The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data.

In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are provided. To derive the unified representation of multi-granularity linguistic assessment information, a fuzzy quantitative transformation for multi-granularity uncertain linguistic information is proposed. The proofs of the desirable properties and some normalized formulas for the trapezoidal fuzzy numbers are presented simultaneously. Next, the objective weight of each assessment indicator is further determined by calculating the Jaccard-Cosine similarity between the trapezoidal fuzzy numbers. Moreover, the trapezoidal fuzzy numbers corresponding to the comprehensive assessment values of each alternative are obtained. The alternatives are effectively ranked according to the distance from the centroid of the trapezoidal fuzzy number to the origin. Finally, based on the proposed consistent fuzzy assessment method, the suitability assessment of forest land resources is achieved under a multi-source heterogeneous data setting.

Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivity (FC) analysis ignore the differences between positive and negative FCs, which provide the potential information complementarity. And they also require additional information to construct a pre-defined graph. Meanwhile, two challenging demand attentions are the imbalance of performance caused by the class distribution and the inherent heterogeneity of multi-site data. In this paper, we propose a novel dynamic graph Transformer network based on dual-view connectivity for ASD Identification. It is based on the Autoencoders, which regard the input feature as individual feature and without any inductive bias. First, a dual-view feature extractor is designed to extract individual and complementary information from positive and negative connectivity. Then Graph Transformer network is innovated with a hot plugging K-Nearest Neighbor (KNN) algorithm module which constructs a dynamic population graph without any additional information. Additionally, we introduce the PolyLoss function and the Vrex method to address the class imbalance and improve the model's generalizability. The evaluation experiment on 1102 subjects from the ABIDE I dataset demonstrates our method can achieve superior performance over several state-of-the-art methods and satisfying generalizability for ASD identification.

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM2) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

PGFNet: Preference-Guided Filtering Network for Two-View Correspondence Learning.

Accurate correspondence selection between two images is of great importance for numerous feature matching based vision tasks. The initial correspondences established by off-the-shelf feature extraction methods usually contain a large number of outliers, and this often leads to the difficulty in accurately and sufficiently capturing contextual information for the correspondence learning task. In this paper, we propose a Preference-Guided Filtering Network (PGFNet) to address this problem. The proposed PGFNet is able to effectively select correct correspondences and simultaneously recover the accurate camera pose of matching images. Specifically, we first design a novel iterative filtering structure to learn the preference scores of correspondences for guiding the correspondence filtering strategy. This structure explicitly alleviates the negative effects of outliers so that our network is able to capture more reliable contextual information encoded by the inliers for network learning. Then, to enhance the reliability of preference scores, we present a simple yet effective Grouped Residual Attention block as our network backbone, by designing a feature grouping strategy, a feature grouping manner, a hierarchical residual-like manner and two grouped attention operations. We evaluate PGFNet by extensive ablation studies and comparative experiments on the tasks of outlier removal and camera pose estimation. The results demonstrate outstanding performance gains over the existing state-of-the-art methods on different challenging scenes. The code is available at https://github.com/guobaoxiao/PGFNet.

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation.

Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.

UCR-Net: U-shaped context residual network for medical image segmentation.

Medical image segmentation prerequisite for numerous clinical needs is a critical step in biomedical image analysis. The U-Net framework is one of the most popular deep networks in this field. However, U-Net's successive pooling and downsampling operations result in some loss of spatial information. In this paper, we propose a U-shaped context residual network, called UCR-Net, to capture more context and high-level information for medical image segmentation. The proposed UCR-Net is an encoder-decoder framework comprising a feature encoder module and a feature decoder module. The feature decoder module contains four newly proposed context attention exploration(CAE) modules, a newly proposed global and spatial attention (GSA) module, and four decoder blocks. We use the proposed CAE module to capture more multi-scale context features from the encoder. The proposed GSA module further explores global context features and semantically enhanced deep-level features. The proposed UCR-Net can recover more high-level semantic features and fuse context attention information from CAE and global and spatial attention information from GSA module. Experiments on the retinal vessel, femoropopliteal artery stent, and polyp datasets demonstrate that the proposed UCR-Net performs favorably against the original U-Net and other advanced methods.

Multiband Spectrum Sensing and Power Allocation for aCognitive Radio-Enabled Smart Grid.

