bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insights on the contribution of doctoral research findings from a school in a South African University towards policy formulation
Authors: Damba Florence Upenyu , Mtshali Ntombifikile Gloria , Chimbari Moses John 
Published: July 2024

AbstractTranslation of health research findings into policy contributes to improvement of health systems. Generally, in sub-Saharan Africa policymakers rarely use research evidence and hence policies are often not informed by research evidence. Unless published or in the case of commissioned research, doctoral health research is often not used for health policy formulation. This paper analysed the potential and utilization of doctoral research from the School of Nursing and Public Health by KwaZulu-Natal Health Department of Health. The study adopted a mixed methods approach that combined elements of qualitative and quantitative research aspects. Qualitative data was collected through content analysis of 29 theses produced in the School of Nursing and Public Health, University of KwaZulu-Natal between 2014 and 2021 and interviews held with four Department of Health personnel as policymakers. When researchers could not get information on how research questions were formulated from content analysis, they checked the student questionnaire for answers. Quantitative data was collected from 79 participants through structured questionnaires. Participants included 47 PhD graduates, 11 final year PhD students and 21 PhD supervisors. Data from content analysis and interviews was analyzed thematically while data from questionnaires was analyzed quantitatively. Eleven (52%) PhD supervisors reported that findings from 22 studies were being considered for policy development and adoption while some had resulted in policy guidelines and frameworks that can be used to formulate policies. Factors such as failure to involve the Department of Health during the formulation of research questions, inappropriate packaging of research findings, policymakers not aware of the availability of research findings, lack of commitment to the dissemination of research results by students and poor demand for research evidence by policymakers hindered the translation of PhD research findings into policy. From the 29 theses reviewed, sixteen (28%) of PhD respondents highlighted that they involved the Department of Health to formulate research questions while forty-two (72%) did not. The theses review also revealed limited identifiable information related to policy formulation. The study confirms the use of PhD research findings for policy formulation. Additionally, it highlights the factors that hinder utilization of PhD work by policymakers. Further research to understand the perspectives of policymakers on factors that contribute to utilization of PhD work as well as how the findings have contributed to policy formulation is recommended since there was not sufficient data collected from policymakers due to Covid-19 restrictions.
Particle-assisted formation of oil-in-liquid metal emulsions
Authors: Kanetkar Shreyas , Shah Najam Ul H , Krisnadi Febby , Uppal Aastha , Gandhi Rohit M , Dickey Michael D , Wang Robert Y , Rykaczewski Konrad 
Published: July 2024

Abstract
Gallium-based liquid metals (LMs) have surface tension an order of magnitude higher than water and break up into micro-droplets when mixed with other liquids. In contrast, silicone oil readily mixes into LM foams to create oil-in-LM emulsions with oil inclusions. Previously, the LM was foamed through rapid mixing in air for an extended duration (over 2 h). This process first results in the internalization of oxide flakes that form at the air-liquid interface. Once a critical fraction of these randomly shaped solid flakes is reached, air bubbles internalize into the LM to create foams that can internalize secondary liquids. Here, we introduce an alternative oil-in-LM emulsion fabrication method that relies on the prior addition of SiO2 micro-particles into the LM before mixing it with the silicone oil. This particle-assisted emulsion formation process provides a higher control over the composition of the LM-particle mixture before oil addition, which we employ to systematically study the impact of particle characteristics and content on the emulsions’ composition and properties. We demonstrate that the solid particle size (0.8 μm to 5 μm) and volume fraction (1%–10%) have a negligible impact on the internalization of the oil inclusions. The inclusions are mostly spherical with diameters of 20–100 μm diameter and are internalized by forming new, rather than filling old, geometrical features. We also study the impact of the particle characteristics on the two key properties related to the functional application of the LM emulsions in the thermal management of microelectronics. In particular, we measure the impact of particles and silicone oil on the emulsion’s thermal conductivity and its ability to prevent deleterious gallium-induced corrosion and embrittlement of contacting metal substrates.
Surface defect detection for intricate pattern fabrics based on deep learning
Authors: Wang Xuerui , Zhu Yongjian , Liu Hongzhan 
Published: July 2024

