bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flow field characteristics and experimental research on inner-jet electrochemical face grinding of SUS420J2 stainless steel
Authors: Wang Feng , He Yafeng , Wu Xiaokai , Kang Min 
Published: July 2022

AbstractElectrochemical grinding (ECG) is processed by the combination of dissolution and grinding. It is very suitable for the processing of difficult-to-cut stainless steel, but its processing performance is restricted by the matching effect of dissolution and grinding. In this work, the processing of the torus surfaces of the stainless steel shaver cap was taken as the research object. A flow field model including the through-hole structure and the rotation of the grinding head was proposed to optimize the flow field distribution and promote the uniform dissolution of materials. The flow field simulation results showed that the rotational flow formed by the high-speed rotation prolonged the electrolyte flow path and was not conducive to the discharge of electrolytic products, and the reasonable selection of the diameter and distribution of the through-hole could reduce the velocity difference. The effects of rotational speed, feed rate, and inlet pressure on the flatness and surface roughness of the torus surfaces were experimentally investigated, and a better matching effect of dissolution and grinding was obtained. Moreover, the experimental results showed that the inner-jet ECG had a good prospect in the batch processing of high-hardness stainless steel parts.
A prospective study using an individualized nomogram to predict the success rate of external cephalic version
Authors: Lin Jing , Liu Wei , Gu Wei , Zhou Ye 
Published: July 2022

AbstractTo establish a clinical-based nomogram for predicting the success rate of external cephalic version (ECV) through a prospective study. This was a single-center prospective study that collected eligible breech pregnant women. 152 participants were enrolled in the training cohort, who received ECV procedures performed by a single operator. We used the training cohort to establish regression equations and prediction models. These variables include maternal factors (age, operation gestational age, pre-pregnancy BMI (Body Mass Index), operation BMI, BMI increase, multipara), ultrasound factors (fetal weight estimation, amniotic fluid index, placental location, type of breech presentation, spinal position), and anesthesia. Univariate and multivariable analyses were used to screen the factors affecting the success of ECV. A nomogram scoring model was established based on these factors. And C-index, DCA (Decision Curve Analysis) and calibration curve, Hosmer–Lemeshow test was used to verify the prediction effect of the model. Finally, 33 participants were enrolled in the testing cohort who received ECV with an unrestricted operator. We used C-index, DCA (decision curve analysis), and Hosmer–Lemeshow to verify the application value of the prediction model. The calibration curves and ROC curves of both the training cohort and testing cohort are plotted for internal and external validation of the model. The ECV success rate of the training cohort was 62.5%. Univariate analysis showed that the predictors related to the success rate of ECV were age, BMI increase value, AFI (amniotic fluid index), breech type, placental location, spinal position, anesthesia, and multipara. The prediction thresholds of the corresponding indexes were calculated according to the Youden index. Multivariate logistic regression analysis showed that BMI increase ≥ 3.85 kg/m2, AFI ≥ 10.6 cm, anesthesia, multipara, and non-anterior placenta were independent predictors of ECV success. Through the internal and external validation, it is confirmed that the model has a good calibration and prediction ability. Our nomogram has a good ability to predict the success rate of ECV.
Multi-center validation of machine learning model for preoperative prediction of postoperative mortality
Authors: Lee Seung Wook , Lee Hyung-Chul , Suh Jungyo , Lee Kyung Hyun , Lee Heonyi , Seo Suryang , Kim Tae Kyong , Lee Sang-Wook , Kim Yi-Jun 
Published: July 2022

AbstractAccurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.
A chemical approach for the future of PLA upcycling: from plastic wastes to new 3D printing materials
Authors: Shao Lin , Chang Yu-Chung , Hao Cheng , Fei Ming-en , Zhao Baoming , Bliss Brian J. , Zhang Jinwen 
Published: July 2022

