Storage Platform Research Articles

STUDY OF THE CURRENT STATE OF DEVELOPMENT OF CONTROL AND AUTOMATION SYSTEMS OF HYDRAULIC MACHINES

Future hydraulic systems will heavily incorporate AI and ML, enhancing control precision and enabling predictive maintenance. AI-driven algorithms will increasingly be used for predictive maintenance, improving the reliability and lifespan of hydraulic systems. This integration will lead to smarter, more efficient operation, and extended equipment lifespans. The integration of IoT technology is crucial for real-time data collection and analysis, facilitating remote monitoring and control that leads to improved operational efficiency and maintenance management. The use of cloud platforms for data analysis and storage is likely to increase, enabling more sophisticated data processing and accessibility. Advanced sensors and predictive maintenance contribute significantly to safety by anticipating system failures. Reliability is also enhanced through reduced downtime and smarter maintenance schedules. The growing convergence of advanced robotics with hydraulic systems is steering these systems towards increased autonomy. Enhanced safety features will be a priority, driven by smarter sensors and control algorithms. The growing complexity of hydraulic systems underscores the need for specialized training and education in system design, maintenance, and operation. Future hydraulic machines are expected to be more customizable and flexible, with modular designs that cater to specific industry needs. A significant challenge is the adaptability of AI and ML models in dynamic, real-world conditions. Continuous learning methods are being developed to address this. In summary, the future of hydraulic machine control systems and automation looks towards greater intelligence, connectivity, efficiency, and adaptability. These trends will be underpinned by ongoing technological advancements, particularly in AI, ML, IoT, and sustainable practices.

Retracted: Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

Retracted: Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

Layer-Dependent Sensing Performance of WS2-Based Gas Sensors.

Two-dimensional (2D) materials, such as tungsten disulfide (WS2), have attracted considerable attention for their potential in gas sensing applications, primarily due to their distinctive electrical properties and layer-dependent characteristics. This research explores the impact of the number of WS2 layers on the ability to detect gases by examining the layer-dependent sensing performance of WS2-based gas sensors. We fabricated gas sensors based on WS2 in both monolayer and multilayer configurations and methodically evaluated their response to various gases, including NO2, CO, NH3, and CH4 at room temperature and 50 degrees Celsius. In contrast to the monolayer counterpart, the multilayer WS2 sensor exhibits enhanced gas sensing performance at higher temperatures. Furthermore, a comprehensive gas monitoring system was constructed employing these WS2-based sensors, integrated with additional electronic components. To facilitate user access to data and receive alerts, sensor data were transmitted to a cloud-based platform for processing and storage. This investigation not only advances our understanding of 2D WS2-based gas sensors but also underscores the importance of layer engineering in tailoring their sensing capabilities for diverse applications. Additionally, the development of a gas monitoring system employing 2D WS2 within this study holds significant promise for future implementation in intelligent, efficient, and cost-effective sensor technologies.

Storage-D: A user-friendly platform that enables practical and personalized DNA data storage.

Deoxyribonucleic acid (DNA) has been suggested as a very promising medium for data storage in recent years. Although numerous studies have advocated for DNA data storage, its practical application remains obscure and there is a lack of a user-oriented platform. Here, we developed a DNA data storage platform, named Storage-D, which allows users to convert their data into DNA sequences of any length and vice versa by selecting algorithms, error-correction, random-access, and codec pin strategies in terms of their own choice. It incorporates a newly designed "Wukong" algorithm, which provides over 20 trillion codec pins for data privacy use. This algorithm can also control GC content to the selected standard, as well as adjust the homopolymer run length to a defined level, while maintaining a high coding potential of ~1.98 bis/nt, allowing it to outperform previous algorithms. By connecting to a commercial DNA synthesis and sequencing platform with "Storage-D," we successfully stored "Diagnosis and treatment protocol for COVID-19 patients" into 200 nt oligo pools in vitro, and 500 bp genes in vivo which replicated in both normal and extreme bacteria. Together, this platform allows for practical and personalized DNA data storage, potentially with a wide range of applications.

