Features Of Matrix Research Articles

Detergent-Free Decellularization Preserves the Mechanical and Biological Integrity of Murine Tendon.

Tissue decellularization has demonstrated widespread applications across numerous organ systems for tissue engineering and regenerative medicine applications. Decellularized tissues are expected to retain structural and/or compositional features of the natural extracellular matrix (ECM), enabling investigation of biochemical factors and cell-ECM interactions that drive tissue homeostasis, healing, and disease. However, the dense collagenous tendon matrix has limited the efficacy of traditional decellularization strategies without the aid of harsh chemical detergents and/or physical agitation that disrupt tissue integrity and denature proteins involved in regulating cell behavior. In this study, we adapted and established the advantages of a detergent-free decellularization method that relies on latrunculin B actin destabilization, alternating hypertonic-hypotonic salt and water incubations, nuclease-assisted elimination of cellular material, and protease inhibitor supplementation under aseptic conditions. Our method maintained the collagen molecular structure (i.e., minimal extent of denaturation), while adequately removing cells and preserving bulk mechanical properties. Furthermore, we demonstrated that decellularized tendon ECM-derived coatings isolated from different mouse strains, injury states (i.e., naive and acutely injured/"provisional"), and anatomical sites harness distinct biochemical cues and robustly maintain tendon cell viability in vitro. Together, our work provides a simple and scalable decellularization method to facilitate mechanistic studies that will expand our fundamental understanding of tendon ECM and cell biology. Impact statement In this study, we present a decellularization method for tendon that does not rely on any detergent or physical processing techniques. We assessed the impact of detergent-free decellularization using tissue, cellular, and molecular level analyses and validated the preservation of gross fiber architecture, collagen molecular structure, and extracellular matrix (ECM)-associated biological cues that are essential for studying physiological cell-ECM interactions. Finally, we demonstrated the applicability of this method for healthy and injured tendon environments, across mouse strains, and for different types of tendons, illustrating the utility of this approach for isolating the contributions of biochemical cues within unique tendon ECM microenvironments.

Optimizing input parameter of the randomized singular value decomposition algorithm for compressing technetium-99m L,L, ethylenedicysteine renal dynamic study in minimum time preserving clinical information.

The SVDsketch [MATLAB function which implements a randomized singular value decomposition (rSVD) algorithm] uses tolerance (tol) to adaptively determine the rank of the matrix sketch approximation. As the tol gets larger, fewer features of input image matrix are used in the matrix sketch. The objective of this study was to optimize the value of tol for compressing technetium-99m (Tc-99m) L,L, ethylenedicysteine (LLEC) renal dynamic (RD) study in minimum time preserving clinical information. At different values of tol [0.00012(default), 0.1, 0.01, and 0.05] 50 Tc-99m LLEC RD studies were compressed. Two nuclear medicine (NM) physicians compared compressed images at tol = 0.1 with its input images. The SVD computation time and compression factor were calculated for each study. The image quality metrics: Error, structural similarity index for measuring image quality, brightness, global contrast factor (GCF), contrast per pixel (CPP), and blur were used for objective assessment of image quality. Percentage error in split function estimated from compressed and original images was calculated. Wilcoxon signed-rank test was applied to find statistically significant difference between renal split function, blur, GCF, CPP, and brightness of the compressed image and the original image at . As per NM physicians, compressed images estimated with tol = 0.1 were identical to the original images. Based on image quality metrics, compressed images were significantly less noisy, brighter, and have better contrast compared with its input images. There was insignificant difference in split renal function estimated from compressed RD study at tol = 0.1 and its original study. The SVD computation and percentage compression per study were found to be 0.04725 s and up to 74.53%. The optimized value of tol for compressing Tc-99m LLEC RD study preserving clinical information was found to be 0.1, and SVD computation time: 0.04725 s.

