Data Storage Research Articles

Lwreg: A Lightweight System for Chemical Registration and Data Storage.

Here, we present lwreg, a lightweight, yet flexible chemical registration system supporting the capture of both two-dimensional molecular structures (topologies) and three-dimensional conformers. lwreg is open source, with a simple Python API, and is designed to be easily integrated into computational workflows. In addition to lwreg itself, we also introduce a straightforward schema for storing experimental data and metadata in the registration database. This direct connection between compound structural information and data generated using those structures creates a powerful tool for data analysis and experimental reproducibility. The software is available at and installable directly from https://github.com/rinikerlab/lightweight-registration.

Advances and Challenges in Random Access Techniques for In Vitro DNA Data Storage.

With digital transformation and the general application of new technologies, data storage is facing new challenges with the demand for high-density loading of massive information. In response, DNA storage technology has emerged as a promising research direction. Efficient and reliable data retrieval is critical for DNA storage, and the development of random access technology plays a key role in its practicality and reliability. However, achieving fast and accurate random access functions has proven difficult for existing DNA storage efforts, which limits its practical applications in industry. In this review, we summarize the recent advances in DNA storage technology that enable random access functionality, as well as the challenges that need to be overcome and the current solutions. This review aims to help researchers in the field of DNA storage better understand the importance of the random access step and its impact on the overall development of DNA storage. Furthermore, the remaining challenges and future research trends in random access technology of DNA storage are discussed, with the goal of providing a solid foundation for achieving random access in DNA storage under large-scale data conditions.

Realization of spinful metaphotonic stokes skyrmions

Abstract Topologically protected skyrmion textures of light have garnered significant attention due to their potential applications in next-generation high-density data storage and logic devices. However, achieving compact and tunable on-chip skyrmion modes remains a formidable challenge. In this work, we present a novel approach empowered by birefringent metasurfaces to generate and manipulate spin-multiplexed photonic skyrmion textures. By encoding independent phase profiles onto orthogonal spin states, we observe the emergence of anti-skyrmions and skyrmioniums via Stokes parameter measurements, elucidating their distinct topological characteristics. This spin-multiplexed metasurface platform not only facilitates high-dimensional multiplexing but also enables the miniaturization of topological quasi-particles, offering promising prospects for applications in optical memory, information processing, and communications.

Recent Progress in Memrsitor Array Structures and Solutions for Sneak Path Current Reduction

AbstractMemristors have diverse potential for improving data storage through linear memory control and synaptic operation in AI and neuromorphic computing. Prior research on optimizing memristors in next‐generation devices has generally indicated that emerging arrays and vertical structures can improve memory density, although special fabrication steps are required to realize improved operation. Until now, many obstructions, such as the sneak path current and forming processes from the initial device in array structure operation at the device level, have limited the development of array‐based memristor devices for further progressing circuits and integrated design. In this paper, memristor array studies are examined that have suggested solutions for sneak path current and forming operation problems at the device level. Ultimately, representative solutions are proposed to progress memristors into array structures by introducing the latest research on one diode‐one RRAM (1D1R), one selector‐one RRAM (1S1R), overshoot suppressed RRAM (OSRRAM), self‐rectifying cell (SRC), charge trap memory (CTM) and their applications. Additionally, essential details demonstrating the practical implementation of these devices in crossbar array memory are investigated. Finally, the advantages and perspectives of these array‐based memristor solutions are summarized.

Complex Sequence-Defined Heteropolymers Enable Controlled Film Growth in Layer-By-Layer Assembly.

