Information Storage Technology Research Articles

Student Graduation Predictions Using Comparison of C5.0 Algorithm With Linear Regression

Technological advances supported by human knowledge have a very good influence on data and information storage technology, including in predicting student graduation (Graduation Prediction) on time, by applying several existing algorithms. In this study, researchers used the C5.0 Algorithm and Linear Regression. The concept of the research is to compare two algorithms, namely C5.0 and Linear Regression to the case of graduating students on time. Based on the length of study, students who graduated correctly amounted to 651 (91%) with a male gender of 427 students and a female gender of 224 students while those who did not pass (late) correctly amounted to 64 (9%) with a male gender totaling 53 students and female gender totaling 11 students from 2017-2020. Comparison results The R2 score from the C5.0 algorithm reached 96.85% (training) and 93.

Survey on knowledge, attitude, and practice of Indian oral health-care professionals regarding QR codes in dental practice and academics

Objectives: QR codes form an integral part of modern-day digitalized information storage technology in almost all the fields. Their utility in oral health-care field also has a huge potential, most of which can be uncovered once the oral health-care professionals are aware about their uses and technique of generation. Materials and Methods: A self-constructed e-questionnaire was validated and circulated through online media to oral health-care professionals in India. Data were subjected to statistical analysis and descriptive statistics were obtained. Comparison of frequencies of categories of variables with groups was done using Chi-square test. Results: The study sample of n = 334 respondents comprised 222 females and 112 males. The age of respondents ranged from 22 to 65 years with a mean age of 28.96 years (S.D = ± 6.877). Majority of respondents were aware about the utility of QR codes in day-to-day aspects as well as dental clinical practice and academics. Although two-thirds of respondents perceived online data management as an easy task, generation of QR codes was considered as relatively more difficult requiring higher technical skills. Conclusion: QR codes can have a wide variety of applications in the field of dentistry such as in prosthodontics, orthodontics, oral pathology, radiology, and forensic odontology. Our findings suggest that most of the dental professionals have convenient access to the technology essential to generating QR code, and yet, about 70% of oral health-care professionals are unaware about their generation technique.

Visual Data Analysis Technology Based on Data Center

Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN PERBANDINGAN ALGORITMA C5.0 DENGAN REGRESSION LINEAR

Technological advances supported by human knowledge have a very good influence on data and information storage technology, including in predicting student graduation (Graduation Prediction) on time, by applying several existing algorithms. In this study, researchers used the C5.0 Algorithm and Linear Regression. The concept of the research is to compare two algorithms, namely C5.0 and Linear Regression to the case of graduating students on time. Based on the length of study, students who graduated correctly amounted to 651 (91%) with a male gender of 427 students and a female gender of 224 students while those who did not pass (late) correctly amounted to 64 (9%) with a male gender totaling 53 students and female gender totaling 11 students from 2017-2020. Comparison results The R2 score from the C5.0 algorithm reached 96.85% (training) and 93.72% (testing) while the R2 score from the Linear Regression reached 33.31% (training) and 40.30% (testing).

Transfer modeling of 1T1R crossbar arrays with line resistances based on matrix algebra method

Progress of neuromorphic computing and next-generation information storage technologies hinges on the development of emerging nonvolatile memory (eNVM) devices, which are typically organized employing the crossbar array architecture. To facilitate quantitative performance analysis of eNVM crossbar array architecture, this paper proposes a way to study the one-transistor-one-resistor (1T1R, R: eNVM devices) crossbar arrays based on matrix algebra method. The comparative analysis of 1T1R crossbar array modeling based on matrix algebra method and compact-model SPICE simulations verifies the accuracy of the proposed method, which can be directly used for static quantitative analysis and evaluation of 1T1R crossbar array performance. With the proposed method, the optimization of array operation schemes and current backflow issue are discussed. Our analysis indicates that the proposed method is capable of flexibly adjusting array parameters and consider the influence of line resistance on array operation, and can provide guidance for improving the sensing margin of the array through multi-parameter co-simulation. The proposed matrix algebra-based 1T1R crossbar array modeling method can bridge the gap between the accuracy and flexibility of the available methods.

