−1 after 200 cycles) and superior rate capability (411 mAh g−1 at 10 A g−1). The novel structure of F–CoSiO 2 anode not only provided abundant active sites for electrochemical reactions and effectively facilitated the migration of Li ions during charge-discharge process, but also contributed to the primary pseudocapacitive charge storage behaviors. This work may open up opportunities in structure design and represent the enormous potential of hierarchical transition metal silicate as practical advanced electrode for lithium storage. [Display omitted] • A novel flower-shaped CoSiO 2 anode material for lithium ion batteries was designed and fabricated firstly. • The 3D architecture assembled by 2D nanosheets generated large surface area and abundant mesopores. • The hierarchical structure greatly shortened mass diffusion and fascinated the transport of Li ions. • The CoSiO 2 material exhibited remarkable cycling and rate performance. [ABSTRACT FROM AUTHOR]

• Forces that detach the soil could be divided into dynamic and resistance force. • Effective force represents the comprehensive mechanical effect of linear erosion. • Mechanical tendency feature exists in the linear erosion process. • Interaction among flow, resistance and morphology results in the tendency. • Mechanical tendency provides theoretical basis for erosion prevention. Elucidating the mechanical mechanisms of linear erosion can contribute to the understanding of soil erosion and its prevention. However, few studies have investigated the development of sediment yield and the mechanical understanding of linear erosion. Experiments, four flow discharge rates and three slope gradients, were conducted for a mechanistic understanding of linear erosion. The conclusions are as follows: (1) The forces that detach soil can be divided into dynamic force (DF) and resistance force (RF) by vector decomposition and synthesis methods. The difference between DF and RF is defined as the effective force (EF), which represents the comprehensive mechanical effect of soil erosion. (2) DF and RF tend to be balanced during linear erosion. EF significantly decreased (p < 0.05), DF decreased, and RF increased. (3) The erosion rate (E r) decreases noticeably with decreasing EF (p < 0.05), from 3.99 kg/min to 2.99 kg/min when the slope is 15° and the discharge is 25 L/min. (4) The tendency for mechanical balance is attributed to the interaction among the flow dynamics, resistance, and morphology of linear erosion. The drop-pits and microgeomorphology developed in the linear erosion increased the water flow resistance and decreased the flow dynamics. This mechanical tendency provides a theoretical basis for linear erosion prevention. Our results can help improve the understanding of soil erosion mechanisms and management of water and soil resources. [ABSTRACT FROM AUTHOR]

The photocatalytic efficiency is limited by poor charge separation efficiency and high carrier transport activation energy (CTAE) of photogenerated electron/hole pairs than traditional semiconductor. Hybridizing nanostructure with two staggered alignment band structure is proved as an effective strategy to mitigate these two challenges but still suffers a strong coulomb electrostatic repulsive force between two heterogeneous semiconductors. Here, we steer a friendly sulfurization process to construct BiVO 4 @Bi 2 S 3 heterojunction with a scenario of cosharing Bi atoms. The intimate atomic-level contact between BiVO 4 and Bi 2 S 3 not only enhances the visible-light absorption and lowers CTAE, but also accelerate carrier's separation efficiency, which enables it to deliver the best photocatalytic performance toward reduction of Cr(VI). BiVO 4 @Bi 2 S 3 only needs less than 40 min to completely reduce 50 ppm Cr(VI) solution. The type II heterojunction photocatalytic mechanism is systematically studied to decipher the carriers' transfer track between BiVO 4 and Bi 2 S 3. Our new finding of engineering inorganic heterojunction by cosharing atoms opens a new avenue to other similar materials for potential applications. ga1 • An in situ sulfurization method is proposed to engineer atomic-level BiVO 4 @Bi 2 S 3 heterojunction. • BiVO 4 @Bi 2 S 3 possesses enhanced visible light absorption, and exhibits faster exciton dissociation efficiency. • The CTAE of photogenerated electron/hole pairs was greatly reduced at BiVO 4 @Bi 2 S 3 compared to undecorated BiVO 4. • The photocatalytic Cr(VI) reduction rate of BiVO 4 @Bi 2 S 3 heterojunction is 35.5 times higher than that of BiVO 4. [ABSTRACT FROM AUTHOR]

