Data Storage Capacity Research Articles

Storage scaling management model

Introduction: The implementation of data storage process requires timely scaling of the infrastructure to accommodate the data received for storage. Given the rapid accumulation of data, new models of storage capacity management are needed, which should take into account the hierarchical structure of the data storage, various requirements for file storage and restrictions on the storage media size. Purpose: To propose a model for timely scaling of the storage infrastructure based on predictive estimates of the moment when the data storage media is fully filled. Results: A model of storage capacity management is presented, based on the analysis of storage system state patterns. A pattern is a matrix each cell of which reflects the filling state of the storage medium at an appropriate level in the hierarchical structure of the storage system. A matrix cell is characterized by the real, limit, and maximum values of its carrier capacity. To solve the scaling problem for a data storage system means to predict the moments when the limit capacity and maximum capacity of the data carrier are reached. The difference between the predictive estimatesis the time which the administrator has to connect extra media. It is proposed to calculate the values of the predictive estimates programmatically, using machine learning methods. It is shown that when making a short-term prediction, machine learning methods have lower accuracy than ARIMA, an integrated model of autoregression and moving average. However, when making a long-term forecast, machine learning methods provide results commensurate with those from ARIMA. Practical relevance: The proposed model is necessary for timely allocation of storage capacity for incoming data. The implementation of this model at the storage input allows you to automate the process of connecting media, which helps prevent the loss of data entering the system.

Evidence synthesis communities in low-income and middle-income countries and the COVID-19 response

Evidence synthesis communities in low-income and middle-income countries and the COVID-19 response

Research on Multifeature Data Routing Strategy in Deduplication

Deduplication is a popular data reduction technology in storage systems which has significant advantages, such as finding and eliminating duplicate data, reducing data storage capacity required, increasing resource utilization, and saving storage costs. The file features are a key factor that is used to calculate the similarity between files, but the similarity calculated by the single feature has some limitations especially for the similar files. The storage node feature reflects the load condition of the node, which is the key factor to be considered in the data routing. This paper introduces a multifeature data routing strategy (DRMF). The routing strategy is made based on the features of the cluster, including routing communication, file similarity calculation, and the determination of the target node. The mutual information exchange is achieved by routing communication, routing servers, and storage nodes. The storage node calculates the similarity between the files stored, and then the file is routed according to the information provided by the routing server. The routing server determines the target node of the route according to the similar results and the node load features. The system prototype is designed and implemented; also, we develop a system to process the feature of cluster and determine the specific parameters of various features of experiments. In the end, we simulate the multifeature data routing and single-feature data routing, respectively, and compare the deduplication rate and data slope between the two strategies. The experimental results show that the proposed data routing strategy using multiple features can improve the deduplication rate of the cluster and maintain a lower data skew rate compared with the single-feature-based routing strategy MCS; DRMF can improve the deduplication rate of the cluster and maintain a lower data skew rate.

Portable Device for the Measurement and Assessment of the Human Equilibrium.

This work presents the design and development of a new alternative tool to measure the Center of Pressure (CoP) displacements, intended to evaluate the human balance. The device is based on a modified commercial balance board used for video games, resulting in a low-cost, portable device capable of computing the CoP, providing 24 of the most used indexes to test the human balance. The proposed standalone device runs on rechargeable batteries, weighs only 3.5 kg, and has a data storage capacity for over 1000 tests. Visual and auditory instructions assist its user interface. Thus, contrary to the commercial systems designed for laboratory use, this device enables the measurement of quantitative balance parameters in non-laboratory places, allowing the study of the balance of vulnerable populations directly on their typical environments. To evaluate the device, 20 older adults (68.60 ± 1.23years) were tested, and the resulting values were compared with a similar study using a force platform; 19 indexes showed a similarity with those reported using force platform and 12 of these were statistically equivalent. The proposed device represents an open-source alternative tool for researchers and healthcare personnel to acquire reliable data to evaluate human balance.

