3D-XY critical fluctuations of the thermal expansivity in detwinned YBa2Cu3O7-d single crystals near optimal doping

The strong coupling of superconductivity to the orthorhombic distortion in YBa2Cu3O7-d makes possible an analysis of the superconducting fluctuations without the necessity of subtracting any background. The present high-resolution capacitance dilatometry data unambiguously demonstrate the existence of critical, instead of Gaussian, fluctuations over a wide temperature region (+/- 10 K) around Tc. The values of the amplitude ratio A+/A-=0.9-1.1 and the leading scaling exponent |alpha|<0.018, determined via a least-squares fit of the data, are consistent with the 3D-XY universality class. Small deviations from pure 3D-XY behavior are discussed.Comment: 11 pages including three figure

The European Union, the Member States, and the Lex Mercatoria

The phenomena linked to the internationalization and globalization of the economy prompt the demand for uniform legal frameworks in supranational governance and encourage forms of “self-regulation”. This spontaneous attempt at harmonizing law at the supranational level is often prepared by market forces and comes to add to the classical legal models while leading to the emergence of a new lex mercatoria. The aim of this paper is to analyze the openings of the European system to the transnational production of law identified under the term new lex mercatoria by verifying all the factors that allow its sources of law to enter into the European legal order. The division of competences between Member States and European Union in this regard is analyzed as well as all their implications in terms of sources of law. The study addresses the relationship between spontaneous and institutional attempts to regulate the markets, as well as their implications, risks and impact on the national legal system. In fact, even though they relate mainly to commercial transactions, questions linked to the rule of law and protection of rights are involved, which require the reconciling of private and public interests

Identification and Characterization of Avihepadnaviruses Isolated from Exotic Anseriformes Maintained in Captivity

Five new hepadnaviruses were cloned from exotic ducks and geese, including the Chiloe wigeon, mandarin duck, puna teal, Orinoco sheldgoose, and ashy-headed sheldgoose. Sequence comparisons revealed that all but the mandarin duck viruses were closely related to existing isolates of duck hepatitis B virus (DHBV), while mandarin duck virus clones were closely related to Ross goose hepatitis B virus. Nonetheless, the S protein, core protein, and functional domains of the Pol protein were highly conserved in all of the new isolates. The Chiloe wigeon and puna teal hepatitis B viruses, the two new isolates most closely related to DHBV, also lacked an AUG start codon at the beginning of their X open reading frame (ORF). But as previously reported for the heron, Ross goose, and stork hepatitis B viruses, an AUG codon was found near the beginning of the X ORF of the mandarin duck, Orinoco, and ashy-headed sheldgoose viruses. In all of the new isolates, the X ORF ended with a stop codon at the same position. All of the cloned viruses replicated when transfected into the LMH line of chicken hepatoma cells. Significant differences between the new isolates and between these and previously reported isolates were detected in the pre-S domain of the viral envelope protein, which is believed to determine viral host range. Despite this, all of the new isolates were infectious for primary cultures of Pekin duck hepatocytes, and infectivity in young Pekin ducks was demonstrated for all but the ashy-headed sheldgoose isolate

Technology Focus: Data Management and Communication (October 2013)

Technology Focus The questions addressed in this feature are: What does the petroleum industry need to do to catch up on the generational breach, and how will the oil industry enhance the current training schemes to serve business needs? With the well-announced generational gap, as well as the current global bonanza, the oil and gas industry is facing one of the most profound crises within current human-resources-management practices. The industry focused its efforts on motivating, recruiting, and preparing the new generation faster and more efficiently than ever; however, has it done this fast enough? The reality is that, no matter how fast we get the new generation of workers onboard, it appears that there is not enough time to catch up, not enough time to recruit and select new personnel and to train newly hired employees with the very few mentors available. Complex problems require a variety of perspectives because it is with the joint comprehension and integration of different technical disciplines that complex problems are solved and solutions are enhanced. Research has shown that learning through experience accelerates critical thinking and innovation among younger generations, and younger cohorts bring a fresh, different perspective in handling technical problems and integration workflows. Several papers selected for this feature are presented with the objective of illustrating innovative technologies and initiatives to accelerate and facilitate the learning process of the Millennials. Scenario-based learning, structured mentoring, on-the-job learning, hands-on teaching, and cross-functional data sharing are just a few of the training strategies available to cope with the challenge at hand. The industry is generally adopting a combination of structured, formal classes with innovative ways to share the knowledge and best practices available from the few available experts. The strategies point toward engaging the young groups in the learning process by self-motivation and innovation. The major findings in the literature include involving students in the learning experience by providing them the opportunity to solve relevant problems, establishing a clear path to practice and apply the new knowledge in real-life situations, and offering a clear path to further training and career development. Will the petroleum industry be able to catch up on the generational breach, even with all of the tools available? What else is needed? Is the oil industry pulling the right amount and quality of resources? Are we offering long-term sustainable career paths? Do we know how and when to motivate the new generations for their professional-growth and learning processes? I tend to think that the mentors speak and act in different frameworks, having learned with different methods and career challenges that the oil and gas industry has to offer. It is part of our duty, then, to maximize the communication strategies to ensure that the knowledge transfer is enabled, efficient, and available for the very sustainability of the oil industry. Recommended additional reading at OnePetro: www.onepetro.org. SPE 156136 Structured Mentoring: A Critical Component of a Global Talent-Management Strategy by Meta Rousseau, Baker Hughes SPE 164365 Filling the Experience Gap in the Drilling-Optimization Continuous-Improvement Cycle Through a Self-Learning Expert System by Cliff Kirby, Baker Hughes, et al. SPE 159948 Using Equipment Simulators for Effective Training, Increasing Competence in Well-Services Operations by Anthony Celano, Baker Hughes, et al.</jats:p

Technology Focus: Data Analytics

Technology Focus Until a decade ago, the practice of data analytics and artificial intelligence in upstream was sporadic, and that was only possible through research organizations and individual effort. Nowadays, we see an increasing trend of image-classification applications for reducing subsurface studies from months to days and time-series analytics for recommending actions and preventing failures in real•time. Despite all efforts, the hydrocarbon industry continues to be perceived by many as the black sheep of all the energy supplies; its sustainability is jeopardized by environmental concerns and the net unit cost. In addition, operating companies are charged by shareholders to increase profitability continuously, leading to longer-than-usual working periods and less-safe working sites. Digital transformation supported by data analytics and artificial intelligence can be the differentiator that allows the upstream industry to persist in the next 100 years, providing a unique foundation for innovation to enhance general public awareness and life quality. Because automated machines yield more results per unit time and less unit cost, genuine concerns have arisen about artificial intelligence reducing the number of jobs or making some positions obsolete. Numerous cases exist in which automated oilfield operations have delivered safer workplaces with fewer human-intensive decision-making processes. These operations and processes run 24/7 with very high availability, reducing people-power requirements by 80% or more. Here, three examples are highlighted that relate how digitization, analytics, and artificial intelligence are transforming how geoscientists will work in the future. Extracting more information from seismic and log data and deriving reservoir states without simulating the porous media are becoming common feats, thanks to high-performance hardware and data analytic applications. In dealing with the digital world, professionals need to carry a set of particular skills, including computing upgrades; system maintenance; and data manipulation, including from exploratory data analysis and programming of exception-based surveillance rules. Some places in our industry, however, exist where machines cannot replace the human touch. The future petroleum engineer, released from traditional mundane tasks, would need to focus mostly on creative work, including, for example, the creation of innovative porous-media recovery mechanisms, groundbreaking business models for hydrocarbons in society, new uses of environmentally friendly materials, and cost-effective facility life-extension options. The future hydrocarbon industry, enabled by digitization, analytics, and artificial intelligence, will demand more creative and innovative mindsets and skills able to ingest analyses from multiple domains; this, in turn, could lead to a safer and more profitable industry with fewer working hours and improved work/life balance. Recommended additional reading at OnePetro: www.onepetro.org. SPE 193080 Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching With Field Data by Marco Giuliani, Eni, et al. OTC 29415 Validating Drilling-States Classifiers With Suboptimal Data Sets by Luis R. Pereira, Transocean, et al. SPE 191643 Inferring Well Connectivity in Waterfloods Using Novel Signal-Processing Techniques by Y. Wang, University of Oklahoma, et al.</jats:p

Technology Focus: Petroleum Data Analytics

Technology Focus One of the many challenges we face today in the petroleum industry is the management of data and information. In some instances, we are overwhelmed by the amount and diversity of formats, and, in other cases, we are blinded from the right information to understand a process (What has happened?), to predict the immediate future (What could happen?), or to make proper decisions (What should we do?). The answer to these questions is data analytics supporting appropriate engineering and management judgment and the modeling of actual energy scenarios. Data analytics for strategic decision making is being constantly developed to mitigate low-oil-price scenarios. For many decades, our technical and business processes have benefited from the wide use of data statistics for decision making. In many instances, predicting and prescribing have relied more on data evidences and trends than on first-principle simulation models. The advancement of computational power, sensor availability, and engineering models has promoted the exponential growth of data types and volumes. Data-driven techniques also have diversified and improved to address such incremental complexities. We are now referring to the professionals who manage and find value from data as “data scientists,” and we are calling the management of large and complex data volumes “big data.” Data analytics, either big or small, is the collection of tools that leverages data collection, aggregation, processing, and analysis for describing insights into the past, predicting future performance, and prescribing actions from the optimization of possible outcomes. Current trends of data analytics differ from traditional statistics in the sense that the new data-driven predictive and prescriptive models go beyond data averaging, outlier detection, correlations, and multiple-parameter regression fitting. Data-analytics tools may include one or more of the following groups: statistics (regression, time series, and factor analysis); pattern recognition (Markov models, principal components, ensemble averaging, classification, and regression); business intelligence (key-performance-indicator dashboards, multidimensional visualization); artificial intelligence for planning, creativity, perception, and social intelligence (knowledge representation, neural networks, support vector machines, Bayesian inference, decision tree, natural-language programming); machine learning (inductive logic programming, rule learning, and clustering); and management of large data sets, distributed and parallel computing, cloud computing services, and data cleansing and profiling. A graduate degree may be required to master some of the techniques around data analytics, and decades may be required to adopt them across the industry, but it is also true that many of these techniques are evolving at such a fast pace that they become obsolete by the time we plan to roll out a trial pilot. We need to learn how to experiment with, implement, and capture results from data analytics faster than ever. We either evolve quickly or disappear. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 176791 Use of Regression and Bootstrapping in Drilling Inference and Prediction by Chiranth M. Hegde, The University of Texas at Austin, et al. SPE 174985 Topological Data Analysis To Solve Big-Data Problem in Reservoir Engineering: Application to Inverted 4D-Seismic Data by Abdulhamed Alfaleh, Saudi Aramco, et al. SPE 179958 Detecting and Removing Outliers in Production Data To Enhance Production Forecasting by Nitinkumar L. Chaudhary, University of Houston, et al.</jats:p