Data Storage Platform Research Articles

An AI-based Wild Animal Detection System and Its Application

An AI-based Wild Animal Detection System and Its Application

Advance Electric vehicle System for Minimization of wiring Harness Using IoV

The rapid growth of the electric vehicle (EV) market demands innovative solutions to optimize vehicle design, improve efficiency, and enhance user experience. Traditional vehicles heavily rely on extensive wiring harnesses to interconnect various components and systems, leading to increased weight, complexity, and production costs. This abstract presents an advanced electric vehicle system that leverages the power of the Internet of Vehicles (IoV) to minimize wiring harnesses, utilizing components such as Wi-Fi, Thinkspeak cloud, Buzzer, solar plat, charging storing in Battery, LED indicators, voltage and current sensor and other smart electronic devices. The proposed system employs Wi-Fi connectivity as a robust wireless communication technology within the vehicle. By utilizing Wi-Fi, the need for physical wiring is significantly reduced, enabling seamless data transfer between vehicle components and the central control unit using ESP-32 now wireless Communications. This approach streamlines the vehicle's internal architecture, leading to a cleaner and lighter design, ultimately improving energy efficiency and reducing manufacturing complexity. Furthermore, the integration of the Thinkspeak cloud serves as a centralized data storage and analysis platform. Thinkspeak cloud enables real-time monitoring and remote access to critical vehicle parameters and performance metrics, facilitating efficient fleet management and predictive maintenance. The cloud-based solution enhances the vehicle's connectivity and allows for over-the-air updates, enabling continuous improvements and adaptability to emerging technologies.

Coding for Polymer-Based Data Storage

Polymer-based data-storage platforms use chains of binary synthetic polymers as recording media and read the content via tandem mass spectrometers. For such systems, we propose the first known family of codes that allows for both unique string reconstruction and correction of multiple mass errors. We consider two approaches: The first approach pertains to asymmetric error-correction and it is based on introducing redundancy that scales linearly with the number of errors and logarithmically with the length of the string. The construction allows for the string to be uniquely reconstructed based only on its erroneous substring composition multiset. The key idea behind our unique reconstruction approach is to interleave (shifted) Catalan-Bertrand strings with arbitrary binary strings and “reflect” them so as to force prefixes and suffixes of the same length to have different weights. The asymptotic code rate of the scheme is one, and decoding is accomplished via a simplified version of the Backtracking algorithm used for the Turnpike problem. For symmetric errors, we use a polynomial characterization of the mass information and adapt polynomial evaluation code constructions for this setting. In the process, we develop new efficient decoding algorithms for a constant number of composition errors.

BIG DATA PROCESSING WITH APACHE SPARK

With the exponential growth of information, it is no surprise that we are in a period of history as the Information Age. The rapid growth of data has presented challenges regarding storage and processing technology. This article refers to Apache Spark, an ecosystem that provides many integrated technologies in Big Data processing, including machine learning libraries and data storage platforms. Apache Spark provides distributed data processing for open source applications, loading data in-memory and making operations for analyzing data of any size, with efficient support for popular programming languages like Java, Scala, R, and Python. The article aims to compare the superior computing power of Saprk compared to Hadoop and how to connect Spark with today's popular data processing tools such as the R language.

A Smart System for Environmental Parameter Monitoring in Underwater Archaeological Sites

In this paper, we present a prototype for the remote monitoring of underwater archaeological sites. The system aims to provide autonomous and real-time data collection from environmental probes and also includes biofouling detection based on image processing. The monitoring system is equipped with sensors to measure various parameters of interest, including temperature, pH, conductivity, dissolved oxygen, and oxidation-reduction potential. To detect biofouling, a camera is integrated into the system, and an analysis is performed on the captured images utilizing indices such as the Normalized Difference Vegetation Index (NDVI) and the PYM method. The system consists of a subsea node placed near the monitoring site, connected to a surface buoy through an umbilical cable. The surface buoy contains an LTE router for data transmission to a remote web platform for data storage and visualization. The hardware of the underwater node includes an embedded computer, responsible for providing functionalities for image processing and interfacing with the web platform, and a micro-controller board to read the sensor measurements. The developed remote monitoring system has been tested in a marine environment and offers a comprehensive solution for long-term environmental parameter analysis, biofouling detection, and real-time data access. It holds great potential for applications in marine research, environmental monitoring, and ecosystem studies.