As part of an Internet of Things (IoT) framework, the Smart Grid (SG) relies on advanced communication technologies for efficient energy management and utilization. Cognitive Radio (CR), which allows Secondary Users (SUs) to opportunistically access and use the spectrum bands owned by Primary Users (PUs), is regarded as the key technology of the next-generation wireless communication. With the assistance of CR technology, the quality of communication in the SG could be improved. In this paper, based on a hybrid CR-enabled SG communication network, a new system architecture for multiband-CR-enabled SG communication is proposed. Then, some optimization mathematical models are also proposed to jointly find the optimal sensing time and the optimal power allocation strategy. By using convex optimization techniques, several optimal methods are proposed to maximize the data rate of multiband-CR-enabled SG while considering the minimum detection probabilities to the active PUs. Finally, simulations are presented to show the validity of the proposed methods.

Terahertz Characterization of Lead-Free Dielectrics for Different Applications.

In this spotlight on applications, we describe our recent progress on the terahertz (THz) characterization of linear and nonlinear dielectrics for broadening their applications in different electrical devices. We begin with a discussion on the behavior of dielectrics over a broadband of frequencies and describe the main characteristics of ferroelectrics, as they are an important category of nonlinear dielectrics. We then move on to look at the influence of point defects, porosities, and interfaces, including grain boundaries and domain walls, on the dielectric properties at THz frequencies. Based on our studies on linear dielectrics, we show that THz characterization is able to probe the effect of porosities, point defects, shear planes, and grain boundaries to improve dielectric properties for telecommunication applications. Further, we demonstrate that THz measurements on relaxor ferroelectrics can be successfully used to study the reversibility of the electric field-induced phase transitions, providing guidance for improving their energy storage efficiency in capacitors. Finally, we show that THz characterization can be used to characterize the effect of domain walls in ferroelectrics. In particular, our studies indicate that the dipoles located within domain walls provide a lower contribution to the permittivity at THz frequencies than the dipoles present in domains. The new findings could help develop a new memory device based on nondestructive reading operations using a THz beam.

Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation.

Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor (KNN) algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix (LNC) between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.

Unexpected role of amphiphilic lignosulfonate to improve the storage stability of urea formaldehyde resin and its application as adhesives.

As the second-largest natural polymer, the utilization of lignin for practical applications has attracted increasing attention. In this study, lignosulfonate was employed to enhance the storage stability of urea formaldehyde (UF) resins. Cryo-scanning electron microscopy was firstly used to observe the influence of lignosulfonate addition on the colloidal morphology of UF resin. Moreover, adding lignosulfonate at different stages during the UF resins synthesis was also investigated to reveal its effect on storage stability. The potential interaction between lignosulfonate and UF resins was then analyzed via FT-IR, 13C CPMAS NMR, and zeta potential. It has been observed that lignosulfonate could increase the electrostatic repulsion of UF resins to avoid aging. No chemical reaction between UF resins and lignosulfonate was observed. After the elucidation of potential interaction, the effect of lignosulfonate on the curing process, thermal stability and adhesive performance of UF resins was systematically evaluated. Finally, as adhesives to fabricate eucalyptus plywood, the shear strength and formaldehyde release of UF resins with 20% addition of lignosulfonate could reach 0.88 MPa and 0.12 mg/L, respectively. Due to the excellent performance, low cost and wide availability of lignosulfonate, it might be industrially used as a stabilizer in the UF resins production.

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.

DNA N6-methyladenine (6mA) is closely involved with various biological processes. Identifying the distributions of 6mA modifications in genome-scale is of great significance to in-depth understand the functions. In recent years, various experimental and computational methods have been proposed for this purpose. Unfortunately, existing methods cannot provide accurate and fast 6mA prediction. In this study, we present 6mAPred-FO, a bioinformatics tool that enables researchers to make predictions based on sequences only. To sufficiently capture the characteristics of 6mA sites, we integrate the sequence-order information with nucleotide positional specificity information for feature encoding, and further improve the feature representation capacity by analysis of variance-based feature optimization protocol. The experimental results show that using this feature protocol, we can significantly improve the predictive performance. Via further feature analysis, we found that the sequence-order information and positional specificity information are complementary to each other, contributing to the performance improvement. On the other hand, the improvement is also due to the use of the feature optimization protocol, which is capable of effectively capturing the most informative features from the original feature space. Moreover, benchmarking comparison results demonstrate that our 6mAPred-FO outperforms several existing predictors. Finally, we establish a web-server that implements the proposed method for convenience of researchers' use, which is currently available at http://server.malab.cn/6mAPred-FO.

Author Correction: Synthesis and characterization of polyethylene glycol-phenol-formaldehyde based polyurethane composite.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Study on MMA and BA Emulsion Copolymerization Using 2,4-Diphenyl-4-methyl-1-pentene as the Irreversible Addition-Fragmentation Chain Transfer Agent.