Tunable and nonvolatile multibit data storage memory based on MoTe2/boron nitride/graphene heterostructures through contact engineering

Heterostructures formed by stacking atomically thin two-dimensional materials are promising candidates for flash memory devices to achieve premium performances, due to the capability of effective carrier modulation and unique charge trapping behavior at the interfaces with atomic flatness. Here, we report a nonvolatile floating-gate flash memory based on MoTe2/h-BN/graphene van der Waals heterostructure, which possesses increased data storage capacity per cell and versatile tunability. The decent memory behavior of the device is enabled by the carriers stored in the floating gate of graphene layer, which tunnel through the dielectric layer of h-BN from the channel layer of MoTe2 under static-electrical field. Consequently, the developed memory device is capable to store 2 bits per cell by applying varied gate bias to implement multi-distinctive current levels. The device also exhibits remarkable erase/program current ratio of ∼105 with 1 µs switch speed and stable retention with estimated ∼30% charge loss after 10 yr. Furthermore, the memory device can operate in both p- and n-type modes through contact engineering, offering wide adaptability for emerging applications in electronic technologies, such as neuromorphic computing, data-adaptive energy efficient memory, and complex digital circuits.

Fidelity of visual long-term memory in the ageing process (Fidelidad de la memoria visual a largo plazo en el proceso de envejecimiento)

ABSTRACT Several studies indicate that the capacity for retaining visual content in long-term memory (LTM) is almost unlimited and vastly superior to the storage capacity for verbal data. The purpose of this study is to evaluate the fidelity of visual memory in adults and older adults and compare it with that of young adults. A Visual Long-Term Memory Test (VLT-MT) was developed in which the participants viewed 512 images, divided into two groups of 256 figures, and performed a subsequent forced-choice task after viewing each group. The results also indicate that the semantic category to which the stimulus belonged influenced the performance of the subjects, and that the capacity to recognize a given object diminished as the number of figures from the same category increased.

Modern Kredi Sınıflandırma Çalışmaları ve Metasezgisel Algoritma Uygulamaları: Sistematik Bir Derleme

Number of proposed advanced analysis methods, which try to successfully predict if applicants are going to default in credit applications show an increasing pattern, especially after the Global Financial Crisis. Alternative to conventional statistical classification methods, machine learning methods arrive on the scene; they have capability to reveal information from the data independently from constraints and assumptions. Along with machine learning methods, metaheuristic algorithms that substantially improves classification performances take part in studies. Combined usages of learning methods and metaheuristic algorithms aim to benefit from the contemporary data storage and process capacities at the highest level and greatly contribute to credit risk assessment field. In this review study, credit classification studies that adopt metaheuristic algorithms in the analyses are examined with a systematic process, for the period after 2000. By forming a general framework, classification methods, metaheuristic algorithm implementations, algorithms’ intended uses and performance assessment criteria are addressed. Examination showed that there is a growing interest, nevertheless method preferences are concentrated over a limited option. It is necessary to incorporate more novel metaheuristics and/or hybrid and combined usages to the studies. It is possible to say that progressive works parallel to the developments in computer and mathematical sciences will continuously contribute to the literature.

DNAVS: an algorithm based on DNA-computing and vortex search algorithm for task scheduling problem

In this paper, we present DNAVS algorithm for the general job-shop scheduling problem (also known as Flexible JSSP). JSSP is an NP-complete problem, which means there is probably no deterministic or exact algorithm that can find the optimum solution in polynomial time for arbitrary instances of the problem, unless, P = NP. In the DNA computing algorithm, although the hardware is not accessible, in the future, we will have those computers that work based on biological hardware. Their most important advantages over silicon-based computers are their capacity for data storage. DNAVS is an improvement of the DNA computing algorithm by using a metaheuristic search algorithm called vortex search (VS) algorithm. The strongness of DNA computing is its parallelization. In the unavailability of DNA computers, the DNAVS reduces the time complexity of DNA computing by employing the VS algorithm. The efficiency of the DNAVS has been tested with standard test instances of job-shop scheduling problems. Our implementation results show that the DNAVS algorithm works effectively even for large scale instances on currently silicon-based computers.

