Volume Of Data Research Articles

Do some levator avulsions improve over time?

To determine whether the sonographic appearance of levator avulsion after vaginal childbirth can improve significantly over the first few years postpartum. Retrospective study of women seen in the context of two prospective perinatal imaging studies. All subjects had undergone an interview, clinical examination and 4D translabial ultrasound (TLUS) on average 4.3 months and 3.1 years post-partum. Volume data sets were analysed at a later date blinded against all other data. The number of abnormal slices at both time points was compared using Mann- Whitney U Test. Patients in whom findings had changed over time were reviewed separately in parallel in order to reduce the impact of differences in slice location and imaging settings. The symmetry test was used to analyse changes between the two postnatal visits. Of 1148 women originally recruited, 315 had had at least two postnatal visits. 42 were excluded, leaving 273 women for analysis.They were first seen on average 4.3 (2.6-9.8) months after childbirth and the last time 3.1 (1.4-8) years postpartum. Cohen's kappa for the two assessments was 0.89, with agreement in 97% (264/273) of cases. At the first visit, complete avulsion was diagnosed in 20, partial avulsion in 32, and no avulsion in 221. While seven partial avulsions appeared sonographically normal at the second visit, there were no statistically significant changes in avulsion category between visits (P=0.4). Tomographic pelvic floor imaging obtained 2.5-10 months after childbirth may be used as a proxy for long- term outcomes. Findings at a mean of 3.1 years showed 97% agreement with imaging obtained at an average of 4.3 months. There was a non-significant reduction in abnormal slices affecting at most 3/12 slices. This may be explained by compensatory hypertrophy of remaining intact muscle. Sonographic normalisation of complete avulsion was not observed. This article is protected by copyright. All rights reserved.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design.

In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model

Abstract With the continuous development of Earth Observation technology, resolution of imagery and gridded data has significantly increased, leading to a rapid increase in data volume. To efficiently acquire and analyze these vast amounts of imagery and gridded data, image tiling technology has been developed to effectively access data of interested areas. Tiling technology divides large-scale image data into smaller tiles, providing fast, accurate, and efficient access support for imagery and gridded data. The spatial grid model, as the foundational framework of the new generation of geographic spatial information, plays a critical role in the retrieval, integration, services, and applications of imagery and gridded data resources. When tiling image data based on the spatial grid model, it always generates both complete and incomplete tiles. Particularly, when conducting image tile retrieval using the same rule-based grid in the retrieval area, incomplete tile phenomena along the boundary regions of the retrieved images often occur, resulting in gaps within the retrieval area. To tackle this issue, in this study, we present a new topological model called the Grid Six-Intersection Model (G-6IM), specifically designed for regular rectangular grids, to accurately represent boundary issues in image tiling. Through a practical case study, we demonstrate the effectiveness and practical application potential of the proposed G-6IM model, providing new insights and guidance for the improvement and optimization of imagery and gridded data tiling technology.

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing.

Erosion depth prediction of chloride ions under stray current using FEM based CNNs

Stray currents can accelerate the transport of corrosive ions, especially Cl−, in concrete materials, which is very detrimental to structural safety. Effectively predicting the erosion depth of Cl− is crucial for evaluating structural safety. This article is based on a finite element model and verifies the erosion depth of Cl− under different voltages, Cl− concentrations, and corrosion time through experimental data. A polynomial was used to fit the quantitative relationship between erosion depth, Cl− concentrations, and corrosion time under single voltage condition. However, this formula only applies to a single voltage and has too many parameters. Therefore, this article also established a CNNs regression model to predict the depth of Cl−, and the results showed the multiple regression ability of CNNs. It has been proven that CNNs can accurately predict the erosion depth, which helps to accurately evaluate structural safety. After comparing experimental values, CNNs, ResNet, and ResNet-attention, it was found that residual networks and attention mechanisms did not significantly improve the prediction accuracy of deep networks, which may be related to insufficient data volume. After expanding the dataset, ResNet performed the best overall, and ResNet-attention had better testing performance, which is related to the powerful feature extraction ability of the attention mechanism.