Remote Monitoring and Controlling Electrical Devices for Industrial Application

Abstract: The advent of advanced technologies has revolutionized the industrial landscape, providing new opportunities for enhancing efficiency and productivity. In this context, remote monitoring and controlling of electrical devices have emerged as a crucial aspect of industrial automation. This paper presents a comprehensive study on the design and implementation of a remote monitoring and controlling system for electrical devices in industry. By integrating sensor networks, wireless communication protocols, and cloud-based platforms, the system establishes a seamless connection between the devices and the centralized monitoring station. This allows operators and engineers to have constant access to vital data and control functionalities, regardless of their physical location. The key components of the system include intelligent sensors deployed in the industrial environment, a reliable wireless communication network for data transmission, and a cloud-based platform for data storage and analysis. The sensors collect various parameters such as voltage, current, temperature, and power consumption, providing a comprehensive overview of the electrical devices' operational status. The wireless network facilitates real-time data transmission to the cloud platform, where the data is securely stored and made available for analysis. The cloud-based platform not only offers data storage but also supports advanced analytics and visualization techniques. This enables operators to gain valuable insights into device performance, identify potential issues, and take proactive measures to optimize operations. Furthermore, the system provides remote control capabilities, allowing operators to adjust device settings, troubleshoot problems, and initiate maintenance actions, all through a user-friendly. The benefits of implementing this remote monitoring and controlling system are numerous. It enables predictive maintenance, reducing downtime and minimizing the risk of equipment failure. Real-time monitoring helps detect anomalies and abnormal behaviours, enabling timely interventions to prevent potential hazards. Additionally, remote access to device control enhances operational flexibility and reduces the need for on-site presence, leading to significant cost savings and improved overall efficiency.

Fuzzy logic system for drug storage based on the internet of things: a survey

<span lang="EN-US">The rapid development of internet of things (IoT) technology over the course of recent history has made it possible to connect a large number of smart things and sensors, as well as to establish an environment in which data can be seamlessly exchanged between them. This has led to an increase in the demand for data analysis and storage platforms such as cloud computing and fog computing. One of the application areas for the internet of things that has garnered a lot of interest from the business world, academic institutions, and the government is healthcare. The IoT and fuzzy logic are being used in the medical business to improve patient safety, the overall quality of care, and the overall efficiency of medical operations. The most important healthcare studies that are pertinent to pharmacies have been used as the basis for this research. The purpose of this research is to investigate recent advancements in medical modules, remotes, and detector patterns, as well as current innovations in IoT and fuzzy logic-based health care, and current policies from around the world, with the intention of determining how well they support the long-term growth of IoT and fuzzy logic in healthcare.</span>

The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems.I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.

Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms

More significant data are available thanks to the present Internet of Things (IoT) application trend, which can be accessed in the future using some platforms for data storage. An external storage space is required for practical purposes whenever a data storage platform is created. However, in the IoT, certain cutting-edge storage methods have been developed that compromise the security and privacy of data transfer processes. As a result, the suggested solution creates a standard mode of security operations for storing the data with little noise. One of the most distinctive findings in the suggested methodology is the incorporation of machine learning algorithms in the formulation of analytical representations. The aforementioned integration method ensures high-level quantitative measurements of data security and privacy. Due to the transmission of large amounts of data, users are now able to assess the reliability of data transfer channels and the duration of queuing times, where each user can separate the specific data that has to be transferred. The created system is put to the test in real time using the proper metrics, and it is found that machine learning techniques improve security more effectively. Additionally, for 98 percent of the scenarios defined, the accuracy for data security and privacy is maximized, and the predicted model outperforms the current method in all of them.

Real-time monitoring of early-age compressive strength of concrete using an IoT-enabled monitoring system: an investigative study

Determination of the early-age compressive strength of concrete is essential for quality assurance, safety, and economy of construction projects. Due to manual operation on construction site, conventional maturity meters are not efficient for live monitoring of the early-age concrete strength. Higher levels of automated and computerised improvements have been made possible by recent developments in wireless communications, sensor technologies, and data processing methods across the construction industry. For real-time monitoring of the early-stage concrete strength, the current study presents an innovative Internet of Things (IoT)-enabled system developed by concrete data sensors (CDS), an Australian-owned private business. The CDS sensor system (the system) communicates with temperature sensors via long-range wide-area network and is linked to a cloud-based platform for data storage. The suggested system’s effectiveness was assessed using three concrete mixtures and developed maturity relationships. It was observed that the predicted early-age compressive strength of the mixes matches well with the actual compressive strength and that the system can effectively automate the characterisation of maturity.