Chemically Controlled Volatile and Nonvolatile Resistive Memory Characteristics of Novel Oxygen-Based Polymers.

Recent advancements in modern microelectronics continuously increase the data storage capacity of modern devices, but they require delicate and costly fabrication processes. As alternatives to conventional inorganic based semiconductors, semiconducting polymers are of academic and industrial interest for their cost-efficiency, power efficiency, and flexible processability. Here, we have synthesized a series of novel oxygen-based polymers through the postmodification reactions of poly(ethylene-alt-maleate) with various oxybenzyl alcohol derivatives. The oxygen-based polymers are thermally stable up to 180 °C, and their nanoscale film devices exhibit reliable, power efficient p-type unipolar volatile and nonvolatile resistive memory characteristics with high ON/OFF current ratios. Additionally, when given a higher number of oxygen atoms in oxyphenyl side groups, the thin film polymer devices demonstrate a wide operational film thickness range. The memory characteristics depend on the oxyphenyl moieties functioning as charge trap sites, where a combination of Schottky emission and trap-limited space charge limited conductions in OFF-state and hopping conduction in ON-state are observed. This study demonstrates the chemical incorporation of oxyphenyl derivatives into polymer dielectrics as a powerful development tool for p-type resistive memory materials.

Dual sequence definition increases the data storage capacity of sequence-defined macromolecules

Sequence-defined macromolecules offer applications in the field of data storage. Challenges include synthesising precise and pure sequences, reading stored information and increasing data storage capacity. Herein, the synthesis of dual sequence-defined oligomers and their application for data storage is demonstrated. While applying the well-established Passerini three-component reaction, the degree of definition of the prepared monodisperse macromolecules is improved compared to previous reports by utilising nine specifically designed isocyanide monomers to introduce backbone definition. The monomers are combined with various aldehyde components to synthesise dual-sequence defined oligomers. Thus, the side chains and the backbones of these macromolecules can be varied independently, exhibiting increased molecular diversity and hence data storage capacity per repeat unit. In case of a dual sequence-defined pentamer, 33 bits are achieved in a single molecule. The oligomers are obtained in multigram scale and excellent purity. Sequential read-out by tandem ESI-MS/MS verifies the high data storage capacity of the prepared oligomers per repeat unit in comparison to other sequence defined macromolecules.

High‐Performance Flexible Organic Nonvolatile Memories with Outstanding Stability Using Nickel Oxide Nanofloating Gate and Polymer Electret

AbstractOrganic nonvolatile memory devices based on organic field‐effect transistors (OFETs) have attracted attention as promising memory components in flexible organic integrated circuits. OFET‐based organic memories can be fabricated using solution processes with high portability and flexibility. However, many challenges still remain, such as achieving high levels of transparency and data storage while maintaining device reliability. In this study, the high‐performance of flexible organic nonvolatile memory devices is demonstrated through the synergistic effect of using a combination of polymer electret (poly(2‐vinyl naphthalene), PVN) and nanofloating gates (nickel oxide nanoparticles (NiOx NPs)). The PVN/NiOx NPs bilayer successfully acts as a charge storage medium with high transparency in the visible range. This system greatly improves memory performance including high data storage capacity, stable cyclic endurance, and quasi‐permanent retention characteristics (>10 years). Furthermore, these devices also show excellent charge storage characteristics after 1000 bending cycles and even under the sharply bent state (bending radius 3 mm). This enhanced mechanical stability of flexible synergistic memory devices demonstrates that NiOx NPs can play an important role as a reliable data storage material for next‐generation wearable electronic devices.

Ultra-high capacity for three-dimensional optical data storage inside transparent fluorescent tape: erratum.

In Opt. Lett.45, 1535 (2020)OPLEDP0146-959210.1364/OL.387278, there was an error regarding the corresponding author assignment. That error is corrected here.

