Extra Data Research Articles

Design and Implementation of Deduplication on F2FS

Data deduplication technology has gained popularity in modern file systems due to its ability to eliminate redundant writes and improve storage space efficiency. In recent years, the flash-friendly file system (F2FS) has been widely adopted in flash memory-based storage devices, including smartphones, fast-speed servers, and Internet of Things. In this article, we propose F2DFS (deduplication-based F2FS), which introduces three main design contributions. First, F2DFS integrates inline and offline hybrid deduplication. Inline deduplication eliminates redundant writes and enhances flash device endurance, while offline deduplication mitigates the negative I/O performance impact and saves more storage space. Second, F2DFS follows the file system coupling design principle, effectively leveraging the potentials and benefits of both deduplication and native F2FS. Also, with the aid of this principle, F2DFS achieves high-performance and space-efficient incremental deduplication. Third, F2DFS adopts virtual indexing to mitigate deduplication-induced many-to-one mapping updates during the segment cleaning. We conducted comprehensive experimental comparisons between F2DFS, native F2FS, and other state-of-the-art deduplication schemes, using both synthetic and real-world workloads. For inline deduplication, F2DFS outperforms SmartDedup, Dmdedup, and ZFS, in terms of both I/O bandwidth performance and deduplication rates. And for offline deduplication, compared to SmartDedup, XFS, and BtrFS, F2DFS shows higher execution efficiency, lower resource usage, and greater storage space savings. Moreover, F2DFS demonstrates more efficient segment cleanings than native F2FS.

LAPOD, Latin-American paleomagnetic online database: An online interface to access paleomagnetic data from Latin America

We present the Latin-American Paleomagnetic Online Database (LAPOD), which includes an online tool for consulting and visualizing paleomagnetic data. This platform allows users to search, filter, and query over 2000 paleomagnetic data entries from locations across Latin America, including North America (Mexico and the Southwestern USA), Central America, and South America. Paleomagnetic data has been pre-validated for geographic locations and ages, enhancing its reliability for users.The platform features a robust database engine with up to 26 filters, allowing users to perform queries with high precision and avoiding data duplication, and to download data in various formats for personal use. It also offers graphical visualization of some data directly on screen. Color indicators are used to indicate data quality and magnetic polarity. All data included in LAPOD are properly referenced to their original authors, with links to the original published articles.LAPOD is proposed as a community-driven platform, allowing users to contribute their data and provide comments on their own and others' data. This fosters discussion about the reliability of paleomagnetic data and finally enhances our knowledge of the Earth's magnetic field in the region. This collaborative approach supports the development of new regional models of paleosecular variation with improved reliability. It may be repeated for other regions in the world, and ultimately lead to improved global field models. To access the platform, please visit: https://paleomagnetismo.org/pmagdb/

Randomness Regularization With Simple Consistency Training for Neural Networks.

Randomness is widely introduced in neural network training to simplify model optimization or avoid the over-fitting problem. Among them, dropout and its variations in different aspects (e.g., data, model structure) are prevalent in regularizing the training of deep neural networks. Though effective and performing well, the randomness introduced by these dropout-based methods causes nonnegligible inconsistency between training and inference. In this paper, we introduce a simple consistency training strategy to regularize such randomness, namely R-Drop, which forces two output distributions sampled by each type of randomness to be consistent. Specifically, R-Drop minimizes the bidirectional KL-divergence between two output distributions produced by dropout-based randomness for each training sample. Theoretical analysis reveals that R-Drop can reduce the above inconsistency by reducing the inconsistency among the sampled sub structures and bridging the gap between the loss calculated by the full model and sub structures. Experiments on 7 widely-used deep learning tasks ( 23 datasets in total) demonstrate that R-Drop is universally effective for different types of neural networks (i.e., feed-forward, recurrent, and graph neural networks) and different learning paradigms (supervised, parameter-efficient, and semi-supervised). In particular, it achieves state-of-the-art performances with the vanilla Transformer model on WMT14 English → German translation ( 30.91 BLEU) and WMT14 English → French translation ( 43.95 BLEU), even surpassing models trained with extra large-scale data and expert-designed advanced variants of Transformer models.

