bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temperature measurement based on the magneto-optic Kerr effect in Ni nanofilms
Authors: Yi Wentong , Zhu Yiwen , Hou De , Ning Guoqiang , Sheng Zhigao , Du Zhongzhou , Liu Wenzhong 
Published: March 2022

Abstract
Thermometry is essential in laboratory and industry settings. We propose a noninvasive temperature measurement method based on the superparamagnetic properties of nickel-plated films. When the nickel (Ni) film is uniformly heated from 100 K to 300 K, we observe that the polarization angle of the reflected polarized light decreases correspondingly. Compared with optical temperature measurement, this method introduces magnetic physical quantity, which increases the penetration of temperature measurement. Compared with magnetic temperature measurement, this method introduces optical physical quantity, which greatly increases the amount of information contained in the measurement channel. On this basis, we introduce the characteristic matrix of membrane system, and the improved the mean field theory which can interpret superparamagnetism. Our experiment has demonstrated that the above theoretical model is feasible from low temperature to normal temperature. The thin film sensor element provides the necessary temperature information for cryogenic wind tunnel design and hypersonic vehicle surface model design in real time
Laser ablation fabrication of a p-NiO/n-Si heterojunction for broadband and self-powered UV–Visible–NIR photodetection
Authors: Chaoudhary Savita , Dewasi Avijit , S Ram Prakash , Rastogi Vipul , Pereira Rui N , Sinopoli Alessandro , Aïssa Brahim , Mitra Anirban 
Published: March 2022

Abstract
We report on the optoelectronic characteristics of p-NiO/n-Si heterojunction photodiode for broadband photodetection, fabricated by depositing a p-type NiO thin film onto a commercial n-type silicon substrate using pulsed laser deposition (PLD) technique. The structural properties of the PLD-grown p-NiO material were analysed by means of x-ray diffraction and x-ray photoelectron spectroscopy, confirming its crystalline nature and revealing the presence of Ni vacancies, respectively. Hall measurements confirmed the p-type semiconducting nature of the NiO thin film having a carrier concentration of 8.4 × 1016 cm−3. The current–voltage (I–V) characteristics of the p-NiO/n-Si heterojunction photodevice were investigated under different wavelengths ranging from UV to NIR. The self-bias properties under different illuminations of light were also explored systematically. Under self-bias condition, the photodiode exhibits excellent responsivities of 12.5 mA W−1, 24.6 mA W−1 and 30.8 mA W−1 with illumination under 365 nm, 485 nm, and 850 nm light, respectively. In addition, the time dependency of the photoresponse of the fabricated photodevice has also been investigated and discussed thoroughly.
Can dose convolution modelling explain bath and shower effects in rat spinal cord?
Authors: Blake Steve W 
Published: March 2022

Abstract

Objective. ‘Bath and shower’ effects were first seen in proton irradiations of rat spinal cord, where a low dose ‘bath’ reduced the smaller field ‘shower’ dose needed for limb paralysis giving the appearance of sensitisation of the cord or disproportionate response. This was difficult to reconcile with existing tissue complication models. The purpose of this investigation is to explore a different approach using a dose convolution algorithm to model the 50% isoeffect endpoint. Approach. Bath and shower dose distributions were convolved with Gaussian functions with widths specified by the σ parameter. The hypothesis was that the maximum value from the convolved distributions was constant for isoeffect across the modelled scenarios. A simpler field length dependent relative biological effectiveness (FLRBE) approach was also used for a subset of the data which gave results independent of σ. Main results. The maximum values from the convolved distributions were constant within ±17% across the bath and shower experiments for σ = 3.5 mm, whereas the maximum dose varied by a factor of four. The FLRBE results were also within ±14% confirming the validity of the dose convolution approach. Significance. A simple approach using dose convolution modelling of the 50% isotoxicity gave compelling consistency with the full range of bath and shower results, while the FLRBE approach confirmed the results for the symmetric field data. Convolution modelling and the effect of time interval were consistent with a signalling factor diffusion mechanism such as the ‘bystander effect’. The results suggest biological effectiveness is reduced for very small field sizes, requiring a higher isoeffect dose. By implication, the bath dose does not sensitise the cord to the shower dose; when biological effectiveness is accounted for, a small increase in the bath dose requires a significantly larger reduction in shower dose for isoeffect.
Temporal and spatial properties of plasma induced by infrared femtosecond laser pulses in air
Authors: Balachninaitė O , Skruibis J , Matijošius A , Vaičaitis V 
Published: March 2022

Abstract
Space and time-resolved electron density and temperature of the plasma, created in air by focused femtosecond laser pulses have been investigated as a function of the pump pulse energy and duration. For the air ionization the infrared (1030 nm) femtosecond (190–500 fs) Yb:KGW laser pulses of up to 1 mJ energy were used. Based on the Stark broadening of the oxygen-I 777.19 nm line we have found that after establishing a local equilibrium the density of laser-created plasma could exceed 1017 cm−3 with the electron temperature of over 5000 °C. Obtained results agree well with the results of previously reported measurements of the plasma density created by the femtosecond near-infrared Ti:sapphire laser pulses.
Physics of thermionic, orificed hollow cathodes: I. Theory and experimental validation
Authors: Taunay P-Y C R , Wordingham C J , Choueiri E Y 
Published: March 2022