As a chain transfer agent, 2,4-diphenyl-4-methyl-1-pentene (αMSD) was first introduced in the emulsion binary copolymerization of methyl methacrylate (MMA) and butyl acrylate (BA) based on an irreversible addition-fragmentation chain transfer (AFCT) mechanism. The effects of αMSD on molecular weight and its distribution, the degree of polymerization, polymerization rate, monomer conversion, particle size, and tensile properties of the formed latexes were systematically investigated. Its potential chain transfer mechanism was also explored according to the 1H NMR analysis. The results showed that the increase in the content of αMSD could lead to a decline in molecular weight, its distribution, and the degree of polymerization. The mass percentage of MMA in the synthesized polymers was also improved as the amounts of αMSD increased. The chain transfer coefficients of αMSD for MMA and BA were 0.62 and 0.47, respectively. The regulation mechanism of αMSD in the emulsion polymerization of acrylates was found to be consistent with Yasummasa's theory. Additionally, monomer conversion decreased greatly to 47.3% when the concentration of αMSD was higher than 1 wt% due to the extremely low polymerization rate. Moreover, the polymerization rate was also decreased probably due to the desorption and lower reactivity of the regenerative radicals from αMSD. Finally, the tensile properties of the resulting polyacrylate films were significantly affected due to the presence of αMSD.

Efficient Robust Model Fitting for Multistructure Data Using Global Greedy Search.

In this paper, a new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers. The proposed method is composed of three steps: first, a conventional greedy search strategy is employed to generate (initial) model hypotheses based on the sequential "fit-and-remove" procedure because of its computational efficiency. Second, to efficiently generate accurate model hypotheses close to the true models, a novel global greedy search strategy initially samples from the inliers of the obtained model hypotheses and samples subsequent data subsets from the whole input data. Third, mutual information theory is applied to fuse the model hypotheses of the same model instance. The conventional greedy search strategy is used to generate model hypotheses for the remaining model instances, if the number of retained model hypotheses is less than that of the true model instances after fusion. The second and the third steps are performed iteratively until an adequate solution is obtained. Experimental results demonstrate the effectiveness and efficiency of the proposed method for model fitting.

Synthesis and characterization of polyethylene glycol-phenol-formaldehyde based polyurethane composite.

A series of phenol-formaldehyde-polyethylene glycol polyether polyols (PF-PEGs) were synthesized through the condensation polymerization and etherification of phenol, formaldehyde, and poly(ethylene glycol) (PEG) under alkaline conditions and subsequently reacted with 1,6-hexamethylene diisocyanate to obtain polyurethane (PU) films using acetone as solvents. The influence of phenol and formaldehyde to PEG mass ratio ((P + F)/PEG) on the hydroxyl number of PF-PEGs and mechanical properties, thermal stabilities, crystallization behaviors, as well as microstructure of polyurethane composite films were studied using chemical analysis, mechanical tests, thermogravimetric analyses (TGA), dynamic mechanical analyses (DMA), X-ray diffraction (XRD), scanning and transmission electron microscopies (SEM and TEM), respectively. Results demonstrated that PF-PEGs with (P + F)/PEG of 50/50 had the highest hydroxyl number of 323 mg K(OH)/g. The incorporation of phenol and formaldehyde into PEG improved the mechanical properties of polyurethane films, glass transition temperature (Tg), and thermal properties but resulted in the brittleness characteristic of the composite films and low crystallization properties. Moreover, the synthesis mechanism of PF-PEGs polyurethane composite films was revealed, which would provide a theoretical base for the preparation of the rigid polyurethane foams based on phenolic resins.

Structural Analysis of Complex Atrial Intramural Microstructure from A Multi-layer Model Based on Siamese Network.

Quantitative analysis of complex atrial intramural microstructure is a crucial step towards understanding the mechanism behind atrial fibrillation (AF) maintenance. Siamese network was adopted to extract features from computationally simulated multi-layer fibrosis structure. Through analysis of the features produced by the feature extractor, the difference between Non-sustained and Sustained simulations was comprehended intuitively and electrophysiologically. Complex conduction pathway marked by the feature extractor might be an indicator for AF radio-frequency ablation clinically.

A Robust Algorithm for Selecting Optimal Regularization Parameter Based on Bilateral Accumulative Area.

The choice of regularization parameters is very important for the reconstruction result in the inverse problem of electrocardiology. In this study, the bilateral accumulative area detector is introduced to estimate the optimal parameter points of the L-curve and Generalized Cross-Validation method to Tikhonov regularization and truncated singular value decomposition. The experimental results suggest that this method can achieve high validity and high robustness for the estimation of regularization parameters compared to conventional methods.

