Matrix Algorithm Research Articles

Combination of Matrix and Genetic Algorithms for Fault Location of Active Distribution Network

Abstract Timely and effective location of faults in distribution networks is important for restoring power supply and dealing with faults. In this paper, a method combining matrix algorithm and genetic algorithm (GA) is proposed to determine the fault section. Firstly, the matrix algorithm is applied to locate the fault section initially by combining the alarm information uploaded from the feeder terminal unit (FTU). Secondly, in order to deal with the problem of outputting wrong location results, the switching function of the active distribution network is constructed to check the location results calculated by the matrix, and the results that do not meet the checking will be treated as a suspicious fault set, and the relevant evaluation function is constructed and optimized using the genetic algorithm, so as to arrive at the correct fault zone location results. The results of the example simulation show that the method used in this paper can achieve fast and fault-tolerant fault location in active distribution networks.

Performance research of edge computing nodes based on heterogeneous environment

Abstract Edge computing realizes real-time parallel storage and computing in a distributed way, which can reduce the data transmission cost. The edge nodes, which are composed of different heterogeneous physical devices and hardware resources, assume the computing and storage capacity of the edge network. At present, the resource characteristics and performance of heterogeneous edge nodes are difficult to characterize. Therefore, a resource-aware performance feature detection model for heterogeneous edge compute nodes is proposed in this study. This model is based on a resource utilization matrix and resource relative performance quantization algorithm to extract the performance characteristics of the same type of resources of the edge nodes, to realize multi-dimensional and systematic feature detection and description of heterogeneous edge nodes. On this basis, a weight-based edge node task scheduling algorithm is designed. The final verification results show that the CPU relative performance ratios of Node 1-Node 2, Node 1-Node 3, and Node 2-Node 3 are 6.23, 3.81, and 0.81 respectively. The performance relationship among the three nodes is Node 1> Node 3> Node 2. The test group and the verification group tend to be consistent overall. This method has a good application prospect.

Fault Location in Distribution Network Based on RNN and Transfer Learning

Fault location in distribution networks is a major challenge that needs to be addressed in power distribution systems. Currently, fault location methods based on matrix algorithms, genetic algorithms, deep learning, and other algorithms have received wide attention from the industry. However, such methods have some drawbacks: (1) they require high accuracy in fault information uploads, which can lead to low fault tolerance; (2) they tend to converge early and get stuck in local optimal solutions; (3) they involve high computational complexity, leading to location delays. In this study, we propose a fault location framework based on recurrent neural network (RNN) and transfer learning. In this method, we first encode the information data collected from distribution terminals, and then use RNN to establish a nonlinear mapping relationship between fault features and fault location intervals, which effectively improves fault tolerance and reduces misjudgment issues. We then use transfer learning to load the pre-trained model onto the target task to address the problem of insufficient data for fault location in distribution networks. Experimental results show that after 15 rounds of training, our T-RNN model has achieved over 80% accuracy. Benefiting from Glorot weight initialization adopted after transfer learning, the model achieves good performance early on compared to the BP model, converges faster, and ultimately achieves a prediction accuracy of 96.5%.

High quality factor and unity circular dichroism enabled by dielectric-Weyl semimetal-dielectric stack

A mid-infrared (mid-IR) chiral absorber based on multilayer films of silicon (Si)/Weyl semimetal (WSM)/Si is proposed and numerically studied. The proposal exhibits high quality factor circular dichroism (CD) with different absorptance properties for right-hand circular polarization (RCP) and left-hand circular polarization (LCP) light. The absorption characteristics have been explored with the generalized 4 × 4 transfer matrix algorithm, and the effects of structural parameters on the circularly polarized (CP) absorptance are systematically investigated. The circular polarization efficiency (η) and CD are defined as crucial parameters for quantifying the chirality response. The results prove that the chiral absorber exhibits extraordinary efficiencies of 1 and 0 for LCP and RCP light at a wavelength of 11.5 μm, respectively. Thus a maximum CD in absorption of unity with a resonant quality factor (Q-factor) of 295 can be obtained. The performance of dual-band chiral responses can be engineered via the thickness of each layer. Moreover, it is identified that the design results in angle-independent performance, which is essential trait for an efficient chiral absorber.

