bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and optimisation of energy‐efficient PM‐assisted synchronous reluctance machines for electric vehicles
Authors: Shao Lingyun , Tavernini Davide , Hartavi Karci Ahu Ece , Sorniotti Aldo 
Published: February 2023

AbstractThe design and optimisation of a permanent magnet‐assisted synchronous reluctance (PMaSynR) traction machine is described to improve its energy efficiency over a selection of driving cycles, when installed on a four‐wheel‐drive electrically powered vehicle for urban use, with two on‐board powertrains. The driving cycle‐based optimisation is defined with the objective of minimising motor energy loss under strict size constraints, while maintaining the peak torque and restricting the torque ripple. The key design parameters that exert the most significant influence on the selected performance indicators are identified through a parametric sensitivity analysis. The optimisation brings a motor design that is characterised by an energy loss reduction of 8.2% over the WLTP Class 2 driving cycle and 11.7% over the NEDC and Artemis Urban driving cycles, at the price of a 4.7% peak torque reduction with respect to the baseline machine. Additional analysis, implemented outside the optimisation framework, revealed that different coil turn adjustments would reduce the energy loss along the considered driving cycles. However, under realistic size constraints, the optimal design solutions are the same.
A high‐efficiency power management unit with wide dynamic range for RF energy harvesting system
Authors: Xie Zushuai , Zhang Wenxu , Zhou Quancai , Wang Yanyan , Wu Jianhui , Gou Jingjing , Liu Lu , Wang Zixuan , Cai Zhikuang 
Published: February 2023

AbstractA high‐efficiency power management unit (PMU) with wide dynamic range for radio frequency (RF) energy harvesting system is proposed in this paper. A digital zero current switching (ZCS) and a digital maximum power point tracking (MPPT) circuits are adopted. A sub‐threshold current reference circuit and a subthreshold voltage detection circuit are proposed to reduce the power consumption of always‐on control circuits and improve the conversion efficiency at low input power. A dual‐oscillator is designed to extend the frequency range and widen the input power range with optimized power consumption under low‐end band and high‐end band, respectively. Switch size and on‐time are adjusted according to the input power, which optimizes the conversion efficiency in a wide power range. Proposed PMU is implemented in a standard 180 nm CMOS process. The post‐layout simulation results show that proposed PMU can achieve cold‐start at the input voltage of 280 mV, the power consumption to maintain the PMU is only 9.6 nW. The input power range of the PMU is 11 nW–677 μW. When the input power is 50 nW, a power conversion efficiency (PCE) of 65% is achieved, and the peak PCE can reach 93% at 23.5 μW.
In-flight validation of the Metis visible-light polarimeter coronagraph on board Solar Orbiter
Authors: Liberatore A. , Fineschi S. , Casti M. , Capobianco G. , Abbo L. , Andretta V. , Da Deppo V. , Fabi M. , Frassati F. , Jerse G. , Landini F. , Moses D. , Naletto G. , Nicolini G. , Pancrazzi M. , Romoli M. , Russano G. , Sasso C. , Spadaro D. , Stangalini M. , Susino R. , Telloni D. , Teriaca L. , Uslenghi M. 
Published: February 2023

Context. The Metis coronagraph is one of the remote sensing instruments of the ESA-NASA Solar Orbiter mission. The goal for the instrument is to enable the study of the solar atmosphere and solar wind by simultaneously acquiring images of the solar corona at two different wavelengths: visible light (VL), within a band ranging from 580 nm to 640 nm, and ultraviolet light, in the HI Lyα 121.6 ± 10 nm. The visible-light channel of the coronagraph includes a polarimeter with electro-optically modulating liquid crystal variable retarders to measure the linearly polarized brightness of the K-corona and derive the electron density.
Aims. In this paper, we present the first in-flight validation results of the Metis polarimetric channel together with a comparison to the on-ground calibrations. This paper seeks to validate the first use of an electro-optical device, the liquid crystal-based polarimeter, in deep space and within a hard radiation environment.
Methods. We used the orientation of the K-corona’s linear polarization vector during the roll maneuvers of the Space Orbiter spacecraft for the in-flight calibration.
Results. The Metis coronagraph on board the Solar Orbiter shows good agreement with the on-ground measurements. The in-flight validation confirms the expected performance of the visible-light channel polarimeter. Furthermore, a comparison between the first polarized brightness value obtained by Metis and the polarized brightness values obtained by the space-based coronagraph LASCO and the ground-based coronagraph K-Cor shows the consistency of the Metis calibrated results.
Nystagmus bei Kindern – eine Übersicht
Authors: Salchow Daniel J. 
Published: February 2023

