bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photoluminescence enhancement in multilayered MoSe2 nanostructures obtained by local anodic oxidation
Authors: Borodin Bogdan R , Benimetskiy Fedor A , Davydov Valery Yu , Smirnov Alexander N , Eliseyev Ilya A , Alekseev Prokhor A 
Published: October 2021

Abstract
Monolayers of transition metal dichalcogenides (TMDCs) exhibit attractive properties and are promising for fabricating photonic and optoelectronic devices, while bulk multilayered structures based on the same materials only recently has revealed many properties useful for nanophotonics. In this regard, the combination of monolayer and multilayer properties in one device (on a single flake) is an important and fruitful task that needs to be solved. In this work, we demonstrate the use of local anodic oxidation to improve the optical properties of multilayer MoSe2 flakes on a gold-covered substrate. Using this method, we fabricated nanostructures demonstrating extraordinarily enhanced photoluminescence (PL), with an intensity up to three orders of magnitude compared to that of the original structure. Low-frequency Raman spectroscopy showed that the nature of this PL enhancement is that the bindings between the layers inside the nanostructures are severely disrupted. This means that the nanostructures consist of quasi-monolayers, which is in good agreement with the intensity and the position of PL peak. Here, we also propose a mechanism of forming these quasi-monolayers. Therefore, this method allows using multilayer TMDC flakes on a conductive substrate to fabricate areas with quasi-monolayer optical properties, exhibiting an enhanced PL intensity.
Study on hardness prediction and parameter optimization for carburizing and quenching: an approach based on FEM, ANN and GA
Authors: Liang Ruijun , Wang Zhiqiang , Yang Shuying , Chen Weifang 
Published: October 2021

Abstract
A proper hardening depth is critical to the load-bearing capacity of a part, and heat treatment, including carburizing and quenching, can highly determine the hardness distribution in the part’s surface after manufacturing. This paper proposes a ‘hardness prediction and parameter optimization’ approach that deploys the finite element method (FEM), the artificial neural network (ANN), and the Genetic Algorithm (GA), to describe the relationships between the carburizing/quenching parameters and the hardening depths and conversely to determine the optimized parameters for a given hardening depth. First, the numerical models for carburizing, quenching, and the hardness field are built respectively. And based on these models, the finite element simulation model is designed to predict the carbon content, the microstructure and the hardness of the part. A BP network is then trained by using the data obtained from the finite element simulation, and the model between the carburizing/quenching parameters and the hardening depths on part is established. The optimization model for the carburizing/quenching parameters is finally established through GA, which can determine the optimized parameters for a given hardening depth. The effectiveness of the ‘prediction-optimization’ approach is verified by a series of experiments. The hardening depth predicted by the proposed approach holds a 10% relative error from that measured in the carburizing and quenching experiment. And the optimized parameters for the heat treatment process can work as a meaningful reference for the heat treatment.
Molten pool structure and temperature flow behavior of green-laser powder bed fusion pure copper
Authors: Lu Pan , Cheng-Lin Zhang , Tong Liu , Xin-Yu Liu , Jiang-Lin Liu , Shun Liu , Wen-Hao Wang , Heng-Hua Zhang 
Published: October 2021

Abstract
Additive Manufacturing(AM) is an advanced direct-manufacturing technology, based on the discrete-stacking principle. Laser Powder Bed Fusion (L-PBF) is one of the most promising technologies in the field of metal AM, with the characteristics of fabricating parts with complex shapes directly. For L-PBF equipment , the core device is lasers, and almost all L-PBF printers are currently equipped with infrared laser. However, due to too low absorption rate of the pure copper surface to infrared laser and high thermal conductivity between pure copper, it is extremely challenging to fabricate pure copper by traditional infrared-laser powder bed fusion(IR L-PFB). In this work, green-laser was applied to replace traditional infrared laser during L-PBF process, molten pool structure and temperature flow behavior of Green-Laser powder bed additive manufacturing pure copper was studied by mesoscopic simulation. Here we show that green-laser greatly improved the absorption rate of the pure copper surface, and the result showed that with lower cost laser process parameters (lower laser power 300W and larger hatching space 0.08 mm), pure copper parts with smoother surface, no-remelting and no obvious defects could be fabricated successfully.
On the stability of small-scale ballooning modes in axisymmetric mirror traps
Authors: KOTELNIKOV Igor , LIZUNOV Andrej , ZENG Qiusun 
Published: October 2021