Thermally tunable broadband circular dichroism with Weyl semimetal/vanadium dioxide planar structure

Weyl semimetals (WSMs) have lately attracted significant interest because of the intrinsic large gyrotropic optical response and strong chiral anomaly. This study presents a mid-infrared (mid-IR) lithography-free platform exhibits broadband near-unity circular dichroism (CD) with WSM-vanadium dioxide (VO2) dual layers. The optical responses with mid-IR radiation have been explored with the generalized 4 × 4 transfer matrix algorithm. It is theoretically shown that the proposal possesses different absorption characteristics for right-handed circularly polarized (RCP) and left-handed circularly polarized (LCP) light. Theoretical results show that a CD in absorption reaching 0.936 and a g factor of 1.76 under 300 K temperature. The merit of the wide-angular chiral capacity can be maintained. The magnitude and resonance of CD can be tuned not only by adjusting the structural parameters, but also the insulator-to-metal transition (IMT) of VO2, which may facilitate the design of more efficient chiral optical devices.

Enhancing cancer detection through AI-driven high content analysis of circulating tumor cells.

3054 Background: The advancements in automated High Content Analysis (HCA) for Circulating Tumor Cells (CTCs) detection necessitate the integration of high-throughput screening (HTS) and Artificial Intelligence (AI) to improve cancer diagnosis efficiency and accuracy. Traditional approaches to cancer detection have faced limitations in speed, cost, and analysis depth, driving the need for innovative solutions in assay development and execution. Methods: We obtained peripheral blood from 150 asymptomatic individuals and 63 newly diagnosed cases of solid organ cancers including Lung, Breast, Head and Neck, Colorectal, Pancreas, Esophagus, Gynecologic cancers. We used differential apoptosis based negative enrichment technique and HCS fluorescent imaging for identification of CTCs and CTC like immune cells from the relevant blood samples. We devised AutoML algorithms which were trained on a cohort of 4,135 cropped region of interest (ROI) images of true CTCs and CTC-mimicking immune cells. These images were annotated with the coordinates of the top-left and bottom-right corners of rectangles to specify ROIs. This method employs a combination of automated sample processing, integration with the Laboratory Information Management System (LIMS) integration, digital filtration, and decision matrix algorithms for sample classification and smooth data flow. The image quality criteria were established prior to dataset analysis. The algorithms underwent training, testing, and validation based on expert recommendations, with dataset splitting of 80% for training, 10% for testing, and 10% for validation phases. Results: The automated deep learning model achieved remarkable performance metrics, presenting an accuracy of 96.66% (95% CI, 96.07%-97.19%), a specificity of 95.34% (95% CI, 94.37%-96.18%), and a sensitivity of 98.14% (95% CI, 97.44%-98.69%) in detecting true CTCs. Additional analysis included the area under the curve and confusion matrix evaluations, indicating the model's robustness in distinguishing between true positive and true negative specimens. This efficiency and precision in data handling, processing, and analysis demonstrate significant advancements in HCA screening, offering enhanced assay development, execution flexibility, and the facilitation of detailed cell-by-cell analysis for cancer detection from a large set of Peripheral Blood Mononuclear Cells (PBMCs) from samples obtained for screening or diagnosis. Conclusions: This study successfully evaluated a deep learning model for the detection of circulating tumor cells (CTCs) in peripheral blood mononuclear cells (PBMCs) using image data from HTS platform. The model achieved superior performance, demonstrating its potential for cancer detection from blood samples.