Analisa Desain High Availability dan Uji Reabilitas Cloud Storage

The growth of the Internet has brought about significant changes in how we store data. Common data storage media include hard disks, flash drives, and CDs. With the advancement of technology, cloud storage has emerged as a new alternative in the world of storage. In this regard, two leading cloud storage platforms, Owncloud and Nextcloud, have evolved into popular options. The purpose of this research is to evaluate the level of High Availability and reliability of Owncloud and Nextcloud cloud storage platforms. This is accomplished by conducting load testing and simulating failure scenarios to assess the platforms' ability to ensure data access during disruptions and enhance performance under high loads. The research methodology used is the Network Development LifeCycle (NDLC). The software employed as the operating system is Ubuntu Server 20.04, and for web server load testing, Netdata and Nagios are used to monitor the system. The results of this research provide comprehensive information on the high availability and reliability of Owncloud and Nextcloud cloud storage platforms in modern computing environments.

Triboelectric gait sensing analysis system for self‐powered IoT‐based human motion monitoring

AbstractQuantitative analysis of gait parameters, such as stride frequency and step speed, is essential for optimizing physical exercise for the human body. However, the current electronic sensors used in human motion monitoring remain constrained by factors such as battery life and accuracy. This study developed a self‐powered gait analysis system (SGAS) based on a triboelectric nanogenerator (TENG) fabricated electrospun composite nanofibers for motion monitoring and gait analysis for regulating exercise programs. The SGAS consists of a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. Within the sensing module, two specialized sensing units, TENG‐S1 and TENG‐S2, are positioned at the forefoot and heel to generate synchronized signals in tandem with the user's footsteps. These signals are instrumental for real‐time step count and step speed monitoring. The output of the two TENG units is significantly improved by systematically investigating and optimizing the electrospun composite nanofibers' composition, strength, and wear resistance. Additionally, a charge amplifier circuit is implemented to process the raw voltage signal, consequently bolstering the reliability of the sensing signal. This refined data is then ready for further reading and calculation by the micro‐controller unit (MCU) during the signal transmission process. Finally, the well‐conditioned signals are wirelessly transmitted to the IoT platform for data analysis, storage, and visualization, enhancing human motion monitoring.image

A Latent Factor-Based Bayesian Neural Networks Model in Cloud Platform for Used Car Price Prediction

The selling price of a used car can be predicted based on its historical information. Accurate and reasonable used car price evaluation will be able to promote the healthy progress of the used car industry. Current used car price prediction models are troubled by data quality and the inability to provide estimates of uncertain information, and the prediction accuracy cannot meet the needs of real-world scenarios. In this work, we propose a price prediction model based on a Bayesian neural networks with latent factor (LFBNN) in a cloud platform, which is capable of performing latent factor extraction operations on structured data of used cars as a way to remove the effect of noise in the dataset on the model performance. Moreover, the weight parameters of the Bayesian neural networks (NNs) are represented as probability distributions, which is equivalent to introducing uncertainty and acting as a regularization effect compared to a NN with fixed weights, thus making it possible to alleviate the overfitting problem. The LFBNN model uses a cloud platform for data transmission and storage, enabling it to run faster. Compared with the current benchmark models, the model proposed in this work achieves excellent experimental results on two real datasets. The experimental results on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{1}$</tex-math></inline-formula> dataset are 1.498 for mse, 1.224 for rmse, and 0.995 for MAE, and 1.519 for mse, 1.232 for rmse, and 1.002 for MAE on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{2}$</tex-math></inline-formula> dataset.

MgH2 nanoparticles confined in reduced graphene oxide pillared with organosilica: a novel type of hydrogen storage material.