Theoretical calculations prove that the Sn 3 O 4 lowers the activation barrier of producing HCOOH benefitting from moderate adsorption energies of intermediates (HCOO* and *H) among SnO, SnO 2 and Sn 3 O 4. As a result, a faradic efficiency for carbonaceous products over 97.7 % at −0.9 V RHE is obtained on the Sn 3 O 4 electrocatalyst. This combination of rigorous experimental and theoretical studies clearly elucidates the advantages of Sn 3 O 4 and sheds light on the rational design of SnO x -based electrocatalysts with special electronic structure for efficient CO 2 RR utilizations. • DFT calculations are performed to decipher the behaviors of SnO, SnO 2 and Sn 3 O 4 toward electroreduction of CO 2. • Sn 3 O 4 possesses the lowest activation barrier for the formation of HCOOH. • Flower-like Sn 3 O 4 delivers a FE carbonaceous of 97.7% and a partial current density of 16.6 mA/cm2 at −0.9 V RHE. • An onset overpotential of 190mV, the lowest reported thus far, is achieved on Sn 3 O 4. • The up-shifted electronic structure of Sn 3 O 4 plays an essential role for the boosted production of carbonaceous products. The catalytic performance of electrocatalyst on carbon dioxide reduction reaction (CO 2 RR) heavily depends on the adsorption strength of intermediates and electronic structure. For the first time, Sn 3 O 4 is identified as the most effective electrocatalyst for CO 2 RR among SnO x -based materials towards carbonaceous products by density functional theory calculation and experimental work. An optimized adsorption strength of intermediates is achieved on Sn 3 O 4 owing to the originally synergistic Sn2+ and Sn4+ effect resulted from the unique electronic structure of Sn 3 O 4. Additionally, with an up-shifted band structure, Sn 3 O 4 imparts the moderate adsorption energies of *H and HCOO* intermediate, which suppresses the hydrogen evolution reaction and promotes the intrinsic catalytic efficiency of CO 2 RR. The synthesized Sn 3 O 4 electrocatalyst delivers a carbonaceous faradic efficiency of ∼97.7% with a partial current density of 16.6 mA/cm2 at −0.9 V RHE. The corresponding overpotential of 190 mV is lower than the values from most of the reported SnO x -based electrocatalysts. [ABSTRACT FROM AUTHOR]

Patient stratification has made a great contribution to efficient and personalized medicine. An important task in patient stratification is to discover quite distinct disease subtypes for effective treatment. In this paper, we propose a new deep learning and clustering model which combines Deep Belief Network (DBN) and Fuzzy C-Means(FCM), called Unsupervised Deep Fuzzy C-Means clustering Network(UDFCMN), to cluster lung cancer patients from lung CT images. In our deep clustering network, images after preprocessing are first encoded into multiple layers of hidden variables to extract hierarchical features and feature distribution and form the high-level representations. Here, to solve the problem of feature homogenization in DBN, we introduce the Winner-Take-All (WTA) idea to meliorate the traditional DBN structure, called WTADBN. Then FCM is used to produce the initial cluster labels with the new representations learnt by stacked WTARBM. Therefore, the FCM-generated cluster labels are used for the fine-tuning of the DBN as ground-truth labels. And an unsupervised image clustering and patient stratification process is completed by cross iteration. We tested our deep FCM clustering algorithm to do experiment on both public dataset from the internet and private dataset from cooperate hospital. For the latter one, the clinical and biological verification was also performed. Experimental results reveal outperformance of UDFCMN as compared to the state-of-the-art unsupervised classification methods. These results also indicate that our approach may have practical applications in lung cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy. • Unsupervised learning can learn hidden features related to survival in images. • Mutations in lung cancer related genes can be shown on CT images. • The proposed model can distinguish clinical difference among lung cancer subtypes. • The stratification of patients helps physicians with important clinical decisions. [ABSTRACT FROM AUTHOR]

• Soluble green polymers are obtained based on thieno[3,4-b]pyrazine based terthienyls. • The polymers are obtained by employing ProDOT and benzene as the donor units. • Three polymers showed multichromic property and satisfactory switching performance. Three novel soluble conjugated conducting polymers PBTPT-1, PBTPT-2, and PBTPT-3 were synthesized via Suzuki coupling reaction by employed different feed ratios of the donor units to the acceptor units. 5,7-bis(5-bromo-thiophen-2-yl)-2,3- bis(4-(decyloxy)phenyl)thieno[3,4-b]pyrazine (M2) was prepared and used as the acceptor unit. Besides, benzene (M1) and propylenedioxythiophene (M3) were adopted as the donor units. The molecular structures of the polymers are confirmed by gel permeation chromatography, infrared spectrum, nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS). The optoelectric properties of the polymers were studied by cyclic voltammetry (CV), spectroelectrochemistry, switching kinetics and chromaticity analysis. The thermal stabilities of the polymers were also conducted. Three polymers have narrow band gaps at around 1.50 eV and pseudo-reversible redox activities. For PBTPT-1, the optical contrasts are 24.56 % at 440 nm, 18.96 % at 695 nm and 66.12 % at 1500 nm, and its coloration efficiencies are 196.36 cm2 C−1 at 440 nm, 199.08 cm2 C−1 at 695 nm and 243.49 cm2 C−1 at 1500 nm. Three polymers possessed multichromic property, and exhibited neutral green color with different saturation degrees. Three polymers revealed superior performances in near infrared region to that in visible region, indicating that the copolymers showed prospect applications in the fabrication of near infrared electrochromic device. [ABSTRACT FROM AUTHOR]