The role of boric acid in optical information storage properties in Eu doped BaSi2O5

Optical information storage technology is emerging, therefore materials suitable for optical write-in and read-out are in demand. In this study the influence of boric acid addition during synthesis on the optical information storage properties of BaSi2O5:Eu material, that has been recently offered as a perspective material for this application. A thorough investigation is carried out to establish the relationship between boric acid addition, phase composition and photostimulated luminescence performance of the samples. The present work demonstrates the influence of the boric acid addition on the trapping center distribution in the material, thus leading to the improvement of photostimulated luminescence intensity. This can lead to the optimization of BaSi2O5:Eu material for optical information storage.

A survey of Blockchain technologies applied to software‐defined networking: Research challenges and solutions

AbstractSoftware‐Defined Networking (SDN) brought a groundbreaking idea to facilitate network system management by decoupling and abstracting the Control plane and Data plane of traditional networks. The centralised control offers network administrators many benefits such as a global view of the network, programmability, dynamic updating of forwarding rules, and software‐based traffic analysis. The SDN architecture has been applied a lot in practice, and especially in Internet of Things (IoT) platforms. With the superiority of SDN, IoT devices can be managed and configured much more easily when combined. However, SDN also raises many challenges in terms of scalability, reliability, and security. Blockchain is another promising solution for secure information storage and transmission technology that operates without a centralised authority. Applying Blockchain technology into SDN can address some of the current issues of SDN by providing decentralised methods to authenticate exchanged network information. This study provides a comprehensive survey on Blockchain technologies applied to SDN in both security and non‐security fields. First, related studies and an overview of SDN and the background of Blockchain technology are presented. Then, the authors review how Blockchain technologies are applied in SDN from two perspectives: non‐security and security‐aware approaches. Finally, challenges and broader perspectives are discussed.

Bluetooth Low Energy Error Correction Based on Convolutional Coding

Contemporarily, the Internet of Things (IoT) is recently a newly emerging technology for connecting small devices into a platform; the IoT has been an increasingly demanded front-edge technology in terms of connecting different devices using information transmission and storage technology. To adapt to the small capacities of device batteries, Bluetooth Low Energy is adopted as the protocol of communication. However, the existing standards do not have a suitable and specific error correction method. As there is no ideal information transmission channel, there must be errors that occurred during message transmission. The performance and capacity of error correction become decisive factors in evaluating how efficient the IoT communication system performs. This article uses convolutional coding—a better-performing coding scheme than block coding—to correct errors in information transmission and reception on Internet of Things devices. It is better competent to control and correct bit errors in information transmission. To achieve this goal, convolutional coding algorithms devised by Dr Justin Coon at the University of Oxford have been referred to. By simulation using MATLAB, it has been found that the error rate is enhanced significantly for high Signal-to-Noise Ratio (SNR) in convolutional codes compared to uncoded messages.

Advances in Flexible Memristors with Hybrid Perovskites.

Hybrid organic-inorganic metal halide perovskite (HOIP)-based memristors have captured strong attention not only as an emerging candidate for next-generation high-density information storage technology but also for use in healthcare technology and the Internet of Things (IoT) because of their unique properties: low weight, flexibility, compatibility, stretchability, and low power consumption. In this Perspective, we review the recent advances of various aspects of flexible memristors focusing on the selection of the flexible substrates, materials, interfaces, several resistive switching mechanisms, and different methodologies of perovskite growth. The current state of the art of the memristor as an artificial synapse, light-induced resistive switching, and logic gates is comprehensively and systematically reviewed. Finally, we briefly discuss the stability factors of perovskites and present the conclusion with a broad outlook on the progress and challenges in the field of perovskite-based flexible memristors.

Applied Trends in Magnetic Rare Earth/Transition Metal Alloys and Multilayers.

Ferrimagnetic thin films formerly played a very important role in the development of information storage technology. Now they are again at the forefront of the rising field of spintronics. From new, more efficient magnetic recording media and sensors based on spin valves to the promising technologies envisaged by all-optical switching, ferrimagnets offer singular properties that deserve to be studies both from the point of view of fundamental physics and for applications. In this review, we will focus on ferrimagnetic thin films based on the combination of rare earths (RE) and transition metals (TM).