Chemical upcycling of PLA: Ethanolamine cleaves polylactic acid to high purity N-LEA. N-LEA reacts with methacrylic anhydride (MAh) yielding crosslinkable DME. Simply combine DME with reactive diluent and photo-initiator to form a 3D printing resin.
Unveiling the hybridization between the Cr-impurity-mediated flat band and the Rashba-split state of the α-Au/Si(111) surface
Authors: Mihalyuk Alexey , Bondarenko Leonid , Tupchaya Alexandra , Utas Tatyana , Chou Jyh-Pin , Gruznev Dimitry , Eremeev Sergey , Zotov Andrey , Saranin Alexander 
Published: July 2022

We have studied the effects of magnetic impurity on the electronic structure of the α-Au/Si(111)-√3×√3 system, when the role of magnetic dopants is played by the Cr adatoms.
A Meta-Analysis of Phonemic Awareness Instruction Provided to Children Suspected of Having a Reading Disability
Authors: Rehfeld David M. , Kirkpatrick Marie , O'Guinn Nicole , Renbarger Rachel 
Published: July 2022

Purpose:
The present meta-analysis sought to investigate the effects of phonemic awareness instruction provided to children suspected of having a reading disability.

Method:
Seven databases were systematically searched, and 1,643 unique manuscripts were reviewed for inclusion. Data were extracted from 138 included manuscripts to evaluate the use of phonemic awareness instruction with children suspected of having a reading disability. A random effects model was then used to conduct a meta-analysis of these data with regard to child outcomes.

Results:

Gains in this population associated with phonemic awareness instruction can vary as a function of the outcome being used. On average, phonemic awareness instruction had a medium effect on composite (
g
= 0.511) and segmentation (
g
= 0.571) outcomes and a small effect on outcomes measuring blending (
g
= 0.341), first sound identification (
g
= 0.428), and deletion (
g
= 0.248). Instruction effects were strongest in kindergarten and first grade, but positive outcomes were also found for older children. There was not a significant relationship between cumulative intervention intensity and child performance.

Conclusions:
The present meta-analysis confirms that phonemic awareness instruction can be effective with children of varying ages and that significant gains can be observed on the key outcome measures of segmentation and blending. Graphemes should be incorporated into phonemic awareness instruction, and future studies need to provide information on dosage beyond just the length and frequency of sessions to clarify which aspects of these interventions are most efficient.

Supplemental Material:

https://doi.org/10.23641/asha.20277714
An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation
Authors: Zhang Yong , Hu Ying , Gao Xiaozhi , Gong Dunwei , Guo Yinan , Gao Kaizhou , Zhang Wanqiu 
Published: July 2022

AbstractIn real life, a large amount of data describing the same learning task may be stored in different institutions (called participants), and these data cannot be shared among participants due to privacy protection. The case that different attributes/features of the same instance are stored in different institutions is called vertically distributed data. The purpose of vertical‐federated feature selection (FS) is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set. To solve this problem, in the paper, an embedded vertical‐federated FS algorithm based on particle swarm optimisation (PSO‐EVFFS) is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time. By optimising both hyper‐parameters of the XGBoost model and feature subsets, PSO‐EVFFS can obtain a feature subset, which makes the XGBoost model more accurate. At the same time, since different participants only share insensitive parameters such as model loss function, PSO‐EVFFS can effectively ensure the privacy of participants' data. Moreover, an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant. Finally, the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies. Experimental results show that the proposed algorithm can significantly improve the classification performance of selected feature subsets while fully protecting the data privacy of all participants.
High efficiency single‐pulse controlled switched reluctance motor drive based on novel multilevel converter
Authors: Cai Yan , Liu Hao , Liu Yunhu , Wan Yaohua , Dong Zhongshan 
Published: July 2022