Engineering Heterostructures of Layered Double Hydroxides and Metal Nanoparticles for Plasmon-Enhanced Catalysis

Artificially designed heterostructures formed by close conjunctions of plasmonic metal nanoparticles (PNPs) and non-plasmonic (2D) lamellar nanostructures are receiving extensive interest. The synergistic interactions of the nanounits induce the manifestation of localized surface plasmon resonance (LSPR) in plasmonic metals in the specific environment of the 2D-light absorbing matrix, impacting their potential in plasmon enhanced catalysis. Specifically, layered double hydroxides (LDH) with the advantages of their unique 2D-layered structure, tuned optical absorption, ease of preparation, composition diversity, and high surface area, have emerged as very promising candidates for obtaining versatile and robust catalysts. In this review, we cover the available PNPs/LDH heterostructures, from the most used noble-metals plasmonic of Au and Ag to the novel non-noble-metals plasmonic of Cu and Ni, mainly focusing on their synthesis strategies toward establishing a synergistic response in the coupled nanounits and relevant applications in plasmonic catalysis. First, the structure–properties relationship in LDH, establishing the desirable features of the 2D-layered matrix facilitating photocatalysis, is shortly described. Then, we address the recent research interests toward fabrication strategies for PNPs/support heterostructures as plasmonic catalysts. Next, we highlight the synthesis strategies for available PNPs/LDH heterostructures, how these are entangled with characteristics that enable the manifestation of the plasmon-induced charge separation effect (PICS), co-catalytic effect, or nanoantenna effect in plasmonic catalysis with applications in energy related and environmental photocatalysis. Finally, some perspectives on the challenges and future directions of PNPs/LDHs heterostructures to improve their performance as plasmonic catalysts are discussed.

Comparison of collagen features of distinct types of caries-affected dentin

ObjectivesTo compare the biodegradability, mechanical behavior, and physicochemical features of the collagen-rich extracellular matrix (ECM) of artificial caries-affected dentin (ACAD), natural caries-affected dentin (NCAD) and sound dentin (SD). MethodsDentin specimens from human molars were prepared and assigned into groups according to the type of dentin: ACAD, NCAD, or SD. ACAD was produced by incubation of demineralized SD with Streptococcus mutans in a chemically defined medium (CDM) with 1% sucrose for 7 days at 37 °C under anaerobic conditions. Specimens were assessed to determine collagen birefringence, biodegradability, mechanical behavior, and chemical composition. Data were individually processed and analyzed by ANOVA and post-hoc tests (α = 0.05). ResultsCDM-based biofilm challenge reduced loss, storage, and complex moduli in ACAD (p < 0.001), while the damping capacity remained unaffected (p = 0.066). Higher red and lower green birefringence were found in ACAD and NCAD when compared with SD (p < 0.001). Differently to ACAD, SD and NCAD presented higher biodegradability to exogenous proteases (p = 0.02). Chemical analysis of the integrated areas of characteristic bands that assess mineral quality (carbonate/phosphate and crystallinity index), mineral to matrix (phosphate/amide I) and post-translational modifications (amide III/CH2, pentosidine/CH2, and pentosidine/amide III) (p<0.05) showed that NCAD was significantly different from SD while ACAD exhibited intermediate values. ConclusionsCDM-based biofilm challenge produced a dentin ECM with decreased mechanical properties and increased collagen maturity. The compositional and structural conformation of the ACAD suggested that CDM-based biofilm challenge showed potential to produce artificial lesions by revealing a transitional condition towards mimicking critical features of NCAD. Clinical significanceThis study highlights the importance of developing a tissue that mimics the features of natural caries-affected dentin ECM for in vitro studies. Our findings suggested the potential of a modified biofilm challenge protocol to produce and simulate a relevant substrate, such as caries-affected dentin.

Tuning the Cell-Adhesive Properties of Two-Component Hybrid Hydrogels to Modulate Cancer Cell Behavior, Metastasis, and Death Pathways.

This work presents a polysaccharide and protein-based two-component hybrid hydrogel integrating the cell-adhesive gelatin-tyramine (G-Tyr) and nonadhesive hyaluronic acid-tyramine (HA-Tyr) through enzyme-mediated oxidative coupling reaction. The resulting HA-Tyr/G-Tyr hydrogel reflects the precise chemical and mechanical features of the cancer extracellular matrix and is able to tune cancer cell adhesion upon switching the component ratio. The cells form quasi-spheroids on HA-Tyr rich hydrogels, while they tend to form an invasive monolayer culture on G-Tyr rich hydrogels. The metastatic genotype of colorectal adenocarcinoma cells (HT-29) increases on G-Tyr rich hydrogels which is driven by the material’s adhesive property, and additionally confirmed by the suppressed gene expressions of apoptosis and autophagy. On the other hand, HA-Tyr rich hydrogels lead the cells to necrotic death via oxidative stress in quasi-spheroids. This work demonstrates the ideality of HA-Tyr/G-Tyr to modulate cancer cell adhesion, which also has potential in preventing primary metastasis after onco-surgery, biomaterials-based cancer research, and drug testing.