Static-Dynamic Fluorescence Patterns Based on Photodynamic Disulfide Reactions for Versatile Information Storage.

Dynamic fluorescence patterns with variable output in response to external stimulus can make the current information storage technologies more flexible and intelligent. Yet it remains a great challenge to create such dynamic patterns because of the complicated synthesis process, high cost, limited stability, and biocompatibility of the functional fluorophores. Herein, a facile approach is presented for creating dynamic fluorescence patterns using the photodynamic surface chemistry based on disulfide bonds. By this method, high-resolution (≈20µm) multicolor dynamic fluorescence patterns that are low-cost and dynamically rewritable can be easily fabricated using classical fluorophores such as fluorescein, rhodamine, and dansyl acid. Owing to the spatio-temporal controllability of light, the fluorescence patterns can be partly or entirely erased/rewritten on demand, and complex gray-level fluorescence images with increased information capacity can be easily generated. The obtained fluorescence patterns exhibit little changes after storing in air and solvent environments for 100 days, demonstrating their high stability. In addition, static patterns can also be created on the same disulfide surface using irreversible disulfide-ene chemistry, to selectively control the dynamicity of the generated fluorescence patterns. The authors show the successful application of this strategy on information protection and transformation.

Effect of a "Zn Bridge" on the Consecutively Tunable Retention Characteristics of Volatile Random Access Memory Materials.

Polyimide memory materials with a donor-acceptor structure based on a charge-transfer mechanism exhibit great potential for next-generation information storage technology due to their outstanding high-temperature resistance and good dimensional and chemical stability. Precisely controlling memory performance by limited chemical decoration is one of core challenges in this field. Most reported work mainly focuses on designing novel and elaborate electron donors or acceptors for the expected memory behavior of polyimides; this takes a lot of time and is not always efficacious. Herein, we report a series of porphyrinated copolyimides coPI-Znx (x=5, 10, 20, 50, 80), where x represents the mole percentage of Zn ion in the central core of the porphyrin. Experimental and theoretical analysis indicate that the Zn ion could play a vital bridge role in promoting the formation and stabilization of a charge-transfer complex by enhancing the hybridization of local and charge transfer (HLCT) excitations of porphyrinated polyimides, endowing coPI-Znx with volatile random access memory performance and continuously tunable retention time. This work could provide one simple strategy to precisely regulate memory performance merely by altering the metal content in porphyrinated polyimides.

Forecasting Technology Trends through the Gap Between Science and Technology: The Case of Software as an E-Commerce Service

Identifying technology trends can be a key success factor for companies to be competitive and take advantage of technological trends before they occur. The companies always work to plan for future products and services. For that, it is important to turn to methods that are used for technology forecasting. These tools help the companies to define potential markets for innovative new products and services. This paper uses text mining techniques along with expert judgment to detect and analyze the near-term technology evolution trends in a Software as a Service (SaaS) case study. The longer-term technology development trend in this case is forecasted by analyzing the gaps between science and technology. This paper contributes to the technology forecasting methodology and will be of interest to those in SaaS technology. Our findings reveal five trends in the technology: 1) virtual networking, 2) the hybrid cloud, 3) modeling methodologies, 4) mobile applications, and 5) web applications. Among the results achieved, we can summarize the interesting ones as follows: it is possible to say that traditional information systems are now evolving into online information systems. On the other hand, the use of a licensing model based on subscriptions triggers the change in perpetual licensing models. The product range that has evolved towards mobile technologies has put pressure on information storage technologies and has led to the search for new methods especially in the development of database systems.

Slow Magnetic Relaxation in Mono- and Bimetallic Lanthanide Tetraimido-Sulfate S(NtBu)4 2- Complexes.