Abstract
It is shown that a steepening of the radial plasma pressure profile leads to a decrease in the critical value of beta, above which, small-scale balloon-type perturbations in a mirror trap become unstable. This may mean that small-scale ballooning instability leads to a smoothing of the radial plasma profile. The critical beta values for the real magnetic field of the gas-dynamic trap and various plasma pressure radial profiles was also calculated. For a plasma with a parabolic profile critical beta is evaluated at the level of 0.72. A previous theoretical prediction for this trap was almost two times lower than maximal beta 0.6 achieved experimentally.
Kuramoto-model-based data classification using the synchronization dynamics of uniform-mode spin Hall nano-oscillators
Authors: Garg Neha , Hemadri Bhotla Sri Vasudha , Muduli Pranaba Kishor , Bhowmik Debanjan 
Published: October 2021

Abstract
Oscillator-based data-classification schemes have been proposed recently using the Kuramoto model, which tries to capture the synchronization behavior of coupled oscillators without considering the underlying physics of the oscillation and the coupling. In this paper, we propose the hardware implementation of a Kuramoto-model-based data-classification scheme through an array of dipole-coupled uniform-mode spin Hall nano-oscillators (SHNOs). Using micromagnetic simulations, which capture the underlying physics of operation of the SHNOs, we first study the variation of synchronization range between two uniform-mode SHNOs as a function of the physical distance between them. Thus we correlate the coupling constant in the Kuramoto model with the dipole-coupling strength between two SHNOs, which our micromagnetic simulation takes into account. Next, we generate the synchronization map for the two-input–two-output dipole-coupled uniform-mode SHNO system through micromagnetics and show that it matches with the one predicted by the Kuramoto model. Thus, we demonstrate here that the synchronization behavior of SHNOs obtained from micromagnetics-based modeling is consistent with that obtained from the Kuramoto model, which ignores the underlying physics of the SHNOs. This suggests that the Kuramoto-model-based data classification scheme can indeed be implemented physically on an array of SHNOs. To verify our claim, we show, through micromagnetic simulation, binary classification of data from a popular machine-learning data set (Fisher’s Iris data set) using an array of uniform-mode SHNOs.
Weighted Littlewood–Paley inequality for arbitrary rectangles in ℝ²
Authors: Borovitskiy V. 
Published: October 2021

Weighted counterparts of the one-sided Littlewood–Paley inequalities for arbitrary rectangles in

R

2

\mathbb {R}^2

are proved.

For a partition

I

\mathcal {I}

of the plane

R

2

\mathbb {R}^2

into rectangles with sides parallel to coordinate axes and a weight

w
(

⋅

,

⋅

)

w(\,\cdot \,, \,\cdot \,)

satisfying the two-parameter Muckenhoupt condition

A

p

/

2

A_{p/2}

for

2
>
p
>
∞

2 > p > \infty

, the following inequality holds:

c

p
,
w

‖
{

M
I

f

}

I
∈

I

‖

L
w
p

(

l
2

)

≤
‖
f

‖

L
w
p

,

\begin{equation*} c_{p, w}\lVert \{M_I f\}_{I \in \mathcal {I}} \rVert _{L^p_w(l^2)} \leq \lVert f \rVert _{L_w^p} , \end{equation*}

where the symbols

M
I

f

^

=

f
^

χ

I

\widehat {M_I f} = \widehat {f} \chi _{I}

denote the corresponding Fourier multipliers.

