bluuebunny/crossref_metadata_2025_split huggingface dataset:
A disturbance rejection adaptive filtering approach for human motion tracking
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Authors: Li Peng , Ge Zirui , Yang Xusheng , Zhang Wen-An , Wang Pengjun 
Published: August 2023

Abstract
This article studies the human motion tracking problem using the disturbance rejection adaptive filter with an inertial sensor. Due to the complexity of human motion, conventional inertial methods easily suffer from motion accelerations (MAs) and ferromagnetic disturbances (FDs). Here, a disturbance rejection adaptive filter is presented to segregate the exterior disturbances from the inertial sensor observations before attitude estimation. This method estimates the MA and FD by Kalman filters. According to disturbance intensity, Sage–Husa adaptive strategies based on fuzzy rules are designed to adjust noise covariance. Moreover, gravity and geomagnetic field estimation are applied as multiplicative extended Kalman filter observations to solve external disturbances problems. Finally, the effectiveness and superiority of the proposed method are verified by an example of human motion tracking.
Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment
Authors: Zheng Naiquan , Chai Hongzhou 
Published: August 2023

Abstract
Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) ground height retrieval technology is based on global navigation satellite system (GNSS) signal reflection, which can achieve efficient and high-precision ground retrieval. However, errors cannot be avoided. And whether there is a linear relationship between the height of the station and the error is unknown. This research uses Hi-Target geodetic GNSS receivers, smart phone devices (Honor 60) and smart tablet devices (Huawei MatePad Pro) to collect a total of 5 d data from DOY65 to DOY69 in 2023, with the station heights of 0.8 m, 1.0 m, 1.2 m, 1.4 m and 1.6 m, respectively. The experimental results show that each satellite can effectively establish a linear relationship between the inversion error and the station height, which can be used in the error compensation research of different station heights under the limitation that the height of reflector is between 0.8 m and 1.6 m. Simultaneously, the error is related to the influence of comprehensive factors such as reflector type, satellite number, and data-receiving equipment. Secondly, two clustering methods, k-means and k-media, are introduced to cluster a and b in the linear relationship y = ax + b of each satellite, and it is proved that the linear relationship between inversion error and station height is obviously related to ground reflection surface (plastic track and concrete ground). Finally, it is verified that the height measurement accuracy of low-cost smart electronic equipment (Root Mean Square Error (RMSE): 0.047 m and 0.042 m) is worse than that of GNSS (RMSE: 0.010 m), but it still has good measurement performance. All in all, this study provides an essential technical reference for the error compensation of different station heights and for the application of GNSS-IR with low-cost smart electronic equipment. Due to its low-cost advantage, it has great potential in developing other surface parameter inversion of GNSS-IR technology.
Polyphosphazene-derived carbon modified nanowires for high-performance electrochemical energy storage
Authors: Zhang Xiaoyan , Wang Hongmei , Hong Caihui , Song Huiling , Han Tongwei , Chu Xinyan , Kambonde Jerricia A N N 
Published: August 2023

Abstract
Two one-dimensional nanowires, Fe3O4 and MnO2 nanowires, were modified with polyphosphazene-derived carbon (PZSC) using in situ polymerization and high-temperature calcination methods. PZSC coated with MnO2 nanowire (MnO2/PZSCNW) was designed as the positive electrode, while PZSC coated with Fe3O4 nanowire (Fe3O4/PZSCNW) was designed as the negative electrode. Both MnO2/PZSCNW (+) and Fe3O4/PZSCNW (−) exhibit much larger specific capacities than the corresponding MnO2 and Fe3O4 nanowires, reaching 75.5 mAh g−1 and 75.9 mAh g−1, respectively. The maximum specific capacity, power and energy density of MnO2/PZSCNW (+)//Fe3O4/PZSCNW (−) in alkaline electrolyte are up to 63.2 mAh g−1, 429.6 W kg−1 and 53.7 Wh kg−1, respectively. After 10 000 cycles, the cell maintains 100% capacity. The experimental results indicate that the polyphosphazene-derived carbon coating can significantly improve the electrochemical performance, providing a feasible solution for constructing high-performance supercapacitors.
CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation
Authors: Chen Yuanbin , Wang Tao , Tang Hui , Zhao Longxuan , Zhang Xinlin , Tan Tao , Gao Qinquan , Du Min , Tong Tong 
Published: August 2023