Kidney Transplant Outcomes From Deceased Donors Who Received Dialysis

Recipient outcomes after kidney transplant from deceased donors who received dialysis prior to kidney donation are not well described. To compare outcomes of transplant recipients who received kidneys from deceased donors who underwent dialysis prior to kidney donation vs recipients of kidneys from deceased donors who did not undergo dialysis. A retrospective cohort study was conducted including data from 58 US organ procurement organizations on deceased kidney donors and kidney transplant recipients. From 2010 to 2018, 805 donors who underwent dialysis prior to kidney donation were identified. The donors who underwent dialysis prior to kidney donation were matched 1:1 with donors who did not undergo dialysis using a rank-based distance matrix algorithm; 1944 kidney transplant recipients were evaluated. Kidney transplants from deceased donors who underwent dialysis prior to kidney donation compared with kidney transplants from deceased donors who did not undergo dialysis. The 4 study outcomes were delayed graft function (defined as receipt of dialysis by the kidney recipient ≤1 week after transplant), all-cause graft failure, death-censored graft failure, and death. From 2010 to 2018, 1.4% of deceased kidney donors (805 of 58 155) underwent dialysis prior to kidney donation. Of these 805 individuals, 523 (65%) donated at least 1 kidney. A total of 969 kidneys (60%) were transplanted and 641 kidneys (40%) were discarded. Among the donors with kidneys transplanted, 514 (mean age, 33 years [SD, 10.8 years]; 98 had hypertension [19.1%] and 36 had diabetes [7%]) underwent dialysis prior to donation and were matched with 514 (mean age, 33 years [SD, 10.9 years]; 98 had hypertension [19.1%] and 36 had diabetes [7%]) who did not undergo dialysis. Kidney transplants from donors who received dialysis prior to donation (n = 954 kidney recipients) were associated with a higher risk of delayed graft function compared with kidney transplants from donors who did not receive dialysis (n = 990 kidney recipients) (59.2% vs 24.6%, respectively; adjusted odds ratio, 4.17 [95% CI, 3.28-5.29]). The incidence rates did not significantly differ at a median follow-up of 34.1 months for all-cause graft failure (43.1 kidney transplants per 1000 person-years from donors who received dialysis prior to donation vs 46.9 kidney transplants per 1000 person-years from donors who did not receive dialysis; adjusted hazard ratio [HR], 0.90 [95% CI, 0.70-1.15]), for death-censored graft failure (22.5 vs 20.6 per 1000 person-years, respectively; adjusted HR, 1.18 [95% CI, 0.83-1.69]), or for death (24.6 vs 30.8 per 1000 person-years; adjusted HR, 0.76 [95% CI, 0.55-1.04]). Compared with receiving a kidney from a deceased donor who did not undergo dialysis, receiving a kidney from a deceased donor who underwent dialysis prior to kidney donation was associated with a significantly higher incidence of delayed graft function, but no significant difference in graft failure or death at follow-up.

Multi‐event‐based distributed cooperative predictive control for the multi‐agent system

AbstractIn this paper, we provide the detailed distributed cooperative predictive control scheme for the multi‐agent system (MAS) affected by the network delays. Firstly, we restate the distributed cooperative predictive control problem by introducing iterative learning‐based MAS, which can guarantee the cooperative predictive control of the MAS. To alleviate the unfavorable network constrains, the multi‐event‐triggered conditions are formulated via considering the predictive and error data sufficiently. With these preparations, the well‐posed Roesser‐type two‐dimensional (2D) system is achieved equivalently to the distributed iterative learning predictive system. On this basis, we employ 2D predictive system analysis and control theory to realize the predictive control for the equivalent 2D system and then obtain the collaborative predictive control for the MAS under multi‐event‐triggered mechanism. The corresponding stability criteria, controller, and observer gains are provided by the executable matrix constraints and algorithms design. To conclude the paper, the numerical example is proposed to illustrate the effectiveness and practicability of the provided methods and algorithms.

Assessment of the energy production of a hybrid PV/T collector based on different fluids for Agadir climate

The aim of this paper is to evaluate the electrical and thermal energies produced by a hybrid PV/T collector for Agadir city climatic conditions. To achieve the goal a theoretical model based on energy balance equations applied on each collector component is developed. The discretization of the equations is done using the finite difference method and solved numerically by TDMA matrix algorithm. The validity of the model is examined by the comparison of the our simulation findings with the available literature results. The thermal and electrical powers outputted by the glazed and unglazed hybrid collector are evaluated over the course of a typical day for Agadir region climate (Morocco), using various heat transfer fluids (water, water-EG mixture and Cu−water nanofluid). It is obtained that the employ of the nanofluid Cu−water improves the energy production of the hybrid collector in comparison to other working fluids. In fact, the high daily thermal and electrical energies are obtained using the nanofluid by approximately 5.548 kWh and 1.273 kWh respectively. The results showed that the produced thermal energy of the solar collector with glass is 37.4% higher than unglazed one. However, the supplied electrical energy by the glazing collector is lower than the unglazed system by almost 11.5%. In Agadir region, we recommend choosing the unglazed PV/T to benefit from the weather conditions and the solar potential of this region.