For

I

\mathcal {I}

as above,

p
p

in the range

0
>
p
>
2

0 > p > 2

, and weights

w
w

satisfying a dual condition

α

r
(
p
)

\alpha _{r(p)}

, the following inequality holds

‖

∑

I
∈

I

f
I

‖

L
w
p

≤

C

p
,
w

‖

{

f
I

}

I
∈

I

‖

L
w
p

(

l
2

)

,
where
supp
⁡

f
I

^

⊆
I
for
I
∈

I

.

\begin{equation*} \Big \|{\sum }_{I \in \mathcal {I}} f_I\Big \|_{L^p_w} \leq C_{p, w} \big \| \left \{ f_I \right \}_{I \in \mathcal {I}} \big \|_{L^p_w(l^2)} , \text { where } \operatorname {supp}{\widehat {f_I}} \subseteq I \text { for } I \in \mathcal {I}. \end{equation*}

The proof is based on the theory of two-parameter singular integral operators on Hardy spaces developed by R. Fefferman and some of its more recent weighted generalizations. The former and the latter inequalities are extensions to the weighted setting, respectively, for Journe’s result of 1985 and Osipov’s result of 2010.
Impact of vaccination on the spread of SARS-CoV-2 infection in north-east Italy nursing homes. A propensity score and risk analysis
Authors: Pierobon Silvia , Braggion Marco , Fedeli Ugo , Nordio Maurizio , Basso Cristina , Zorzi Manuel 
Published: October 2021

Abstract

Background
In the Veneto Region, 421,000 coronavirus 2019 disease (COVID-19) cases and 11,000 deaths have been reported since 21 February 2020. The pandemic spread particularly in nursing homes (NH).

Objective
This study estimated the impact of SARS-CoV-2 infection among NH residents, focusing on the risk of hospitalisation and death due to COVID-19 compared with the general older population. It also provided evidence of risk changes over time.

Methods
Older people, resident in Veneto, were enrolled from the regional registry of the population. We collected also information about demographic characteristics, chronic diseases, COVID-19 positivity, NH institutionalization, hospitalisation and date of death. Patients were assigned to NH or non-NH residents groups through a propensity score 1:1 matching. The follow-up period was defined as 21 February 2020 – 3 May 2021 and then divided into three waves. Risk ratios (RRs) and 95% confidence interval were estimated by using Poisson models with robust estimation of variance.

Results
NH residents showed a higher risk of COVID-19 infection (RR = 6.28; 6.03–6.54), hospitalisation for COVID-19 (RR = 2.20; 2.05–2.36) and death with COVID-19 (RR = 6.07; 5.58–6.61).

Conclusion
NH residents shared common spaces with other patients and healthcare professionals and were more exposed to infections. Nonetheless, in Italy from late December 2020 to May 2021, 95% of NH residents and their healthcare professionals received at least one vaccine dose and RRs for all outcomes decreased in NH.
Are we allowed to visit now? Concerns and issues surrounding vaccination and infection risks in UK care homes during COVID-19
Authors: Giebel Clarissa , Hanna Kerry , Cannon Jacqueline , Marlow Paul , Tetlow Hilary , Mason Stephen , Shenton Justine , Rajagopal Manoj , Gabbay Mark 
Published: October 2021

Abstract

Background
vaccination uptake in the UK and increased care home testing are likely affecting care home visitation. With scant scientific evidence to date, the aim of this longitudinal qualitative study was to explore the impact of both (vaccination and testing) on the conduct and experiences of care home visits.

Methods
family carers of care home residents with dementia and care home staff from across the UK took part in baseline (October/November 2020) and follow-up interviews (March 2021). Public advisers were involved in all elements of the research. Data were analysed using thematic analysis.

Results
across 62 baseline and follow-up interviews with family carers (n = 26; 11) and care home staff (n = 16; 9), five core themes were developed: delayed and inconsistent offers of face-to-face visits; procedures and facilitation of visits; variable uptake of the COVID-19 vaccine; misinformation, education and free choice; frustration and anger among family carers. The variable uptake in staff, compared to family carers, was a key factor seemingly influencing visitation, with a lack of clear guidance leading care homes to implement infection control measures and visitation rights differently.