Abstract
A model aimed at illuminating the physics of thermionic, orificed hollow cathodes is developed and validated with experimental data. The model is intended to describe the variation of total (neutrals, ions, and electrons) static pressure with controllable parameters. That pressure must be properly evaluated because it influences important plasma parameters in the cathode such as the attachment length and the electron temperature, which directly impact the lifetime of thermionic inserts. The model, which combines a zero-dimensional approach to the conservation of energy and momentum for the combined plasma-neutral fluid and a charge-exchange-limited ambipolar diffusion model, allows for the computation of all plasma quantities, including the total fluid pressure. The model depends on the operating conditions (discharge current and mass flow rate), cathode geometry, and the gas species, along with two non-controllable parameters: the neutral gas temperature and the sheath potential. Total pressure data at up to 307 A of cathode discharge current were obtained experimentally and were used, along with data from the literature, to validate the model. Good agreement is obtained for all quantities. The model is used in a companion paper to clarify the role of magnetic and gasdynamic pressure in the scaling of total pressure, to derive scaling laws applicable to thermionic, orificed hollow cathodes, and to propose novel cathode design rules.
Enhanced critical axial tensile strain limit of CORC® wires: FEM and analytical modeling
Authors: Anvar V A , Wang K , Weiss J D , Radcliff K , van der Laan D C , Hossain M S A , Nijhuis A 
Published: March 2022

Abstract
Conductor on Round Core (CORC®) cables and wires are composed of spiraled high-temperature superconducting (HTS) rare-earth barium copper oxide (REBCO) tapes, wound in multiple layers, and can carry very high currents in background magnetic fields of more than 20 T. They combine isotropic flexibility and high resilience to electromagnetic and thermal loads. The brittle nature of HTS tapes limits the maximum allowable axial tensile strain in superconducting cables. An intrinsic tensile strain above about 0.45% will introduce cracks in the REBCO layer of straight HTS tapes resulting in irreversible damage. The helical fashion at which the REBCO tapes are wound around the central core allows tapes to experience only a fraction of the total axial tensile strain applied to the CORC® wire. As a result, the critical strain limit of CORC® wires can be increased by a factor of more than 10 that of REBCO tapes. Finite element (FE) and analytical models are developed to predict the performance of CORC® wires under axial tensile strain. A parametric analysis is carried out by varying the winding angle, the Poisson’s ratio of the CORC® wire core, the core diameter, and the tape width. The results show that a small variation in winding angle can have a significant impact on the cable’s axial tensile strain tolerance. While the radial contraction of the helically wound tapes in a CORC® wire under axial tensile strain depends on its winding angle, it is mostly driven by the Poisson’s ratio of the central core, affecting the tape strain state and thus its performance. Contact pressure from multiple layers within the CORC® wire also affects the CORC® wire performance. The FE model can be used to optimize the cable design for specific application conditions, resulting in an irreversible strain limit of CORC® cables and wires as high as 7%.
Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People
Authors: Liu Wei , Zheng Wei-Long , Li Ziyi , Wu Si-Yuan , Gan Lu , Lu Bao-Liang 
Published: March 2022

Abstract

Objective. Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective. Approach. Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition. Main results. From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that the γ and β bands exhibit decreasing activities for Chinese, while for German and French, θ and α bands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures. Significance. Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.
Electrophoretic study of G-quadruplex aptamer interactions with different short single-strand complementary oligonucleotides
Authors: Samokhvalov A V , Zherdev A V , Dzantiev B B 
Published: March 2022

Abstract
Aptamers are single-stranded nucleic acids, typically 20-80 nucleobases (nb) in length, which can bind different compounds with high affinity and selectively. Their ligand-binding properties can be attenuated by adding short complementary strands. These interactions open new opportunities for aptamer-based assays. Strong dependence between the length and electrophoretic mobility of short nucleic acids makes polyacrylamide gel electrophoresis a powerful tool for studying their complexes. The interactions between the 36 nb DNA G-quadruplex aptamer (5’-GAT-CGG-GTG-TGG-GTG-GCG-TAA-AGG-GAG-CAT-CGG-ACA-3’) specific to ochratoxin A and 9 complementary single-stranded DNA (ssDNA) were studied. The length of ssDNA varied from 5 to 9 nb. To maintain ligand-binding conformation of the aptamer, a high ionic strength buffer was used. The best resolution between the aptamer and its complex was provided for the gel with 15% monomer and a monomer/cross-linker ratio of 15:1. Bands of free aptamer and ssDNA were observed for all studied variants. If the ssDNA length was less than 9 nb, the position of the aptamer’s band remained unchanged, independent of the aptamer/ssDNA ratios, and additional bands did not appear. The longest ssDNA (5’-CGC-CAC-CCA-3’) did not lead to the appearance of a new band, but it slowed the aptamer’s migration depending on the ssDNA concentration. Under a 27-fold excess of the given ssDNA, the relative mobility of the aptamer band changed from 0.566 to 0.468. Thus, electrophoresis visualizes aptamer-ssDNA interactions and can be used in the development of aptamer-based analytical systems.
A general take on a Tecnomatix Process Simulate´s Digital Twin creation and its exchange of information with the TIA Portal and PLC SIM Advanced
Authors: Ružarovský R , Skýpala R 
Published: March 2022

