Data Processing Research Articles

Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data.

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis.

Adaptive Governance: Empowering Data Sharing for Public Health Impact - Insights from North Carolina Department of Health and Human Services

Objective and ApproachAt the 2022 IPDLN Conference, we presented work led by the North Carolina Department of Health and Human Services (NCDHSS), in partnership with university-based researchers, to build legal and ethical processes for routine data sharing. This session provides significant updates on the methods, results, and implications of these efforts–namely, that NCDHHS implemented an enterprise-wide data governance process and a legal framework that has enabled timely, impactful use of cross-sector data. We relied on Participatory Action Research and Deliberative Dialogue methods to engage a diverse range of partners in a data landscape overview and the co-creation of new data sharing processes that better enables the enterprise to adapt to a changing world. ResultsFour key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy, created a data sharing guidebook, staffed their Data Office, and implemented a new legal framework. In addition to describing how these actions support data use across a large US state health and human services agency, we provide three use cases demonstrating the impact of this work. ConclusionsEstablishing routine data sharing presents legal, technical, and cultural challenges, particularly in large agencies. Through a collaborative, participatory approach, the NCDHHS successfully established enterprise-wide data governance and a legal framework to support data-driven policymaking and, ultimately, improve health outcomes for residents. ImplicationsThis research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, linkage, and use.

Constraints on Characteristics and Distribution of Gas Hydrate and Free Gas Using Broad-Band Processing of Three-Dimensional Seismic Data

Constraints on Characteristics and Distribution of Gas Hydrate and Free Gas Using Broad-Band Processing of Three-Dimensional Seismic Data

The Demographic Frame

The Demographic Frame is a comprehensive, secure, access-controlled database of person-level data consisting of demographic, social, and economic characteristics of individuals derived from census, survey, administrative, and third-party data sources. The Demographic Frame is facilitating innovations in data collection, processing, analyses, and products for census and survey programs.

A preliminary discussion on data privacy and data security issues

With the expeditious evolution of technologies such as the internet, big data, cloud computing, and artificial intelligence, data has ascended to the status of an invaluable and indispensable resource within the intricate tapestry of modern society. Yet, the labyrinthine ethical quandaries that intertwine with the processes of data collection, storage, processing, and application have progressively surfaced, capturing the rapt attention of academia, industry, and government as they grapple with this nascent challenge. However, with the extensive application of data technology, issues of data ethics have become increasingly prominent, posing unprecedented challenges to personal privacy, data security, social equity, and justice. This article aims to delve deeply into the connotation and extension of data privacy and data security, as well as its significant role in current society. By examining relevant research findings at this stage, analyzing the major challenges faced by data ethics and exploring corresponding strategies, this paper delves into the multifaceted challenges faced by data privacy and data security, highlights the inadvertent data collection by smart devices, the potential for re-identification of blurred and anonymized information through big data analysis, as well as the ongoing threats posed by hacker attacks, internal vulnerabilities, and the complexity of data circulation.

The Development Potential of Urbanization in China's Third-tier Cities: A Case Study of Taizhou Area

With the acceleration of China's urbanization process, the development potential of third-tier cities has become increasingly prominent, becoming an important part of the national urbanization strategy. Taking the Taizhou area as an example, this study first conducts a comprehensive evaluation of the current status of urbanization development in Taizhou, aiming to reveal its development level and characteristics in various aspects such as economy, society, culture, and environment. On this basis, a multi-dimensional evaluation system including urbanization level, quality of citizenization, urban functions, and urban governance is constructed to comprehensively consider Taizhou's performance in several key areas of urbanization development. In terms of data processing, the study uses the Taizhou Statistical Yearbook from 2018 to 2022 and other related reports as the main data sources to ensure the comprehensiveness and accuracy of the evaluation. Through the dimensionless processing of data and the weights of indicators determined by the Analytic Hierarchy Process (AHP), the development potential of urbanization in Taizhou was quantified and evaluated. The research results show that the urbanization development in Taizhou has shown a steady growth trend, especially significant progress in the quality of citizenization and urban functions, but there is still room for improvement in some key areas. Based on the research results, targeted policy recommendations are proposed: first, to improve the quality of citizenization, including deepening the reform of the household registration system and improving the level of public services; second, to optimize the pattern of urbanization, strengthen regional collaborative development and county town construction; third, to promote the construction of new types of cities, covering the improvement of urban innovation capabilities and the construction of smart cities; fourth, to deepen the integrated development of urban and rural areas, promote resource sharing and coordinated economic growth, aiming to guide Taizhou to achieve more balanced and sustainable urbanization development, and provide a reference for similar cities.