AbstractDue to the high back electromotive force and short commutation time at high speed, the phase current of switched reluctance motor (SRM) is difficult to reach a large value, which limits the output performance of the motor in high‐speed operation. This paper proposes a novel multilevel converter (MLC) with fast excitation and fast demagnetisation capability, which allows each phase to control independently even in overlapping conduction operation and is suitable for SRM with any number of phases. The excitation current in the rising inductance region is increased by high‐voltage fast excitation, thus increasing the phase current level of the motor at high speed; the current tailing in the falling inductance region is suppressed by high‐voltage fast demagnetisation, thereby reducing the generation of negative torque. Based on the novel MLC, a maximum torque per ampere (MTPA) control strategy is designed, and closed‐loop control of the boost capacitor voltage is developed. By optimising control, the output torque and efficiency of switched reluctance motor drive (SRD) are greatly improved, and the constant power operation range is extended. Simulation study and experimental results verify the effectiveness of the proposed scheme.
Hierarchical zinc oxide nanobrushes ultraviolet photodetector
Authors: Alenezi Mohammad R. , Almeshal Abdullah M. , Alkhaledi Abdullah NFNR. 
Published: July 2022

AbstractIn order to synthesise hierarchical zinc oxide (ZnO) nanomaterials with a high surface‐to‐volume ratio, a well‐controlled multistage hydrothermal method was developed. Hierarchical ZnO nanobrushes (ZNBs) built from initial mono‐morphological nanomaterials, ZnO nanowires (ZNWs), and ZnO nanoplates (ZNPs) were developed using sequential nucleation and growth following a hydrothermal process. Hierarchical nanomaterials which are comprised of one‐dimensional (1D) nanowire building blocks were obtained via a zinc ion source (zinc nitrate) during the second growth phase. In comparison to their first mono‐morphological equivalents, the hierarchical nanomaterials which were grown showed improved ultraviolet (UV) detection, as well as improved sensitivity (∼104), an ultra‐fast response time (235 ms), and a good recovery time (310 ms). Improvements to the response and recovery times were a result of distinct nanojunction barrier‐dominated resistance. Moreover, the enhanced sensitivity of the ZNB photodetector (PD) was because of the reduced dimensionality and the ultrahigh surface‐to‐volume ratio. This work lays the groundwork for lower‐cost, larger‐scale, and lower‐temperature production of high‐performance nanostructured ZnO‐based PDs on transparent and flexible substrates.
Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review
Authors: Kabiri‐Renani Yahya , Daneshvar Mohammadreza , Mohammadi‐Ivatloo Behnam 
Published: July 2022

AbstractThe electric industry is developing towards a more efficient, reliable, and resilient electric power network. In this way, utilizing distributed energy resources (DERs), especially renewable DERs (RDERs) are a paradigm change. DERs offer many advantages in power systems, including transmission loss reduction, environmental benefits of RDERs, and enhancement of security, reliability, and resiliency of the network. However, high penetration of DERs increases uncertainty and challenges the efficient and reliable operation of the power system. This paper provides a thorough review of the transactive distribution platform which is essential to address the aforementioned challenges. This distribution platform includes a transactive distribution system operator providing a seamless and coordinated control to dynamically balance supply and demand and follow uncertain generations of RDERs. Indeed, this review paper highlights the capability of transactive energy (TE) by considering its key aspect in providing energy sharing opportunities for the integration of DERs into smart grids (SGs). TE is acombination of economic and control methods that enable optimum, reliable, sustainable, and efficient operation of SGs. Furthermore, a fully transactive framework offers more choices for DERs to control and manage the energy transactions in the retail market, as well as improves the inter operability among various market players.
Cloud data center participation in smart demand response programs for energy cost minimisation
Authors: Sheikholeslami Seyed Mohammad , Rabiei Amir Masoud , Mohammad‐Taheri Mahmoud , Abouei Jamshid 
Published: July 2022