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

Innovative Cloud Computing Techniques for Big Data Administration Getting Past Roadblocks and Developing New Paths

The digital era's introduction of big data has fundamentally altered the landscape of information management, providing organizations across a wide range of industries with unprecedented opportunities and difficulties. This scientific inquiry digs into the convergence of cloud computing and big data, presenting a thorough history of their evolution and underlining their critical roles in modern data governance. Through a meticulous examination of the critical role of effective big data management across diverse industries, the paper emphasizes the critical importance of cloud computing solutions in addressing the multifaceted complexities arising from data volume, velocity, variety, veracity, and security. Conventional big data handling approaches inside cloud computing frameworks, such as batch processing, stream processing, data warehousing, and virtualization, are thoroughly evaluated in light of their inherent constraints. The paper then examines modern cloud computing solutions that have been precisely tuned to handle the issues of scalability, real-time processing, and security inherent in large data management. The investigation is complemented by the inclusion of informative case studies and real-world examples that demonstrate the practical applicability of these pioneering ideas across a wide range of sectors, from entertainment to manufacturing. Furthermore, the paper defines emerging technologies and trends in cloud computing, highlights persistent obstacles, and outlines prospects for innovation and future research in the field. Ethical and regulatory aspects such as data privacy, fairness, openness, and compliance are extensively reviewed and emphasized. Finally, the article recommends for ongoing innovation, academic investigation, and ethical discernment to fully realize the promise of big data while limiting related hazards, emphasizing the dynamic nature of big data governance within the context of cloud computing. Key Words: Cloud computing, Big data, Hadoop ,Spark, AI and machine learning integration Blockchain

Semi-supervised image manipulation localization with residual enhancement

Images have become a significant medium for information transmission, while image forensics has garnered widespread attention from researchers. Due to the scarcity of finely annotated images in the field of image manipulation detection and localization, existing methods for locating manipulated images can only utilize a limited amount of data for model training. However, deep learning models typically require a large number of finely annotated images to fully leverage their powerful fitting capabilities. In this paper, we propose a semi-supervised image manipulation localization framework, employing semi-supervised learning to use unannotated images for deep learning model training. To achieve this goal, we first design a residual enhancement module that contains an encoding-decoding structure. The regression target of this branch is the pristine regions in the images to generate the reconstructed images. Next, we perform a residual operation between the reconstructed and original images to roughly locate the anomalous regions and refine the features extracted by the encoder. Secondly, we employ weakly-supervised learning by adding an image-level classification head to the final layer of the encoder. The classification head generates a class active map based on image-level classification results, which further guides the model’s feature augmentation. Finally, we formulate a specialized semi-supervised framework tailored for image manipulation detection, enabling the utilization of a large volume of unannotated data for model training. Extensive experimental results highlight the notable efficacy of the proposed approach, and outperforms state-of-the-art approaches.

Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users’ SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants’ user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.

Face Recognition Based Attendance Management System

The goal of the "Face Recognition Based Attendance Management System" project is to create an automated attendance system that efficiently and reliably marks attendance using facial recognition technology. As people enter a specified region, the technology would take pictures of them and compare them to a database of people who have already registered. The system would record the person's attendance and save the data for further use after a successful identification. By completing this project, manual attendance taking—which may be laborious and error-prone—would no longer be necessary. Additionally, it would give administrators access to real-time attendance data, which would simplify the tracking of attendance and enable data-driven decision-making . Large volumes of data would be handled by the system, which would be developed using machine learning and computer vision techniques. All things considered, this project has the power to completely change how attendance is kept track of in a variety of places, including companies, colleges, and schools. It provides a very precise and effective solution that can lower expenses, save time, and boost output all around. Keyword- Attendance Management System, Face Recognition, Camera, NumPy, OpenCV.

