Data Storage Research Articles

Harnessing Persistent Photocurrent in a 2D Semiconductor-Polymer Hybrid Structure: Electron Trapping and Fermi Level Modulation for Optoelectronic Memory.

Recently, 2D semiconductor-based optoelectronic memory has been explored to overcome the limitations of conventional von Neumann architectures by integrating optical sensing and data storage into one device. Persistent photocurrent (PPC), essential for optoelectronic memory, originates from charge carrier trapping according to the Shockley-Read-Hall (SRH) model in 2D semiconductors. The quasi-Fermi level position influences the activation of charge-trapping sites. However, the correlation between quasi-Fermi level modulations and PPC in 2D semiconductors has not been extensively studied. In this study, we demonstrate optoelectronic memory based on a 2D semiconductor-polymer hybrid structure and confirm that the underlying mechanism is charge trapping, as the SRH model explains. Under light illumination, electrons transfer from polyvinylpyrrolidone to p-type tungsten diselenide, resulting in high-level injection and majority carrier-type transitions. The quasi-Fermi level shifts upward with increasing temperature, improving PPC and enabling optoelectronic memory at 433 K. Our findings offer valuable insights into optimizing 2D semiconductor-based optoelectronic memory.

Cloud-Based Remote Real-Time Monitoring and Control System for Spring Fatigue Testing Machine

In recent years, the utilization of cloud technology has witnessed a surge, particularly in the domains of industrial automation and intelligent scenarios. However, the prevailing spring fatigue testing machine is still in the traditional single-machine working mode. In this mode, there are many problems such as low automation of testing equipment, poor experimental site environment, and the need for experimenters to be on duty for a long time. In order to solve the above problems, this paper builds a cloud-based remote monitoring and control system based on the high-temperature constant-force spring fatigue testing machine. The system is based on Browser/Server architecture, and clients can access it anytime and anywhere using a browser in a public network environment. The server is hosted on a public cloud platform and includes website service, data storage service, WebSocket real-time communication service, and remote video monitoring service. Clients can remotely monitor and control the testing machine in real time through the cloud. After experimental verification, the real-time monitoring and control messages delay is 11 ms, and the video monitoring delay is 291 ms, which can meet the actual needs of remote spring fatigue testing. This remote monitoring and control system improves the automation of the spring fatigue testing machine and improves the working environment of the experimenters. In addition, it can be applied to other reliability testing machines in the laboratory, and can further help build a workshop-level remote monitoring and control platform.

Databases in Edge and Fog Environments: A Survey

While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy, and scalability. This article dissects the advantages provided by databases in edge and fog environments by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment and analyzes the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.

Multi-level authentication for security in cloud using improved quantum key distribution

ABSTRACT Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models

Decentralization of Identity using Ethereum and IPFS

Introduction: The Identity of a user in digital world is an important factor for an individual. Identity management has been handled by various models which, over the period of time have been prone various security breaches. The foremost integral part of any identity model is the centralized storage, access of data. Considering this, in recent years, there has been evolution from centralization to de-centralization of Identity management. With respect to this new aspect, this paper proposes a solution to the centralized management problems, as a decentralized Identity Management System. The said approach utilizes Ethereum blockchain, IPFS, both supporting distributed data accessibility and data storage respectively. This paper also sheds light on the W3C specification of DID (De-centralized Identifier) which supports the Self-Sovereign Identity principles of Identity Management and are vital for de-centralization of Identity Management

Using Coordination Chemistry to Control "Click" Reactions: The Selective Formation of Asymmetrically Ligated Polyoxometalates.

