bluuebunny/crossref_metadata_2025_split huggingface dataset:
Startup acquisitions, relocation, and employee entrepreneurship
Authors: Kim J. Daniel 
Published: April 2022

AbstractResearch SummaryThis study examines the impact of startup acquisition on the acquired employees' propensity for entrepreneurship. As a new owner, the acquirer may not prioritize acquired employees' ideas to be commercialized internally, thereby increasing the odds that they are pursued externally. Leveraging employee‐employer matched data from U.S. Census on high‐tech startup acquisitions and their workforce in 1990–2011, I find that startup acquisitions substantially increase the rate of employee entrepreneurship both within and outside the target firm's industry. Post‐acquisition entrepreneurship is disproportionally concentrated among individuals with high human capital. Moreover, this effect is amplified when the target startup is relocated to the acquirer's location, and largely muted when left in its original location. Overall, this study highlights startup acquisitions as an important organizational precursor to employee entrepreneurship.Managerial SummaryThe past few decades have shown a significant increase in established firms acquiring startups. Although startup acquisitions can be a new source of technological innovation, talent, and market power, their long‐term outcomes likely depend on the ability to retain the acquired personnel. Using comprehensive data in the United States in 1990–2011, this study shows that startup acquisitions push many acquired employees to leave and launch their own ventures, some of which can be new competitors. This effect is disproportionately driven by the departures of founders and other top earners, who are generally the most valuable individuals to retain after the acquisition. Acquirers can mitigate these effects by allowing the acquired startup to remain in its original location rather than relocating it inside their existing establishments.
Shear performance of vertical joints with different lapping splices for precast concrete frame–shear wall structures
Authors: Li Yongquan , Tong Jiaxin , Yang Yuanzhang , Sun Bochao , Zhao Weijian 
Published: April 2022

AbstractPrecast concrete structures have been widely applied in the construction industry worldwide. However, the current construction method for the reinforced concrete frame–shear wall structure (FSWS) is semi‐precast because the shear wall is commonly cast‐in‐place. This paper proposes a full precast FSWS and investigates three new vertical joints with different lap splices that can facilitate on‐site construction. Push‐off tests are conducted to investigate the shear performance on the aspect of cracking and failure patterns, the evolution of deformation, stiffness reduction, and energy dissipation. A finite element model with delicate consideration of the contact is developed and validated against experimental data. A further parametric study is conducted. This study shows that the lap splice with a more uniformly distributed reinforcement layout yields the crack distribution more uniformly and increases the ductility of the joints. The proposed three vertical joints exhibit an improvement in ductility compared to the monolithic one. Furthermore, the shear keys change the failure mechanism of the precast joints into the multi‐struts failure mechanism thus increasing the shear strength. The parametric study shows that the decreasing of the splice lap length could increase the shear capacity of the vertical joints but decreases the ductility. However, the ductility reduction depends on the variation of lap splices.
Water insecurity in the Global North: A review of experiences in U.S. colonias communities along the Mexico border
Authors: Wutich Amber , Jepson Wendy , Velasco Carmen , Roque Anais , Gu Zhining , Hanemann Michael , Hossain Mohammed Jobayer , Landes Laura , Larson Rhett , Li Wen Wen , Morales‐Pate Olga , Patwoary Nargish , Porter Sarah , Tsai Yu‐shiou , Zheng Madeleine , Westerhoff Paul 
Published: April 2022

