Data Structure Research Articles

Attention-Guided Low-Rank Tensor Completion.

Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional structures from a limited set of observed entries. Despite recent significant successes, the original structures of data tensors are still not effectively preserved in LRTC algorithms, yielding less accurate restoration results. Moreover, LRTC algorithms often incur high computational costs, which hinder their applicability. In this work, we propose an attention-guided low-rank tensor completion (AGTC) algorithm, which can faithfully restore the original structures of data tensors using deep unfolding attention-guided tensor factorization. First, we formulate the LRTC task as a robust factorization problem based on low-rank and sparse error assumptions. Low-rank tensor recovery is guided by an attention mechanism to better preserve the structures of the original data. We also develop implicit regularizers to compensate for modeling inaccuracies. Then, we solve the optimization problem by employing an iterative technique. Finally, we design a multistage deep network by unfolding the iterative algorithm, where each stage corresponds to an iteration of the algorithm; at each stage, the optimization variables and regularizers are updated by closed-form solutions and learned deep networks, respectively. Experimental results for high dynamic range imaging and hyperspectral image restoration show that the proposed algorithm outperforms state-of-the-art algorithms.

Transfer graph feature alignment guided multi-source domain adaptation network for machinery fault diagnosis

In recent years, the application of unsupervised multi-source domain adaptation (MSDA) techniques for fault diagnosis has gained significant traction. Current research typically overlooks or fails to effectively capture critical data structure information during feature extraction. Another challenge is optimising the integration of information from multiple source domains to diagnose the target domain while avoiding negative transfer. To address these challenges, this study proposes a transfer graph feature alignment guided multi-source domain adaptation network (MDTGAL). In the proposed method, a transfer graph sample generator module (GSG) is constructed to model the data structure between source and target domains, and multiple graph feature extractors are employed to learn the data structure information from different domain combinations. A regularisation technique is introduced to extract the domain-invariant features by aligning the parameters across multiple independent graph feature extractors. In addition, a weighted soft-voting mechanism based on the polynomial kernel-induced maximum mean difference metric (PK-MMD) is designed to fuse the outputs from multiple classifiers, to comprehensively account for the influence of each source domain. The proposed method was tested on multi-source domain transfer tasks involving various operating conditions of rotating machinery. The experimental results demonstrate that the MDTGAL exhibits superior cross-domain diagnostic performance, outperforming existing mainstream methods. In addition, this study explored the impact of varying numbers of source domains on the diagnostic accuracy of the target domain, providing insights into selecting the correct number of source domains for specific MSDA tasks.

Unveiling DNA Sequences: A Comparison of Machine Learning and Deep Learning Techniques for Prediction

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>DNA is the biological macromolecule unit that carries the information of all protein, amino acid sequences. With the help of this protein sequence, we explore the mutated gene and disease-causing mutated genomic pattern. Currently, the progression of genomic innovation is the source of DNA arrangement information developing at a dangerous rate—external factors have stimulated the volume of research into DNA genomes. Initially, the development process of DNA sequencing is accomplished with the support of the Database, data structures, and sequence similarity. The method is capable of extracting a particular property in DNA. We employ the deep learning algorithm to pull out protein sequences' features. The DNA sequence is classified based on the in-build protein structures extracted into the Fasta file. Therefore, the DNA sequence of E.coli with 106 data sets and 57 nucleotides is tested experimentally. Finally, we compared the results with the existing decision tree algorithm, KNN- Classification, random forest, and neural networks. The deep learning algorithm yields higher efficiency of 98% compared to other machine learning algorithms. This highlights the potential of deep learning in genomics research and its ability to yield superior results in classifying DNA sequences.</p></td></tr></tbody></table></div>

Robust Principal Component Analysis Based on Fuzzy Local Information Reservation.

Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential structure of data. In other words, the principal component space contains not only information related to the essential structure of data but also some unrelated information. This frequently occurs when the intrinsic dimensionality of data is unknown or when it has complex distribution characteristics such as multi-modalities, manifolds, etc. Therefore, it is unreasonable to identify noise and useful information based solely on reconstruction error. For this reason, PCA is unsuitable as a preprocessing technique for most applications, especially in noisy environment. To solve this problem, this paper proposes robust PCA based on fuzzy local information reservation (FLIPCA). By analyzing the impact of reconstruction error on sample discriminability, FLIPCA provides a theoretical basis for noise identification and processing. This not only greatly improves its robustness but also extends its applicability and effectiveness as a data preprocessing technique. Meanwhile, FLIPCA maintains consistent mathematical descriptions with traditional PCA while having few adjustable hyperparameters and low algorithmic complexity. Finally, we conducted comprehensive experiments on synthetic and real-world datasets, which substantiated the superiority of our proposed algorithm.