Conclusions
we make five recommendations in this paper to enable improved care home visitation in the ongoing, and in future, pandemics. Visits need to be enabled and any changes to visiting rights must be used as a last resort, reviewed regularly in consultation with residents and carers and restored as soon as possible as a top priority, whilst more education needs to be provided surrounding vaccination for care home staff.
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences
Authors: Ao Chunyan , Zou Quan , Yu Liang 
Published: October 2021

Abstract2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.
The micro–meso–macro architecture of a proposed collaborative emergent strategy for the hydrogen market
Authors: Tranfield John 
Published: October 2021

Abstract
In 2018, an intergovernmental climate-change panel concluded that, to maintain a global temperature increases of &lt;1.5oC, net-zero greenhouse-gas emissions would be required by 2050. Since then, &gt;110 countries have pledged net-zero carbon ambitions by 2050 and hydrogen has been identified at national levels as key to achieving this. Governments have pledged &gt;US$ 70 billion to further advance hydrogen infrastructure and technology, with an additional investigation on &gt;200 proposed hydrogen-based projects expecting completion before 2030, totalling a value of US$ 300 billion. Reaching these aggressive targets will require a disciplined cohesion of collaborative strategies to develop an integrated macro infrastructure system. This article discusses the current infancy of the hydrogen market, introduces and defines a new ‘collaborative emergent strategy’ based on the emergent strategy concept by Mintzberg and Waters, and links its developmental viability through a staged micro–meso–macro architecture. Successful strategic business case studies and current market opportunities across multiple industries are reviewed as they all vie for a strategic early market position.
High-Throughput CRISPR–Cas13 SARS-CoV-2 Test
Authors: Manning Brendan J , Khan Wahab A , Peña Jennifer M , Fiore Elizabeth S , Boisvert Heike , Tudino Marisa C , Barney Rachael E , Wilson Mary K , Singh Subha , Mowatt Joel A , Thompson Hannah J , Tsongalis Gregory J , Blake William J 
Published: October 2021

Abstract

Background
The ability to control the spread of COVID-19 continues to be hampered by a lack of rapid, scalable, and easily deployable diagnostic solutions.

Methods
We developed a diagnostic method based on CRISPR (clustered regularly interspaced short palindromic repeats) that can deliver sensitive, specific, and high-throughput detection of Sudden Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). The assay utilizes SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) for the qualitative detection of SARS-CoV-2 RNA and may be performed directly on a swab or saliva sample without nucleic acid extraction. The assay uses a 384-well format and provides results in &lt;1 hour.

Results
Assay performance was evaluated with 105 (55 negative, 50 positive) remnant SARS-CoV-2 specimens previously tested using Food and Drug Administration emergency use authorized assays and retested with a modified version of the Centers for Disease Control and Prevention (CDC) quantitative PCR with reverse transcription (RT–qPCR) assay. When combined with magnetic bead-based extraction, the high-throughput SHERLOCK SARS-CoV-2 assay was 100% concordant (n = 60) with the CDC RT–qPCR. When used with direct sample addition the high-throughput assay was also 100% concordant with the CDC RT–qPCR direct method (n = 45). With direct saliva sample addition, the negative and positive percentage agreements were 100% (15/15, 95% CI: 81.8–100%) and 88% (15/17, 95% CI: 63.6–98.5%), respectively, compared with results from a collaborating clinical laboratory.

Conclusions
This high-throughput assay identifies SARS-CoV-2 from patient samples with or without nucleic acid extraction with high concordance to RT–qPCR methods. This test enables high complexity laboratories to rapidly increase their testing capacities with simple equipment.
On the importance of representative datasets in ECG-based artificial intelligence
Authors: Gumpfer N , Wegener S , Prim J , Gruen D , Hannig J , Keller T , Guckert M 
Published: October 2021

Abstract

Background/Introduction
ECG-based artificial intelligence (AI) is an emerging field in digital cardiology. Training on diseased records vs. healthy controls is common practice. We aimed to evaluate if such an approach can lead to unwanted behaviour in real-world settings and thus unnecessarily reduce diagnostic precision of the developed AI model.