ITGB2 related to immune cell infiltration as a potential therapeutic target of inflammatory bowel disease using bioinformatics and functional research.

Inflammatory bowel disease (IBD) is a chronic systemic inflammatory condition regarded as a major risk factor for colitis-associated cancer. However, the underlying mechanisms of IBD remain unclear. First, five GSE data sets available in GEO were used to perform 'batch correction' and Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). Candidate molecules were identified using CytoHubba, and their diagnostic effectiveness was predicted. The CIBERSORT algorithm evaluated the immune cell infiltration in the intestinal epithelial tissues of patients with IBD and controls. Immune cell infiltration in the IBD and control groups was determined using the least absolute shrinkage selection operator algorithm and Cox regression analysis. Finally, a total of 51 DEGs were screened, and nine hub genes were identified using CytoHubba and Cytoscape. GSE87466 and GSE193677 were used as extra data set to validate the expression of the nine hub genes. CD4-naïve T cells, gamma-delta T cells, M1 macrophages and resting dendritic cells (DCs) are the main immune cell infiltrates in patients with IBD. Signal transducer and activator of transcription 1, CCR5 and integrin subunit beta 2 (ITGB2) were significantly upregulated in the IBD mouse model, and suppression of ITGB2 expression alleviated IBD inflammation in mice. Additionally, the expression of ITGB2 was upregulated in IBD-associated colorectal cancer (CRC). The silence of ITGB2 suppressed cell proliferation and tumour growth invitro and invivo. ITGB2 resting DCs may provide a therapeutic strategy for IBD, and ITGB2 may be a potential diagnostic marker for IBD-associated CRC.

Communication security analysis of fully electronic interlocking systems

Communication security in fully electronic interlocking systems is one of the key factors ensuring the safe and reliable operation of the entire system. EN50159 is an important standard in the European railway communication sector, aimed at ensuring the safety, reliability, and efficiency of railway transportation. According to the communication security standards of EN50159, there are six types of security risks in closed communication: data duplication, deletion, insertion, misordering, delay, and corruption. This paper analyzes and explains the aspects of communication security that need to be considered based on the safety communication standards of China's railway signaling system and the signal safety standards in EN50159, focusing primarily on communication security and reliability.

Suicide thoughts screening with social media using cat swarm-intelligent adaptive recurrent network

The research on detecting suicidal ideation through social media in early stages and faces several difficulties. Many persons with a propensity for suicide express their ideas and beliefs on social media. Numerous studies have found that accessible social media posts have useful criteria for rapidly identifying people thosewho are considering suicide. Finding and comprehending the risk factors and warning signals that may lead to suicide, is the most challenging aspect of suicide prevention. By automatically detecting the abrupt changes in a user behavior, this can be accomplished. Social media interactions can be used to gather textual and behavioral data using natural language processing (NLP) techniques. The framework can use this data to spot trends in social interactions that point to suicide intent. We can anticipate suicidal ideation using classification techniques based on deep learning. We can accomplish this by identifying these emotions in user postings using a combination of Cat Swarm-Intelligent Adaptive Recurrent Network (CSI-ARN) models. Adding extra training data and using an attention model to improve model performance can increase accuracy. This study examines social media comments for suicidal thoughts using a CSI-ARN model. The suggested model outperformed baseline models with 90.3% accuracy and 92.6% F1-score.

Data Deduplication Based on Content Locality of Transactions to Enhance Blockchain Scalability

Data Deduplication Based on Content Locality of Transactions to Enhance Blockchain Scalability

Efficacy and safety of a digital check-in and triage kiosk in emergency departments: a systematic review protocol

IntroductionIncreasing demand for healthcare services worldwide has led to unprecedented challenges in managing patient flow and delivering timely care in emergency care settings. Overcrowding, prolonged waiting times, reduced patient satisfaction...

Enhancing the Autonomy of Establishing Indicator Informatization.

