bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of STEM students' spatial abilities based on a novel net cube imagination test
Authors: Pawlak-Jakubowska Anita , Terczyńska Ewa 
Published: October 2023

AbstractThis study aimed to determine the level of spatial ability among STEM students. A universal multiple-choice test was prepared. The validity of the test and the effectiveness of its application were tested. The test is an extension of those currently in use. It contains tasks on spatial perception, spatial visualization, mental folding, rotation of spatial elements, and representation of spatial elements on a plane. The test consists of 16 tasks showing a cube with lines located on the walls. The student's task was to determine the development of the cube and mentally construct a cube based on the development. The results of the test determined the level of progress of the group (105 participants), and showed that a significant number of students have difficulties in perceiving and working with a three-dimensional object. On average 55% of the questions were answered correctly. For the group tested, reading a flat drawing and determining axonometry proved easier than other task. Students who attended technical high school or had design experience scored better. During the course, measures to improve teaching were introduced. Spatial model work was strengthened and initial tasks were adjusted according to the level of the group. Emphasis on teamwork and consultation was introduced for those with the lowest scores. The applied modifications in classroom management had a good effect. The average of the final grade was B. The test is a useful tool for academics and students to study spatial ability and improve teaching activities for STEM students.
Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals
Authors: Khayal Inas S. , O’Malley A. James , Barnato Amber E. 
Published: October 2023

AbstractRacial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.
A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis
Authors: Zhang Jiadong , Sun Kaicong , Yang Junwei , Hu Yan , Gu Yuning , Cui Zhiming , Zong Xiaopeng , Gao Fei , Shen Dinggang 
Published: October 2023

AbstractMedical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy.
A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments
Authors: Zhou Zhiqian , Ren Junkai , Zeng Zhiwen , Xiao Junhao , Zhang Xinglong , Guo Xian , Zhou Zongtan , Lu Huimin 
Published: October 2023

AbstractWhen deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots. However, until now, efficient and safe robot navigation in such dynamic environments is still an open problem. The critical reason is that the inconsistency between navigation efficiency and motion safety is greatly intensified by the high dynamics and uncertainties of pedestrians. To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict‐Averse Safe Reinforcement Learning (CASRL) for autonomous robot navigation in dynamic environments. Specifically, it first separates the collision avoidance sub‐task from the overall navigation task and maintains a safety critic to evaluate the safety/risk of actions. Later, it constructs two task‐specific but model‐agnostic policy gradients for goal‐reaching and collision avoidance sub‐tasks to eliminate their mutual interference. Then, it further performs a conflict‐averse gradient manipulation to address the inconsistency between two sub‐tasks. Finally, extensive experiments are performed to evaluate the superiority of CASRL. Simulation results show an average 8.2% performance improvement over the vanilla baseline in eight groups of dynamic environments, which is further extended to 13.4% in the most challenging group. Besides, forty real‐world experiments fully illustrated that the CASRL could be successfully deployed on a real robot.
Design of spread spectrum communication system based on multipath signal synthesis
Authors: Xiaoxu Guo , Zhaobin Xu , Jiacheng Dai , Jia Yang , Xiaojun Jin , Zhonghe Jin 
Published: October 2023

AbstractA low‐complexity anti‐multipath interference spread spectrum communication system is proposed to address shortcomings in traditional anti‐multipath technology, such as complex structures and high resource consumption. The system utilizes a fast Fourier transform to capture pseudo‐noise code signals and simultaneously detect multipath signals. Moreover, the signal‐to‐noise ratio (SNR) of each path signal is calculated, and the multipath signal is synthesized through the designed optimal SNR synthesis method to improve the SNR of the demodulated signal to reduce the bit error rate. MATLAB is used to perform functional simulations of the system to verify its feasibility. The simulation results show that the system can successfully detect and synthesize multipath signals and achieve lower communication error rates than traditional code division multiple access (CDMA) systems.
Impact of power flow on the apparent impedance calculated by the distance protection in transmission lines
Authors: Leonardo Fonseca , Fernando Chapi , Antonio Fonseca , Franklin Quilumba , Fabian Pérez‐Yauli 
Published: October 2023

