bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scalable approach to fabricate paper-based biomass reduced graphene sensor for the detection of exhaled diabetic breath
Authors: Ajay Rakkesh R , Durgalakshmi D , Balakumar S 
Published: August 2022

Abstract
Herein, we demonstrate a microwave-assisted chemical reduction technique to exfoliate a few layers of graphene from the natural waste material, ‘coconut shell’. The microwave irradiation coconut shell is subjected to structural, morphological and functional groups characterization methods including SEM, Raman, FTIR and XPS spectroscopic analyses. The formation of biomass reduced graphene (BRG) has been confirmed through Raman and FTIR spectroscopic analyzes with the presence of D, G and 2D and other functional spectral bands, respectively. The surface topography of the BRG exhibits two-dimensional mat structures with wrinkle topography, imaged by electron microscopic techniques. The metallic behaviour of the BRG is evaluated by band structure calculation using density functional theory. The synthesized nanostructure has been evaluated for exhaled diabetic breath sensing application by fabricating sensor device on the paper-based substrate by roll-to-roll coating technique. The BRG sensor exhibited enhanced sensing response at a very lower concentration of diabetic biomarker with long term stability and rapid response/recovery time of 1.11 s/41.25 s, respectively. Based on our findings, the microwave-assisted BRG is a potential candidate for fabricating highly scalable, inherently safe, economically viable and excellent sensing performance to detect exhaled diabetic breath at room temperature.
Symmetric domain segmentation in WS2 flakes: correlating spatially resolved photoluminescence, conductance with valley polarization
Authors: Kayal Arijit , Barman Prahalad Kanti , Sarma Prasad V , Shaijumon M M , Kini R N , Mitra J 
Published: August 2022

Abstract
The incidence of intra-flake heterogeneity of spectroscopic and electrical properties in chemical vapour deposited (CVD) WS2 flakes is explored in a multi-physics investigation via spatially resolved spectroscopic maps correlated with electrical, electronic and mechanical properties. The investigation demonstrates that the three-fold symmetric segregation of spectroscopic response, in topographically uniform WS2 flakes are accompanied by commensurate segmentation of electronic properties e.g. local carrier density and the differences in the mechanics of tip-sample interactions, evidenced via scanning probe microscopy phase maps. Overall, the differences are understood to originate from point defects, namely sulfur vacancies within the flake along with a dominant role played by the substrate. While evolution of the multi-physics maps upon sulfur annealing elucidates the role played by sulfur vacancy, substrate-induced effects are investigated by contrasting data from WS2 flake on Si and Au surfaces. Local charge depletion induced by the nature of the sample-substrate junction in case of WS2 on Au is seen to invert the electrical response with comprehensible effects on their spectroscopic properties. Finally, the role of these optoelectronic properties in preserving valley polarization that affects valleytronic applications in WS2 flakes, is investigated via circular polarization discriminated photoluminescence experiments. The study provides a thorough understanding of spatial heterogeneity in optoelectronic properties of WS2 and other transition metal chalcogenides, which are critical for device fabrication and potential applications.
Low-dose CT allows for accurate proton therapy dose calculation and plan optimization
Authors: Elhamiasl Masoud , Salvo Koen , Poels Kenneth , Defraene Gilles , Lambrecht Maarten , Geets Xavier , Sterpin Edmond , Nuyts Johan 
Published: August 2022

Abstract

Objective. Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization. Approach. To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam. Main results. The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%. Significance. The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.
Locating sources of complex quantum networks
Authors: Hongjue Wang , Fangfeng Zhang 
Published: August 2022

