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In this Letter, we present a novel, to the best of our knowledge, line-wise scanning-based super-resolution (LSSR) imaging method. To reduce point spread functions overlapping among pixels, we specifically present a super-resolution (SR) imaging architecture to capture a series of low-resolution images using a line-based optical multiplexing technique, which is able to achieve a good balance between imaging quality and speed. In addition, we propose an efficient joint reconstruction algorithm based on total variation and low-rank constraints to generate a high-resolution image from these low-resolution images that contain different spatial details. Meanwhile, existing stripe noises are efficiently suppressed. Experiments on real data show that LSSR imaging has significant advantages over other state-of-the-art methods in terms of visual quality and quantitative measurement.

Motivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data, in particular when only a single sample is available., Results: In this study, we developed a novel approach, i.e. single-sample landscape entropy (SLE) method, to identify the tipping point during disease progression with only one sample data. Specifically, by evaluating the disorder of a network projected from a single-sample data, SLE effectively characterizes the criticality of this single sample network in terms of network entropy, thereby capturing not only the signals of the impending transition but also its leading network, i.e. dynamic network biomarkers. Using this method, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects, including influenza virus infection, lung cancer metastasis, prostate cancer and acute lung injury., Availability and Implementation: https://github.com/rabbitpei/SLE., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Frequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity of some unknown compounds. The main challenge is its efficiency, as (i) it is computationally intensive to test for graph isomorphisms, and (ii) the graph collection to be mined and mining results can be very large. Existing solutions often require days to derive mining results from biological networks even with relative low support threshold. Also, the whole mining results always cannot be stored in single node memory., Results: In this paper, we implement a parallel acceleration tool for classical frequent subgraph mining algorithm called cmFSM. The core idea is to employ parallel techniques to parallelize extension tasks, so as to reduce computation time. On the other hand, we employ multi-node strategy to solve the problem of memory constraints. The parallel optimization of cmFSM is carried out on three different levels, including the fine-grained OpenMP parallelization on single node, multi-node multi-process parallel acceleration and CPU-MIC collaborated parallel optimization., Conclusions: Evaluation results show that cmFSM clearly outperforms the existing state-of-the-art miners even if we only hold a few parallel computing resources. It means that cmFSM provides a practical solution to frequent subgraph mining problem with huge number of mining results. Specifically, our solution is up to one order of magnitude faster than the best CPU-based approach on single node and presents a promising scalability of massive mining tasks in multi-node scenario. More source code are available at:Source Code: https://github.com/ysycloud/cmFSM .

Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to "diagnose disease", sDNB is based on the information of differential associations, thereby having the ability to "predict disease" or "diagnose near-future disease". Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.

Singular value decomposition (SVD)-based 2D image reconstruction methods are developed and evaluated for a broad class of inverse problems for which there are no analytical solutions. The proposed methods are fast and accurate for reconstructing images in a non-iterative fashion. The multi-resolution strategy is adopted to reduce the size of the system matrix to reconstruct large images using limited memory capacity. A modified high-contrast Shepp-Logan phantom, a low-contrast FORBILD head phantom, and a physical phantom are employed to evaluate the proposed methods with different system configurations. The results show that the SVD methods can accurately reconstruct images from standard scan and interior scan projections and that they outperform other benchmark methods. The general SVD method outperforms the other SVD methods. The truncated SVD and Tikhonov regularized SVD methods accurately reconstruct a region-of-interest (ROI) from an internal scan with a known sub-region inside the ROI. Furthermore, the SVD methods are much faster and more flexible than the benchmark algorithms, especially in the ROI reconstructions in our experiments.

The photon counting detector based spectral CT system is attracting increasing attention in the CT field. However, the spectral CT is still premature in terms of both hardware and software. To reconstruct high quality spectral images from low-dose projections, an adaptive image reconstruction algorithm is proposed that assumes a known reference image (RI). The idea is motivated by the fact that the reconstructed images from different spectral channels are highly correlated. If a high quality image of the same object is known, it can be used to improve the low-dose reconstruction of each individual channel. This is implemented by maximizing the patch-wise correlation between the object image and the RI. Extensive numerical simulations and preclinical mouse study demonstrate the feasibility and merits of the proposed algorithm. It also performs well for truncated local projections, and the surrounding area of the region- of-interest (ROI) can be more accurately reconstructed. Furthermore, a method is introduced to adaptively choose the step length, making the algorithm more feasible and easier for applications.