Lanthanide ions are particularly well‐suited for the design of single‐molecule magnets owing to their large unquenched orbital angular momentum and strong spin‐orbit coupling that gives rise to high magnetic anisotropy. Such nanoscopic bar magnets can potentially revolutionize high‐density information storage and processing technologies, if blocking temperatures can be increased substantially. Exploring non‐classical ligand scaffolds with the aim to boost the barriers to spin‐relaxation are prerequisite. Here, the synthesis, crystallographic and magnetic characterization of a series of each isomorphous mono‐ and dinuclear lanthanide (Ln=Gd, Tb, Dy, Ho, Er) complexes comprising tetraimido sulfate ligands are presented. The dinuclear Dy complex [{(thf)2Li(NtBu)2S(tBuN)2DyCl2}2 ⋅ ClLi(thf)2] (1c) shows true signatures of single‐molecule magnet behavior in the absence of a dc field. In addition, the mononuclear Dy and Tb complexes [{(thf)2Li(NtBu)2S(tBuN)2LnCl2(thf)2] (2b,c) show slow magnetic relaxation under applied dc fields.

Influence of randomly distributed vacancy defects on thermal transport in two-dimensional group-III nitrides

Efficient thermal transport control is a fundamental issue for electronic device applications such as information, communication, and energy storage technologies in modern electronics in order to achieve desired thermal conditions. Structural defects in materials provide a mechanism to adjust the thermal transport properties of these materials on demand. In this context, the effect of structural defects on lattice thermal conductivities of two-dimensional hexagonal binary group-III nitride (XN, X=B, Al, and Ga) semiconductors is systematically investigated by means of classical molecular dynamics simulations performed with recently developed transferable inter-atomic potentials accurately describing defect energies. Here, two different Green–Kubo based approaches and another approach based on non-equilibrium molecular dynamics are compared in order to get an overall understanding. Our investigation clearly shows that defect concentrations of 3% decrease the thermal conductivity of systems containing these nitrites up to 95%. Results hint that structural defects can be used as effective adjustment parameters in controlling thermal transport properties in device applications associated with these materials.

NaMgF3:Tb3+@NaMgF3 Nanoparticles Containing Deep Traps for Optical Information Storage

AbstractPersistent luminescence (PersL) materials containing deep traps have attracted great attention in the field of optical information storage. However, the lack of nanomaterials with satisfactory light storage ability has become one of the main obstacles to the practical applications. In this work, NaMgF3:Tb3+@NaMgF3:Tb3+ nanoparticles are reported which exhibit excellent light storage ability into deep traps upon X‐ray irradiation and controllable photon emissions under thermal stimulation. A surface passivation strategy by constructing a core–shell structure is adopted, which is proved valid to greatly enhance the PersL efficiency. To understand the possible mechanism on the light storage and thermally stimulated PersL in the NaMgF3:Tb3+@NaMgF3 nanoparticles, an energy level diagram is built and the electronic transition processes are clarified. According to the proposed mechanism, the Tb3+ ions possibly serve as both emitting centers and trap centers in the NaMgF3 host during the X‐ray irradiation. Due to the excellent dispersibility and stability in water, luminescent inks containing the nanoparticles are successfully prepared and the applications to inkjet printing optical information storage and information decryption are demonstrated. The developed NaMgF3:Tb3+@NaMgF3 PersL nanoparticles may inspire further research on lanthanide‐activated fluoride PersL nanoparticles and provide new opportunities to the next‐generation information storage and biomedical technologies.

Ab initio description of oxygen vacancies in epitaxially strained hbox {SrTiO}_{{3}} at finite temperatures