Hydrogen is a promising alternative fuel that can push forward the energy transition because of its high energy density (142 MJ kg-1), variety of potential sources, low weight and low environmental impact, but its storage for automotive applications remains a formidable challenge. MgH2, with its high gravimetric and volumetric density, presents a compelling platform for hydrogen storage; however, its utilization is hindered by the sluggish kinetics of hydrogen uptake/release and high temperature operation. Herein we show that a novel layered heterostructure of reduced graphene oxide and organosilica with high specific surface area and narrow pore size distribution can serve as a scaffold to host MgH2 nanoparticles with a narrow diameter distribution around ∼2.5 nm and superior hydrogen storage properties to bulk MgH2. Desorption studies showed that hydrogen release starts at relatively low temperature, with a maximum at 348 °C and kinetics dependent on particle size. Reversibility tests demonstrated that the dehydrogenation kinetics and re-hydrogenation capacity of the system remains stable at 1.62 wt% over four cycles at 200 °C. Our results prove that MgH2 confinement in a nanoporous scaffold is an efficient way to constrain the size of the hydride particles, avoid aggregation and improve kinetics for hydrogen release and recharging.

An eco-friendly polycaprolactone/graphite composite as a robust freestanding electrode platform for supercapacitive energy storage.

We present the successful development and characterization of a novel eco-friendly polycaprolactone-graphite (PCLGr) composite as a freestanding platform, serving as a bulk conducting chip electrode for supercapacitor applications. Notably, this is the first report of using this biodegradable polymer for making such a self-standing conductive platform. Traditional polymer and carbon-based electrodes often rely on insulating supports or non-eco-friendly materials, which we have addressed in our work. Direct deposition of the redox material, polyaniline (PANI), onto the electrode via the galvanostatic method has been achieved. The specific capacitance of PANI demonstrates comparability to previous studies utilizing conventional current collectors. Notably, the electrode exhibits exceptional stability in highly acidic environments. Comprehensive characterization utilizing bulk conductivity measurements, XRD, TGA, DSC, SEM, and stress-strain analyses shows advanced properties of the electrode. It complements the evaluation of PANI's supercapacitive performance through cyclic voltammetry, charge-discharge measurements, and impedance spectroscopy. We achieved a specific capacitance of ≈162 F g-1 at 0.5 A g-1. This innovative electrode presents a promising alternative to conventional counterparts across various electrochemical applications.

Functionalization of three-dimensional epitaxial graphene with metal nanoparticles.

We demonstrate the first successful functionalization of epitaxial three-dimensional graphene with metal nanoparticles. The functionalization is obtained by immersing three-dimensional graphene in a nanoparticle colloidal solution. This method is versatile and demonstrated here for gold and palladium, but can be extended to other types of nanoparticles. We have measured the nanoparticle density on the top surface and in the porous layer volume by scanning electron microscopy and scanning transmission electron microscopy. The samples exhibit a wide coverage of nanoparticles with minimal clustering. We demonstrate that high-quality graphene promotes the functionalization, leading to higher nanoparticle density both on the surface and in the pores. X-ray photoelectron spectroscopy shows the absence of contamination after the functionalization process. Moreover, it confirms the thermal stability of the Au- and Pd-functionalized three-dimensional graphene up to 530 °C. Our approach opens new avenues for utilizing three-dimensional graphene as a versatile platform for catalytic applications, sensors, and energy storage and conversion.

Porous liquids: an integrated platform for gas storage and catalysis.

Porous liquids (PLs) represent a new frontier in materials design, combining the unique features of fluidity in liquids and permanent porosity in solids. By engineering well-defined pores into liquids via designed structure modification techniques, the greatly improved free volume significantly enhances the gas transport and storage capability of PL sorbents. Triggered by the promising applications of PLs in gas separation, PLs are further explored in catalysis particularly to integrate the gas storage and catalytic transformation procedure. This emerging field has demonstrated promising progress to advance catalytic procedures using PLs as catalysts, with performance surpassing that of the pure liquid and porous host counterparts. In this perspective article, the recent discoveries and progress in the field of integrated gas storage and catalysis by leveraging the PL platforms will be summarized, particularly compared with the traditional homogeneous or heterogeneous catalytic procedures. The unique features of PLs endow them with combined merits from liquid and solid catalysts and beyond which will be illustrated first. This will be followed by the unique techniques being utilized to probe the porosity and active sites in PLs and the structural evolution during the catalytic procedures. The catalytic application of PLs will be divided by the reaction categories, including CO2-involving transformation, O2-involving reaction, H2S conversion, hydrogenation reaction, and non-gas involving cascade reactions. In each reaction type, the synthesis approaches and structure engineering techniques of PLs, structure characterization, catalytic performance evaluation, and reaction mechanism exploration will be discussed, highlighting the structure-performance relationship and the advancement benefiting from the unique features of PLs.