Inspired by the Compressed Sensing (CS) theory, it has been proved that the interior problem of computed tomography (CT) can be accurately and stably solved if a region-of-interest (ROI) is piecewise constant or polynomial, resulting in the CS-based interior tomography. The key is to minimize the total variation (TV) of the ROI under the constraint of the truncated projections. Coincidentally, the Split-Bregman (SB) method has attracted a major attention to solve the TV minimization problem for CT image reconstruction. In this paper, we apply the SB approach to reconstruct an ROI for the CS-based interior tomography assuming a piecewise constant imaging model. Furthermore, the ordered subsets (OS) technique is used to accelerate the convergence of SB algorithm, leading to a new OS-SB algorithm for interior tomography. The conventional OS simultaneous algebraic reconstruction technique (OS-SART) and soft-threshold filtering (STF) based OS-SART are also implemented as references to evaluate the performance of the proposed OS-SB algorithm for interior tomography. Both numerical simulations and clinical applications are performed and the results confirm the advantages of the proposed OS-SB method.

Background: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients., Methods and Results: We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes., Conclusions: A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care., (Copyright © 2015. Published by Elsevier Ireland Ltd.)

In traditional zonal wavefront sensing for adaptive optics, after local wavefront gradients are obtained, the entire wavefront can be calculated by assuming that the wavefront is a continuous surface. Such an approach will lead to sub-optimal performance in reconstructing wavefronts which are either discontinuous or undersampled by the zonal wavefront sensor. Here, we report a new method to reconstruct the wavefront by directly measuring local wavefront phases in parallel using multidither coherent optical adaptive technique. This method determines the relative phases of each pupil segment independently, and thus produces an accurate wavefront for even discontinuous wavefronts. We implemented this method in an adaptive optical two-photon fluorescence microscopy and demonstrated its superior performance in correcting large or discontinuous aberrations.

Background: Haplotypes are important for assessing genealogy and disease susceptibility of individual genomes,but are difficult to obtain with routine sequencing approaches. Experimental haplotype reconstruction based on assembling fragments of individual chromosomes is promising, but with variable yields due to incompletely understood parameter choices., Results: We parameterize the clone-based haplotyping problem in order to provide theoretical and empirical assessments of the impact of different parameters on haplotype assembly. We confirm the intuition that long clones help link together heterozygous variants and thus improve haplotype length. Furthermore, given the length of the clones, we address how to choose the other parameters, including number of pools, clone coverage and sequencing coverage, so as to maximize haplotype length. We model the problem theoretically and show empirically the benefits of using larger clones with moderate number of pools and sequencing coverage. In particular, using 140 kb BAC clones, we construct haplotypes for a personal genome and assemble haplotypes with N50 values greater than 2.6 Mb. These assembled haplotypes are longer and at least as accurate as haplotypes of existing clone-based strategies, whether in vivo or in vitro., Conclusions: Our results provide practical guidelines for the development and design of clone-based methods to achieve long range, high-resolution and accurate haplotypes.

The microvasculature underlies the supply networks that support neuronal activity within heterogeneous brain regions. What are common versus heterogeneous aspects of the connectivity, density, and orientation of capillary networks? To address this, we imaged, reconstructed, and analyzed the microvasculature connectome in whole adult mice brains with sub-micrometer resolution. Graph analysis revealed common network topology across the brain that leads to a shared structural robustness against the rarefaction of vessels. Geometrical analysis, based on anatomically accurate reconstructions, uncovered a scaling law that links length density, i.e., the length of vessel per volume, with tissue-to-vessel distances. We then derive a formula that connects regional differences in metabolism to differences in length density and, further, predicts a common value of maximum tissue oxygen tension across the brain. Last, the orientation of capillaries is weakly anisotropic with the exception of a few strongly anisotropic regions; this variation can impact the interpretation of fMRI data.

