Data Integrity Research Articles

North American Cancer Center Clinical Research Capacity and Benchmarking in the Postpandemic Era.

Cancer center clinical trial offices (CCTOs) support trial development, activation, conduct, regulatory adherence, data integrity, and compliance. In 2018, the Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) Steering Committee conducted and published survey results to benchmark North American CCTOs, including trial volume, accrual, full time equivalents (FTEs), and budget. The survey was readministered in 2023 to assess contemporary CCTO performance and capacity with results presented here. The 28 question 2023 survey was sent to directors of AACI's clinical member cancer centers. Survey participation was voluntary, no compensation was provided, and data requested covered operations during 2022. Definitions were consistent with National Cancer Institute (NCI) CCTO reporting requirements and AACI staff anonymously compiled results for descriptive statistical reporting. The survey response rate was 61% (60/99). The median annual CCTO budget was $11.5 million (M) US dollars (USD) versus $8.2M USD in 2018. These budgets support a median of 150 FTEs versus 104 previously, and a median total of 384 versus 280 interventional treatment trials and a median of 479 versus 531 interventional treatment accruals. Sources of support for CCTO annual budgets were primarily from industry revenue (45.3%) or institutional support (31.7%). Nearly 60% of centers reported activating NCI-sponsored studies within 90 days but only 9% reported meeting a 90-day activation timeline for industry sponsored studies. Contemporary benchmarks for CCTO operations through this survey demonstrate larger staff sizes, larger budgets, more trials supported, but fewer patients enrolled to interventional treatment trials in comparison with 2018. These data shine a critical light on the increasing complexity of cancer clinical trials, the importance of external funding sources, and necessary operational efficiency upgrades to provide cutting-edge cancer research and care.

Data Privacy and Security Concerns in AI-Integrated Educational Platforms

As educational institutions increasingly embrace AI technologies to enhance learning experiences, a paramount concern arises regarding the privacy and security of sensitive student data. This research paper delves into the challenges and implications associated with the integration of AI in educational platforms, focusing specifically on data privacy and security issues. The study aims to identify potential risks, assess current safeguards, and propose strategies to mitigate threats, ensuring a responsible and secure implementation of AI in education. By examining the intersection of technological advancement and privacy concerns, this research contributes to the ongoing dialogue on ethical considerations in the realm of AI-driven education transformation. Furthermore, this research endeavors to propose robust frameworks and strategies to safeguard the integrity and confidentiality of data in AI driven educational environments. By addressing these concerns head-on, we strive to contribute valuable insights to the ongoing discourse on balancing the transformative benefits of AI in education with the imperative to protect the privacy and security of all stakeholders involved.

Iterative Error‐Driven Ensemble Labeling (IEDEL) Algorithm for Enhanced Data Quality Control for the Atmospheric Radiation Measurement (ARM) Program User Facility

AbstractFor over three decades, the Atmospheric Radiation Measurement (ARM) Program user facility has provided researchers with invaluable benchmark atmospheric data. Ensuring the accuracy and integrity of ARM data is vital, and to achieve this, the ARM Data Quality Office (DQO) has implemented customized quality control tests tailored to each variable, with guidance from instrument mentors. These tests are designed to pinpoint common issues, such as data exceeding valid ranges or persisting with little change over extended periods, and ARM offers tools for users to review and exclude contaminated data efficiently. However, certain quality issues, such as spikes in time series or data drift over time, sometimes evade detection by existing tests and require manual identification by data analysts and instrument mentors through visualization tools. To tackle these challenges more efficiently, the DQO has developed and implemented the Iterative Error‐Driven Ensemble Labeling (IEDEL) algorithm with unanimous voting and transfer learning techniques to efficiently generate labeled data at scale. This initiative has empowered the creation of high‐performing machine learning models, enabling real‐time monitoring of data quality issues within the ARM data and thereby enhancing data integrity and accessibility.