Abstract
To tackle the challenges in the current detection of patterned fabric defects, such as unbalanced and limited data volume, incomplete extraction of defect features by the network, aliasing effect in feature fusion by the pyramid network, and low accuracy in identifying small targets, this paper proposes a novel single-stage network named ASC-you only look once (YOLO). Firstly, the CoT-CSP module is introduced into the Backbone network of the YOLOv5s to model the global contextual relationships of defective features. This module compensates for the limitations of the original Cross-Stage Partial Network, which can only extract local feature information. It effectively enhances the comprehensive extraction of various irregular defect features in fabrics and reduces redundant interactions with texture background noise. Subsequently, three types of AttentionC3 modules are proposed and integrated into the Neck enables the model to focus on valuable details related to defect features through weight allocation, significantly enhancing the capability to identify small defects. Finally, an adaptive spatial feature fusion network is designed to optimize the pyramid network by redistributing weights across different feature layers for efficient utilization of multi-level feature information. Additionally, a random erasing approach is proposed to individually enhance the defect images of specific types with a limited number and adjust the corresponding labels simultaneously. This process aims to improve the robustness of the ASC-YOLO network against data noise. Experimental results demonstrate that ASC-YOLO achieves 95.8% accuracy, with mean average precision@0.5 reaching 83.1%. Meanwhile, the experiments also verify its superiority over other fabric detection methods and its ability to effectively improve the accuracy of fabric defect detection.
Advancements in atomic-scale interface engineering for flexible electronics: enhancing flexibility and durability
Authors: Wen Di , Yuan Ruige , Cao Kun , Yang Fan , Chen Rong 
Published: July 2024

Abstract
Flexible electronics, such as wearable displays, implantable electronics, soft robots, and smart skin, have garnered increasing attention. Despite notable advancements in research, a bottleneck remains at the product level due to the prevalent use of polymer-based materials, requiring encapsulation films for lifespan extension and reliable performance. Multilayer composites, incorporating thin inorganic layers to maintain low permeability towards moisture, oxygen, ions, etc, exhibit potential in achieving highly flexible barriers but encounter challenges stemming from interface instability between layers. This perspective offers a succinct review of strategies and provides atomic-scale interface modulation strategy utilizing atomic layer integration technology focused on enhancing the flexibility of high-barrier films. It delves into bendable multilayers with atomic-scale interface modulation strategies, encompassing internal stress and applied stress modulation, as well as stretchable composite structural designs such as gradient/hybrid, wavy, and island. These strategies showcase significant improvements in flexibility from bendable to stretchable while maintaining high barrier properties. Besides, optimized manufacturing methods, materials, and complex structure design based on atomic-scale interface engineering are provided, better aligning with the future development of flexible electronics. By laying the groundwork for these atomic-scale strategies, this perspective contributes to the evolution of flexible electronics, enhancing their flexibility, durability, and functionality.
Ceria nanocatalyst-supported oxidative organic transformations of aromatic alcohols and p-nitrotoluene
Authors: Naaz Farha , Alshehri Saad M , Ahmad Tokeer 
Published: July 2024

