bluuebunny/crossref_metadata_2025_split huggingface dataset:
3D magnetic-field morphology of the Perseus molecular cloud
Authors: Tahani M. , Lupypciw W. , Glover J. , Plume R. , West J. L. , Kothes R. , Inutsuka S. , Lee M.-Y. , Robishaw T. , Knee L. B. G. , Brown J. C. , Doi Y. , Grenier I. A. , Haverkorn M. 
Published: February 2022

Context.Despite recent observational and theoretical advances in mapping the magnetic fields associated with molecular clouds, their three-dimensional (3D) morphology remains unresolved. Multi-wavelength and multi-scale observations will allow us to paint a comprehensive picture of the magnetic fields of these star-forming regions.Aims.We reconstructed the 3D magnetic field morphology associated with the Perseus molecular cloud and compared it with predictions of cloud-formation models. These cloud-formation models predict a bending of magnetic fields associated with filamentary molecular clouds. We compared the orientation and direction of this field bending with our 3D magnetic-field view of the Perseus cloud.Methods.We used previous line-of-sight and plane-of-sky magnetic field observations as well as Galactic magnetic field models to reconstruct the complete 3D magnetic field vectors and morphology associated with the Perseus cloud.Results.We approximated the 3D magnetic field morphology of the cloud as a concave arc that points in the decreasing longitude direction in the plane of the sky (from our point of view). This field morphology preserves a memory of the Galactic magnetic field. In order to compare this morphology to cloud-formation model predictions, we assume that the cloud retains a memory of its most recent interaction. After incorporating velocity observations, we find that the line-of-sight magnetic field observations are consistent with predictions of shock-cloud-interaction models.Conclusions.To our knowledge, this is the first time that the 3D magnetic fields of a molecular cloud have been reconstructed. We find the 3D magnetic field morphology of the Perseus cloud to be consistent with the predictions of the shock-cloud-interaction model that describes the formation mechanism of filamentary molecular clouds.
Utopia, war, and justice
Authors: Mooney Tara , Price Gareth 
Published: February 2022

Abstract
Between 2014 and 2017, Islamic State in Iraq and Syria (ISIS) morphed from a terrorist organization into a proto-state that held a large swathe of territory, within which its strict interpretation of sharia law was enforced. We argue that ISIS discursively constructed itself politically by appealing to elements of both a traditional Islamic caliphate and a modern legitimate state. Linguistic and semiotic data is presented from political communications through social media that revolve around three key themes, specifically the construction of a utopian state founded on justice and engaged in righteous war, which all rely fundamentally on a conception of the modern state as holding a “monopoly on the legitimate use of force”, in Weber’s definition. We analyze this data from a critical discourse analysis (CDA) perspective, within a theoretical framework that draws on the political sociology of the state and political communications in asymmetric conflicts.
Optimizing sprint interval exercise for post‐exercise hypotension: A randomized crossover trial
Authors: Ketelhut Sascha , Möhle Martin , Gürlich Tina , Hottenrott Laura , Hottenrott Kuno 
Published: February 2022

ABSTRACTThis study aimed to examine the effects of manipulating the rest intervals during sprint interval training (SIT) on post‐exercise hypotension and within‐session oxygen consumption.Thirty healthy, trained adults (aged 30.9 ± 8.7 years; 14 males, 16 females; BMI 22.1 ± 2.3 kg/m2; VO2max 50.7 ± 7.8 ml/kg/min) completed two different SIT protocols (4x 30‐seconds all‐out cycling sprints) with a one‐week washout period. Sprint bouts were separated by either 1 (R1) or 3 (R3) minutes of active recovery. Both before and throughout the 45 min after the training, peripheral systolic (pSBP) and diastolic (pDBP) blood pressure, central systolic (cSBP) and diastolic (cDBP) blood pressure, aortic pulse wave velocity (aPWV), stroke volume (SV), and heart rate (HR) were assessed. Throughout the SIT protocols, oxygen consumption (VO2) was monitored.There were no significant differences in time spent at 75%, 85%, 95%, and 100% of maximal VO2 between R1 and R3. After R3, there was a significant reduction in pSBP, pDBP, cSBP, cDBP, and aPWV. After R1, there were no changes in the respective parameters. There were significant interaction effects in pSBD (p < 0.001), pDBP (p < 0.001), cSBP (p < 0.001), cDBP (p = 0.001), and aPWV (p = 0.033). HR significantly increased after both conditions. Only R1 resulted in a significant reduction in SV.Longer resting intervals during SIT bouts seem to result in more substantial post‐exercise hypotension effects. Time spent at a high percentage of maximal VO2 was not affected by rest interval manipulation.
Extracting white blood cells from blood on microfluidics platform: a review of isolation techniques and working mechanisms
Authors: Laxmi Vijai , Joshi Suhas S , Agrawal Amit 
Published: February 2022