AbstractThe reach of primary protection zones of transmission lines distance relays is affected when large power flows circulate through the transmission line prior to a short circuit occurrence. This can cause improper operation of the protection system in certain scenarios. In practice, protection zone adjustments are made by considering the apparent impedances obtained from short‐circuit simulations in the line of interest using standard methods such as IEC 60909‐0 or ANSI/IEEE C37.010. However, these short‐circuit calculation methods omit the existing operating conditions prior to the fault. Therefore, this work proposes a set of equations developed for calculating the apparent impedance seen by the distance relay in view of different types of short circuits, including the effects of power flow prior to the fault. These equations are then used to establish the distance primary protection zone settings. The analysis uses the mathematical expressions proposed of voltages and currents at the relay location, and the impedance diagrams obtained from simulated short circuits. Subsequently, the settings obtained with these equations are compared to those achieved using the IEC 60909‐0 standard, allowing for the differences in settings and their effectiveness. Finally, this procedure is applied to a part of the Ecuadorian Transmission System.
Study on surface discharge of nonlinear conductive epoxy resin/SrTiO3 composites in 80–300 K temperature zone
Authors: Xing Yunqi , Wang Bin , Wang Mingyang , Chi Jiakai , Jia Peng , Huang Rongjin 
Published: October 2023

AbstractBisphenol F epoxy resin (EP) is often used in terminal current lead insulation of superconducting equipment because of its good insulation performance, high mechanical strength, good toughness at cryogenic temperatures, and resistance to cold shock and heat shock. However, due to the wide temperature range of 80–300 K and the strong electric field, the EP‐N2 interface is prone to surface flashover, resulting in terminal insulation failure. To improve the reliability of the current lead insulation, non‐linear conductive EP/strontium titanate (SrTiO3) composites were prepared by modification with nano filling. The changes of dielectric, surface discharge, flashover, and trap distribution characteristics of composite materials were studied, and the mechanism of SrTiO3 on the surface flashover of composite materials was analysed. The results show that the conductivity of the composite increases with the rise of SrTiO3 filling content, and the amplitude of improvement is greater under the strong electric field, showing a more significant non‐linearity. The composite has a lower trap energy level and a greater number of shallow traps compared to pure EP, which accelerates surface charge de‐trapping and reduces charge accumulation, effectively enhancing the discharge and surface flashover voltage of the composite.
Blind motion deblurring using improved DeblurGAN
Authors: Ji Wentao , Chen Xing , Li Yihong 
Published: October 2023

AbstractTo develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery.
Nonparametric point cloud filter
Authors: Sun Yefa , Wang Jinli 
Published: October 2023

AbstractThis paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters.
Parameter optimization of reactive power device planning in hybrid AC/DC networks based on objective‐oriented scenario selection method
Authors: Niu Tao , Wu Wenguo , Fang Sidun , Li Fan , Chen Guanhong 
Published: October 2023

AbstractAs one of the most common voltage/var‐related contingencies, commutation failures in hybrid AC/DC networks deteriorate the voltage profiles. Thus, systems may suffer from large‐scale cascading risks induced by the lack of dynamic reactive power support. Conventional power system planning usually aims to optimize the device capacity and location, ignoring the influences of intrinsic device parameters on high‐voltage direct current (HVDC) dynamics during commutation failures. If the intrinsic device parameters are also taken into account in the planning problem, it would allow for faster dynamic voltage/var support during system faults or disturbances to recover from voltage drops. However, considering the intrinsic parameters, allocations and capacities of a device, the optimal planning problem brings several challenges, such as scenario selections and computational burdens. To overcome these issues, an objective‐oriented scenario selection based power system planning approach is proposed in this paper. First, the detailed operations of converter stations are formulated both considering the steady state and system dynamics. Next, the quantitative relation between the power losses and deviation in system conditions is then derived to approximately describe the operational characteristics. Then, an objective‐oriented representative scenario selection method is introduced, aiming to reduce the number of selected scenarios, guaranteeing both computational accuracy and efficiency. Finally, a modified IEEE‐118 system is used to test the proposed algorithm, and the superiority and computational efficiency are observed by the simulation results.
Reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration
Authors: Wang Haipeng , Lu Hongchang , Sun Kai , Wu Xuewei , He Yuling , Du Xiaodong 
Published: October 2023