ZusammenfassungNystagmus ist definiert als unwillkürliche, periodische Bewegung, bei der eines oder beide Augen vom Fixationspunkt wegdriften (sog. Drift). Auf die Drift, die langsame Phase, folgt entweder eine schnelle, refixierende Bewegung (sog. Refixationssakkade oder schnelle Phase) oder eine langsamere Bewegung zur Wiederaufnahme der Fixation. In dieser Übersichtsarbeit sollen die wichtigsten Formen von Nystagmus bei Kindern erörtert werden, für eine Übersicht bei Erwachsenen sei auf 1 verwiesen.
Influence of Lithium- and Zinc-Containing Bioactive Glasses on Pulpal Regeneration
Authors: Tran An Thi Loc , Sukajintanakarn Charnsak , Senawongse Pisol , Sritanaudomchai Hathaitip , Ruangsawasdi Nisarat , Lapthanasupkul Puangwan , Kitkumthorn Nakarin , Monmaturapoj Naruporn , Khamsut Chutikarn , Naruphontjirakul Parichart , Pongprueksa Pong 
Published: February 2023

Abstract
Objective To evaluate the potential of modified bioactive glasses containing lithium and zinc as pulp capping materials by investigating the odontogenic differentiation and mineralization response in the tooth culture model.
Materials and Methods Lithium- and zinc-containing bioactive glasses (45S5.1Li, 45S5.5Li, 45S5.1Zn, 45S5.5Zn, 45S5.1Zn sol-gel, and 45S5.5Zn sol-gel), fibrinogen-thrombin, and biodentine were prepared to assess Axin2 gene expression at 0, 30 minutes, 1 hour, 12 hours, and 1 day and DSPP gene expression at 0, 3, 7, and 14 days in stem cells from human exfoliated deciduous teeth (SHEDs) using qRT-PCR. The experimental bioactive glasses incorporated with fibrinogen-thrombin and biodentine were placed on the pulpal tissue in the tooth culture model. Histology and immunohistochemistry were analyzed at 2 weeks and 4 weeks.
Results Axin2 gene expression for all experimental groups was significantly higher than the control at 12 hours. The DSPP gene expression for all experimental groups was significantly higher than the control at 14 days. The presence of mineralization foci was significantly higher at 4 weeks for the modified bioactive glasses 45S5.5Zn, 45S5.1Zn sol-gel, and 45S5.5Zn sol-gel as well as Biodentine compared with the fibrinogen-thrombin control.
Conclusion Lithium- and zinc-containing bioactive glasses increased Axin2 and DSPP gene expression in SHEDs and can potentially enhance pulp mineralization and regeneration. Zinc-containing bioactive glasses are a promising candidate to be used as pulp capping materials.
Differential Expression Profile of Salivary oncomiRNAs among Smokeless Tobacco Users
Authors: AL-Rawi Natheer H. , Rizvi Zuha , Mkadmi Sarra , Abu Kou Rawan , Elmabrouk Neibal , Alrashdan Mohammad S. , Koippallil Gopalakrishnan Aghila Rani 
Published: February 2023