Fault Diagnosis and Detection Based on Efficiency Loss Test of Photovoltaic Power Station

The existing operation and maintenance system of photovoltaic power station can analyze partial equipment problems, but due to the data storage, processing and transmission capacity can hardly meet the requirements of a large number of operation data acquisitions and processing of photovoltaic power station, the operation status of photovoltaic power station cannot be comprehensively evaluated. This paper proposes to analyze the overall efficiency and key link efficiency of the photovoltaic system through long-term monitoring, extract the efficiency model parameters of the key components and important links of the photovoltaic power station, and establish the performance evaluation implementation process. At the same time, based on different actual detection conditions, photovoltaic fault diagnosis is carried out by time domain reflectometry, prediction theory comparison and sensor optimization.

Digital Management of Sports Industry Based on Big Data Era

In the era of big data, the sports industry is facing transformation and upgrading. Big data has become the key technology to lead the cross-border sports industry and strengthen the sports industry. In the era of big data, the development direction of sports industry is based on the development concept of big data sharing of sports industry, relying on the refinement and personalized service of big data to the market, aiming to build a sports industry chain of industrial integration and build a big data platform of sports industry, so as to realize online sports consumption and offline sports experience. Based on the above background, this paper aims to study the digital management of sports industry in the era of big data. With the maturity of big data technology, the combination of big data and sports industry has become inevitable. Based on the simple analysis of the characteristics and background of the era of big data, this paper discusses the opportunities and challenges faced by the big data sports industry, and proposes to promote the research of digital management of sports industry from the aspects of improving data storage and processing capacity. In the era of big data, sports industry management should be able to use big data technology to analyze valuable information from massive data. In the era of big data, the storage of massive information requires data analysis and processing, and timely feedback of information, which all bring great challenges to the sports industry.

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and data integration with tools for data visualization, analysis, and management. Currently, four models have been developed as components and integrated into the overall platform, demonstrating data prepossessing (e.g. sub gridding), data interaction, model execution, and visualization capabilities. The developed data management technologies provide a suite of capabilities, enabling diverse computation capabilities, data storage capacity, connectivity, and accessibility. The developed Virtual Watershed Platform explored the use of virtual reality and 3d visualization for scientific experimentation and learning, provided web services for the transfer of data between models and centralized data storage, enabled the statistical distribution of hydrometeorological model input, and coupled models using multiple methods, both to each other and to a distributed data management and visualization system.

DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey

CubeSats, which are limited by size and mass, have limited functionality. These miniaturised satellites suffer from a low power budget, short radio range, low transmission speeds, and limited data storage capacity. Regardless of these limitations, CubeSats have been deployed to carry out many research missions, such as gravity mapping and the tracking of forest fires. One method of increasing their functionality and reducing their limitations is to form CubeSat networks, or swarms, where many CubeSats work together to carry out a mission. Nevertheless, the network might have intermittent connectivity and, accordingly, data communication becomes challenging in such a disjointed network where there is no contemporaneous path between source and destination due to satellites’ mobility pattern and given the limitations of range. In this survey, various inter-satellite routing protocols that are Delay Tolerant (DTN) and Non Delay Tolerant (Non-DTN) are considered. DTN routing protocols are considered for the scenarios where the network is disjointed with no contemporaneous path between a source and a destination. We qualitatively compare all of the above routing protocols to highlight the positive and negative points under different network constraints. We conclude that the performance of routing protocols used in aerospace communications is highly dependent on the evolving topology of the network over time. Additionally, the Non-DTN routing protocols will work efficiently if the network is dense enough to establish reliable links between CubeSats. Emphasis is also given to network capacity in terms of how buffer, energy, bandwidth, and contact duration influence the performance of DTN routing protocols, where, for example, flooding-based DTN protocols can provide superior performance in terms of maximizing delivery ratio and minimizing a delivery delay. However, such protocols are not suitable for CubeSat networks, as they harvest the limited resources of these tiny satellites and they are contrasted with forwarding-based DTN routing protocols, which are resource-friendly and produce minimum overheads on the cost of degraded delivery probability. From the literature, we found that quota-based DTN routing protocols can provide the necessary balance between delivery delay and overhead costs in many CubeSat missions.

