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XLcloud aims to define and demonstrate the principles of HPC as a Service (High Performance Computing) for all those applications that involve highly intensive calculations. XLcloud is designed as a collaborative tool that enables users to work together on highly sophisticated software in the Cloud, thus sidestepping the need for individuals to have to purchase expensive software on their own. XLcloud combines the expertise of companies and academics that are innovative in the field of high performance computer architectures and flow visualization HD/3D and video. While many Internet-based applications are now available on the Cloud, high-performance applications still face a number of technological obstacles before they are cloud ready. For example, high performance applications often need large capacity processing, lots of storage and huge network bandwidth, needs that are difficult to meet in today’s Cloud. In developing HPC as a Service (HaaS), XLcloud has identified the following high performance applications involving research and industrial innovation: * Research environments such as universities or technical institutes where high performance computing is a requirement, * Online game applications, * Online streaming video in real time, * Search engines in real time, * Online three-dimensional imaging for medical use.

This project aims to offer an easy-to-extend Java DAS server framework. It offers several advantages: * Implementing data sources is very easy but also flexible and powerful. * Data caching is built into the system, with access to the caching mechanism made available to the data sources. * All aspects of the server are highly configurable, including selecting options where the DAS 1.53 specification offers choices to the implementor. * The latest Java technologies have been used throughout the system to optimise performance and simplify data source development. * Wherever possible the same terminology is used in the API as in the DAS specification and XML - again, making data source development more easy. * The server allows XSLT transforms of the DAS XML to be configured to provide a simple DAS client view (limited to the single DAS source). More details of the DAS protocol, DAS servers and DAS clients can be found at http://www.biodas.org/wiki/Main_Page. The first version of this server is a complete implementation of Distributed Sequence Annotation System (DAS) Version 1.53. If you are interested in learning more about DAS 1.53, the specification is highly recommended as a concise and complete description of the DAS protocol that can be obtained from: http://biodas.org/documents/spec.html

IronPDF Java library offers an extensive compatibility range, making it a go-to solution for a wide array of developers. It fully supports JVM languages like Java, Scala, and Kotlin, making it incredibly versatile. This Java PDF library is also compatible with Java 8 and above, providing optimum performance across multiple platforms. It's been designed with a wide range of users in mind Here's a look at what it supports: JVM Languages: Java, Scala, Kotlin.Platforms: Java 8 and above.Operating Systems: Microsoft Windows, Linux, Docker, Azure, AWS.IDEs: Jetbrains IntelliJ IDEA, Eclipse. You can deploy IronPDF Java across various platforms, including Microsoft Windows, Linux, Docker, Azure, and AWS. It is also fully compatible with popular IDEs like Jetbrains IntelliJ IDEA and Eclipse, facilitating smooth project development and management. Your pom.xml file is essentially the backbone of your project when you're using Maven. It's here where you introduce new dependencies that you wish to include. To make IronPDF Java package a part of your Maven project, you simply need to add the following snippets to your pom.xml: Remember to replace '20xx.xx.xxxx' with the latest version of IronPDF. IronPDF Java simplifies the process of creating PDF files. Convert HTML files, HTML strings, or URLs directly to new PDF documents in a few lines of code. The variety of file formats it handles is vast, as it can even transform images into PDF documents and vice versa. Need to use base 64 encoding, base URLs, or custom file paths? No problem! IronPDF Java has got you coveredFor more detail about installing and using IronPDF Java. When you run your project for the first time post-integration, IronPDF's engine binaries will automatically be downloaded. The engine starts its journey when you call any IronPDF function for the first time and takes a breather when your application is either closed or enters an idle state. It is not an open source java PDF library but here's the best part - IronPDF Java is offering a 30-day free trial. So, why wait? Give it a go and boost your PDF operations today.

The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.