Understanding Consumer Trends in Setting Up a Lucrative Marketing Strategies

Businesses all over the world strive to convince customers to buy their goods and services. To do this, they research consumer needs and develop marketing plans that will persuade customers to buy. Numerous marketing strategies are used to sway consumer behavior, which ultimately affects their purchasing decision. In present business world that is highly competitive, understanding consumers trend in setting up a lucrative marketing strategies dictate the tune in achieving a successful and effectual marketing rewards. The main objective of this study is to explore how consumer trends can be utilized to develop effective marketing strategies. To achieve this goal, the study analyzed both primary and secondary data. Primary data was collected through observation, interviews, and surveys, while secondary data was obtained from a variety of sources such as newspapers, magazines, trade journals, blogs, online data storage platforms, and government institutions like the Chamber of Commerce, Census Bureau, and labor department. Appropriate marketing tools analysis such as SWOT, PEST and situational analysis are adopted in the study to analyse the relevant data collected from both the primary and secondary sources in order to achieve the objectives of the study. The analysis indicates that grasping consumer trends plays a crucial role in designing effective marketing strategies. It helps in formulating appropriate techniques for each strategy and ensures their effectiveness. Understanding consumer trends is the key to making the strategies responsive to their needs and preferences. Creating successful marketing strategies requires steadfastness, planning, research, and analysis of important policies to understand consumer trends.

IoT Based Food Freshness Detection by Using Python

Abstract: In the contemporary landscape of interconnected devices and the Internet of Things (IoT), the pursuit of sustainable practices has led to innovative solutions in diverse domains. This project introduces an IoT-based system for food freshness detection, leveraging the capabilities of the Python programming language. The primary objective is to design a versatile and accessible platform that monitors the freshness of perishable goods in real-time, contributing to reduced food wastage and enhanced consumer awareness. The project centers around the integration of sensors, particularly gas sensors, with a Raspberry Pi microcontroller. These sensors capture volatile compounds emitted during the degradation of food items, providing crucial data for freshness assessment. The Python programming language is employed to orchestrate sensor data processing, implement intelligent freshness determination logic, and facilitate communication with IoT platforms. Key components of the project include the establishment of MQTT communication for real-time data transfer, integration with cloud platforms (such as AWS IoT or ThingSpeak) for data storage and visualization, and the optional development of a user interface for remote monitoring. Through rigorous testing and calibration, the system aims to achieve a reliable and accurate assessment of food freshness across diverse environmental conditions. The anticipated outcomes include a functional and scalable IoT-based food freshness detection system. By providing users with real-time insights into the condition of perishable goods, the project strives to empower consumers to make informed decisions, reduce food wastage, and contribute to sustainable food management practices

A Comparative Study of Electrode Parameters of Vanadium Redox Flow Batteries for Improved Design and Performance

