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This book discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. It provides state of the art knowledge needed for successful deployment of an SDN, including how to explain to the non-technical business decision makers in an organization the potential benefits and risks, in shifting parts of a network to the SDN model; how to make intelligent decisions about when to integrate SDN technologies in a network; how to decide if an organization should be developing its own SDN applications or looking to acquire them from an outside vendor; how to accelerate the ability to develop an SDN application; discusses the evolution of the switch platforms that enable SDN; addresses when to integrate SDN technologies in a network; provides an overview of sample SDN applications relevant to different industries; includes practical examples of how to write SDN applications. --

Design, implement, and execute continuous delivery pipelines with a level of flexibility, control, and ease of maintenance that was not possible with Jenkins before. With this practical book, build administrators, developers, testers, and other professionals will learn how the features in Jenkins 2 let you define pipelines as code, leverage integration with other key technologies, and create automated, reliable pipelines to simplify and accelerate your DevOps environments.

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates 1 . Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems 2 . Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models 3 . Bayesian optimization has also been recently applied in chemistry 4 – 9 ; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run. Bayesian optimization is applied in chemical synthesis towards the optimization of various organic reactions and is found to outperform scientists in both average optimization efficiency and consistency.

PurposeThe use of life cycle assessment (LCA) as a decision support tool can be hampered by the numerous uncertainties embedded in the calculation. The treatment of uncertainty is necessary to increase the reliability and credibility of LCA results. The objective is to provide an overview of the methods to identify, characterize, propagate (uncertainty analysis), understand the effects (sensitivity analysis), and communicate uncertainty in order to propose recommendations to a broad public of LCA practitioners.MethodsThis work was carried out via a literature review and an analysis of LCA tool functionalities. In order to facilitate the identification of uncertainty, its location within an LCA model was distinguished between quantity (any numerical data), model structure (relationships structure), and context (criteria chosen within the goal and scope of the study). The methods for uncertainty characterization, uncertainty analysis, and sensitivity analysis were classified according to the information provided, their implementation in LCA software, the time and effort required to apply them, and their reliability and validity. This review led to the definition of recommendations on three levels: basic (low efforts with LCA software), intermediate (significant efforts with LCA software), and advanced (significant efforts with non-LCA software).Results and discussionFor the basic recommendations, minimum and maximum values (quantity uncertainty) and alternative scenarios (model structure/context uncertainty) are defined for critical elements in order to estimate the range of results. Result sensitivity is analyzed via one-at-a-time variations (with realistic ranges of quantities) and scenario analyses. Uncertainty should be discussed at least qualitatively in a dedicated paragraph. For the intermediate level, the characterization can be refined with probability distributions and an expert review for scenario definition. Uncertainty analysis can then be performed with the Monte Carlo method for the different scenarios. Quantitative information should appear in inventory tables and result figures. Finally, advanced practitioners can screen uncertainty sources more exhaustively, include correlations, estimate model error with validation data, and perform Latin hypercube sampling and global sensitivity analysis.ConclusionsThrough this pedagogic review of the methods and practical recommendations, the authors aim to increase the knowledge of LCA practitioners related to uncertainty and facilitate the application of treatment techniques. To continue in this direction, further research questions should be investigated (e.g., on the implementation of fuzzy logic and model uncertainty characterization) and the developers of databases, LCIA methods, and software tools should invest efforts in better implementing and treating uncertainty in LCA.

Calculating solvent accessible surface areas (SASA) is a run-of-the-mill calculation in structural biology. Although there are many programs available for this calculation, there are no free-standing, open-source tools designed for easy tool-chain integration. FreeSASA is an open source C library for SASA calculations that provides both command-line and Python interfaces in addition to its C API. The library implements both Lee and Richards' and Shrake and Rupley's approximations, and is highly configurable to allow the user to control molecular parameters, accuracy and output granularity. It only depends on standard C libraries and should therefore be easy to compile and install on any platform. The library is well-documented, stable and efficient. The command-line interface can easily replace closed source legacy programs, with comparable or better accuracy and speed, and with some added functionality.

Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.

LArSoft is a set of detector-independent software tools for the simulation, reconstruction and analysis of data from liquid argon (LAr) neutrino experiments The common features of LAr time projection chambers (TPCs) enable sharing of algorithm code across detectors of very different size and configuration. LArSoft is currently used in production simulation and reconstruction by the ArgoNeuT, DUNE, LArlAT, MicroBooNE, and SBND experiments. The software suite offers a wide selection of algorithms and utilities, including those for associated photo-detectors and the handling of auxiliary detectors outside the TPCs. Available algorithms cover the full range of simulation and reconstruction, from raw waveforms to high-level reconstructed objects, event topologies and classification. The common code within LArSoft is contributed by adopting experiments, which also provide detector-specific geometry descriptions, and code for the treatment of electronic signals. LArSoft is also a collaboration of experiments, Fermilab and associated software projects which cooperate in setting requirements, priorities, and schedules. In this talk, we outline the general architecture of the software and the interaction with external libraries and detector-specific code. We also describe the dynamics of LArSoft software development between the contributing experiments, the projects supporting the software infrastructure LArSoft relies on, and the core LArSoft support project.

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.

The advancement of sport, especially sport coaching in Indonesia, depends on the level of success of sport science and technology transfer process to sport community. This study aims (1) to describe patterns of sports science and technology transfer process to community, and (2) to analyze the level of accessibility and familiarity of the tools of technology-based physical ability test among coaches and sport community. This is a continuation of embryo research on modeling selection and utilization of technology-based devices through the Nominal Group Technique (NGT) at the research stage next year. The experiment was conducted by applying the combination method of various approaches. The approaches includes: Bibliographical Studies, Survey Descriptive, Nominal Groups Technique (NGT), as well as certain stages of a study on modeling the selection and use of hardware and software tools of modern physical ability test on sports community achievements in Surakarta. The results showed that basically the coaches have a tendency as follows: (1) 98.7% respondents felt that most coaches are ready to make improvements by training themselves in using sports science and technology, (2) 97.3% felt that a refreshing model of science and technology-based exercise planning process organized by the stakeholder group exercise can be followed periodically and coached evenly, (3) Respondents did not agree with the statement that community sports coach had appreciation for applied sports science and technology development, the lowest mean item score is the issue of \"appreciation of science and technology development in sports\". It is high time for coaches in Indonesia to increase their appreciation to sport science and technology, and sport community.