With the continuous progress of science and technology, electric power equipment detection systems are developing in the direction of artificial intelligence. To achieve good automatic detection results, a high-quality and speedy algorithm is designed to intelligently detect indoor switchgear components in substations. This proposed method can detect the status of components based on image processing technology, which belongs to the field of condition monitoring. In this paper, the targets to be detected include multi-colour buttons or lights and the ammeters or voltmeters of the electrical switchgear. Two hybrid improved algorithms are used to optimise the you only look once v5s (YOLOv5s) network framework for increasing the detection speed and performance. Firstly, deeper feature map extraction is achieved using HorNet recursive gated convolution to replace the original C3 module for more efficient results. Then, a bidirectional feature pyramid network (BiFPN) algorithm is used to achieve the bidirectional propagation of feature information in the feature pyramid. This method can promote better fusion of feature information at different levels and help to convey feature and location information in the image. Finally, the improved YOLOv5s-BH model is used to detect the targets in substations. The experimental results show that the proposed method provides encouraging detection results for indoor switchgear components in substations. [ABSTRACT FROM AUTHOR]

The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be potentially applied to the recognition of traffic signs in intelligent driving vehicles and automatic inspection of power systems. Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP@0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network's rank-1 performance by 3.86%. [ABSTRACT FROM AUTHOR]

This study investigates the joint replenishment and delivery problem taking trade credits the delay in payment permitted by suppliers and capacity-constrained into consideration (CJRD-TC). The CJRD-TC aims to minimize the total system costs by finding a reasonable replenishment and delivery schedule policy. In this paper, an opposite-based RUNge Kutta optimization algorithm (OBRUN) is proposed to compare with other intelligent algorithms, including the differential evolution (DE), RUNge Kutta optimization algorithm (RUN), sine cosine algorithm (SCA), and adaptive differential evolution with optimal external archive (JADE). The proposed algorithm is tested on 14 optimization functions. The values of average, standard deviation, and the best are the lowest for 12 functions, which demonstrates OBRUN is superior to other algorithms. We also provide graphs of search history, 2D views of functions, trajectory curve, average fitness history, and convergence curve of OBRUN, the balance between exploration and exploitation over the course of iterations can be observed. The convergence curves of five algorithms are also depicted, which show OBRUN has a fast convergence rate and accuracy than other algorithms. We conduct numerical experiments, parameter tuning, and sensitivity analysis for each of the small-scale, medium-scale, and large-scale number of items. The OBRUN can always find better solutions with the lowest mean values and the best-found total cost results than other algorithms. Indicators of the average optimality gap and the optimality gap dominate by the value of zero. These computational results have demonstrated OBRUN is a suitable and effective candidate for managers to solve the practical CJRD-TC problems. [ABSTRACT FROM AUTHOR]

We propose an indefinite proximal subgradient-based algorithm (IPSB) for solving nonsmooth composite optimization problems. IPSB is a generalization of the Nesterov's dual algorithm, where an indefinite proximal term is added to the subproblems, which can make the subproblem easier and the algorithm efficient when an appropriate proximal operator is judiciously setting down. Under mild assumptions, we establish sublinear convergence of IPSB to a region of the optimal value. We also report some numerical results, demonstrating the efficiency of IPSB in comparing with the classical dual averaging-type algorithms. [ABSTRACT FROM AUTHOR]

Effective perception of the surrounding environment and the balance between accuracy and processing speed are crucial for the successful application of real-time semantic segmentation algorithm in the fields of autonomous driving, drones, and smart security. In this paper, a lightweight feature reuse network MHANet for real-time semantic segmentation is proposed. The main novelties of our method are improved ResNet and attention-based fusion mechanism. And the effectiveness of our method is verified by a large number of experiments. Without any pre-training process, the performance of real-time segmentation is improved by using deep fusion of segmentation maps with different resolutions. At the same time, our network converges faster than other networks using pre-training when trained from scratch. Compared with existing methods, the results obtained with our method on the Camvid dataset improve in accuracy (mIoU) ranging from 2% to 6% and in efficiency (FPS) ranging from 15% to 18%. The results achieved 71.87% mIoU of accuracy in the Cityscapes test set, processing images at 203 FPS. Experiments show that manual designed MHANet is effective in improving the performance of real-time semantic segmentation without any pre-training. [ABSTRACT FROM AUTHOR]