Patient First Strategies for Reducing Inequities in HAI Prevention

Background: Inequities in healthcare-associated infections (HAI) incidence and prevention measures are critically important to understand (Chen,2021). While evaluations are beginning to characterize these disparities by infection type (Gettler, 2023), our work expands this by characterizing disparities by prevention strategies. By better understanding how evidence-based prevention strategies are implemented at the patient level, infection preventionists and hospital epidemiologists can better design strategies that provide equitable care to all patients. Methods: Beginning January 2023, gender, race, ethnicity, spoken language, and age group fields were added to daily chlorhexidine gluconate (CHG) treatment and C. difficile test order compliance data captured via electronic medical record. In July 2023, fields on recorded race, ethnicity, and gender were added to well-established foley and vascular access real-time peer audit tools that are used by infection preventionists (IPs). Each prevention strategy variable was summarized by demographic variables and differences in compliance were measured using chi-square tests. Results: 899 vascular audits and 420 foley audits were completed by IPs between July – December 2023. In 2023, there were 114,066 opportunities for CHG Treatment and 1,991 C. difficile test orders. Missing data varied by metric but ranged from 0-60%. Statistically significant differences by race were found in 3 of 8 components (i.e., intact seal, secured catheter and absence of dependent loop) in the foley audit (p < 0 .01) and compliance with C. difficile test ordering (p < 0 .01). No differences in race were found in vascular access audits or CHG treatment. No differences in gender or ethnicity were noted in foley, vascular access audits, CHG treatment compliance, or C. difficile testing. Differences in gender and age were found in CHG treatment compliance (p < 0 .001). Conclusions: By focusing more on patient level process measures rather than only presenting stratified outcomes data, we can identify targeted opportunities for improvement in health equity before our patients develop an HAI. Further evaluations should also focus on assessing the clinical relevance of statistical findings to better inform intervention strategies. Separately, efforts are needed to improve completeness and integrity of demographic data in the electronic medical record.

Integrating optical security management with optical‐layer controller architecture for enhanced network security

AbstractTo guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.

Network Security and Cryptography: Ensuring Data Integrity and Confidentiality in the Digital Age

Network Security and Cryptography: Ensuring Data Integrity and Confidentiality in the Digital Age

Enhancing Landslide Prediction Through Advanced Transformer-Based Models: Integrating SAR Imagery and Environmental Data

Landslides, a significant natural hazard driven predominantly by rainfall infiltration, pose a continuous threat to the terrain, buildings, and ecosystem of Caiazzo, located in southern Italy (Caserta). This research undertakes a novel approach to predict and understand the complex patterns of landslide deformations in this region. Our study leverages the advanced capabilities of COSMO-SkyMed satellite imagery, integrating a comprehensive dataset that includes factors such as elevation, slope, Topographic Wetness Index, Stream Power Index, geology, flow direction, curvature, Normalized Difference Vegetation Index, and several other relevant indices. We employ Transformer-based models, renowned for their effectiveness in capturing long-range dependencies within sequential data. The Transformer models in our study are adept at analyzing the temporal sequences of environmental factors and their intricate interactions, thereby offering a more nuanced understanding of the temporal patterns leading to landslides. These Transformer models are designed to process the entire sequence of satellite data obtained by the Coherent Pixels - Temporal Phase Coherence within the SUBSOFT package to ensure data integrity and precision, encompassing 132 images in ascending geometry and 143 in descending geometry, spanning from 2013 to 2021. By doing so, they efficiently identify critical patterns and dependencies over time, such as the interplay between rainfall events and subsequent soil and vegetation changes. The models are fine-tuned to our specific dataset, ensuring high precision and accuracy in landslide deformation prediction. Our evaluation metrics, including Mean Absolute Error, Root Mean Square Error, and R^2 score, demonstrate the superior performance of the Transformer-based approach, with significant improvements over the conventional Deep Learning model. The visual correlation of our predictions with actual landslide occurrences further corroborates the effectiveness of this method. This transformative approach not only enhances our understanding and predictive capability for landslides in Caiazzo but also sets a benchmark for landslide prediction in geologically vulnerable regions worldwide.

Data-Driven Web-Based Service Travel Resume Information System

The Energy and Mineral Resources Department (ESDM) of South Sumatra Province has significantly enhanced its operational efficiency by implementinga Web-Based Official Travel Resume Information System. This system was developed to address the inefficiencies and risks associated with the previous manual method of managing official travel data. The new system leverages PHP, MySQL, Javascript, JQuery, and Bootstrap to provide a robust, secure, and user-friendly platform for handling travel reports, assignment orders, and official travel documentation. The digital transformation has streamlined data entry, improved data integrity, and facilitated real-time information access,enabling better decision-making and strategic planning. A System Usability Scale (SUS) test was conducted with 120 participants to evaluate the system's usability. The results showed a mean SUS score of 78.5, indicating good usability, with a standard deviation of 8.3. High scores were recorded for the system's ease of use, integration of functions, and user confidence, while lower scores highlighted initial complexity and a need for additional user training. The successful implementation and positive usability results underscore the system's effectiveness in enhancing the ESDM Department's operational capabilities. Recommendations for further improvements include enhancing mobile accessibility, providing ongoing user training, integrating advanced reporting features, and refining the user interface to ensure continued user satisfaction and system efficiency.

Data integrity of 14 randomised controlled trials

BackgroundA review of the literature on iron treatments for iron-deficient anaemia in pregnancy indicated duplication of baseline and outcome tables in two separate randomised controlled trials (RCTs) that share only a single author. AimTo assess the integrity of randomised clinical trials from Dr A.M. Darwish, Assiut University, Egypt. DesignAssessment of Research Integrity. MethodsWe tabulated the characteristics of studies, compared baseline and outcome tables between articles and looked for implausible findings. We used the distribution of baseline p-values to assess whether the summary statistics of baseline characteristics were consistent with properly conducted randomisation. ResultsWe identified 14 RCTs (1,405 participants) published between October 2004 and September 2019. Two pairs of studies showed considerable similarities in baseline characteristics, while another pair of studies was plagiarized. The analysis of baseline p-values indicated a low probability that all the studies featured randomised treatment allocation. ConclusionOur analysis of the RCTs of Dr Darwish suggests possible integrity problems. We recommend a critical investigation of the studies that have not been retracted. Until that has been completed, these studies should not be used to inform clinical practice.

Blockchain-Based Shared Data Integrity Auditing and Deduplication

Blockchain-Based Shared Data Integrity Auditing and Deduplication

Dissecting the Onion: Identifying and Remediating Issues Surrounding Data Integrity in Online Survey Research

Dissecting the Onion: Identifying and Remediating Issues Surrounding Data Integrity in Online Survey Research

Exploring AWS S3: In-Depth Analysis of Bucket Management

Amazon Simple Storage Service (S3) is a cornerstone in modern data management strategies with in Amazon Web Services (AWS). It is pivotal in providing scalable, durable, and accessible object storage infrastructure. A key advantage of S3 is its scalability, enabling seamless storage capacity expansion without upfront investments or provisioning delays. This flexibility empowers organizations to accommodate dynamic data growth while maintaining operational agility, which is crucial in today's rapidly evolving digital landscape. S3's exceptional durability, boasting an impressive 99.999999999% reliability, ensures steadfast data integrity and resilience against potential failures, mitigating concerns about data loss and bolstering confidence in managing mission-critical information effectively. Accessibility is another crucial aspect, with robust access controls and APIs facilitating secure data retrieval, storage, and management from any location. This accessibility fosters collaboration and innovation by enabling seamless data interaction and utilization across geographical boundaries.S3's integration capabilities with other AWS services enhance operational efficiency and optimize data analytics workflows. It is a foundational element in data analytics, facilitating data ingestion, storage, and processing to derive actionable insights from diverse datasets. Furthermore, S3's versatility extends beyond storage, supporting applications such as content delivery, backup and archiving, disaster recovery, and machine learning model training. This versatility drives innovation across industries and underscores S3's role in driving digital transformation and fostering a culture rooted in data-driven decision-making. In conclusion, Amazon S3's unparalleled scalability, durability, and accessibility make it indispensable for organizations navigating the data-centric landscape of modern business environments, underlining the imperative of harnessing its full potential for organizational success.

Blockchain-enabled Data Mining for Pre-Condition Monitoring in Railway Infrastructure

Early detection of abnormalities in railway systems is crucial for risk mitigation and cost-effective maintenance. Damaged wheels in freight trains cause a significant threat to transport infrastructure integrity, particularly when wheel flats occur. Monitoring wheel conditions relies on sensing mechanisms, requiring robust data integrity checks. This paper proposes a data-driven approach utilising blockchain for pre-condition monitoring to enhance data reliability. To achieve this goal, a data mining procedure is developed to establish a local blockchain. The blockchain continuously expands with new blocks, with each block linked to the previous one via a hash function. Only validated data are recorded in order to enhance the reliability of train measurements.

The impact of Block chain-Based System on Goods Tracking and management in Industrial Environment

In the domain of Industrial Internet of Things (IIoT), the demand for robust and secure methods for goods tracking and management has become growingly critical. Conventional methods face significant challenges, including authentication, computational overhead, cyber security, and data integrity. To address these issues, this paper proposes a block-chain based system for goods management and tracking with enhanced authentication mechanism by leveraging the decentralized nature of block-chain technology and integrating Elliptic Curve Digital Signature Algorithm (ECDSA) with Elliptic Curve Cryptography-Zero Knowledge Proof (ECC-ZKP). The proposed solution aims to ensure the authenticity and the integrity for all the transaction while providing high level privacy-preserving verification without revealing information. The research in this paper demonstrates that the proposed block-chain-based system significantly enhances security performance, key management and operational efficiency, addressing the existing challenges in IIoT goods tracking and management, providing a resilient framework for more secure industrial operations in managing goods.

A Model for Data Confidentiality and Integrity during Replication in Fog & Edge Computing Environment

A Model for Data Confidentiality and Integrity during Replication in Fog & Edge Computing Environment

LTEM: Lightweight Trust Evaluation Model in IoT Environment

In the domain of the Internet of Things (IoT), data reliability is important, particularly within critical sectors like healthcare, environmental surveillance, and smart grids. Nevertheless, data transfer from the physical domain to the digital layer is susceptible to trust-related challenges encompassing data integrity, genuineness, and credibility. Predominantly, prevailing models for trust assessment primarily concentrate on the conduct of nodes, thus disregarding the direct evaluation of data packets. This particular constraint results in an insufficient validation of data credibility, thereby failing to consider pivotal elements like timeliness and accuracy. Furthermore, utilizing cloud-based packet evaluation frameworks frequently leads to inaccuracies, unreliability, and energy inefficiencies owing to the transfer of untrusted data. The current study introduces a streamlined trust assessment framework called the lightweight trust evaluation model (LTEM), custom-built for IoT settings to combat these obstacles. LTEM meticulously examines node behavior and data packets via a multi-tiered approach encompassing nodes, cluster heads (CH), and base stations (BS). Moreover, the proposed model's architecture considers energy usage by averting the transmission of untrusted data. Simulation results showcase the supremacy of LTEM compared to existing models by achieving a detection rate of 99% for untrusted data packets, outperforming the detection rates ranging from 30% to 75% observed in other models. Moreover, LTEM enhances the operational efficiency of sensor nodes regarding energy consumption, achieving an average energy utilization of 1.33J out of 4J, resulting in savings of approximately 2.67J on average, thereby extending the lifespan of nodes.

Enhancing cybersecurity protocols in the era of big data and advanced analytics

In the modern digital landscape, the exponential growth of big data and the proliferation of advanced analytics present both unprecedented opportunities and significant challenges for cybersecurity. This review explores the imperative of enhancing cybersecurity protocols to safeguard sensitive information and ensure the integrity of digital infrastructures in an era characterized by vast data generation and sophisticated analytical techniques. As organizations across various sectors leverage big data to drive innovation and gain competitive advantages, they simultaneously face heightened risks from cyber threats. Advanced analytics, including machine learning and artificial intelligence, offer potent tools for detecting and mitigating these threats. However, the integration of such technologies into cybersecurity frameworks demands a comprehensive and forward-thinking approach. Key to this enhancement is the development of robust data governance policies that ensure data integrity, confidentiality, and availability. These policies must address the complexities introduced by diverse data sources, varied data formats, and the velocity at which data is generated and processed. Additionally, the implementation of machine learning algorithms can significantly improve threat detection capabilities by identifying patterns and anomalies indicative of cyber threats, thus enabling proactive defense mechanisms. Moreover, enhancing cybersecurity protocols involves the adoption of encryption techniques and secure communication channels to protect data both at rest and in transit. Continuous monitoring and real-time analytics are crucial for maintaining situational awareness and promptly responding to potential breaches. The utilization of big data analytics also facilitates the identification of vulnerabilities and the assessment of risk profiles, allowing for the prioritization of security measures based on threat severity and impact. Despite the technological advancements, challenges such as data privacy concerns, algorithmic biases, and the need for skilled cybersecurity professionals persist. Addressing these challenges requires a multi-faceted strategy encompassing regulatory compliance, ethical considerations, and ongoing education and training. In conclusion, enhancing cybersecurity protocols in the era of big data and advanced analytics is essential for protecting critical digital assets and maintaining trust in digital ecosystems. By integrating cutting-edge analytical tools and establishing comprehensive data governance frameworks, organizations can effectively mitigate cyber risks and leverage the full potential of big data for sustainable growth and innovation.

Principles of Cloud Computing Infrastructure IaaS

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.

SECURITY IN CLOUD COMPUTING

By offering scalable and easily available computer resources over the internet, cloud computing has radically changed IT infrastructures all around. Ensuring the security of cloud systems remains a major difficulty even if they are used widely in many different fields. This abstract explores the intricate elements of cloud computing security including technical advancement, challenges, and methods. Data breaches, insider threats, and compliance complexities are among the main challenges; strong protections like authentication, authorization, and encryption will help to maintain data integrity and security. By redefining trust models and access restrictions in cloud systems, blockchain and zero-trust architecture are absolutely essential in increasing security. Emphasizing the importance of tying security measures with legal obligations, regulatory compliance systems such GDPR, HIPAA, and ISO/IEC 27001 set rigorous demands on cloud providers and users. Service level agreements (SLAs) help to define and explain providers' and customers' security obligations. They list procedures for problem resolution and service guarantees. This abstract ends with stressing the always shifting nature of challenges in cloud security and the pressing need of adaptive security solutions to effectively lower developing hazards. It offers a whole view that is absolutely essential for comprehending and controlling the complexity of protecting cloud computing systems.

Python's Role in Accelerating Web Application Development with Django

Efficient, secure, and scalable web services are critical in today's digital environment. This study explores the field of web service development and addresses issues such system efficiency, project duration, and changing requirements. The goal of this study is to ensure reliable and efficient web services by streamlining the development process and utilizing the Django framework. The study highlights the application of Django's Model-Template-View (MTV) design pattern, which is customized for a listing management system. Smooth data interaction and effective system performance are ensured by the study's seamless integration of Django's capabilities with MySQL for database management. To further improve system performance, focus is given to standardizing data sharing protocols and expediting user authentication procedures. The automation of web page construction with Python, HTML, and CSS modules, which increases system efficacy, is a crucial component of the research. Furthermore, the study shows how crucial security measures are, such as encryption for data confidentiality and integrity, which are made possible by the REST API interaction with the front end and the Django REST framework. Along with discussing key attacks and weaknesses that are frequent in web technology, the article also offers strategies to reduce these risks. Through the use of suitable SDLC models and testing procedures, it recognizes the crucial role that software engineering processes play in the development process. Focusing attention to Django's feature set, scalability, and wide library support, the study proves its advantages over competing web frameworks. The goal of the research is to provide developers and students with the skills they need to fully utilize Django's capabilities, making it easier to create dependable, successful and safe online applications. This study advances knowledge in the field of web technology and service development by lighting the development, difficulties, and current solutions in these fields. The study's ultimate goal is to open the door for comprehensive, modern, and efficient web service creation in order to satisfy the growing need for effective web solutions in the current digital era.