Digital platforms for regional economic research: a review and methodology proposal

Relevance. This article addresses the need for a comprehensive approach to the analysis of socio-economic data on territorial units. The existing statistical observation system provides a vast amount of data on the socio-economic development of countries, macro-regions, sub-regions, and municipalities. Despite the wealth of data available, research efforts often remain narrowly focused on specific scientific tasks. In the field of regional economy, many research methods have been developed, but there are almost no approaches to the combined use of these methods. Digital research platforms can solve these problems by providing a mechanism for complex analysis of data. Research objective. This study aims is to examine the essence of platformization in scientific research and to present a detailed overview of the functionality of existing digital research platforms on regional and spatial development to substantiate the methodology of distributed regional research. The authors examine and systematize the features of 40 digital platforms worldwide that are related to regional research, using methods such as comparative analysis, extended case method, and cross-case analysis. Data and methods. The authors examine and systematize the features of 40 digital platforms worldwide that are related to regional research by using methods such as comparative analysis, extended case method, and cross-case analysis. Results. The proposed methodology includes a system of criteria and a typology that includes five main types of platforms for regional research: information and communication platforms, distributed work and data storage platforms, service platforms, computing platforms, and transaction platforms. These types are described and their advantages and disadvantages are highlighted. Conclusions. Digital platforms should become the key form of organizing scientific research in the field of regional economics, as they allow for a comprehensive analysis of socio-economic data and scenario experiments on the "digital twins" of regions. The study proposes a general methodology for conducting distributed regional studies. This methodology provided a foundation for RegScienceGRID platform. Overall, this study highlights the potential of digital research platforms in regional studies and provides a useful methodology for evaluating and utilizing these platforms.

The temperature and pressure Remote intelligent control system for biogas digester based on STM32

The application of biogas digesters in modern life has become more and more extensive. However, the current level of automation in biogas management was too low. and people are afraid of odor and dirty when operating biogas fermentation. In order to solve the shortage of modern energy and improve the safety and efficiency of biogas digesters, a temperature and pressure remote intelligent control management system based on STM32 for biogas digesters was designed. This system took STM32 as the main control chip, through the DS18B20 and PMS pressure measurement module for temperature and pressure measurement, combined with ESP 8266 01sWiFi module and cloud platform for data transmission communication and storage, and then displayed to the user by the QT software interface, the remote intelligent control of the biogas digester in the cloud was realized, so that the efficiency and safety management of the biogas digester were could improved. This management system of the biogas digester would promote the energy industry and sustainable development of human beings and facilitated rural revitalization.

Construction of Digital Case Base System Based on DB2 Database

DB2 database supports from mainframe to single-user environment, and makes database localization and remote connection transparent, simplifies the operation and maintenance process of database system, and has become the most important platform for data storage and processing of mainframe system. Digital teaching resources simplify the knowledge and teaching difficulties through multimedia devices, making the communication and interaction between teachers and students more convenient. Teaching case base is one of the digital teaching resources, and the construction of digital teaching case base for recitation based on DB2 database meets the needs of teaching reform and development in the information age. The DB2 object architecture and DB2 Viper architecture are analysed, the database design and functional design of digital teaching case base system for recitation are carried out, and the specific application scheme is proposed.

Flexomagnetism and vertically graded Néel temperature of antiferromagnetic Cr2O3 thin films

Antiferromagnetic insulators are a prospective materials platform for magnonics, spin superfluidity, THz spintronics, and non-volatile data storage. A magnetomechanical coupling in antiferromagnets offers vast advantages in the control and manipulation of the primary order parameter yet remains largely unexplored. Here, we discover a new member in the family of flexoeffects in thin films of Cr2O3. We demonstrate that a gradient of mechanical strain can impact the magnetic phase transition resulting in the distribution of the Néel temperature along the thickness of a 50-nm-thick film. The inhomogeneous reduction of the antiferromagnetic order parameter induces a flexomagnetic coefficient of about 15 μB nm−2. The antiferromagnetic ordering in the inhomogeneously strained films can persist up to 100 °C, rendering Cr2O3 relevant for industrial electronics applications. Strain gradient in Cr2O3 thin films enables fundamental research on magnetomechanics and thermodynamics of antiferromagnetic solitons, spin waves and artificial spin ice systems in magnetic materials with continuously graded parameters.

