Google (Ireland)

Abstract Purpose This study explores emerging digital innovations in pharmaceutical manufacturing, as well as the opportunities and challenges they present for product quality and batch release. A secondary objective was to discuss the regulatory compliance of these digital innovations. Method A comprehensive systematised literature review was performed using Web of Science, PubMed, and Scopus research databases. Screening of the identified studies was performed using Covidence online software. The Mixed Methods Appraisal Tool (MMAT) was adapted for the risk of bias assessment. Results 38 studies were included after screening and risk of bias assessment. The thematic analysis identified 5 empirical themes: ‘PAT and RTRT’ ‘AI’, ‘Process Modelling’ ‘IoT’, and ‘Data Management’. The main opportunities identified included prediction of product quality and variability, deviation root cause analysis, real-time process monitoring, and adaptive control to prevent out of specification products. This study didn’t identify any regulatory challenges to these technologies, however challenges included a lack of personnel with the required technical expertise, data security risks, validation challenges, and potential for inaccuracy. Conclusions Emerging digital technologies are being used to support pharmaceutical quality. A review of current guidance did not uncover any regulatory obstacles to theimplementation of the identified technologies, once part of the registered manufacturing process. More opportunities than challenges were identified in the literature. Based on this study, we propose a responsible strategy for future digital innovation implementation and conclude that there is value in (1) Adopting digital innovations in pharmaceutical manufacturing and (2) Supporting digital education for QPs and other quality professionals in pharmaceutical manufacturing.

We construct a special class of semiclassical Fourier integral operators whose wave fronts are symplectic micromorphisms. These operators have very good properties: they form a category on which the wave front map becomes a functor into the cotangent microbundle category, and they admit a total symbol calculus in terms of symplectic micromorphisms enhanced with half-density germs. This new operator category encompasses the semi-classical pseudo-differential calculus and offers a functorial framework for the semi-classical analysis of the Schr\"odinger equation. We also comment on applications to classical and quantum mechanics as well as to a functorial and geometrical approach to the quantization of Poisson manifolds.

In response to Matin Durrani's editorial "Women's world", in which he states why it is right to air in this magazine the challenges female physicists face.

The problem of recommending items to users is of high practical importance. For instance, many web services try to find relevant recommendations for the users, e.g., finding relevant movies, social-media friends, restaurants, shopping items, etc. The expansion of the Web and the ever-growing number of people who use web services render the problem of recommendation challenging. The Locality Sensitive Hashing (LSH, for short) is the most known scalable technique for nearest-neighbor search in high dimensional data, and hence the LSH is widely used in most industrial recommendation systems. This paper presents an implementation of the LSH using Google's MapReduce engine. We apply the LSH to a real case study at Google, where we recommend for each web-host a set of outlinks based on the outlink similarity amongst the web-hosts. We identify some performance limitations of the LSH that occur due to specific properties in the data, and that become significant when the scale of the data is large. Furthermore, we present SHRec, a novel technique for scalable recommendation that addresses these performance limitations. Based on real deployment of both SHRec and LSH on Google's infrastructure, and using real data of the crawled Web at Google, where a sample host-level graph of 1.5 Billion web-hosts is extracted, we demonstrate that SHRec is more scalable than LSH. In particular, we show that SHRec is one order of magnitude faster than LSH while achieving better recommendation quality.

This study builds on our previous work on beardedness [1] and explores whether wearing spectacles in a LinkedIn profile picture affects a female candidate’s prospects of being invited for a job interview and whether this is contingent on the type of job vacancy. Results of a 2 (spectacle use: spectacles versus no spectacles) × 3 (job type: expertise, trustworthiness, attractiveness) experiment conducted among 139 participants show that bespectacled candidates are perceived as having more expertise and –to our surprise- also being more attractive than candidates not wearing spectacles. Moreover, a candidate’s perceived credibility is a significant predictor of the intention to invite the candidate for a job interview. Theoretical and practical implications of these findings are discussed.

Web 2.0 and Knowledge Management (KM) have a considerable overlap. It appears promising to apply Web 2.0 applications for supporting and improving sharing and creation of knowledge. Yet, little research examining the impact of Web 2.0 on KM has been conducted. This chapter presents research examining the suitability and impact of Web 2.0 applications on KM in organizations. Two extensive exploratory case studies were conducted involving 11 interviews with key personnel of two student-run organizations. It is demonstrated how Web 2.0 applications can be used for a number of KM practices mostly related to the areas of asset management and knowledge creation and innovation. Moreover, they suggest that among all the Web 2.0 principles, User-Generated Content and Unbounded Collaboration exert the biggest influence on creating and sharing of knowledge within organizations. The study contributes to the general understanding of how Web 2.0 and KM practices can be interlinked with each other.

As video-on-demand (VoD) access is progressively becoming consum ers' preferred way to access video content, there are many chal lenges in designing large-scale VoD systems that are capable of high standards in scalability and reliability. Video-streaming reliability has always been synonymous with reduced scalabil ity and higher cost in terms of bandwidth and complexity. This article addresses this perfor mance trade-off. Our system offers VoD services while reconciling this trade-off in scalability and reliability. Our VoD system supports a significantly higher number of VoD sessions than other systems, while still providing an improved reliability through a distributed error-control mechanism.

This paper outlines how sponsorships can be beneficial or harmful to events. Using an experimental design and focusing on association transfers surrounding a snowboarding event, we illustrate that the sponsoring brand associations have a significant effect on the associations of the event. Our results indicate that in this instance some associations are transferred; others are not significant. Event managers must track which of these association transfers are occurring in order to understand and maintain their desired positioning.

The Google Online Marketing Challenge is perhaps the world's largest in-class competition for higher education students. Merging education with hands-on advertising, the Challenge exposes students to the increasingly important field of online marketing, engages students with local businesses and gives students the thrill of an international competition. Feedback from participating students, academics and businesses in the inaugural Challenge was overwhelmingly positive. Based on experience and feedback, Google plans an improved version in 2009. Global organisations such as CHRIE should consider a special division for hospitality and tourism students in the 2009 Challenge.

Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of de-identifying records is to remove identifying fields such as social security number, name etc. However, recent research has shown that a large fraction of the US population can be identified using non-key attributes (called quasi-identifiers) such as date of birth, gender, and zip code [15]. Sweeney [16] proposed the k-anonymity model for privacy where non-key attributes that leak information are suppressed or generalized so that, for every record in the modified table, there are at least k−1 other records having exactly the same values for quasi-identifiers. We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. To ensure privacy of the data records, we impose the constraint that each cluster must contain no fewer than a pre-specified number of data records. This technique is more general since we have a much larger choice for cluster centers than k-Anonymity. In many cases, it lets us release a lot more information without compromising privacy. We also provide constant-factor approximation algorithms to come up with such a clustering. This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k. We further observe that a few outlier points can significantly increase the cost of anonymization. Hence, we extend our algorithms to allow an e fraction of points to remain unclustered, i.e., deleted from the anonymized publication. Thus, by not releasing a small fraction of the database records, we can ensure that the data published for analysis has less distortion and hence is more useful. Our approximation algorithms for new clustering objectives are of independent interest and could be applicable in other clustering scenarios as well.