Abstract
Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. One such technique is the U-Net framework based on the U-shaped convolutional neural networks (CNN) and its variants. However, these methods have limitations in simultaneously capturing both the global and the remote semantic information due to the restricted receptive domain caused by the convolution operation’s intrinsic features. Transformers are attention-based models with excellent global modeling capabilities, but their ability to acquire local information is limited. To address this, we propose a network that combines the strengths of both CNN and Transformer, called CoTrFuse. The proposed CoTrFuse network uses EfficientNet and Swin Transformer as dual encoders. The Swin Transformer and CNN Fusion module are combined to fuse the features of both branches before the skip connection structure. We evaluated the proposed network on two datasets: the ISIC-2017 challenge dataset and the COVID-QU-Ex dataset. Our experimental results demonstrate that the proposed CoTrFuse outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation. The codes are available at https://github.com/BinYCn/CoTrFuse.
Leveraging global binary masks for structure segmentation in medical images
Authors: Kazemimoghadam Mahdieh , Yang Zi , Chen Mingli , Ma Lin , Lu Weiguo , Gu Xuejun 
Published: August 2023

Abstract
Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels’ intensity information for inference. Acquiring sufficient training data is another challenge limiting models’ applications. Here, we proposed to leverage the consistency of organs’ anatomical position and shape information in medical images. We introduced a framework leveraging recurring anatomical patterns through global binary masks for organ segmentation. Two scenarios were studied: (1) global binary masks were the only input for the U-Net based model, forcing exclusively encoding organs’ position and shape information for rough segmentation or localization. (2) Global binary masks were incorporated as an additional channel providing position/shape clues to mitigate training data scarcity. Two datasets of the brain and heart computed tomography (CT) images with their ground-truth were split into (26:10:10) and (12:3:5) for training, validation, and test respectively. The two scenarios were evaluated using full training split as well as reduced subsets of training data. In scenario (1), training exclusively on global binary masks led to Dice scores of 0.77 ± 0.06 and 0.85 ± 0.04 for the brain and heart structures respectively. Average Euclidian distance of 3.12 ± 1.43 mm and 2.5 ± 0.93 mm were obtained relative to the center of mass of the ground truth for the brain and heart structures respectively. The outcomes indicated encoding a surprising degree of position and shape information through global binary masks. In scenario (2), incorporating global binary masks led to significantly higher accuracy relative to the model trained on only CT images in small subsets of training data; the performance improved by 4.3%–125.3% and 1.3%–48.1% for 1–8 training cases of the brain and heart datasets respectively. The findings imply the advantages of utilizing global binary masks for building models that are robust to image intensity variations as well as an effective approach to boost performance when access to labeled training data is highly limited.
Biaxial ultrasound driving technique for small animal blood–brain barrier opening
Authors: Pellow Carly , Li Siyun , Delgado Sagid , Pike G Bruce , Curiel Laura , Pichardo Samuel 
Published: August 2023

Abstract
Biaxial driving can more efficiently convert electrical power to forward acoustic power in piezoelectric materials, and the interaction between the orthogonal electric fields can produce a combination of extensional and shear deformations as a function of the phase difference between them to allow dynamic steering of the beam with a single-element. In this study, we demonstrate for the first time the application of a single-element biaxially driven ring transducer in vivo for blood–brain barrier opening in mice, and compare it to that achieved with a conventional single-element highly focused (F# = 0.7) spherical transducer operating at a similar frequency. Transcranial focused ultrasound (0.45 MPa, 10 ms pulse length, 1 Hz repetition frequency, 30 s duration) was applied bilaterally to mice with a 40 μl/kg bolus of DefinityTM microbubbles, employing either a single-element biaxial ring (1.482 MHz, 10 mm inner diameter, 13.75 mm outer diameter) or spherical (1.5 MHz, 35 mm diameter, F# = 0.7; RK50, FUS Instruments) transducer on each side. Follow-up MRI scans (T1 pre- and post- 0.2 mmol/kg Gd injection, T2) were acquired to assess blood–brain barrier opening volume and potential damage. Compared to blood–brain barrier opening achieved with a conventional single-element spherical focused transducer, the opening volume achieved with a single-element biaxial ring transducer was 35% smaller (p = 0.002) with a device of a ring diameter of 40% the aperture size. Axial refocusing was further demonstrated with the single-element biaxial ring transducer, yielding a 1.63 mm deeper, five-fold larger opening volume (p = 0.048) relative to its small-focus mode. The biaxial ring transducer achieved a more localized opening compared to the spherical focused transducer under the same parameters, and further enabled dynamic axial refocusing with a single-element transducer with a smaller fabrication footprint.
Accuracy evaluation of different machine learning force field features
Authors: Han Ting , Li Jie , Liu Liping , Li Fengyu , Wang Lin-Wang 
Published: August 2023

