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Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.

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.

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss.

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE.

Seeking reliable correspondences between two scenes is crucial for solving feature-based point cloud registration tasks. In this paper, we propose a novel outlier rejection network, called Channel-Spatial Contextual Enhancement Network (CSCE-Net), to obtain rich contextual information on correspondences, which can effectively remove outliers and improve the accuracy of point cloud registration. To be specific, we design a novel channel-spatial contextual (CSC) block, which is mainly composed of the Channel-Spatial Attention (CSA) layer and the Nonlocal Channel-Spatial Attention (Nonlocal CSA) layer. The CSC block is able to obtain more reliable contextual information, in which the CSA layer can selectively aggregate the mutual information between the channel and spatial dimensions. The Nonlocal CSA layer can compute feature similarity and spatial consistency for each correspondence, and the CSA layer and Nonlocal CSA layer can support each other. In addition, to improve the distinguishing ability between inliers and outliers, we present an advanced seed selection mechanism to select more dependable initial correspondences. Extensive experiments demonstrate that CSCE-Net outperforms state-of-the-art methods for outlier rejection and pose estimation tasks on public datasets with varying 3D local descriptors. In addition, the network parameters of CSCE-Net are reduced from 1.05M to 0.56M compared to the recently learning-based outlier rejection method PointDSC.

A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method.

An effective method is proposed to solve the group decision-making problems with uncertain multiplicative linguistic information. First, to compare two uncertain multiplicative linguistic variables, a possibility degree formula is introduced in accordance with the operation laws. Some desirable properties of possibility degree are provided. Then, the uncertain multiplicative linguistic hybrid weighted geometric averaging (ULHWG) operator is developed to aggregate uncertain multiplicative linguistic variables into an overall preference value. Thus, a comprehensive algorithm for linguistic group decision-making is proposed based on the deviation measure and the ULHWG operator. Finally, a numerical example is provided to illustrate the practicality and validity of the proposed method.

In this paper, we design a variable-gain first order sliding mode, in which the adaptive gain is state-independent and designed by two types of smooth, monotonically increasing and bounded functions. We modify the structure of the sliding mode even further by using an amplifying overall scaling factor. Both of the variable gain and the scaling factor can improve the control accuracy. Then we apply the newly designed sliding mode to the attitude synchronization of spacecraft. Simulation results show that, compared with the constant gain sliding mode, the proposed design ensures much higher control precision evaluated by the attitude synchronization error without more fuel consumption.

Intelligent transportation systems (ITSs), which can fulfill people's increasing requirements on mobility, have arisen as a popular research area. In order to implement the ITSs, a large amount of data needs to be executed within requested time. Due to the remote locations, cloud centers are no longer sufficient in data off-loading. A heterogeneous vehicular network, which consists of three kinds of computation resources, i.e., a cloud center, an edge node, and multiple vehicles, is, therefore, constructed to solve the aforementioned issues. First, considering the heterogeneity of each computation resource, the delay bound can be derived based on the martingale theory. Especially, data off-loading to the cloud center or to the edge node is assisted by road side units (RSUs). The communication link from the source node to the cloud center or to the edge node is modeled as a two-hop one. By utilizing the min-plus algebra, the two-hop link can be abstracted and analyzed in an equivalent single system further. Then, given a requested delay time, the maximum off-loading capacity of each resource can be obtained from the derived delay bound. When the tasks are smaller than the network available off-loading capacity, an optimal task allocation problem can, therefore, be constructed to minimize the overall delay violation probability. The numerical results are presented to show the performance of the proposed task allocation scheme in the heterogeneous vehicular networks. In addition, it can be verified that the proposed optimal task allocation scheme performs better than the benchmarks in the literature.

Although quantum maximal-distance-separable (MDS) codes that satisfy the quantum singleton bound have become an important research topic in the quantum coding theory, it is not an easy task to search for quantum MDS codes with the minimum distance that is larger than (q/2) + 1. The pre-shared entanglement between the sender and the receiver can improve the minimum distance of quantum MDS codes such that the minimum distance of some constructed codes achieves (q/2) + 1 or exceeds (q/2) + 1. Meanwhile, how to determine the required number of maximally entangled states to make the minimum distance of quantum MDS codes larger than (q/2) + 1 is an interesting problem in the quantum coding theory. In this paper, we utilize the decomposition of the defining set and q2-cyclotomic cosets of constacyclic codes with the form q = αm + t or q = αm + α - t and n = (q2 + 1/α) to construct some new families of entanglement-assisted quantum MDS codes that satisfy the entanglement-assisted quantum singleton bound, where q is an odd prime power and m is a positive integer, while both α and t are positive integers such that α = t2 + 1. The parameters of these codes constructed in this paper are more general compared with the ones in the literature. Moreover, the minimum distance of some codes in this paper is larger than (q/2) + 1 or q + 1.

