Maximum File Size Research Articles

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. 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. 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. 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. 

WORKING WITH LARGE OBJECTS IN APPLICATION DEVELOPMENT

In this paper are considered the specifics of the manipulation of large objects in the database of an application - recording, storage, processing, transmission over the network. Some literature sources dealing with this topic will be reviewed and after that some specifics of working with large objects will be presented. The paper will also address the advantages of using large objects in application development. Some new software products that assist the storage of large objects will also be reviewed. Large Objects (LOBs) are a set of data types in Oracle DBMS, Microsoft SQL Server etc. LOBs are designed to store large amount of data. Large objects are for huge files such as an audio and video files. LOBs can support a maximum file size ranging from 2 GB to 8 GB. Large objects are suitable for storing semi-structured and unstructured data. Storing data in large objects allows data to be accessed and manipulated efficiently within an application. The data in the application s database, with the accumulation of many audio and video files, will at some point begin to meet the rules of big data, and its processing and storage must be carried out by specialized big data environments. The problems of transferring and storing large objects in specialized big data environments will be discussed. Data transfer between different environments will be addressed. The format and content of the data must be preserved, and this leads to the need to comply with several conditions, which will be considered.

Challenges In Using WhatsApp as An Online Arabic Learning Center

This study aims to examine the challenges and obstacles faced in using WhatsApp as an online Arabic learning center and efforts to overcome them. The data from this qualitative study were obtained from observations of 71 students and 2 Arabic language teachers. The data was then validated and strengthened by interviews with 5 students and 2 Arabic language teachers. The results of this study show that the challenges and obstacles in using WhatsApp as an online Arabic learning center do not only come from students, student guardians, and teachers but can also come from the limitations of the WhatsApp application itself such as the maximum file size limit that can be sent and the record button prone to be canceled. In overcoming the existing obstacles, the teacher converts the files to be sent first, lowers the level of questions in assignments, pays attention to the accuracy of students' answers in doing assignments, and collaborates the use of WhatsApp with Google Drive and Instagram. Finally, by looking at the role of the WhatsApp application in the current learning process, the researcher hopes that the WhatsApp application developer will consider to provide WhatsApp for Education like WhatsApp for Business which has existed for a long time.

SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">defect prediction models</i> have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable—i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners’ perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rule-based explanations for the predictions of defect prediction models. Finally, we develop and evaluate a visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80 percent of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics—i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.

Capacity of Wireless Distributed Storage Systems With Broadcast Repair

In wireless distributed storage systems, storage nodes are connected by wireless channels, which are broadcast in nature. This paper exploits this unique feature to design an efficient repair mechanism, called broadcast repair, for wireless distributed storage systems in the presence of multiple-node failures. Due to the broadcast nature of wireless transmission, we advocate a new measure on repair performance called repair-transmission bandwidth. In contrast to repair bandwidth, which measures the average number of packets downloaded by a newcomer to replace a failed node, repair-transmission bandwidth measures the average number of packets transmitted by helper nodes per failed node. The storage system we considered can undergo an unlimited number of repair rounds. We obtain an upper bound on the maximum file size that can be supported by a cut analysis of a finite graph. The achievability is shown by codes constructed over a refined information flow graph, which is unbounded. In addition, the optimal storage-bandwidth tradeoff is obtained. The performance of broadcast repair is compared both analytically and numerically with that of cooperative repair, the basic repair method for wired distributed storage systems with multiple-node failures. While cooperative repair is based on the idea of allowing newcomers to exchange packets, broadcast repair is based on the idea of allowing a helper to broadcast packets to all newcomers simultaneously. We show that broadcast repair outperforms cooperative repair, offering a better tradeoff between storage efficiency and repair-transmission bandwidth.

The XinJiang Astronomical Observatory NSRT Pulsar Data Archive

The sensitivity of our pulsar observation system with typical integration time of 16 minutes has reached 0.4 mJy after we have upgraded the receiver cooling system of NanShan 25-m radio telescope (NSRT) at Xinjiang Astronomical Observatory (XAO) in 2002. About 280 pulsars were observed after the upgrade. The NanShan station pulsar data is transmitted to the data center at XAO headquarters via a 300Mb/s dedicated data link for long-term storage, and the remote backup is realized by synchronous data transmission between NanShan station and XAO headquarters. Metadata extraction, archiving, and releasing are completed by the data release servers which are located in XAO headquarters. At present, more than 84000 pulsar observation data files in the PSRFITS format have been released with a maximum file size of 1GB. XAO pulsar data online service provides cone search and multiobject constraint query. We have published our data based on Virtual Observatory (VO) standards. The online pulsar data service is registered in the International Virtual Observatory Alliance’s Registry. Users can access the data through common VO tools.

PC1Dmod 6.2 – Improved Simulation of c-Si Devices with Updates on Device Physics and User Interface

In this paper we present a new update to PC1Dmod, which extends the original PC1D program by implementing Fermi-Dirac statistics and a range of state-of-the-art models in order to improve the accuracy of c-Si device simulation. In PC1Dmod 6.2 the list of models is further expanded to include a parameterization of incomplete ionization of dopants (Altermatt et al., J. Appl. Phys.100, 113715, 2006), with parameters for phosphorus, boron, arsenic, gallium and aluminum. The results have been verified against a previous implementation of the model. We show that the inclusion of incomplete ionization is of particular importance for moderately doped surface regions with doping densities in the range ∼5 × 1017 to ∼5×1019 cm-3, resulting in a deviation of up to 15% in the simulated recombination current density and sheet resistance. The effect of incomplete ionization is also shown to be more pronounced in devices made from compensated Si material. Furthermore, the default solar spectrum has also been updated in PC1Dmod 6.2, taking advantage of the increased maximum file size to include a more realistic representation. The generation profiles calculated using PC1Dmod 6.2 together with input from OPAL 2 agree well with results from Sentaurus TCAD for both planar and pyramidally textured surfaces, thus facilitating a more direct comparison between PC1Dmod 6.2 and other simulation programs utilizing standard spectra. The simulation comparison is also extended to the electrical performance of the modeled devices, showing good agreement in the calculated short-circuit current density for a range of planar and textured solar cells with varying emitter doping. Finally, several new output options, including emitter saturation current and injection-dependent lifetime curves have been added, enabling a simpler and more direct way for users to access and plot important device properties.

K-Means Based Prediction of Transcoded JPEG File Size and Structural Similarity

The problem of efficiently adapting JPEG images to satisfy given constraints such as maximum file size and resolution arises in a number of applications, from universal media access for mobile browsing to multimedia messaging services. However, optimizing for perceived quality (user experience) commands a non-negligible computational cost which in the authors work, they aim to minimize by the use of low-cost predictors. In previous work, the authors presented predictors and predictor-based systems to achieve low-cost and near-optimal adaption of JPEG images under given constraints of file size and resolution. In this work, they extend and improve these solutions by including more information about images to obtain more accurate predictions of file size and quality resulting from transcoding. The authors show that the proposed method, based on the clustering of transcoding operations represented as high-dimensional vectors, significantly outperforms previous methods in accuracy.

Effect of power settings on temperature change at the root surface when using a holmium YAG laser in enlarging the root canal

The aim of this study was to determine the maximum amount of power, in watts, that a Holmium YAG laser could deliver via a 245-micron fiberoptic to the canal surface and still not raise the temperature (delta T) of the cementum by > 5 degrees C. Sixty single-rooted teeth were divided into three groups according to power selection (0.50, 0.75, and 1.00 W). The three main outcome variables were: change in apical temperature, change in coronal temperature, and maximum size of an endodontic file that could fit into the canal after lasing. The group means for apical delta T were: 1.00 W = 2.2 degrees C, 0.75 W = 2.68 degrees C, and 0.50 W = 1.58 degrees C. The group means for coronal delta T were: 1.00 W = 1.15 degrees C, 0.75 W = 0.99 degree C, and 0.50 W = 0.56 degree C. The group means for file size were: 1.00 W = 41.25, 0.75 W = 38.75, and 0.50 W = 40.75. The canal size was increased from a size 25 file up to approximately a size 40 file with all power groups. There were no significant differences between the groups for change in apical temperature (p = 0.32), coronal temperature (p = 0.17), or maximum file size (p = 0.86) when adjustments were made for tooth dimensions. In all groups studied, the delta T was < 5 degrees C. This represents a safe and predictable laser procedure.