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Data storage capacity, once hoarded, is now plentiful and inexpensive. It is a welcome trend for health care IT professionals, who have more reasons than ever to keep digital data that can be used for clinical and financial activities. The HIMSS/Hewlett-Packard Leadership survey of 1,200 participants at the 1996 HIMSS Annual Conference and Exhibition indicated that 65% of the respondents planned on more than doubling their data storage requirements by 1998. However, as new uses for data increase, new complexities are beginning to surface. Image and video will increasingly become a data storage challenge.

Zn and Co incorporated with Fe 2 O 4 show different magnetic behaviors, the former can be adjusted between ferromagnetic and antiferromagnetic while the later one displays ferromagnetic ordering. This work presents a mixed system of Co–Zn with Fe 2 O 4 for its potential applications in optical communication, data storage, and magneto-optical devices. Zn x Co 1- x Fe 2 O 4 ( x  = 0.00, 0.25, 0.50, 0.75, 1.00) nanostructures were prepared through Sol–Gel method followed by annealing at 800 °C. XRD results showed the cubic nature of all samples with grain sizes of 14–21 nm having high values of specific surface area (71–81 m 2  g −1 ). A linear increase in lattice constant from 8.3934 to 8.4322 Å is reported with the increase in Zn +2 concentration from 0 to 1, i.e., following Vegard’s law. Magnetic properties for x  = 0.5 sample studied up to 5 K, and an improvement has been reported, e.g., M s from 40 to 88 emu.g −1 , H c up to 2236 Oe, n B from 1.72 to 3.75 μ B , H ex up to 32.44 Oe, and α Y - K ranged from 50° to 16°. Switching is reported in direct B.G for x  = 0.25, x  = 0.50, and in indirect B.G for x  = 0.75. Prepared nanostructures have long ranged nonlinear refractive index (0.48–5.34) and metallization criterion from 0.37 to 0.45. Increased Zn +2 content in CoFe 2 O 4 resulted a decrease in Urbach energy from 318 to 141 meV. High values of lattice dielectric constant, direct B.G and large values of activation energies, optical and electrical conductivities suggested that prepared samples (especially with x  = 0.50) have potential for use in efficient electronic devices such as sensors, photoconductors, solar cells and high-capacity energy storage devices such as supercapacitors. Dielectric and optoelectronic properties for Zn x Co 1- x Fe 2 O 4 surpassed all the previous work.

In order to upgrade existing electronic technology, we need simultaneously to advance power supply devices to match emerging requirements. Owing to the rapidly growing wearable and portable electronics markets, the demand to develop flexible energy storage devices is among the top priorities for humankind. Flexible supercapacitors (FSCs) have attracted tremendous attention, owing to their unrivaled electrochemical performances, long cyclability and mechanical flexibility. Carbon nanotubes (CNTs), long recognized for their mechanical toughness, with an elastic strain limit of up to 20%, are regarded as potential candidates for FSC electrodes. Along with excellent mechanical properties, high electrical conductivity, and large surface area, their assemblage adaptability from one-dimensional fibers to two-dimensional films to three-dimensional sponges makes CNTs attractive. In this review, we have summarized various assemblies of CNT structures, and their involvement in various device configurations of FSCs. Furthermore, to present a clear scenario of recent developments, we discuss the electrochemical performance of fabricated flexible devices of different CNT structures and their composites, including additional properties such as compressibility and stretchability. Additionally, the drawbacks and benefits of the study and further potential scopes are distinctly emphasized for future researchers.

Background Established assessment scales used for Parkinson’s disease (PD) have several limitations in tracking symptom progression and fluctuation. Both research and commercial-grade wearables show potential in improving these assessments. However, it is not known whether pervasive and affordable devices can deliver reliable data, suitable for designing open-source unobtrusive around-the-clock assessments. Our aim is to investigate the usefulness of the research-grade wristband Empatica E4, commercial-grade smartwatch Fitbit Sense, and the Oura ring, for PD research. Method The study included participants with PD ( N  = 15) and neurologically healthy controls ( N  = 16). Data were collected using established assessment scales (Movement Disorders Society Unified Parkinson’s Disease Rating Scale, Montreal Cognitive Assessment, REM Sleep Behavior Disorder Screening Questionnaire, Hoehn and Yahr Stage), self-reported diary (activities, symptoms, sleep, medication times), and 2-week digital data from the three devices collected simultaneously. The analyses comprised three steps: preparation (device characteristics assessment, data extraction and preprocessing), processing (data structuring and visualization, cross-correlation analysis, diary comparison, uptime calculation), and evaluation (usability, availability, statistical analyses). Results We found large variation in data characteristics and unsatisfactory cross-correlation. Due to output incongruences, only heart rate and movement could be assessed across devices. Empatica E4 and Fitbit Sense outperformed Oura in reflecting self-reported activities. Results show a weak output correlation and significant differences. The uptime was good, but Oura did not record heart rate and movement concomitantly. We also found variation in terms of access to raw data, sampling rate and level of device-native processing, ease of use, retrieval of data, and design. We graded the system usability of Fitbit Sense as good, Empatica E4 as poor, with Oura in the middle. Conclusions In this study we identified a set of characteristics necessary for PD research: ease of handling, cleaning, data retrieval, access to raw data, score calculation transparency, long battery life, sufficient storage, higher sampling frequencies, software and hardware reliability, transparency. The three analyzed devices are not interchangeable and, based on data features, none were deemed optimal for PD research, but they all have the potential to provide suitable specifications in future iterations.

With the rapid development of information technology, blended learning has become a crucial aspect of modern education. However, the fragmented use of various teaching platforms, such as Xuexitong and Rain Classroom, has led to the dispersion of teaching data. This not only increases the cognitive load on teachers and students but also hinders the systematic recording of teaching activities and learning outcomes. Moreover, existing blended learning evaluation systems exhibit significant shortcomings in large-scale data storage and secure sharing. To address these issues, this study designs a blended teaching evaluation management system based on blockchain and searchable encryption. First, an on-chain and off-chain collaborative storage model is established using the Ethereum blockchain and the InterPlanetary File System (IPFS) to ensure secure and large-scale storage of student work data. Next, a role-based access control scheme utilizing smart contracts is proposed to effectively prevent unauthorized access. Simultaneously, a searchable encryption scheme is designed using AES-CBC-256 and SHA-256 algorithms, enabling data sharing while safeguarding data privacy. Additionally, the smart contract comprehensively records students’ grade information, including weekly regular scores, midterm scores, final scores, overall scores, and their rankings, ensuring transparency in the evaluation process. Based on these technical solutions, a general-purpose teaching evaluation management system (B-Education) is developed. The experimental results demonstrate that the system accurately records teaching activities and learning outcomes, improving the transparency of teaching evaluations while ensuring data security and privacy. The system’s gas consumption remains within a reasonable range, demonstrating good flexibility and usability. Educational institutions can flexibly configure course evaluation criteria and adjust the weighting of various grades based on their specific needs. This study provides an innovative solution for blended teaching evaluation, offering significant theoretical value and practical implications.

The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of Science TM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

The development of mobile devices and positioning technology has facilitated the rapid growth of location-based social networks (LBSNs). Users in these networks can share geo-related information in real-time, including locations, trajectories, geo-tagged pictures, and tweets. LBSNs record massive amounts of spatiotemporal data and offer a great opportunity to analyze human and location-specific spatiotemporal characteristics. It plays an important role in various applications, such as marketing, recommendations, and urban planning. In this study, we collect relevant literature about LBSNs research in the past 10 years and use a topic model, latent Dirichlet allocation (LDA), to uncover the highly heterogeneous area of research related to LBSNs. Then, we conduct a systematic review of those works. In doing so, we organize identified literature into eight fine-grained directions. For each direction, we sum up the major research focus and contributions. We also systematize future research into four main themes concerning data simulation and fusion, privacy-aware methods, new applications and services, and technological innovations.

With the exponential increase in energy demands (commercial as well as residential), the traditional grid infrastructure significantly shifted to intelligent ICT-based Smart Grid (SG) infrastructure. In the SG environment, only efficient energy management may not be sufficient as the SG dynamics have significant impacts on multimedia communications such as video surveillance of the technical/non-technical losses of energy and many more. The inevitable energy losses can be identified by process and analyze the massive amount of heterogeneous data, i.e., Big Data (BD) generated through smart devices such as sensors, Smart Meters (SMs), and others. The key challenges in analyzing multimedia BD are computational complexity, operational integration complexity, data security, and privacy. To overcome the aforementioned issues, this paper proposes a blockchain-based data analytics scheme called ChoIce , which offers secure data collection, analysis, and decision support for the SG systems. It works in two phases; (i) secure data collection over Ethereum and (ii) BD analytics and decision-making using deep learning (DL). The robust and secure data analytics, efficient network management, and high-performance computing for BD are crucial towards the optimization of SG operation. The performance of ChoIce is evaluated considering parameters such as the data storage cost, multimedia communication latency, and prediction accuracy. Thus, the results of ChoIce shows that it outperforms in contrast to other state-of-the-art approaches.