Abstract
Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
Velocity of interfaces with short and long ranged elasticity under sinusoidal creep
Authors: Savolainen Juha , Alava Mikko 
Published: August 2023

Abstract
Plenty of research on elastic interfaces has been done on systems where the interface is pushed with a constant force. We studied the average velocity of an interface under a sinusoidal driving in the creep region, considering both short-range elastic systems, such as magnetic domain walls during a hysteresis loop, and long-ranged systems such as fractures. We obtained a modified version of the creep velocity with approximate power-law behavior and a material-dependent exponent for short ranged systems and simpler behavior for long-range elasticity. We discuss whether the model can be applied to fatigue fractures or if extra physics is needed.
Unveiling herd behavior in financial markets
Authors: Neuman Yair , Cohen Yochai 
Published: August 2023

Abstract
Herd behavior is a powerful source of growth in financial markets. However, as available energy resources limit exponential growth, we should expect periods where an upward trend is balanced toward equilibrium or reverse its direction toward decline. This paper proposes a novel approach for modeling herd behavior and predicting a trend reversal in financial markets. Our approach relies on two key metrics: asymmetry and ‘steps to symmetry.’ We use Machine Learning to identify hidden patterns in the fluctuations of these metrics and use the patterns for predicting a transition from exponential growth. Analyzing three datasets of stock prices, we present solid empirical evidence supporting the proposed approach.
First-principles atomic level stresses: application to a metallic glass under shear
Authors: Lobzenko Ivan , Tsuru Tomohito , Shiihara Yoshinori , Iwashita Takuya 
Published: August 2023

Abstract
Unlike crystalline alloys, metallic glasses (MGs) do not possess distinctive defects but exhibit a highly heterogeneous response to shear deformation. The difficulties in describing such non-uniform behaviour hamper the prediction of the mechanical properties of MGs. Using the first-principles athermal quasi-static shear simulation on a CuZr glass, we investigate the mechanical responses of various atomic-level parameters, such as the first-principles atomic stresses and electronic properties (an atomic charge, chemical bonds, etc), and their correlations. We find that the atomic von Mises stress is correlated with a

D

min

2

parameter, which is commonly employed and also serves as a unique measure of the degree of non-uniform responses. We also show little correlation between the mechanical and electronic properties during the relaxation process, while we perceive a high correlation between the change in chemical and topological bonds. We discuss the physical insights behind these correlations.
Cutoff phenomenon and entropic uncertainty for random quantum circuits
Authors: Oh Sangchul , Kais Sabre 
Published: August 2023

Abstract
How fast a state of a system converges to a stationary state is one of the fundamental questions in science. Some Markov chains and random walks on finite groups are known to exhibit the non-asymptotic convergence to a stationary distribution, called the cutoff phenomenon. Here, we examine how quickly a random quantum circuit could transform a quantum state to a Haar-measure random quantum state. We find that random quantum states, as stationary states of random walks on a unitary group, are invariant under the quantum Fourier transform (QFT). Thus the entropic uncertainty of random quantum states has balanced Shannon entropies for the computational basis and the QFT basis. By calculating the Shannon entropy for random quantum states and the Wasserstein distances for the eigenvalues of random quantum circuits, we show that the cutoff phenomenon occurs for the random quantum circuit. It is also demonstrated that the Dyson-Brownian motion for the eigenvalues of a random unitary matrix as a continuous random walk exhibits the cutoff phenomenon. The results here imply that random quantum states could be generated with shallow random circuits.
Implementation of a multidisciplinary inpatient drug desensitization program
Authors: Martino Janine G , Cang William , Jaskowiak Jaclyn , Tavakoli Aran , Clapp Christopher , Marro Hannah , Kim Alexander 
Published: August 2023

Abstract

Purpose
The purpose of this project was to analyze the process of adult inpatient drug desensitization and to create a multidisciplinary standardized procedure with the aim of improving patient safety, efficiency, length of stay, and patient satisfaction.

Summary
A team consisting of physicians, pharmacists, nurses, critical care specialists, and operational capacity managers was created. The electronic health system was queried to evaluate the frequency of desensitizations, level of care, implicated medications, and length of stay. An electronic desensitization request form was created along with a desensitization team and email listserver for notification of key stakeholders.

