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Patent data have been exploited to track invention and international technology diffusion. We review evidence on research activity, international patenting, and income differences across countries. Guided by that evidence, we construct a model of ideas in the world economy that includes the decision of whether and where to patent them. The model makes precise connections between international patent statistics and cross-country differences in innovation, technology diffusion, market size, and strength of patent protection. We use it to organize our discussion of existing empirical studies, which typically focus on one of five core relationships: (i) national pools of knowledge and international spillovers from basic research; (ii) aggregate productivity and international technology diffusion from applied research; (iii) international patenting and the production of ideas, international diffusion, market sizes, and intellectual property regimes; (iv) the value of ideas and diffusion, market sizes, and the intellectual property regimes; or (v) investment in research and research productivity, the cost of research, and the value of ideas. While distinguishing between these five relationships proves useful, they are, of course, logically intertwined. Taking these interconnections into account will contribute to the goal of building a quantitative model of the creation, diffusion, and adoption of ideas in the global economy.

In the last two decades the subject of intellectual property rights (IPR) took on major significance as an element of global trade regulation and commercial policy. Implementation of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) at the World Trade Organization (WTO) in 1995 obliged member countries, over various transition periods, to adopt and enforce minimum standards of protection for patents, copyrights, trademarks, trade secrets, and related policies. This mandate forced legislative and administrative changes in virtually all countries, but had particular impact in developing nations, which had generally weaker IPR standards prior to TRIPS. Since 1995 there have been additional multilateral negotiations, largely at the World Intellectual Property Organization, over stronger global standards for patents and copyrights for digital electronic goods. Most controversially, in its negotiations of bilateral free trade areas the United States aggressively demands highly rigorous standards, beyond those called for in TRIPS, for patent rules governing pharmaceutical products and new biotechnological goods in the agricultural and life sciences.

Resurgent interest in the life and work of the Italian Cambridge economist Piero Sraffa is leading to New Directions in Sraffa Scholarship. This chapter introduces readers to some of these developments. First and perhaps foremost is the fact that as of September 2016 Sraffa’s archival material has been uploaded onto the website of the Wren Library, Trinity College, Cambridge University, as digital colour images; this chapter introduces readers to the history of these events. This history provides sharp relief on the extant debates over the role of the archival material in leading to the final publication of Production of Commodities by Means of Commodities, and readers are provided a brief sketch of these matters. The varied nature of Sraffa scholarship is demonstrated by the different aspects of Sraffa’s intellectual legacy which are developed and discussed in the various entries of our Symposium. The conclusion is reached that we are on the cusp of an exciting phase change of tremendous potential in Sraffa scholarship.

In recent years, the problem of classification with imbalanced data has been growing in popularity in the data-mining and machine-learning communities due to the emergence of an abundance of imbalanced data in many fields. In this chapter, we compare the performance of six classification methods on an imbalanced dataset under the influence of four resampling techniques. These classification methods are the random forest, the support vector machine, logistic regression, k-nearest neighbor (KNN), the decision tree, and AdaBoost. Our study has shown that all of the classification methods have difficulty when working with the imbalanced data, with the KNN performing the worst, detecting only 27.4% of the minority class. However, with the help of resampling techniques, all of the classification methods experience improvement on overall performances. In particular, the Random Forest, in combination with the random over-sampling technique, performs the best, achieving 82.8% balanced accuracy (the average of the true-positive rate and true-negative rate).

We then propose a new procedure to resample the data. Our method is based on the idea of eliminating “easy” majority observations before under-sampling them. It has further improved the balanced accuracy of the Random Forest to 83.7%, making it the best approach for the imbalanced data.

This chapter focuses on the early history of feminist explorations in criminology in the UK in particular, but with reference to developments elsewhere. The chapter discusses the achievements of early feminist perspectives in criminology and assesses their impact in terms of ‘transforming and transgressing’ the criminological enterprise. In particular, the author focuses on the case for transformations in traditional research methodologies and looks at the different ways in which feminist writers in criminology grappled with the question of how to produce good quality knowledge. The chapter takes a chronological approach, identifying developments pre-1960s in a phase which might be described as an ‘awakening’ and then describing initiatives in the 1960s and 1970s. The discovery that ‘woman’ was a conceptual term which could be incorporated into the criminological framework really took off in the 1970s with the publication of Carol Smart’s pioneering work. Notwithstanding faster developments in other disciplines, slowly, mainstream criminology took stock of feminism’s early claims.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.