Digitally-encoded poly(phosphodiesters) (d-PPDE) with highly complex primary structures are evaluated for layer-by-layer (LbL) assembly. To be easily decoded by mass spectrometry (MS), these digital polymers contain many different monomers: 2 coding units allowing binary encryption, 1 cleavable spacer allowing controlled MS fragmentation, and 3 mass tags allowing fragment identification. These complex heteropolymers are therefore composed of 6 different motifs. Despite this strong sequence heterogeneity, it is found that they enable a highly controlled LbL film formation. For instance, a regular growth is observed when alternating the deposition of negatively-charged d-PPDE and positively-charged poly(allyl amine hydrochloride) (PAH). Yet, in this approach, the interdistance between consecutive coded d-PPDE layers remains relatively small, which may be an issue for data storage applications, especially for the selective decoding of the stored information. Using poly(sodium 4-styrene sulfonate) (PSS) as an intermediate non-coded polyanion, it is shown that a controlled interdistance between d-PPDE layers can be easily achieved, while still maintaining a regular LbL growth. Last but not least, it is found in this work that d-PPDE of relatively small molecular weight (i.e., significantly smaller than those of PAH and PSS) still enables a controlled LbL assembly.

Common sleep data pipeline for combined data sets.

Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizing results. However, working with large amounts of sleep data can be challenging, both from a hardware perspective and because of the different preprocessing steps necessary for distinct data sources. Here we review the possible obstacles and present an open-source pipeline for automatic data loading. Our solution includes both a standardized data store as well as a 'data serving' portion which can be used to train neural networks on the standardized data, allowing for different configuration options for different studies and machine learning designs. The pipeline, including implementation, is made public to ensure better and more reproducible sleep research.

Algorithm for Key Transparency with Transparent Logs

Background Cryptography plays a crucial role in securing digital communications and data storage. This study evaluates the Transparent Key Management Algorithm utilizing Merkle trees, focusing on its performance and security effectiveness in cryptographic key handling. Methods The research employs simulated experiments to systematically measure and analyze key operational metrics such as insertion and verification times. Synthetic datasets are used to mimic diverse operational conditions, ensuring rigorous evaluation under varying workloads and security threats. Implementation is carried out using R programming, integrating cryptographic functions and Merkle tree structures for integrity verification. Results Performance analysis reveals efficient insertion and verification times under normal conditions, essential for operational workflows. Security evaluations demonstrate the algorithm's robustness against tampering, with approximately 95% of keys verified successfully and effective detection of unauthorized modifications. Simulated attack scenarios underscore its resilience in mitigating security threats. Conclusions The Transparent Key Management Algorithm, enhanced by Merkle trees and cryptographic hashing techniques, proves effective in ensuring data integrity, security, and operational efficiency. Recommendations include continuous monitoring and adaptive algorithms to bolster resilience against evolving cybersecurity challenges, promoting trust and reliability in cryptographic operations.

Evaluation of the Financial Reporting System of LKM Gapoktan Tani Manunggal Temanggung using Black Box Testing

This study employs Boundary Value Analysis (BVA), a kind of Black-Box Testing, to assess the financial information system of LKM Gapoktan Tani Manunggal Temanggung. Through testing the system's performance in a variety of edge situations pertaining to user registration, login, financial data entry, balance calculation, report creation, data storage, backup, alerts, and system accessibility, the research seeks to evaluate the functionality of the system. The outcomes demonstrate how well the system manages boundary circumstances, producing precise outputs and guaranteeing dependability across various functionality and devices. The study emphasizes the system's resilience and points out areas that may benefit more development, such as enhanced security, better user experience, integration testing, and sophisticated testing methods.

Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.

Maize plant height automatic reading of measurement scale based on improved YOLOv5 lightweight model

Background Plant height is a significant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms. Method This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales. Results The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm’s 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm’s results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Parallel detection structure exploiting the extrinsic information from the serial detection in the holographic data storage system

Parallel detection structure exploiting the extrinsic information from the serial detection in the holographic data storage system

Innovative Application of Intelligent Internet of Things in Smart Cities

The Internet of Things, as an important component of the new generation of information technology system, regards the Internet as the main carrier of information, enabling all ordinary physical objects that can be independently addressed to achieve interconnectivity. This is the key to promoting urban modernization and building smart cities. This paper analyzes and studies the application of Internet of Things technology in smart cities. Based on the understanding of relevant concepts, it aims to understand the relationship between IoT technologies and smart city construction, clarify the role of Internet of Things in smart city construction, and the challenges currently faced by IoT technologies in smart city application. Therefore, taking the smart community as the starting point, the application of the smart Internet of Things in the smart community is completed through eight steps: demand analysis, planning and design, sensor and device deployment, data collection, data storage, data analysis, control and scheduling, and user application development. The experiment results show that after establishing a smart community control platform based on smart IoT technologies, the failure rate of the comprehensive information sending end, receiving end, output system, and hardware system is low, the user experience satisfaction is high, and the platform performance is strong.

