Solihull College

In this study, equal channel angular pressing (ECAP) technique was used as the severe plastic deformation method. The ECAP process was applied to the samples of Al–Si–Mg alloy, which further were joined via the friction welding (FW) technique. ECAP was applied to the samples for up to two passes. Microstructural and mechanical characterization of the material, tensile and hardness tests, as well as the optical microscope (OM), scanning electron microscope (SEM), and fractured-surface examinations were performed after the homogenization process and after each ECAP pass. In the study, the FW joining of Al–Si–Mg alloy samples preliminarily subjected to ECAP process and the effects of microstructure on the mechanical properties of FW joining of ECAP materials were examined. The aim of the study is to examine the mechanical effect of the material, which is pre-processed with severe plastic deformation, on the friction welding. As a result, in mechanical properties, a 30–40% increase in hardness was observed in ECAP 1 and 2 passes compared to homogenized materials, while an increase in yield strength was observed in direct proportion to the increase in the number of ECAP passes. Reduction in grain sizes after the ECAP process positively affected the strength and hardness values.

A revolutionary era in healthcare has begun with the merging of artificial intelligence (AI) with sensor technology, which has completely changed how patient care, diagnosis, and treatment approaches are provided. This abstract explores the transformative potential of AI-powered sensors in a range of healthcare disciplines. Healthcare data analysis, disease identification, and treatment planning have all been transformed by AI and machine learning (ML) algorithms. AI-driven literature mining approaches, in particular, have made it possible to process large biomedical text databases quickly and efficiently, revealing priceless insights into intricate biological relationships and disease causes. ML's ability to detect concepts in medical literature is demonstrated by the neural concept recognizer, a neural dictionary model that uses convolutional neural networks. It outperforms conventional rule-based approaches and shows how knowledge can be applied to a variety of terminologies. These developments highlight how AI-powered tools have the potential to transform healthcare decision-making processes and speed up research by offering quick and thorough insights. To fully reap the rewards of AI-powered sensors in healthcare, a number of obstacles must be overcome. Maintaining current ontologies, integrating various data sources with ease, guaranteeing algorithm transparency, reducing biases in training datasets, and protecting patient privacy are a few of these. AI-powered sensors have the potential to transform healthcare delivery completely and advance medical research, but to reach their full potential and improve patient outcomes and the healthcare ecosystem as a whole significant efforts must be made to overcome current obstacles.

Simple SummaryAlthough animals are being employed for a growing number of roles to support people, the terms used to describe those animals (e.g., “therapy animal” and “emotional support animal”) can be confusing. The same term may be used to describe different types of work, or the same role can be described with different terms. This paper presents the results of a collaboration between over 100 researchers, practitioners, and end users of animal-based supports from all over the world. We created working definitions for the following nine terms: “assistance animal”, “companion animal”, “educational/school support animal”, “emotional support animal”, “facility animal”, “service animal”, “skilled companion animal”, “therapy animal”, and “visiting/visitation animal”. In this paper, we describe the defining characteristics of each animal type and how it is different from the other types. We recommend phasing out the terms “skilled companion animal” and “service animal”, because they are similar to other terms. We discuss how our definitions may be received in different parts of the world.The nomenclature used to describe animals working in roles supporting people can be confusing. The same term may be used to describe different roles, or two terms may mean the same thing. This confusion is evident among researchers, practitioners, and end users. Because certain animal roles are provided with legal protections and/or government-funding support in some jurisdictions, it is necessary to clearly define the existing terms to avoid confusion. The aim of this paper is to provide operationalized definitions for nine terms, which would be useful in many world regions: “assistance animal”, “companion animal”, “educational/school support animal”, “emotional support animal”, “facility animal”, “service animal”, “skilled companion animal”, “therapy animal”, and “visiting/visitation animal”. At the International Society for Anthrozoology (ISAZ) conferences in 2018 and 2020, over 100 delegates participated in workshops to define these terms, many of whom co-authored this paper. Through an iterative process, we have defined the nine terms and explained how they differ from each other. We recommend phasing out two terms (i.e., “skilled companion animal” and “service animal”) due to overlap with other terms that could potentially exacerbate confusion. The implications for several regions of the world are discussed.

Abstract Cryptodome and dome collapse is associated with volcanic hazards, such as explosive eruptions, pyroclastic density currents, and volcanic edifice collapse. The study of the growth and evolution of volcanic domes provides vital information on the link between dome growth and the development of weakness zones that may cause collapse. The Cerro Bayo cryptodome is superbly exposed in the eroded Miocene Chachahuén volcano in the Neuquén basin, Argentina. Cerro Bayo is a >0.3‐km3 trachyandesitic cryptodome that intruded within the uppermost kilometer of the Chachahuén volcano. Here we investigate the emplacement of the Cerro Bayo cryptodome using structural mapping, photogrammetry and measurement of magma flow indicators, brittle deformation features, and magnetic fabrics with anisotropy of magnetic susceptibility. Magma flow fabrics near the margin are concentric and indicate contact‐parallel flow and internal inflation of the body. Magmatic and magnetic fabrics and fracture patterns in the interior of the cryptodome are more complex and outline several structural domains. These domains are separated by magmatic shear zones that accommodated intrusion growth. The shear zones locally overprint the earlier formed concentric fabric. The nature of the structural domains shows that the emplacement of Cerro Bayo occurred in three stages that resemble the endogenous to exogenous growth of volcanic domes. The formation of magmatic shear zones during cryptodome formation may have a profound effect on cryptodome stability by creating weakness zones that increase the risk of collapse.

Commentary to: Psychosocial interventions in opiate substitution treatment services: does the evidence provide a case for optimism or nihilism?

Should nurses have to fund the nursing and midwifery council now that the government has announced an end to self-regulation? we asked our readers panel

An estimated 10 million people in the UK are affected by insomnia. We asked our readers panel if enough attention is paid to the health benefits of a good night’s sleep

Abstract For a long time now, David Balding has been considered one of the most gifted mathematicians working in the field of forensic interpretation. His 1994 paper on population subdivision, relatedness and database searching must be one of the top papers of the 90's and led to significant changes in the way that DNA was interpreted in Europe and Australia. This book shows him to be a gifted writer as well.