AbstractSelective isolation of human blood cells has numerous applications in disease diagnostic, prognostics, drug discovery, and drug delivery. In particular, isolation of white blood cells (WBCs) is required for the detection of various diseases such as leukemia, human immunodeficiency virus infection, Epstein–Barr virus (EBV), and cancers. Although the conventional methods of centrifugation and flow cytometry are broadly employed to isolate WBCs in clinical practice, they experience several limitations such as the requirement of the large volume of samples and reagents, trained personnel, large setup, and have an adverse effect on the quality of cells. In contrast, microfluidics-based methods have appeared as a superior approach of cells isolation with advantages such as low cost, ease to operate, compact in size, and requiring a lower sample volume. This review focuses on various microfluidics techniques for isolating WBCs from blood. Here, we have discussed the working mechanism of different microfluidics techniques, microdevice designs, and their performance parameters to isolate WBCs. In addition, a brief description of the numerous advantages and limitations of the existing microdevices and their future prospects aiming to develop an affordable, user-friendly point-of-care solution is provided.
Unconventional superconductivity in UTe2
Authors: Aoki D , Brison J-P , Flouquet J , Ishida K , Knebel G , Tokunaga Y , Yanase Y 
Published: February 2022

Abstract
The novel spin-triplet superconductor candidate UTe2 was discovered only recently at the end of 2018 and already attracted enormous attention. We review key experimental and theoretical progress which has been achieved in different laboratories. UTe2 is a heavy-fermion paramagnet, but following the discovery of superconductivity, it has been expected to be close to a ferromagnetic instability, showing many similarities to the U-based ferromagnetic superconductors, URhGe and UCoGe. This view might be too simplistic. The competition between different types of magnetic interactions and the duality between the local and itinerant character of the 5f Uranium electrons, as well as the shift of the U valence appear as key parameters in the rich phase diagrams discovered recently under extreme conditions like low temperature, high magnetic field, and pressure. We discuss macroscopic and microscopic experiments at low temperature to clarify the normal phase properties at ambient pressure for field applied along the three axis of this orthorhombic structure. Special attention will be given to the occurrence of a metamagnetic transition at H
m = 35 T for a magnetic field applied along the hard magnetic axis b. Adding external pressure leads to strong changes in the magnetic and electronic properties with a direct feedback on superconductivity. Attention is paid on the possible evolution of the Fermi surface as a function of magnetic field and pressure. Superconductivity in UTe2 is extremely rich, exhibiting various unconventional behaviors which will be highlighted. It shows an exceptionally huge superconducting upper critical field with a re-entrant behavior under magnetic field and the occurrence of multiple superconducting phases in the temperature-field-pressure phase diagrams. There is evidence for spin-triplet pairing. Experimental indications exist for chiral superconductivity and spontaneous time reversal symmetry breaking in the superconducting state. Different theoretical approaches will be described. Notably we discuss that UTe2 is a possible example for the realization of a fascinating topological superconductor. Exploring superconductivity in UTe2 reemphasizes that U-based heavy fermion compounds give unique examples to study and understand the strong interplay between the normal and superconducting properties in strongly correlated electron systems.
Classification of macular abnormalities using a lightweight CNN-SVM framework
Authors: Wang Xuqian , Gu Yu 
Published: February 2022