Faults-as-address simulation

Fault-as-address-simulation (FAAS) is a simulation mechanism for testing combinations of circuit line faults, represented by the bit addresses of element logical vectors. The XOR relationship between the test set T and the truth table L of the element forms a deductive vector for fault simulation, using truth table addresses or the logic vector bits. Addresses are used in the simulation matrix to mark those n-combinations of input faults detected at the element's output. The columns of the simulation matrix are treated as n-row addresses to generate an element output row via a deductive vector. There is no transport of input faults to the element output, Only the 1-signals written in the non-input row coordinates of the circuit simulation matrix. The simulation matrix is initially filled with 1-signals along the main diagonal. The line faults detected on the test set of circuits are determined by the inverse of lines good values, which have 1-values in the matrix row corresponding to the output circuit element. The deductive vector is obtained by the XOR-relations between the test set and logical vector in three table operations. The advantage of the proposed FAAS mechanism is the predictable complexity of the algorithm and memory consumption for storing data structures when simulating a test set.

Remaining useful life prediction with spatio-temporal graph transform and weakly supervised adversarial network: An application in power components

Remaining useful life (RUL) prediction is crucial in improving the reliability of insulated gate bipolar transistors (IGBTs) and has attracted widespread attention all over the world. Owing to the heterogeneity and complex internal connectivity of the IGBTs, accurate RUL prediction by traditional deep learning (DL) methods remains challenging. Herein, a novel prognostic framework is proposed for their RUL prediction. In brief, the non-smooth spike voltage of IGBTs is transformed into a graph matrix that contains rich correlation information. Then, spatio-temporal fusion graph network (STFGN) is proposed to realize RUL prediction. It contains spatial-scale and temporal-scale extractors, where the former one based on GraphSage can obtain unique IGBTs data structure information, while the latter based on bidirectional long short-term memory (BiLSTM) captures the time-series degradation features of IGBTs. Moreover, the weakly supervised adversarial learning strategy is considered to realize cross-domain prediction using scarce labeled data, which enhance the positive transfer. Four transfer tasks under variable working conditions are considered to prove the proposed method. The experimental results demonstrate that the average SF and RMSE metrics of the proposed method are 0.429 and 0.108, which is superior to other related methods.

VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns

With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning (ML), obtaining power consumption data is becoming more and more important. Collecting real-world energy data using sensors is time consuming, expensive, error-prone and in some situations not possible. Therefore, we present the VA-Creator, a framework to create Virtual Appliances (VAs). These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. To be able to generate all appliance types available in a typical household we use various kinds of NN, including Multilayer Perceptrons (MLPs), Long Short-Term Memorys (LSTMs) and a specific Generative Adversarial Network (GAN) as well as different ML techniques such as XGBoost, selecting the appropriate technique depending on each appliance’s characteristics. We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping (DTW) as well as the classification performance of an MLP descriminator as metrics. Additionally, to ensure that the VAs allow to meaningfully train ML models, we use them to generate synthetic data and then train Non intrusive Load Monitoring (NILM) models in an extensive evaluation. The presented evaluation provides evidence that the VA models produce realistic and meaningful results.

EFFT: An Event-Based Method for the Efficient Computation of Exact Fourier Transforms.

We introduce eFFT, an efficient method for the calculation of the exact Fourier transform of an asynchronous event stream. It is based on keeping the matrices involved in the Radix-2 FFT algorithm in a tree data structure and updating them with the new events, extensively reusing computations, and avoiding unnecessary calculations while preserving exactness. eFFT can operate event-by-event, requiring for each event only a partial recalculation of the tree since most of the stored data are reused. It can also operate with event packets, using the tree structure to detect and avoid unnecessary and repeated calculations when integrating the different events within each packet to further reduce the number of operations. eFFT has been extensively evaluated with public datasets and experiments, validating its exactness, low processing time, and feasibility for online execution on resource-constrained hardware. We release a C++ implementation of eFFT to the community.