Abstract
Digital twins are rapidly establishing themselves as a necessary step in digital change also in the design of automated and robotic manufacturing systems. The digital twin serves as an interface between physical industrial products and digital worlds. This allows you to map the continuous availability of data throughout the lifecycle from product planning and development, production and commissioning to use and recycling. An important step towards this goal is to create digital twins of the components in order to create a database usable for the design of new production systems in a virtual environment. One of the possibilities of using the digital twins is testing and virtual commissioning of production systems. Instead of the classic CAD model, the digital twin replaces a detailed digital image of individual components, including their full functionality. The Digital Twin comprises the CAD model, kinematic behavior model a most important is the logical behavior model. Our goal is to create a digital twin component of the production system, integrate a CAD model, create a kinematic model and a logical behavior model. This digital twin will have signals assigned to it for logical behavior. The result is the verification and testing of the information exchange between the virtual control system and the virtual model, using the Software-in-the-Loop method and the Tecnomatix Process Simulate software environment on the digital twin side and the Siemens TIA Portal with PLC SIM Advanced on the controller side. The result of the knowledge is that the exchange of information is functional and usable for virtual commissioning.
Digital Twin design at the material flow level
Authors: Delgado Sobrino D R , Rychtarik V , Cagáňová D 
Published: March 2022

Abstract
Mass customization in industry is perceived as contemporary challenge for many organizations. Requirements for quality, functionality and uniqueness of products grow at a disproportionate pace and at the same time, price remains to be the the customer’s main attribute for purchasing the product. One of the tools for reducing the cost is precisely the Digital Twin concept, which besides is the essence of the so-called Simulation 4.0. This concept focuses on the creation of digital prototypes of products and production processes at any of its different instances including the material flow or plant level. Up until recently, Simulation had been known for being static in nature and using pre-determined or generated values, i.e.: “dead” simulations without dynamics or interactions with real data. However, Simulation 4.0 is built on the principles of the Digital Twin technology, which is flexible, time-changing, and capable of interaction with the real world. Simulation 4.0 uses many tools, such as IoT, Big Data, Cloud Computing technologies used to process live data. Live data are afterward sent to local or global databases. This paper elaborates on all these topics and presents a real-life example for the online tracking of products and parts in a production environment, as a clear manifestation of digital twin design at the material flow level.
Verification of the voice control modification of robot -DOBOT Magician depending to change voice frequency
Authors: Holubek R , Janícek M , Tirian G O 
Published: March 2022

Abstract
Voice control is becoming increasingly popular nowadays. Voice control is used, for example, in the car when entering navigation, in smartphones it is possible to search for the desired thing on the Internet using smartphones, or a smart home voice assistant will make grocery purchases if necessary. Is the reliability of voice control close to perfection or does it have its flaws? Continuous improvement of voice control proves that reliability still has unexplored possibilities for improvement. In collaborative robotics, multiword voice commands are more successful. In the longer time it takes to say the whole command, it is easier to identify the voice command and assign the correct task. Modifying the voice frequency also improves the success of the commands. This article examines single-word and multi-word voice commands and verifies modifications to the commands of a collaborative robot depending on the change in voice frequency.
MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations
Authors: Ding Yulian , Lei Xiujuan , Liao Bo , Wu Fang-Xiang 
Published: March 2022

AbstractMotivationMicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases.ResultsIn this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.
SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression
Authors: Liu Yusong , Wang Tongxin , Duggan Ben , Sharpnack Michael , Huang Kun , Zhang Jie , Ye Xiufen , Johnson Travis S 
Published: March 2022

Abstract
High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).
DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity
Authors: Cai Tian , Abbu Kyra Alyssa , Liu Yang , Xie Lei 
Published: March 2022

AbstractMotivationDrug discovery has witnessed intensive exploration of predictive modeling of drug–target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug–target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions.ResultsTo address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs.Availability and implementationAll data used are downloaded from Pfam (Mistry et al., 2020), GLASS (Chan et al., 2015) and IUPHAR/BPS and the data from reference (Sakamuru et al., 2021). Readers are directed to their official website for original data. Code is available on GitHub https://github.com/XieResearchGroup/DeepREAL.Supplementary informationSupplementary data are available at Bioinformatics online.