In this paper, a hierarchical region merging method is proposed for partitioning synthetic aperture radar (SAR) image into un-overlapping scene area, such as forest regions, urban regions, agricultural regions, and so on. The proposed method mainly consists of two steps: initial over-segmentation and hierarchical regions merging. The over-segmentation uses the watershed transform to the thresholded Bhattacharyya-coefficient-based edge strength map (BESM), and the hierarchical regions merging applies a new region merging cost weighted by a gradually increasing orientated edge strength penalty. There is a defect that the ratio-based edge detector widely used in homogeneous SAR image fails to distinguish the transitions between uniform and texture regions in high spatial resolution SAR image, and yields an initial over-segmentation result with some regions straddling multiple uniform or texture areas. To overcome this, the Bhattacharyya coefficient is used to replace the ratio-based edge detector for extracting the ESM of a SAR image by using a bi-rectangle-window configuration. Multi-scale windows are utilized to capture additional edge information. A new region merging cost is proposed based on the Kuiper's distance, weighted by a new gradually increasing orientated edge strength penalty term. The hierarchical region merging criterion is obtained with the increasing of the strength of the edge penalty. The effectiveness of the proposed method is demonstrated by comparing it qualitatively and quantitatively with several state-of-the-art methods.

Generally, it is not easy to construct quantum maximal-distance-separable (MDS) codes with the minimum distance greater than $\frac {q}{2}+1$ . The minimum distance of quantum MDS codes can achieve $\frac {q}{2}+1$ or exceed $\frac {q}{2}+1$ by adopting pre-shared entanglement. In this work, some new families of entanglement-assisted quantum MDS codes that satisfy the quantum Singleton bound are constructed and the number of maximally entangled states required is determined to make the minimum distance of some constructed codes achieve $\frac {q}{2}+1$ or exceed $\frac {q}{2}+1$ by utilizing the decomposition of the defining set and $q^{2}$ -cyclotomic cosets of constacyclic codes with length $\frac {q^{2}+1}{\gamma }$ , where $\gamma =t^{2}+1$ , $t$ is a power of 2 and $q=t^{e}>4$ with $e\equiv 1~\textrm {mod}~4$ or $e\equiv 3~\textrm {mod}~4$ . Moreover, the parameters of these codes constructed in this paper are more general relative to the ones in the literature and the minimum distance of some codes constructed in this paper is larger than $\frac {q}{2}+1$ .

The prospect of being able to move through security without the inconvenience of separating liquids from bags is an exciting one for passengers, and there are important operational benefits for airports as well. Here, two terahertz (THz) systems, 100 GHz sub-THz line scanner and attenuation total reflection-based THz time domain spectroscopy (TDS), have been used to demonstrate the capability of identifying different liquid samples. Liquid samples’ THz complex permittivities are measured and their differences have contributed to the variation of 100 GHz 2D images of volatile liquids with different volumes inside of cannister bottles. The acquired attenuation images at 100 GHz can easily be used to distinguish highly absorbed liquids (Water, Ethanol, Fuel Treatment Chemicals) and low loss liquids (Petrol, Diesel, Kerosene and Universal Bottle Cleaner). The results give a promising feasibility for mm-wave imager and THz spectroscopy to efficiently identify different volatile liquids.

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.

The hesitant multiplicative preference relation (HMPR) was initially put forward in 2013. Utilizing the HMPR, the decision makers can give some possible preference values from the Saaty's 1-9 scale for pairwise comparisons over alternatives. However, until now, there is little research on the consistency and consensus of HMPRs. In this paper, we focus on exploiting the regression method and the feedback mechanism to improve the consistency and consensus for HMPRs and developing an efficient group decision-making model with HMPRs. First, a regression method based on complete consistency is developed to reduce HMPRs to multiplicative preference relations (MPRs). After that, a novel consistency checking and revising method based on the threshold estimation method and the feedback mechanism is given to improve the consistency level of the reduced MPRs. Then, a group consensus index is put forward to calculate the deviation degree between the reduced MPR and the group MPR and it is utilized to develop a consensus reaching process based on the feedback mechanism to improve the group consensus level of the reduced MPRs. Next, a complete group decision-making model with HMPRs is developed to rank all the alternatives and select the best one. Finally, a numerical example with respect to the investment of shared bikes is presented to demonstrate the proposed group decision-making model and then we also compare our proposed model with the existing one.

