Terms Of Storage Research Articles

The role of topography, climate, soil and the surrounding matrix in the distribution of Veredas wetlands in central Brazil

Wetlands are among the most important ecosystems in the world in terms of endemic biodiversity, carbon storage and hydrological process. Veredas wetlands are distributed across the Brazilian savanna (i.e. Cerrado biome) and are permanently protected areas. Veredas wetlands have a hydromorphic soil, providing water to the main rivers of central Brazil and allowing the occurrence of several endemic species of plants and animals. Although recent studies on biotic and abiotic characteristics have been conducted in several areas of Veredas, the studies are local and there is a lack of information about large-scale patterns. Here we used remote sensing data to explore the role of climate, soil, topography and surrounding matrix explaining Veredas occurrence in the Triângulo Mineiro and Alto Paranaíba (TMAP), a mesoregion of the State of Minas Gerais, Southeastern Brazil. Veredas were more frequent in the western region of TMAP, in areas with lower altitudes, temperature and precipitation seasonality, soil cation exchange capacity, silt and sand content, and slope. Moreover, farming was the most frequent land use in areas surrounding Veredas. Veredas are associated with recharging of the water table and water flow that maintains rivers in the Upper Paraná River water basin. We trust the present assessment will be of help for the development of conservation strategies and biodiversity studies.Graphical abstractResearch questions, data processing, statistical analysis and illustration of the outputs generated.

High electrochemical performance of PANI/CdO nanocomposite based on graphene oxide as a hybrid electrode materials for supercapacitor application

In the last decade, the production of clean and sustainable energy sources for energy storage purposes have grown dramatically due to the population growth and increasing demand for energy in the world. In this regard, supercapacitors have proved to be promising candidates in energy storage applications. Therefore, in this study, polyaniline/cadmium oxide/graphene oxide (PANI/CdO/GO) nanocomposite was prepared by co-precipitation method to evaluate the electrochemical performance. The structural and surface properties, morphology and particle size distribution were analyzed by XRD diffraction spectroscopy (XRD), field emission scanning electron microscope (FESEM), transmission electron microscope (TEM), N2 adsorption-desorption, and Fourier transform infrared spectroscopy (FT-IR). Furthermore, the synthesized nanocomposite was applied as an active electrode material and its performance was investigated by cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS) in terms of energy storage. The results of these tests confirmed that PANI/CdO/GO nanocomposite provides great electrochemical behavior, including specific capacity of 647 F g−1, energy density of 116.6 W h kg−1, power density of 388 W kg−1 compared to the other electrode. According to the stability test, the initial capacity maintenance was about 82% after 500 charge-discharge cycles, which indicated relatively good electrochemical stability. Moreover, the impedance spectroscopy studies showed that the nanocomposite possessed much lower internal strength and charge transfer reaction resistance in comparison to the other synthesized materials. Based on these results, it was found that the prepared nanocomposite has a good performance in the field of energy storage.

A Vehicular Edge Computing-Based Architecture and Task Scheduling Scheme for Cooperative Perception in Autonomous Driving

Cooperative perception is an important domain of autonomous driving that helps to improve road safety and traffic efficiency. Nevertheless, the large amount of sensed data and complicated algorithms make storage and computation for autonomous vehicles (AVs) challenging. Furthermore, not every AV needs to individually process all sensed data from other AVs because the environmental information is the same in a small region. Inspired by vehicular edge computing (VEC), where AVs are interconnected with the help of roadside units (RSUs) for better storage and computation capabilities, we propose a VEC-based architecture for cooperative perception and design a key task scheduling algorithm for the above challenges. Specifically, a time slot-based VEC architecture with the help of an RSU is designed, and the task scheduling problem in the proposed architecture is formulated as a multitask multitarget scheduling problem with assignment restrictions. A two-stage heuristic scheme (TSHS) is designed for the problem. Finally, extensive simulations indicate that the proposed architecture with the TSHS can enable cooperative perception, with a fast running speed and advanced performance, that is superior to that of the benchmarks, especially when most AVs face limitations in terms of storage and computation.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 1

Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.

Delayed Supplementation Strategy of Extracellular Vesicles from Adipose-Derived Mesenchymal Stromal Cells with Improved Proregenerative Efficiency in a Fat Transplantation Model.

Background Mesenchymal stromal cells (MSCs) and their secreted extracellular vesicles (MSC-EVs) possess similar proregenerative effects when injected into defects immediately following trauma. However, MSC-EVs are superior to MSCs in terms of storage and rejection reflection, while immediate administration of MSC-EVs is related to several target cells for most donor cells die within few weeks. Besides, the inflammatory cascade is incited, providing an unfavorable environment for target cells. We hypothesized that delayed injection of MSC-EVs might have priority on tissue regeneration than instant injection. Method Extracellular vesicles isolated from adipose-derived mesenchymal stromal cells (ADSC-EVs) were administered into human umbilical vein endothelial cells (HUVECs) in vitro at different doses. The migration of HUVECs was assessed using the scratch wound healing assay, whereas the length of tubes and number of vessel-like structures formed by HUVECs were determined using tube formation assay. Next, 24 BALB/c nude mice were randomly divided into three groups (n = 8). For the EV-delayed group, ADSC-EVs were injected into transplanted fat a week later than the EV-immediate group. The volume and weight of grafts were measured at 3 months after fat transplantation. Further, the number of CD31-possitive vessels and CD206-possitive cells in the fat grafts was quantified. Results Compared with the EV-immediate group, the EV-delayed group had a higher fat tissue retention volume (0.11 ± 0.02 mL versus 0.08 ± 0.01 mL), more neovessels (31.00 ± 4.60 versus 24.20 ± 3.97), and fewer cysts. Furthermore, there were more Ki67-positive cells (25.40 ± 7.14 versus 16.20 ± 4.17) and CD206-positive M2 macrophages cells (23.60 ± 3.44 versus 14.00 ± 3.85) in the EV-delayed group than in the EV-immediate group. Conclusion Delayed injection of ADSC-EVs promotes fat graft volume retention by stimulating angiogenesis. These findings suggest that delayed supplementation might be a more effective strategy for the application of MSC-EVs in tissue regeneration.

Solar Energy Utilization Techniques, Policies, Potentials, Progresses, Challenges and Recommendations in ASEAN Countries

Sustainable development goals not only contributes towards a clean environment but also towards better climatic conditions. Within Asia next to China and India, the Association of Southeast Asian Nations (ASEAN) are the actively developing countries in terms of economy and technologies. On the verge of achieving development, the ASEAN countries highly depend on fossil fuels for their energy needs. The ASEAN countries have taken visionary steps towards increasing the renewable energy mix with the conventional grid without hampering the ongoing development; this study presents the solar energy utilization policies, potential, progresses, and challenges adopted in ASEAN countries; furthermore, in these nations there is a huge potential of solar energy being located near the equator, therefore, they should focus on both solar to electrical and solar to thermal energy applications; however, in order to meet the peak demand and ensure the reliability of renewable energy like solar power, the development of advanced energy storage systems could be the key areas, and concrete efforts are required. Therefore, this article is a spotlight on government policies and goals focusing on energy potential, major progress in terms of energy storage and challenges in implementation of renewable energy systems in ASEAN countries; furthermore the recommended highlights on policies to accelerate the exploitation of renewable energy usage among the people are also discussed in detail, besides, the insights on reduction of carbon footprints over the next decade through incorporation of advanced energy storage systems. The issues discussed in this article will be helpful for exploring the desired energy storage systems and energy policies to be followed, which will eventually attract the stakeholders for small and medium-scale entrepreneurs for the development of renewable energy business in the region, if implemented on the ground.

Energy and economic study of using Pumped Hydropower Storage with renewable resources to recover the Furnas reservoir

Hydroelectric power plants have been losing competitiveness to renewable resources in Brazil, specifically wind and solar power resources, given the current water crisis in the country, which has left main reservoirs in the Brazilian hydroelectric power system depleted. However, other renewable energy resources show disadvantages in terms of intermittence and energy storage. Pumped Hydropower Storage (PHS) can help mitigate this problem, supplying energy with fewer variations. This paper studied using PHS concepts together with solar and/or wind renewable resources to recover water levels at the Furnas reservoir in terms of energy and economic performance. The energy studies were developed by sizing the PHS and identifying solar and wind resource potential for supplying energy for pumps in the hydropower plant. The economic analyses considered three different scenarios (I; II and III), and used the NPV (Net Present Value), and LCOE (Levelized Cost of Electricity) methods for a period of 30 years. Energy calculations showed that 1692 MWh/day of energy could be generated using PHS. However, 2729 MWh/day of energy would be needed to store generated energy, and a 1037 MWh negative energy balance will be compensated for by using daily wind and/or solar photovoltaic power production. This would allow the Furnas reservoir to regain 0.45 m of its water level per year. The economic studies showed economic viability for PHS scenarios I and III, with estimated NPV at 151.5 million dollars, and 64.5 million dollars for the last year of the project, respectively. Scenario II was unviable, giving rise to several strong discussions on this application within Brazil.

Modelling and Optimization of the Processing of a Healthy Snack Bar Made of Grape and Tomato Pomaces.

A snack made of 36% by byproducts of grape and tomato pomaces was developed, also including other ingredients, such as oats, chia, quinoa, honey and peanut butter. The recipe was defined as tasty and healthy by a focus group. The snack was produced by using forced air at three different drying temperatures (50 °C, 60 °C and 70 °C). The Newton, Page, Henderson and Pabis, and Midilli–Kucuk models fit the drying curves well. The average values for the Newton’s model drying constants were k50 = 2.71 × 10−1 ± 3 × 10−3 min−1, k60 = 2.76 10−1 ± 4 × 10−3 min−1 and k70 = 3.91 × 10−1 ± 8 × 10−3 min−1 at 50 °C, 60 °C and 70 °C, respectively. The product’s quality was assessed in terms of storage with respect to water activity and texture (hardness, springiness, cohesiveness, chewiness and resilience). There were no differences among the three tested processing temperatures in terms of their influence the final product’s quality. As there were no significant differences between initial and final water activity and texture attributes at any temperature and they were mainly unaltered during storage, the snack bar was considered stable during this period. This new snack, which includes byproducts from the food industry, reduces food waste and contributes to a circular economic model, simultaneously presenting environmental and economic advantages.

Non-Structured DNN Weight Pruning-Is It Beneficial in Any Platform?

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured manner, which preserves the full matrix structure with a lower pruning rate. Weight quantization leverages the redundancy in the number of bits in weights. Compared to pruning, quantization is much more hardware-friendly and has become a "must-do" step for FPGA and ASIC implementations. Thus, any evaluation of the effectiveness of pruning should be on top of quantization. The key open question is, with quantization, what kind of pruning (non-structured versus structured) is most beneficial? This question is fundamental because the answer will determine the design aspects that we should really focus on to avoid the diminishing return of certain optimizations. This article provides a definitive answer to the question for the first time. First, we build ADMM-NN-S by extending and enhancing ADMM-NN, a recently proposed joint weight pruning and quantization framework, with the algorithmic supports for structured pruning, dynamic ADMM regulation, and masked mapping and retraining. Second, we develop a methodology for fair and fundamental comparison of non-structured and structured pruning in terms of both storage and computation efficiency. Our results show that ADMM-NN-S consistently outperforms the prior art: 1) it achieves 348× , 36× , and 8× overall weight pruning on LeNet-5, AlexNet, and ResNet-50, respectively, with (almost) zero accuracy loss and 2) we demonstrate the first fully binarized (for all layers) DNNs can be lossless in accuracy in many cases. These results provide a strong baseline and credibility of our study. Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structured pruning is not competitive in terms of both storage and computation efficiency. Thus, we conclude that structured pruning has a greater potential compared to non-structured pruning. We encourage the community to focus on studying the DNN inference acceleration with structured sparsity.

Research on the rheological properties of bitumen modified with waste photopolymer

Recently, the use of waste materials in bitumen modification has become very important in terms of protecting natural resources, reducing environmental pollution, and increasing efficiency. Today, thousands of tons of photopolymer plates are produced every year for printing works and they become waste directly after use. Moreover, the products produced by 3D printers using photopolymer materials have the potential to become waste over time. Photopolymer (PhP) seems to be a suitable material for bitumen modification due to the precious polymers it contains. In this study, PhP obtained from waste solid sheet plates was ground and added to bitumen at 4 different rates. The performance of PhP modified bitumen was compared to pure bitumen and styrene–butadienestyrene (SBS) modification. Softening point, penetration, viscosity, dynamic shear rheometer (DSR), multi-stress creep recovery (MSCR) bending beam rheometer (BBR), storage stability tests and Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis were conducted. In conclusion, it was determined that PhP offered superior properties compared to pure binder with its high softening point, viscosity, complex modulus and low phase angle values. The 9 % PhP modification was similar in many respects to the 5 %SBS modification. Addition of 7 % PhP to pure bitumen (PG 58) made the binder capable of handling extremely heavy traffic conditions at 64 °C. The PhP modification behaved more flexibly than SBS at low temperatures and was somewhat more stable in terms of storage. There was physical changes in the structure even though there was no chemical reaction in the binder with the use of PhP and SBS.

Blockchain Tree Powered Green Communication for Efficient and Sustainable Connected Autonomous Vehicles

Connected and Autonomous Vehicles (CAV) is a system of inter-connectivity and communication between smart automated vehicles. Blockchain has emerged as a technology focusing on data security with a decentralized architecture and has been successful to a very high degree. Unfortunately, this technology was not originally meant for low-power IoT devices and is very computationally expensive with high power requirements and a tendency to introduce delays. With the explosion of the Internet of Things (IoT) enabled autonomous vehicles, boosted further by the advent of smart cities, energy requirements have become an issue that can no longer be ignored. As a consequence, research into green technologies has been gaining popularity. With this area of focus, blockchain with its exponential power consumption levels is heavily flawed and thus presents itself as a prime candidate for improvement. In this paper, a framework is proposed to further evolve blockchain technology to integrate it with a semi-centralized data storage mechanism, while keeping network control decentralized. The proposed Blockchain Tree (BCT) approach entails a combination of a tree structure network composed of blockchains, based on a time-based upward data propagation mechanism that optimizes the architecture to reduce communication delay, cut down its high power consumption levels and ensure sustainability in the long run. As is evident in the comparative study, such a blockchain-based network structure offers drastic improvements in terms of speed, energy savings, storage and computation. The proposed model reduces the storage requirement of the blockchain by a factor of 0.17. The energy requirements and time complexity have been optimized by approximately 195% in the early stages of optimization. All the parameters get further optimized by orders of magnitude with more transactions and users.

Implementation of Drug Logistics Management in Pharmaceutical Installations in Sigi Regency Health Office

Drug logistics management at the health office is one of the important aspects of the Public Health Center. The purpose of the study was to determine the planning, budgeting of procurement, storage, distribution, and elimination in drug logistics management at the Pharmacy Installation of the Sigi Regency Health Office. This type of research was qualitative with a case study approach. The informants in this study consisted of key informants (the Head of the Pharmacy Installation of the Sigi Regency Health Office), the usual informants (the Pharmacy Installation Staff of the Sigi Regency Health Office), and ordinary informants (the person in charge of managing the pharmacy installation at the Public Health Center). Data collection through triangulation techniques, including in-depth interviews, observation, and documentation using interview guidelines. The results showed that the implementation of drug logistics management at the Pharmacy Installation of the Sigi Regency Health Office in terms of planning, budgeting, procurement, storage, distribution, and elimination, was appropriate as it should be but still had some obstacles. So it is still necessary to improve the implementation of logistics management that is more effective and efficient. It is recommended that the coordination of the Sigi Regency Health Office and the Public Health Center is more optimal in the implementation of drug management.

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.

Exporting sunshine: Planning South America’s electricity transition with green hydrogen

Europe and North America have numerous studies on 100% renewable power systems. South America, however, lacks research on zero-carbon energy systems, especially understanding South America as an interconnected region, despite its great renewable energy sources, increasing population, and economic productivity.This work extends the cost-optimization energy planning model LEELO and applies it to South America. This results in the to-date most complete model for planning South America’s power sector, with a high temporal (8760 time steps per year) and spatial (over 40 nodes) resolution, and 30 technologies involved. Besides the base case, we study how varying spatial resolution for South America impacted investment results (43, 30, 16, 1 node). Finally, we also evaluate green hydrogen export scenarios, from 0% to 20% on top of the electricity demand.Our study reveals that South America’s energy transition will rely, in decreasing order, on solar photovoltaic, wind, gas as bridging technology, and also on some concentrated solar power. Storage technologies equal to about 10% of the total installed power capacity would be required, aided by the existing hydropower fleet. Not only is the transition to renewables technically possible, but it is also the most cost-efficient solution: electricity costs are expected to reach 32 €/MWh from the year 2035 onwards without the need for further fossil fuels.Varying the spatial resolution, the most-resolved model (43 nodes) reveals 11% and 6% more costs than the one-node and one-node-per-country (16) models, respectively, with large differences in investment recommendations, especially in concentrated solar and wind power. The difference between 43 and 30 nodes is negligible in terms of total costs, energy storage, and technology mix, indicating that 30 nodes are an adequate resolution for this region. We then use the 30-node model to analyze hydrogen export scenarios. The electricity costs drop, as hydrogen is not only a load but also a flexibility provider. Most green hydrogen is produced in Chile, Argentina, and northeast Brazil. For future work, we propose to do an integrated energy plan, including transport and heat, for the region, as well as modeling local hydrogen demands. This work aims to inform policymakers of sustainable transitions, and the energy community.

A post processing pipeline to prepare raw data for machine learning algorithms in cardiac magnetic resonance imaging

Abstract Funding Acknowledgements Type of funding sources: None. Background Artificial Intelligence is an emergent tool in clinical practice for post processing of medical images. Machine Learning (ML) pipelines are created for data of interest extraction and algorithm application. A common issue in data extraction is represented by noisy datasets, like those of CMR studies, characterized by multiple images, acquired by different techniques, axis orientation and contrast timing. Purpose A ML pipeline for extraction of LGE images from raw DICOM data is presented. Additionally, steps for normalization of image number and automatically heart localization are outlined. Methods and Results 642 consecutive CMR studies were analyzed. Pipeline, Part 1. By looking at the metadata in raw files, ‘SequenceName’ tag was used to discard cine images, ‘ScanningSequence’ tag to select Gradient Recall and Inversion Recovery techniques (Inversion Time > 100 ms), ‘SequenceVariant’ tag to discard Steady State images (See Fig. 1). Orientation of the major axis was computed and ‘Axial’ or ‘Coronal’ images removed. Scans were grouped together by image orientation (requesting a min and max number of elements per group) and only the group with the largest number of files was selected. Finally, DICOMs were grouped by image shape (demanding a min number of elements), and only the series with the highest resolution was retained. Then, for each subject, the extracted series consists of a 3D-array (N,H,W), with N number of slices, and (H,W) image resolution. The attributes were not homogeneous between subjects. Pipeline, Part 2. Given a desired final number of slices and resolution, the 3D-array was reshaped through a spline interpolation. In order to have a focus on the heart, a Region of Interest (ROI) extractor was implemented, based on a YOLO network for object detection. The network was applied on all the slices (Fig. 2); then the images were cropped by keeping the largest bounding box. This step allowed us to remove the background by only selecting the relevant ROIs. To manage the data more easily, images were saved as a NumPy Array, while other useful Dicom metadata (e.g. weight, age, …) were stored using the json standard. At the end of the ML pipeline, images can be reduced to a common resolution and forwarded to ML algorithms. By using this pipeline, a great amount of information not needed for LGE analysis can be discarded, granting a significant reduction in terms of data storage. In our series, the original dataset extended for about 200 GB; by requesting 10 slices per subject with a resolution of 128 by 128 pixels (also extracting heart ROI) the final dimension was reduced to 108 MB. Conclusions In this work, we presented a post-processing pipeline for CRM images and LGE analysis. Given in input raw CRM the pipeline is able to (i) remove unuseful data, (ii) extract heart ROIs storing also Dicom metadata, (iii) normalize slices and image resolution, and (iv) store the processed CRM into ready-format for ML techniques. Fig.1:Pipeline Schematic RepresentationFig.2:YOLO Heart Extraction

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Background/Objectives: Relational databases are a commonly utilized technology that allows for the storage, administration, and retrieval of various data schemas. However, for certain big databases, executing queries can become a time-consuming and inefficient procedure. Furthermore, storing enormous volumes of data necessitates servers with greater capacity and scalability. Relational databases have limits when it comes to dealing with scalability for big amounts of data. On the other hand, non-relational database systems, often known as NoSQL, were created to better fulfill the demands of key-value storing of enormous volumes of records. However, there are several NoSQL options, and the majority have not yet been extensively compared. The goal of this research is to examine different NoSQL databases and evaluate their performance in terms of typical data storage and retrieval. Methods: In this study, we use the YCSB tool to measure the performance of three NoSQL databases: MongoDB, Cassandra, and Redis. We test six different workloads with 100000, 250000, 500000, 750000, and 1000000 operations. Our test was designed with five different operations, i.e., 100000, 250000, 500000, 750000, and 1000000, with six different workloads to see which database is most suitable for applications which use a large amount of data to process. Findings: MongoDB is a superior performing NoSQL database among Cassandra and Redis. The numerous optimizations used by the designers of NoSQL solutions to improve performance, such as good cache memory operation, have a direct impact on the execution time. In all workloads except workload D, MongoDB has significantly reduced latency across all operation counts. Novelty: We also measure the average latency of different workload scenarios that include a mix of read, write, and update activities. Keywords: NoSQL; YCSB; big data; cloud computing; MongoDB; Cassandra; Redis