Ultra-high capacity for three-dimensional optical data storage inside transparent fluorescent tape.

In this Letter, we show an ultralarge capacity for three-dimensional optical data storage inside transparent fluorescent tape using the two-photon absorption photo-bleaching method. We can obtain transparent fluorescent tape by means of the simple dip method. We successfully demonstrate recording and reading of six layers of binary data bits with lateral separation of 2 µm and longitudinal layer separation of 3 µm. Thus, this result leads to a storage density of approximately ${80}\;{{\rm Gbits/cm}^3}$80Gbits/cm3. Therefore, we can realize authentic ultrahigh capacity optical data storage using long transparent fluorescent tape in the future, like magnetic tape, and fundamentally solve the data explosion disaster.

Dealing With Technological Trajectories: Where We Have Come From and Where We Are Going

Technological progress is widely recognized as having considerable impact on human societies, which have become more and more dependent on human-made tools that entail intricate scientific processes, considered <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">technology</i> [item 1) in the Related Works]. The adoption of such tools is borne out of the necessity of survival at first [item 2) in the Related Works], and subsequently as a means to improve access to food, shelter, and clothing; arguably things that we cannot live without. Nevertheless, the urgency of understanding the role of technology in human life has never been as vital as today. Indeed, one could say that technological progress is the key ingredient driving human civilization: the invention of ever more complex tools traces the history of human civilization and its development. Each generation of technology lays the foundation for the next, the invention of simple machines, such as the wheel, for example, enabled the invention of the wheelbarrow, which enabled in turn building larger structures. In parallel, the wheel enabled the construction of gears to transmit power from crankshaft to driveshaft and also the rotation of magnetic tapes permitting the capture of binary data leading to the spinning hard disk drives and their superior digital data storage capacity.

Data Hiding under QR Code using Visual Secret Sharing and Advanced Partitioning Based on Specific Relationship

The Visual Secret Sharing is the technique in which secret information is hidden in the form image which then divided into shares and these shares are used to decrypt the secret information. The number of shares required is given by the creator of the secret. Minimum those many shares are needed in order to decrypt the secret. If a single share is missing then the secret cannot be decrypted. The Quick Response (QR) code gives quick access to the information contained in it. QR code is 2D representation of the barcode which has capability to store information and can be easily read by machine. Due to easy access to the information stored in it, it is necessary to use some encryption or other protection to the data. Proposed approach to visual secret sharing scheme divides a secret QR code into different shares. In addition, the secret message is recovered by using XOR operation of the shares. This can effortlessly be achieved with the use of smartphones or different QR scanning gadgets. Using optimal partitioning methods the decryption of the message is made difficult to the hackers giving additional security to the data. The message accuracy can be checked by comparing the original message with shared message using hashing techniques. Because the QR code are small in size and has high data storage capacity, it is also resistant to damage so that is information is not lost even if some portion of the QR code is damaged it is best image to be used in visual secret sharing. The proposed approach reduces the risk of data transmission attacks.

Uses of pharmacovigilance databases: An overview

Over the past decades, assessment of drug safety and of their benefits harms balance has been profoundly modified by the availability of large databases and computerized automated statistical approaches. Improvement of digital data storage capacity has been applied to pharmacovigilance reports. VigiBase, the international pharmacovigilance database, is now aggregating over 21 million individual case safety reports in 2020. Identification and investigation of drug safety signals -concerning notably rare and unknown adverse drug reactions- is one of the major tasks in pharmacovigilance that can be amplified by automated signal detection. Several quantitative statistical methods exist, each with its own strengths and limits. Integrating signal detection, pharmacovigilance databases can be used for a wide variety of retrospective observational studies illustrated here by concrete examples. Confirming these signals by orthogonal validation using pre-clinical platforms and prospective trials is helpful. Pharmacovigilance databases represent a considerable source of information. However, the quality of signal detection and of pharmacoepidemiology studies in the field of adverse drug reaction closely depends on the quality of the individual data recorded.