In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones. [ABSTRACT FROM AUTHOR]

Background: Increasing attention has been paid to the hemodynamic evaluation of cerebral arterial stenosis. We aimed to demonstrate the performance of angiography-based quantitative flow ratio (QFR) to assess hemodynamic alterations caused by luminal stenoses, using invasive fractional pressure ratios (FPRs) as a reference standard. Methods: Between March 2013 and December 2019, 29 patients undergoing the pressure gradient measurement of cerebral atherosclerosis were retrospectively enrolled. Wire-based FPR was defined by the arterial pressure distal to the stenotic lesion (Pd) to proximal (Pa) pressure ratios (Pd/Pa). FPR < 0.70 or FPR < 0.75 was assumed as hemodynamically significant stenosis. The new method of computing QFR from a single angiographic view, i.e., the Murray law-based QFR, was applied to the interrogated vessel. An artificial intelligence algorithm was developed to realize the automatic delineation of vascular contour. Results: Fractional pressure ratio and QFR were assessed in 38 vessels from 29 patients. Excellent correlation and agreement were observed between QFR and FPR [ r = 0.879, P < 0.001; mean difference (bias): −0.006, 95% limits of agreement: −0.198 to 0.209, respectively). Intra-observer and inter-observer reliability in QFR were excellent (intra-class correlation coefficients, 0.996 and 0.973, respectively). For predicting FPR < 0.70, the area under the receiver-operating characteristic curves (AUC) of QFR was 0.946 (95% CI, 0.820 to 0.993%). The sensitivity and specificity of QFR < 0.70 for identifying FPR < 0.70 was 88.9% (95% CI, 65.3 to 98.6%) and 85.0% (95% CI, 62.1 to 96.8%). For predicting FPR < 0.75, QFR showed similar performance with an AUC equal to 0.926. Conclusion: Computational QFR from a single angiographic view achieved comparable results to the wire-based FPR. The excellent diagnostic performance and repeatability empower QFR with high feasibility in the functional assessment of cerebral arterial stenosis. [ABSTRACT FROM AUTHOR]

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In this communication, we present flexible and efficient solutions of large-scale electromagnetic scattering problems using the parallel discontinuous Galerkin (DG) surface integral equation (SIE) method. A simplified formulation of the conventional DG is used by removing the interior penalty term, thereby avoiding computational burden of the contour integral in the conventional DG. It is demonstrated numerically that the simplification causes convergence deficiency and changes the accuracy convergence rate of the solution for objects with sharp corners. The former is overcome by constructing preconditioners using the near-field matrix of the whole region, and the latter is proved to have a neglectable influence on the accuracy of the final solutions as long as normal mesh density requirement is satisfied. The wellscaling ternary parallelization approach of the multilevel fast multipole algorithm (MLFMA) is incorporated into the DG domain decomposition framework to reduce the complexity of the solution and strength its capability for large-scale problems, with the distribution of the nearfield-related calculation adjusted to adapt the nonuniform meshes for a better workload balance. Numerical results are included to validate the accuracy and demonstrate the scalability and versatility of the proposed method. In addition, we demonstrated the effectiveness of our algorithm by solving a high-definition complicated aircraft model with inlets and exhausts involving over one billion unknowns. [ABSTRACT FROM AUTHOR]

