bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Study on Solvothermal and Sonochemical Synthesis of Zinc Oxide Nanomaterials and Unraveling the Efficacy in Sunscreen Formulations for Enhanced UV Protection
Authors: Veerabhadraiah Srikanth R. , Bhat Sachin A. , Sharma Rohit Kumar , Yelamaggad C. V. , Naik Nagaraja 
Published: May 2024

AbstractAs the demand for highly efficient UV‐protective sunscreens continues to rise, the exploration of advanced materials becomes imperative. This paper introduces a novel approach to synthesizing zinc oxide (ZnO) nanomaterials through one‐step solvothermal (ZnO−A) and sonochemical (ZnO−B) methods, eliminating the need for stabilizers or capping agents. The materials have undergone rigorous characterization, focusing on structural and optical properties, and are subsequently evaluated for their UV shielding efficacy. The synthesized ZnO nanomaterials are seamlessly incorporated into a pure cream matrix at varying concentrations (5, 10 and 15 wt. %), offering a versatile application for skin protection against UVA/UVB irradiation. Remarkably, the cream with 15 wt. % ZnO−A emerges as a standout performer, demonstrating immense UV blocking efficacy with 96.44 % for UVA and 97.22 % for UVB. The in vitro sun protection factor (SPF) value of 30.76 further underscores its exceptional performance. This research not only establishes a comprehensive comparative analysis of ZnO−A and ZnO−B nanomaterials but also emphasizes their potential as active ingredients in next‐generation skin care sunscreen formulations. The findings contribute to the ongoing efforts in advancing UV protection technologies, addressing both efficacy and safety considerations.
Hofmeister‐Effect‐Driven Hybrid Glycerogels for Perfect Wide‐Temperature Shape Fixity and Shape Recovery in Soft Robotics Applications
Authors: Janipour Shahroudkolaei Mona , Mredha Md. Tariful Islam , Chuang Kuo‐Chih , Jeon Insu 
Published: May 2024

AbstractShape memory gels have emerged as crucial elements in soft robotics, actuators, and biomedical devices; however, several problems persist, like the trade‐off between shape fixity and shape recovery, and the limited temperature range for their application. This article introduces a new class of shape memory hybrid glycerogels (GGs) designed to address these limitations. The well‐modulated internal structure of the GGs, facilitated by the Hofmeister salting‐out effect, strategically incorporates a higher crystallite content, abundant crosslinking points, and a high elastic modulus. Unlike reported shape memory gels, the GG exhibits a perfect triple‐step shape memory behavior in air with 100% shape fixity in a wide programming temperature range (75–135 °C) and simultaneously achieves 100% shape recoverability. The gel recovers its shape at −40 °C under near‐infrared light across a wide programming temperature range (25–135 °C), showing unexpected initiation even at subzero temperatures. Inspired by the mechanics of composite structures, a method is proposed to integrate the GG seamlessly with a shape memory alloy, which further expands the temperature range that yields perfect shape memory properties. Finally, two light‐controlled fluttering and crawling soft robot prototypes are engineered to illustrate the versatility and potential applications of the composite gel in soft robotics.
Varieties of How Actors Use Human Rights Treaties in Subnational Policy Processes
Authors: Miaz Jonathan , Schmid Evelyne , Niederhauser Matthieu , Kaempfer Constance , Maggetti Martino 
Published: May 2024

AbstractIn this chapter, we examine the varieties of how actors use international human rights treaties in policy processes at the subnational level. As explained in Chapters 1 and 2, our case studies are the Istanbul Convention (IC) and the Convention on the Rights of Persons with Disabilities (CRPD) in Swiss cantons. Here, we identify the most relevant actors and how they come to know about a treaty, and we then categorise various uses according to the actors and to the stage of the policy process in which the actors intervene. We find that various subnational actors—including bureaucrats, elected politicians (members of cantonal parliaments or governments), civil society representatives and academic experts—use treaties according to their own local interests, agendas and strategies. The uses of the treaties often occur along a continuous and incremental process involving back and forth and both top-down and bottom-up dynamics. In the policy process, subnational actors use treaties to (a) set issues on the policy agenda and (b) to support claims (new ones or existing ones). If the relevant subnational political authorities use a treaty with a view to further its implementation, we qualify this type of use as an engagement, which we will address in Chapter 5.
Conceptual framework
Authors: Klingebiel Stephan , Hartmann Flora L. , Madani Elisa , Paintner Jonas , Rohe Rebekka A. , Trebs Lisa , Wolk Teodor 
Published: May 2024