Abstract
Macular abnormalities are the main reason for central vision loss, especially in elderly people. Due to global population aging, a heavy burden will be placed on the health care system. Therefore, it is urgent and necessary to develop an automatic and intelligent tool to identify macular abnormalities. Optical coherence tomography is a non-invasive rapid imaging technique to diagnose macular abnormalities. We propose a lightweight convolutional neural network–support vector machine (CNN-SVM) framework consisting of a novel lightweight CNN backbone and an SVM classifier for the accurate detection of macular abnormalities. The CNN-SVM framework achieves excellent performance based on various metrics (precision, recall, F1-score, and accuracy) with an accuracy of 99.8% and demonstrates strong interpretability using heatmap visualization, outperforming several state-of-the-art models (Joint-Attention Network, OpticNet, MobileNet-V3, DenseNet-169, ResNet-50, lesion-aware CNN, Atten-ResNet, least-squares generative adversarial network and others). The proposed CNN-SVM framework is a feasible and reliable tool for the classification of macular abnormalities and shows potential for diagnostic ophthalmology in clinical practice.
Properties and applications of boron nitride nanotubes
Authors: Turhan Emine Ayşe , Pazarçeviren Ahmet Engin , Evis Zafer , Tezcaner Ayşen 
Published: February 2022

Abstract
Nanomaterials have received increasing attention due to their controllable physical and chemical properties and their improved performance over their bulk structures during the last years. Carbon nanostructures are one of the most widely searched materials for use in different applications ranging from electronic to biomedical because of their exceptional physical and chemical properties. However, BN nanostructures surpassed the attention of the carbon-based nanostructure because of their enhanced thermal and chemical stabilities in addition to structural similarity with the carbon nanomaterials. Among these nanostructures, one dimensional-BN nanostructures are on the verge of development as new materials to fulfill some necessities for different application areas based on their excellent and unique properties including their tunable surface and bandgap, electronic, optical, mechanical, thermal, and chemical stability. Synthesis of high-quality boron nitride nanotubes (BNNTs) in large quantities with novel techniques provided greater access, and increased their potential use in nanocomposites, biomedical fields, and nanodevices as well as hydrogen uptake applications. In this review, properties and applications of one-dimensional BN (1D) nanotubes, nanofibers, and nanorods in hydrogen uptake, biomedical field, and nanodevices are discussed in depth. Additionally, research on native and modified forms of BNNTs and also their composites with different materials to further improve electronic, optical, structural, mechanical, chemical, and biological properties are also reviewed. BNNTs find many applications in different areas, however, they still need to be further studied for improving the synthesis methods and finding new possible future applications.
Preface for International Conference on Recent Trends in Applied Research (ICoRTAR2021) Proceedings
Authors: Chakraverty S , Edeki SO , Adinya I , González-Gaxiola O , Hammouch Z , Fotso TJ , Owino JN , Jena RM , Bhattacharya T , Zelibe SC , Animasaun IL , Jena SK , Andam Boiquaye PS , Athavale VA 
Published: February 2022

The 2nd International Conference on Recent Trends in Applied Research (ICoRTAR2021) is a virtual conference based on enabling platforms to present research results relating to global issues. ICoRTAR2021 is hosted by ResearchAcad HUB with channels and human resources in India, Nigeria, Kenya, Mexico, Morocco, Ghana, and Cameroon. Thus, the conference was held virtually from October 08-09, 2021.The Theme of this second series (ICoRTAR2021) is ‘Effective Research Practices Amidst Covid-19 Pandemic’. ICoRTAR202 aims to bring together faculty members, leading scientists, academicians, research & graduate scholars, industrial professionals, and decision-makers to discuss current trends in research with implications on global phenomena such as COVID-19, economic growth & development, and so on. Basically, the ICORTAR2021 participants discussed extensively vital topics, recent trends, and issues in Applied Science, Mathematical Sciences, Computational Science, Engineering Science, Physics and Nanoscience, Financial Modeling, Artificial Intelligence, and Stochastic Dynamics. Five plenary sessions with different aspects of the ICORTAR2021 Theme were held successfully. The first keynote speaker, Professor Snehashish Chakraverty from the National Institute of Technology, Rourkela, INDIA, spoke on ‘Estimating Corona Positive Cases via Uncertainty Modeling.’ The second keynote speaker, Professor/Dr. Sannan khan, from the University Putra Malaysia, spoke on ‘Online Learning: A Panacea in the Time of COVID-19 Crisis.’
List of editorial board/organizing committee are available in the pdf.
Direct Solution of the Black-Scholes PDE Models with Non-Integer Order
Authors: Ogunyebi SN , Fadugba SE , Ogunlade TO , Adebayo KJ , Babalola BT , Faweya O , Emeka HO 
Published: February 2022