The glycocalyx affects the mechanotransductive perception of the topographical microenvironment

The cell/microenvironment interface is the starting point of integrin-mediated mechanotransduction, but many details of mechanotransductive signal integration remain elusive due to the complexity of the involved (extra)cellular structures, such as the glycocalyx. We used nano-bio-interfaces reproducing the complex nanotopographical features of the extracellular matrix to analyse the glycocalyx impact on PC12 cell mechanosensing at the nanoscale (e.g., by force spectroscopy with functionalised probes). Our data demonstrates that the glycocalyx configuration affects spatio-temporal nanotopography-sensitive mechanotransductive events at the cell/microenvironment interface. Opposing effects of major glycocalyx removal were observed, when comparing flat and specific nanotopographical conditions. The excessive retrograde actin flow speed and force loading are strongly reduced on certain nanotopographies upon strong reduction of the native glycocalyx, while on the flat substrate we observe the opposite trend. Our results highlight the importance of the glycocalyx configuration in a molecular clutch force loading-dependent cellular mechanism for mechanosensing of microenvironmental nanotopographical features.Graphical

Recommendation of Knowledge Graph Convolutional Networks Based on Multilayer BiLSTM and Self-Attention

To solve the problems of cold start, sparse data, and poor recommendation performance in collaborative filtering recommendation, an end-to-end framework algorithm based on BiLSTM and BAGCN was proposed. In order to discover the higher-order structural information in the knowledge graph, stacked BiLSTM is used to extract the features of embedded entities and relationships, respectively, and the depth dependence features of user-item interaction matrix are mined. The neighborhood representation of each entity is then calculated by sampling adjacent entities of a fixed size. Then, the self-attention mechanism is used to learn the semantic association between entities and neighboring entities to obtain the final neighborhood information. Aggregators are used to combine neighborhood information and bias information when computing node representations. By extending the sampling of adjacent entities to multihop simulation of higher-order adjacent information, users’ potential long-distance interests can be captured. Compared with the baseline model, the superiority of this method is verified.

Chemical strategies to engineer hydrogels for cell culture.

Two-dimensional and three-dimensional cell culture systems are widely used for biological studies, and are the basis of the organoid, tissue engineering and organ-on-chip research fields in applications such as disease modelling and drug screening. The natural extracellular matrix of tissues, a complex scaffold with varying chemical and mechanical properties, has a critical role in regulating important cellular functions such as spreading, migration, proliferation and differentiation, as well as tissue morphogenesis. Hydrogels are biomaterials that are used in cell culture systems to imitate critical features of a natural extracellular matrix. Chemical strategies to synthesize and tailor the properties of these hydrogels in a controlled manner, and manipulate their biological functions in situ, have been developed. In this Review, we provide the rational design criteria for predictably engineering hydrogels to mimic the properties of the natural extracellular matrix. We highlight the advances in using biocompatible strategies to engineer hydrogels for cell culture along with recent developments to dynamically control the cellular environment by exploiting stimuli-responsive chemistries. Finally, future opportunities to engineer hydrogels are discussed, in which the development of novel chemical methods will probably have an important role.

Optimized Decellularization Protocol for Large Peripheral Nerve Segments: Towards Personalized Nerve Bioengineering.

Nerve injuries remain clinically challenging, and allografts showed great promise. Decellularized nerve allografts possess excellent biocompatibility and biological activity. However, the vast majority of decellularization protocols were established for small-size rodent nerves and are not suitable for clinical application. We aimed at developing a new method of decellularizing large-diameter nerves suitable for human transplantation. Repeated rounds of optimization to remove immunogenic material and preserve the extracellular structure were applied to the porcine sciatic nerve. Following optimization, extensive in vitro analysis of the acellular grafts via immunocytochemistry, immunohistology, proteomics and cell transplantation studies were performed. Large segments (up to 8 cm) of the porcine sciatic nerve were efficiently decellularized and histology, microscopy and proteomics analysis showed sufficient preservation of the extracellular matrix, with simultaneous consistent removal of immunogenic material such as myelin, DNA and axons, and axonal growth inhibitory molecules. Cell studies also demonstrated the suitability of these acellular grafts for 3D cell culture studies and translation to future large animal studies and clinical trials. By using non-human donors for peripheral nerve transplantation, significant drawbacks associated with the gold standard can be eliminated while simultaneously preserving the beneficial features of the extracellular matrix.