Leveraging Artificial Intelligence to Combat Money Laundering and Related Crimes in the South African Banking Sector

Abstract Money laundering and related financial crimes, such as fraud and terrorism financing, pose a significant threat to the integrity and stability of South African financial markets. This article explores the application and use of artificial intelligence (AI) to detect and prevent money laundering in South African banking institutions. The implementation of big data technologies, data processing analytics and AI could enhance the detection and prevention of money laundering activities in South Africa's banking sector. AI should be carefully utilised to improve the detection of suspicious activities and the accuracy of financial intelligence, and to combat evolving money laundering techniques. The article also examines the benefits and challenges of implementing AI as an anti-money laundering (AML) measure in the South African banking sector. These include the need for quality data, integration with existing regulatory systems, regulatory compliance and ethical considerations. The article highlights the potential use of AI in transaction monitoring, customer due diligence, outcomes-based risk assessment and the improved detection of suspicious transactions. This could be done by utilising AI to enhance the effectiveness and efficiency of AML measures. The importance of effective coordination between banking institutions, regulatory authorities and law enforcement bodies is also highlighted as a key component of leveraging AI to combat money laundering and related financial crimes in South Africa's banking sector.

Perancangan Sistem Informasi Pengolahan Data Morbiditas Rawat Inap Guna Menunjang Tata Kelola Pelaporan Rawat Inap

Morbidity is a crucial aspect that must be reported regularly, specifically quarterly (RL4a). This study aims to support the governance of inpatient unit reporting, especially in the aspect of morbidity, and make it easier for medical record officers as system users to process morbidity data of inpatient unit patients into a report that is needed quickly, accurately, and efficiently. This research is qualitative, and data is collected using observation techniques. This research's system development method is the waterfall model, which is a consecutive development method analogous to a waterfall that flows across all project stages. Designing an inpatient morbidity data processing system to support inpatient reporting governance is described using a flow map, Context diagram, Data flow diagram, and Entity relationship diagram. Then, it was implemented into the website using Microsoft Visual Studio Code using the Hypertext Preprocessor, Javascript programming language, and supported by MySQL as a data store. This study results in a system that can quickly process the morbidity of inpatient units, make the extensive ten disease reports, and reduce user error. Therefore, the governance of inpatient reporting can run accurately and efficiently.

Application of Attribute-Based Encryption in Military Internet of Things Environment

The Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying the MIoT in such an environment is to acquire, analyze, and merge vast amounts of data from many different IoT devices and disseminate them in a secure, reliable, and context-dependent manner. This challenge is one of the main challenges in a federated environment and forms the basis for establishing trusting relationships and secure communication between IoT devices belonging to different partners. In this work, we focus on the problem of fulfillment of the data-centric security paradigm, i.e., ensuring the secure management of data along the path from its origin to the recipients and implementing fine-grained access control mechanisms. This problem can be solved using innovative solutions such as applying attribute-based encryption (ABE). In this work, we present a comprehensive solution for secure data dissemination in a federated MIoT environment, enabling the use of distributed registry technology (Hyperledger Fabric), a message broker (Apache Kafka), and data processing microservices implemented using the Kafka Streams API library. We designed and implemented ABE cryptography data access control methods using a combination of pairings-based elliptic curve cryptography and lightweight cryptography and confirmed their suitability for the federations of military networks. Experimental studies indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications.

Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

Investigating Factors Affect Perceived Value And E-Word Of Mouth (E-WOM) In Choosing Islamic Digital Banks

The Covid-19 pandemic has sped up the need for digitalization in doing daily activities including banking. Thus, banks is now in competition to give the best digital platform to serve their customers. One of the most effective form of promotion to introduce a new services is word of mouth. This study aims to investigate the effect of E-word of mouth (E-WOM) on customer decisions to choose an Islamic Bank: A Case Study of Indonesia’s Islamic Digital Banks. This research includes survey research by taking samples and using questionnaires as the main data. The study targetted 130 customers who have active accounts in the form of savings or time deposits. Primary data processing by factor analysis by testing the research model and the hypothesis of the relationship between variables, this processing uses smart PLS statistical software with the Structural Equation Modeling method with Partial Least Squares. The results of the analysis show that religious motivation and service quality have a positive effect on consumer ratings, where consumer ratings and service quality have a positive effect on E-word of mouth while convenience has no positive effect on consumer ratings and E-word of mouth on respondents. The current study contributes novelty to adjust the existing model with Islamic Digital Bank (IDB).

PharmaBench: Enhancing ADMET benchmarks with large language models

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

Perancangan Sistem Informasi Pengolahan Data Rekam Medis Elektronik Guna Menunjang Tata Kelola Pelaporan Rawat Jalan

Recording and reporting medical records is the main activity in health service institutions, and hospital information systems play an essential role in this process. Outpatient data processing at XYZ Hospital uses spreadsheets and Google Sheets even though it has yet to implement SIMRS fully. Because the data in the SIMRS must be changed for each spreadsheet report, there are several reporting problems. Therefore, the authors begin to develop a web-based outpatient reporting system after researching the need for more efficient applications. This study uses a qualitative approach to data collection through interviews and observations, focusing on developing a waterfall software. The results showed that searching and managing patient data and making outpatient recapitulation reports became easier for medical record officers. Implementing this system brings several concrete impacts, including reducing human errors in data recording, increasing the speed of access to medical information, and increasing time efficiency in making reports. This system also allows better data integration between hospital departments, improving patient care coordination and the overall quality of health services.

Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System

Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general.

Optimization of Daily Warehouse Storage PT. XYZ with Shared Storage Method

PT XYZ is a company engaged in ship repair and maintenance services and port management services. The problems faced by PT XYZ in carrying out its business lines are related to the effective and efficient operational managerial activities of raw material warehousing. Problem solving steps are carried out by testing data processing and analysis using the shared storage method as a solution step related to the problem of warehousing layout and optimization. Problem solving with the shared storage method is a method of reconditioning the warehousing layout with the principle of First in First out or fast moving where the layout of goods is classified based on the order of the flow of goods movement according to the operational needs of PT XYZ activities. Based on the calculation results, if the company applies the proposed layout, there is an average distance savings of 4 m2. The layout of the placement of raw materials based on the proposed improvements has a more organized condition and facilitates the flow of goods movement as well as inputting the flow of goods in and out which can be more efficient if the goods are organized and inputted administratively, this also affects the quality of goods.

Towards the Automation of Non-destructive Fault Recognition: Enhancement of Robustness and Accuracy Through AI Powered Acoustic and Thermal Signal Analysis in Time, Frequency- and Time-Frequency Domains

AbstractNon-destructive inspection and analysis techniques are crucial for quality assessment and defect analysis in various industries. They enable for screening and monitoring of parts and products without alteration or impact, facilitating the exploration of material interactions and defect formation. With increasing complexity in microelectronic technologies, high reliability, robustness and thus, successful failure analysis is essential. Machine learning (ML) approaches have been developed and evaluated for the analysis of acoustic echo signals and time-resolved thermal responses for assessing their ability for defect detection. In the present paper different ML architectures were evaluated, including 1D and 2D convolutional neural networks (CNNs) after transforming time domain data into the spectral- and wavelet domains. Results showed that 2D CNNs processing data in wavelet domain representation performed best, however at the expense of additional computational effort. Furthermore, ML-based analysis was explored for lock-in thermography to detect and locate defects in the axial dimension based on thermal emissions. While promising, further research is needed to fully realize its potential.