Abstract
Hydrothermally derived nanocubes of CeO2 (10 nm) were explored as an efficient heterogeneous catalyst in the partial oxidation of aromatic alcohols to the corresponding aldehydes and aerobic oxidation of p-nitrotoluene to p-nitrobenzoic acid. The CeO2 nanocatalyst was characterized by x-ray diffraction, transmission electron microscopy (TEM), energy dispersive spectroscopy, x-ray photoelectron spectroscopy, Brunauer–Emmett–Teller (BET) surface area analysis, Fourier transform infrared spectroscopy, thermal gravimetric analysis and ultraviolet–visible spectroscopy. TEM/high-resolution TEM micrographs reveal a morphology of mostly cubic nanostructures with exposed highly active {100} and {110} facets. The surface area of nanoceria was determined by BET analysis and found to be 33.8 m2 g−1. To demonstrate the universality of the catalytic system, the selective oxidation of different substrates of benzylic alcohol and complete oxidation of p-nitrotoluene was investigated under mild conditions. Absolute selectivity towards their respective aldehydes was found to be 99.50% (benzaldehyde), 90.18% (p-chlorobenzaldehyde), 99.71% (p-nitrobenzaldehyde), 98.10% (p-fluorobenzaldehyde), 94.66% (p-anisaldehyde) and 86.14% (cinnamaldehyde). Moreover, the catalytic oxidative transformation of nitrotoluene results in 100% conversion with 99.29% selectivity towards nitrobenzoic acid.
Preparation and sensing performance of wet-spun fabric triboelectric nanogenerator for energy harvesting
Authors: Li Meng , Zhang Yingying , Wang Ping , Zhang Yan , Hu Jiancheng , Li Yuanyuan 
Published: July 2024

Abstract
Flexible, wearable triboelectric nanogenerators (TENGs) monitoring human movement and health signals have received more attention recently. In particular, developing a flexible TENG combining stress, strain, electrical output performance and durability becomes the current research focus. Herein, a highly stretchable, self-powered coaxial yarn TENGs were manufactured using a low-cost, efficient continuous wet-spinning method. Carbon nanotube/conductive thermoplastic polyurethane (MWCNT/CTPU) and polyvinylidene fluoride-hexafluoropropylene were utilized for the coaxial fibers conductive layers and dielectric layers, respectively. Fibers were continuously collected over a length of 10 m. Excellent electrical output with an open-circuit voltage (Voc) of 11.4 V, short-circuit current (Isc) of 114.8 nA, and short-circuit transfer charge (Qsc) of 6.1 nC was achieved. In addition, fabric TENGs with different two and three dimensional structures were further prepared by the developed coaxial fibers. The corresponding electrical output properties and practical performance were discussed. Results showed that the four-layer three-dimensional angle interlocking structure exhibited the optimal performance with an open-circuit voltage (Voc) of 38.4 V, short-circuit current (Isc) of 451.5 nA, and short-circuit transfer charge (Qsc) of 23.1 nC.
Multi-institutional experimental validation of online adaptive proton therapy workflows
Authors: Bobić Mislav , Choulilitsa Evangelia , Lee Hoyeon , Czerska Katarzyna , Christensen Jeppe B , Mayor Alexandre , Safai Sairos , Winey Brian A , Weber Damien C , Lomax Antony J , Paganetti Harald , Nesteruk Konrad P , Albertini Francesca 
Published: July 2024

AbstractObjective.To experimentally validate two online adaptive proton therapy (APT) workflows using Gafchromic EBT3 films and optically stimulated luminescent dosimeters (OSLDs) in an anthropomorphic head-and-neck phantom.Approach.A three-field proton plan was optimized on the planning CT of the head-and-neck phantom with 2.0 Gy(RBE) per fraction prescribed to the clinical target volume. Four fractions were simulated by varying the internal anatomy of the phantom. Three distinct methods were delivered: daily APT researched by the Paul Scherrer Institute (DAPTPSI), online adaptation researched by the Massachusetts General Hospital (OAMGH), and a non-adaptive (NA) workflow. All methods were implemented and measured at PSI. DAPTPSIperformed full online replanning based on analytical dose calculation, optimizing to the same objectives as the initial treatment plan. OAMGHperformed Monte-Carlo-based online plan adaptation by only changing the fluences of a subset of proton beamlets, mimicking the planned dose distribution. NA delivered the initial plan with a couch-shift correction based on in-room imaging. For all 12 deliveries, two films and two sets of OSLDs were placed at different locations in the phantom.Main results.Both adaptive methods showed improved dosimetric results compared to NA. For film measurements in the presence of anatomical variations, the [min-max] gamma pass rates (3%/3 mm) between measured and clinically approved doses were [91.5%–96.1%], [94.0%–95.8%], and [67.2%–93.1%] for DAPTPSI, OAMGH, and NA, respectively. The OSLDs confirmed the dose calculations in terms of absolute dosimetry. Between the two adaptive workflows, OAMGHshowed improved target coverage, while DAPTPSIshowed improved normal tissue sparing, particularly relevant for the brainstem.Significance.This is the first multi-institutional study to experimentally validate two different concepts with respect to online APT workflows. It highlights their respective dosimetric advantages, particularly in managing interfractional variations in patient anatomy that cannot be addressed by non-adaptive methods, such as internal anatomy changes.
Dark Matter halo parameters from overheated exoplanets via Bayesian hierarchical inference
Authors: Benito María , Karchev Konstantin , Leane Rebecca K. , Põder Sven , Smirnov Juri , Trotta Roberto 
Published: July 2024

