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ABSTRACTReliable and reproducible analysis of surfaces is critically dependent on how samples have been collected, handled, and prepared before analysis. Although there are important, well‐established general guidelines to minimize unintended alteration of the surfaces to be analyzed, specific handling steps depend on the nature of the samples and information desired. Assessments of the validity and reliability of the results require adequate information about sampling, handling, and processing. ISO standard 20579 part 1 describes the information to be recorded and reported for samples that are prepared or submitted for surface analysis. This paper summarizes elements of the standard, which includes annexes that justify and inform the sample handling processes. Also included is a checklist of information that needs to be recorded and reported and an appendix containing an example of a form that might be used to report the information.

ObjectivesThe purpose of this study was to identify which, and to what extent, demographic and operational factors are indicative of likelihood for a new call handler or paramedic to remain in role within the first two years of employment at an ambulance trust using data held in the trust’s bespoke data warehouse.DesignThe study uses a retrospective observational cohort design using routinely collected data.SettingOne ambulance trust focused on a large, predominantly urban area in the UK.ParticipantsThe study used the data of all employees of the trust who started employment as call handlers (869) or paramedics (1672) between 1 January 2018 and 31 July 2023.Primary and secondary outcome measures‘Time-to-event’ analysis of ‘likelihood to remain in post within the first two years of employment’ as call handlers or paramedics via accelerated failure time regression.ResultsSeveral factors showed a significant contribution to the likelihood of remaining in post within an ambulance National Health Service Trust. Among the findings, short-term sick leave in the first two years of employment was associated with increased retention for paramedics (0.040, 95% CI 0.030, 0.060). In addition, female call handlers were found to have increased retention (0.29, 95% CI 0.043, 0.54), and paramedic retention increased with time outside of ‘job cycle time’ (JCT) activities (ie, activities other than responding to calls) (0.097, 95% CI 0.057, 0.14).ConclusionsThis study presents a method for extracting new insights from routinely collected operational data, identifying common drivers and specific predictors for retention among the ambulance NHS workforce. It emphasises the importance of workforce-centred retention strategies, highlighting the need for non-JCT time, which in turn would allow paramedics to have time to reflect and recuperate to avoid burnout and attrition. The study also suggests that a lack of sick leave might indicate a lack of trust and self-care culture, potentially leading to paramedic staff attrition. Our approach to retention analytics provides a new mechanism for trusts to monitor and respond to their attrition risks in a timely, proactive fashion.

Artificial intelligence (AI) is no longer a farfetched reality depicted in movies from decades ago.The AI revolution has begun transforming every aspect of organizational life.Business-to-business (B2B) sales organizations are among the business sectors profoundly affected by AI.Unlike previous sales technologies that disrupted B2B sales, such as sales force automation (SFA), customer relationship management (CRM), or social media platforms that--digitized and streamlined existing work routines of salespersons and sales managers-AI introduces a qualitatively different set of dynamics.Its advanced capabilities to mimic human behavior and intervein in decisionmaking, reshape traditional ways of approaching, developing, and maintaining buyer-seller relationships, reconfiguring how work sales work is organized.While sales organizations' interest in adopting AI has grown rapidly, recent industry reports reveal that the optimization of these technologies has fallen short of managerial expectations, with many forms struggling to realize the expected value from AI investments.Guided by the overarching research question "How do AI-enabled transformations reshape the socio-technical systems of sales organizations?", the thesis develops both conceptual and empirical insights by drawing on socio-technical systems theory (STS), which emphasizes that technologyinduced change cannot be understood in isolation but unfolds through interactions between people, structures, technologies, and work tasks.The thesis is comprised of three articles.Article I introduces STS as an external grand theory to the sales technology research domain to understand technology-enabled transformations holistically.By synthesizing literature across disciplines, Article I establishes the theoretical scaffolding for the thesis by foregrounding the core constructs of STS-socio-technical inertia, microfoundations, and sociomateriality.Article II empirically explores how AI reconfigures B2B sales processes across the pre-sales, sales, and postsales phases, illustrating that transformations brough forth by AI into the sales processes are recursive and uneven, characterized by tensions such as efficiency vs. contextual sensitivity of AI insights as well as managerial oversight vs. salesperson autonomy.Article III situates the analysis within the banking industry, where regulatory constraints, legacy infrastructures, and trust-based customer relationships shape how AI is integrated into customer advisory and sales practices.The findings underscore the notion that AI not only automates and augments sales tasks but also reshapes professional identities, redistributes decision-making authority, and redefines what constitutes as professional expertise.Taken together, the three articles in this thesis underline that AI-enabled transformations in sales organizations can be best understood as a sociotechnical phenomenon unfolding through five socio-technical mechanisms: centralization and power redistribution, data institutionalization and technology innovation, AI individualization and institutionalization of human-AI collaborations, identity negotiation and role reconfiguration, and legitimation of expertise and skill reconstitution.These mechanisms reveal that tensions arising from AI-enabled transformations are not mere obstacles but socio-technical entanglements that expose misalignments in sociotechnical sub-systems and that provide opportunities for sales organizations to learn and innovate.