An Efficient Image Cryptosystem Utilizing Difference Matrix and Genetic Algorithm.

Aiming at addressing the security and efficiency challenges during image transmission, an efficient image cryptosystem utilizing difference matrix and genetic algorithm is proposed in this paper. A difference matrix is a typical combinatorial structure that exhibits properties of discretization and approximate uniformity. It can serve as a pseudo-random sequence, offering various scrambling techniques while occupying a small storage space. The genetic algorithm generates multiple ciphertext images with strong randomness through local crossover and mutation operations, then obtains high-quality ciphertext images through multiple iterations using the optimal preservation strategy. The whole encryption process is divided into three stages: first, the difference matrix is generated; second, it is utilized for initial encryption to ensure that the resulting ciphertext image has relatively good initial randomness; finally, multiple rounds of local genetic operations are used to optimize the output. The proposed cryptosystem is demonstrated to be effective and robust through simulation experiments and statistical analyses, highlighting its superiority over other existing algorithms.

Towards high-performance polarimeters with large-area uniform chiral shells: a comparative study on the polarization detection precision enabled by the Mueller matrix and deep learning algorithm

Polarization detection and imaging technologies have attracted significant attention for their extensive applications in remote sensing, biological diagnosis, and beyond. However, previously reported polarimeters heavily relied on polarization-sensitive materials and pre- established mapping relationships between the Stokes parameters and detected light intensities. This dependence, along with fabrication and detection errors, severely constrain the working waveband and detection precision. In this work, we demonstrated a highly precise, stable, and broadband full-Stokes polarimeter based on large-area uniform chiral shells and a post-established mapping relationship. By precisely controlling the geometry through the deposition of Ag on a large-area microsphere monolayer with a uniform lattice, the optical chirality and anisotropy of chiral shells can reach about 0.15 (circular dichroism, CD) and 1.7, respectively. The post-established mapping relationship between the Stokes parameters and detected light intensities is established through training a deep learning algorithm (DLA) or fitting the derived mapping-relationship formula based on the Mueller matrix theory with a large dataset collected from our home-built polarization system. For the detection precision with DLA, the mean squared errors (MSEs) at 710 nm can reach 0.10% (S1), 0.41% (S2), and 0.24% (S3), while for the Mueller matrix theory, the corresponding values are 0.14% (S1), 0.46% (S2), and 0.48% (S3). The in-depth comparative studies indicate that the DLA outperforms the Mueller matrix theory in terms of detection precision and robustness, especially for weak illumination, small optical anisotropy and chirality. The averaged MSEs over a broad waveband ranging from 500 nm to 750 nm are 0.16% (S1), 0.46% (S2), and 0.61% (S3), which are significantly smaller than those derived from the Mueller matrix theory (0.45% (S1), 1% (S2), and 39.8% (S3)). The optical properties of chiral shells, the theory and DLA enabled mapping-relationships, the combination modes of chiral shells, and the MSE spectra have been systematically investigated.

Grading the Dominant Pathological Indices in Liver Diseases from Pathological Images Using Radiomics Methods

Purpose: This study aims to diagnose the severity of important pathological indices, i.e., fibrosis, steatosis, lobular inflammation, and ballooning from the pathological images of the liver tissue based on extracted features by radiomics methods.
 Materials and Methods: This research uses the pathological images obtained from liver tissue samples for 258 laboratory mice. After preprocessing the images and data augmentation, a collection of texture feature sets extracted by gray-level-based algorithms, including Global, Gray-level Co-Occurrence Matrix (GLCM), Gray-level Run length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM) algorithms. Then, advanced methods of classification, namely Support Vector Machine (SVM), Random Forest (RF), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Multi-layer Perceptrons (MLP) are employed. This procedure is provided separately for each of the four indices of fibrosis level in 6 grading classes, steatosis in 5 grading classes, inflammation in 4 grading classes, and ballooning in 3 grading classes. For a comparison of the output of these algorithms, the accuracy value obtained from the evaluation data is presented for the performance of different methods.
 Results: The results showed that, compared to other methods, the Gaussian SVM algorithm provides a better response to the classification of the grading of liver disease among all the indices from the pathological images due to its structural features. This value of accuracy was calculated at 84.30% for fibrosis, 90.55% for steatosis, 81.11% for inflammation, and 95.98% for ballooning.
 Conclusion: This fully automatic framework based on advanced radiomics algorithms and machine learning from pathological images can be very useful in clinical procedures and be considered as an assistant or a substitute for pathologists’ diagnoses.

