bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy
Authors: Kropat Marcel , Laleicke Paul Frederik , Acosta Juan Jose 
Published: June 2022

Abstract
The chemical composition of wood determines the color development when applying chemical stains to the surface of wood. However, different species and individuals from the same species can show variations in the chemical composition, resulting in the risk of nonuniform color development in industrial staining processes between different batches of wood. In the present study, near-infrared (NIR) models were developed to predict wood specimen color development after applying three different concentrations of the chemical stains iron acetate and sodium bicarbonate. The modeling dataset included the NIR spectra of the untreated wood, stain treatment, concentration, and the International Commission on Illumination (CIE) L*a*b* color value before stain application for 210 specimens from five commercial wood species, including red oak (Quercus rubra), white oak (Quercus alba), yellow poplar (Liriodendron tulipifera), southern yellow pine (Pinus spp.), and western red cedar (Thuja plicata). The models were developed by partial least squares regression (PLSR), using 13 different mathematical transformations on the NIR spectra as well as the raw spectral data. Models with single stains and global-species/stain models were developed and compared. The models for iron acetate showed promising results in predicting the color development with the coefficient of determination for cross-validation ( ≥ 0.92), while the models for sodium bicarbonate showed acceptable results with of 0.71 to 0.89. However, a global model including both stains resulted in an unsatisfying prediction of the CIE L*a*b* color values, with of 0.46 to 0.76. The NIR models can be useful for online predictions of color development in industrial staining processes of wood with chemical stains.
Two-dimensional measurement of resonance in MEMS resonators using stroboscopic differential interference contrast microscopy
Authors: Iimori Mirai , Zhang Ya 
Published: June 2022

We report the two-dimensional (2D) measurement of resonance in MEMS resonators using stroboscopic differential interference contrast (DIC) microscopy, for the investigation of the linear and nonlinear oscillations of MEMS resonators. The DIC microscopy measures the interference of two sheared illumination light beams reflected from the sample surface to determine the differential surface deflection. By modulating the illumination light at the resonance frequency, the DIC image of the MEMS resonator periodically change its brightness and contrast with the sweeping illumination phase, which have been used to derive the oscillation amplitude and the resonance mode shape of the MEMS resonator. Comparing with conventional interference microscopy, the DIC microscopy can observe the surface deflection larger than the wavelength of the illumination light, enabling the measurement of nonlinear oscillations with a large oscillation amplitude. We demonstrate that the stroboscopic DIC microscopy can measure the 2D mechanical resonance with a high vertical resolution at the nanometer(nm)-scale, and a large measurement range of ∼1 µm, which is very promising for the investigation of linear and nonlinear oscillations of MEMS resonators.
Realizing fast temperature measurement and simulating Maxwell’s demon with nearly nondestructive detection in cold atoms
Authors: Wang Xin , Sun Yuan , Liu Liang 
Published: June 2022

Optical detection and manipulation of the thermal properties is an essential subject of cold atoms in the quantum era. For laser cooled alkali atoms, we have experimentally realized deterministic temperature measurement with time cost below 1 ms and effective filtering of colder atoms with temperature less than 1 μK, with the help of nearly nondestructive detection. The quick temperature measurement is accomplished by carefully resolving the diffusion dynamics of atoms with the information provided by a single probe laser pulse in the form of bucket detection, while suppressing the amplitude and phase noises of probe laser. The separation of colder atoms is attainable as the velocity differences of atoms translate into nontrivial position differences, when the diffusion sustains for a few tens of milliseconds. In particular, these efforts are based on a labeling process that distinguishes the cold atoms under study from the others by specific internal states, while the nearly nondestructive detection is implemented via driving a cycling transition with continuous optical pulses. Moreover, such a position-dependent labeling process can be further modified to become velocity-dependent, with which we have demonstrated a Maxwell’s demon-type operation on cold atoms, as Maxwell’s demon’s intricate abilities can be understood as measuring the velocity of an individual particle and then performing feedback according to a straightforward dichotomy of the velocity value.
Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester
Authors: Walker Mark C. , Willner Inbal , Miguel Olivier X. , Murphy Malia S. Q. , El-Chaâr Darine , Moretti Felipe , Dingwall Harvey Alysha L. J. , Rennicks White Ruth , Muldoon Katherine A. , Carrington André M. , Hawken Steven , Aviv Richard I. 
Published: June 2022

Objective
To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.

Methods
All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability.

Results
The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88–98%), sensitivity 92% (95% CI: 79–100%), specificity 94% (95% CI: 91–96%), and the area under the ROC curve 0.94 (95% CI: 0.89–1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area.