Quality indicators are essential for hospital management, clinical decision-making, and patient care. In today's technologically advanced era, it is increasingly critical to derive effective statistical data analysis from intricate datasets. However, the administrative department within our hospital often fails to prioritize information needs due to limited resources. Consequently, the collection of indicator data continues to rely on paper-based operations.To tackle common issues in collecting indicator data, such as incomplete or inaccurate data, time-consuming aggregation and statistical analysis, and a lack of timeliness, we have implemented appropriate Business Intelligence (BI) tools. We provide educational training to empower users to create visual templates for indicators that are tailored to their specific requirements. This approach saves time, reduces data duplication, and minimizes writing errors. It enhances supervisors' understanding of the current situation and future prospects in terms of data analysis and timeliness. Ultimately, this initiative improves healthcare quality and management effectiveness.

Feature value quantization and reduction process for predicting heart attack possibility and the level of severity by a machine learning model

Heart disease is a prevalent condition nowadays that, if undiagnosed, can be deadly. To predict heart disease, researchers designed many machine learning models. In this study, we propose a model that chooses fewer attribute columns for training, and we use these chosen features to determine the heart problem severity. Correlation Repeated Heat map and Information Gain were used for selecting the features. To train our model we used the UCI Cleveland heart disease dataset. We removed duplicate data to improve the accuracy score, and we also encoded the categorical data collection using the OneHot(OH) encoding method, which can improve prediction accuracy. Support Vector, Logistic Regression, K-Nearest Neighbour, Naive Bayes, Decision Tree, Random Forest, Adaboost, and XGBoost are the eight classifier algorithms that are used in this process overall. Based on repeated heat map correlation, we compare the accuracy score each time. In this proposed method, the Adaboost classification algorithm used by the fbs row heat map achieves the highest accuracy for heart disease detection and it is 92%. By choosing features according to the information gain value, we compare the accuracy score each time in information gain. For both XGBoost and Logistic Regression, we got an accuracy score of 93.44%. However, compared to the XGBoost classification technique, Logistic Regression requires less time. Accuracy, precision, recall, f1-score, sensitivity, specificity, and the AUC of ROC charts were used to evaluate the performance of the model. Overall, the results of our model demonstrate that it is reliable and accurate in identifying cardiac disease and its level of severeness.

A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach

Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works.

An efficient learning based approach for automatic record deduplication with benchmark datasets

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow

ABSTRACT The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

SDNet: Spatial adversarial perturbation local descriptor learned with the dynamic probabilistic weighting loss

Local descriptor is an important upstream component in computer vision tasks. Despite considerable advances with deep learning-based descriptors, recent descriptors are not robust enough to handle widespread viewpoint changes in image matching tasks such as localization and 3D reconstruction. In this study, SDNet, a robust descriptor utilizing spatial adversarial perturbations, trained with a novel dynamic probabilistic weighting loss to enhance performance under such challenges. First, to increase the robustness and generalization ability of the network across spatially transformed instances, a innovative module for generating hard negative samples via spatial adversarial perturbations is designed. By maximizing adversarial loss, this module generates more complex patches, significantly enhancing the geometric robustness of the descriptor. Importantly, this module integrates seamlessly with existing patch-based descriptors without necessitating extra training data. Second, to mitigate the imbalance in the matching relationship between generated positive and negative pairs, the label weighted triplet loss is proposed, which markedly improves descriptor performance. Third, a comprehensive theoretical analysis of preceding studies is carried out from a gradient perspective, and a probabilistic dynamic weighting approach that adaptively emphasizes weighting functions with higher likelihoods is proposed to improve training performance of the descriptor. Extensive experiments are carried out on mainstream datasets. These comprehensive experiments demonstrate the effectiveness of SDNet, and the proposed method achieves significant improvements on the UBC, HPatches and ETH datasets, outperforming current state-of-the-art methods. The code is available at https://github.com/webd111/sdnet.

Optimizing Storage Efficiency Through Pipelining and Parallelization for Enhanced Data Deduplication

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management.

