bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kikuchi‐Fujimoto disease following SARS‐CoV‐2 infection: A rare disease with increased incidence during the COVID‐19 pandemic?
Authors: Leung Cheuk Cheung Derek , Chan Hiu Ching Christy , Chan Ming Chiu , Chan Yu Hong , Ho Man Ying , Chen Chun Hoi , Ngai Ching Man , Yeung Yiu Cheong 
Published: June 2024

AbstractKikuchi‐Fujimoto Disease (KFD), also known as Kikuchi disease or Kikuchi histiocytic necrotizing lymphadenitis, is a rare and self‐limiting condition characterized by cervical lymphadenopathy and fever, primarily affecting young Asian adults. The aetiology of KFD remains unknown, although various infectious agents have been suggested as potential triggers. With the emergence of the COVID‐19 pandemic, cases of post‐COVID‐19 KFD and post‐COVID‐19 vaccine KFD have been reported. In this article, we present the first case of post‐COVID‐19 KFD in Hong Kong. A 24‐year‐old man developed fever and painful neck swelling 1 month after recovering from COVID‐19. Diagnostic evaluation, including ultrasound‐guided fine needle aspiration cytology (FNAC), confirmed the diagnosis of KFD. The patient's symptoms resolved spontaneously with supportive care. This case underscores the importance of considering KFD as a potential differential diagnosis in patients presenting with cervical lymphadenopathy and fever following COVID‐19 recovery or vaccination.
Fault estimation for a class of nonlinear distributed systems using the adaptive observer technique
Authors: Liu Yulan , Yan Ge , Chen Tianrui 
Published: June 2024

AbstractIn this article, a fault estimation method is proposed for a class of nonlinear distributed systems, in which the adaptive observer technique is exploited to estimate the parameters related to the fault in the interconnection items. First, we consider the simple case that the interconnection items only contain measurable variables. And then the result will be extended to the case that the interconnection items contain unmeasurable variables. Due to the existing of nonlinear item, the state and parameter estimations cannot be decoupled from each other, which makes the analysis of the convergence of the state and parameter estimations much more difficult. To overcome this problem, by designing of an integral Lyapunov function, and by exploiting the converse theorem of Lyapunov, a sufficient condition for the exponential convergence of the proposed algorithm is derived. Moreover, the detail relationship between the observer gain, Lipschitz parameter of the nonlinear item and the PE level of the estimation system is also derived. Simulation results show the effectiveness of the proposed fault identification scheme.
Event‐triggered extended dissipativity control for semi‐Markov jump systems under multiple types of deception attacks: A semi‐Markov model approach
Authors: Wang Qian , Zhang Xiaojun , Zhong Shouming , Cheng Jun , Shi Kaibo 
Published: June 2024

AbstractThe event‐triggered extended dissipativity control problem for semi‐Markov jump systems (SMJSs) on the basis of multiple types of deception attacks is discussed in this paper. The mode‐ and disturbance‐dependent event‐triggered mechanism (ETM), unlike the traditional ETM, is designed to identify the triggered moments which can prevent network resources from being wasted. Additionally, as the attack evolves into a more intelligent and complex strategy, It is hypothesized that there are multiple deception attacks that can be applied to the communication channels between the sensor and the controller, which are modeled by a more general semi‐Markov process (SMP). As a result, by using stochastic analysis techniques and system‐mode‐dependent and attack‐mode‐dependent Lyapunov‐Krasovskii functional (LKF), a new sufficient condition is established to guarantee the stochastic stability and extended dissipativity of the closed‐loop SMJSs. Four numerical cases are given to illustrate the effectiveness of the developed method.
Emotions in search of words: Does alexithymia predict treatment outcome in chronic musculoskeletal pain?
Authors: Lanzara Roberta , Conti Chiara , Lalli Vittorio , Cannizzaro Paolo , Affaitati Gianna Pia , Giamberardino Maria Adele , Williams Alison , Porcelli Piero 
Published: June 2024