Edge Computing in Healthcare: Innovations, Opportunities, and Challenges

Edge computing promising a vision of processing data close to its generation point, reducing latency and bandwidth usage compared with traditional cloud computing architectures, has attracted significant attention lately. The integration of edge computing in modern systems takes advantage of Internet of Things (IoT) devices and can potentially improve the systems’ performance, scalability, privacy, and security with applications in different domains. In the healthcare domain, modern IoT devices can nowadays be used to gather vital parameters and information that can be fed to edge Artificial Intelligence (AI) techniques able to offer precious insights and support to healthcare professionals. However, issues regarding data privacy and security, AI optimization, and computational offloading at the edge pose challenges to the adoption of edge AI. This paper aims to explore the current state of the art of edge AI in healthcare by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and analyzing more than 70 Web of Science articles. We have defined the relevant research questions, clear inclusion and exclusion criteria, and classified the research works in three main directions: privacy and security, AI-based optimization methods, and edge offloading techniques. The findings highlight the many advantages of integrating edge computing in a wide range of healthcare use cases requiring data privacy and security, near real-time decision-making, and efficient communication links, with the potential to transform future healthcare services and eHealth applications. However, further research is needed to enforce new security-preserving methods and for better orchestrating and coordinating the load in distributed and decentralized scenarios.

Access control for trusted data sharing

In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seamless and secure data sharing both within and outside organizations. Given that 6G networks promise ultra-low latency, enhanced connectivity, and massive data throughput, the need for robust data access control mechanisms becomes imperative. These mechanisms ensure security and trust among entities, particularly in multi-tenant environments where multiple organizations share infrastructure and data resources. In this paper, we have designed and implemented a novel access control mechanism tailored for a distributed data streaming system developed by Nokia Bell Labs. Our approach leverages fine-grained policies, dynamic enforcement, and transparency mechanisms to enhance trust between data owners and consumers. By facilitating secure multi-tenancy data sharing, our solution contributes to the seamless exchange of data across diverse entities within the next-generation communication ecosystem. We demonstrate that our proposed access control mechanism incurs minimal overhead while ensuring data confidentiality and integrity. The introduction of such advancements in data sharing markets strengthens the overall ecosystem by providing heightened transparency and enhanced control over data, promoting collaboration and innovation in the 6G era.

Identification of Transposon Insertion Sites in MaizeMu-Tagged Mutants Using Mu-Seq

Mutator(Mu) transposons facilitate untargeted insertional mutagenesis in maize by moving within the genome and disrupting genes. Such an approach has been used to generate collections such as theBonnMuresource, aMu-tagged maize population for functional genomics studies. Mutant-Seq (Mu-Seq) is a sequencing-based method for the high-throughput identification and mapping ofMuinsertion sites. The approach involves the construction of multiplexed sequencing libraries (known as Mu-Seq libraries) fromMu-tagged populations, followed by high-throughput sequencing and data processing using the Mu-Seq Workflow Utility (MuWU) tool, to determine the location ofMuinsertions. Here, we provide a detailed protocol for Mu-Seq, from the generation of the maizeMu-tagged mutant population to data analysis. Researchers can use this approach to develop mutant collections customized to specific genetic backgrounds of interest, which can aid in characterizing genotype-specific mutations and identifying candidate genes linked to visible mutant phenotypes.

Applying machine learning to optical metrology: a review

Abstract This literature review investigates the integration of machine learning (ML) into optical metrology, unveiling enhancements in both efficiency and effectiveness of measurement processes. With a focus on phase demodulation, unwrapping, and phase-to-height conversion, the review highlights how ML algorithms have transformed traditional optical metrology techniques, offering improved speed, accuracy, and data processing capabilities. Efficiency improvements are underscored by advancements in data generation, intelligent sampling, and processing strategies, where ML algorithms have accelerated the metrological evaluations. Effectiveness is enhanced in measurement precision, with ML providing robust solutions to complex pattern recognition and noise reduction challenges. Additionally, the role of parallel computing using graphics processing units (GPUs) and field programmable gate arrays (FPGAs) is emphasised, showcasing their importance in supporting the computationally intensive ML algorithms for real-time processing. This review culminates in identifying future research directions, emphasising the potential of advanced ML models and broader applications within optical metrology. Through this investigation, the review articulates a future where optical metrology, empowered by ML, achieves improved levels of operational efficiency and effectiveness.