AbstractThis paper presents a reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration, and mainly addresses issues: 1) insufficient consideration of the characteristics for the components and distributed generations (DGs) of distribution network such as time sequences, randomness and intermittency; 2) changes in the feeder area of the distribution network after the access of DGs; and 3) multi‐dimensional reliability index for distribution network with DGs has not been formed perfectly. Firstly, Markov reliability model for distribution network components/DGs is established in our study. Then, considering the fault recovery and network reconfiguration of the feeder area, island partitioning method of distribution network with DGs is also proposed. Moreover, a reliability evaluation model is presented for distribution network with DGs based on sequential Monte Carlo method. Case study is performed on the improved IEEE RBTS BUS6 F4 feeder system, and the research results indicate the validity and effectiveness of the proposed method. In addition, the system reliability indicators, such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), and average system availability index (ASAI), are studied and discussed in detail from three dimensions: the access of DGs, the types of DGs, and the capacity of DGs.
Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis
Authors: Shi Wenchao , Li Tinghui , Li Huiwen , Ren Juan , Lv Meiyu , Wang Qi , He Yaowu , Yu Yao , Liu Lijie , Jin Shoude , Chen Hong 
Published: October 2023

AbstractThe coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.
Mg II h&k fine structure prominence modelling and the consequences for observations
Authors: Peat A. W. , Labrosse N. , Gouttebroze P. 
Published: October 2023

Aims. Using 2D Mg II h&k solar prominence modelling, our aim is to understand the formation of complex line profiles and how these are seen by the Interface Region Imaging Spectrograph (IRIS). Additionally, we see how the properties of these simulated observations are interpreted through the use of traditional 1D prominence modelling.
Methods. We used a cylindrical non-local thermodynamic equilibrium (NLTE) 2D complete redistribution (CRD) code to generate a set of cylindrical prominence strands, which we stacked behind each other to produce complex line profiles. Then, with the use of the point spread functions (PSFs) of IRIS, we were able to predict how IRIS would observe these line profiles. We then used the 1D NLTE code PROM in combination with the Cross Root Mean Square method (xRMS) to find the properties recovered by traditional 1D prominence modelling.
Results. Velocities of magnitude lower than 10 km s−1 are sufficient to produce asymmetries in the Mg II h&k lines. However, convolution of these with the PSFs of IRIS obscures this detail and returns standard looking single peaks. By increasing the velocities by a factor of three, we recover asymmetric profiles even after this convolution. The properties recovered by xRMS appear adequate at first, but the line profiles chosen to fit these profiles do not satisfactorily represent the line profiles. This is likely due to the large line width of the simulated profiles.
Conclusions. Asymmetries can be introduced by multithread models with independent Doppler velocities. The large line width created by these models makes it difficult for traditional 1D forward modelling to find good matches. This may also demonstrate degeneracies in the solution recovered by single-species 1D modelling.
Unravelling the post-collision properties of the Cartwheel galaxy: A MUSE exploration of its bar and inner region
Authors: Mondal Chayan , Barway Sudhanshu 
Published: October 2023

Aims. We aim to investigate the characteristics of the bar and inner disc in the collisional ring galaxy Cartwheel.
Methods. We used integral field unit (IFU) observations from the Multi-Unit Spectroscopic Explorer (MUSE) of the Very Large Telescope (VLT) to investigate the stellar kinematics, age, and nature of ionised gas in the inner region of the Cartwheel galaxy. We produced stellar line of sight velocity (V), velocity dispersion (σ), h3 velocity moment, stellar population age, and emission-line maps of the galaxy using the Galaxy IFU Spectroscopy Tool (GIST) pipeline.
Results. The observed nature of the intensity, V, and σ profiles together support the existence of a stellar bar, as earlier revealed from near-infrared (NIR) Ks-band imaging. A weak correlation between V/σ and h3 is found within the bar radius, providing more kinematic evidence for a stellar bar that survived the drop-through collision. The overall weak anti-correlation between V/σ and h3 in the disc implies that the stellar orbits in the disc are less stable, which might be due to the impact of the collision. The mass-weighted age map of the galaxy shows that the stellar populations in the bar region are relatively old, with an increasing gradient from the bar edge to the centre, further evidence that the bar was present before the galaxy underwent collision. Using a BPT diagram, we analysed a central unresolved source detected earlier with NIR imaging and do not find evidence of an active galactic nucleus. Our findings signify the preservation of the pre-collisional structures in the inner region of the Cartwheel, an important point to note when attempting to determine the evolution of collisional galaxy systems, particularly when investigating pre-collisional central regions in theoretical studies.