Abstract
Objective The aim of this study was to evaluate the expression of selected salivary oncomiRNAs among smokeless tobacco users and nonsmokers.
Materials and Methods Twenty-five subjects with chronic smokeless tobacco habit (> 1 year) and 25 nonsmokers were selected for this study. MicroRNA was extracted from saliva samples using the miRNeasy Kit (Qiagen, Hilden, Germany). The forward primers used in the reactions include hsa-miR-21-5p, hsa-miR-146a-3p, hsa-miR-155-3p, and hsa-miR-199a-3p. Relative expression of miRNAs was calculated using the 2-ΔΔCt method. Fold change is calculated by raising 2 to the power of the negative ΔΔCT value.
Statistical Analysis Statistical analysis was carried out using GraphPad Prism 5 software. A p-value less than 0.05 was considered statistically significant.
Results The four tested miRNAs were found overexpressed in saliva of subjects with smokeless tobacco habit when compared with saliva from nontobacco users. miR-21 expression was 3.74 ± 2.26 folds higher among subjects with smokeless tobacco habit compared to nontobacco users (p < 0.01). The expression for miR-146a (5.56 ± 8.3 folds; p < 0.05), miR-155 (8.06 ± 23.4 folds; p < 0.0001) and miR-199a (14.39 ± 30.3 folds; p < 0.05) was significantly higher among subjects with smokeless tobacco habit.
Conclusion Smokeless tobacco leads to salivary overexpression of the miRs 21, 146a, 155, and 199a. Monitoring the levels of these four oncomiRs may provide insight about the future development of oral squamous cell carcinoma, especially in patients with smokeless tobacco habits.
An assessment of the spatial distribution of bulk billing-only GP services in Australia in relation to area-based socio-economic status
Authors: Graham Bree , Kruger Estie , Tennant Marc , Shiikha Yulia 
Published: February 2023

Background The accessibility to affordable primary healthcare services contributes to population health and health equity. A key aspect to accessibility is the geographical distribution of primary healthcare services. Limited studies have assessed the nationwide spatial distribution of bulk billing-only medical practices or ‘no-fee’ services. The aim of this study was to provide a nationwide approximation of bulk billing-only services and evaluate the socio-demographic status and population characteristics in relation to the distribution of bulk billing-only GP services. Methods The methodology in this study used Geographic Information System (GIS) technology to map the locations of all bulk bulking-only medical practices collected in mid-2020 and linked this with population data. The population data and practice locations were analysed at the level of Statistical Areas Level 2 (SA2) regions and used the most recent Census data. Results The study sample included (n = 2095) bulk billing-only medical practice locations. The nationwide average Population-to-Practice (PtP) ratio was 1 practice to 8529 people for regions with access to bulk billing-only practice, and 57.4% of the Australian population lives within an SA2 that has access to at least one bulk billing-only medical practices. No significant associations were identified between practice distribution and area socio-economic status. Conclusion The study identified areas with low access to affordable GP services, with many SA2 regions having no access to bulk billing-only practices. Findings also indicate that there was no association between area socio-economic status and the distribution of bulk billing-only services.
The potential distribution of the yellow monitor, Varanus flavescens (Hardwick & Gray) under multiple climate, land cover and dispersal scenarios in Nepal
Authors: Baral Suraj , Kunwar Amar , Adhikari Dipendra , Kandel Kanti , Mandal Dev Narayan , Thapa Arjun , Neupane Dinesh , Thapa Tej B. 
Published: February 2023