Since solid-state drives (SSD) have high read speed, they are integrated into a traditional hard disk drive (HDD)-based storage systems for improving the overall performance. The data migration, which is responsible for moving the data between the HDD and SSD so as to maximize the I/O performance, may result in the overhead in terms of unnecessary page migrations. To deal with it, we develop a novel buffer-aware data migration scheme to boost the performance for hybrid storage systems consisting of HDD and SSD by exploiting the content information in the buffer cache. It first introduces two new data states to reclassify the data, which are placed on the hybrid storage systems equipped with HDD and SSD. Then, it tries to improve the efficiency of data migration by copying the newly updated data in the buffer cache instead of the old version in the HDD or SSD into the other device. We also model the process of data migration operation and assess the effectiveness of the buffer-aware data migration scheme theoretically. Experimental results reveal that the buffer-aware data migration scheme can reduce the runtime time by up to 18% and the write count by up to 14% over the buffer-unaware data migration scheme under three benchmarks and a real workload.

The probabilistic hesitant multiplicative preference relations (PHMPRs) can model the opinions of the decision makers for pairwise comparisons over alternatives using some possible values from Saaty's 1-9 scale with the probability information. The existing study normalized the PHMPRs by adding additional preference values with the probability information into the shorter element before modifying the consistency and consensus. As a supplement, in this paper, we focus on dealing with the consistency and consensus for PHMPRs by means of multiplicative preference relations. We first present the definition of geometric consistency index for PHMPRs and develop an automatic iterative algorithm for checking and improving the geometric consistency index for PHMPRs. We also put forward the concept of geometric consensus degree for PHMPRs and then devise an automatic consensus reaching algorithm to modify the geometric consensus degree of PHMPRs. After that, a novel complete group decision making model with PHMPRs is put forward. Finally, an illustrative example is shown to verify the proposed model and we compare it with the existing study to show its superiority.

Non-rigid point set registration is a fundamental problem in many fields related to computer vision, medical image processing, and pattern recognition. In this paper, we develop a new point set registration method by using an adaptive weighted objective function, which formulates the alignment of two point sets as a mixture model estimation problem. The correspondences and the transformation are jointly recovered by using the expectation-maximization algorithm to obtain the promising results. First, the correspondences are established using local feature descriptors, and the adaptation parameters for the mixture model are computed from these correspondences. Then, the underlying transformation is recovered by minimizing the adaptive weighted objective function deduced from the mixture model. We demonstrate the advantages of the proposed method on various types of synthetic and real data and compare the results against those obtained using the state-of-the-art methods. The experimental results show that the proposed method is robust and outperforms the other registration approaches.

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.

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.

Because the solid-state drive (SSD) shows high access performance, it is usually integrated into existing hard disk drive (HDD)-based storage hierarchy to form HDD/SSD hybrid storage systems. To further improve the performance of HDD/SSD hybrid storage systems, data migration schemes have been put forward to migrate the sequential data between HDD and SSD. However, the existing schemes cannot be aware of dirty data in the buffer and then incur a large number of unnecessary page migrations. In this paper, we devise an efficient data migration scheme considering dying data for HDD/SSD hybrid storage systems. In this scheme, a new liveness state, called the dying state, is utilized to identify the live data, which will become dead shortly due to its corresponding dirty version in the buffer. To decrease the page migration count, this scheme uses the benefit-to-cost ratio to select a block for data migration and copies the up-to-date version of dying data into the free space instead of migrating the dying data. Our experimental results show that our proposed scheme can perform better than existing data migration schemes that are not aware of dying data under various benchmarks.

In orthogonal frequency division multiplexing relay networks with in-phase and quadrature imbalances and full-duplex relay station (RS), how to optimize pilot pattern and power allocation using the criterion of minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain least-squares channel estimator has a heavy impact on self-interference (SI) cancellation. First, the design problem of pilot pattern is casted as a convex optimization. From the Karush-Kuhn-Tucker conditions, the optimal analytical expression is derived when source and RS powers are given or fixed. Second, under the total transmit power sum constraint of source node and RS, an optimal power allocation (OPA) strategy is proposed to further alleviate the effect of Sum-MSE. Simulation results show that the proposed OPA performs better than equal power allocation (EPA) in terms of Sum-MSE, and the Sum-MSE performance gain increases with deviating ρ from the value of ρo minimizing the Sum-MSE, where ρ is defined as the average ratio of the residual SI channel gain at RS to the intended channel gain from source to RS. For example, the OPA achieves approximately 5-dB signal-to-noise ratio gain over EPA by shrinking or stretching ρ with a factor 4. More importantly, the more ρ decreases or increases, the more the performance gain becomes significant.