Abstract
The source location of quantum network is an important basic research in the direction of quantum networks, which has important scientific and application values in the frontier fields include quantum state tomography, quantum computing, quantum communication, etc. In this paper, we conduct innovative research on quantum network source location algorithm and theory. A matrix vectorization technique is used to establish a linear system evolution model for quantum network system, and then a high-precision and high-efficiency source location algorithm based on compressed sensing is proposed for large-scale complex quantum networks. All the results of numerical simulation on various model and real networks show the effectiveness and feasibility of the proposed algorithm.
Optical-acoustic excitation of broadband terahertz antiferromagnetic spin waves
Authors: Zhang Jinglin , Ge Xu , Yu Shaojie , Yu Lu , Dong Diandian , Song Jianhui , Chen Yangyi , Li JiaPu , Luo Wei , Liang Shiheng , Otani Yoshichika , You Long , Yang Xiaofei , Zhang Yue 
Published: August 2022

Abstract
We propose an optical-acoustic means to excite broadband terahertz antiferromagnetic (AFM) spin wave in a metal/insulator/antiferromagnet heterostructure. The AFM spin wave is excited by an ultrafast strain wave triggered by a femtosecond pulsed laser based on photoacoustic conversion. This spin wave comprises an AFM exchange spin wave and a magnetoelastic spin wave. Their dispersion curves are overlapped in a wide frequency range by manipulating the Dzyaloshinskii–Moriya interaction, which is accompanied by lifting the degeneration of the spin-wave modes with opposite chirality. This optical-acoustic excitation of spin waves exploits the laser-induced ultrafast strain waves and avoids the thermal effect from the laser. It paves a way to develop novel AFM devices that can apply for ultrafast information processing and communication.
Erratum: Capture and decay of electroweak WIMPonium
Authors: Asadi Pouya , Baumgart Matthew , Fitzpatrick Patrick J. , Krupczak Emmett , Slatyer Tracy R. 
Published: August 2022

Abstract
Due to a correction to the relative sign between the two diagrams in figure 1, a new version of the original paper is provided, since this sign propagates through the paper and gives rise to changes in some equations and figures. This affects both capture rate into the boundstates and transition rates between different boundstates. The topline qualitative results of the paper remain unchanged.

The spectrum of Weakly-Interacting-Massive-Particle (WIMP) dark matter generically possesses bound states when the WIMP mass becomes sufficiently large relative to the mass of the electroweak gauge bosons. The presence of these bound states enhances the annihilation rate via resonances in the Sommerfeld enhancement, but they can also be produced directly with the emission of a low-energy photon. In this work we compute the rate for SU(2) triplet dark matter (the wino) to bind into WIMPonium — which is possible via single-photon emission for wino masses above 5 TeV for relative velocity v < O(10-2) — and study the subsequent decays of these bound states. We present results with applications beyond the wino case, e.g. for dark matter inhabiting a nonabelian dark sector; these include analytic capture and transition rates for general dark sectors in the limit of vanishing force carrier mass, efficient numerical routines for calculating positive and negative-energy eigenstates of a Hamiltonian containing interactions with both massive and massless force carriers, and a study of the scaling of bound state formation in the short-range Hulthén potential. In the specific case of the wino, we find that the rate for bound state formation is suppressed relative to direct annihilation, and so provides only a small correction to the overall annihilation rate. The soft photons radiated by the capture process and by bound state transitions could permit measurement of the dark matter's quantum numbers; for wino-like dark matter, such photons are rare, but might be observable by a future ground-based gamma-ray telescope combining large effective area and a low energy threshold.
Lithography-free fabrication and optical characterizations of nanotextured nickel dewetting thin film for broadband absorbers
Authors: Fang Rongpeng , Yu Zhenshan , Lin Yu-Sheng 
Published: August 2022