Context Human-induced changes in climate and land cover have altered the distribution of fauna around the globe. Some reptiles have been found to be vulnerable to these changes; therefore, studies to identify the impact of the changes on other groups of reptiles are necessary. Aims We aimed to study the impact of climate and land cover change on the yellow monitor (Varanus flavescens) in Nepal. We also aimed to identify the current distribution range and predict the potential distribution under multiple climate change, corresponding land cover change, and dispersion scenarios in the near- and mid-future. Methods We used available presence locations with a candidate set of the least-correlated environmental variables and an Ensemble of Small Models (ESM), a Species Distribution Model (SDM) approach suitable for species with small sample size. Additionally, dispersal scenarios of 1 km, 5 km, and 10 km were added to the model to determine the future distribution under the dispersal scenarios. Key results We found soil particle size, distance to forest, precipitation of wettest quarter, bulk density, and elevation were the five most important variables contributing to the distribution of the species. The Terai lowland and wide valleys in Outer Himalayas are currently suitable but are expected to experience a substantial decrease under most future climate projections and dispersal scenarios. Conclusions The distribution is mostly dependent on soil-related variables; however, climatic variables might have a greater impact on future suitability. Implications Limiting emissions contributing to climatic changes, conserving the soil outside the protected areas, and the potential areas where the species will not experience habitat loss might contribute to the conservation of the species.
Diagnosing perilymphatic fistula with 3D flair MRI
Authors: Kemps Glen , Mistry Jayran , Connor Steve , Obholzer Rupert , Ainsworth Carolyn 
Published: February 2023

Introduction
The diagnosis of a perilymphatic fistula (PLF) has long been a challenge, as its presentation is variable and can mimic other vestibular disorders. Currently, it is suggested that resolution of symptoms after treatment should be used as a gold standard in diagnosing PLF. Recently, improvement in Magnetic Resonance Imaging (MRI) have caused PLF to be diagnosed more consistently with a higher sensitivity and specificity, preceding surgery.

Case report
We report a 43-year-old patient, who was seen with progressive vertigo since 15 years, triggered by bending forward, and lasting from 20 min to 2 h. During these spells, she would also have pulse-synchronous tinnitus and mild left-sided mixed hearing loss. Physical examination was normal and vestibular function tests did not reveal evidence of peripheral or central audiovestibular dysfunction. Delayed acquisition MRI, with 3-dimensional fluid attenuated inversion recovery (3D FLAIR) of the inner ear displayed a left ‘round window sign’ (RWS): focal enhancement in the round window niche. There were no signs of vestibular or cochlear hydrops to suggest Meniere’s syndrome. Her symptoms resolved after surgery.

Conclusion
RWS on 3D FLAIR MRI sequence seems to be a sensitive and specific pre-operative diagnostic phenomenon for the detection of PLF.
Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search
Authors: Lin Na , Yang Huimin , Li Ya , Wang Xuping 
Published: February 2023

Abstract
This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.
Research on the Prediction of Ancient Glass Artifact Types based on XGBoost and Random Forest Algorithm
Authors: Li Zeyan , Wang Yanting , Zhao Shuyue 
Published: February 2023

Abstract
Ancient Chinese glass is susceptible to weathering by environmental influences, and its chemical composition has changed considerably thus affecting the category judgment. The classification of ancient glass is mainly divided into two types: high potassium and lead-barium. In this paper, in order to determine the type of ancient glass according to its chemical composition, the multicollinearity phenomenon of many chemical compositions is reduced by using LDA model and then the parameters of XGBoost and RF models are optimized by using GA algorithm and the five-fold cross-validation of existing data sets, and then the weights of XGBoost and RF are optimized by GA algorithm, and finally the GA - XGBoost - RF dual optimization model. The predicted categories can be derived by bringing the chemical composition content of unknown categories of artifacts into the well-trained model, which provides more accurate and faster categorization guidance for ancient glass artifact types to a certain extent.
Design of Recommendation Algorithm Based on Knowledge Graph
Authors: Fang Xue , Li Lin , Wei Zheng 
Published: February 2023