Epitaxially grown hbox {SrTiO}_{{3}} (STO) thin films are material enablers for a number of critical energy-conversion and information-storage technologies like electrochemical electrode coatings, solid oxide fuel cells and random access memories. Oxygen vacancies ({mathrm{V}_{{mathrm{O}}}}), on the other hand, are key defects to understand and tailor many of the unique functionalities realized in oxide perovskite thin films. Here, we present a comprehensive and technically sound ab initio description of {mathrm{V}_{{mathrm{O}}}} in epitaxially strained (001) STO thin films. The novelty of our first-principles study lies in the incorporation of lattice thermal excitations on the formation energy and diffusion properties of {mathrm{V}_{{mathrm{O}}}} over wide epitaxial strain conditions (-4 le eta le +4%). We found that thermal lattice excitations are necessary to obtain a satisfactory agreement between first-principles calculations and the available experimental data for the formation energy of {mathrm{V}_{{mathrm{O}}}}. Furthermore, it is shown that thermal lattice excitations noticeably affect the energy barriers for oxygen ion diffusion, which strongly depend on eta and are significantly reduced (increased) under tensile (compressive) strain. The present work demonstrates that for a realistic theoretical description of oxygen vacancies in STO thin films is necessary to consider lattice thermal excitations, thus going beyond standard zero-temperature ab initio approaches.

Electrohydrodynamic printing of a dielectric elastomer actuator and its application in tunable lenses

Optical lenses driven by dielectric elastomer (DE) actuators with tunable focal lengths are presented here. They are inspired by the architecture of the crystalline lens and the ciliary muscle of the human eye and have prompted a growing interest. The most commonly used DEs in tunable lenses have often required highly transparent films and also the need to encapsulate clear liquid silicone to act as the lens. There is a restriction on the properties of the tunable lens imposed by materials limitations. Here, the fabrication of a fully 3D printed tunable lens with an inhomogeneous structure is described. It exhibited a 29% change in focal length from 33.6 mm to 26.1 mm under a dynamic driving voltage signal control. Furthermore, it displayed excellent stability when the focal length was tuned from far to near (30.1 mm to 25.3 mm) for 200 cycles. The tunable lens obtained mimics the working principle of the human eye in auto adjusting the focal length and has evident potential applications in imaging, information storage, beam steering and bifocal technology.

An alternative approach to nucleic acid memory

DNA is a compelling alternative to non-volatile information storage technologies due to its information density, stability, and energy efficiency. Previous studies have used artificially synthesized DNA to store data and automated next-generation sequencing to read it back. Here, we report digital Nucleic Acid Memory (dNAM) for applications that require a limited amount of data to have high information density, redundancy, and copy number. In dNAM, data is encoded by selecting combinations of single-stranded DNA with (1) or without (0) docking-site domains. When self-assembled with scaffold DNA, staple strands form DNA origami breadboards. Information encoded into the breadboards is read by monitoring the binding of fluorescent imager probes using DNA-PAINT super-resolution microscopy. To enhance data retention, a multi-layer error correction scheme that combines fountain and bi-level parity codes is used. As a prototype, fifteen origami encoded with ‘Data is in our DNA!\\n’ are analyzed. Each origami encodes unique data-droplet, index, orientation, and error-correction information. The error-correction algorithms fully recover the message when individual docking sites, or entire origami, are missing. Unlike other approaches to DNA-based data storage, reading dNAM does not require sequencing. As such, it offers an additional path to explore the advantages and disadvantages of DNA as an emerging memory material.

RETRACTED ARTICLE: Research on athlete recognition based on image feature extraction and artificial intelligence classification

With the continuous development of modern technology, the continuous update and evolution of information collection, storage and information processing technology and equipment, and the problems of information security, the development of electronic identity verification related technologies has continuously attracted widespread social attention. Athlete’s personal identification authentication refers to the effective information identification and automatic authentication of mobilized personal identity. Among them, the athlete’s personal identification function is used to help judge who he is. In the context of the current unstable international military, political, economic and social situation in the country and the still severe domestic anti-terrorism situation, in order to effectively protect the personal property, life and personal property of a country and its people, improve the grassroots level of our people the basic living standards of the masses, the development of convenient, efficient, and humanized national athlete personal identification information identification system technology has developed into urgent problems. In order to accurately simulate human machine images and accurately recognize facial features of human images, artificial intelligence has been widely used in the field of image recognition technology in recent years. Related scientific research staffs have proposed many recognition methods and calculation models. Through the training of the model training cluster, the corresponding input recognition target model is obtained. After the accuracy of the input target recognition image and the input model target are compared, the model input can be judged in the future whether the accuracy of the target image is the same as the model target input image. In summary, the main key point of image recognition is still to extract image features.