AbstractThe chapter introduces the main concepts of our research. Firstly, we expand on the concept of knowledge and argue why it is important to focus on the relationship between knowledge and power structures. Secondly, we explain how power structures manifest in knowledge interactions and how we operationalise this theoretical concept to conduct our empirical research. Thirdly, we introduce the concept of modalities of knowledge interaction and present our typology of modalities of knowledge interaction.
Methods for Data Collection and Analysis
Authors: Klingebiel Stephan , Hartmann Flora L. , Madani Elisa , Paintner Jonas , Rohe Rebekka A. , Trebs Lisa , Wolk Teodor 
Published: May 2024

AbstractIn this chapter we elaborate on the methods we use to collect and analyse our data. We followed a multi-method data collection process in which we triangulated data obtained via different collection methods. The methods we use comprise document analysis, semi-structured interviews, participant observations, surveys, and workshops in which group discussions take place.
Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative
Authors: Ammon Danny , Kurscheidt Maximilian , Buckow Karoline , Kirsten Toralf , Löbe Matthias , Meineke Frank , Prasser Fabian , Saß Julian , Sax Ulrich , Stäubert Sebastian , Thun Sylvia , Wettstein Reto , Wiedekopf Joshua P. , Wodke Judith A. H. , Boeker Martin , Ganslandt Thomas 
Published: May 2024

ZusammenfassungDie Arbeitsgruppe Interoperabilität der Medizininformatik-Initiative (MII) ist die Plattform für die Abstimmung übergreifender Vorgehensweisen, Datenstrukturen und Schnittstellen zwischen den Datenintegrationszentren (DIZ) der Universitätskliniken und nationalen bzw. internationalen Interoperabilitätsgremien. Ziel ist die gemeinsame inhaltliche und technische Ausgestaltung einer über das Forschungsdatenportal für Gesundheit nutzbaren verteilten Infrastruktur zur Sekundärnutzung klinischer Versorgungsdaten. Wichtige Rahmenbedingungen sind dabei Datenschutz und IT-Sicherheit für die Nutzung von Gesundheitsdaten in der biomedizinischen Forschung. Hierfür werden in dezidierten Taskforces geeignete Methoden eingesetzt, um prozessuale, syntaktische und semantische Interoperabilität für Datennutzungsprojekte zu ermöglichen. So wurde der MII-Kerndatensatz, bestehend aus mehreren Modulen mit zugehörigen Informationsmodellen, entwickelt und mittels des Standards HL7® FHIR® implementiert, um fachliche und technische Vorgaben für die interoperable Datenbereitstellung von Versorgungsdaten durch die DIZ zu ermöglichen. Zur näheren Beschreibung dieser Datensätze dienen internationale Terminologien und konsentierte Metadaten. Die Gesamtarchitektur, einschließlich übergreifender Schnittstellen, setzt die methodischen und rechtlichen Anforderungen an eine verteilte Datennutzungsinfrastruktur z. B. durch Bereitstellung pseudonymisierter Daten oder föderierte Analysen um. Mit diesen Ergebnissen der Arbeitsgruppe Interoperabilität stellt die MII eine zukunftsweisende Lösung für den Austausch und die Nutzung von Routinedaten vor, deren Anwendbarkeit über den Zweck der Forschung hinausgeht und eine wesentliche Rolle in der digitalen Transformation des Gesundheitswesens spielen kann.
The case for a broader approach to AI assurance: addressing “hidden” harms in the development of artificial intelligence
Authors: Thomas Christopher , Roberts Huw , Mökander Jakob , Tsamados Andreas , Taddeo Mariarosaria , Floridi Luciano 
Published: May 2024

AbstractArtificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.
The Brownian transport map
Authors: Mikulincer Dan , Shenfeld Yair 
Published: May 2024

AbstractContraction properties of transport maps between probability measures play an important role in the theory of functional inequalities. The actual construction of such maps, however, is a non-trivial task and, so far, relies mostly on the theory of optimal transport. In this work, we take advantage of the infinite-dimensional nature of the Gaussian measure and construct a new transport map, based on the Föllmer process, which pushes forward the Wiener measure onto probability measures on Euclidean spaces. Utilizing the tools of the Malliavin and stochastic calculus in Wiener space, we show that this Brownian transport map is a contraction in various settings where the analogous questions for optimal transport maps are open. The contraction properties of the Brownian transport map enable us to prove functional inequalities in Euclidean spaces, which are either completely new or improve on current results. Further and related applications of our contraction results are the existence of Stein kernels with desirable properties (which lead to new central limit theorems), as well as new insights into the Kannan–Lovász–Simonovits conjecture. We go beyond the Euclidean setting and address the problem of contractions on the Wiener space itself. We show that optimal transport maps and causal optimal transport maps (which are related to Brownian transport maps) between the Wiener measure and other target measures on Wiener space exhibit very different behaviors.
GenAI and me: the hidden work of building and maintaining an augmentative partnership
Authors: Boulus-Rødje Nina , Cranefield Jocelyn , Doyle Cathal , Fleron Benedicte 
Published: May 2024