Computational study of the HLTF ATPase remodeling domain suggests its activity on dsDNA and implications in damage tolerance

The Helicase-Like Transcription Factor (HLTF) is member of the SWI/SNF-family of ATP dependent chromatin remodellers known primarily for maintaining genome stability. Biochemical and cellular assays support its multiple roles in DNA Damage Tolerance. However, the lack of sufficient structural data limits the comprehension of the molecular basis of its modes of action.In this work we have modelled and characterized the HLTF ATPase remodeling domain by using bioinformatic tools and all-atoms molecular dynamics simulations.In-silico results suggested that its binding to dsDNA is mainly mediated by the positively charged residues Arg563 and Lys913, found conserved in HLTF homologs, and Arg620 and Lys999, found only in HLTF. Interestingly, these residues are mutated in cancer cells. During translocation on dsDNA, HLTF remains persistently bound through the N-terminal ATPase subunit. However, DNA advancement occurs only in the presence of the synergic-anticorrelated action of both motor lobes. In contrast, the C-terminal facilitates substrate remodeling through DNA deformation and generation of bulges according to a wave-model. Finally, the large conformational change suggested between the two motor-remodeling subunits might be activated upon the release of PARP1 on stalled fork and be responsible for the intervention of HLTF-HIRAN in the formation of D-loop and 4-way junction DNA structures.

Comparative Analysis of Machine Learning Algorithms: KNN, SVM, Decision Tree and Logistic Regression for Efficiency and Performance

This paper presents a comparative study of machine learning models namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression on two datasets: 20Newsgroups and Wine datasets. Based on basic accuracy, and precision, and recall of the models, F1 measures were assessed. When compared with other classifiers such as KNN and Decisions Trees, SVM and Logistic regression gave better results especially in the case of the 20Newsgroups dataset dominated by textual high dimensional data. KNN had poor recall results and Decision Tree was moderate. In the Wine dataset, since the structure of data is comparatively less complex in our context then all the models yielded almost similar results with accuracy and precision factors very close to 1.0 which of course manifested that the choice of the model does not affect much on simple data. These results stress the importance of the choice of the appropriate model for tasks with a certain level of data complexity; detailed models show the greatest efficiency at the accomplishment of complicated tasks, though at the same time, they are not required for simple, structured data.

Bridging theory and data: A computational workflow for cultural evolution

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

AbstractThe surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

Alterations of gray matter asymmetry in internet gaming disorder.

Structural asymmetry is a subtle but pervasive property of the human brain, which has been found altered in various psychiatric and neurocognitive disorders. However, little is known regarding potential alterations of structural asymmetry underlying internet gaming disorder (IGD). Therefore, this study aimed to investigate the structural features of gray matter asymmetry in IGD. High-resolution structural magnetic resonance imaging data were collected from 104 individuals with IGD and 104 recreational game users (RGUs). We applied a whole-brain voxel-based asymmetry (VBA) approach to determine the asymmetrical aberrations of gray matter in relation to IGD. Furthermore, the local abnormalities of structural asymmetry were employed as features to examine the effect of classification using a support vector machine (SVM). The results indicated that individuals with IGD as compared to RGUs showed asymmetrical alterations of gray matter in the medial prefrontal cortex (mPFC), orbitofrontal cortex, precuneus, middle temporal gyrus, superior parietal lobule and inferior temporal gyrus, regions implicated in hedonic motivation, self-reflection, information integration and visuospatial attention processing. Moreover, these atypical asymmetrical features can distinguish IGD subjects from RGUs with high accuracy. These results suggested that disrupted structural asymmetry of motivational reward, visuospatial and default mode circuits might be potential biomarkers for identifying pathological gaming dependence. These findings extended our understanding of structural underpinnings of IGD and provided new insights for developing effective interventions to alleviate compulsive gaming usage.

Enhancing Recovery of Structural Health Monitoring Data Using CNN Combined with GRU

Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions during the operation of the infrastructure. However, for various reasons, data from SHM systems may be interrupted or faulty, leading to serious consequences. This study proposes using a Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRUs) to recover lost data from accelerometer sensors in SHM systems. CNNs are adept at capturing spatial patterns in data, making them highly effective for recognizing localized features in sensor signals. At the same time, GRUs are designed to model sequential dependencies over time, making the combined architecture particularly suited for time-series data. A dataset collected from a real bridge structure will be used to validate the proposed method. Different cases of data loss are considered to demonstrate the feasibility and potential of the CNN-GRU approach. The results show that the CNN-GRU hybrid network effectively recovers data in both single-channel and multi-channel data loss scenarios.