Parallel Algorithms for Numerical Solution of Spatially Three-Dimensional Diffusion-Convection Equations in Coastal Systems Based on Splitting Schemes

Introduction. To prevent the occurrence and mitigate the consequences of hazardous and catastrophic phenomena associated with sediment transport in natural systems, it is necessary to develop operational and scientifically justified forecasts, identify critical states at which the emergence of emergency situations is possible. For these purposes, it is necessary to create an accurate and efficient toolkit, including algorithms for numerical solution of a model problem that takes into account the specifics of natural systems. In this work, parallel algorithms for numerical solution of a spatially three-dimensional diffusion-convection problem of sediment are presented, which allow a significant reduction in computation time (by more than 4 times) compared to calculations conducted using a sequential algorithm.Materials and Methods. For the parallel solution of the spatially three-dimensional diffusion-convection problem, an implicit splitting scheme is constructed, in which the original continuous problem is replaced by a chain of two-dimensional and one-dimensional problems. The splitting schemes proposed in the work are physically justified and take into account the specifics of coastal marine systems, for which the influence of micro-turbulent diffusion and advective transport of substances are comparable, and the Peclet number does not exceed unity when approximating real problems. For the parallel numerical implementation, a method of decomposing the grid domain into two families of vertical planes parallel to the coordinate planes Oxz and Oyz, combined with the Seidel method for solving two-dimensional grid problems in horizontal planes and the tridiagonal matrix algorithm when solving one-dimensional three-point problems in the vertical direction, is used. Within the framework of the parallel computing software implementation, a parallel algorithm is presented that implements the diffusion-convection problem on a computing system using MPI technology.Results. A comparative analysis of parallel and sequential algorithms is obtained using a model problem.Discussion and Conclusions. The developed software allows its practical use for solving specific hydrophysical problems, including as part of a software complex.

Multi-Source energy optimization method for supersonic aircraft based on multi-objective adaptive covariance matrix and chaotic search group algorithm

In the trend of increasing aircraft electrical power levels, future supersonic aircraft will have an urgent need for multi-source, adaptive, and high-energy-efficient approach to electrical power generation. In this paper, a multi-source energy optimization method based on multi-objective optimization algorithm MOACCGA is proposed. The method considers fuel consumption rate and thrust as the objective functions, with engine safety as a constraint. Based on the establishment of a small bypass ratio turbofan engine with multi-source energy extraction system, the influence mechanism of energy extraction on engine performance is analyzed. It is concluded that the generation of multi-source energy is optimizable, and the objective function has strong nonlinear and multi-peak distribution in the solution space. Then, the multi-objective adaptive covariance matrix and chaotic search group algorithm (MOACCGA) is proposed to solve the multi-source energy optimization problem. The algorithm utilizes the evolutionary approach with an adaptive covariance matrix to adapt to the strong nonlinearity of the system. Additionally, it overcomes the problem of getting trapped in local optima within the solution space characterized by multiple peak distributions by employing chaotic search. Finally, the multi-source energy optimization method is validated through simulations. In each condition, multi-source energy optimization can save fuel consumption by 0.7% of the total fuel consumption and reduce thrust loss by 1.5% of the total thrust.

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved Chan­Vese Segmentation, Hybrid HRSVM­CNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray­ level co­occurrence matrix

Only half of the authors of overviews of exercise-related interventions use some strategy to manage overlapping primary studies—a metaresearch study