Abstract
The commodity data in the shopping system contains a wealth of feature information, and different commodities are related by these features. Traditional collaborative filtering algorithms represent commodity data in a structured way, but they have issues such as low commodity similarity calculation accuracy, poor recommendation effect, and unfriendly recommendation results. The commodity recommendation algorithm based on the knowledge graph put forward in this paper initially automatically extracts the entities and entity relationships in the commodity data as the vertices and edges of the graph and then stores the commodity data in the Neo4j graph database. Finally, the similarity between commodities is calculated based on the graph’s path similarity, and the list of TOP-K commodities with the highest similarity is chosen for recommendation. The experimental findings indicate that the recommendation algorithm based on the knowledge graph can not only more accurately represent the similarity between commodities, but also that the graph structure can display the recommended path in a more user-friendly manner, and has better recommendation explainability as well as higher recommendation trust.
A low-light image enhancement method based on AUDD-GAN
Authors: Zhao Longyang , Li Xinran , Meng Ruimin 
Published: February 2023

Abstract
A large number of images taken under conditions of insufficient lighting, often with overall darkness, low contrast, noise and inconspicuous details, thus affecting people’s observation of the image content and the subsequent use of the image. This paper presents a novel method, Attention U-Net Dual Discriminator-Generative Adversarial Networks (AUDD-GAN), which combines attention mechanism, U-Net network structure and the dual discriminator. First, the method generates a normalized attention map to guide the subsequent modules to perform illumination enhancement to prevent overexposure or underexposure problems; second, the U-Net generative network extracts characteristics of diverse levels to acquire richer detail information; then, a dual discriminator is used as the discriminative network, where PatchGAN is used as the local discriminator to achieve better enhancement effects; ultimately, we combine multiple loss functions as the joint loss function. AUDD-GAN is verified to enhance the global contrast of the image while enhancing the detail features more effectively.
Application of Self-supervised Learning in Non-intrusive Load Monitoring
Authors: Tan Zhukui , Liu Bin , Xu Yutao , Feng Shengyong , Zhao Haixiang 
Published: February 2023

Abstract
With the proposal of smart grid, the demand of both source and load for fine monitoring and control of power load is becoming increasingly prominent. Non-intrusive load monitoring is a technical means to better meet this demand. However, the research at home and abroad focuses on the existing data sets and labeled data to improve the accuracy of load identification, while the research on the training method of the model under the massive unlabeled monitoring data in the actual scene is still in a relatively blank stage. Aiming at the problem of how to make full use of unlabeled monitoring data for model training, a non-intrusive-load monitoring method based on self-supervised learning is proposed in this paper. This method designs a self-supervised learning task, so that the model can make full use of the massive unlabeled monitoring data for training, eliminating the step of manually labelling the data; Based on the encoder-decoder structure, a deep learning model is established, and the load is identified through the load characteristic vector output by the encoder, so that the method has generalization performance. In this paper, AMPds2 data set is used to verify the method, and test examples verify the effectiveness of the method.
Convolutional Neural Networks Combined with Machine Vision for Mechanical Compressor Defect Detection
Authors: Li Kunshan 
Published: February 2023

Abstract
The study aims to detect the defects in the production line of compressor, promote the development of convolution neural network (CNN) in defect diagnosis and recognition, and expand the application of intelligent algorithm tools in the detection and recognition of defect and fault. The detection and recognition of the defects in the compressor workpiece were discussed based on the optimization of CNN. First, the active learning under the background of machine learning (ML) was introduced into the selection of sample training model for the annotation of massive data sets in diagnosing the compressor defect, and compared with the random selection method. Second, aiming at the limitation of deep CNN, through the introduction of deep separable convolution and reverse residual structure, an improved space-separable residual CNN model was proposed, and its training process was observed and analyzed. Finally, the improved space-separable residual CNN model was applied to the defect detection and recognition of compressor workpiece, and the effect of defect recognition was evaluated. The results showed that the accuracy of the active learning was higher than that of the random selection, and it can save about 18.76% of the cost of manual annotation data. The recognition accuracy of the improved CNN model is more than 90%, and the accuracy curve of the training set and the test set basically coincided in the later training period. The average recognition accuracy of the compressor defect detection model was 96.87%, and the normal workpiece could be fully recognized. The combination of improved and optimized CNN and ML has great potential in the detection of compressor defect.