Conclusions
Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.
Female sex hormones and symptoms of obstructive sleep apnea in European women of a population-based cohort
Authors: Sigurðardóttir Erla S. , Gislason Thorarinn , Benediktsdottir Bryndis , Hustad Steinar , Dadvand Payam , Demoly Pascal , Franklin Karl A. , Heinrich Joachim , Holm Mathias , van der Plaat Diana A. , Jõgi Rain , Leynaert Benedicte , Lindberg Eva , Martinez-Moratalla Jesus , Sainz De Aja Leire , Pesce Giancarlo , Pin Isabelle , Raherison Chantal , Pereira-Vega Antonio , Real Francisco Gómez , Triebner Kai 
Published: June 2022

Background
The prevalence of obstructive sleep apnea is higher in women after menopause. This is suggested to be a result of an altered sex hormone balance but has so far not been confirmed in a population-based study.

Objective
To investigate whether serum concentration of estrogens and progesterone are associated with the prevalence of sleep apnea symptoms in middle-aged women of the general population.

Methods
We analyzed data from 774 women (40–67 years) from 15 study centers in seven countries participating in the second follow-up of the European Community Respiratory Health Survey (2010–2012). Multiple logistic regression models were fitted with self-reported symptoms of sleep apnea as outcomes and serum concentrations of various estrogens and progesterone as predictors. All analyses were adjusted for relevant covariates including age, BMI, education, study center, smoking habits, and reproductive age.

Results
Among all included women, a doubling of serum concentrations of estrone and progesterone was associated with 19% respectively 9% decreased odds of snoring. Among snorers, a doubling of the concentrations of 17β-estradiol, estrone and estrone 3-sulfate was associated with 18%, 23% and 17% decreased odds of breathing irregularly, and a doubling of the progesterone concentration was further associated with 12% decreased odds of waking up suddenly with a chocking sensation. Other evaluated associations were not statistically significant.

Conclusions
Middle-aged women with low serum estrogen and progesterone levels are more likely to snore and report symptoms of obstructive sleep apnea.
The use of social robots with children and young people on the autism spectrum: A systematic review and meta-analysis
Authors: Kouroupa Athanasia , Laws Keith R. , Irvine Karen , Mengoni Silvana E. , Baird Alister , Sharma Shivani 
Published: June 2022

Background
Robot-mediated interventions show promise in supporting the development of children on the autism spectrum.

Objectives
In this systematic review and meta-analysis, we summarize key features of available evidence on robot-interventions for children and young people on the autism spectrum aged up to 18 years old, as well as consider their efficacy for specific domains of learning.

Data sources
PubMed, Scopus, EBSCOhost, Google Scholar, Cochrane Library, ACM Digital Library, and IEEE Xplore. Grey literature was also searched using PsycExtra, OpenGrey, British Library EThOS, and the British Library Catalogue. Databases were searched from inception until April (6th) 2021.

Synthesis methods
Searches undertaken across seven databases yielded 2145 articles. Forty studies met our review inclusion criteria of which 17 were randomized control trials. The methodological quality of studies was conducted with the Quality Assessment Tool for Quantitative Studies. A narrative synthesis summarised the findings. A meta-analysis was conducted with 12 RCTs.

Results
Most interventions used humanoid (67%) robotic platforms, were predominantly based in clinics (37%) followed home, schools and laboratory (17% respectively) environments and targeted at improving social and communication skills (77%). Focusing on the most common outcomes, a random effects meta-analysis of RCTs showed that robot-mediated interventions significantly improved social functioning (g = 0.35 [95%CI 0.09 to 0.61; k = 7). By contrast, robots did not improve emotional (g = 0.63 [95%CI -1.43 to 2.69]; k = 2) or motor outcomes (g = -0.10 [95%CI -1.08 to 0.89]; k = 3), but the numbers of trials were very small. Meta-regression revealed that age accounted for almost one-third of the variance in effect sizes, with greater benefits being found in younger children.

Conclusions
Overall, our findings support the use of robot-mediated interventions for autistic children and youth, and we propose several recommendations for future research to aid learning and enhance implementation in everyday settings.

PROSPERO registration
Our methods were preregistered in the PROSPERO database (CRD42019148981).
Gait stability in ambulant children with cerebral palsy during dual tasks
Authors: Wist Sophie , Carcreff Lena , Bruijn Sjoerd M. , Allali Gilles , Newman Christopher J. , Fluss Joel , Armand Stéphane 
Published: June 2022

Aim
The aim of this cross-sectional study was to measure the effect of dual tasks on gait stability in ambulant children with cerebral palsy (CP) compared to typically developing (TD) children.