Some isomers of boron-nitrogen doped phenanthrene – DFT treatment

Phenanthrene is an even alternant aromatic hydrocarbon. In adjacent positions simultaneously boron and nitrogen doped phenanthrene and some of their derivatives have found some technological applications. In the present study, isomers of phenanthrene perturbed as mentioned above have been considered within the restrictions of density functional theory at the level of B3LYP/6-311++G(d,p) level. All the isomeric structures presently considered are thermally favored and electronically stable at the standard states. Various structural and quantum chemical data have been collected and discussed, including UV-VIS spectra. Also the NICS (0) data have been obtained for the isomers.

Mathematical foundation of High-Dimensional Data Analysis: Leveraging Topology and Geometry for Enhanced Model Interpretability in AI

One of the most important challenges for modern AI and machine learning is the analysis of high-dimensional data. Traditional methods face serious complications in such cases due to high complexity of datasets: the curse of dimensionality, overfitting, and lack of transparency of model behavior. In this paper, we adopt a novel approach to analyze high-dimensional data; topological and geometric techniques will be exploited, taking advantage of better model interpretability and deeper insights into the structure. Precisely, we discuss Topological Data Analysis, mainly Persistent Homology (Edelsbrunner et al., 2002), which allows the extraction of topological features-like loops and connected components that enable the extracting knowledge about the global structure of data. We also see how some concepts of differential geometry and Riemannian geometry (Do Carmo, 1976) can be used to cast light on manifold data structure lying at the heart of any attempt at modeling intrinsic patterns in high-dimensional spaces. We will review how these mathematical pillars, combined with state-of-the-art techniques for dimensionality reduction like t-SNE, UMAP, Principal Component Analysis, are able to provide interpretable and low-dimensional representations of high-dimensional data that can be used to understand models and make decisions. Case studies are also included, which explain the practical working of these methods in AI systems and show how much complex models can be made transparent using these, especially in domains that are very critical, such as healthcare (Caruana et al., 2015), finance (Chen et al., 2018), and autonomous systems ( Wang et al., 2019). We also discuss some of the difficulties in using these methods for practical applications: computational complexity; the need for large-scale data processing (Bengio et al., 2007); and integration of topological and geometric intuition with the rest of the machine learning pipeline (Zhu et al., 2020). We conclude with possible future directions of research toward fine-tuning these methods and exploring their broader applicability to AI in its quest for more robust, interpretable, and reliable AI models. Given this work, we focus on how linking topology, geometry, and AI bears great promise for solving one of today's critical challenges: model interpretability in high-dimensional data analysis.

Blood Management System Using Java with GUI and Array List-based Database

This research paper presents the development of a Blood Management System (BMS) using Java with a graphical user interface (GUI), ArrayList as a data structure for managing records, and file handling for persistence. The system provides an efficient and user-friendly platform for blood donation management, focusing on functionalities accessible to an administrative user (admin) only. Key features include donor data management (adding, updating, searching, deleting), stock management of blood groups, and essential application controls like login/logout and exit functionalities. This system aims to streamline the management of blood banks by providing quick access to critical information such as donor location, blood group, and stock availability. The source code for the Blood Management System is accessible at https://github.com/BloodBankManagment.

PENGGUNAAN STRATA TAJUK OLEH SATWA BURUNG DI DESA LORULUN KECAMATAN WERTAMRIAN, KABUPATEN KEPULAUAN TANIMBAR

This research aims to determine the use of canopy strata for birds and determine bird activity based on the use of canopy strata in Lorulun Village, Wertamrian District, Tanimbar Islands Regency. Bird watching in Lorulun Village uses the point count method which is made in five plots. To collect vegetation structure data vertically, namely using a canopy stratification approach, the technical implementation is by recording the types of birds found. The data obtained in this research was then analyzed descriptively qualitatively and quantitatively. The results of the research carried out were that there were 20 types of bird species found at the research location with distribution in strata A, there were 7 species of birds, Strata B, there were 7 species of birds, Strata C, there were 8 species of birds, Strata D, there was 1 species of birds. birds, Strata E there are 2 types of bird animals in the use of per strata, while in the use of vegetation strata there are bird species that use more than one stratum, 16 types of bird animals and activity patterns of bird species in each stratum include: rest activities, social activities and activities. Eat. Including the activity patterns of bird species in each strata, including: resting activities, social activities and eating activities.