Data Structure Research Articles

Influence of loss function and electron dose on ptychography of 2D materials using the Wirtinger flow

Iterative phase retrieval is based on minimising a loss function as a measure of the consistency of an initial guess and underlying experimental data. Under ideal experimental conditions, real data contains Poissonian noise due to counting statistics. In this work, we use the Wirtinger Flow concept in combination with four common loss functions, being the L1 loss, the mean-squared error (MSE), the amplitude loss and the Poisson loss. Since only the latter reflects the counting statistics as an asymmetric Poisson distribution correctly, our simulation study focuses on two main cases. Firstly, high-dose momentum-resolved scanning transmission electron microscopy (STEM) of an MoS2 monolayer is considered for phase retrieval. In this case, it is found that the four losses perform differently with respect to chemical sensitivity and frequency transfer, which we interprete in terms of the substantially different signal level in the bright and dark field part of diffraction patterns. Remedies are discussed using further simulations, addressing the use of virtual ring detectors for the dark field, or restricting loss calculation to the bright field. Secondly, a dose series is presented down to 100 electrons per diffraction pattern. It is found that all losses yield qualitatively reasonable structural data in the phase, whereas only MSE and Poisson loss range at the correct amplitude level. Chemical contrast is, in general, reliably obtained using the Poisson concept, which also provides the most continuous spatial frequency transfer as to the reconstructed object transmission function.

Applications of Power Electronics using AI Technology: Overview

In this century our world, there are huge impacts of AI. This paper discusses an overview of the Artificial Intelligence (AI) applications used in power electronic systems. AI works in three distinctive life-cycle phases, design, control, and maintenance. Also, it includes optimisation, classification, regression, and data structure exploration. Here we discuss, the applications of four categories of AI: expert system, fuzzy logic, metaheuristic method, and machine learning. This paper shows various techniques of AI, applications of AI and different methods are fault diagnosis and prognosis. Future scope of AI in power electronics is discuss in this review paper.

Diagnostics for categorical response models based on quantile residuals and distance measures

Polytomous categorical data are frequent in studies, that can be obtained with an individual or grouped structure. In both structures, the generalized logit model is commonly used to relate the covariates on the response variable. After fitting a model, one of the challenges is the definition of an appropriate residual and choosing diagnostic techniques. Since the polytomous variable is multivariate, raw, Pearson, or deviance residuals are vectors and their asymptotic distribution is generally unknown, which leads to difficulties in graphical visualization and interpretation. Therefore, the definition of appropriate residuals and the choice of the correct analysis in diagnostic tools is important, especially for nominal data, where a restriction of methods is observed. This paper proposes the use of randomized quantile residuals associated with individual and grouped nominal data, as well as Euclidean and Mahalanobis distance measures, as an alternative to reduce the dimension of the residuals. We developed simulation studies with both data structures associated. The half-normal plots with simulation envelopes were used to assess model performance. These studies demonstrated a good performance of the quantile residuals, and the distance measurements allowed a better interpretation of the graphical techniques. We illustrate the proposed procedures with two applications to real data.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

Efficient Parallel Processing of R-Tree on GPUs

R-tree is an important multi-dimensional data structure widely employed in many applications for storing and querying spatial data. As GPUs emerge as powerful computing hardware platforms, a GPU-based parallel R-tree becomes the key to efficiently port R-tree-related applications to GPUs. However, traditional tree-based data structures can hardly be directly ported to GPUs, and it is also a great challenge to develop highly efficient parallel tree-based data structures on GPUs. The difficulty mostly lies in the design of tree-based data structures and related operations in the context of many-core architecture that can facilitate parallel processing. We summarize our contributions as follows: (i) design a GPU-friendly data structure to store spatial data; (ii) present two parallel R-tree construction algorithms and one parallel R-tree query algorithm that can take the hardware characteristics of GPUs into consideration; and (iii) port the vector map overlay system from CPU to GPU to demonstrate the feasibility of parallel R-tree. Experimental results show that our parallel R-tree on GPU is efficient and practical. Compared with the traditional CPU-based sequential vector map overlay system, our vector map overlay system based on parallel R-tree can achieve nearly 10-fold speedup.

Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).MethodsWe used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk “disease-like” or low-risk “CN-like”. Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.ResultsIn predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).ConclusionWhen predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

FDIE: a graph-based framework for extracting design information from annotated building facade images

ABSTRACT Images of building facades, rich in design information, are important for architectural studies. In recent years, numerous studies have explored the automatic recognition and annotation of basic elements (e.g. windows) in these images. However, most application of the recognized elements remains at the pixel level, such as 3D reconstruction, and the design information hidden behind the elements cannot be processed using common data analysis methods because the annotations are still unstructured data. This prevents us from further discovering the composition pattern of building facades from the images. To enable the recognized elements to be analyzed, a framework for facade design information extraction (FDIE) from annotated building facade images based on graph theory is proposed in this paper. The design information is divided into three categories, i.e. graphic, relationship, and sum information, and extracted as structured data. An image restoration test demonstrates that more than 95% of the design information can be extracted using the FDIE. These structured data provide an important basis for intelligent analysis of composition pattern. Several examples and cases partly illustrate the application prospects of the extracted information. Other graph-based analysis methods are also expected to be applied to the extracted design information.

Unraveling the mesoscale organization induced by network-driven processes

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

Consensus and discriminative non-negative matrix factorization for multi-view unsupervised feature selection

Multi-view unsupervised feature selection (MUFS) has been proven to be an efficient dimensionality reduction technique for multi-view data. Existing methods have two main challenges: (1) The consistency information from different views is not fully exploited. (2) The cluster structure of the original data is not well utilized. To settle these problems, we propose a novel consensus and discriminative non-negative matrix factorization (CDNMF) for multi-view unsupervised feature selection. Specifically, CDNMF obtains a robust low-dimensional latent space by NMF with an ℓ2,1-norm constraint. To select more discriminative features, we further impose a sparsity constraint on the learned latent features. Moreover, CDNMF performs k-means clustering on all views separately to obtain the pseudo-labels of each view, which are used to guide the learning of consensus information among views. To solve our model, we develop an efficient iterative optimization algorithm. Extensive experimental results on ten benchmark datasets demonstrate that the proposed significantly outperforms several existing feature selection methods in clustering tasks.

Structural and molecular dynamics simulation studies of CBL-interacting protein kinase CIPK and its complexes related to plant salinity stress.

Calcium-dependent signaling in plants is responsible for several major cellular events, including the activation of the salinity-responsive pathways. Calcium binds to calcineurin B-like protein (CBL), and the resulting CBL-Ca2+ complex binds to CBL-interacting protein kinase (CIPK). The CBL-CIPK complex enhances the CIPK interaction with an upstream kinase. The upstream kinase phosphorylates CIPK that, in turn, phosphorylates membrane transporters. Phosphorylation influences transporter activity to kick-start many downstream functions, such as balancing the cytosolic Na+-to-K+ ratio. The CBL-CIPK interaction is pivotal for Ca2+-dependent salinity stress signaling. Computational methods are used to model the entire Arabidopsis thaliana CIPK24 protein structure in its autoinhibited and open-activated states. Arabidopsis thaliana CIPK24-CBL4 complex is predicted based on the protein-protein docking methods. The available structural and functional data support the CIPK24 and the CIPK24-CBL4 complex models. Models are energy-minimized and subjected to molecular dynamics (MD) simulations. MD simulations for 500ns and 300ns enabled us to predict the importance of conserved residues of the proteins. Finally, the work is extended to predict the CIPK24-CBL4 complex with the upstream kinase GRIK2. MD simulation for 300ns on the ternary complex structure enabled us to identify the critical CIPK24-GRIK2 interactions. Together, these data could be used to engineer the CBL-CIPK interaction network for developing salt tolerance in crops.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

Vascular access specialist teams versus standard practice for catheter insertion and prevention of failure: a systematic review

ObjectiveBillions of vascular access devices (VADs) are inserted annually for intravenous therapy worldwide. However, their use is not without challenges. Facilitating the process and enhancing results, hospital authorities have created...

A comparison of two models for detecting inconsistency in network meta-analysis.

The application of network meta-analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta-analysis as whether the results can be reliably used as a clinical guidance. Among the existing methods for detecting inconsistency, two commonly used models are the design-by-treatment interaction model and the side-splitting models. While the original side-splitting model was initially estimated using a Bayesian approach, in this context, we employ the frequentist approach. In this paper, we review these two types of models comprehensively as well as explore their relationship by treating the data structure of network meta-analysis as missing data and parameterizing the potential complete data for each model. Through both analytical and numerical studies, we verify that the side-splitting models are specific instances of the design-by-treatment interaction model, incorporating additional assumptions or under certain data structure. Moreover, the design-by-treatment interaction model exhibits robust performance across different data structures on inconsistency detection compared to the side-splitting models. Finally, as a practical guidance for inconsistency detection, we recommend utilizing the design-by-treatment interaction model when there is a lack of information about the potential location of inconsistency. By contrast, the side-splitting models can serve as a supplementary method especially when the number of studies in each design is small, enabling a comprehensive assessment of inconsistency from both global and local perspectives.