Large-scale energy storage has become an urgent priority to integrate variable renewable energy sources into the electricity grid. Redox flow batteries offer attractive energy storage platforms due to the ease of integration of renewable energy, higher round-trip efficiency, location flexibility, and scalability [1]. Vanadium redox flow batteries (VRFBs) have already received widespread attention due to their recent commercialization for large-scale storage applications [2]. The electrode plays a significant role in determining the performance of the VRFB, which is composed of a porous microstructure characterized by fiber diameter, porosity, specific surface area, and permeability. These properties control various parameters that affect the performance of the VRFB system, such as pressure differences between the inlet and outlet of the electrode, overpotentials, cell potential, and crossover from the membrane. Various studies have reported varying electrode porosity's influence on cell charge-discharge cycles of VRFB systems [3–5]. However, these studies lack a consistent link between porous electrode microstructures and cell performance. Hence, the interrelated nature of the individual parameters and accurately predicting their effect on cell performance pose a challenge in predicting cell performance.In this work, we present a comparative study of porous electrode parameters using a 2-dimensional physics-based model of a VRFB incorporating mass, momentum, charge transport, and kinetics. We have established a consistent relationship between electrode parameters and cell performance which is incorporated into the model to verify the dependency of overpotential, pressure difference, and crossover of ions on porosity and fiber diameter by utilizing a correlation for the surface area and permeability as a function of these parameters in the porous electrode. Pressure difference at the inlet and out of the electrode is one of the driving forces for the crossover of ions across one electrode to another, decreasing with the higher value of fibre diameters during the charging of the cell (as shown in Figure 1(a)). The effect of the operating parameter, flow rate, is negligible on the ion crossover. However, the crossover is impacted by the current, which further increases by increasing the current value, as shown in Figure 1(b). We also found that the system efficiency increases with an increase in the fibre diameter and reaches a maximum at the range of 40-70 µm, but voltage efficiency decreases with the higher value of fibre diameter, as shown in Figure 1(c). This study/analysis/work will include the impact on the crossover of vanadium ions by varying the electrode and operating parameters. The system's efficiency with and without considering the pumping energy will also be presented for various parameters. Our key findings provide important insight for optimizing VRFB design. Developing a 2D model that considers multiple key parameters enables a better understanding of the complex interactions between electrode microstructure and system performance.

Supramolecular polymers with dual energy storage mechanism for high-performance supercapacitors

In this paper, we prepared the supramolecular polymers (MWCNT-APP-s) with a dual energy storage mechanism as the electrode materials by the coordination of four transition metal ions with the small molecule chelator (APP) and functionalized carbon nanotubes, respectively. Among four MWCNT-APP-s, MWCNT-APP-Fe has the characteristics of moderate micropore/mesopore, significant hydrophobicity, redox property and functional groups. Interestingly, the redox reaction of Fe3+/Fe2+ and -CN-/-CN- transformation give MWCNT-APP-Fe an energy storage basis of pseudocapacitance, while MWCNTs and the micro/mesopore structure in MWCNT-APP-Fe provide a double-layer energy storage platform. As expected, on base of the dual energy storage mechanism, the symmetric supercapacitor assembled with MWCNT-APP-Fe has a higher specific capacity (Cs, 421 F g−1 at 1 mV s−1) as well as a long-lasting stability of 94.8% capacity retention with 99% Coulombic efficiency after 10,000 cycles at 20 mV s−1. More notably, the relevant aqueous Zn2+ hybrid supercapacitor provides a high capacity (Cm) of 191 mAh g−1 at 0.5 A g−1 and a long duration of over 2000 cycles at 50 A g−1, with a capacity retention of 92.4%. In summary, MWCNT-APP-Fe with a dual energy storage mechanism enables a potential application as an electrode material for high-performance supercapacitor.

Why Can’t I just use Dropbox? A comparison of cloud file storage platforms used for research

Objective: Many researchers use cloud file storage platforms such as Box and Google Drive as the sole data management platform for all of their research data throughout the course of their projects. Researchers have lost access to their preferred platforms due to changes in licensing agreements and cost to their institutions, leaving researchers to figure out a new system on their own. This paper describes differences between these platforms that affect research data workflows.&amp;nbsp;Methods: We selected four commonly used cloud file storage vendors (Box, Dropbox, Google Drive, and Microsoft SharePoint/OneDrive) to assess. The authors read public user documentation for the platforms and identified several differences, such as maximum file size, that could affect research data workflows. For each difference, we recorded the specifics of each platform then narrowed the scope to features that vary across the platforms.&amp;nbsp;Results: We identified three areas where cloud platforms differed in ways that affect data management: data stewardship, storage capacity, and file organization. Data stewardship is affected by variations in the platforms’ approaches to individual vs. group ownership of files and how user roles are defined and assigned. The platforms also differ in limits on total capacity, individual file size, total number of files, and the amount of data that can be moved in and out of the platform. Some of these limits vary by access method (e.g. API vs web interfaces). Finally many differences affect how data can be organized, such as number of files per folder, synchronization, and file sharing restrictions.&amp;nbsp;Conclusions: Some research data workflows may be negatively affected by moving between cloud file storage platforms. Researchers will need assistance identifying differences between the platforms and modifying disrupted data workflows.&amp;nbsp;