Strengthening‐toughening of graphene nanoplates and in situ ZrB2 nanoparticles reinforced AA6111 matrix composites with discontinuous layered structures

Simultaneously achieving high strength and ductility is a critical issue for graphene nanoplates (GNPs) reinforced aluminum matrix composites. Layered structures and in situ nanoparticles were applied to achieve the strengthening and toughening of GNPs reinforced aluminum matrix composites from the structure and composition design, respectively. Therefore, the discontinuous layered AA6111 matrix composites synergistically reinforced with GNPs and in situ ZrB2 nanoparticles were prepared by the composite melt preparation method and hot rolling. The discontinuous layered structure features of graphene, particles, and matrix were investigated. The interfaces between reinforcements and the matrix were further characterized by high-resolution transmission electron microscopy. It was found that the improved load transfer efficiency of GNPs was mainly attributed to the formation of a high-strength amorphous alumina interface between GNPs and the aluminum, as well as the pinning effect of the Al2Cu particles on GNPs' surface. By analyzing characteristics of the dislocation configuration, GNPs and nanoparticles boosted the dislocation storage capacity of the composite, while the particles dispersed the stress concentration at the edge tip of GNPs, thereby improved the composite's ductility. The composite achieved a 50% (349 MPa) increase in yield strength with only a 30% compromise on ductility (13.8%), leading to a notable improvement in toughness. The research revealed an efficient way for manufacturing high strength-ductility metal matrix composites with discontinuous layered structures via well-bonded interfaces employing GNPs and nanoparticles.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

BackgroundDiffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.MethodsThis prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.ResultsAccording to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.ConclusionsThis study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Three-dimensional rapid visualization of flame temperature field via compression and noise reduction of light field imaging

Light field (LF) imaging, as an emerging temperature measurement technique, can simultaneously capture the intensity and direction information of radiation in the combustion flow field and has recently attracted extensive attention. However, the dense sampling of the LF introduces a large amount of redundant information and makes the plenoptic data susceptible to noise pollution, which can significantly affect the performance of 3D temperature field reconstruction. To address this problem, a novel approach, i.e., light field compression and noise reduction (LFCNR), is proposed to extract the main features of the projection matrix and separate the noise-related and signal-related subspaces of the plenoptic data, thus improving the reconstruction accuracy and efficiency. The performance of the proposed LFCNR method and the effect of the hyper-parameters on reconstructed quality are investigated thoroughly. It has been observed that the proposed LFCNR-based method for 3D temperature tomography imaging has higher accuracy, more efficiency, and better anti-noise ability compared to the well-established method. LFCNR can improve the reconstructed temperature accuracy by approximately 20%, and the calculation time is shortened to 10%. Priori smoothing is introduced into the optimization objective to constrain the fluctuation of the retrieval temperature, called LFCNR-PS, which can further improve the reconstructed accuracy of axisymmetric and non-axisymmetric fields about 1.79% and 1.23%. Furthermore, experiments of the LFCNR-based method are carried out to reconstruct the 3D temperature field of ethylene diffusion flames. The reconstructed temperature slices demonstrate that the flame topology can be clearly and rapidly identified at different depths. The efficiency and robustness of the proposed method can be applied to the harsh environment, which will be beneficial to the reliable operation of thermal equipment, the study of turbulent chemical reaction mechanisms, fuel utilization, and the optimization design of the engine combustion chamber.

Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence

This paper introduces a Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) for extracting the nonlinear features of a matrix in standard NMF. This method uses a nonlinear transformation to act on the feature matrix for constructing a NMF model based on the objective function of Kullback-Leibler Divergence, and the Taylor series expansion and the Newton iteration formula of solving root are used to obtain the iterative update rules of the basis matrix and the feature matrix. Experimental results show that the proposed method obtains the nonlinear features of data matrix in a more efficient way. In object recognition and clustering tasks, better accuracy can be achieved over some typical NMF methods.