Methods
The children of the CP (n = 20) and TD groups (n = 20) walked first without a dual task, then while counting forward and finally while alternatively naming fruits and animals (DTf/a). They then completed the same cognitive exercises while sitting comfortably. We calculated the distance between the foot placement estimator (FPE) and the real foot placement in the anterior direction (DFPEAP) and in the mediolateral direction (DFPEML) as a measure of gait stability, in a gait laboratory using an optoelectronic system. Cognitive scores were computed. Comparisons within and between groups were analysed with linear mixed models.

Results
The dual task had a significant effect on the CP group in DFPEAP and DFPEML. The CP group was more affected than the TD group during dual task in the DFPEML. Children in both groups showed significant changes in gait stability during dual tasks.

Interpretation
The impact of dual task on gait stability is possibly due to the sharing of attention between gait and the cognitive task. All children favoured a ‘posture second’ strategy during the dual task of alternatively naming animals and fruits. Children with CP increased their mediolateral stability during dual task.
Comparison of external R&D and internal R&D: Based on the perspective of S&T development of China’s pharmaceutical manufacturing industry
Authors: Wu Di , Wang Su , Chang Senhao , Lian Guiyu , Chen Yuwen 
Published: June 2022

Purpose
The science and technology (S&T) innovation in China’s pharmaceutical industry has entered a bottleneck. The choice between external and internal research and development (R&D) has become a significant challenge for S&T development. To provide scientific suggestions for companies to choose an R&D strategy and enhance S&T development, we analyzed and compared the impacts of two R&D strategies on S&T output.

Methods
We selected the data related to China’s pharmaceutical manufacturing industry from 2000 to 2019, established regression equations by the E-G two-step method, and used the VAR model for impulse responses and variance decompositions to research the relationship between two R&D strategies and S&T output.

Results
There is a stable long-term equilibrium relationship between two R&D strategies, including external and internal R&D, and S&T output in China’s pharmaceutical manufacturing industry. When internal R&D increases by 1%, S&T output increases by 0.7382% with a 5-year lag. When S&T output increases by 1%, external R&D increases by 2.0749% with a 2-year lag.

Conclusion
Compared with external R&D, internal R&D can boost S&T output.
A Rare Presentation of Sarcoidosis Masquerading as Colonic Polyps on Screening Colonoscopy
Authors: Ufondu Obiora , Hillerson Natalie , Shwetar Mohammed J. , Mills Anne , Kumral Dennis 
Published: June 2022

ABSTRACT
Sarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation. Gastrointestinal involvement in sarcoidosis is a very rare occurrence, with the colon being affected in few patients. We present a case of sarcoidosis presenting as multiple colonic polyps found on routine colorectal cancer screening colonoscopy. Histopathology of the polyps showed noncaseating granulomas.
Pratiche di stesura della tesi di laurea in italiano LS
Authors: Załęska Maria 
Published: June 2022

In the Italian academic discourse research, Master’s degree theses used to be studied mainly from the perspective of linguistic competencies of their authors. However, strictly linguistic skills are not the only challenge for undergraduate, who must also convey intellectual content in appropriate discursive and rhetorical forms. The paper explores the patterns of a peculiar form of “collaborative writing”, i.e. the cooperation between the thesis supervisor and the student. Using the example of Master’s theses in lin- guistics, written in Italian as a foreign language, three basic didactic approaches are discussed in the paper. In the didactics of content the supervisors emphasize knowledge, i.e. linguistic concepts expected to be applied in a Master’s thesis. In the didactics of forms, the supervisors focus on the linguistic code, that is, on grammatical and lexical correctness of the Master’s thesis. In the didactics of forms of content the supervisors use regularities observed in academic discourse, such as genre and rhetorical structures, as valuable teaching resources likely to help students develop metacommunicative awareness.
SNARF
Authors: Vaidya Kapil , Chatterjee Subarna , Knorr Eric , Mitzenmacher Michael , Idreos Stratos , Kraska Tim 
Published: June 2022

We present Sparse Numerical Array-Based Range Filters (SNARF), a learned range filter that efficiently supports range queries for numerical data. SNARF creates a model of the data distribution to map the keys into a bit array which is stored in a compressed form. The model along with the compressed bit array which constitutes SNARF are used to answer membership queries.
We evaluate SNARF on multiple synthetic and real-world datasets as a stand-alone filter and by integrating it into RocksDB. For range queries, SNARF provides up to 50x better false positive rate than state-of-the-art range filters, such as SuRF and Rosetta, with the same space usage. We also evaluate SNARF in RocksDB as a filter replacement for filtering requests before they access on-disk data structures. For RocksDB, SNARF can improve the execution time of the system up to 10x compared to SuRF and Rosetta for certain read-only workloads.
QueryFormer
Authors: Zhao Yue , Cong Gao , Shi Jiachen , Miao Chunyan 
Published: June 2022

