bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factorial design analytics on effects of material parameter uncertainties in multiphysics modeling of additive manufacturing
Authors: Giam Amanda , Chen Fan , Cai Jiaxiang , Yan Wentao 
Published: April 2023

AbstractA bottleneck in Laser Powder Bed Fusion (L-PBF) metal additive manufacturing (AM) is the quality inconsistency of its products. To address this issue without costly experimentation, computational multi-physics modeling has been used, but the effectiveness is limited by parameter uncertainties and their interactions. We propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics models. Data is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material parameters. Crucial physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge, which are used to establish a validation checkpoint for our study. Initial data visualization with half-normal probability plots, interaction plots and standard deviation plots, is used to assess if the checkpoint is being met. We then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable selection. Analytics yield statistically and phyiscally validated findings with practical implications, emphasizing the importance of parameter interactions under uncertainty, and their relation to the underlying physics of L-PBF.
Synthesizing efficient circuits for Hamiltonian simulation
Authors: Mukhopadhyay Priyanka , Wiebe Nathan , Zhang Hong Tao 
Published: April 2023

AbstractWe provide an approach for compiling quantum simulation circuits that appear in Trotter, qDRIFT and multi-product formulas to Clifford and non-Clifford operations that can reduce the number of non-Clifford operations. The total number of gates, especially CNOT, reduce in many cases. We show that it is possible to implement an exponentiated sum of commuting Paulis with at most m (controlled)-rotation gates, where m is the number of distinct non-zero eigenvalues (ignoring sign). Thus we can collect mutually commuting Hamiltonian terms into groups satisfying one of several symmetries identified in this work. This allows an inexpensive simulation of the entire group of terms. We further show that the cost can in some cases be reduced by partially allocating Hamiltonian terms to several groups and provide a polynomial time classical algorithm that can greedily allocate the terms to appropriate groupings.
Recurrent Respiratory Papillomatosis (RRP)—Meta-analyses on the use of the HPV vaccine as adjuvant therapy
Authors: Goon Peter , Sauzet Odile , Schuermann Matthias , Oppel Felix , Shao SenYao , Scholtz Lars-Uwe , Sudhoff Holger , Goerner Martin 
Published: April 2023

AbstractRecurrent Respiratory Papillomatosis(RRP) is a rare disease with severe morbidity. Treatment is surgical. Prevailing viewpoint is that prophylactic HPV vaccines do not have therapeutic benefit due to their modus operandi. Studies on HPV vaccination alongside surgery were meta-analysed to test effect on burden of disease. Databases were accessed Nov and Dec 2021 [PubMed, Cochrane, Embase and Web of Science]. Main outcome measured was: Mean paired differences in the number of surgeries or recurrences per month. Analyses was performed using: Random effect maximal likelihood estimation model using the Stata module Mataan(StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX:StataCorp LLC.) Our results found n = 38 patients, suitable for syntheses with one previous meta-analyses (4 published, 2 unpublished studies) n = 63, total of n = 101 patients. Analyses rendered an overall reduction of 0.123 recurrences or surgeries per month (95% confidence interval [0.064, 0.183]). Our meta-analyses concludes that HPV vaccine is a beneficial adjunct therapy alongside surgery
Coherent radio bursts from known M-dwarf planet-host YZ Ceti
Authors: Pineda J. Sebastian , Villadsen Jackie 
Published: April 2023

AbstractObserving magnetic star–planet interactions (SPIs) offers promise for determining the magnetic fields of exoplanets. Models of sub-Alfvénic SPIs predict that terrestrial planets in close-in orbits around M dwarfs can induce detectable stellar radio emission, manifesting as bursts of strongly polarized coherent radiation observable at specific planet orbital positions. Here we present 2–4 GHz detections of coherent radio bursts on the slowly rotating M dwarf YZ Ceti, which hosts a compact system of terrestrial planets, the innermost of which orbits with a two-day period. Two coherent bursts occur at similar orbital phases of YZ Ceti b, suggestive of an enhanced probability of bursts near that orbital phase. We model the system’s magnetospheric environment in the context of sub-Alfvénic SPIs and determine that YZ Ceti b can plausibly power the observed flux densities of the radio detections. However, we cannot rule out stellar magnetic activity without a well-characterized rate of non-planet-induced coherent radio bursts on slow rotators. YZ Ceti is therefore a candidate radio SPI system, with unique promise as a target for long-term monitoring.
A social niche breadth score reveals niche range strategies of generalists and specialists
Authors: von Meijenfeldt F. A. Bastiaan , Hogeweg Paulien , Dutilh Bas E. 
Published: April 2023

