Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

This database was established to oversee documents issued in support of fishery research activities including experimental fishing permits (EFP), letters of acknowledgement (LOA), temporary possession permits (TPP), exempted educational activity authorizations (EEAA), and scientific research permits (SRP) . Specifically, the primary objectives are: 1. Oversee research document applications; 2. Track vessels authorized to operate under these documents; and 3. Monitor the activity and catch from vessels operating under these documents.

RDD Dataset 11

## Overview

RDD Dataset 11 is a dataset for instance segmentation tasks - it contains Road 81nj annotations for 616 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

RDD Dataset 11 Box 2

## Overview

RDD Dataset 11 Box 2 is a dataset for object detection tasks - it contains Jalankotak 6VGX ZZ3l annotations for 444 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

RDD+Roboflow

## Overview

RDD+Roboflow is a dataset for object detection tasks - it contains Rdd Objects annotations for 55,447 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

RDD AABB

## Overview

RDD AABB is a dataset for object detection tasks - it contains Road HFwh annotations for 612 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

RDD YOLO V9 V2

## Overview

RDD YOLO V9 V2 is a dataset for object detection tasks - it contains Rdd annotations for 3,343 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones. The images are collected from roads in India, Japan and the Czech Republic.

The National Software Reference Library (NSRL) collects software from various sources and incorporates file profiles computed from this software into a Reference Data Set (RDS) of information. The RDS can be used by law enforcement, government, and industry organizations to review files on a computer by matching file profiles in the RDS. This alleviates much of the effort involved in determining which files are important as evidence on computers or file systems that have been seized as part of criminal investigations. The RDS is a collection of digital signatures of known, traceable software applications. There are application hash values in the hash set which may be considered malicious, i.e. steganography tools and hacking scripts. There are no hash values of illicit data, i.e. child abuse images.

Experimental tests have been performed to characterize the aerosols representative of radiological dispersion devices (RDDs, a.k.a. “dirty bombs”) by applying to chosen surrogate compound rapid high temperature transients, vaporizing the sample and forming aerosols mainly by rapid cooling of the vapour. The materials, which were tested in their non-radioactive form, had been chosen from the radioactive sources widely used in industries and nuclear medicine applications, Co, CsCl, Ir and SrTiO3. Our analyses permitted the characterization of the inhalable fraction of the aerosols released, and the study of the influence of cladding materials on the aerosol release and on its characteristics.

The Robotic Drug Dispensing System (RDDS) market is experiencing robust growth, driven by the increasing demand for automation in healthcare settings to improve efficiency, reduce medication errors, and enhance patient safety. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7 billion by 2033. This growth is fueled by several key factors. Hospitals and pharmacies are increasingly adopting automated systems to manage the complexities of medication dispensing, particularly in high-volume environments. The rise in chronic diseases and the consequent increase in medication consumption further fuels the need for efficient and error-free dispensing solutions. Technological advancements, such as the integration of artificial intelligence and improved robotics, are enhancing the capabilities of RDDS, making them more versatile and cost-effective. Furthermore, stringent regulatory requirements regarding medication safety and traceability are driving adoption, making RDDS a critical investment for healthcare providers. The market is segmented by application (pharmacy, clinic, hospital, others) and type (picking robot system, integrated system). Hospitals currently represent the largest segment, driven by their high medication volume and stringent safety protocols. However, the pharmacy segment is expected to demonstrate significant growth due to increasing automation adoption in retail pharmacies. Similarly, the integrated system segment is anticipated to experience faster growth compared to the picking robot system segment due to its greater efficiency and comprehensive capabilities. Geographically, North America currently holds the largest market share, followed by Europe, driven by advanced healthcare infrastructure and high adoption rates. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, fueled by increasing healthcare expenditure and growing awareness of automation benefits. Competition within the market is intense, with major players such as Omnicell, Capsa Healthcare, and BD Rowa vying for market share through innovation, strategic partnerships, and geographical expansion.

The regression discontinuity design (RDD) is a valuable tool for identifying causal effects with observational data. However, applying the traditional electoral RDD to the study of divided government is challenging. Because assignment to treatment in this case is the result of elections to multiple institutions, there is no obvious single forcing variable. Here we use simulations in which we apply shocks to real-world election results in order to generate two measures of the likelihood of divided government, both of which can be used for causal analysis. The first captures the electoral distance to divided government and can easily be utilized in conjunction with the standard sharp RDD toolkit. The second is a simulated probability of divided government. This measure does not easily fit into a sharp RDD framework, so we develop a probability restricted design (PRD) which relies upon the underlying logic of an RDD. This design incorporates common regression techniques but limits the sample to to those observations for which assignment to treatment approaches “as-if random.” To illustrate both of our approaches, we reevaluate the link between divided government and the size of budget deficits.

Explore the historical Whois records related to rdds.hamburg (Domain). Get insights into ownership history and changes over time.

The global Rosai-Dorfman Disease (RDD) Therapeutics market is expected to garner a market value of US$ 431 Million in 2023 and is expected to accumulate a market value of US$ 839.95 Million by registering a CAGR of 6.9% in the forecast period 2023 to 2033.

Report Attribute	Details
Expected Market Value (2023)	US$ 431 Million
Anticipated Forecast Value (2033)	US$ 839.95 Million
Projected Growth Rate (2023 to 2033)	6.9% CAGR

Report Scope

Report Attribute	Details
Market Value in 2023	US$ 431 Million
Market Value in 2033	US$ 839.95 Million
Growth Rate	CAGR of 6.9% from 2023 to 2033
Base Year for Estimation	2022
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Quantitative Units	Revenue in USD Million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis
Segments Covered	Drug Class Treatment End User Distribution Channel Region
Regions Covered	North America Latin America Europe South Asia East Asia Oceania MEA
Key Countries Profiled	USA Canada Brazil Mexico Rest of Latin America Germany United Kingdom France Spain Italy Rest of Europe India Malaysia Singapore Thailand Rest of South Asia China Japan South Korea Austria New Zealand GCC countries South Africa Israel Rest of MEA
Key Companies Profiled	Teva Pharmaceuticals Ltd. Zydus Pharmaceuticals, Inc. Sun Pharmaceuticals Industries Ltd. Advanz Pharmaceticals Novartis AG Mylan N.V. Aurobindo Pharma Dr. Reddy’s Laboratories Ltd Viatris Inc. Pfizer Inc.
Customization	Available Upon Request

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