AbstractChronic pain, with its complex and multidimensional nature, poses significant challenges in identifying effective long‐term treatments. There is growing scientific interest in how psychopathological and personality dimensions may influence the maintenance and development of chronic pain. This longitudinal study aimed to investigate whether alexithymia can predict the improvement of pain severity following a treatment‐as‐usual programme for chronic musculoskeletal pain over and above psychological cofactors (emotional distress, catastrophizing, and self‐efficacy). A consecutive sample of 129 patients with diagnosed chronic musculoskeletal pain referred to two tertiary care centres was recruited and treated for 16 weeks. Clinical pain, psychological distress, self‐efficacy, catastrophizing, and alexithymia were assessed with validated self‐report measures at the first medical visit (T0) and at 16‐week follow‐up (T1). Compared with non‐responder patients (n = 72, 55.8%), those who responded (i.e., reduction of >30% in pain severity; n = 57, 44.2%) reported an overall improvement in psychological variables except alexithymia. Alexithymia showed relative stability between baseline and follow‐up within the entire sample and remained a significant predictor of treatment outcome even when other predictive cofactors (i.e., pain interference, depressive symptoms, and catastrophizing) were considered simultaneously. Our results suggest that identifying patients with a co‐occurrence between alexithymia, depressive symptoms, catastrophizing, and the stressful experience of chronic pain can be clinically relevant in pain prevention and intervention programs.
Rethinking Global Englishes and Moving toward Reparative Redress for Language‐Minoritized and Racialized TESOL Practitioners
Authors: Imperial Rowland Anthony 
Published: June 2024

AbstractIn this article, I propose an ontological break in Global Englishes‐oriented research and teaching practice, and a critical‐ethical movement beyond the five foundational paradigms of GELT. I do this by first drawing on two philosophical perspectives on liberation and justice—Enrique Dussel's (2013) ethics of liberation and Olúfẹ́mi O. Táíwò's (2022) constructive and distributive model of reparative justice—and then conceptually linking them to two critical perspectives outside of the Global Englishes paradigm, that is, Flores & Rosa's (2015, 2022) raciolinguistic perspective and Canagarajah's (2023) decolonial crip linguistics perspective. The conceptual work that I present here involves mapping out a critical‐ethical framework, a pedagogy for repair, that seeks to redress rather than reproduce structural injustices in ELT. The framework prioritizes the uptake of ethical research questions and positions and provides a heuristic for rethinking ELT in ways that allow us to be wholly committed to continuing TESOL's “transformative journey as an adaptable profession” (Rose & Galloway, 2019, p. 222). I argue that it is by critically addressing issues of injustice and ethically centering our work on the lives of language minoritized and racialized ELT/TESOL practitioners that we will ensure the long‐term adaptability and sustainability of the teaching profession.
Knowledge Transfer From Business Schools to Firms Through Academics: An AMO Perspective in an Emerging Economy
Authors: Boafo Christopher , Dornberger Utz 
Published: June 2024

ABSTRACTSociety's growing demand for knowledge transfer from higher education institutions to firms through academics is a notable trend. Nonetheless, the nuances of how business school academics perceive their engagement in knowledge transfer to firms remain inadequately explored. This research endeavors to bridge this knowledge gap by drawing upon interviews conducted with 52 business school academics in Ghana. The synthesized findings derived from the interpretive phenomenological data analysis provide crucial insights grounded in the ability–motivation–opportunity theory framework. Within this framework, “opportunity” describes image‐ and project‐opportunity context drivers, such as media engagement, goodwill, in‐service training students, and projects by international development organizations fostered through relationship‐building and networking. “Motivation” explains the established national, societal, and self‐serving mandates, stimulating institutional‐, society‐, and person‐driven motivations. “Ability” encompasses the capacity of academics to employ both generic and relational mechanisms. The interplay among ability, motivation, and opportunity catalyzes the creation of various knowledge content types linked to specific market contexts.
Studies on the association of malondialdehyde as a biomarker for oxidative stress and degree of malignancy in dogs with mammary adenocarcinomas
Authors: Schroers Maike , Walter Beate , Fischer Stine , Cremer Jessica , Bauer Eva‐Maria , Zablotzki Yury , Majzoub‐Altweck Monir , Meyer‐Lindenberg Andrea 
Published: June 2024