The Cu(I)-catalyzed azide-alkyne cycloaddition reaction between (NBu4)2[V6O13((OCH2)3CCH2N3)2] and 3-ethynylpyridine led to the formation of products capable of forming poorly soluble coordination compounds with transition metal ions such as Cu(I) and Zn(II). The formation of these poorly soluble phases is an important feature that was used to determine the course of reactions, allowing the selective preparation of symmetric bis-pyridyltriazolyl and asymmetric monopyridyltriazolyl derivatives with relatively high yields and high substrate conversions. The asymmetric compound (NBu4)2[V6O13((OCH2)3CCH2-N3C2H-C5H4N)((OCH2)3CCH2N3)] (V6asym) was utilized in the subsequent "click" postfunctionalization reaction with 1,4-diethynylbenzene, resulting in a covalently bound V6asym-V6asym dimer. This dimeric compound was subjected to scanning probe microscopy studies on gold surfaces, which revealed no electronic coupling between the hexavanadate cores within the dimer upon potential-induced switching. This observation indicates that such dimers and higher-order oligomers composed of polyoxometalate-ligand-polyoxometalate bridges can be exploited as active capacitor/memristor units, relevant to increase the data storage capacity of standard memory devices with innovative molecular switching mechanisms.

The Optimization of Chillers Air-Conditioning in Thailand Supermarkets using a Retail Energy Management System (REMS)

Supermarkets in Thailand continue to grow and there will be more than 1500 branches by 2023 open 18-24 hours a day. In this research, we will present how to increase energy efficiency in retail stores by reducing the energy consumption of compressors of chiller air conditioning systems by using energy management systems in retail stores. Retail energy management systems in retail stores use the Internet of Things to connect to a device to store data and analyze and process from big data, then Artificial Intelligence controls and commands to make the air conditioning system energy efficient. Controlling 10 AHUs using central sensors at the sales area instead of return sensors allows us to save 25% of energy or 12,886 kWh/Year. As for the cold-water pump, it is controlled by an inverter. By using sensors to measure the water flow rate, we save 33% of energy or 19,755 kWh/Year. When controlling the water pump and AHU, the chiller compressor will save energy because when the air handling unit and the water pump have reduced work, the work of the compressor to cool water is reduced as well, resulting in energy savings of 12% or 25,211 kWh/Day. Using such a system reduces the overall energy consumption of the chiller air conditioning system. 17% or 57,852 per year has a payback period of 3 years and can also be extended to other systems within the store or other stores.

Deep learning model for fiber optic distributed acoustic sensing seismic data compression based on autoencoder and recurrent neural networks

Because of the extremely high sampling rate of fiber optic distributed acoustic sensing (DAS) equipment, the amount of DAS seismic data collected is enormous, which poses great challenges to the transmission and storage of DAS seismic data. Therefore, it is essential to study the compression methods of DAS seismic data. Existing data compression methods such as wavelet transform, cosine transform, and convolutional autoencoder (CAE) still have room for improvement in the compression performance and compression ratio (CR). Thus, we have proposed what we believe to be is a novel deep learning model called the recurrent autoencoder (RAE) for high-performance compression of DAS seismic data. Under different CRs, we have designed performance evaluation experiments for RAE models based on different RNN modules with different loss functions. When the CR of the RAE model is 8, the signal-to-noise ratio (SNR) of the reconstructed DAS seismic data reaches 40.60 dB, which is better than that of the CAE’s 11.58 dB. The ultimate CR was increased to 512 without reducing the compression quality, which is 4.12 times higher than the CAE model. The LSTM with a weighted loss function improves the SNR to 43.66 dB at a CR of 8, which is 3.06 dB higher than the LSTM with additive loss function. The results show that the RAE model proposed with a weighted loss function in this paper has excellent DAS seismic data compression performance and provides a high CR, which can be widely applied in large-scale DAS seismic data compression.

Exchange bias behaviour in cobalt ferrite-cobalt oxide CoFe2O4/CoO nanocomposites for data storage applications

Abstract In this study cobalt ferrite-cobalt oxide CFO/CoO nanoparticles were synthesized using hydrothermal chemical method for studying the exchange bias (EB) phenomena: a key parameter for data storage applications. The structural analysis was carried out by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Energy dispersive spectroscopy (EDS), which confirm formation of platelets like CFO/CoO nanoparticles with appropriate chemical composition of different elements with an average crystallite size of 15 nm. Magnetic study revealed the ferromagnetic behaviour of nanoparticles from (5K-360K) and blocking temperature has found to be above 360 K, which decreases nonmonotonically with increasing the applied field HFC. The field cool (FC) magnetic hystersis (MH) loop studies have revealed that the samples exhibit very large exchange bias effect. The values of exchange bias (HEB) found to be maximum at low temperature and decreases with increasing temperature before vanishing at the blocking temperature. The aim of the study is to determine role of cooling field strength for the exchange bias in magnetically contrasted CFO/CoO nanoparticles suitable for data storage applications.