With the development of smart manufacturing, quality has become an indispensable issue in the manufacturing process. Although there is increasing publication about inspection allocation problems, inspection allocation optimization research considering resource capability is scarce. This paper focuses on the inspection allocation problem with resource constraints in the flexible manufacturing system. Combined with the inspection resource capability model, a bi-objective model is developed to minimize the cost and balance loads of the inspection station. A modified NSGA-II algorithm with adaptive mutation operators is suggested to deal with the proposed model. Finally, a simulation experiment is conducted to test the performance of the modified algorithm and the results demonstrate that modified NSGA-II can obtain acceptable inspection solutions. [ABSTRACT FROM AUTHOR]

Background: Immunotherapy is a vital component in cancer treatment. However, due to the complex genetic bases of cancer, a clear prediction index for efficacy has not been established. Tumor mutation burden (TMB) is one of the essential factors that affect immunotherapeutic efficacies, but it has not been determined whether the mutation is associated with the survival of Skin Cutaneous Melanoma (SKCM) patients. This study aimed at evaluating the correlation between TMB and immune infiltration.Methods: Somatic mutation profiles (n = 467), transcriptome data (n = 471), and their clinical information (n = 447) of all SKCM samples were downloaded from The Cancer Genome Atlas (TCGA) database. For each sample, TMB was calculated as the number of variants per megabase. Based on K-M survival analysis, they were allocated into the high-TMB and low-TMB groups (the optimal cutoff was determined by the 'surv_cutpoint' algorithm of survival R package). Then, Gene ontology (GO) and Gene Set Enrichment Analyses (GSEA) were performed, with immune-associated biological pathways found to be significantly enriched in the low-TMB group. Therefore, immune genes that were differentially expressed between the two groups were evaluated in Cox regression to determine their prognostic values, and a four-gene TMB immune prognostic model (TMB-IP) was constructed.Results: Elevated TMB levels were associated with better survival outcomes in SKCM patients. Based on the cutoff value in OS analysis, they were divided into high-TMB and low-TMB groups. GSEA revealed that the low-TMB group was associated with immunity while intersection analysis revealed that there were 38 differentially expressed immune-related genes between the two groups. Four TMB-associated immune genes were used to construct a TMB-IP model. The AUC of the ROC curve of this model reached a maximum of 0.75 (95%CI, 0.66-0.85) for OS outcomes. Validation in each clinical subgroup confirmed the efficacy of the model to distinguish between high and low TMB-IP score patients.Conclusions: In SKCM patients, low TMB was associated with worse survival outcomes and enriched immune-associated pathways. The four TMB-associated immune genes model can effectively distinguish between high and low-risk patients. [ABSTRACT FROM AUTHOR]