AbstractBackgroundMammary adenocarcinomas are one of the most common tumour diseases in bitches. The relationship between oxidative stress and the degree of malignancy of the tumour has not been sufficiently researched in veterinary medicine.ObjectivesThe main objective was to investigate the potential role of MDA as a practice‐relevant biomarker for the assessment of systemic oxidative stress and to determine whether this parameter can indicate the malignancy grade of a mammary adenocarcinoma.MethodsIn the present pilot study, MDA plasma concentrations were analysed in 55 bitches with (n = 28) and without (n027) malignant adenocarcinomas of the mammary gland using two different measurement methods and the relationship to tumour size was investigated.ResultsThe mean MDA concentration measured by enzyme‐linked immunosorbent assay (ELISA) was 289 ng/mL (range 365–634 ng/mL) in dogs with grade 1 adenocarcinoma (n = 13), 288.5 ng/mL (range 85–752 ng/mL) in dogs with grade 2 adenocarcinoma (n = 10), 332 ng/mL (range 239–947 ng/mL) in dogs with grade 3 (n = 5) adenocarcinoma and 293 ng/mL (range 175–549 ng/mL) in dogs without a mammary tumour (n = 27). When MDA was measured by HPLC, the average MDA concentration in the study group (n = 11) was 0.24 µmol/L (range 0.16–0.37) and that of the control group (n = 15) was 0.27 µmol/L (range 0.16–1.62). Thus, there were no significant differences between the study group with malignant adenocarcinomas and the control group in both examination methods (p > 0.05). Furthermore, there was no correlation between the MDA concentrations and the approximate volume of the mammary tumour.ConclusionThe results highlight the challenges of providing a prognosis for the malignancy of a mammary adenocarcinoma based on MDA concentrations in plasma using ELISA or HPLC. As a result, histopathological examination remains the gold standard for diagnosing and differentiating adenocarcinomas of the mammary gland.
Consumer experiences navigating health care provider directories and support of federal policy action
Authors: Haeder Simon F. , Xu Wendy Yi 
Published: June 2024

AbstractMost Americans rely on managed care networks and provider directories to access care. Little is known about how patients experience navigating directories, how it affects their lives, and whether Americans support federal action on the issue. We fielded a large, and demographically diverse survey of US adults (N = 10,739) from January to April 2022. The survey queried respondents about their experience using provider directories, problems they encountered, and support for federal policy action. Analyses were conducted using Weighted Ordinary Least Squares regression. We found that 43% of Americans used provider directories last year. Among these, provider directory inaccuracy affected 56%, with inaccurate network status (34%), providers having left the practice (18%), and wrong contact information (15%) serving as primary problems. A majority of those using provider directories faced challenges in accessing care subsequently. This included 28% delaying care, 10% seeking out Urgent Care and 6% Emergency Care, respectively, 5% going out of network, and 10% forging care. A substantial number suffered health (54%) and financial (51%) problems as a result and about half took some action as a result. Overall, Americans strongly supported various federal actions to improve directory accuracy. Provider directory inaccuracy affects a larger number of Americans, many of whom are severely affected by the challenges. Broad public support should encourage federal action on the issue.
Using the full potential of Airborne Laser Scanning (aerial LiDAR) in wildlife research
Authors: Cosgrove Cameron F. , Coops Nicholas C. , Martin Tara G. 
Published: June 2024