Abstract
Plasmonic structures based on metamaterials are widely studied and have been extensively researched in various applications. However, the fabrication of regular nanostructures always requires expensive equipment and a strict working environment, lacking the ability for large-scale fabrication. In this study, we propose and demonstrate simple nanotextured nickel (Ni) dewetting thin films on silicon (Si) and quartz substrates by using different thermal annealing temperatures. They achieve a broadband absorption range with near zero reflectivity due to the standing-wave resonances of surface plasmon polariton, and the resonance is relative to the material of the substrate. The topographies of the nanotextured Ni dewetting thin films vary with thermal annealing temperatures at different dewetting stages. The corresponding reflection and absorption resonant wavelengths of the devices are redshifted by increasing the thermal annealing temperatures. The main absorption resonances are at wavelengths of 610 nm, 580 nm, 625 nm, and 660 nm on the Si substrate. While the reflectivity of the sample around the visible range is lower than 40%, it is suitable for broadband absorption for green and yellow spectra. Moreover, the resonant wavelengths are blueshifted by increasing the incident angles. The demonstrated devices are also sensitive to the ambient media. The reflection resonant wavelengths are redshifted by increasing the environmental refraction indexes. The corresponding reflected colors are changed from green to yellow . These devices exhibit a highest sensitivity of 500 nm RIU−1 and can be used for color sensors. This proposed approach has large-scale fabrication capacity and provides promising applications for broadband absorbers, reflective displays, environmental sensors, and other optoelectronic fields.
Structure and function of hard carbon negative electrodes for sodium-ion batteries
Authors: Mittal Uttam , Djuandhi Lisa , Sharma Neeraj , Andersen Henrik L 
Published: August 2022

Abstract
Practical utilisation of renewable energy from intermittent sustainable sources such as solar and wind relies on safe, reliable, cost-effective, and high-capacity energy storage systems to be incorporated into the grid. Among the most promising technologies aimed towards this application are sodium-ion batteries(SIBs). Currently, hard carbon is the leading negative electrode material for SIBs given its relatively good electrochemical performance and low cost. Furthermore, hard carbon can be produced from a diverse range of readily available waste and renewable biomass sources making this an ideal material for the circular economy. In facilitating future developments on the use of hard carbon-based electrode materials for SIBs, this review curates several analytical techniques that have been useful in providing structure-property insight and stresses the need for overall assessment to be based on a combination of complementary techniques. It also emphasises several key challenges in the characterisation of hard carbons and how various in situ and operando techniques can help unravel those challenges by providing us with a better understanding of these systems during operation thereby allowing us to design high-performance hard carbon materials for next-generation batteries.
Global forests are influenced by the legacies of past inter-annual temperature variability
Authors: Hansen Winslow D , Schwartz Naomi B , Williams A Park , Albrich Katharina , Kueppers Lara M , Rammig Anja , Reyer Christopher P O , Staver A Carla , Seidl Rupert 
Published: August 2022

Abstract
Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.
Use of Personal Protective Equipment Among Healthcare Workers During the First and the Second Wave of the COVID-19 Pandemic
Authors: Cramer Christine , Hansen Karoline Kærgaard , Kinnerup Martin Byskov , Flachs Esben Meulengracht , Vestergaard Jesper Medom , Biering Karin , Nielsen Kent , Würtz Anne Mette , Dalbøge Annett , Würtz Else Toft , Kjærsgaard Mona , Kolstad Henrik Albert , Schlünssen Vivi 
Published: August 2022

Abstract

Introduction
To treat and properly care for COVID-19 patients it is vital to have healthy healthcare workers to ensure the continued function of the healthcare system and to prevent transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to patients, co-workers, and the community. Personal protective equipment (PPE) can prevent healthcare workers from being infected with and transmitting SARS-CoV-2. Experience and training are pivotal to ensure optimal protection. This study aims to examine the use and failure of PPE and compliance with PPE guidelines during the first and the second wave of the COVID-19 pandemic among Danish healthcare workers.

Methods
Healthcare workers from the Central Denmark Region and the Capital Region of Denmark were invited to participate April–June 2020 during the first wave and November 2020–April 2021 during the second wave. Day-by-day, participants reported work procedures, use and failure of PPE, and compliance with PPE guidelines. Register-based information on sex, age, department, and profession was available for all participants.