Abstract
Dark Matter (DM) can become captured, deposit annihilation energy, and hence increase the heat flow in exoplanets and brown dwarfs. Detecting such a DM-induced heating in a population of exoplanets in the inner kpc of the Milky Way thus provides potential sensitivity to the galactic DM halo parameters. We develop a Bayesian Hierarchical Model to investigate the feasibility of DM discovery with exoplanets and examine future prospects to recover the spatial distribution of DM in the Milky Way. We reconstruct from mock exoplanet datasets observable parameters such as exoplanet age, temperature, mass, and location, together with DM halo parameters, for representative choices of measurement uncertainty and the number of exoplanets detected. We find that detection of ℴ(100) exoplanets in the inner Galaxy can yield quantitative information on the galactic DM density profile, under the assumption of 10% measurement uncertainty. Even as few as ℴ(10) exoplanets can deliver meaningful sensitivities if the DM density and inner slope are sufficiently large.

https://github.com/mariabenitocst/exoplanets
Energy extraction via magnetic reconnection in magnetized black holes
Authors: Zhang Shao-Jun 
Published: July 2024

AbstractThe Comisso-Asenjo mechanism is a novel mechanism proposed recently to extract energy from black holes through magnetic reconnection of the surrounding charged plasma, in which the magnetic field plays a crucial role. In this work, we revisit this process by taking into account the backreaction of the magnetic field on the black hole's geometry. We employ the Kerr-Melvin metric to describe the local near-horizon geometry of the magnetized black hole. By analyzing the circular orbits in the equatorial plane, energy extraction conditions, power and efficiency of the energy extraction, we found that while a stronger magnetic field can enhance plasma magnetization and aid energy extraction, its backreaction on the spacetime may hinder the process, with a larger magnetic field posing a greater obstacle. Balancing these effects, an optimal moderate magnetic field strength is found to be most conducive to energy extraction. Moreover, there is a maximum limit to the magnetic field strength associated with the black hole's spin, beyond which circular orbits in the equatorial plane are prohibited, thereby impeding energy extraction in the current scenario.
Spintronics with two-dimensional materials and van der Waals heterostructures
Authors: Roche Stephan , van Wees Bart , Garello Kevin , Valenzuela Sergio O 
Published: July 2024

Abstract
We briefly summarize more than fifteen years of intense research in 2D materials (2DM)-based spintronics, which has led to an in-depth understanding of fundamental spin transport mechanisms, novel functionalities in magnetic tunnel junctions and spin orbit torque devices, and the formidable and unprecedented capability of proximity effects to make graphene a spin active material. Although the portfolio of functional 2DM-based devices and related heterostructures is continuously increasing, we outline key technological challenges that are still impeding practical spintronic applications in spin-logics and non-volatile memory technologies. We conclude by mentioning current and future directions which will maintain the momentum of the field of ultracompact spintronics based on 2DM and van der Waals heterostructures.
Robust large-gap quantum spin Hall states in stabilized bismuthene on Si(111)-

α
−

3

×

3

-Au
Authors: Llona Bheim M , Chou Hsin-Lei , Lan Liang-Wei , Wu Shih-Yu , Hsu Chia-Hsiu , Chuang Feng-Chuan , Lin Hsin , Kuo Chien-Cheng 
Published: July 2024