An Accurate Prediction Algorithm of RUL for Bearings: Time-Frequency Analysis Based on MRCNN

Remaining useful life (RUL) prediction of mechanical components is of high research value in the field of prognostics and health management (PHM). However, RUL prediction problems are completely challenging due to the complicacy of bearings’ operating environment. In this paper, we transform the vibration acceleration signal collected by sensors into a time-frequency domain matrix through continuous wavelet transform (CWT) and then extract the features of the time-frequency domain matrix through the proposed multiscale residual convolutional neural network (MRCNN), which enables the model to extract more local and global features while constructing more accurate health indicators (HI). In order to highlight the degradation trend of mechanical components, the obtained health indicators are smoothed by exponential moving average (EMA). Finally, linear regression is exploited to predict the RUL of the bearing. Performance evaluations based on the public dataset PRONOSTIA demonstrate the effectiveness of our proposed algorithm, which is superior to existing data-driven algorithms in terms of prediction accuracy.

Microstructures and dielectric properties of fluorene polyester ternary nanocomposites optimized by zero‐dimensional and two‐dimensional fillers

AbstractDue to the limitations of dielectric, insulating and mechanical properties, pure polymers could not satisfy the requirements of technological development. Using inorganic fillers with excellent performance to modify it is the mainstream method at present. Ternary hybrid polymer composites can use fillers with excellent dielectric properties to improve their dielectric properties, showing potential in improving electrical insulation, dielectric, and other fields. In this work, fluorene polyester (FPE) films of different components are prepared by the solution blending method using zero‐dimensional silica (SiO2) nanoparticles and two‐dimensional boron nitride nanosheets (BNNS) as fillers. Compared with SiO2/FPE composite films, ternary films exhibit better dielectric properties, short‐term breakdown strength and corona‐resistant lifetime. By investigating the interface features of BNNS, SiO2 nanoparticles and FPE matrix as well as the microstructure and properties of the films, the synergism mechanism of BNNS and SiO2 nanoparticles on the performance of FPE composite films is explored. The dielectric properties of the composites are investigated, including dielectric constant, breakdown strength, and corona‐resistant lifetime. This article focuses on clarifying the key role of BNNS in the electrical properties of ternary films and provides a theoretical basis for expanding the application of 2D materials in the field of high insulation.

Blood-Vessel-Inspired Hierarchical Trilayer Scaffolds: PCL/Gelatin-Driven Protein Adsorption and Cellular Interaction.

Fabrication of scaffolds with hierarchical structures exhibiting the blood vessel topological and biochemical features of the native extracellular matrix that maintain long-term patency remains a major challenge. Within this context, scaffold assembly using biodegradable synthetic polymers (BSPs) via electrospinning had led to soft-tissue-resembling microstructures that allow cell infiltration. However, BSPs fail to exhibit the sufficient surface reactivity, limiting protein adsorption and/or cell adhesion and jeopardizing the overall graft performance. Here, we present a methodology for the fabrication of three-layered polycaprolactone (PCL)-based tubular structures with biochemical cues to improve protein adsorption and cell adhesion. For this purpose, PCL was backbone-oxidized (O-PCL) and cast over a photolithography-manufactured microgrooved mold to obtain a bioactive surface as demonstrated using a protein adsorption assay (BSA), Fourier transform infrared spectroscopy (FTIR) and calorimetric analyses. Then, two layers of PCL:gelatin (75:25 and 95:5 w/w), obtained using a novel single-desolvation method, were electrospun over the casted O-PCL to mimic a vascular wall with a physicochemical gradient to guide cell adhesion. Furthermore, tensile properties were shown to withstand the physiological mechanical stresses and strains. In vitro characterization, using L929 mouse fibroblasts, demonstrated that the multilayered scaffold is a suitable platform for cell infiltration and proliferation from the innermost to the outermost layer as is needed for vascular wall regeneration. Our work holds promise as a strategy for the low-cost manufacture of next-generation polymer-based hierarchical scaffolds with high bioactivity and resemblance of ECM’s microstructure to accurately guide cell attachment and proliferation.