ObjectivesThe conduct of systematic reviews (SRs) and overviews share several similarities. However, because the unit of analysis for overviews is the SRs, there are some unique challenges. One of the most critical issues to manage when conducting an overview is the overlap of data across the primary studies included in the SRs. This metaresearch study aimed to describe the frequency of strategies to manage the overlap in overviews of exercise-related interventions. Study Design and SettingA systematic search in MEDLINE (Ovid), Embase (Ovid), Cochrane Library, Epistemonikos, and other sources was conducted from inception to June 2022. We included overviews of SRs that considered primary studies and evaluated the effectiveness of exercise-related interventions for any health condition. The overviews were screened by two authors independently, and the extraction was performed by one author and checked by a second. We found 353 overviews published between 2005 and 2022 that met the inclusion criteria. ResultsOne hundred and sixty-four overviews (46%) used at least one strategy to visualize, quantify, or resolve overlap, with a matrix (32/164; 20%), absolute frequency (34/164; 21%), and authors' algorithms (24/164; 15%) being the most used methods, respectively. From 2016 onwards, there has been a trend toward increasing the use of some strategies to manage overlap. Of the 108 overviews that used some strategy to resolve the overlap, ie, avoiding double or multiple counting of primary study data, 79 (73%) succeeded. In overviews where no strategies to manage overlap were reported (n = 189/353; 54%), 16 overview authors (8%) recognized this as a study limitation. ConclusionAlthough there is a trend toward increasing its use, only half of the authors of the overviews of exercise-related interventions used a strategy to visualize, quantify, or resolve overlap in the primary studies’ data. In the future, authors should report such strategies to communicate more valid results.

Consensus Clustering With Co-Association Matrix Optimization.

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization; 2) the information from the original dataset is rarely considered; and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

Temporal network compression via network hashing

Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The largest outcome of these processes is directly linked to the structure of the underlying network. Indeed, a node of a network at a given time cannot affect more nodes in the future than it can reach via time-respecting paths. This set of nodes reachable from a source defines an out-component, which identification is costly. In this paper, we propose an efficient matrix algorithm to tackle this issue and show that it outperforms other state-of-the-art methods. Secondly, we propose a hashing framework to coarsen large temporal networks into smaller proxies on which out-components are more easily estimated, and then recombined to obtain the initial components. Our graph hashing solution has implications in privacy respecting representation of temporal networks.

Entropy generation analysis of a ternary hybrid nanofluid (Au-CuO-GO/blood) containing gyrotactic microorganisms in bifurcated artery

PurposeThe purpose of this paper is to study haemodynamic flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, magnetohydrodynamic (MHD) flow and aneurysm conditions. The findings of this study offer significant insights into the intricate interplay encompassing electro-osmosis, MHD flow, microorganisms, Joule heating and the ternary hybrid nanofluid.Design/methodology/approachThe governing equations are first non-dimensionalised, and subsequently, a coordinate transformation is used to regularise the irregular boundaries. The discretisation of the governing equations is accomplished by using the Crank–Nicolson scheme. Furthermore, the tri-diagonal matrix algorithm is applied to solve the resulting matrix arising from the discretisation.FindingsThe investigation reveals that the velocity profile experiences enhancement with an increase in the Debye–Hückel parameter, whereas the magnetic field parameter exhibits the opposite effect, reducing the velocity profile. A comparative study demonstrates the velocity distribution in Au-CuO hybrid nanofluid and Au-CuO-GO ternary hybrid nanofluid. The results indicate a notable enhancement in velocity for the ternary hybrid nanofluid compared to the hybrid nanofluids. Moreover, an increase in the Brinkmann number results in an augmentation in entropy generation.Originality/valueThis study investigates the flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, MHD flow and aneurysm conditions. The governing equations are non-dimensionalised, and a coordinate transformation is applied to regularise the irregular boundaries. The Crank–Nicolson scheme is used to model blood flow in the presence of a ternary hybrid nanofluid (Au-CuO-GO/blood) within the arterial domain. The findings shed light on the complex interactions involving stenosis, MHD flow, aneurysms, Joule heating and the ternary hybrid nanofluid. The results indicate a decrease in the wall shear stress (WSS) profile with increasing stenosis size. The MHD effects are observed to influence the velocity distribution, as the velocity profile exhibits a declining nature with an increase in the Hartmann number. In addition, entropy generation increases with an enhancement in the Brinkmann number. This research contributes to understanding fluid dynamics and heat transfer mechanisms in bifurcated arteries, providing valuable insights for diagnosing and treating cardiovascular diseases.

Revealing connectivity in residential Architecture: An algorithmic approach to extracting adjacency matrices from floor plans

In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture.