GB Of Data Research Articles

EVALUATION OF THE EFFICIENCY OF MODERN DATA ENCRYPTION TOOLS USING THE NAVEOL ALGORITHM

The paper evaluates the effectiveness of the proposed algorithm NaVeOl_Cript_UNO in comparison with the most common ones. 1,49 GB data was encoded, the number of repetitions was 500. The BlowFish 2000 algorithm completed the encryption in 186 ± 1,5 s, the NaVeOl_Cript_UNO algorithm completed the procedure in 98 ± 0,6 s. The information processing speed using the NaVeOl algorithm is 1,9 times faster than using the BlowFish algorithm. The absolute encryption time for a file of 1,49 GB in size on one core was 22,37 ± 0,03 s, the rate was 66,6 ± 0,5 Mb/s. For each encoded byte, an average of 44 machine cycles was spent, which is 54 times faster compared to the A5/1 algorithm. The performance of the algorithm in the limit of 202 KB and above does not have a clearly defined dependence on the size of the encoded file and the polynomial function with a significance level of 0,95. The impact of the algorithm type on entropy was evaluated using a 100 000 KB file. The file is filled with null values. It was encoded using BlowFish and NaVeOl_Cript, with the key 0123456789. The compression ratio was used to estimate the entropy of the files. As a result of compression of the source file, a 6 KB archive was obtained. The result obtained shows that the algorithms BlowFish and WinRAR 6.02 x64 are significantly inferior to the algorithm NaVeOl_Cript in terms of reducing redundancy in the file and, as a result, increasing entropy and encoding complexity.

A Study on the Impact of Cloud Computing Performance Efficiency on Task Resource Scheduling

Abstract In this paper, the inertia weighting strategy of the particle swarm is improved by using the properties of periodicity and fixed upper and lower bounds of sinusoidal function to model the task scheduling problem in cloud computing as a mathematical problem, and the improved particle swarm algorithm is discretized, and the improved discrete particle swarm algorithm is applied to task scheduling by corresponding encoding method. The task scheduling algorithm (PSOACO) that fuses the fast convergence and small computational power of the particle swarm algorithm with the global exploration capability of the ant colony algorithm for scheduling tasks is proposed. Two test cases, PageRank and wordcount, are selected to measure the performance of the PSO-ACO algorithm. In the performance comparison running the PageRank test case, the PSO-ACO algorithm obtains a performance speedup ratio of 3.8 times that of the native Domino when 50,000 pages are added. In the execution time comparison for the wordcount test case with an additional data set, the PSO-ACO algorithm is nearly 2.8 times faster than the native Domino when adding 1GB of data. Thus, the fusion algorithm reduces the task completion time and achieves a balance between the algorithm’s computational effort and the scheduling’s convergence performance.

Challenges in Inter-UAV 60 GHz Wireless Communication Utilizing Instantaneous Proximity Opportunities in Flight

Communication using millimeter wave (mmWave) and terahertz bands between unmanned aerial vehicles (UAVs) is a crucial technology for the realization of non-terrestrial networks envisioned for Beyond 5G. While these frequency bands offer remarkably high-speed transmission capabilities of tens of Gbps and above, they possess strong directivity and limited communication range due to the requirement of high-gain antennas to compensate for substantial propagation loss. When a UAV employs radio of such a high-frequency band, the available communication time can be less than one second, and the feasibility of leveraging this ultra-narrow zone, which is only accessible for a short duration in a confined space, has not been investigated. This paper presents the theory behind the ultra-narrow zone in frequencies beyond mmWave and explores the data transfer characteristics at 60 GHz between two UAVs. We demonstrate the transmission of 120 MB of data within approximately 500 milliseconds utilizing the instantaneous proximity opportunity created as the UAVs pass each other. Additionally, we evaluate data transfer while the UAVs maintain a fixed distance, to sustain the 60 GHz link, successfully transmitting over 10 GB of data in the air with a throughput of approximately 5 Gbps.

SOCIALIZATION OF THE USE OF GOOGLE DRIVE AT PUSKESMAS AMBACANG KOTA PADANG

Computer technology has developed significantly, from Windows application programs to increasingly sophisticated data storage media. In the past, we usually stored data using diskettes with small storage sizes, then Flashdisk and Hardisk appeared, until the entry of a new era, namely Cloud storage technology. Cloud technology itself has broad functions, one of which is as an online data storage medium. Like the familiar cloud-based data storage service, Google Drive can store up to 15GB of data for free. In addition to data storage like Google Drive, there is also cloud data storage, such as DropBox, OneDrive, and others. By using Google Drive, users experience many benefits, including being able to share data with those who need data. There is no need to store files on a flash disk or hard disk, where our files are directly stored on Google servers, which can be used at any time, modified. In using Google Drive, many Ambacang Health Center staff do not understand Google Drive. The purpose of holding this Community Service is so that all staff can understand Google Drive how to use it and its features, the results achieved are that all participants understand and understand the use of Google Drive. This socialization has been carried out 2 times, on May 27 and June 17, 2023, with lecture and discussion methods.

Unveiling the possible floral visitors and invisible pollination networks from Deep RNA-seq Profile

Floral visitors are key components of successful plant sexual reproduction, although the lack of systematic monitoring, limits our understanding of the status of visitors to flowers. The classical methods of identification and quantification of floral visitors may require ecologists to reach and observe tall trees in dense forests and knowledge of entomology and taxonomy. The advancement of next-generation sequencing technologies has revolutionized traditional analytical methods in many fields. The pollen gets deposited on animals’ bodies, and the salivary glands, muscle cells, and secretions may retain on the flower parts. These insect body parts and secretions collected with flowers contain information about the insects that visit those flowers. By considering this, we present a workflow from freely available bioinformatics software for floral visitor detection based on the hypothesis that a small amount of Illumina Hi-seq RNA data is sufficient for detecting floral visitors. We assembled the workflow using deep Illumina Hi-seq data generated for Cinnamomum verum flowers and field observations. The workflow was also validated using five additional datasets from NCBI. Assembly was performed de novo and eliminated the redundancy. For the taxonomy analysis, assembled transcripts were blasted against insect taxid using DIAMOND BLAST. The same procedure was followed while randomly extracting 1-5 GB of raw data separately, examining the number of species and insect specific genes on the output. The floral visitors detected in transcriptome data of cinnamon were confirmed in the field. Interestingly, >95% of the floral visitors detected from the complete transcriptome data are represented in 1 GB of data. It needs about 0.1g of tissue to generate 1–5 GB of data for an affordable price. More importantly, this workflow could be utilized as the foundation for a detailed study of any plant species, which would help to streamline the identification and quantification of pollinators.

Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach

Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classical trajectory compression algorithms can only be executed in a single-threaded manner and are limited to running in a single-node environment. Therefore, these trajectory compression algorithms are insufficient to compress this incremental data, which often amounts to hundreds of gigabytes, within an acceptable time frame. This paper utilizes Spark, a popular big data processing engine, to parallelize a set of classical trajectory compression algorithms. These algorithms consist of the DP (Douglas–Peucker), the TD-TR (Top-Down Time-Ratio), the SW (Sliding Window), SQUISH (Spatial Quality Simplification Heuristic), and the V-DP (Velocity-Aware Douglas–Peucker). We systematically evaluate these parallelized algorithms on a very large GPS trajectory dataset, which contains 117.5 GB of data produced by 20,000 taxis. The experimental results show that: (1) It takes only 438 s to compress this dataset in a Spark cluster with 14 nodes; (2) These parallelized algorithms can save an average of 26% on storage cost, and up to 40%. In addition, we design and implement a pipeline-based solution that automatically performs preprocessing and compression for continuous GPS trajectories on the Spark platform.

COVID-19 Impacts on the IT Job Market: A Massive Job Ads Analysis

The Covid-19 pandemic has had a significant impact on many economic sectors. The most severe immediate financial effects of Covid-19 include job losses and decreased hiring, and we can expect generalized lower economic growth in the long term. While such phenomena are there for all to see, measuring their scope is complex. In this work, we performed a massive analysis of job postings (ads) taken from LinkUp, a popular job search web platform, to better understand the occupational trends in IT. We analyzed about nine million ads for computer and mathematical experts to measure the impact of the virus on the IT job market. We also extended our investigations to almost 109 million advertisements (about 300 GB of data) for all kinds of positions to overview the effects of Covid-19 on the job market at large. The results show that the Covid-19 crisis hit the job market during the first two quarters of 2020, causing the number of job advertisements to drop across all sectors (except one). Specifically, the IT sector lost between 15% and 48% of the ads, depending on the specific professional figure. Since the last quarter of 2020, the ad numbers have recovered for some sectors, and by the first 2021 quarter, all of them have more job ads than in the last five years. Finally, we used text analysis to understand the trends of interest in teleworking. We found that in the second quarter of 2020, the number of ads explicitly mentioning telework was almost three times the average of the previous quarters.

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences.

Learning low-dimensional continuous vector representation for short k-mers divided from long DNA sequences is key to DNA sequence modeling that can be utilized in many bioinformatics investigations, such as DNA sequence retrieval and classification. DNA2Vec is the most widely used method for DNA sequence embedding. However, it poorly scales to large data sets due to its extremely long training time in kmer embedding. In this paper, we propose a novel efficient graph-based kmer embedding method, named Kmer-Node2Vec, to tackle this concern. Our method converts the large DNA corpus into one kmer co-occurrence graph, and extracts kmer relation on the graph by random walks to learn fast and high-quality kmer embedding. Extensive experiments show that our method is faster than DNA2Vec by 29 times for training on a 4GB data set, and on par with DNA2Vec in terms of task-specific accuracy of sequence retrieval and classification.

A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)

Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under consideration based on processing time to handle complicated data in controlled amount of time. The proposed MMFM’s performance is assessed with real-time weather prediction dataset where the data is acquired from sensor (observed) and image data. MMFM is used to conduct semantic analytics and similarity-based classification on this collection. The processing time based on records and samples are investigated for the various data sizes, instances, and entries. It is found that the proposed MMFM gets 70 seconds of processing time for 2GB data and 0.99 seconds while handling 5000 records for various classification instances.

The Solar Upper Transition Region Imager (SUTRI) Onboard the SATech-01 Satellite

The Solar Upper Transition Region Imager (SUTRI) onboard the Space Advanced Technology demonstration satellite (SATech-01), which was launched to a Sun-synchronous orbit at a height of ∼500 km in 2022 July, aims to test the on-orbit performance of our newly developed Sc/Si multi-layer reflecting mirror and the 2k×2k EUV CMOS imaging camera and to take full-disk solar images at the Ne vii 46.5 nm spectral line with a filter width of ∼3 nm. SUTRI employs a Ritchey–Chrétien optical system with an aperture of 18 cm. The on-orbit observations show that SUTRI images have a field of view of ∼ 41.′6 × 41.′6 and a moderate spatial resolution of ∼8″ without an image stabilization system. The normal cadence of SUTRI images is 30 s and the solar observation time is about 16 hr each day because the earth eclipse time accounts for about 1/3 of SATech-01's orbit period. Approximately 15 GB data is acquired each day and made available online after processing. SUTRI images are valuable as the Ne vii 46.5 nm line is formed at a temperature regime of ∼0.5 MK in the solar atmosphere, which has rarely been sampled by existing solar imagers. SUTRI observations will establish connections between structures in the lower solar atmosphere and corona, and advance our understanding of various types of solar activity such as flares, filament eruptions, coronal jets and coronal mass ejections.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-Based FPGAs

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM's characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBM-based FPGAs. In the first phase, 16 merge trees work in parallel to fully utilize 32 HBM channels' bandwidth. In the second phase, TopSort reuses the logic from phase one to form a wider merge tree to merge the partially sorted results from phase one. TopSort also adopts HBM-specific optimizations to reduce resource overhead and improve bandwidth utilization. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7× and 2.7× faster than state-of-the-art CPU and FPGA sorters.

Financial Performance on Reliance vs Idea

Telecommunication sector in India is the second largest in the whole global economy, with over 1.2 billion subscribers. Due to high population, Indian telecom industry has been very competitive, with many brands being operating in the market. Due to challenging business environment many exited the market. And in today’s scenario only handful of companies are operating which are Airtel, Reliance Jio, BSNL, Idea and many others. Among the following Reliance Jio being the strongest and the most emerging player in the market, came into competition on 5th September 2016.The strategy which helped Reliance Jio capture the maximum market share was free internet service to Jio prime customers which compelled its rivals to lower the data rates. Upon its launch Reliance welcome Offer was introduced where 4GB data was provided to customers on daily basis. And there were many add-on features which made JIO data package favourable for customers of all age and cadre. This all had an impact on the other players in the Indian telecom market, with other telecos’ facing a sharp drop in their monthly average revenue per user, which was approximately 42%; this being also called “The JIO effect”. Coming on, there were two major telecommunication companies, Vodafone and Idea whose business was grossly affected and therefore these collaborated to face this Jio effect. These got merged on 31st August 2018 to form Vodafone Idea Ltd. in order to fight this neck to neck battle with Reliance Jio.

Monitoring Land Degradation Dynamics to Support Landscape Restoration Actions in Remote Areas of the Mediterranean Basin (Murcia Region, Spain).

This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model.

Impact of Household PV Generation on the Voltage Quality in 0.4 kV Electric Grid—Case Study

This article (case study) discusses the influence of household photovoltaic generation on the voltage quality in a three-phase 0.4 kV grid. The research analyzes remotely acquired data from two specially designed three-phase Y-connected power meters to understand the PV system’s influence on the grid. The values of the voltages, currents, THD, individual harmonics, power factor, and K-factor are obtained every 200 ms over several months, creating more than 20 GB of data. The loads are typical household electrical appliances and EV portable chargers. The main conclusion after analyzing the measurements is that three-phase PV generation in the presence of household loads can create or increase grid unbalance; important current values into the neutral wire and THD and K-factor increase in some cases. The results also uncovered that without current sensing at the household connection point to the three-phase grid to control the PV inverter, the balanced phase output power of the grid-tied inverter is harmful to the 0.4 kV grid balance.

Genetic heterogeneity and enrichment of variants in DNA-repair genes in ameloblastoma.

Ameloblastomas are a group of relativelycommon odontogenic tumors that frequentlyoriginate from the dental epithelium. These tumors are aggressive in nature and present as slow-growing painless cortical expansion of the jaw. Histologically, the follicular and plexiform subtypes constitute two-thirds of solid/multicystic ameloblastomas. The objective of this study was to understand the genetic architecture of follicular and plexiform ameloblastomas using deep whole-exome sequencing. Archived formalin-fixed paraffin-embedded tissue blocks of follicular (n= 4) and plexiform (n= 6) ameloblastomas were retrieved and genomic DNAs were isolated from the tumor tissue dissected from the formalin-fixed paraffin-embedded block. The exomes were enriched using the Integrated DNA TechnologiesExome Research Panel (IDT, Coralville, IA) and paired-end sequencing was completed on an Illumina NovaSeq 6000 with an average output of 20 GB of data resulting in a mean coverage of 400×. Variant analysis was completed using custom-developed software: Rapid Understanding of Nucleotide variant Effect Software and variant integration and knowledge interpretation in genomes. Our analyses focused on examining somatic variants (gnomAD minor allele frequency ≤1%) in genes found on an FoodandDrugAdministration-approved clinical cancer sequencing panel (FoundationOne®CDx). In follicular tumors, variants (>20% of the reads) were identified in BRAF, KMT2D, and ABL1 genes. In plexiform tumors, variants (>20% of the reads) were identified in ALK, BRAF, KRAS, KMT2D, SMO, KMT2A, and BRCA2 genes. Enrichment analysis showed a significant role of DNA repair genes in the development of these tumors. The variants identified in follicular and plexiform ameloblastomas were enriched in DNA-repair genes. The observed genetic heterogeneity in these ameloblastomas may contribute to the aggressive nature and recurrence risk of these tumors.

GRAMMATICAL COLLOCATION OF IMPERATIVES IN PRODUCT USER GUIDE

This research was conducted to find out what kind of English grammatical collocation used in Imperatives in Product User Guide. The research applied a qualitative descriptive method, the data from which were collected, analyzed and concluded without drawing a general conclusion. The data of this study are grammatical collocations in imperatives found in KRISBOW Impact Drill 500W User Guide. The analysis of the 69 collected data shows there are as many as 1 type GB data (1.4%), 2 GD type data (2.9%), 12 type Gd data (17.4%), 1 type GE data (1.4%), 2 type GH data (2.9%), 1 type GI data (1.4%), 8 type GL data (11.6%), 8 type GN data (11.6%), 29 type GP data (42.02%), 3 type GQ data (4.3%), 2 type Gs data (2.9%). In conclusion, type GP (Verb + (O) + Adverbial) is the most frequently used grammatical collocation in the form of imperatives in the User Guide.

The Plan B Toolkit: Debug a high-speed ADC using decimation [Shop Talk: What You Didn’t Learn in School

Debugging high-speed ADCs is a unique challenge because of the enormous rate at which they spew data. Modern devices can fling out 20 GB of data per second. That’s like 250 DVDs worth of information per minute! How’re you going to deal with that? You’re not. That’s the secret.

Framework for the Assessment of Data Masking Performance Penalties in SQL Database Servers. Case Study: Oracle

Dynamic data masking (DDM) is a powerful data-security technique for protecting personal and other sensitive information in databases from unauthorized access. A DDM can be used to mask or obfuscate information in real time, as it is accessed by unauthorized users. This prevents sensitive information from being exposed, while still allowing authorized users to access the data. In current multilayered applications, data masking may be incorporated as special modules placed anywhere between the storage and user interface. In this paper, we consider the solution of implementing masking directly in the persistence layer so that data do not travel unmasked along the network. The data at rest are unchanged (i.e., unmasked), but when users query the database, the sensitive columns in the results are displayed in a masked format, which makes it impossible to identify the original data. Given the diversity of masking features proposed by commercial and open-source database servers, this study proposes a framework for assessing the performance penalty of SQL queries when using database masking relative to the original (unmasking) scenario. We implemented and applied the framework to a basic masking scenario in the Oracle database server using the TPC-H benchmark database. Exploratory analysis and Machine Learning models suggest that DDM has a weak impact on query performance. This could be a powerful incentive for incorporating DDM in real-world software applications when up to 100GB data is stored using Oracle database server.

Assessing Population Structure and Signatures of Selection in Wanbei Pigs Using Whole Genome Resequencing Data

Wanbei pig (WBP) is one of the indigenous pig resources in China and has many germplasm characteristics. However, research on its genome is lacking. To assess the genomic variation, population structure, and selection signatures, we resequenced 18 WBP for the first time and performed a comprehensive analysis with resequenced data of 10 Asian wild boars. In total, 590.03 Gb of data and approximately 41 million variants were obtained. Polymorphism level (θπ) ratio and genetic differentiation (fixation index)-based cross approaches were applied, and 539 regions, which harbored 176 genes, were selected. Functional analysis of the selected genes revealed that they were associated with lipid metabolism (SCP2, APOA1, APOA4, APOC3, CD36, BCL6, ADCY8), backfat thickness (PLAG1, CACNA2D1), muscle (MYOG), and reproduction (CABS1). Overall, our results provide a valuable resource for characterizing the uniqueness of WBP and a basis for future breeding.