Volatile memory characteristics of CMOS-compatible HZO ferroelectric layer for reservoir computing

Recently, ferroelectric memory utilizing hafnium oxide has emerged as an attractive option compared to existing memory technologies, primarily due to its scalability and energy-efficient advantages. Among them, hafnium zirconium oxide (HZO) is examined for its short-term memory characteristics to achieve a reservoir computing system known to exhibit remarkable polarization properties, being able to switch between distinct polarization states under the influence of an electric field. These unique properties are of utmost importance in ferroelectric memory applications, where they play a pivotal role in the storage and retrieval of binary data. In this study, we identify and experiment with the electrical characteristics of a ferroelectric tunnel junction (FTJ) device with a metal-ferroelectric-semiconductor (MFS) structure using TiN as the top electrode and HZO as the ferroelectric layer. Moreover, we assess the performance of the device by evaluating its maximum 2Pr (remnant polarization) and tunneling electro resistance (TER) values in different conditions of cell area. Furthermore, we analyze and show short-term memory (STM) characteristics and synaptic properties with 5 cycles of potentiation and depression under conditions of stable dynamic range by coordinating identical and incremental pulses. In the case of incremental pulses (> 95%), the MNIST pattern recognition accuracy is higher than in the case of identical pulses (> 94%). Through a sequence of procedures, the synaptic characteristics of FTJs are confirmed to assess their suitability for use as an artificial synaptic device.

Rapid variable-step computation of dynamic convolutions and Volterra-type integro-differential equations: RK45 Fehlberg, RK4

We introduce a novel, time-efficient adaptive Runge-Kutta computational scheme tailored for systematically solving linear and nonlinear Volterra-type Integro-Differential Equations (VTIDEs). This scheme is particularly effective for equations featuring a specific class of convolution memory, K(t,s)=aκ(t−s) within convolution integrals, where a,κ∈R. Such equations frequently arise in fields like viscoelasticity, population dynamics, epidemiology, control systems, financial mathematics, and neuroscience, where current states are influenced by historical data, especially in dynamical systems within physics. Our proposed scheme demonstrates remarkable computational efficiency, achieving a cost-effective computation time of O(Nt) for Nt iterations, a significant improvement over existing schemes for VTIDE, which typically exhibit complexities of O(Ntlog⁡Nt) and O(Nt(log⁡Nt))2. The key feature of our scheme lies in its ability to elegantly and efficiently handle convoluted integrals of varying complexity such as those including nonlinearities and derivatives. Specifically, our approach eliminates the need for backstage integration, allowing the main Runge-Kutta scheme to operate efficiently with only a few data points at each time step. This not only saves time but also prevents potential data storage issues while rendering numerical implementation straight forward and tractable. Moreover, this article introduces flexible discretizations for integration and differentiation, accommodating unequally spaced data points. We implement a fast RK45-Fehlberg algorithm (TE-RK45-Fehlberg), which we use to solve three examples of nonlinear VTIDE presented in this study. We further demonstrate the effectiveness of our approach by using an example, where we employ a novel parabolic step size generator function to efficiently generate step sizes for a computationally fast RK4 scheme (Flex-RK4). For all examples tackled using TE-RK45-Fehlberg and Flex-RK4, we generate plots that compare numerical data with exact solutions, providing insight into the accuracy and reliability of our approach. We also show how the scheme provides straightforward ways of transforming it into implicit forms and obtaining stability curves.