A photochromic material-based platform for high-precision UV light detection and erasable optical information storage

The ultraviolet (UV) light detection has important applications in photochemistry, material aging, plant cultivation, and integrated circuit lithography. And photochromic materials have attracted more and more attention in technological applications due to their different physical and chemical properties before and after irradiation with a specific excitation light source. Therefore, it is a very interesting work to realize the detection of ultraviolet light intensity based on photochromic materials with good response to ultraviolet light. Here, we design a dual excitation detection mode based on the inorganic photochromic material SrHfO3: Ho3+, and propose a simple and high-precision UV intensity detection method. The doping of Ho3+ ions optimizes the photochromic effect of the SrHfO3 host material and also enables the material to integrate the up-conversion and the downshifting luminescence modes. Different luminescence quenching degrees appear in response to photochromic effect between up-conversion and the downshifting luminescence modes, thereby constructing a dual excitation mode for high-precision UV light intensity detection. In addition, flexible films and 3D micro-rod arrays using the as-obtained photochromic material were designed and fabricated a photochromism platform toward the implementation of optical information storage. This work not only provides a photochromic platform for optical data storage, but also opens a way for simplification of UV intensity detection with high accuracy.

PSIV-A-3 Body Temperature Monitoring Using Subcutaneously and Vaginal Sensors in Grazing Cows

Abstract Body temperature is an indicator of animal health and can be used to detect harmful physiological events in advance of clinical symptoms. Traditionally, body temperature in cattle is determined with a rectal thermometer and requires the animal to be restrained. Although systems to automatically monitor cattle health in confinement operations exist, assessing the real-time temperature of grazing cows is not common. Our object was to demonstrate an integrated sensor network for monitoring pastured cattle body temperature in comparison with temperature measured by a vaginal logger. The integrated sensor network (Mahindra & Mahindra; Mumbai, India) communicates with the data obtained from a subcutaneous temperature sensor (EmbediVet, Livestock Labs Inc.; Pittsburg, PA) to a cloud-based data storage platform. The vaginal temperature was measured by an implantable temperature logger (Micro-T 16-bit; Star Oddi, Iceland). Vaginal devices were inserted into the vagina using a controlled internal drug release (CIDR) cleaned of progesterone. The subcutaneous temperature sensors were surgically implanted through a 2cm vertical incision in the neck. Data from the implantable sensor were transmitted via Bluetooth communication to a solar-powered base station. The subcutaneous sensors and vaginal loggers were deployed on 10 grazing cattle over a period of 6 months. Data from only 4 subcutaneous sensors consistently reported data. Temperature values measured by the subcutaneous sensors had no statistical relationship (P=0.595) with body temperature measured by the vaginal temperature loggers. However, the subcutaneous sensors were able to detect changes in body temperature over time within-animal, indicating that errors in predicting temperature were more likely due to frameshift issues than an inability to track true temperature. Further improvements in data-processing and higher sample size are needed to maximize the usability of this sensor network for monitoring cattle body temperature.

Paired and Unpaired Deep Learning Methods for Physically Accurate Super-Resolution Carbonate Rock Images

X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise the pore-scale geometry of subsurface porous rocks. Recent developments in super-resolution (SR) methods using deep learning allow for the digital enhancement of low-resolution (LR) images over large spatial scales, creating SR images comparable to high-resolution (HR) ground truth images. This circumvents the common trade-off between resolution and field-of-view. An outstanding issue is the use of paired LR and HR data, which is often required in the training step of such methods but is difficult to obtain. In this work, we rigorously compare two state-of-the-art SR deep learning techniques, using both paired and unpaired data, with like-for-like ground truth data. The first approach requires paired images to train a convolutional neural network (CNN), while the second approach uses unpaired images to train a generative adversarial network (GAN). The two approaches are compared using a micro-CT carbonate rock sample with complicated micro-porous textures. We implemented various image-based and numerical verifications and experimental validation to quantitatively evaluate the physical accuracy and sensitivities of the two methods. Our quantitative results show that the unpaired GAN approach can reconstruct super-resolution images as precise as the paired CNN method, with comparable training times and dataset requirements. This unlocks new applications for micro-CT image enhancement using unpaired deep learning methods; image registration is no longer needed during the data processing stage. Decoupled images from data storage platforms can be exploited to train networks for SR digital rock applications. This opens up a new pathway for various applications related to multi-scale flow simulations in heterogeneous porous media.

Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework

Spatiotemporal air quality datasets are typically collected hourly in monitoring stations deployed non-uniformly across a metropolitan city. These datasets are not only big, which poses challenges on the storage and processing capacity of centralized computing systems but also imbalanced and spatially heterogeneous, which may result in biased air quality prediction. To address these challenges, we designed and developed a parallel air quality prediction system equipped with a spatiotemporal data partitioning method, a distributed machine learning algorithm, Hadoop’s distributed data storage platform and its resource scheduler/manager, and Spark’s efficient and in-memory execution environment, which is suitable for running iterative algorithms, e.g., machine learning. Our proposed spatiotemporal partitioning method accounted for imbalance and spatial heterogeneity features of big air quality data in predictive models, which comply with the load-balancing requirement of distributed computing systems. Distributed Random Forest algorithm in the H2O library of the Spark framework was selected as the distributed machine learning algorithm to develop the air quality predictive model. This algorithm is an ensemble forest with algorithm-level adjustments to perform as efficiently as possible for big imbalanced datasets. An application of the parallel quality prediction system for Tehran, Iran showed that the parallel prediction system had considerable speedup gain and improved both the overall accuracy and class precision of air quality prediction when working with imbalanced big spatiotemporal air quality datasets. A future research direction is to add data streaming and visualization functions to the system to provide rapid and reliable air quality prediction for supporting environmental health management. • A parallel air quality prediction system based on Spark and Hadoop frameworks. • A spatiotemporal big data partitioning method based on location and year of data. • Implemented H2O Distributed Random Forest Algorithm using Sparkling Water library. • Achieved overall accuracy of 68% and precision improvements for minority classes. • Observed an increasing trend in speedup gain when adding more parallel processors.

Penerapan Firebase Realtime Database Pada Aplikasi Media Informasi dan Pendaftaran Training IT Berbasis Android

Jogja IT Training Center (JITC) is a business entity that provides training in the field of information technology. JITC provides various kinds of related training. As a business entity engaged in the service sector, the dissemination of information is very important. Currently, JITC uses social media Facebook and Instagram to convey information to the public, but currently, it is not managed properly due to limited resources so the delivery of information and student registration is hampered. The student registration method is done manually and has to come to the office, so the number of students has decreased and resulting in a decrease in income. The conventional registrar data collection has many shortcomings, namely the data is not well documented, resulting in data loss and the search process takes a lot of time. Based on the existing problems, an android-based information and registration media application was built, this mobile application added the Firebase Realtime Database as a data storage platform for information and registrant data so that the training data can be accessed online and in real-time which can be integrated into one service. Firebase Realtime Database has many features and is accessed for free and has a fairly large data storage limit. The result of this research is an android-based application for information providers and registration services for new students.

Molecular Encryption and Steganography Using Mixtures of Simultaneously Sequenced, Sequence-Defined Oligourethanes.

Molecular encoding in abiotic sequence-defined polymers (SDPs) has recently emerged as a versatile platform for information and data storage. However, the storage capacity of these sequence-defined polymers remains underwhelming compared to that of the information storing biopolymer DNA. In an effort to increase their information storage capacity, herein we describe the synthesis and simultaneous sequencing of eight sequence-defined 10-mer oligourethanes. Importantly, we demonstrate the use of different isotope labels, such as halogen tags, as a tool to deconvolute the complex sequence information found within a heterogeneous mixture of at least 96 unique molecules, with as little as four micromoles of total material. In doing so, relatively high-capacity data storage was achieved: 256 bits in this example, the most information stored in a single sample of abiotic SDPs without the use of long strands. Within the sequence information, a 256-bit cipher key was stored and retrieved. The key was used to encrypt and decrypt a plain text document containing The Wonderful Wizard of Oz. To validate this platform as a medium of molecular steganography and cryptography, the cipher key was hidden in the ink of a personal letter, mailed to a third party, extracted, sequenced, and deciphered successfully in the first try, thereby revealing the encrypted document.