Enhancing Cloud Data Deduplication with Dynamic Chunking and Public Blockchain

The majority of cloud service providers (CSPs) store and remove customer data according to certain principles. The majority of them have designed their cloud platform to have very high levels of consistency, speed, availability, and durability. Their systems are built with these performance characteristics in mind, and the requirement to ensure precise and rapid data deletion must be carefully balanced. In the public blockchain, this paper suggests employing the rapid content-defined Chunking algorithm for data duplication. Acute data is frequently outsourced by individuals and organizations to distant cloud servers since doing so greatly reduces the headache of maintaining infrastructure and software. However, because user data is transmitted to cloud storage providers and stored on a remote cloud, ownership and control rights are nonetheless separated. Users thus have significant challenges when attempting to confirm the integrity of private information. According to the experiment results, the suggested dynamic chunking has a fast processing time that is on par with fixed-length chunking and significantly improves deduplication processing capability.

P-760 The effect of assisted hatching on pregnancy outcomes in 1,833,485 single embryo transfer based on Japan Assisted Reproductive Technology Registry System

Abstract Study question Does assisted hatching (AH) affect pregnancy outcomes especially when it was performed selectively on specified group? Summary answer No clinically significant effect of AH on pregnancy outcomes was demonstrated for the entire single embryo transfer (SET) cycle after adjusting for covariates. What is known already AH is a technique used in assisted reproduction by artificially thinning or making a small hole in the zona pellucida in order to facilitate the hatching process. It is suggested that AH may improve the chance of embryo implantation and success of pregnancy. However, there is insufficient evidence to conclude that AH improves pregnancy outcomes, despite its widespread and longtime use. In 2014, the Practice Committee of ASRM recommended that AH should not be conducted routinely for all patients. On the other hands, it has been suggested that AH could be effective for patients with poor prognosis. Study design, size, duration A retrospective observational study based on the national ART registry maintained by the Japan Society of Obstetrics and Gynecology (JSOG) was performed. The study included 1,833,485 single embryo transfer (SET) for in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) from January 2010 to December 2019. This study was approved by the Registration and Research Subcommittee of the JSOG and Tokyo University Ethics Committee. Participants/materials, setting, methods To determine the effect of AH, the rate of clinical pregnancy and live birth were compared in SET with and without AH as primary outcome measures. In addition, the prevalence of miscarriage, multiple pregnancy, ectopic pregnancy, placental malposition, and placenta accreta were analyzed as secondary outcomes. We performed inverse probability of treatment weighting and outcome regression analyses. Main results and the role of chance Out of 1,833,485 SET cycles, AH was conducted in 983,242 cycles (53.6%). In all SET cycles, clinical pregnancy rate (CRP) and live birth rate (LBR) were 31.0% vs. 26.2%, and 21.6% vs. 18.7% in AH vs. without AH group, respectively. AH was more frequently performed in patients with advanced maternal age, frozen-thawed ET cycles (88% vs. 47%, AH vs. without AH), blastocyst transfer cycles (70% vs. 42%), patients with tubal factor (17% vs 14.3%), and hormonal replacement cycles (66% vs. 43%). After adjusting for these covariates, the propensity-weighted CPR (28.4% vs. 29.1%, AH vs. without AH) and LBR (20.1% vs. 20.6%) were slightly yet significantly lower in AH group. As secondary outcomes, AH may lead to increased risk of multiple pregnancy (0.21% increase), ectopic pregnancy (0.02%), and placenta accreta (0.03%), and 0.24% decreased risk of miscarriage. On the other hands, subgroup analysis suggested that AH could be effective in the following cycles: frozen-thawed cycles with either blast or cleavage with maternal age under 35 years, and fresh blastocyst transfer cycles with maternal age over 40 years. Limitations, reasons for caution The limitation of this study is that the information regarding the history of repeated implantation failure and methods of AH were not included in this registry system. Another limitation is that duplicate data from same patients who underwent multiple embryo transfers exists in this retrospective study for 10 years. Wider implications of the findings Despite of frequent use of AH in Japan, AH in SET cycles for all patients is not recommended because of increased risk of adverse events without improvement of pregnancy outcomes. On the other hands, AH may improve pregnancy outcomes for some groups associated with maternal age and duration of culture. Trial registration number not applicable’

An analysis of retracted studies in cardiology in the last two decades.

