Efficient data management in a large-scale epidemiology research project

In the context of an increasing number of multi-centric studies providing data from different sites and sources the necessity for central data management (CDM) becomes undeniable. This is exacerbated by a multiplicity of featured data types, formats and interfaces. In relation to methodological medical research the definition of central data management needs to be broadened beyond the simple storage and archiving of research data. This paper highlights typical requirements of CDM for cohort studies and registries and illustrates how orientation for CDM can be provided by addressing selected data management challenges. Therefore in the first part of this paper a short review summarises technical, organisational and legal challenges for CDM in cohort studies and registries. A deduced set of typical requirements of CDM in epidemiological research follows. In the second part the MOSAIC project is introduced (a modular systematic approach to implement CDM). The modular nature of MOSAIC contributes to manage both technical and organisational challenges efficiently by providing practical tools. A short presentation of a first set of tools, aiming for selected CDM requirements in cohort studies and registries, comprises a template for comprehensive documentation of data protection measures, an interactive reference portal for gaining insights and sharing experiences, supplemented by modular software tools for generation and management of generic pseudonyms, for participant management and for sophisticated consent management. Altogether, work within MOSAIC addresses existing challenges in epidemiological research in the context of CDM and facilitates the standardized collection of data with pre-programmed modules and provided document templates. The necessary effort for in-house programming is reduced, which accelerates the start of data collection.

It has been shown that detecting and treating children with hearing loss at an early age will improve their speech and language development. The implementation of universal hearing screening programs is therefore one of the major goals in pediatric audiology. One problem of present screening programs is high lost to follow-up rates. The aim of this paper was to evaluate the effectiveness of a patient tracking system based on central data management and data transmission via the internet. The screening program was organized as a TEOAE (transitory evoked otoacoustic emissions)-based three-stage procedure and covered a complete German federal state (Saarland). The measurements of the first stage took place in well-baby nurseries (WBNs) as well as neonatal intensive care units (NICUs). Measurements of the second stage were performed by pediatricians and ENT physicians on an outpatient basis. The third stage comprised diagnostic ABR (auditory brainstem response) measurements performed by pediatric audiologists. Using a central data management system we were able to identify and follow every child not screened or with negative results. The overall coverage in the first stage was 93% of all newborns. A screening result of the second stage could be obtained for 90% of the children. Four out of 3,830 children screened turned out to have a relevant hearing loss and were fitted with hearing aids. A TEOAE-based newborn hearing screening system can be organized effectively. Central data management as well as interdisciplinary organization of the follow-up are important prerequisites for the success of a hearing screening program. A follow-up system as proposed in this paper can also be implemented in AABR (automated auditory brainstem response)-based programs.

Research on the human components of global change: Next steps

In today's society, the rapid development of the Internet makes People's Daily life become more intelligent and diversified. Today's society has entered a multifaceted era where everything is interconnected. Artificial intelligence technology is gradually replacing some traditional human services, such as intelligent robot customer service instead of traditional human customer service, intelligent face scanning security check in railway stations instead of traditional manual ticket checking, unmanned supermarket automatic checkout has liberated some social labor costs. All these changes are the result of the development of artificial intelligence technology in today's society. In recent years, unicorn startups focused on biometrics have sprung up all around us, such as BTU and its MEG VII (Face ++). Thanks to the development of Internet and artificial intelligence technology, in many application fields, the traditional access control and identity authentication technology based on password verification is gradually transforming to the scheme based on biometric identification verification. Secure identity authentication is very important to the application of Internet. Face recognition is the most popular technology among all biometric identification technologies. In the field of biometric identification technology, it has become the most widely used technology in the field of identity authentication because of its unique non-invasive, support for infrared and visible light, no need for user cooperation and many other advantages. In the field of education, examinee identification, pedestrian identification detection at the entrance of railway stations, face electronic payment, intelligent video surveillance system, intelligent attendance and access control system, intelligent unmanned supermarkets and customs clearance ports become the pioneer fields of face recognition applications. It can be seen that the era of “national face brushing” has arrived, and the application of face recognition technology will only be more and more widespread in the current era and in the future. However, due to the sensitivity of biometric data and the heterogeneity and openness of network environment, the privacy leakage of biometric data is difficult to avoid. At present, fog computing and edge computing have been paid more and more attention in many fields. In the case that cloud service providers are unable to provide sufficient security, edge computing shows its advantages. In this paper, mobile edge computing is introduced for the first time into the face privacy protection identity authentication system based on cloud server outsourcing computing. It can not only greatly reduce the interaction frequency between users and cloud server, improve the availability and fault tolerance of the system, but also contribute to the implementation of privacy protection scheme. A deep constitutional neural network for face feature extraction is trained using deep learning framework Cafe. Cosine similarity is used to complete face verification. A privacy protection scheme based on the secure nearest neighbor algorithm is proposed, which can not only protect the security of the face feature data at the edge computing node, but also allow the edge computing node to complete the face recognition operation against the encrypted face feature data. In addition, the encryption scheme does not require large computing resources, and the accuracy of face recognition in cipher text is exactly the same as that in explain. At present, most of the solutions either have high computational complexity or poor security performance. How to reduce the computational complexity and improve the real-time performance of the system while ensuring the high security of the private data has important research significance and value. Therefore, in the cloud server outsourcing computing environment, how to complete biometric identification on the premise of protecting the privacy of biological data has become a research hot spot.

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

In today’s digital era, safeguarding critical data is paramount, particularly within enterprise environments. Oracle databases are widely used for managing mission-critical data, making it essential to implement robust security measures to protect against data loss, corruption, and unauthorized access. This paper focuses on optimizing Oracle database security through automated backup and recovery solutions. The traditional manual processes for database backup and recovery are prone to errors, time delays, and human intervention, which may lead to security vulnerabilities. To mitigate these risks, this study explores the integration of automation technologies in the backup and recovery processes of Oracle databases, ensuring higher efficiency, consistency, and reliability. Automated solutions leverage scheduling, encryption, and real-time monitoring to provide a secure backup environment, minimizing the chances of data breaches. Moreover, they enable rapid recovery in the event of a disaster, thereby enhancing database availability and reducing downtime. The research discusses best practices for configuring Oracle database backups, including full, incremental, and differential backups, and highlights the role of encryption and access controls in securing backup data. Additionally, the study examines the use of cloud-based backup systems, which offer scalability and off-site storage, further enhancing security and disaster recovery capabilities. By adopting these automated solutions, organizations can optimize the security of their Oracle databases, ensuring data integrity, confidentiality, and availability. The paper concludes by recommending strategies for integrating automated backup and recovery solutions into existing Oracle database environments, aiming to streamline operations and mitigate security risks in the long term

The secure management of healthcare and education systems has become an essential component in ensuring data privacy, trust, and transparency in handling sensitive information. Healthcare systems manage vast amounts of personal medical data, while education systems handle confidential student records and academic results. Both sectors rely on secure methods to ensure that data is properly handled, shared, and stored. Historically, centralized databases were used to manage these systems, offering control but leaving vulnerabilities, such as data breaches, unauthorized access, and manipulation of information. Many organizations used centralized security protocols like firewalls, encryption, and access control systems, but they often fell short of providing full security due to single points of failure. The integration of Blockchain technology offers a decentralized solution, eliminating central control and providing a secure, transparent, and immutable way to manage sensitive data. Blockchain’s unique ability to record and verify transactions on a distributed ledger ensures that once data is entered, it cannot be altered, reducing the risk of fraud or errors in both healthcare and education records. The motivation behind implementing Blockchain in these sectors is to address the security risks and inefficiencies of centralized systems, offering a solution where all stakeholders can have a clear, tamper-proof view of the data. The primary problems faced with centralized management include vulnerabilities to hacking, data loss, and the complexity of audits. The proposed system, utilizing Blockchain, ensures decentralized data storage, enhanced privacy, and transparent access management, fostering a trusted environment for both healthcare and education systems. By using smart contracts and cryptographic security, the integrity of healthcare and education data is guaranteed, ensuring that all stakeholders—from patients to students, and from educators to healthcare providers—can trust the system’s reliability and security.

Beginning in 1998, the Pacific Northwest National Laboratory (PNL), US Postal Inspection Service Forensic Laboratory (USPIS), and the Data Fusion Laboratory, Drexel University (DFL) have been collaborating on a large scale research project ''Handwriting Individuality--Moving From Art to Science''. In April 1998 a survey was distributed to the community of forensic document examiners (FDEs) requesting input on the habit areas used and their utility in distinguishing handwriting. The information obtained from this survey was intended to provide the data necessary to select the criteria and begin the evaluation of the handwriting samples currently in the project. Preliminary results of the survey were made available to the community at the American Society of Questioned Document Examiners (ASQDE) meeting in August 1998 and the American Academy of Forensic Sciences (AAFS) meeting in February 1999. This report provides final documentation of the survey and its results. This survey has two objectives: (1) to compile a list of handwriting features and characteristics used by professional forensic document examiners in the examination and comparison of handwriting and (2) to gather information about the significance of these features and characteristics. These objectives are met by having the FDEs provide an indication of their experience in the frequency of habit area evaluation and the utility of the habit area for discrimination.

ObjectiveTo access Electronic Health Record (EHR) data, hospitals have implemented Clinical Data Warehouses (CDWs) using Extract Transform and Load (ETL) processes. While ETL performances are typically evaluated individually, our study examines the cumulative impact of ETLs on data availability. MethodsUsing a real multi-hospital CDW as a case study, we modeled EHR data processing from the software sources to the CDW's data store. We simulated a scenario where researchers aimed to reconstruct breast cancer care trajectories using EHR data. We calculated the size and characteristics of the data store population, and compared them to the original population. ResultsEHR data are recorded in various software depending on data category, hospital, and year, each requiring specific series of ETLs for integration in the CDW. Despite acceptable transfer rates for each ETL (range 73 %-100 %), cumulative losses led to study populations in the data store being up to 90 % smaller than anticipated when researchers required data exhaustivity for patients. Population size decreased steeply with the more data categories required. No difference was found in population characteristics between the data store and the original cohorts. Discussion & ConclusionResearchers should scrutinize data availability in CDWs as missing data could result from outsourced care, incomplete input, or underperforming ETLs. Integrating more data sources in CDWs increases the number of data routes, necessitating time for ETL implementation and maintenance, and increases data loss risks. Though commonly perceived as a “black box”, data transformation can significantly influence the reliability of populations studied in CDWs. Public interest SummaryTo access data generated during care, researchers build Clinical Data Warehouses (CDWs). CDWs are infrastructures composed of a series of processing steps to extract the data from the data source, transform it according to the needs and load it into a data store. Usually, the performances of these processing steps are evaluated one a time. However, each data point goes through a series of processing steps before being made available for research. In this study, we aim to evaluate the impact of the entire data processing pipeline on the availability of data points in a CDW by simulating a study on breast cancer and evaluating the impact on the size and the characteristics of the final cohort. The cumulative losses of the processing steps resulted in a population 90 % smaller than anticipated. The characteristics of the final population showed no difference to those of the original cohort.

This article is about a Spectrum Sensing Network (SSN) which generates an accurate radio environment map (e.g. power over frequency, time, and location) from a given application area. It is intended to be used in combination with cognitive Program Making and Special Events (PMSE) devices (e.g. wireless microphones) to improve their operation reliability. The SSN consists of a distributed network of multiple scanning radio receivers and a central data management and storage unit. The parts of the SSN are presented in detail and the advantages and use cases of such a sensing network structure will be outlined.

Established in 2003 by the Office of Rare Diseases Research (ORDR), in collaboration with several National Institutes of Health (NIH) Institutes/Centers, the Rare Diseases Clinical Research Network (RDCRN) consists of multiple clinical consortia conducting research in more than 200 rare diseases. The RDCRN supports longitudinal or natural history, pilot, Phase I, II, and III, case-control, cross-sectional, chart review, physician survey, bio-repository, and RDCRN Contact Registry (CR) studies. To date, there have been 24,684 participants enrolled on 120 studies from 446 sites worldwide. An additional 11,533 individuals participate in the CR. Through a central data management and coordinating center (DMCC), the RDCRN's platform for the conduct of observational research encompasses electronic case report forms, federated databases, and an online CR for epidemiological and survey research. An ORDR-governed data repository (through dbGaP, a database for genotype and phenotype information from the National Library of Medicine) has been created. DMCC coordinates with ORDR to register and upload study data to dbGaP for data sharing with the scientific community. The platform provided by the RDCRN DMCC has supported 128 studies, six of which were successfully conducted through the online CR, with 2,352 individuals accrued and a median enrollment time of just 2months. The RDCRN has built a powerful suite of web-based tools that provide for integration of federated and online database support that can accommodate a large number of rare diseases on a global scale. RDCRN studies have made important advances in the diagnosis and treatment of rare diseases.

Secure Medical Data Storage with Blockchain Technology

A large scale research and development project for China Fusion Engineering Test Reactor (CFETR) named the comprehensive research facility for fusion technology (CRAFT) started in 2019 and is scheduled for completion in 2025. A superconducting conductor experiment testing platform (Super-X for short) is a sub-task of CRAFT to evaluate the future superconducting part performance. The maximum testing magnetic field at bore center is 15 T, the dimensions of testing space are 100 × 160 × 550 mm, the field homogeneity of testing space is superior to 95%, and the maximum testing current is 100 kA. The direct current (DC) magnet is the key sub-system for the facility providing the background magnetic field. The engineering design for DC magnet has been accomplished in the middle of this year, and its technical parameters meet the required. The main work focuses on the structural design of the split coils, the axial preload and the hanging assembly. This paper presents the progress on the engineering design of the DC magnet.

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

The history of scientific research of Novaya Zemlya archipelago at the end of the 19th – the beginning of the 20th century is analysed based on archival and published sources. The study of the history of scientific research on the vast northern territory is closely connected with the history of the role of national science in development of the Arctic territories of Russia. At the end of the 19th century geo-political situation encouraged the Government of Russia to colonize Novaya Zemlya actively. Large-scale scientific research was organized to study thoroughly its physical and geographic characteristics and to explore natural resources. In wide political and socio-economic context the period taken for the study is topical in terms of attaching this territory to Russia. Systematic studies of Novaya Zemlya started in the Soviet period when the young Government of the USSR started to regard the archipelago as an important military outpost of the country and one of the sources of natural productive forces of the country. The leading role of the Academy of Sciences and the All-Union Arctic Research Institute in the research of Novaya Zemlya is shown. Special attention is paid to the initiatives of the scientific society in the research of the archipelago.