СПОСОБИ ПІСЛЯЗБИРАЛЬНОЇ ОБРОБКИ ГІБРИДІВ КУКУРУДЗИ ТА ЇХ ВПЛИВ НА ЯКІСТЬ НАСІННЯ ПРИ ТРИВАЛОМУ ЗБЕРІГАННІ

Topicality. The seed stock creation and storage is a prerequisite in seed production of maize hybrids in order to provide sowing material in case of various adverse conditions (crop failure, natural disaster, etc.). Depending on the usage and the storage term, there are the insurance, reserve and breeding seed stocks. During storage, the seed germination may reduce; therefore, seed stock should be refreshed with seeds prepared for long-term storage. Issues. In order to prepare seeds for long-term storage, post-harvest processing aimed at forming longevity of hybrid corn seeds should be carried out. Thus, scientific and practical problem is the definition and optimization of technical and technological processes of the preparation of seeds for storage. Aim. To study the features of the formation of maize hybrid seed longevity, to establish methods and factors of post-harvest processing that ensure long-term storage and high seed quality. Materials and methods. In the experiments, we used maize hybrid seeds developed at the State Enterprise Institute of Grain Crops of NAAS. The moisture levels of the harvested seeds were different, and the seeds were processed for further long-term storage. When harvesting moisture is high, post-harvest processing includes different methods of drying, separation by fractional sieve and gravity methods, chemical treatment (seed dressing). Seeds were stored at different moisture content (7–8, 9–11, 12–14 %), in the different packages (paper, fabric, polyethylene), in the dressing and undressing state for 3–4 years. During storage, we have determined the laboratory germination by various methods, field germination, and the unity of sprout emergence. Results. We have established that the formation of longevity and seed quality of maize hybrids depend on features of harvesting and postharvest processing, such as harvest moisture, methods and temperature regimes of drying, sieve and gravity separation with the selection of various seed fractions, terms of chemical seed dressing. It was found that the laboratory seed germination indicator determined by cold test characterizes the economic suitability of maize hybrids for long-term storage. Conclusions. It was established that to obtain the high-quality seeds capable of long-term storage the following is recommended: to dry the cobs at humidity to temperature ratio of 31–36 % to 36–40 °C or 19–30 % to 40–44 °C; to grade seeds by sieving and gravity separator that to obtain two seed fractions, namely conditionally large and medium; to store undressed seed and carry out the dressing stocks immediately before sowing. Under long-term storage of untreated seeds, it is proposed to protect the seed against harmful objects with a combination of abiotic factors as low seed moisture content, cooling, limiting the oxygen access. When the maize hybrid seed with a moisture content of 9–11 % is hermetically packaged, it is guaranteed the storage period of the seed up to 3–5 years, so this method is most practical and cost-effective. It is recommended to determine the suitability of seeds for long-term storage by the germination indicators determined by the cold test. Keywords: maize seeds, germination, factors and processes of postharvest processing, long-term storage.

Hybrid Approach for Improving the Performance of Data Reliability in Cloud Storage Management.

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research

An Edge-Based Architecture for Offloading Model Predictive Control for UAVs

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

Design and Performance Analysis of Super Highspeed Flywheel Rotor for Electric Vehicle

The optimal design of a super highspeed flywheel rotor could improve flywheel battery energy density. The improvement of flywheel battery energy density could enhance the performance of the flywheel lithium battery composite energy storage system. However, there are still many problems in the structure, material and flywheel winding of super highspeed flywheels. Therefore, in this paper, electric flywheel energy and power density parameters are designed based on CPE (Continuous Power Energy) function and vehicle dynamics. Then, according to the design index requirements, the structure, size and material of the electric flywheel rotor are designed. Furthermore, the numerical analysis model of stress and displacement of multi-ring interference fit flywheel rotor under plane stress state is established. On this basis, the influence laws of flywheel rotor wheel flange numbers and interlaminar interference on stress distribution of flywheel rotor are analyzed, and the assembly form of wheel flange is determined. Finally, the stress check of the flywheel rotor is completed. The results show that the super highspeed flywheel rotor designed in this paper meets vehicle dynamics requirements in terms of energy storage and power. In terms of strength, it meets the design requirements of static assembly stress and dynamic stress at maximum speed.