AbstractDemand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations.
Current sensor fault‐tolerant control strategy for surface‐permanent‐magnet‐synchronous motor drives based on improved full‐order sliding‐mode observer
Authors: Khlaief Amor , Saadaoui Oussama , Abassi Moez , Chaari Abdelkader 
Published: July 2022

AbstractThis paper exhibits a sensorless speed control method based on full‐order‐sliding‐mode observer applied to a fault‐tolerant permanent‐magnet‐synchronous motor (PMSM) drive system. So, this paper proposes a rapid method of fault switch detection in the power converters aiming to make sure of the continuity of service despite the fault presence of an opening phase. In fact, the full‐order‐sliding‐mode observer (FO‐SMO) observer is used to replace the mechanical sensor and a redundant inverter leg is equally employed to replace the faulty leg. The proposed new fast fault diagnosis method has the features of a simple algorithm, independent of the transient states and being simply integrated without any additional sensors.
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework
Authors: Gebhard Timothy D. , Bonse Markus J. , Quanz Sascha P. , Schölkopf Bernhard 
Published: July 2022

Context.High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter.Aims.Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process.Methods.Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapted the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work; however, other (nonlinear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors.Results.When we applied our method to four data sets from the VLT/NACO instrument, our algorithm provided a better false-positive fraction than a popular baseline method in the field. Additionally, we found that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present a first piece of evidence that using the observing conditions as additional predictors can improve the results.Conclusions.Our HSR-based method provides an alternative, flexible, and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.
Identifying quiescent compact objects in massive Galactic single-lined spectroscopic binaries
Authors: Mahy L. , Sana H. , Shenar T. , Sen K. , Langer N. , Marchant P. , Abdul-Masih M. , Banyard G. , Bodensteiner J. , Bowman D. M. , Dsilva K. , Fabry M. , Hawcroft C. , Janssens S. , Van Reeth T. , Eldridge C. 
Published: July 2022

Context. The quest to detect dormant stellar-mass black holes (BHs) in massive binaries (i.e. OB+BH systems) is challenging; only a few candidates have been claimed to date, all of which must still be confirmed.
Aims. To search for these rare objects, we study 32 Galactic O-type stars that were reported as single-lined spectroscopic binaries (SB1s) in the literature. In our sample we include Cyg X-1, which is known to host an accreting stellar-mass BH, and HD 74194, a supergiant fast X-ray transient, in order to validate our methodology. The final goal is to characterise the nature of the unseen companions to determine if they are main-sequence (MS) stars, stripped helium stars, triples, or compact objects such as neutron stars (NSs) or stellar-mass BHs.
Methods. After measuring radial velocities and deriving orbital solutions for all the systems in our sample, we performed spectral disentangling to extract putative signatures of faint secondary companions from the composite spectra. We derived stellar parameters for the visible stars and estimated the mass ranges of the secondary stars using the binary mass function. Variability observed in the photometric TESS light curves was also searched for indications of the presence of putative companions, degenerate or not.
Results. In 17 of the 32 systems reported as SB1s, we extract secondary signatures, down to mass ratios of ∼0.15. For the 17 newly detected double-lined spectroscopic binaries (SB2s), we derive physical properties of the individual components and discuss why they have not been detected as such before. Among the remaining systems, we identify nine systems with possible NS or low-mass MS companions. For Cyg X-1 and HD 130298, we are not able to extract any signatures for the companions, and the minimum masses of their companions are estimated to be about 7 M⊙. Our simulations show that secondaries with such a mass should be detectable from our dataset, no matter their nature: MS stars, stripped helium stars or even triples. While this is expected for Cyg X-1, confirming our methodology, our simulations also strongly suggest that HD 130298 could be another candidate to host a stellar-mass BH.
Conclusions. The quest to detect dormant stellar-mass BHs in massive binaries is far from over, and many more systems need to be scrutinised. Our analysis allows us to detect good candidates, but confirming the BH nature of their companions will require further dedicated monitorings, sophisticated analysis techniques, and multi-wavelength observations.