ABSTRACT A free-range organic broiler (Gallus gallus domesticus) premises in Staffordshire was infected by high pathogenicity avian influenza virus (HPAIV) H5N8 during the 2020–2021 epizootic in the United Kingdom (UK). Following initial confirmation of the infection in poultry, multiple wild bird species were seen scavenging on chicken carcasses. Detected dead wild birds were subsequently demonstrated to have been infected and succumbed to HPAIV H5N8. Initially, scavenging species, magpie (Pica pica) and raven (Corvus corax) were found dead on the premises but over the following days, buzzards (Buteo buteo) were also found dead within the local area with positive detection of HPAIV in submitted carcasses. The subacute nature of microscopic lesions within a buzzard was consistent with the timeframe of infection. Finally, a considerable number of free-living pheasants (Phasianus colchicus) were also found dead in the surrounding area, with carcasses having higher viral antigen loads compared to infected chickens. Limited virus dissemination was observed in the carcasses of the magpie, raven, and buzzard. Further, an avirulent avian paramyxovirus type 1 (APMV-1) was detected within poultry samples as well as in the viscera of a magpie infected with HPAIV. Immunohistochemistry did not reveal colocalization of avian paramyxovirus antigens with lesions, supporting an avirulent APMV-1 infection. Overall, this case highlights scenarios in which bi-directional transmission of avian viral diseases between commercial and wild bird species may occur. It also underlines the importance of bio separation and reduced access when infection pressure from HPAIV is high.

The prelims comprise: Half-Title Page Title Page Copyright Page Table of Contents List of Contributors Foreword Preface Acknowledgments

Epidemiology and Animal Health Economics Book Editor(s):Laura Urdes, Laura Urdes University of Agricultural Sciences and Veterinary Medicine of Bucharest, Bucharest, RomaniaSearch for more papers by this authorChris Walster, Chris Walster The Island Veterinary Associates, Stafford, UKSearch for more papers by this authorJulius Tepper, Julius Tepper Long Island Fish Hospital, NY, USSearch for more papers by this author First published: 03 March 2023 https://doi.org/10.1002/9781119839729.part2 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Pathology and Epidemiology of Aquatic Animal Diseases for Practitioners RelatedInformation

Free Access Index Book Editor(s):Laura Urdes, Laura Urdes University of Agricultural Sciences and Veterinary Medicine of Bucharest, Bucharest, RomaniaSearch for more papers by this authorChris Walster, Chris Walster The Island Veterinary Associates, Stafford, UKSearch for more papers by this authorJulius Tepper, Julius Tepper Long Island Fish Hospital, NY, USSearch for more papers by this author First published: 03 March 2023 https://doi.org/10.1002/9781119839729.index AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Pathology and Epidemiology of Aquatic Animal Diseases for Practitioners RelatedInformation