Background: The aim of this study is to analyze retracted studies in cardiovascular field.Methodology: PubMed and Embase databases were used to identify retracted publications from 2002 to 2022. Various characteristics of articles were retrieved, and an analysis was performed using R software.Results: We finally included 979 articles. Authors from China have the highest number of retracted studies (35.5%), followed by the USA (22.1%), and Japan (4%). The most common causes of retraction are mistakes and honest errors (24.5%) and duplicate data (17.7%). From 2002 to 2022, there has been a significant increase in retracted studies and a decrease in the impact factor of journals, number of citations, and time to retraction.Conclusion: The trend of retracting publications in cardiology is increasing.

O-083 Sexual function in women with Premature Ovarian Insufficiency (POI): systematic review and meta-analysis

Abstract Study question What is the impact of idiopathic premature ovarian insufficiency (POI) on sexual function in women? Summary answer Women with POI exhibit significant reduced sexual function and a higher risk of sexual dysfunction than healthy control women. What is known already POI is a life-changing diagnosis with profound physical and psychological consequences. The impact of POI on sexual complaints, seems multifaceted. Many women report vaginal dryness, dyspareunia (painful intercourse), and diminished libido, but also psychosexual function could be impaired, with potential effects on sexual identity, sexual function, and sexual relationships. Furthermore, the POI diagnosis can have a significant impact on a woman's self-esteem and body image, which could result in problems with sexual function. The ESHRE Guideline emphasizes the importance of providing psychological support and counseling to address the emotional and psychosexual impact of POI. Study design, size, duration This systematic review and meta-analysis is reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The following online databases were systematically searched: EMBASE, Medline(Ovid), Web of Science, Coachrane, PsychInfo and Google Scholar up to January 2023. Participants/materials, setting, methods A total of ten studies were included in the systematic review and five studies involving 352 women with POI were included in the meta-analysis. Random effects models were used for analyses, with data reported as Hedges’ g and 95% confidence interval, and the risk of heterogeneity was evaluated. Main results and the role of chance Eight of the ten studies concluded that women with POI have a reduced sexual function. An overall medium Hedges’ g effect size of −0.72 was found (ranging between −0.20 to −1.29) in favor of control women, with moderate heterogeneity (I2 = 64%). When we stratified studies in which women were using hormone replacement therapy (HRT) we calculated an even higher Hedges’ g effect size of −0.82 (95% CI −1.18, −0.47). Pooled data from the different FSFI domains, revealed that women with POI scored significantly lower on all domains. Especially the domain ‘dyspareunia’ exhibited the greatest dissimilarity (Hedges g of −0.63, 95%CI −0.86, −0.39) in women with POI compared to control. Limitations, reasons for caution We have reported a moderate heterogeneity which may be caused by differences in age, duration of diagnosis, infertility status, and socioeconomic background in the various studies. Additionally, limitations such as selection bias, data duplication, and questionnaire variation were identified. Wider implications of the findings The results show that women with POI have reduced sexual function, and HRT use did not improve sexual function. This suggest that oral HRT is less effective for sexual dysfunction. There could be an important role for psychological factors in improving sexual well-being among women with POI. Trial registration number CRD42023437203 (PROSPERO)

Selected Issues of Insurgency of the Central Register of Voters (CRV)

On 4 August ws, item 497) – the Central Register of Voters was launched. This register replaced more than 2,500 previously functioning registers. The change in this respect – in the legislator’s opinion – was dictated primarily by the necessity to eliminate possible irregularities related to discrepancies of data in individual registers and the possibility of duplication of voter data, whichwould result in errors in the election process. However, another equally important argument cannot be overlooked, namely to make the electoral process and the elections themselves more accessible to voters. The above assumptions need to be examined and the question of whether they have actually been achieved needs to be answered. I provide such an answer in the following article. The study uses dogmatic-legal and theoretical-legal methods.