AbstractIt has been argued that emergent AI systems should be viewed as working partners rather than tools. Building on this perspective, this study investigates the process through which academics develop a working partnership with generative AI chatbots using a relationship lens and collaborative autoethnographic methods. Based on a collaborative autoethnographic analysis of our experiences of working with AI, we identify five stages of relationship development in building a successful working partnership with generative AI: Playing Around, Infatuation, Committing, Frustration, and Enlightenment and Readjustment. In reporting each stage, we provide vignettes to illustrate the emotional and cognitive challenges and rewards involved, the developing skills, and the experienced anthropomorphic dimension of this emerging relationship. Drawing on prior theory, we identify and examine three types of work that are required in developing this working partnership: articulation work, relationship work, and identity work. We analyse how each type of work unfolds with the stages of relationship development and consider implications for research and practice.
DC-Graph: a chunk optimization model based on document classification and graph learning
Authors: Zhou Jingjing , Zhang Guohao , Alfarraj Osama , Tolba Amr , Li Xuefeng , Zhang Hao 
Published: May 2024

AbstractExisting machine reading comprehension methods use a fixed stride to chunk long texts, which leads to missing contextual information at the boundaries of the chunks and a lack of communication between the information within each chunk. This paper proposes DC-Graph model, addressing existing issues in terms of reconstructing and supplementing information in long texts. Knowledge graphs contain extensive knowledge, and the semantic relationships between entities exhibit strong logical characteristics, which can assist the model in semantic understanding and reasoning. By categorizing the questions, this paper filters the content of long texts based on categories and reconstructs the content that aligns with the question category, compressing and optimizing the long text to minimize the number of document chunks when inputted into BERT. Additionally, unstructured text is transformed into a structured knowledge graph, and features are extracted using graph convolutional networks. These features are then added as global information to each chunk, aiding answer prediction. Experimental results on the CoQA, QuAC, and TriviaQA datasets demonstrate that our method outperforms both BERT and Recurrent Chunking Mechanisms, which share the same improvement approach, in terms of F1 and EM score. The code is available at (https://github.com/guohaozhang/DC-Graph.git).
Genome-Wide Identification and Expression Analysis of the STAT Family in Reeve’s Turtle (Mauremys reevesii)
Authors: Song Yi , Zhou Zeshuo , Huang Shichen , Li Zhiyuan , Zhu Xuechi , Zhou Hongming , Jiang Yuxin , Dissanayake Duminda S. B. , Georges Arthur , Xiong Lei 
Published: May 2024

AbstractThe Stat (signal transducer and activator of transcription) gene family plays a vital role in regulating immunity and the processes of cellular proliferation, differentiation, and apoptosis across diverse organisms. Although the functions of Stat genes in immunity have been extensively documented in many mammals, limited data are available for reptiles. We used phylogenetic analysis to identify eight putative members of the Stat family (Stat1-1, Stat1-2, Stat2, Stat3, Stat4, Stat5b, Stat6-1, and Stat6-2) within the genome of M.reevesii, a freshwater turtle found in East Asia. Sequence analysis showed that the Stat genes contain four conserved structural domains protein interaction domain, coiled-coil domain, DNA-binding domain, and Src homology domain 2. In addition, Stat1, Stat2, and Stat6 contain TAZ2bind, Apolipo_F, and TALPID3 structural domains. The mRNA levels of Stat genes were upregulated in spleen tissues at 4, 8, 12, and 16 h after administration of lipopolysaccharide, a potent activator of the immune system. Stat5b expression at 12-h LPS post-injection exhibited the most substantial difference from the control. The expression of Stat5b in spleen tissue cellular was verified by immunofluorescence. These results suggest that Stat5b plays a role in the immune response of M.reevesii and may prove to be as a positive marker of an immune response in future studies.
Healthcare disparities, screening, and molecular testing in the changing landscape of non–small cell lung cancer in the United States: a review
Authors: Kurzrock Razelle , Chaudhuri Aadel A. , Feller-Kopman David , Florez Narjust , Gorden Jed , Wistuba Ignacio I. 
Published: May 2024

