GANN: Graph Alignment Neural Network for Semi-Supervised Learning

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors

Surfactant Induced Reservoir Wettability Alteration: Recent Theoretical and Experimental Advances in Enhanced Oil Recovery

Reservoir wettability plays an important role in various oil recovery processes. The origin and evolution of reservoir wettability were critically reviewed to better understand the complexity of wettability due to interactions in crude oil-brine-rock system, with introduction of different wetting states and their influence on fluid distribution in pore spaces. The effect of wettability on oil recovery of waterflooding was then summarized from past and recent research to emphasize the importance of wettability in oil displacement by brine. The mechanism of wettability alteration by different surfactants in both carbonate and sandstone reservoirs was analyzed, concerning their distinct surface chemistry, and different interaction patterns of surfactants with components on rock surface. Other concerns such as the combined effect of wettability alteration and interfacial tension (IFT) reduction on the imbibition process was also taken into account. Generally, surfactant induced wettability alteration for enhanced oil recovery is still in the stage of laboratory investigation. The successful application of this technique relies on a comprehensive survey of target reservoir conditions, and could be expected especially in low permeability fractured reservoirs and forced imbibition process

Influence of micro-rolling on the strength and ductility of plasma-arc additively manufactured Ti–6Al–4V alloys

The aim of this study was to reduce the number of defects, refine the grain size, and improve the mechanical properties of Ti–6Al–4V. We investigated the effect of interpass micro-rolling on the microstructures and mechanical properties of Ti–6Al–4V alloys produced via plasma arc additive manufacturing (PAAM). The non-rolled plasma-arc additively manufactured (PAAMed) sample exhibited coarse columnar crystals, basket-weave microstructures, and Widmanstätten structures, while the interpass-micro-rolled PAAMed sample exhibited fine equiaxed crystals and basket-weave microstructures. Interpass micro-rolling could significantly reduce the number density and size of defects during PAAM, and the rolled samples exhibited lower mechanical property anisotropy than the non-rolled samples. Moreover, the rolled sample exhibited a higher yield strength (∼797.66 and 793.45 MPa), tensile strength (∼939.73 and 935.71 MPa), and elongation (∼12.83% and 14.73%) in the X- and Z-directions than the unrolled sample. These enhanced mechanical properties can be attributed to αGB fragmentation, grain boundary strengthening, dislocation strengthening, and a low crack density. The micro-cracks preferentially nucleated around defects in the non-rolled sample, resulting in high crack and defect densities

Deep Graph Clustering via Dual Correlation Reduction

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github

A New Method for Forest Canopy Hemispherical Photography Segmentation Based on Deep Learning

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method

The microstructural features and strain recovery characteristics of Ti–V–Al shape memory alloy with minor Sc addition

In the present work, minor Sc addition was employed to control the microstructural features, further optimizing the mechanical/functional performances of Ti–V–Al based shape memory alloy. The results revealed that phase constituents of Ti–V–Al based shape memory alloys were dominated by single αˊˊ martensite phase, regardless of Sc content. Nevertheless, Sc addition caused the significant lattice distortion of Ti–V–Al based shape memory alloy. Meanwhile, the grain size of Ti–V–Al based shape memory alloys were reduced due to Sc addition. Besides, the martensite morphologies evolved from self-accommodation configuration to optimal orientation configuration, but the types of twins kept unchanged. All Ti–V–Al based shape memory alloys showed the reversible αˊˊ→β martensitic transformation in heating curves, irrespective of Sc content. With Sc content increasing, the martensitic transformation temperatures increased linearly due to the chemical composition effect and mechanical effect. Moderate Sc addition was favor to the improvement of thermal cycling stability of Ti–V–Al based shape memory alloys, which can enhance its functional stability. In proportion, the mechanical and functional properties of Ti–V–Al based shape memory alloys firstly increased and then decreased. Ti–V–Al based shape memory alloy with moderate Sc content of 0.025 at.% possess the higher fracture strength of 748.4 MPa, superior ductility of 18.7% and the larger shape memory effect strain of 4.31% under the 8% pre-strain condition. The higher performances in Ti–V–Al shape memory alloy with 0.025 at.% Sc can be attributed to the compressive effect of solution strengthening and grain refinement

Cathelicidin-OA1, a novel antioxidant peptide identified from an amphibian, accelerates skin wound healing

Abstract Cathelicidins play pivotal roles in host defense. The discovery of novel cathelicidins is important research; however, despite the identification of many cathelicidins in vertebrates, few have been reported in amphibians. Here we identified a novel cathelicidin (named cathelicidin-OA1) from the skin of an amphibian species, Odorrana andersonii. Produced by posttranslational processing of a 198-residue prepropeptide, cathelicidin-OA1 presented an amino acid sequence of ‘IGRDPTWSHLAASCLKCIFDDLPKTHN′ and a molecular mass of 3038.5 Da. Functional analysis showed that, unlike other cathelicidins, cathelicidin-OA1 demonstrated no direct microbe-killing, acute toxicity and hemolytic activity, but did exhibit antioxidant activity. Importantly, cathelicidin-OA1 accelerated wound healing against human keratinocytes (HaCaT) and skin fibroblasts (HSF) in both time- and dose-dependent manners. Notably, cathelicidin-OA1 also showed wound-healing promotion in a mouse model with full-thickness skin wounds, accelerating re-epithelialization and granulation tissue formation by enhancing the recruitment of macrophages to the wound site, inducing HaCaT cell proliferation and HSF cell migration. This is the first cathelicidin identified from an amphibian that shows potent wound-healing activity. These results will help in the development of new types of wound-healing agents and in our understanding of the biological functions of cathelicidins