Winter Bird Diversity and Abundance in Small Farmlands in a Megacity of Southern China

Urban expansion often accompanies a loss of natural habitats and arable lands but an increase in urban population. In China, vegetable-dominant small farmlands are common in urban/peri-urban areas. Some farmlands are also associated with government policy that aims to enhance local farmers’ livelihoods as well as increase food availability for city citizens. While small urban farmlands create open greenery cover that may provide birds with resources such as food and shelter, little attention has been given to understanding bird diversity in urban farmlands. Using two hierarchical models (multi-species occupancy model and N-mixture model), we examined how species richness and abundance of birds were associated with environmental characteristics within and surrounding urban farmlands in Guangzhou, one of the largest cities in China. We conducted crop and bird surveys at urban farmlands during two winter seasons between December 2019 and January 2021. Species richness increased with non-woody (herbaceous) vegetation cover within a farmland. Abundance of three species was also positively associated with the local non-woody vegetation variable. Two species were more abundant at farmlands with higher crop diversity. Compositional features of matrix surrounding a farmland (a 500-m circular area) did not affect species richness. However, species richness and abundance of one species tended to decrease with increasing farmland fragmentation (patch density of farmlands) within a 1-km circular area. These findings suggest that (1) birds could be more influenced by environmental features within farmlands than matrix features surrounding farmlands, (2) local uncultivated herbaceous vegetation is an important environmental feature, and (3) diverse crops in farmlands may benefit some birds. They also indicate that the landscape pattern of farmlands, such as degree of fragmentation, could affect bird diversity in urban farmlands.

SNIFF: A Scalable Network Inference Framework for Measuring End-to-End Performance

For Internet measurement, a key challenge is how to measure end-to-end performance accurately and effectively. To achieve this goal, several recent studies suggest applying advanced matrix completion techniques to recover the complete matrix using a small percentage of samples. While the low-rank features of matrices are generally utilized in existing studies, important features, which are unique in our domain context and can be used to significantly reduce path sampling overhead with guaranteed recovery accuracy, have not been fully explored. To fill the gap, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable network inference framework</i> , namely, SNIFF, to monitor the end-to-end performance of a set of paths over time. In our framework, we extensively investigate real datasets and identify key features in measurement data, including the low-rank feature of the path-time matrix, the smoothness of performance over a short period, and the pair-wise linear correlation of paths. Based on these features, we develop three non-uniform sampling strategies that increase the sampling rate of important paths while reducing the rate if two paths are highly correlated. Moreover, we formulate two types of optimization problems for matrix completion that take into account important characteristics of data and we develop efficient algorithms to solve the corresponding problems. To evaluate the proposed framework, we conduct extensive numerical experiments for measuring end-to-end delay and throughput performance. The results confirm the feasibility and advantages of the proposed framework and algorithms.

Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN

Operational monitoring of pipelines can prevent environmental and economic losses. However, pipeline data have the characteristics of high dimension and nonlinear coupling, which makes it difficult to determine the relationship between the data and process, resulting in a high rate of misjudgment of the operating condition. To address this issue, an operating condition recognition model based on kernel principal component analysis (KPCA)-convolutional neural network (CNN) is proposed. Deeppipe refers to the use of deep learning algorithms to solve pipeline-related problems. Considering the spatial and time-series characteristics of the pipeline, the inlet and outlet pressure matrixes of the initial station, intermediate station, and terminal station are constructed. Subsequently, the features of the pressure matrix in the time domain, frequency domain, and energy domain are extracted. KPCA is employed to obtain the reconstructed feature matrix, which is used as the input of the proposed CNN recognition model. Taking two multiproduct pipelines as examples, the effectiveness of the KPCA-CNN recognition model is verified while compared with traditional nonlinear classification models (e.g., artificial neural network, decision tree, random forest, and others). The results show that the proposed model has the highest accuracy, precision, recall, and F1 score, and all reach 100%, which has a certain guiding significance for the monitoring and management of onsite pipelines.

Thresholding Approach for Low-Rank Correlation Matrix Based on MM Algorithm.

Background: Low-rank approximation is used to interpret the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in an estimation that is far from zero, even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. Methods: To overcome this, we propose a novel approach to estimate a sparse low-rank correlation matrix based on threshold values. We introduce a new cross-validation function to tune the corresponding threshold values. To calculate the value of a function, the MM algorithm is used to estimate the sparse low-rank correlation matrix, and a grid search was performed to select the threshold values. Results: Through numerical simulation, we found that the false positive rate (FPR), interpretability, and average relative error of the proposed method were superior to those of the tandem approach. For the application of microarray gene expression, the FPRs of the proposed approach with and 5 were , , and , respectively, while the FPR of the tandem approach was . Conclusions: We propose a novel approach to estimate sparse low-rank correlation matrices. The advantage of the proposed method is that it provides results that are interpretable using a heatmap, thereby avoiding result misinterpretations. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.