Design and analysis of a segmented blade for a 50 MW wind turbine rotor

Extreme-size wind turbines face logistical challenges due to their sheer size. A solution, segmentation, is examined for an extreme-scale 50 MW wind turbine with 250 m blades using a systematic approach. Segmentation poses challenges regarding minimizing joint mass, transferring loads between segments and logistics. We investigate the feasibility of segmenting a 250 m blade by developing design methods and analyzing the impact of segmentation on the blade mass and blade frequencies. This investigation considers various variables such as joint types (bolted and bonded), adhesive materials, joint locations, number of joints and taper ratios (ply dropping). Segmentation increases blade mass by 4.1%–62% with bolted joints and by 0.4%–3.6% with bonded joints for taper ratios up to 1:10. Cases with large mass growth significantly reduce blade frequencies potentially challenging the control design. We show that segmentation of an extreme-scale blade is possible but mass reduction is necessary to improve its feasibility.

Doubly Robust Crowdsourcing

Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score. By imitating workers with supervised learners and using them in a doubly robust estimation framework, we prove that the variance of estimation can be substantially reduced, even if the learner is a poor approximation. Synthetic and real-world experiments show that by combining the doubly robust approach with adaptive worker/item selection rules, we often need much lower label cost to achieve nearly the same accuracy as in the ideal world where all workers label all data points.

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.

The US Defense Economy

The United States defence economy is remarkable for a number of reasons - including sheer size. It receives a significant (albeit decreasing) share of GDP and has a significant international footprint. Its purpose is to provide the resources for national defence - against a set of complex and capable adversaries. The main players in the defence economy are households, and the Federal Government. The associated interactions determine the resources provided for national defence and their allocation among various defence needs. This Element focuses primarily on interactions between government and industrial suppliers within the institutional peculiarities of the defence marketplace. This includes the developments that have determined the course of defence industry consolidation post-Cold War. The authors also highlight the persistent gap between resources available for defence and the means to execute the National Security Strategy. Finally, they offer some tentative thoughts regarding developments likely to shape the defence economy's future.

“There Is No Form in the Middle”

Across his vast body of multimedia art, Kevin Jerome Everson pursues sophisticated formal exercises that deploy representational devices with the aim of achieving “massive abstractions.” Focusing on the sculptural potential of film as a time-based medium, Everson crafts his films as sculptural objects. It is a process that works toward a point of critical density in which time's material effects on a space, a body, or the screen are rendered visible. In order to reach this point, Everson has developed a rigorous practice that includes casting his own solid rubber props, carefully choreographing films that repeat formal and bodily gestures, and making oblique references to cinematic history and its foundational relationship to factory labor. In this interview the liquid blackness editors speak with Everson about his “massive” creative project; its pursuit of layered self- referentiality; the work's sheer size (measured in labor hours, custom props molded, and film titles); his fine art training; artistry as the mastery of craft; the high art of Richard Pryor; his hesitant, delicate approach to blackness; and the possibility of a midwestern, or specifically Mansfield, Ohio, artistic sensibility.

Generalized and scalable trajectory inference in single-cell omics data with VIA

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

The Luit in Bhupen Hazarika’s Songs: A Metaphor for Exploring Assam’s Linguistic and Ethnic Politics

The river Brahmaputra, also known as Luit, has always occupied an important place in the cultural mindscape of the people of Assam, a state in the northeast of India. A source of great pride because of its sheer size and the myths and lore associated with it, it has nevertheless brought untold misery to people over the years because of annual flooding. Authors and musicians of the land have found in the Luit an apt metaphor to tell stories of love, loss, belonging and pain. In the songs of Bhupen Hazarika (1926-2011), a renowned music composer from Assam, the Brahmaputra becomes a character through which the poet expresses both his anguish at the sufferings of the masses and his joy at the all- embracing nature of the valley. In songs like “Mahabahu Brahmaputra”, Hazarika tries to appeal to the people of Assam to maintain harmony and promote the land as one of plurality and hospitality. This song becomes significant when seen in the context of the Assam movement (a six-year long agitation to halt the illegal migration of people from neighbouring Bangladesh) and Hazarika’s own conflicted attitude towards it. This article is an attempt to examine how the Luit has been represented in a selection of Hazarika’s songs – the ways the river becomes a potent presence of deeply political and social overtones and a metaphor to underscore the turbulent history of Assam.

Review on Dark Web and Its Impact on Internet Governance

Cyber attackers use the Dark Web, a collection of facilities that are not visible to search engines and normal users, to explore a variety of illegal products and services. In this paper, the Dark Web and its impact on internet governance were analyzed. The findings of a review of the literature provide in-depth knowledge on the increasing number of crimes committed on the Dark Web, considering the economic, social, along ethical consequences of cybercrime on the Dark Web, as well as analyzing the consequences and methods for locating the criminals, as well as their drawbacks. Fraudsters, militants, and government-sponsored secret agents used the Dark Web where is among the most popular difficult together with unidentifiable channels to achieve their illicit goals. Crimes that were committed on the Dark Web are similar to criminal offenses committed in the real world. Nevertheless, the sheer size of the Dark Web, the unpredictability of the ecosystem, as well as the privacy and confidentiality afforded by Dark Web services, were also critical challenges in tracing criminals. Measuring the yachting Dark Web crime risks is a critical step in discovering alternative approaches to cybercrime. The study reveals that Dark Web services are available to arrest criminals, as well as digital facts and evidence, should be analyzed and applied in a way that allows Internet Governance.

Music Index with Full Text

Music Index with Full Text is an expansion of Music Index (formerly The Music Index Online), an EBSCO music periodical database that provides comprehensive coverage of the music field from 1970 to the present. Over 800 journals are indexed, and coverage includes various music styles and topics. Music Index with Full Text has added full text journal coverage from approximately 200 journals.The EBSCO interface is familiar to many users and offers easy integration with other heavily used music databases, such as RILM and RIPM. While the sheer size of citations and variety of music materials and styles is beneficial, Music Index might not be as useful to researchers focusing purely on historical music scholarship. The addition of full text journals is welcome, but the content of the journals varies widely in scope and content.

Improving BGP Convergence And Reachability Through Stable Path Aggregation (SPAGG)

BGP is the standard inter-domain routing protocol of the internet. It has proven to be scalable enough to accommodate the exceptional growth of the Internet. However, because of the sheer size of the Internet and the complexity of its topology, the behaviour of BGP can be unpredictable sometimes. Researchers have been proposing various changes and enhancements in the past 10 to 15 years to improve the security, stability and convergence of BGP. Some of the solutions have been adopted, but BGP is still suffering from possible deficiencies when it comes to convergence time and stability at specific situations and scenarios. In this thesis, we focus on providing a reasonable solution for the problem of BGP instability but without causing long convergence, which leads eventually into minimizing BGP churn and path exploration. We, first, analyse the current BGP standard protocol and previous proposed solutions. Then, we study current problems associated with a recently proposed improvement, suggest a new algorithm that avoids path selection problem at the aggregator and the path shortening problem. We also describe its implementation in OPNET. Finally, we show the results from our simulation and compare them to the results of previous work suggested. Our results show a great improvement of the convergence of BGP while preserving reachability and optimality all the time.