Design and implementation of intelligent monitoring system for agricultural environment in IoT

In the context of population growth and limited resources, the efficiency and sustainability of agricultural production became particularly important. Smart agricultural technology and Internet of Things (IoT) technology have helped farmers with better management tools to improve crop yield and quality. This research takes the IoT as the core and combines sensor, actuator and cloud platform technologies to build a smart greenhouse control system. Environmental parameters such as temperature, humidity, and light are obtained through sensors. And it is uploaded to the cloud platform for storage and analysis. At the same time, light, ventilation, water, and fertilizer inside the greenhouse are controlled automatically by our system to achieve the best crop growth condition. System design adopts a modular design concept. We separately realize the functions of sensor, actuator and cloud platform, where data is transmitted via wireless communication and integrated together. The experimental results show that the cloud control system of smart agricultural greenhouse can effectively monitor the greenhouse environment, optimize efficiency of energy and resource utilization, and help farmers manage greenhouse conveniently and quickly.

Optimization for Storage Scheduling of Steel Plates Based on Cloud Manufacturing Platform

With the development of cloud manufacturing, the automation of storage scheduling becomes popular in the steel industry. However, the high customization of steel plates makes the storage scheduling too complex to be optimized. To overcome this challenge, we propose to utilize the big data of steel plate orders and warehouse status to optimize the storage scheduling of steel plates. The agglomerative hierarchical clustering is firstly adopted to reduce the complexity of excessive steel plate specifications, then an optimization problem is defined to formulate the storage scheduling of steel plates with safety. A two-stage heuristic (TSH) algorithm is proposed to solve the optimization problem with low complexity. In TSH, steel plates are firstly assigned to multiple stacks, and then the arrangement of each stack is determined. Experiments are executed based on a cloud manufacturing platform for steel plates production and storage, and the results prove the effectiveness of the proposed works.

Experimental investigation on thermochemical heat storage using CaO/CaCO3 in a cyclone reactor

In this study, a cyclone reactor with a secondary flow effect was applied to the CaCO3/CaO thermochemical energy storage system. An experimental platform for thermochemical energy storage with a cyclone reactor was built. A cold-state experiment was performed to study the feeding and conveying performance of CaCO3 and CaO particles. The experimental results reveal that the average feeding speeds of CaCO3 and CaO were 25.7–29.6 g/min and 3.5–11.0 g/min, respectively. The effects of different heating temperatures (750–950 °C) and inlet air velocities (4–14 m/s) on the reaction performance were studied. The results indicate that CaCO3 decomposes above 750 °C. The reaction degree increased with increased reaction temperature, and the peak value of the outlet CO2 concentration peaked (4.72 %) when the heating temperature was 950 °C, where the reaction degrees under different air velocities were similar. In addition, the inlet air velocity and the outlet relative humidity decreased owing to the formation of CO2 and CaO. In the energy release experiment, it was difficult to smoothly feed CaO into the cyclone reactor for synthesis owing to its characteristics, and no obvious change in the CO2 concentration was observed at the outlet. The energy storage reaction and flow characteristics were experimentally studied to provide a reference for the design of thermochemical energy storage reactors.

The Expansion of Data Science: Dataset Standardization

With recent advances in science and technology, more processing capability and data have become available, allowing a more straightforward implementation of data analysis techniques. Fortunately, available online data storage capacity follows this trend, and vast amounts of data can be stored online freely or at accessible costs. As happens with every evolution (or revolution) in any science field, organizing and sharing these data is essential to contribute to new studies or validate obtained results quickly. To facilitate this, we must guarantee interoperability between existing datasets and developed software, whether commercial or open-source. This article explores this issue and analyzes the current initiatives to establish data standards and compares some of the existing online dataset storage platforms. Through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, it is possible to better understand the strategy that should be taken to improve the efficiency in this field, which directly depends on the data’s characteristics. The development of dataset standards will directly increase the collaboration and data sharing between academia and industry, allowing faster research and development through direct interoperability.