Rankboost is a well-known algorithm that iteratively creates and aggregates a collection of "weak rankers" to build an effective ranking procedure. Initial work on Rankboost proposed two variants. One variant, that we call Rb-d and which is designed for the scenario where all weak rankers have the binary range { 0 , 1 } , has good theoretical properties, but does not perform well in practice. The other, that we call Rb-c, has good empirical behavior and is the recommended variation for this binary weak ranker scenario but lacks a theoretical grounding. In this paper, we rectify this situation by proposing an improved Rankboost algorithm for the binary weak ranker scenario that we call Rankboost + . We prove that this approach is theoretically sound and also show empirically that it outperforms both Rankboost variants in practice. Further, the theory behind Rankboost + helps us to explain why Rb-d may not perform well in practice, and why Rb-c is better behaved in the binary weak ranker scenario, as has been observed in prior work. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) heterogeneous network (HetNet) has been regarded as a promising means to improve the cellular system capacity in the 5G era. In this paper, we investigate the joint user association and resource allocation problem in a multi-band mmWave HetNet where different bands have different propagation characteristics. According to whether a user can transmit on multiple mmWave bands simultaneously, two different access schemes are considered: the single-band access scheme and the multi-band access scheme. For the single-band access scheme, we first find a closed-form expression for the optimal time fraction allocation and then develop an iterative algorithm for joint user association and power allocation based on the Lagrangian dual decomposition methods and the Newton-Raphson method. For the multi-band access scheme, we develop a near-optimal solution based on the Markov approximation framework. Our analytical results reveal that different users can only access at most one band simultaneously although the multi-band access scheme allows a user to transmit on multiple bands. Finally, numerical results demonstrate that the multi-band access scheme performs better than the single-band access scheme, especially in the light load scenario. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition.Methods: In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC).Results: The entropy, uniformity, mean intensity, standard deviation, skewness (P < 0.05), except kurtosis (P = 0.104) of thyroid tissue with nodules have a significant difference from those of normal thyroid tissue. The optimal classification was obtained from the presented method. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.880, 0.821, 0.933, 0.917, 0.854, and 0.953 respectively.Conclusion: First order texture features can be used as imaging biomarkers, and the presented system can be used to assist radiologists to recognize the nodules in CT image. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate a new and practical decision support model of the coordinated replenishment and delivery (CRD) problem with multi-warehouse (M-CRD) to improve the performance of a supply chain. Two algorithms, tabu search-RAND (TS-RAND) and adaptive hybrid different evolution (AHDE) algorithm, are developed and compared as to the performance of each in solving the M-CRD problem.Design/methodology/approach The proposed M-CRD is more complex and practical than classical CRDs, which are non-deterministic polynomial-time hard problems. According to the structure of the M-CRD, a hybrid algorithm, TS-RAND, and AHDE are designed to solve the M-CRD.Findings Results of M-CRDs with different scales show that TS-RAND and AHDE are good candidates for handling small-scale M-CRD. TS-RAND can also find satisfactory solutions for large-scale M-CRDs. The total cost (TC) of M-CRD is apparently lower than that of a CRD with a single warehouse. Moreover, the TC is lower for the M-CRD with a larger number of optional warehouses.Practical implications The proposed M-CRD is helpful for managers to select the suitable warehouse and to decide the delivery scheduling with a coordinated replenishment policy under complex operations management situations. TS-RAND can be easily used by practitioners because of its robustness, easy implementation, and quick convergence.Originality/value Compared with the traditional CRDs with one warehouse, a better policy with lower TC can be obtained by the new M-CRD. Moreover, the proposed TS-RAND is a good candidate for solving the M-CRD. [ABSTRACT FROM AUTHOR]

This paper presents a scheme for satellite multi-sensor fault-tolerant attitude estimation. It can both detect a faulty sensor online and conduct fault tolerance in time. First, a satellite attitude estimator based on error states is presented as the unified filter algorithm, in which the filter state equation is built with the gyro model and satellite kinematics, and the measurement equations are built with angle sensors. Meanwhile, typical fault models of the angle sensors are given. Then, a fault-tolerant federated Kalman filter (FTFKF) scheme is designed. Its three sub-filters include the respective angle sensors and share the public gyro measurement. Under the sensor fault condition, a FTFKF can detect the faulty sensor by contrasting and analysing the dimensionless fault detection factors and then selectively fusing the sub-filters’ outputs to enable the satellite attitude determination accuracy to approach normal. The comparative analysis of simulation results in typical fault cases shows that the proposed satellite multi-sensor fault-tolerant attitude estimation scheme may achieve the expected fault-tolerant performance. It is promising for enhancing the reliability of satellite attitude determination and control systems. [ABSTRACT FROM AUTHOR]

This paper proposes a first- and second-order unconditionally stable direct discretization method based on a surface mesh consisting of piecewise triangles and its dual-surface polygonal tessellation for solving the N-component Cahn–Hilliard system. We define the discretizations of the gradient, divergence, and Laplace–Beltrami operators on triangle surfaces. We prove that the proposed schemes, which combine a linearly stabilized splitting scheme, are unconditionally energy-stable. We also prove that our method satisfies the mass conservation. The proposed scheme is solved by the biconjugate gradient stabilized (BiCGSTAB) method, which can be straightforwardly applied to GPU-accelerated biconjugate gradient stabilized implementation by using the Matlab Parallel Computing Toolbox. Several numerical experiments are performed and confirm the accuracy, stability, and efficiency of our proposed algorithm. • Direct discretization methods for N-component Cahn–Hilliard system were proposed. • The unconditional energy stability of the proposed method was proved. • Various tests were performed to confirm the accuracy, stability, and efficiency of our method. [ABSTRACT FROM AUTHOR]

Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certain local range and not being able to jump out. We applied the enhanced algorithm to four classic chemical problems. Meanwhile, we changed the equal time division method or unequal time division method commonly used to solve chemical dynamic optimization problems, and proposed a new interval distribution method—the non-fixed points discrete method, which can more accurately represent the optimal control trajectory. The comparison and analysis of the simulation test results with other algorithms for solving chemical dynamic optimization problems show that the EBSO algorithm has good performance to a certain extent, which further proves the effectiveness of the EBSO algorithm and has a better optimization ability. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved Kalman filtering algorithm, to solve problems associated with processing of direct current (DC) signals in optical current transformers (OCTs). The algorithm was realized as follows. Based on the characteristics of the OCT, mixed noise was included with the parameters and state information of the measurement model, to ensure that it could be processed. State predictions and update equations for the improved Kalman algorithm were obtained, according to the minimum mean squared error criterion, using the established model. Finally, real-time noise estimation and correction was achieved by designing a recursive noise filter. Results from simulations and experiments demonstrate that the improved signal processing algorithm is effective in filtering mixed noise from DC signals measured by an OCT. Furthermore, the proposed algorithm is able to achieve real-time noise correction during the filtering process. [ABSTRACT FROM AUTHOR]

Abstract: Deutsch''s algorithm is the simplest quantum algorithm which shows the acceleration of quantum computer. In this paper, we theoretically advance a scheme to implement quantum Deutsch''s algorithm in spin-orbital angular momentum space. Our scheme exploits a newly developed optical device called "q-plate", which can couple and manipulate the spin-orbital angular momentum simultaneously. This experimental setup is of high stability and efficiency theoretically for there is no interferometer in it. [Copyright &y& Elsevier]

Abstract: A novel approach to sentence generation – SegSim, Sentence Generation by Similarity Matching – is outlined, and is argued to possess a number of desirable properties making it plausible as a model of sentence generation in the human brain, and useful as a guide for creating sentence generation components within artificial brains. The crux of the approach is to do as much as possible via similarity matching against a large knowledge base of previously comprehended sentences, rather than via complex algorithmic operations. To get the most out of this sort of matching, a certain amount of relatively simple rule-based processing needs to be done in pre- and post-processing steps. However, complex algorithmic operations are required only for the generation of sentences representing complex or unfamiliar thoughts. This, it is suggested, is the sort of sentence generation approach that makes sense in a system that – like a real or artificial brain – combines the capability for effective local application of logical rules with the capability for massively parallel, scalable, inexpensive similarity matching. [ABSTRACT FROM AUTHOR]