Structural and geomorphological constraints on the activity of the Sparta Fault (Greece)

The ancient city of Sparti (Greece) suffered extensive damage from a M w ∼7.2 earthquake in 464 B.C.E. resulting in ∼ 20,000 fatalities, but questions remain on the short (∼10 4 yrs) and long-term (10 5 – 10 6 yrs) activity of this important structure. This paper presents new structural data and fluvial geomorphologic analysis from the Sparta Fault, and in particular considers the northern fault segment that is less well known. A new topographic profile on the well-developed post-glacial fault scarp from the northern strand indicates a 7.53 m offset over the last ∼15 ka, suggesting a throw rate of ∼0.5 mm/yr. The longitudinal profiles of rivers flowing across the fault allow elucidation of longer-term fault activity. Along the strike of the fault rivers exhibit up to two slope-break knickpoints, which decrease in height from south to north. These knickpoints are interpreted to have formed owing to the initiation of faulting and a subsequent slip-rate acceleration. The post-glacial fault scarp and fluvial geomorphology both indicate that entire fault is active and has an asymmetrical throw profile that results in the highest slip-rate in the south.

Dynamic community detection algorithm based on hyperbolic graph convolution

PurposeCommunity detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.Design/methodology/approachHSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.FindingsExperimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.Originality/valueThis model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

Validation of an Electronic Health Record-Based Machine Learning Model Compared to Clinical Risk Scores for Gastrointestinal Bleeding

Background & AimsGuidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may outperform existing scores and can be integrated within the electronic health record (EHR) to provide real-time risk assessment without manual data entry. We present the first EHR-based machine learning model for GIB. MethodsThe training cohort comprised 2,546 patients and internal validation of 850 patients presenting with overt GIB (hematemesis, melena, hematochezia) to emergency departments of 2 hospitals from 2014-2019. External validation was performed on 926 patients presenting to a different hospital with the same EHR from 2014-2019. The primary outcome was a composite of red-blood-cell transfusion, hemostatic intervention (endoscopic, interventional radiologic, or surgical), and 30-day all-cause mortality. We used structured data fields in the EHR available within 4 hours of presentation and compared performance of machine learning models to current guideline-recommended risk scores, Glasgow-Blatchford Score (GBS) and Oakland Score. Primary analysis was area under the receiver-operating-characteristic curve (AUC). Secondary analysis was specificity at 99% sensitivity to assess proportion of patients correctly identified as very-low-risk. ResultsThe machine learning model outperformed the GBS (AUC=0.92 vs. 0.89;p<0.001) and Oakland score (AUC=0.92 vs. 0.89;p<0.001). At the very-low-risk threshold of 99% sensitivity, the machine learning model identified more very-low-risk patients: 37.9% vs. 18.5% for GBS and 11.7% for Oakland score (p<0.001 for both comparisons). ConclusionsAn EHR-based machine learning model performs better than currently recommended clinical risk scores and identifies more very-low-risk patients eligible for discharge from the emergency department.

Insight into binding of endogenous neurosteroid ligands to the sigma-1 receptor

The sigma-1 receptor (σ1R) is a non-opioid membrane receptor, which responds to a diverse array of synthetic ligands to exert various pharmacological effects. Meanwhile, candidates for endogenous ligands of σ1R have also been identified. However, how endogenous ligands bind to σ1R remains unknown. Here, we present crystal structures of σ1R from Xenopus laevis (xlσ1R) bound to two endogenous neurosteroid ligands, progesterone (a putative antagonist) and dehydroepiandrosterone sulfate (DHEAS) (a putative agonist), at 2.15-3.09 Å resolutions. Both neurosteroids bind to a similar location in xlσ1R mainly through hydrophobic interactions, but surprisingly, with opposite binding orientations. DHEAS also forms hydrogen bonds with xlσ1R, whereas progesterone interacts indirectly with the receptor through water molecules near the binding site. Binding analyses are consistent with the xlσ1R-neurosteroid complex structures. Furthermore, molecular dynamics simulations and structural data reveal a potential water entry pathway. Our results provide insight into binding of two endogenous neurosteroid ligands to σ1R.

Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts

AbstractWe consider the availability of new harmonized data sources and novel machine learning methodologies in the construction of a social vulnerability index (SoVI), a multidimensional measure that defines how individuals’ and communities may respond to hazards including natural disasters, economic changes, and global health crises. The factors underpinning social vulnerability—namely, economic status, age, disability, language, ethnicity, and location—are well understood from a theoretical perspective, and existing indices are generally constructed based on specific data chosen to represent these factors. Further, the indices’ construction methods generally assume structured, linear relationships among input variables and may not capture subtle nonlinear patterns more reflective of the multidimensionality of social vulnerability. We compare a procedure which considers an increased number of variables to describe the SoVI factors with existing approaches that choose specific variables based on consensus within the social science community. Reproducing the analysis across eight countries, as well as leveraging deep learning methods which in recent years have been found to be powerful for finding structure in data, demonstrate that wealth-related factors consistently explain the largest variance and are the most common element in social vulnerability.

Self-Diffusion in Sr-Containing Iron-Polyphosphate Glasses by Molecular Dynamics Simulations

Among the many possible applications of iron phosphate glasses, one of them is that they are promising materials in waste vitrification, particularly for radioactive waste. In vitrified form, waste elements should be permanently immobilized in a glass network as they are susceptible to harsh environmental conditions. The self-diffusion of the vitrified material species may limit the potential usefulness of the glasses. This paper presents the possibility of using molecular dynamics simulations to study this process and the substitution of SrO into an iron phosphate glass network. It was evidenced that the self-diffusion mechanism differed significantly depending on whether the glass was in a solid or liquid state. The proposed method also offered a relatively easy prediction of glass characteristic temperatures, such as transformation and flow. We also observed, and here describe, an aggregation process of the glass elements that may drive their crystallization. The obtained results are discussed in light of the experimental and theoretical structural feature literature data.

Demonstration of Physicochemical and Functional Similarity of Biosimilar Pegfilgrastim-cbqv to Pegfilgrastim.

Pegfilgrastim-cbqv/CHS-1701 (UDENYCA®) (hereafter referred to as pegfilgrastim-cbqv) was approved in 2018 by the US Food and Drug Administration as a biosimilar for pegfilgrastim (Neulasta®) (hereafter referred to as pegfilgrastim). Both pegfilgrastim-cbqv and pegfilgrastim are conjugates of recombinant human granulocyte colony stimulating factor (r-metHuG-CSF) with a 20kDa polyethylene glycol (PEG) indicated to decrease the incidence of infection, as manifested by febrile neutropenia, in patients receiving myelosuppressive anticancer drugs. The demonstration of analytical similarity for PEG-protein conjugates presents unique challenges since both the protein and PEG attributes must be characterized. The current study demonstrates the analytical similarity of pegfilgrastim-cbqv and the reference product, pegfilgrastim. In addition to the physicochemical and functional characterization of the protein, the study assessed attributes specific to PEGylation including PEG size and polydispersity, site of attachment, linker composition, and PEGylation process-related variants. The structural, functional, and stability attributes of pegfilgrastim-cbqv and pegfilgrastim were compared using state-of-the-art analytical methods. For the protein, the primary structure, disulfide structure, and secondary and tertiary structures were assessed using traditional protein characterization techniques such as mass spectrometry (MS), circular dichroism (CD), intrinsic fluorescence, and differential scanning calorimetry (DSC), as well as more advanced techniques such as two-dimensional (2D) nuclear magnetic resonance (NMR) and hydrogen deuterium exchange (HDX). For the PEG moiety, the site of attachment, occupancy, linker composition, size and polydispersity were compared using mass spectrometry (both intact and after endoprotease digestion), multiangle light scattering detection (MALS), and Edman degradation. Purity assessments included the assessment of both protein variants and PEGylation variants using chromatographic and electrophoretic analytical separation techniques. The functional similarity between pegfilgrastim-cbqv and pegfilgrastim was compared using both a cell-based bioassay and surface plasmon resonance (SPR). The degradation rates and stability profiles were compared under accelerated and stressed conditions. Biosimilarity was demonstrated by a thorough assessment of physiochemical and functional attributes, as well as comparative stability, of pegfilgrastim-cbqv relative to pegfilgrastim. These studies demonstrated identical primary structure and disulfide structure, highly similar secondary and tertiary structure, as well as functional similarity. The impurity profile of pegfilgrastim-cbqv was comparable to that of pegfilgrastim with only minor differences in PEGylation variants and a slight offset in the PEG molar mass. These differences were not clinically relevant. The degradation profiles were qualitatively and quantitatively similar under accelerated and stress conditions. The structural, functional, and stability data demonstrate that pegfilgrastim-cbqv is highly similar to the reference product, pegfilgrastim.