Purpose
Several studies have shown that deep neural networks are able to exceed performance of medical experts. However, when these models are applied to different cohorts, results vary strongly. We hypothesise that this is because the datasets used for training were not representative for the target population.

Methods
Based on the public ECG database PTB-XL we sampled three distinct subsets of n=150 records representing ECG groups labelled for diagnoses 'old myocardial infarction' (M), 'normal ECG' (N), or 'other cardiac abnormality' (O). These groups were combined to three datasets ([M, N] (n=300), [M, O] (n=300), [M, N, O] (n=450)), representing different approaches to data sampling. On each dataset, we trained a separate but equally structured deep neural network using 100-fold bootstrapping. The diagnostic performance of each model was validated on unseen data from all datasets with sensitivity, specificity and area under the receiver operator characteristic curve.

Results
Evaluation of the three differently trained models shows best diagnostic performance on the M vs. N records and worst on the M vs. O records. However, in the out-of-dataset setting, the best-performing model (trained on [M, N]) shows weaker performance on the [M, N, O] and [M, O] datasets. Sensitivity for the same model remained equal, as identical M records were used throughout corresponding bootstrapping folds. Detailed results are presented in Table 1.

Conclusions
Our results suggest that the model trained on a dataset including only diseased records vs. healthy controls [M, N] learned to recognise healthy (N) instead of diseased (M) records, which explains why it performed poorly on datasets including records showing other cardiac abnormalities (O). Such behaviour is a common problem in AI and requires special attention in dataset sampling. For small cohorts, it is tempting to increase the amount of training data by using healthy controls. However, we have shown that this can be a poor option, since classifiers can more easily rely on features that are not actually related to the target disease. Training and validation of classifiers should therefore be performed on representative datasets that are as close as possible to the target population.

Funding Acknowledgement
Type of funding sources: Public Institution(s). Main funding source(s): Forschungscampus Mittelhessen, Flexi Funds Table 1
Greater Inclusion of Asian Americans in Aging Research on Family Caregiving for Better Understanding of Racial Health Inequities
Authors: Yellow Horse Aggie J , Patterson Sarah E 
Published: October 2021

AbstractWith the substantial demographic changes in racial composition in the United States since 1965, research on racial health inequities must build upon the Black-White binary to assess the complex ways “race” affects health and aging. Considering variation in the prevalence and meanings of aging across racialized groups requires concerted efforts to expand and disaggregate samples. Aligned with the goals of the intersectionality framework, we argue that greater inclusion of Asian Americans is critical to advance both theoretical and methodological considerations that enable us to investigate the lived experiences of Asian Americans. Using caregiving as an example, we discuss how systemic, cultural, and interpersonal marginalization from racism and other oppressive systems intertwine with “race” to produce the race effects. Greater inclusion of Asian Americans helps further provide the opportunity to conceptualize culture as dynamic and interacting with structure to produce different racial patterns. Meaningful inclusion of Asian Americans in research requires more systemic effort to collect accurate, reliable, and quality data for Asian Americans that can be disaggregated by other important axes of stratification.
Undermining the Role of Women in the Economy: The Interplay Between Paid Work and Unpaid Care Work in India
Authors: Vyas Neha 
Published: October 2021

Abstract
Women are overrepresented in informal employment in the Indian labour market. They also devote more time to unpaid care work than men do. The poor working conditions of women are attributable to this double burden of work. Due to the lack of regulatory measures to protect the interest of informal women workers along with rigid gender norms, women’s participation in paid work is drastically reduced. As far as unpaid care work is concerned, feminist economists have been striving to make such work visible for a long time now. There have been some developments in labour statistics, with time use surveys quantifying women’s paid and unpaid contributions to the economy. This article delves into the examination of unpaid care work in India with the help of the Indian government’s recent Time Use Survey. It attempts to study the connection between paid work and unpaid care work and its implications for the working conditions of women in India. Finally, it evaluates the ‘right to work’ as a possible solution by using the example of employment guarantee schemes in India.