AbstractSince the late 1970s, the term “colonias” (in English) has described low‐income, peri‐urban, and rural subdivisions north of the U.S.‐Mexico border. These communities are in arid and semi‐arid regions—now in a megadrought—and tend to have limited basic infrastructure, including community water service and sanitation. Recent scholarship has demonstrated how colonias residents experience unjust and inequitable dynamics that produce water insecurity in the Global North. In this review, we explain why U.S. colonias are an important example for theorizing water insecurity in the United States and beyond in the Global North. Tracing the history of water infrastructure development in U.S. colonias, we show how colonias are legally and socially defined by water insecurity. We draw on the published literature to discuss key factors that produce water insecurity in U.S. colonias: political exclusion, municipal underbounding, and failures in water quality monitoring. We show that water insecurity had led to negative outcomes—including poor water access, risks to physical health, and mental ill‐health—in U.S. colonias. We present four possible approaches to improving water security in U.S. colonias: (1) soft paths & social infrastructure for water delivery, (2) decentralized water treatment approaches, such as point‐of‐use, point‐of‐entry, and fit‐for‐purpose systems; (3) informality, including infrastructural, economic, and socio‐cultural innovations; and (4) political, policy, and law innovations and reforms. At the same time, we reflect seriously on how water security can be ethically achieved in partnership and aligning with the visions of U.S. colonias residents themselves.This article is categorized under:
Human Water > Water Governance
Engineering Water > Water, Health, and Sanitation
Statistical inference for stochastic differential equations
Authors: Craigmile Peter , Herbei Radu , Liu Ge , Schneider Grant 
Published: April 2022

AbstractMany scientific fields have experienced growth in the use of stochastic differential equations (SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs can simultaneously capture the known deterministic dynamics of underlying variables of interest (e.g., ocean flow, chemical and physical characteristics of a body of water, presence, absence, and spread of a disease), while enabling a modeler to capture the unknown random dynamics in a stochastic setting. We focus on reviewing a wide range of statistical inference methods for likelihood‐based frequentist and Bayesian parametric inference based on discretely‐sampled diffusions. Exact parametric inference is not usually possible because the transition density is not available in closed form. Thus, we review the literature on approximate numerical methods (e.g., Euler, Milstein, local linearization, and Aït‐Sahalia) and simulation‐based approaches (e.g., data augmentation and exact sampling) that are used to carry out parametric statistical inference on SDE processes. We close with a brief discussion of other methods of inference for SDEs and more complex SDE processes such as spatio‐temporal SDEs.This article is categorized under:
Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Algorithms and Computational Methods > Maximum Likelihood Methods
Statistical Models > Fitting Models
Technologies and Applications for Big Data Value
Authors: Curry Edward , Auer Sören , Berre Arne J. , Metzger Andreas , Perez Maria S. , Zillner Sonja 
Published: April 2022

AbstractThe continuous and significant growth of data, together with improved access to data and the availability of powerful computing infrastructure, has led to intensified activities around Big Data Value (BDV) and data-driven Artificial Intelligence (AI). Powerful data techniques and tools allow collecting, storing, analysing, processing and visualising vast amounts of data, enabling data-driven disruptive innovation within our work, business, life, industry and society.The adoption of big data technology within industrial sectors facilitates organisations to gain a competitive advantage. Driving adoption is a two-sided coin. On one side, organisations need to master the technology necessary to extract value from big data. On the other side, they need to use the insights extracted to drive their digital transformation with new applications and processes that deliver real value. This book has been structured to help you understand both sides of this coin and bring together technologies and applications for Big Data Value.This chapter defines the notion of big data value, introduces the Big Data Value Public-Private Partnership (PPP) and gives some background on the Big Data Value Association (BDVA)—the private side of the PPP. It then moves on to structure the contributions of the book in terms of three key lenses: the BDV Reference Model, the Big Data and AI Pipeline, and the AI, Data and Robotics Framework.
3D Human Big Data Exchange Between the Healthcare and Garment Sectors
Authors: Durá Gil Juan V. , Remon Alfredo , Rodriguez Iván Martínez , Pariente-Lobo Tomas , Salmeron-Majadas Sergio , Perrone Antonio , Ciuhu-Pijlman Calina , Znamenskiy Dmitry , Karavaev Konstantin , Codina Javier Ordono , Boura Laura , Silva Luísa , Redon Josep , Real Jose , Cipresso Pietro 
Published: April 2022

Abstract3D personal data is a type of data that contains useful information for product design, online sale services, medical research and patient follow-up.Currently, hospitals store and grow massive collections of 3D data that are not accessible by researchers, professionals or companies. About 2.7 petabytes a year are stored in the EU26.In parallel to the advances made in the healthcare sector, a new, low-cost 3D body-surface scanning technology has been developed for the goods consumer sector, namely, apparel, animation and art. It is estimated that currently one person is scanned every 15 min in the USA and Europe. And increasing.The 3D data of the healthcare sector can be used by designers and manufacturers of the consumer goods sector. At the same time, although 3D body-surface scanners have been developed primarily for the garment industry, 3D scanners’ low cost, non-invasive character and ease of use make them appealing for widespread clinical applications and large-scale epidemiological surveys.However, companies and professionals of the consumer goods sector cannot easily access the 3D data of the healthcare sector. And vice versa. Even exchanging information between data owners in the same sector is a big problem today. It is necessary to overcome problems related to data privacy and the processing of huge 3D datasets.To break these silos and foster the exchange of data between the two sectors, the BodyPass project has developed: (1) processes to harmonize 3D databases; (2) tools able to aggregate 3D data from different huge datasets; (3) tools for exchanging data and to assure anonymization and data protection (based on blockchain technology and distributed query engines); (4) services and visualization tools adapted to the necessities of the healthcare sector and the garment sector.These developments have been applied in practical cases by hospitals and companies of in the garment sector.
Data-Driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins
Authors: Unal Perin , Albayrak Özlem , Jomâa Moez , Berre Arne J. 
Published: April 2022

AbstractThis chapter presents a Digital Twin Pipeline Framework of the COGNITWIN project that supports Hybrid and Cognitive Digital Twins, through four Big Data and AI pipeline steps adapted for Digital Twins. The pipeline steps are Data Acquisition, Data Representation, AI/Machine learning, and Visualisation and Control. Big Data and AI Technology selections of the Digital Twin system are related to the different technology areas in the BDV Reference Model. A Hybrid Digital Twin is defined as a combination of a data-driven Digital Twin with First-order Physical models. The chapter illustrates the use of a Hybrid Digital Twin approach by describing an application example of Spiral Welded Steel Industrial Machinery maintenance, with a focus on the Digital Twin support for Predictive Maintenance. A further extension is in progress to support Cognitive Digital Twins includes support for learning, understanding, and planning, including the use of domain and human knowledge. By using digital, hybrid, and cognitive twins, the project’s presented pilot aims to reduce energy consumption and average duration of machine downtimes. Data-driven artificial intelligence methods and predictive analytics models that are deployed in the Digital Twin pipeline have been detailed with a focus on decreasing the machinery’s unplanned downtime. We conclude that the presented pipeline can be used for similar cases in the process industry.
Model-Based Engineering and Semantic Interoperability for Trusted Digital Twins Big Data Connection Across the Product Lifecycle
Authors: Lázaro Oscar , Alonso Jesús , Holom Roxana-Maria , Rafetseder Katharina , Kritzinger Stefanie , Ubis Fernando , Fritz Gerald , Wiesinger Alois , Sehrschön Harald , Nguyen Jimmy , Luniewski Tomasz , Zietak Wojciech , Clavel Jerome , Perez Roberto , Hildebrand Marlene , Kiritsis Dimitris , Garious Hugues-Arthur , de la Maza Silvia , Ventura-Traveset Antonio , Hierro Juanjo , Boege Gernot , Ahle Ulrich 
Published: April 2022

AbstractWith the rising complexity of modern products and a trend from single products to Systems of Systems (SoS) where the produced system consists of multiple subsystems and the integration of multiple domains is a mandatory step, new approaches for development are demanded. This chapter explores how Model-Based Systems Engineering (MBSE) can benefit from big data technologies to implement smarter engineering processes. The chapter presents the Boost 4.0 Testbed that demonstrates how digital twin continuity and digital thread can be realized from service engineering, production, product performance, to behavior monitoring. The Boost 4.0 testbed demonstrates the technical feasibility of an interconnected operation of digital twin design, ZDM subtractive manufacturing, IoT product monitoring, and spare part 3D printing services. It shows how the IDSA reference model for data sovereignty, blockchain technologies, and FIWARE open-source technology can be jointly used for breaking silos, providing a seamless and controlled exchange of data across digital twins based on open international standards (ProStep, QIF), allowing companies to dramatically improve cost, quality, timeliness, and business results.
A Data Science Pipeline for Big Linked Earth Observation Data
Authors: Koubarakis Manolis , Bereta Konstantina , Bilidas Dimitris , Pantazi Despina-Athanasia , Stamoulis George 
Published: April 2022

AbstractThe science of Earth observation uses satellites and other sensors to monitor our planet, e.g., for mitigating the effects of climate change. Earth observation data collected by satellites is a paradigmatic case of big data. Due to programs such as Copernicus in Europe and Landsat in the United States, Earth observation data is open and free today. Users that want to develop an application using this data typically search within the relevant archives, discover the needed data, process it to extract information and knowledge and integrate this information and knowledge into their applications. In this chapter, we argue that if Earth observation data, information and knowledge are published on the Web using the linked data paradigm, then the data discovery, the information and knowledge discovery, the data integration and the development of applications become much easier. To demonstrate this, we present a data science pipeline that starts with data in a satellite archive and ends up with a complete application using this data. We show how to support the various stages of the data science pipeline using software that has been developed in various FP7 and Horizon 2020 projects. As a concrete example, our initial data comes from the Sentinel-2, Sentinel-3 and Sentinel-5P satellite archives, and they are used in developing the Green City use case.
Supporting Semantic Data Enrichment at Scale
Authors: Ciavotta Michele , Cutrona Vincenzo , De Paoli Flavio , Nikolov Nikolay , Palmonari Matteo , Roman Dumitru 
Published: April 2022

AbstractData enrichment is a critical task in the data preparation process in which a dataset is extended with additional information from various sources to perform analyses or add meaningful context. Facilitating the enrichment process design for data workers and supporting its execution on large datasets are only supported to a limited extent by existing solutions. Harnessing semantics at scale can be a crucial factor in effectively addressing this challenge. This chapter presents a comprehensive approach covering both design- and run-time aspects of tabular data enrichment and discusses our experience in making this process scalable. We illustrate how data enrichment steps of a Big Data pipeline can be implemented via tabular transformations exploiting semantic table annotation methods and discuss techniques devised to support the enactment of the resulting process on large tabular datasets. Furthermore, we present results from experimental evaluations in which we tested the scalability and run-time efficiency of the proposed cloud-based approach, enriching massive datasets with promising performance.
Processing Big Data in Motion: Core Components and System Architectures with Applications to the Maritime Domain
Authors: Giatrakos Nikos , Deligiannakis Antonios , Bereta Konstantina , Vodas Marios , Zissis Dimitris , Alevizos Elias , Akasiadis Charilaos , Artikis Alexander 
Published: April 2022

AbstractRapidly extracting business value out of Big Data that stream in corporate data centres requires continuous analysis of massive, high-speed data while they are still in motion. So challenging a goal entails that analytics should be performed in memory with a single pass over these data. In this chapter, we outline the challenges of Big streaming Data analysis for deriving real-time, online answers to application inquiries. We review approaches, architectures and systems designed to address these challenges and report on our own progress within the scope of the EU H2020 project INFORE. We showcase INFORE into a real-world use case from the maritime domain and further discuss future research and development directions.
Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective
Authors: Berre Arne J. , Tsalgatidou Aphrodite , Francalanci Chiara , Ivanov Todor , Pariente-Lobo Tomas , Ruiz-Saiz Ricardo , Novalija Inna , Grobelnik Marko 
Published: April 2022

AbstractBig Data and AI Pipeline patterns provide a good foundation for the analysis and selection of technical architectures for Big Data and AI systems. Experiences from many projects in the Big Data PPP program has shown that a number of projects use similar architectural patterns with variations only in the choice of various technology components in the same pattern. The project DataBench has developed a Big Data and AI Pipeline Framework, which is used for the description of pipeline steps in Big Data and AI projects, and supports the classification of benchmarks. This includes the four pipeline steps of Data Acquisition/Collection and Storage, Data Preparation and Curation, Data Analytics with AI/Machine Learning, and Action and Interaction, including Data Visualization and User Interaction as well as API Access. It has also created a toolbox which supports the identification and use of existing benchmarks according to these steps in addition to all of the different technical areas and different data types in the BDV Reference Model. An observatory, which is a tool, accessed via the toolbox, for observing the popularity, importance and the visibility of topic terms related to Artificial Intelligence and Big Data technologies has also been developed and is described in this chapter.
Privacy-Preserving Technologies for Trusted Data Spaces
Authors: Bonura Susanna , Carbonare Davide Dalle , Díaz-Morales Roberto , Fernández-Díaz Marcos , Morabito Lucrezia , Muñoz-González Luis , Napione Chiara , Navia-Vázquez Ángel , Purcell Mark 
Published: April 2022

AbstractThe quality of a machine learning model depends on the volume of data used during the training process. To prevent low accuracy models, one needs to generate more training data or add external data sources of the same kind. If the first option is not feasible, the second one requires the adoption of a federated learning approach, where different devices can collaboratively learn a shared prediction model. However, access to data can be hindered by privacy restrictions. Training machine learning algorithms using data collected from different data providers while mitigating privacy concerns is a challenging problem. In this chapter, we first introduce the general approach of federated machine learning and the H2020 MUSKETEER project, which aims to create a federated, privacy-preserving machine learning Industrial Data Platform. Then, we describe the Privacy Operations Modes designed in MUSKETEER as an answer for more privacy before looking at the platform and its operation using these different Privacy Operations Modes. We eventually present an efficiency assessment of the federated approach using the MUSKETEER platform. This chapter concludes with the description of a real use case of MUSKETEER in the manufacturing domain.
Investive Statusarbeit als Modus der Lebensführung: Das theoretische Modell
Authors: Kumkar Nils C. , Holubek-Schaum Stefan , Gottschall Karin , Hollstein Betina , Schimank Uwe 
Published: April 2022

ZusammenfassungDieses Kapitel stellt ein generelles soziologisches Modell von Lebensführung und daran anschließend ein Modell investiver Statusarbeit im Speziellen vor. Damit wird ein Idealtypus als theoretischer Suchscheinwerfer ausgerichtet, der das empirische Vorgehen anleitet.
Erfassung und apparatives Monitoring des Ernährungsstatus von Patient*innen auf der Intensiv- und Intermediate Care Station
Authors: Weimann Arved , Hartl Wolfgang H. , Adolph Michael , Angstwurm Matthias , Brunkhorst Frank M. , Edel Andreas , de Heer Geraldine , Felbinger Thomas W. , Goeters Christiane , Hill Aileen , Kreymann K. Georg , Mayer Konstantin , Ockenga Johann , Petros Sirak , Rümelin Andreas , Schaller Stefan J. , Schneider Andrea , Stoppe Christian , Elke Gunnar 
Published: April 2022

ZusammenfassungDie Erhebung des Ernährungsstatus zum Zeitpunkt der Aufnahme im Intensiv- oder Intermediate Care Bereich hat sowohl prognostische als auch therapeutische Relevanz im Hinblick auf die Planung einer individualisierten medizinischen Ernährungstherapie (engl. „medical nutrition therapy“, MNT). Diese Planung wird im Rahmen der Erstversorgung eines vital bedrohlichen Krankheitsbilds nachvollziehbar nicht priorisiert, jedoch im weiteren Verlauf häufig auch oft nicht mehr angemessen durchgeführt. Vor allem bei längerer Verweildauer besteht das Risiko einer Mangelernährung mit Aufbau eines kumulativen, prognoserelevanten Makro- und/oder Mikronährstoffdefizits. Bisher gibt es für Patient*innen auf Intensiv- und Intermediate Care Einheiten keine strukturierten Empfehlungen zur Erhebung des Ernährungsstatus. Das vorliegende Positionspapier der Sektion Metabolismus und Ernährung der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin (DIVI) beinhaltet konsensbasierte Empfehlungen zur Erfassung und zum apparativen Monitoring des Ernährungsstatus von Patient*innen auf Intensiv- und Intermediate Care Stationen. Diese Empfehlungen ergänzen die aktuelle S2k-Leitlinie „Klinische Ernährung in der Intensivmedizin“ der Deutschen Gesellschaft für Ernährungsmedizin (DGEM) und der DIVI.