Lignocellulosic butanol residue (BR), obtained as the by-product of lignocellulosic butanol production, was used for the preparation of lignin-based phenol-resorcinol-formaldehyde resins (LPRFRs) by condensation polymerization. The lignin was first phenolated under sodium hydroxide catalysis at 90 to 92 °C at various phenolation times (1.0 to 4.0 h). The structural differences between BR and phenolated BR (PBR) were studied using Fourier transform infrared (FT-IR) spectroscopy, ultraviolet (UV) spectroscopy, thermogravimetric analysis (TGA), and gel permeation chromatography (GPC). The BR phenolated for 3.0 h had high phenol hydroxyl content, low molecular weight, and good thermal stability. The LPRFRs with 30 wt.% BR had the lowest free formaldehyde and phenol. With the substitution of BR for phenol, the hydrophilicity of LPRFRs increased. In addition, the mechanical, fragility, thermal properties, and morphology of lignin-phenol-resorcinol-formaldehyde foams (LPRFFs) were also investigated. The LPRFFs had excellent comprehensive properties when 30 wt.% PBR was substituted for phenol. These experimental findings could provide a new avenue for further study and application of bio-phenol-resorcinol foams.

Recently, directional modulation has become an active research area in wireless communications due to its security. Unlike existing research work, we consider a multi-beam directional modulation (MBDM) scenario with imperfect desired direction knowledge. In such a setting, a robust synthesis scheme is proposed for MBDM in broadcasting systems. In order to implement the secure transmission of a confidential message, the beamforming vector of the confidential message is designed to preserve its power as possible in the desired directions by minimizing its leakage to the eavesdropper directions while the projection matrix of artificial noise (AN) is to minimize the effect on the desired directions and force AN to the eavesdropper directions by maximizing the average receive signal-to-artificial-noise ratio at desired receivers. Simulation results show that compared with conventional methods, the proposed robust scheme achieves much better bit error rate performance along desired directions for a given signal-to-noise ratio (SNR). From the secrecy-rate aspect, the proposed scheme performs better than conventional methods for almost all SNR regions. In particular, in the medium and high SNR regions, the rate improvement of the proposed scheme over conventional methods is significant.

BackgroundRecent 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.MethodsThe 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.Results1. 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.ConclusionThe 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.

The partitioned Bonferroni mean (PBM) operator can efficiently aggregate inputs, which are divided into parts based on their interrelationships. To date, it has not been used to aggregate linguistic Pythagorean fuzzy numbers (LPFNs). In this paper, we extend the PBM operator and partitioned geometric Bonferroni mean (PGBM) operator to the linguistic Pythagorean fuzzy sets (LPFSs) and use them to develop a novel multiattribute group decision-making model under the linguistic Pythagorean fuzzy environment. We first define some novel operational laws for LPFNs, which take into consideration the interactions between the membership degree (MD) and nonmembership degree (NMD) from two different LPFNs. Based on these novel operational laws, we put forward the interaction PBM (LPFIPBM) operator, the weighted interaction PBM (LPFWIPBM) operator, the interaction PGBM (LPFIPGBM) operator, and the weighted interaction PGBM (LPFWIPGBM) operator. Then, we study some properties of these proposed operators and discuss their special cases. Based on the proposed LPFWIPBM and LPFWIPGBM operators, a novel multiattribute group decision-making model is developed to process the linguistic Pythagorean fuzzy information. Finally, some illustrative examples are introduced to compare our proposed methods with the existing ones.

Many applications of intuitionistic fuzzy sets depend on ranking or comparing intuitionistic fuzzy numbers. This paper presents a novel ranking method for intuitionistic fuzzy numbers based on the preference attitudinal accuracy and score functions. The proposed ranking method considers not only the preference attitude of decision maker, but also all the possible values in feasible domain. Some desirable properties of preference attitudinal accuracy and score functions are verified in detail. A total order on the set of intuitionistic fuzzy numbers is established by using the proposed two functions. The proposed ranking method is also applied to select renewable energy. The advantage and validity of the proposed method are shown by comparing with some representative ranking methods.

To appropriately realize the performance of a web service, it is essential to give it a comprehensive testing. Although an elastic test could be guaranteed in traditional cloud testing systems, the geographical test that supports real user behavior simulation remains a problem. In this paper, we propose a testing system based on a crowdsourcing model to carry out the distributed test on a target web server automatically. The proposed crowdsourcing-based testing system (CTS) provides a reliable testing model to simulate real user web browsing behaviors with the help of web browsers scattered all around the world. In order to make the entire test process the same as the real situation, two test modes are proposed to simulate real user activity. By evaluating every single resource of web service automatically, a tester can not only find out internal problems but also understand the performance of the web service. In addition, the complete geographical test is available with the performance measurements coming from different regions in the world. Several experiments are performed to validate the functionality and usability of CTS. It demonstrates that CTS is a complete and reliable web service testing system, which provides unique functions and satisfies different requirements.

Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate detection mechanisms of functional modules in PPIN, including open database, existing detection algorithms, and recent solutions. After that, we describe the proposed approach based on the simplified swarm optimization (SSO) algorithm and the knowledge of Gene Ontology (GO). The proposed solution implements the SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species datasets for experiment: fruitfly, mouse, scere, and human. The testing and analysis result show that the proposed solution is feasible, efficient, and could achieve a higher accuracy of prediction than existing approaches.