Machine learning has become a prominent method in many database optimization problems such as cost estimation, index selection and query optimization. Translating query execution plans into their vectorized representations is non-trivial. Recently, several query plan representation methods have been proposed. However, they have two limitations. First, they do not fully utilize readily available database statistics in the representation, which characterizes the data distribution. Second, they typically have difficulty in modeling long paths of information flow in a query plan, and capturing parent-children dependency between operators.
To tackle these limitations, we propose QueryFormer, a learning-based query plan representation model with a tree-structured Transformer architecture. In particular, we propose a novel scheme to integrate histograms obtained from database systems into query plan encoding. In addition, to effectively capture the information flow following the tree structure of a query plan, we develop a tree-structured model with the attention mechanism. We integrate QueryFormer into four machine learning models, each for a database optimization task, and experimental results show that QueryFormer is able to improve performance of these models significantly.
Explanations for Inequality and Partisan Polarization in the U.S., 1980–2020
Authors: Suhay Elizabeth , Tenenbaum Mark , Bartola Austin 
Published: June 2022

AbstractWe examine an understudied component of partisan polarization—disagreement over whether the U.S. economy rewards merit. Drawing on data from party platforms and surveys over four decades, we illustrate large, and increasing, partisan divides in beliefs regarding whether an unequal society, or unequal behavior, is the cause of socioeconomic inequality. Republican politicians and citizens are optimistic about the American Dream and pessimistic about poor people’s behavior; Democratic politicians and citizens are pessimistic about the Dream and optimistic about poor people’s ability to succeed if given the chance. These patterns hold for beliefs about economic inequality along both class and race lines. Variation in societal versus individual blame is consistently associated with views on social welfare, taxation, and affirmative action. We conclude that Americans’ beliefs about the fairness of the economy represent a crucial component of a redistributive versus anti-redistributive ideology that is increasingly associated with the two political parties.
Nature, Culture and COVID-19—Towards a Global History of Pandemics
Authors: Fangerau Heiner , Labisch Alfons 
Published: June 2022

Abstract
From a historical perspective, SARS-CoV-2 and COVID-19 have simultaneously led to known and previously unknown events as well. These seamlessly linked events can only be grasped with a new, integrative perspective of the relationship between culture and disease. Such a view requires a historiography that captures the full spectrum of an epidemic event, from the causes of emerging pathogens to their global spread and impact on different national, regional, and local communities. Integrative approaches to a global history of epidemics essentially include the following:
–Understanding the dynamic relationship between nature and culture to empirically capture changes in local and regional biospheres and their interaction in global contexts.
–Investigating the culturally determined scientific and social negotiation processes that lead to the naming, characterization and communication of initially unknown causes of disease in relation to the culturally determined countermeasures that begin with their emergence.
–Analysis of the effects of worldwide densification through new technical possibilities and new forms of globally organised production and the associated traffic of trade, transport and communication in historical perspective. These empirical approaches represent nodes in a seamless web of interacting factors. Such an approach necessarily has to bring together a wide range of disciplines and perspectives.
Signal propagation parameters estimation through designed multi layer fibre with higher dominant modes using OptiFibre simulation
Authors: Zaki Rashed Ahmed Nabih , Ahammad SK. Hasane , Daher Malek G. , Sorathiya Vishal , Siddique Abrar , Asaduzzaman Sayed , Rehana Hasin , Dutta Nitul , Patel Shobhit K. , Nyangaresi Vincent Omollo , Jibon Rayhan Habib , Ibrahim Anas 
Published: June 2022

Abstract
The aim and scope of the paper is to simulate the signal propagation parameters estimation through designed multi-layer fibre with higher dominant modes by using OptiFibre simulation software. The multi-layer fibre profile has a length of 1000 m is designed and clarified with six layers. RI difference profile variations are clarified with radial distance variations. Modal/group index, group delay, dispersion, mode field diameter and total fibre losses are demonstrated with the fibre wavelength variations. All the dominant mode field distribution for multi-layer fibre are simulated and demonstrated. The other modes for designed multi-layer fibre with the theoretical fibre cutoff values for the different modes based the designed multi-layer fibre are analyzed and clarified clearly in details.