'Where have my patients gone?': A simulation study on real-world data processing in Clinical Data Warehouses

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.

Do Not Let the Beginning Trap you! On Inhibition, Associative Creative Chains, and Hopfield Neural Networks

ABSTRACTCreative thinking stems from the cognitive process that fosters the creation of new ideas and problem‐solving solutions. Artificial intelligence systems and neural network models can reduce the intricacy of understanding creative cognition. For instance, the generation of ideas could be symbolized as patterns of binary code in which clusters of neurons synchronize their firing and store information inside a neural network, forming connections based on correlation. The Hopfield neural network (HNN) is a simple model known for its biological plausibility in storing and retrieving neuron patterns. We implemented certain modifications to HNN as a step toward the larger framework of creative thinking‐based association. These modifications included introducing pattern weights control, which provides a robust representation for content addressable memory and conceptual links in stored data. We identified two mechanisms controlling the transition from analytical to associative‐based thinking. The first mechanism refers to the activation threshold of neurons, which acts as an on/off switch for the network. The second was the inhibition of stored concepts, similar to an on/off switch that guides the network to search for associative links and when to stop. Our findings suggest that neurons step back from the contextual focus and find alternatives when analytical thinking is insufficient. These alternatives are linked to seemingly unrelated ideas, using inhibition as an analogy to the hyperparameters. Using hyperparameters to inhibit the stored patterns, we could control the creation of associative links.

Leonhard Med, a trusted research environment for processing sensitive research data.

This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a "bare-metal" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.

A QR code-enabled framework for fast biomedical image processing in medical diagnosis using deep learning

In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making.

Mobile forensics tools and techniques for digital crime investigation: a comprehensive review

<p>Extracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities.</p>

Dual Access Control for Cloud Storage and Sharing

In the field of distributed computing, a significant innovation is now being developed. The process of storing information is extremely difficult for every single person on the planet. Data storage and retrieval can be accomplished in the most straightforward and expedient manner possible through the utilisation of distributed computing. When it comes to distributed computing, security is the most important consideration. An alternative approach to provide access control for distributed computing is what this research work intend to illustrate. Distributed computing can benefit from this design's protective admission control. Additionally, it makes use of a watch and a progressive design in order to facilitate access control, that is more exact. It is possible for us to send, download, and erase papers to and from the fog with ease using this method.

Efficient generation of octave-separating orbital angular momentum beams via forked grating array in lithium niobite crystal

Abstract The concept of orbital angular momentum (OAM) of light has not only advanced fundamental physics research but also yielded a plethora of practical applications, benefitting from the abundant methods for OAM generation based on linear, nonlinear and combined schemes. The combined scheme could generate octave-separating OAM beams, potentially increasing the channels for optical communication and data storage. However, this scheme faces a challenge in achieving high conversion efficiency. In this work, we have demonstrated the generation of multiple OAM beams at both fundamental frequency and second harmonic (SH) wavelengths using a three-dimensional forked grating array with both spatial χ (1) and χ (2) distributions in a lithium niobate nonlinear photonic crystal platform. The enhancements of the fundamental and SH OAM beams have been achieved by employing linear Bragg diffraction and nonlinear Bragg diffraction, respectively, i.e., quasi-phase matching. The experimental results show that OAM beams with variable topological charges can be enhanced at different diffraction orders via wavelength or angle tuning, achieving conversion efficiencies of 60.45 % for the linear OAM beams and 1.08 × 10−4 W −1 for the nonlinear ones. This work provides a promising approach for parallel detection of OAM states in optical communications, and extends beyond OAM towards the control of structured light via cascaded linear and nonlinear processes.