Internet of thing based health monitoring system using wearable sensors networks

Maintaining mental and physical health is becoming increasingly important for maintaining independent living, particularly as the population of people suffering from chronic illnesses like diabetes, heart disease, obesity, and other conditions rises and the average age of many societies keeps rising. Using sensors, monitoring health remotely, and ultimately recognising daily activities have all been proposed as potential strategies. In this work, fatigue threshold and environmental bounds are assessed and provided via an external interface to a microcontroller unit (MCU) in addition to the required restrictions. Rerouting the required boundaries into the long range (LoRa) and Bluetooth module, the MCU is responsible for editing and analysing the raw data to remove the oxygen immersion, pulse, and temperature data. These important restrictions are sent to many terminals, such as PCs and mobile devices, using the remote Bluetooth and LoRa module. For data storage and retrieval, any IoT platform may be used. With caution, the patient is discharged home after the medical experts have carefully evaluated the diseases in light of the new features. To telemonitor patients with heart conditions, the test results show that the framework is efficient and dependable for collecting, sending, and presenting electrocardiogram (ECG) data constantly.

Forecasting daily solar radiation: An evaluation and comparison of machine learning algorithms

Rising energy demand, resource depletion, and environmental issues tied to fossil fuels demand a transition to renewable energy. Solar power, abundant and well-established, presents a promising solution to address our expanding energy requirements. The sun radiates an astonishing amount of energy every second, far more than humanity’s current and future energy needs. Accurate solar radiation prediction is crucial for optimizing solar panel design, placement, and grid integration. This paper aims to predict daily global solar radiation data for six Pakistani cities: Karachi, Lahore, Islamabad, Quetta, Peshawar, and Multan. It highlights the importance of advanced algorithms and introduces an innovative data collection method using pyranometer sensors and microcontrollers, making data storage and analysis more affordable and efficient while reducing the financial burdens associated with traditional equipment. Focusing on Pakistan’s diverse solar radiation potential, this research evaluates eight machine learning algorithms using seven key statistical metrics to understand and compare their performance in predicting solar radiation. Four algorithms, k-nearest neighbors, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression (SVR), consistently exhibit remarkable precision, achieving outstanding R2 values of up to 99%. This highlights the crucial role of algorithm selection in solar radiation prediction, with SVR emerging as the top choice. SVR’s precise and reliable forecasts empower renewable energy planning and decision-making. This study provides valuable guidance for decision-makers to optimize solar energy utilization across diverse geographical regions and contributes invaluable insights to the field of renewable energy forecasting.

Penggunaan Sistem Blockchain Di Perpustakaan Sekolah Dalam Meningkatkan Kinerja Guru Bahasa Indonesia Pasca Pandemi Covid-19

The school library is one of the important components in the educational ecosystem affected by the pandemic, including the private high school SMA XYZ in South Tangerang, which is the subject of this study. The research objective is to describe the effectiveness of the Blockchain system in accommodating data storage and its impact on efforts to improve the performance of Indonesian language teachers at SMA XYZ in utilizing the library. This study adopts a descriptive qualitative approach involving Indonesian language teachers and library staff. Data were collected through interviews and participatory observation. The collected data were validated through observation, documentation, and providing written questionnaires, administered directly in front of the researchers. If there were discrepancies in the data, further discussion with the sources would be conducted. Validated data were compared and summarized. The results of data analysis indicate that Indonesian language teachers have not maximized the use of the school library. The service system and data management in the library are also not optimal. The Blockchain system is proposed as an effort to improve teacher performance because it enables transparency, reliability, and security in information storage and exchange. In the context of the school library, the Blockchain system can be used to record and manage teachers' learning history, awards, and assessments by students and colleagues. Poorly organized book inventory management and borrowing processes make it difficult to utilize library facilities. The research findings indicate that the Blockchain system effectively addresses various challenges mentioned above.

Implementación de un Sistema de gestión de la información de ventas aplicando inteligencia de negocios en las PYMES del cantón La Maná

Small and Medium Enterprises (SMEs) are essential to the global economy, promoting employment, innovation and sustainable development. Effective sales information management is critical to your success, involving the collection, storage, analysis and application of data about customers, products, distribution channels, prices and market trends. Proper management of this data allows SMEs to understand customer demands, identify market opportunities and optimize their sales strategies.However, SMEs face significant challenges in this area, such as technological limitations, budget constraints and data complexity, which can lead to manual processes, lack of visibility in the supply chain and loss of competitiveness. The research carried out in the La Maná canton, in Cotopaxi, Ecuador, supports the implementation of a sales information management system due to its intense commercial activity and the presence of SMEs in sectors such as agriculture and commerce.The study adopted a mixed methodology, which combined literature review and field research, using inductive and deductive approaches. Managers, administrators and workers from two companies were surveyed. The results indicate that this methodology is effective in achieving the research objectives, underscoring the importance of integrating various methodologies to obtain a complete understanding of the topic

Edge computing and AI-driven intelligent traffic monitoring and optimization

Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.

Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

SummaryData aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

A Multidisciplinary Approach to the Evaluation of Air Quality and Thermo-Hygrometric Conditions for the Conservation of Heritage Manuscripts and Printed Materials in Historic Buildings: A Case Study of the Sala del Dottorato of the University of Perugia as a Model for Heritage Preservation and Occupants’ Comfort

The Sala del Dottorato (Hall of Graduates) is a magnificent library in the University of Perugia which plays the double role of providing optimal conservation of valuable books and manuscripts while also hosting important events. This double role is closely connected to contrasting indoor microclimatic conditions. This paper presents the results of a multidisciplinary study, begun in 2019, which investigates optimal conditions for the conservation of volumes by monitoring thermo-hygrometric and air quality parameters. The study describes the current conditions of the Hall (in terms of air temperature, relative humidity and concentration of CO2), highlighting critical aspects, defining strategies for their mitigation and control, and outlining future developments. Improvement measures relate to the installation of a permanent monitoring system with alarm settings and data storage, technical interventions on the windows, and the restoration of several volumes. The paper shows the importance of monitoring as an instrument of control in real time and provides guidelines for management to be implemented according to indoor microclimatic conditions.

Blockchain empowered access control for digital twin system with attribute-based encryption

Digital twin is a pivotal and burgeoning technique that plays a crucial role in the realms of digital transformation and intelligent advancement. To bolster diverse applications and realize digital transformation, it is imperative to share the generated device data among multiple stakeholders involved in the digital twin system product life cycle. Since the device data contains sensitive and secret information, strict access control is required. Attribute-based encryption (ABE) is commonly employed for the construction of a cryptographic-enforced data access control scheme. ABE enables data confidentiality and one-to-many fine-grained access control due to its flexibility and high scalability. Unfortunately, traditional ABE schemes adopted in digital twin system has been hindered by the single point of failure and trust problems. Blockchain is a secure distributed ledger technology with decentralization and tamper-proofing properties. In this article, we present a blockchain-empowered data access control scheme for digital twin system with attribute-based encryption. Firstly, blockchain is employed to construct a complete, tamper-resistant, and efficient architecture for digital twin system to address challenges of access control. Subsequently, to ensure the confidentiality of digital twin data, we employ ciphertext-policy ABE (CP-ABE) to achieve fine-grained and one-to-many access control. Finally, we combine blockchain-based architecture with the interplanetary file system (IPFS) to propose a data storage scheme to release storage pressure on the blockchain and improve system efficiency. Security analysis and performance evaluation exhibit that the proposed access control scheme can effectively protect the data security of digital twin system with efficient storage.

Screening Rainwater Harvesting Potentialities in the EU Industrial Sector: A Framework for Site-Specific Assessment

The industrial sector’s water consumption is projected to increase by 400% by 2050, placing significant stress on freshwater reserves. To address this challenge, innovative solutions for water management are crucial. This paper proposes a comprehensive framework for Rainwater Harvesting (RWH) in industrial settings, offering a methodology to assess the potential for RWH implementation across EU industrial sites. The framework integrates internal and publicly available datasets, including EU climate change monthly average rainfall data from the Copernicus Climate Data Store, to create current and prospective scenarios for RWH. The methodology evaluates critical parameters co-created with industrial stakeholders, such as catchment area, water quality, and industrial water requirements. This approach allows for site-specific assessments, enabling industries to reduce freshwater consumption and support sustainability goals within the Horizon 2050 framework. Our findings indicate that implementing RWH systems can significantly contribute to a sustainable and circular economy by reducing annual freshwater consumption, promoting resource reuse, and lowering industrial water costs. This framework provides industries with a tool to assess RWH feasibility, supporting their efforts to prepare for increased water demands and contribute to environmental conservation.