Abstract
Bismuthene is a promising large-gap two-dimensional topological material with potential applications in quantum devices. However, fabricating a stable bismuthene on a substrate that preserves its edge states and large energy gap at room temperature has been challenging. In this study, we successfully stabilized bismuthene on the 2D electron gas Si(111)-α-

3

×

3

-Au surface despite its delicate atomic structures, enabling direct access to its quantum spin Hall states. Scanning tunneling microscopy with localized

d

I

/

d

V

mapping on in-situ prepared structures revealed that the bismuthene surface exhibits a stable, shallow-buckled, insulative interior and an almost planar metallic edge. We found a 0.75 eV-bandgap throughout the interior and a closing gap at the island’s boundary. By using island-based differential conductance mapping, we identified localized edge states and the Dirac point at an energy of −0.10 eV within the bandgap. These results support the 2D-TI nature of bismuthene on Au / Si(111), paving the way for the development of bismuthene-based quantum devices.
Spectral-bias and kernel-task alignment in physically informed neural networks
Authors: Seroussi Inbar , Miron Asaf , Ringel Zohar 
Published: July 2024

Abstract
Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations. As in many other deep learning approaches, the choice of PINN design and training protocol requires careful craftsmanship. Here, we suggest a comprehensive theoretical framework that sheds light on this important problem. Leveraging an equivalence between infinitely over-parameterized neural networks and Gaussian process regression, we derive an integro-differential equation that governs PINN prediction in the large data-set limit—the neurally-informed equation. This equation augments the original one by a kernel term reflecting architecture choices. It allows quantifying implicit bias induced by the network via a spectral decomposition of the source term in the original differential equation.
Real-time forecast of temperature-related excess mortality at small-area level: towards an operational framework
Authors: Mistry Malcolm N , Gasparrini Antonio 
Published: July 2024

Abstract
The development of innovative tools for real-time monitoring and forecasting of environmental health impacts is central to effective public health interventions and resource allocation strategies. Though a need for such generic tools has been previously echoed by public health planners and regional authorities responsible for issuing anticipatory alerts, a comprehensive, robust and scalable real-time system for predicting temperature-related excess deaths at a local scale has not been developed yet. Filling this gap, we propose a flexible operational framework for coupling publicly available weather forecasts with temperature-mortality risk functions specific to small census-based zones, the latter derived using state-of-the-art environmental epidemiological models. Utilising high-resolution temperature data forecast by a leading European meteorological centre, we demonstrate a real-time application to forecast the excess mortality during the July 2022 heatwave over England and Wales. The output, consisting of expected temperature-related excess deaths at small geographic areas on different lead times, can be automated to generate maps at various spatio-temporal scales, thus facilitating preventive action and allocation of public health resources in advance. While the real-case example discussed here demonstrates an application for predicting (expected) heat-related excess deaths, the framework can also be adapted to other weather-related health risks and to different geographical areas, provided data on both meteorological exposure and the underlying health outcomes are available to calibrate the associated risk functions. The proposed framework addresses an urgent need for predicting the short-term environmental health burden on public health systems globally, especially in low- and middle-income regions, where rapid response to mitigate adverse exposures and impacts to extreme temperatures are often constrained by available resources.
Anti-Scientific Americans?
Authors: Motta Matthew 
Published: July 2024

Abstract
Chapter 1 begins by providing recent anecdotal accounts of the ways in which anti-intellectualism negatively impacts American political life. It then draws on both recent scholarship and longstanding theoretical insights from Hofstadter’s (1963) work on the subject to conceptualize anti-intellectualism as the dislike and distrust of scientists, academics, and other experts. To do this, the chapter navigates thorny issues regarding who counts as an expert, contrasts anti-intellectualism from related constructs (e.g., populism), and discusses how this definition differs from previous standards employed in the past. Chapter 1 concludes by outlining a plan for the book and issuing an important word of caution: experts are people too. Because even experts are not infallible, the chapter argues that Americans ought to express healthy skepticism toward the role that experts play in the policymaking process.