AbstractSpecies' habitats are strongly influenced by the 3‐dimensional (3D) structure of ecosystems. The dominant technique used to measure 3D structure is Airborne Laser Scanning (ALS), a type of LiDAR (Light Detection and Ranging) technology. Airborne Laser Scanning captures fine‐scale structural information over large spatial extents and provides useful environmental predictors for habitat modeling. However, due to technical complexities of processing ALS data, the full potential of ALS is not yet realized in wildlife research, with most studies relying on a limited set of 3D predictors, such as vegetation metrics developed principally for forestry applications. Here, we highlight the full potential of ALS data for wildlife research and provide insight into how it can be best used to capture the environmental conditions, resources, and risks that directly determine a species' habitat. We provide a nontechnical overview of ALS data, covering data considerations and the modern options available for creating custom, ecologically relevant, ALS predictors. Options included the following: i) direct point cloud approaches that measure structure using grid, voxel, and point metrics, ii) object‐based approaches that identify user‐defined features in the point cloud, and iii) modeled environmental predictors that use additional modeling to infer a range of habitat characteristics, including the extrapolation of field acquired measurements over ALS data. By using custom ALS predictors that capture species‐specific resources, risks, and environmental conditions, wildlife practitioners can produce models that are tailored to a species' ecology, have greater biological realism, test a wider range of species‐environment relationships across scales, and provide more meaningful insights to inform wildlife conservation and management.
Thermal effects of electromagnetic radiation on the skin tissue by considering fourth‐order MGT bioheat model
Authors: Abouelregal Ahmed E. , Megahid Sami F. , Sedighi Hamid M. 
Published: June 2024

AbstractUnderstanding the biothermal response of skin tissue exposed to electromagnetic (EM) radiation and variable thermal fields is crucial for mitigating risks associated with such exposures. This knowledge can empower the development of safe and efficacious evidence‐based treatments for various skin conditions within the medical field. This study employs the fourth‐order Moore–Gibson–Thomson (MGT) concept to establish a theoretical framework for biothermal analysis. This investigation aims to elucidate the biothermal response of skin tissues to EM radiation. The developed model facilitates the prediction of thermal reactions within human skin and the subsequent evaluation of bioheat transfer efficiency in biological tissues. The proposed model is implemented on a one‐dimensional representation of a skin layer and incorporates the effects of an induced electric field and a time‐harmonic heat source. Additionally, a linear dependence of metabolic heat generation on tissue temperature is considered. By employing Laplace transforms, the analytical solutions for tissue temperature are presented. The derived analytical solutions are compared with established theories to assess the accuracy of the proposed model. The results reveal that the modified MGT bioheat transfer model predicts lower temperatures compared to the conventional Pennes model, attributable to the incorporation of the thermal relaxation time constant.
AI-Enhanced UWB-Based Localisation in Wireless Networks
Authors: Dehmollaian Eshagh , Etzlinger Bernhard , Peterseil Philipp , Springer Andreas 
Published: June 2024

AbstractThanks to low complex and affordable hardware, low power consumption, and pulse-based communication, ultra-wideband (UWB) technology has brought the possibility of positioning in wireless networks for various applications with high precision. Nowadays, the widespread use of this technology for location-based applications together with the integration of this technology in smartphones, motivates more research on the use of this technology for localisation systems. Current research results emphasize that artificial intelligence (AI) algorithms can help to improve the positioning performance of UWB technology due to the use of large amounts of data. In this work, we provide an overview of the challenges and their AI-based solutions in UWB-based localisation systems. This is followed by an overview of related work and an application example.
Location Awareness in HealthCare
Authors: van de Laar Frank , Klabunde Karin 
Published: June 2024