AbstractInequitable access to care continues to hinder improvements in diagnosis and treatment of lung cancer. This review describes healthcare disparities in the changing landscape of non–small cell lung cancer (NSCLC) in the United States, focusing on racial, ethnic, sex-based, and socioeconomic trends. Furthermore, strategies to address disparities, overcome challenges, and improve patient outcomes are proposed. Barriers exist across lung cancer screening, diagnosis, and treatment regimens, varying by sex, age, race and ethnicity, geography, and socioeconomic status. Incidence and mortality rates of lung cancer are higher among Black men than White men, and incidences in young women are substantially greater than in young men. Disparities may be attributed to geographic differences in screening access, with correlating higher incidence and mortality rates in rural versus urban areas. Lower socioeconomic status is also linked to lower survival rates. Several strategies could help reduce disparities and improve outcomes. Current guidelines could improve screening eligibility by incorporating sex, race, and socioeconomic status variables. Patient and clinician education on screening guidelines and patient-level barriers to care are key, and biomarker testing is critical since ~ 70% of patients with NSCLC have an actionable biomarker. Timely diagnosis, staging, and comprehensive biomarker testing, including cell-free DNA liquid biopsy, may provide valuable treatment guidance for patients with NSCLC. Efforts to improve lung cancer screening and biomarker testing access, decrease bias, and improve education about screening and testing are needed to reduce healthcare disparities in NSCLC.
Efficacy of thermal analysis in the detection of bacterial strains in periprosthetic joint infections
Authors: Lőrinczy Dénes , Dandé Árpád , Nöt László G. 
Published: May 2024

AbstractIn musculoskeletal surgery, joint and periprosthetic infections are rare but significant complications with a high risk of morbidity. Rapid diagnosis, surgical intervention and definitive antibiotic therapy are key factors to the success of the treatment and the recovery of former limb function. In addition to the current diagnostic procedures, there is a need to introduce new, therapy-centered diagnostic procedures in view of the increase in antibiotic resistance. In our current study, we set out to demonstrate the practical utility of differential scanning calorimetry and the isoperibolic calorimetry in diagnostics of joint and periprosthetic infections. We have shown significant differences among the different grade of arthritis. Under experimental conditions, synovial fluid samples were artificially superinfected with the most frequently occurring bacteria at a concentration of 103–105 CFU mL−1 and monitored in isothermal mode at 37 °C. The isoperibolic tests clearly showed a specific, concentration-dependent representative curve (duration of proliferation and its maximum rates) for each bacterial strain of the study. In our opinion, the different thermal analytical methods can be useful in the diagnosis of septic inflammatory processes and, in the rapid selection of definitive antibiotic therapy.
Preliminary feasibility assessment of a targeted, pharmacist-led intervention for older adults with polypharmacy: a mixed-methods study
Authors: Liu Lisheng , Brokenshire Bernadette , Davies Deborah , Harrison Jeff 
Published: May 2024

Abstract
Background
Polypharmacy is associated with the prescription of inappropriate medications and avoidable medication-related harm. A novel pharmacist-led intervention aims to identify and resolve inappropriate medication prescriptions in older adults with polypharmacy.

Aim
To conduct a preliminary feasibility assessment of the intervention in primary care, testing whether specific components of the intervention procedures and processes can be executed as intended.

Method
The mixed-methods study was approved by the New Zealand Health and Disability Ethics Committees and public health agency. Patients from a New Zealand general practice clinic were recruited over 4 weeks to receive the intervention. The preliminary feasibility assessment included measures of intervention delivery, patient-reported outcome measures, and perspectives from ten patients and six clinicians. Data were analysed quantitatively and qualitatively to determine if a full-scale intervention trial is warranted. The study's progression criteria were based on established research and guided the decision-making process.

Results
The intervention met the study's progression criteria, including patient recruitment, retention, and adherence to the intervention procedures. However, several modifications were identified, including: (1) enhancing patient recruitment, (2) conducting a preliminary meeting between the patient and pharmacist, (3) supporting pharmacists in maintaining a patient-centred approach, (4) reviewing the choice of patient-reported outcome measure, (5) extending the 8-week follow-up period, (6) allocating more time for pharmacists to conduct the intervention.

Conclusion
The study found the intervention feasible; however, additional development is required before progressing to a full-scale trial. This intervention has the potential to effectively reduce medication-related harm and improve outcomes for older adults with polypharmacy.

Trial registration number
ACTRN12621000268842 Date registered: 11/03/2021
Adaptive histogram equalization in constant time
Authors: Härtinger Philipp , Steger Carsten 
Published: May 2024

AbstractAdaptive Histogram Equalization (AHE) and its contrast-limited variant CLAHE are well-known and effective methods for improving the local contrast in an image. However, the fastest available implementations scale linearly with the filter mask size, which results in high execution times. This presents an obstacle in real-world applications, where large filter mask sizes are desired while maintaining low execution times. In this work, we propose an efficient algorithm for AHE that reduces the per-pixel computational complexity to $$\mathcal {O}(1)$$

O
(
1
)

. To the best of our knowledge, this is the first time that a constant-time algorithm is proposed for AHE and CLAHE. In contrast to commonly used fast implementations, our method computes the exact result for each pixel without interpolation artifacts. We benchmark and compare our method to existing algorithms. Our experiments show that our method exhibits superior execution times independent of the filter mask size, which makes AHE and CLAHE fast enough to be usable in real-world applications.