Technical Perspective: Efficient and Reusable Lazy Sampling

When interactively working with data, query latency is very important. In particular when ad-hoc queries are written in an explorative manner, it is essential to quickly get feedback in order to refine and correct the query based upon result values. This interactive use case is difficult to support if the underlying data is large, as analyzing large volumes of data is inherently expensive.

Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm

Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm

STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Kirkwood-Buff Analysis of Binary and Ternary Systems Consisting of Alcohols (Methanol, Ethanol, 1-Propanol, and 2-Propanol), Water, and n-Hexane to Understand the Formation of Surfactant-Free Microemulsions.

Surfactant-free microemulsion (SFME) represents a class of fluid mixtures that can form microheterogeneous structures without detergents, offering an environmentally benign alternative to traditional microemulsions. However, the formation mechanism is still elusive. This work applies the Kirkwood-Buff theory to mixtures of alcohols, water, and n-hexane to elucidate the SFME formation mechanism. To ensure robust calculation of the Kirkwood-Buff integrals (KBIs), we construct a data set of densities and excess free energies of binary and ternary systems. Multiple excess Gibbs free energy models are assessed against this data set to select the most suitable model reproducing the experimental results. In addition, we introduce statistical methods to determine the optimal polynomial order of the Redlich-Kister correlation for the excess volume data. We first validate our methodology in binary systems. Then, we extend the calculation method to ternary mixtures. The KBI calculation results reveal that the alcohol-hexane and water-hexane interactions do not significantly affect SFME formation. In contrast, the interplay among water-water, water-alcohol, and alcohol-alcohol interactions critically influences the ability of a liquid mixture to form SFME structures. SFME systems exhibit the facile formation of water aggregates enveloped by alcohols, whereas non-SFME systems demonstrate homogeneous alcohol/water droplets dispersed in an oil continuous medium.

Cross-tokamak disruption prediction based on domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges based on domain adaptation (DA). The approach aims to predict disruption by finding a feature space that is universal to all tokamaks. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamaks (source domain) based on a DA algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying DA in the cross-tokamak disruption prediction task. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL called supervised CORAL (S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak. This approach provides a light, interpretable and few data-required ways by aligning features to predict disruption using small data volume from the future tokamak.

Revolutionizing Content Digestion: Unleashing the Power of Bidirectional and Auto-Regressive Transformers in AI-Powered Automatic Text Summarization

Revolutionizing Content Digestion: Unleashing the Power of Bidirectional and Auto-Regressive Transformers in AI-Powered Automatic Text Summarization

Unveiling COVID-19’s Influence on Diet-Related Lifestyle: Insights from Google Trends Query Volumes

Aim: This study aimed to showcase the particular lifestyle and behavioural pattern changes during the pandemic using Google Trends (GT). Background: The pandemic hit by the Coronavirus Disease 2019 (COVID-19) globally has brought basic living into the context of surprise. Since the lockdown and social restrictions, the population has faced many changes in their daily routine. objective: To demonstrate how COVID-19 affects eating habits and lifestyle in India. Objective: The purpose of the study was to demonstrate how COVID-19 has affected eating habits and lifestyle in India. Method: A retrospective analysis was carried out with the aid of Google Trend Relative Search Volume (RSV) data obtained for interest areas, such as COVID-19 cases, symptoms, preference of medicines, choice of nutrients, reduced physical activity, and watching varied Over-the-top media service (OTT) platforms as the source of entertainment; these were the variables that were taken into consideration to derive correlation coefficient values. Results: There was a strong positive correlation found between zinc and vitamin C, zinc and vitamin D, and vitamin C and D, which was 0.83, 0.78, and 0.72, respectively. Symmetrical observation has been observed in Netflix and Amazon Prime as 0.81; a notable negative correlation was found between COVID-19 and gym as -0.32. Conclusion: This study can be used as a checkpoint list for future predictions and past timelines and how they have a cohabitant role in the spread of the epidemic and its line of mitigation strategies. other: NIL

Tele-Trafficking of Virtual Data Storage Obtained from Smart Grid by Replicated Gluster in Syntose Environment

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods.