The technology of Incremental Sheet Forming - a brief review of the history

This paper describes the history of Incremental Sheet Forming (ISF) focusing on technological developments. These developments are in general protected by patents, so the paper can also be regarded as an overview of ISF patents in addition to a description of the early history. That history starts with the early work by Mason in 1978 and continues up to the present day. An extensive list of patents including Japanese patents is provided.\ud \ud The overall conclusion is that ISF has received the attention of the world, in particular of the automotive industry, and that most proposed or suspected applications focus on the flexibility offered by the process. Only one patent has been found that is explicitly related to the enhancement of formability. Furthermore, most patents refer to TPIF (Two-Point Incremental Forming) as a process.\ud \ud Besides simply presenting a historical overview the paper can act as an inspiration for the researcher, and present a rough idea of the patentability of new developments

Black hole and de Sitter solutions in a covariant renormalizable field theory of gravity

It is shown that Schwarzschild black hole and de Sitter solutions exist as exact solutions of a recently proposed relativistic covariant formulation of (power-counting) renormalizable gravity with a fluid. The formulation without a fluid is also presented here. The stability of the solutions is studied and their corresponding entropies are computed, by using the covariant Wald method. The area law is shown to hold both for the Schwarzschild and for the de Sitter solutions found, confirming that, for the $\beta=1$ case, one is dealing with a minimal modification of GR.Comment: 7 paages, latex fil

enteroviral infections and development of type 1 diabetes the brothers karamazov within the cvbs

Type 1 diabetes (T1D) is the result of a selective autoimmune destruction of pancreatic islet β-cells, occurring in genetically predisposed subjects, possibly triggered or accelerated by environmental agents (1). Both innate (2) and adaptive (3) immune responses are involved in islet inflammation in T1D. The role of environmental factors has become increasingly relevant, as indicated by the marked recent rise of incidence (4), impossible to explain based on genetic changes alone. One of the environmental risk factors identified by several independent studies in man and in animal models (5) is represented by enteroviral infections, which have been epidemiologically associated to T1D development (6). Enteroviruses may contribute to the pathological events leading to β-cell damage by several different mechanisms, such as virus-induced cytolysis or islet inflammation leading to subclinical β-cell destruction (7). However, it should also be taken into account that in specific settings viral infections may also protect from diabetes development (8). In this issue, two closely related articles written by Oikarinen et al. (9) and Laitinen et al. (10) provide important information on the potential roles of enteroviruses, and more specifically of group B coxsackieviruses (CVB), in modulating susceptibility to T1D development. Neutralizing antibodies against CVBs have been measured in a longitudinal sample series from a large prospective birth cohort in Finland (9) as well as cross-sectionally in children with newly diagnosed T1D and control subjects (10) matched according to sampling time, gender, age, and country,

Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science

Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Personalized Medicine and Machine Learning: A Roadmap for the Future

: In the last ten years, many advances have been made in the treatment and diagnosis of immune-mediated diseases [...]

Diagnosis, Clinical Features and Management of Interstitial Lung Diseases in Rheumatic Disorders: Still a Long Journey

: Interstitial lung disease (ILD) is one of the most frequent pulmonary complications of autoimmune rheumatic diseases (ARDs), and it is mainly associated with connective tissue diseases (CTDs) and rheumatoid arthritis (RA) [...]