Abstract
This paper presents a direct solution of Black-Scholes PDE models with non-integer order via the non-integer iterative method. The Black-Scholes PDE models of non-integer order became popular and accepted globally by option traders for the valuation of financial derivatives over the period of time and the study of its numerical approaches has a wide range of applications in the real world.. The performance of the method has been confirmed and measured.
Iterative Method for Approximate-Analytical Solutions of Linear Schrödinger Equation
Authors: Akinlabi G.O. , Braimah J.A. , Abolarinwa A. , Edeki S.O. 
Published: February 2022

Abstract
In this study, the modified Picard Iterative Method (MPIM) is used to provide analytic and numerical solutions to linear Schrödinger Equations. These approximate-analytical solutions for the examples under consideration are easily computed. The suggested method is employed without any transformation, discretization, linearization, or limiting assumptions. The obtained results are similar to their exact forms. As a result, the approach is highly suggested for both linear and non-linear time-space fractional partial differential models with applications in various applied disciplines.
Direct Visual Odometry by Fusing Luminosity and Depth Information
Authors: Yin Jinming , Zhou Haibo , Zhang Haoxin , Li Chenming , Sun Guoqing 
Published: February 2022

Abstract
In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. The algorithm proposed in this paper uses Kinect 2 to collect RGB images with the corresponding depth information, and selects points with large changes of gray gradient to construct a luminosity error function and uses the corresponding depth information to construct a depth error function. The two error functions are merged into one function and converted into the least squares function of the pose of camera, the Levenberg-Marquardt algorithm is used to solve the camera pose. Finally, the Graph optimization theory and the g2o library are used to optimize the initial pose. Experiments show that the algorithm can reduce the error to a certain extent and reduce the drift caused by illumination changes.
Analysis on Influence Degree of Static and Dynamic Kinematics Error of EMA Pitch Servo Mechanism
Authors: Wan Ziping , Xie Xin , He Aihua , Fan Dapeng 
Published: February 2022

Abstract
Aiming at the problem that the influence degree of static and dynamic accuracy of EMA pitch servo mechanism is not clear, this paper analyzes the influence degree of static and dynamic error of EMA pitch servo mechanism based on high-precision pointing system. In terms of static error, based on the static error kinematics model, by establishing the static error probability model and solving the static error model, it is pointed out that the geometric error is the largest factor affecting the static kinematics error. In terms of the influence degree of dynamic error, based on the dynamic error kinematics model, through the establishment of the dynamic error separation process, and based on the test and mechanical experiment platform, it is pointed out that the general trend error is the largest factor affecting the dynamic kinematics error. Through the analysis of the influence degree of static and dynamic kinematic errors, the accuracy improvement direction of the EMA pitch servo mechanism is defined.
The Premise of Automatic Four-wheel Alignment: Intelligent Identification and Processing of Rim Targets
Authors: Wang Yifan , Yao Meibao 
Published: February 2022

Abstract
With high demand of automatic development of four-wheel alignment technology, this paper explores the core technology of automatic detection of four-wheel alignment instrument: intelligent identification and processing of rim target. According to the working process of 3D vision four-wheel aligner, starting from the target positioning, this paper proposes a method based on matching idea to realize the target recognition and positioning; Then the target area image is segmented to obtain the center coordinates on it, which provides a basis for the subsequent calculation of four-wheel positioning parameters. Simulation results demonstrate the effectiveness of our method.
Design and Analysis of a Three-DOF Assembly Robot
Authors: Wang Wei , Zhao Jinzhi , Zhu Chunyi , Ma ZiMeng , Sun WenPei , Wang Bin 
Published: February 2022