Results
In total, 21 684 and 10 097 healthcare workers participated during the first and the second wave, respectively. During the first wave, 1.7% used filtering face piece-2 or -3 (FFP2 or FFP3) respirators and 8.2% used face masks [fluid resistant (type IIR) masks, masks with visor (typically type IIR), and other unspecified face masks] during physical contact with patients. During the second wave, the corresponding figures increased to 17.8% and 80.7%. During respiratory procedures, the use of FFP2 or FFP3 respirators increased from 5.6 to 24.3%, and the use of face masks from 14.7 to 77.8%. The no PPE use decreased from 21.3% during the first wave to 0.4% in the second wave, during respiratory procedures. Total PPE failures decreased from 0.7 to 0.4% from the first to second wave. The proportion not complying with PPE guidelines declined from 3.6 to 2.2% during physical contact with patients and from 6.5 to 4.6% during respiratory procedures. PPE failure and non-compliance varied by age, sex and type of department. Frequent reasons for non-compliance were forgetfulness and lack of time, and during the first but not during the second wave, limited availability of PPE.

Conclusion
We found a substantial increase in the use of PPE and a substantial decrease in PPE failures from the first to the second wave of COVID-19 in Denmark. However, there is still a need for continuous focus on compliance in use of PPE among healthcare workers.
Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM
Authors: Wang Lei , Zhong Haolin , Xue Zhidong , Wang Yan 
Published: August 2022

AbstractMotivationProtein domains are the basic units of proteins that can fold, function and evolve independently. Protein domain boundary partition plays an important role in protein structure prediction, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Although there are many methods that have been developed to predict domain boundaries from protein sequence over the past two decades, there is still much room for improvement.ResultsIn this article, a novel domain boundary prediction tool called Res-Dom was developed, which is based on a deep residual network, bidirectional long short-term memory (Bi-LSTM) and transfer learning. We used deep residual neural networks to extract higher-order residue-related information. In addition, we also used a pre-trained protein language model called ESM to extract sequence embedded features, which can summarize sequence context information more abundantly. To improve the global representation of these deep residual networks, a Bi-LSTM network was also designed to consider long-range interactions between residues. Res-Dom was then tested on an independent test set including 342 proteins and generated correct single-domain and multi-domain classifications with a Matthew’s correlation coefficient of 0.668, which was 17.6% higher than the second-best compared method. For domain boundaries, the normalized domain overlapping score of Res-Dom was 0.849, which was 5% higher than the second-best compared method. Furthermore, Res-Dom required significantly less time than most of the recently developed state-of-the-art domain prediction methods.Availability and implementationAll source code, datasets and model are available at http://isyslab.info/Res-Dom/.
No means ‘No’: a non-improper modeling approach, with embedded speculative context
Authors: Tiwary Priya , Madhubalan Akshayraj , Gautam Amit 
Published: August 2022

Abstract

Motivation
The medical data are complex in nature as terms that appear in records usually appear in different contexts. Through this article, we investigate various bio model’s embeddings (BioBERT, BioELECTRA and PubMedBERT) on their understanding of ‘negation and speculation context’ wherein we found that these models were unable to differentiate ‘negated context’ versus ‘non-negated context’. To measure the understanding of models, we used cosine similarity scores of negated sentence embeddings versus non-negated sentence embeddings pairs. For improving these models, we introduce a generic super tuning approach to enhance the embeddings on ‘negation and speculation context’ by utilizing a synthesized dataset.

Results
After super-tuning the models, we can see that the model’s embeddings are now understanding negative and speculative contexts much better. Furthermore, we fine-tuned the super-tuned models on various tasks and we found that the model has outperformed the previous models and achieved state-of-the-art on negation, speculation cue and scope detection tasks on BioScope abstracts and Sherlock dataset. We also confirmed that our approach had a very minimal trade-off in the performance of the model in other tasks like natural language inference after super-tuning.

Availability and implementation
The source code, data and the models are available at: https://github.com/comprehend/engg-ai-research/tree/uncertainty-super-tuning.