The effect of complex intramural microstructure caused by structural remodeling on the stability of atrial fibrillation: Insights from a three-dimensional multi-layer modeling study.

BACKGROUND: Recent researches have suggested that the complex three-dimensional structures caused by structural remodeling play a key role in atrial fibrillation (AF) substrates. Here we aimed to investigate this hypothesis using a multi-layer model representing intramural microstructural features. METHODS: The proposed multi-layer model was composed of the endocardium, connection wall, and epicardium. In the connection wall, intramural fibrosis was simulated using fibrotic patches randomly scattered in the myocardial tissue of fibrotic layers, while endo-epicardial dissociation was simulated using myocardial patches randomly scattered in the fibrotic tissue of isolation layers. Multiple simulation groups were generated to quantitatively analyze the effects of endo-epicardial dissociation and intramural fibrosis on AF stability, including a stochastic group, interrelated groups, fibrosis-degree-controlled groups, and dissociation-degree-controlled groups. RESULTS: 1. Stable intramural re-entries were observed to move along complete re-entrant circuits inside the transmural wall in four of 65 simulations in the stochastic group. 2. About 21 of 23 stable simulations in the stochastic group were distributed in the areas with high endo-epicardial dissociation and intramural fibrosis. 3. The difference between fibrosis-degree-controlled groups and dissociation-degree-controlled groups suggested that some distributions of connection areas may affect AF episodes despite low intramural fibrosis and endo-epicardial dissociation. 4. The overview of tracking phase singularities revealed that endo-epicardial dissociation played a visible role in AF substrates. CONCLUSION: The complex intramural microstructure is positively correlated with critical components of AF maintenance mechanisms. The occurrence of intramural re-entry further indicates the complexity of AF wave-dynamics.

Multi-window detection for P-wave in electrocardiograms based on bilateral accumulative area.

BACKGROUND AND OBJECTIVE: P-wave detection is one of the most challenging aspects in electrocardiograms (ECGs) due to its low amplitude, low frequency, and variable waveforms. This work introduces a novel multi-window detection method for P-wave delineation based on the bilateral accumulative area. METHOD: The bilateral accumulative area is calculated by summing the areas covered by the P-wave curve with left and right sliding windows. The onset and offset of a positive P-wave correspond to the local maxima of the area detector. The position drift and difference in area variation of local extreme points with different windows are used to systematically combine multi-window and 12-lead synchronous detection methods, which are used to screen the optimization boundary points from all extreme points of different window widths and adaptively match the P-wave location. RESULTS: The proposed method was validated with ECG signals from various databases, including the Standard CSE Database, T-Wave Alternans Challenge Database, PTB Diagnostic ECG Database, and the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database. The average sensitivity Se was 99.44% with a positive predictivity P+ of 99.37% for P-wave detection. Standard deviations of 3.7 and 4.3ms were achieved for the onset and offset of P-waves, respectively, which is in agreement with the accepted tolerances required by the CSE committee. CONCLUSION: Compared with well-known delineation methods, this method can achieve high sensitivity and positive predictability using a simple calculation process. The experiment results suggest that the bilateral accumulative area could be an effective detection tool for ECG signal analysis.

The plant virus microscope image registration method based on mismatches removing.

The electron microscopy is one of the major means to observe the virus. The view of virus microscope images is limited by making specimen and the size of the camera's view field. To solve this problem, the virus sample is produced into multi-slice for information fusion and image registration techniques are applied to obtain large field and whole sections. Image registration techniques have been developed in the past decades for increasing the camera's field of view. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Alternatively, the methods are conceived just to provide visually pleasant registration for high overlap ratio image sequence. This work presents a method for virus microscope image registration acquired with detailed visual information and subpixel accuracy, even when overlap ratio of image sequence is 10% or less. The method proposed focus on the correspondence set and interimage transformation. A mismatch removal strategy is proposed by the spatial consistency and the components of keypoint to enrich the correspondence set. And the translation model parameter as well as tonal inhomogeneities is corrected by the hierarchical estimation and model select. In the experiments performed, we tested different registration approaches and virus images, confirming that the translation model is not always stationary, despite the fact that the images of the sample come from the same sequence. The mismatch removal strategy makes building registration of virus microscope images at subpixel accuracy easier and optional parameters for building registration according to the hierarchical estimation and model select strategies make the proposed method high precision and reliable for low overlap ratio image sequence.