Shuangshen granules (SSG), a nationally patented Chinese medicinal formula, including Panax quinquefolium L., Panax notoginseng (Burkill) F. H. Chen, and Cordyceps sinensis (Berk.) Sacc., has demonstrated remarkable therapeutic effects on pancreatic cancer in clinical treatment for nearly 10 years. Previous pharmacological researches have found that its main components, including ginsenosides and cordycepin have anticancer or preventive effects on pancreatic ductal adenocarcinoma (PDAC), which may be associated with immune metabolism. However, the underlying pharmacological mechanism of SSG in the truncation effect of PDAC progression is still unclear. To comprehensively understand the infiltrating immune cells during the different stages of the PDAC development chain and search for immune-related biomarkers that could potentially serve as drug targets through bioinformatic analysis. Meanwhile, the truncation effect of SSG on PDAC progression was also investigated. The gene expression profiles at different PDAC developmental stages, including normal pancreas, pancreatic intraepithelial neoplasia (PanIN), and PDAC, were retrieved from the GEO database. The GEO2R tool was used to identify differentially expressed genes among the three groups. Functional enrichment analysis was performed with the GSEA software and Metascape platform. The CIBERSORT algorithm evaluated immune cell infiltration in the three groups, and immune-related biomarkers were identified. Correlation analysis was employed to examine the association between immune cells and the biomarkers. One of these biomarkers was selected for immunohistochemistry validation in human samples. Lastly, the effectiveness of SSG against PDAC progression and the influence on the selected biomarker were validated in vivo. The underlying pharmacological mechanisms were also explored. One dataset was obtained, where the functional enrichment of DEGs primarily involved immune effector processes and cytokine production of immune cells. The differential immune cells reflected during the progression from PanIN to PDAC were B memory cells, monocytes, M2 macrophages, and activated dendritic cells. The upregulation of ACTA2 was closely associated with M2 macrophage regulation. The immunohistochemistry on human samples validated significant differences in ACTA2 expression levels as the PDAC progressed. Moreover, animal experiments revealed that the national patented drug SSG ameliorated the pathological changes, decreased the expression of ACTA2 and its functional protein α-smooth muscle actin during PDAC progression. The underlying pharmacological mechanism was related to the regulation of macrophage polarization and downregulation of TGF-β/Smad signaling pathway. The immunosuppressive environment changes during the PDAC progression. ACTA2 is a potential immuned-target for drug prevention of PDAC, while SSG could be a promising drug candidate. Pictures in graphical abstract were download from http://image.baidu.com/. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Spectral clustering algorithm (SC) has recently received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that the commonly used distance measures in SC cannot simultaneously consider global and local consistencies and are sensitive to various noises. As a result, the obtained similarity matrices are unable to capture the actual data structure and thus produce poor clustering results, especially when the data exhibits nonlinear and local manifold structures characteristics. In order to address those limitations, we present a spectral clustering algorithm using density-sensitive distance measure with global and local consistencies in this paper. In the presented algorithm, a novel manifold distance with exponential term and scaling factor is introduced as the pairwise similarity measure. By modifying its exponential term and scaling factor, we can flexibly adjust the ratio of the similarities between the data within the same manifold to the similarities between the data across different manifolds. This flexible adjustment is beneficial to the obtained similarity matrix more accurately reflecting the global and local consistencies of data structures. In addition, to eliminate the effect of noises on the clustering performance, we also incorporate the relative density sensitive term into the distance measure to take into account the local distribution characteristics of the data. Finally, to further improve clustering performance, we provide the SC-based k value determination method for k nearest neighbors (KNN) graph. In the experimental part, the effect of parameters on the performance of the proposed technique is discussed and some suggestions about the determination of the parameters are given. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing spectral clustering techniques with good robustness. [ABSTRACT FROM AUTHOR]

Abstract: The production scheduling for a bell-type batch annealing process in heat treatment of steel coils is a typical multi-machine multi-stage re-entrant problem with complex constraint of resources including energy source. It is very difficult to build its mathematical model due to the large number of constraints in the process. This paper proposes an optimal scheduling method for the bell-type batch annealing process that combines an improved genetic algorithm with discrete event simulation techniques. The optimal scheduling method developed in this paper has been applied to the annealing shop in Shanghai Baosteel Yichang Sheet Co. Ltd, China, for nearly 2 years. The industrial application result shows that the proposed optimal scheduling method improves production efficiency and gains economic benefits. [Copyright &y& Elsevier]

The fisheye lens has the characteristics of wide viewing angle and low overall cost. Panoramic images can be further generated based on fisheye camera. In this paper, we propose a high-quality fisheye image stitching algorithm(LLBI-AW). In correction part, traditional bilinear interpolation algorithm leads to edge blur, a latitude and longitude-based interpolation algorithm (LLBI) is proposed to address this problem. Direction of the target point 0.is introduced through the latitude and longitude, weights are determined by the distance which between each point and the direction, and then performs interpolation. In fusion part, for the ghost problem in the overlapped area of the image after stitching, a weighted fusion algorithm (AW) based on the arc function is proposed. We establish an arc function to determine weights of left and right images, and then perform fusion calculation. The experimental results show that the information entropy, standard deviation and Brenner gradient of LLBI-AW algorithm are improved compared with the traditional algorithm, and the central region of the stitching image has a natural transition and high image quality. [ABSTRACT FROM AUTHOR]