AbstractWaiting for patients and looking for or loosing assets in healthcare costs millions/year and may cause considerable annoyance for caregivers and additional health risks for patients. Localization solutions for healthcare may help to reduce these significantly. This paper provides an overview on localization for Healthcare. It starts with an overview of applicable technologies for both indoor and outdoor localization that may be applied in healthcare logistics solutions. The next section describes various aspects that needs to be considered for an end-to-end localization solution. It is followed by a section on use-cases for healthcare with different operational requirements that may deploy technologies and methods as presented. The final section is on building a use-case concept demonstrator taking into account the technologies and considerations addressed in the previous sections. Security and privacy aspects are also considered to comply with EU healthcare regulations, especially when dealing with personal location data.
Artificial Intelligence for Wireless Avionics Intra-Communications
Authors: Robles Ramiro Samano , Venkatesha Prasad R. , Arts Ad , Rzymowski Mateusz , Kulas Lukasz 
Published: June 2024

AbstractThis chapter presents a summary of the description and preliminary results of the use case related to the implementation of artificial intelligence tools in the emerging technology called wireless avionics intra-communications (WAICs). WAICs aims to replace some of the cable buses of modern aircraft. This replacement of infrastructure leads to: (1) complexity reduction of future airplanes, (2) creation of innovative services where wireless links are more flexible than wireline links, and mainly (3) a considerable weight reduction, which in turn leads to fuel consumption efficiency, increase of payload, as well as range extension. Therefore, WAICs is expected to have a large impact on the aeronautics industry, propelling a new generation of greener, more efficient, and less expensive aeronautical services. However, there are still several reliability, trust, interoperability and latency issues that need to be addressed before this technology becomes commercial. It is expected that AI will boost the applicability of this technology, contributing to the realization of the concept of “fly-by-wireless”.
The Development of Ethical and Trustworthy AI Systems Requires Appropriate Human-Systems Integration
Authors: Moertl Peter , Ebinger Nikolai 
Published: June 2024

AbstractThe development of Artificial Intelligence (AI) technologies experiences worldwide an ongoing challenge to become trustworthy and ethical for users and the general public. This challenge currently stands between the promise of AI to create immense societal and individual impact and its realization. Because of this, possible large marketplaces still remain hesitant or closed. We have investigated this problem and identified potential solutions in the InSecTT project, a large international EU research and development project that investigates ethical, smart technologies. Thereby, working with industrial and research partners, we assert that developing trustworthy AI technologies is not foremost a technical challenge, but increasingly an organizational and process challenge that results from applying traditional ways of conceiving, designing, and selling technologies to technologies with very new types of user and societal implications. Because AI technologies can shift the role that humans and society see as acceptable, traditional development processes that rely on the strict separation of specialties are overburdened. In our view, a research and development approach for trustworthy AI systems should put human concerns and needs at the start of the development process to effectively integrate humans and systems and thereby have a chance to meet EU guidelines for developing ethical AI. Our proposed approach to develop such ethical, trustworthy AI systems centers around the assessment of trustworthiness risks through intensive user involvement prior to the elicitation of system requirements that are then managed throughout the system’s life cycle. Also, the approach includes concrete recommendations to establish the organizational prerequisites for producing ethical, trustworthy AI systems. In this chapter, we describe the Human-Systems Integration (HSI) approach and motivate the underlying principles in some detail. The chapter intends to inform managers of technical organizations and product managers, as well as principal investigators, to set up the prerequisites for trustworthy AI, while also soliciting inputs for further refinement and discussion.
Structuring the Technology Landscape for Successful Innovation in AIoT
Authors: Priller Peter , Jerne Michael 
Published: June 2024

AbstractThe InSecTT consortium started with a ground-breaking vision: to combine IoT and AI, or in short: AI + IoT = AIoT. But how can research and development in such a large project with many partners be structured and managed to lead to successful innovations? Especially in such a highly competitive market of digital solutions? This section provides an overview of processes and methods, from requirement engineering, followed by technology structuring, leading into joined development of re-usable technical building blocks. To validate and proof the approach, these technologies were integrated into a wide range of demonstrators in many different industrial domains. An iterative approach allowed to learn and improve along that way. Additional measures like use case marketplaces allowed to identify and exploit synergies. Finally, measures to support the exploitation of results are introduced.