AbstractGeneralists can survive in many environments, whereas specialists are restricted to a single environment. Although a classical concept in ecology, niche breadth has remained challenging to quantify for microorganisms because it depends on an objective definition of the environment. Here, by defining the environment of a microorganism as the community it resides in, we integrated information from over 22,000 environmental sequencing samples to derive a quantitative measure of the niche, which we call social niche breadth. At the level of genera, we explored niche range strategies throughout the prokaryotic tree of life. We found that social generalists include opportunists that stochastically dominate local communities, whereas social specialists are stable but low in abundance. Social generalists have a more diverse and open pan-genome than social specialists, but we found no global correlation between social niche breadth and genome size. Instead, we observed two distinct evolutionary strategies, whereby specialists have relatively small genomes in habitats with low local diversity, but relatively large genomes in habitats with high local diversity. Together, our analysis shines data-driven light on microbial niche range strategies.
Probe-based bacterial single-cell RNA sequencing predicts toxin regulation
Authors: McNulty Ryan , Sritharan Duluxan , Pahng Seong Ho , Meisch Jeffrey P. , Liu Shichen , Brennan Melanie A. , Saxer Gerda , Hormoz Sahand , Rosenthal Adam Z. 
Published: April 2023

AbstractClonal bacterial populations rely on transcriptional variation across individual cells to produce specialized states that increase fitness. Understanding all cell states requires studying isogenic bacterial populations at the single-cell level. Here we developed probe-based bacterial sequencing (ProBac-seq), a method that uses libraries of DNA probes and an existing commercial microfluidic platform to conduct bacterial single-cell RNA sequencing. We sequenced the transcriptome of thousands of individual bacterial cells per experiment, detecting several hundred transcripts per cell on average. Applied to Bacillus subtilis and Escherichia coli, ProBac-seq correctly identifies known cell states and uncovers previously unreported transcriptional heterogeneity. In the context of bacterial pathogenesis, application of the approach to Clostridium perfringens reveals heterogeneous expression of toxin by a subpopulation that can be controlled by acetate, a short-chain fatty acid highly prevalent in the gut. Overall, ProBac-seq can be used to uncover heterogeneity in isogenic microbial populations and identify perturbations that affect pathogenicity.
A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling
Authors: Ahmad Sohaib , Ullah Kalim , Zahid Erum , Shabbir Javid , Aamir Muhammad , Alshanbari Huda M. , El-Bagoury Abd Al-Aziz Hosni 
Published: April 2023

AbstractThis article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error (MSE) are derived up to first degree of approximation. From our generalized class of estimators, we obtained two improved estimators. The gain in second proposed estimator is more as compared to first estimator. Three real data sets and a simulation are accompanied to measure the performances of our generalized class of estimators. The MSE of our proposed estimators is minimum and consequently percentage relative efficiency is higher as compared to their existing counterparts. From the numerical outcomes it has been shown that the proposed estimators perform well as compared to all considered estimators in this study.
Composition and function of the Galapagos penguin gut microbiome vary with age, location, and a putative bacterial pathogen
Authors: Rohrer Sage D. , Jiménez-Uzcátegui Gustavo , Parker Patricia G. , Chubiz Lon M. 
Published: April 2023

AbstractMicrobial colonization plays a direct role in host health. Understanding the ecology of the resident microbial community for a given host species is thus an important step for detecting population vulnerabilities like disease. However, the idea of integrating microbiome research into conservation is still relatively new, and wild birds have received less attention in this field than mammals or domesticated animals. Here we examine the composition and function of the gut microbiome of the endangered Galapagos penguin (Spheniscus mendiculus) with the goals of characterizing the normal microbial community and resistome, identifying likely pathogens, and testing hypotheses of structuring forces for this community based on demographics, location, and infection status. We collected fecal samples from wild penguins in 2018 and performed 16S rRNA gene sequencing and whole genome sequencing (WGS) on extracted DNA. 16S sequencing revealed that the bacterial phyla Fusobacteria, Epsilonbacteraeota, Firmicutes, and Proteobacteria dominate the community. Functional pathways were computed from WGS data, showing genetic functional potential primarily focused on metabolism—amino acid metabolism, carbohydrate metabolism, and energy metabolism are the most well-represented functional groups. WGS samples were each screened for antimicrobial resistance, characterizing a resistome made up of nine antibiotic resistance genes. Samples were screened for potential enteric pathogens using virulence factors as indicators;Clostridium perfringenswas revealed as a likely pathogen. Overall, three factors appear to be shaping the alpha and beta diversity of the microbial community: penguin developmental stage, sampling location, andC. perfringens. We found that juvenile penguins have significantly lower alpha diversity than adults based on three metrics, as well as significantly different beta diversity. Location effects are minimal, but one site has significantly lower Shannon diversity than the other primary sites. Finally, when samples were grouped byC. perfringensvirulence factors, we found dramatic changes in beta diversity based on operational taxonomic units, protein families, and functional pathways. This study provides a baseline microbiome for an endangered species, implicates both penguin age and the presence of a potential bacterial pathogen as primary factors associated with microbial community variance, and reveals widespread antibiotic resistance genes across the population.
Engineering-geological study of relationships between soil and rock workability, type and volume of excavated materials, and earthwork costs (six case studies)
Authors: Marschalko Marian , Popielarczyk Dariusz , Matuskova Simona , Niemiec Dominik , Neuman David , Glacova Veronika 
Published: April 2023

AbstractThe engineering-geological study deals with the study of significance and relationship of soil and rock workability (factor representing the engineering-geological structure of rock massif) and the remaining earthwork parameters influencing the cost of construction work, such as excavation type and its technology, and excavated cubic volume. The comparative tool was the cost of earthwork as it reflects the real value of the given parameters during the implementation of earthwork. Soil and rock workability is the most important parameter of rock massif engineering-geological structure during any earthwork. The investor pays the contractor for earthwork based on workability classes which have their accounting value expressed as a volume unit of earthwork per particular project. The research results arise from a comparison of 6 sewer system construction project case studies in the north-east of the Czech Republic. The research shows that the most important factor during the implementation of earthwork is the specific engineering-geological structure (52%), which reflects in the parameter of soil and rock workability classes, using which all earthwork is priced. The second most important factor (33%) is the type of excavation and its technology. The least important is the excavated cubic volume (15%), which means the overall cubic volume of earthwork. The results were obtained within three evaluation approaches, where the comparison unit was one cubic meter of excavated volume during earthwork.
Demonstration of laser cooling in a novel all oxide GAYY silica glass
Authors: Thomas Jyothis , Meyneng Thomas , Tehranchi Amirhossein , Gregoire Nicolas , Monet Frederic , Seletskiy Denis , Messaddeq Younès , Kashyap Raman 
Published: April 2023

AbstractWe demonstrate laser induced cooling in ytterbium doped silica (SiO2) glass with alumina, yttria co-doping (GAYY-Aluminum: Yttrium: Ytterbium Glass) fabricated using the modified chemical vapour deposition (MCVD) technique. A maximum temperature reduction by − 0.9 K from room temperature (296 K) at atmospheric pressure was achieved using only 6.5 W of 1029 nm laser radiation. The developed fabrication process allows us to incorporate ytterbium at concentration of 4 × 1026 ions/m3 which is the highest value reported for laser cooling without clustering or lifetime shortening, as well as to reach a very low background absorptive loss of 10 dB/km. The numerical simulation of temperature change versus pump power well agrees with the observation and predicts, for the same conditions, a temperature reduction of 4 K from room temperature in a vacuum. This novel silica glass has a high potential for a vast number of applications in laser cooling such as radiation-balanced amplifiers and high-power lasers including fiber lasers.
A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate
Authors: Kistenev Yury V. , Borisov Alexei V. , Samarinova Alisa A. , Colón-Rodríguez Sonivette , Lednev Igor K. 
Published: April 2023