Abstract
The paper designs a Three-DOF assembly robot based on the planar quadrilateral mechanism. It is mainly used for the handling and assembly of parts, featuring small size, low cost and simple transmission principle. The robot combined with the characteristics of the planar quadrilateral mechanism, a detailed kinematic analysis is carried out and the forward and inverse solutions are obtained. The paper use MATLAB to analyze its end working space and motion trajectory according to the constraint equation. Finally, the motion simulation is carried out using ADAMS, which verifies the effectiveness of the design and provides a theoretical basis for the kinematics research and structural optimization of the assembly robot.
Energy-aware Distributed Formation Control for Multiple Unmanned Vehicles
Authors: Xie Qifeng , Xia Yaoxin , Wang Zhe , Zhang Jianjie , Kong Delei , Li Heng 
Published: February 2022

Abstract
Multi-agent system is an important branch of distributed artificial intelligence. As a new field of engineering control science, it has developed vigorously in recent years. From a practical point of view, most existing agents, such as unmanned vehicles and unmanned aerial vehicles, are powered by mobile power supply. Therefore, it is of practical significance to consider the battery state and explore how to improve the overall battery life when designing the multi-agent control strategy. In this paper, we first introduced a classical algorithm based on the consensus theory, and on this basis, the overtaking action in the formation is realized by changing the topology, so as to simulate the individuals (action individuals) who consume more energy in the formation; then we discussed how to improve the endurance of high-energy-consuming individuals in formation through average energy consumption in strategy design, and proposed an algorithm based on energy-aware. Finally, the MATLAB simulation results show that compared with the classical algorithm, the proposed algorithm can improve the endurance of formation.
Dynamics Modeling and Vibration Analysis of Planar 3-RRR Flexible Parallel Robot
Authors: Chen Xinyu , Wang Leilei , Chen Yayu , Guo Fei 
Published: February 2022

Abstract
To address the issue of complicated dynamics modeling and difficult solution of 3-RRR flexible parallel robot, we propose a method of building a flexible parallel robot dynamics model using screw bond graph. Firstly, the structure of the flexible parallel robot is analyzed; secondly, the multi-energy domain dynamics model of the robot is built using screw bond graph and vector bond graph considering the elastomeric transformation in the middle flexible linkage; finally, based on the model, the closed-loop control strategy for suppressing harmful vibration of the system, and the speed loop error compensation for different control parameters is also studied. Author establishes the vector bond and spin bond conversion junction type based on the power conservation transfer principle, which realizes the combination of rigid and flexible system in this paper. And we construct the dynamical model of multi-energy domain flexible parallel robot system.
A Fusion Feature for the Oestrous Sow Sound Identification Based on Convolutional Neural Networks
Authors: Chen Peng , Yin Dan , Yang Bo , Tang Wensheng 
Published: February 2022

Abstract
Affected by expert experience and individual differences in sows, artificial identification of sows in estrus is prone to various problems leading to low efficiency. A reliable audio classifier helps to identify the sows for estrus. In this work we propose a novel approach for estrus sound recognition using a fusion of two representative features as inputs and Convolutional Neural Networks (CNN) as training model. We demonstrate that the fusion feature generalizes better on the CNN model with training data under a real farm environment and gives remarkably higher test performance. Our model is also compared with some works that are published recently and achieve state-of-the-art performance.
Research on Object Detection in Campus Scene Based on Faster R-CNN
Authors: Tong Yan , Ma Hang , Zhang Shizhuo , Wu Xibao , Chen Wenbai 
Published: February 2022

AbstractThis paper focuses on the research of object detection technology in Smart Campus scene. Firstly, an object detection algorithm is constructed based on Faster R-CNN two-stage detector, which is trained on COCO2017 and VOC0712 public datasets, and the performance of the trained detector is evaluated. On this basis, the actual image data is collected from the campus scene, and divided into different groups according to the shooting angle, lighting conditions, occlusion, etc. After using the algorithm to detect, the results are analyzed systematically. In general, occlusion between objects is more likely to lead to unsatisfactory detection results. In the future, we still need to find a non-maximum suppression method which can effectively distinguish the same object repeated detection and occlusion between objects.