AbstractTraces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were “titrated” numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.
Descriptor engineering in machine learning regression of electronic structure properties for 2D materials
Authors: Dau Minh Tuan , Al Khalfioui Mohamed , Michon Adrien , Reserbat-Plantey Antoine , Vézian Stéphane , Boucaud Philippe 
Published: April 2023

AbstractWe build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor’s construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R$$^{2}$$

2

greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R$$^{2}$$

2

of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions. We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models.
Valley-optical absorption in planar transition metal dichalcogenide superlattices
Authors: Hashemi R. , Shojaei S. , Rezaei B. , Liu Zheng 
Published: April 2023

AbstractIn this study, we investigate the optical absorption of a planar superlattice comprising alternatively arranged two-dimensional Transition Metal DiChalcogenide semiconductors. Within a semi-classical model and using the Dirac-like equation in the presence of light interaction as a perturbation, we obtained the governing Hamiltonian. Using this Hamiltonian, we derived a fully analytical relationship for the absorption coefficient of the structure. By calculating the effective mass for different bands and using the Drude-Lorentz model, our approach is able to determine the oscillator strength and the effective refractive index of the structure. We found that the spin–orbit coupling has important effect on the absorption coefficient and energy bands where it reduces the absorption coefficient of the structure from typical value of $$11.54 ({10}^{5}{\mathrm{cm}}^{-1})$$

11.54
(

10

5

cm

-
1

)

–$$5.937({10}^{5}{\mathrm{cm}}^{-1})$$

5.937
(

10

5

cm

-
1

)

, also the valence band experiences a significant blue shift, while the conduction band shows minor changes due to spin orbit coupling. Moreover, the role of incident light angle and light polarization were studied in details at different valleys of $$K$$
K
and $${K}^{^{\prime}}$$

K

′

. The most important finding is that by changing the polarization of incident light, it is possible to increase the absorption coefficients of $$K$$
K
and $${K}^{^{\prime}}$$

K

′

valleys by up to 30 times. For light propagation direction close to perpendicular to the plane of the superlattice, the right-circular polarization is absorbed only by $$K$$
K
valley in contrast to the left-circular polarization, which is absorbed by the $${K}^{^{\prime}}$$

K

′

valley. Our model might be used to design newly developed 2D optovalleytronic devices.
Autofocusing MALDI MS imaging of processed food exemplified by the contaminant acrylamide in German gingerbread
Authors: Wittek Oliver , Römpp Andreas 
Published: April 2023

AbstractAcrylamide is a toxic reaction product occurring in dry-heated food such as bakery products. To meet the requirements laid down in recent international legal norms calling for reduction strategies in food prone to acrylamide formation, efficient chromatography-based quantification methods are available. However, for an efficient mitigation of acrylamide levels, not only the quantity, but also the contaminant’s distributions are of interest especially in inhomogeneous food consisting of multiple ingredients. A promising tool to investigate the spatial distribution of analytes in food matrices is mass spectrometry imaging (MS imaging). In this study, an autofocusing MALDI MS imaging method was developed for German gingerbread as an example for highly processed and instable food with uneven surfaces. Next to endogenous food constituents, the process contaminant acrylamide was identified and visualized keeping a constant laser focus throughout the measurement. Statistical analyses based on relative acrylamide intensities suggest a higher contamination of nut fragments compared to the dough. In a proof-of-concept experiment, a newly developed in-situ chemical derivatization protocol is described using thiosalicylic acid for highly selective detection of acrylamide. This study presents autofocusing MS imaging as a suitable complementary method for the investigation of analytes’ distributions in complex and highly processed food.
An integrative machine learning framework for classifying SEER breast cancer
Authors: Manikandan P. , Durga U. , Ponnuraja C. 
Published: April 2023

AbstractBreast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.