Conclusion
A standardized, safe, and efficient process for inpatient drug desensitization requires advance planning and participation from all disciplines involved. Operational and logistical aspects of desensitization are crucial components of rapid drug desensitization. A structured, multidisciplinary approach to inpatient desensitization is feasible.
Symmetry’s revenge
Authors: Schmid Joseph C 
Published: August 2023

Abstract
James Henry Collin recently developed a new symmetry breaker favouring the ontological argument’s possibility premiss over that of the reverse ontological argument. The symmetry breaker amounts to an undercutting defeater for the reverse possibility premiss based on Kripkean cases of a posteriori necessity. I argue, however, that symmetry re-arises in two forms. First, I challenge the purported asymmetry in epistemic entitlements to the original and reverse possibility premisses. Second, relevantly similar Kripkean cases equally undercut the original possibility premiss.
Measuring tissue water potential in marine macroalgae via an updated Chardakov method
Authors: Gibson V L , Richards Donà A , Smith C M 
Published: August 2023

Abstract.
Regulation of tissue water potential is a key mechanism in macroalgal osmotic responses to changing external osmotic conditions, which are common in tidally influenced estuarine and intertidal systems. Nevertheless, significant knowledge gaps exist in our understanding of osmotic responses in macroalgae because few methods measure osmotic potential within macroalgal tissues. Leaf psychrometers have furthered understanding of osmotic potentials in terrestrial plant water relations, yet these have not been developed to measure the range of highly negative potential values found in marine macroalgae. To address these gaps, we present an effective, updated version of the Chardakov method to measure tissue water potential in macroalgae. Here, we present a case study examining macroalgal response in tissue water potential by two morphologically and evolutionarily distinct species, Ulva lactuca (Chlorophyta) and Hypnea musciformis (Rhodophyta) to four paired salinity and nutrient treatments at two temperatures. These treatments simulate a gradient from full coastal ocean conditions to brackish submarine groundwater discharge, an ecosystem type found on basaltic shorelines. Both algae demonstrated plasticity in osmotic response to submarine groundwater discharge with significant positive correlations between tissue water potential and proportion of submarine groundwater discharge in the treatment. These results are the first to describe macroalgal response in tissue water potential, a first step to understanding algal physiological ecology in such complex coastal environments. This revised Chardakov method is a valuable tool to better understand species-specific osmotic responses to ecologically relevant conditions, and can augment the study of other tidal systems and ontogenetic stages.
Polarized Discourses of Abortion in English: A Corpus-based Study of Semantic Prosody and Discursive Salience
Authors: Malory Beth 
Published: August 2023

Abstract
Amidst ongoing global debate about reproductive rights, questions have emerged about the role of language in reinforcing stigma around termination. Amongst some ‘pro-choice’ groups, the use of pro-life is discouraged, and anti-abortion is recommended. In UK official documents, termination of pregnancy is generally used, and abortion is avoided. Lack of empirical research focused on lexis means it is difficult to draw conclusions about the role language plays in this polarized debate, however. This paper, therefore, explores whether the stigma associated with abortion may reflect negative semantic prosody. Synthesizing quantitative corpus linguistic methods and qualitative discourse analysis, it presents findings that indicate that abortion has unfavourable semantic prosody in a corpus of contemporary internet English. These findings are considered in relation to discursive salience, offering a theoretical framework and operationalization of this theory. Through this lens, the paper considers whether the discursive salience of extreme anti-abortion discourses may strengthen the negative semantic prosody of abortion. It, therefore, combines a contribution to theory around semantic prosody with a caution to those using abortion whilst unaware of its possibly unfavourable semantic prosody.
Capturing Thresholds and Continuities: Individual Differences as Predictors of L2 Sociolinguistic Repertoires in Adult Migrant Learners in Austria
Authors: Wirtz Mason A , Pfenninger Simone E 
Published: August 2023

Abstract
This cross-sectional study addresses for the first time the non/linear association between individual learner differences of social, proficiency-related, and socioaffective nature (length of residence [LoR], varietal proficiency, exposure, and socioaffect) and differential outcomes in L2 sociolinguistic repertoires against the backdrop of the Austro-Bavarian naturalistic context. Forty adult migrant L2 German learners participated in a virtual reality experiment involving interactions with dialect-speaking and standard German-speaking interlocutors. The goal was to explore differences in participants’ interpersonal varietal behavior, that is, their addressee-relational variable use of standard German, Austro-Bavarian dialect, and mixture varieties. The results of Bayesian multinomial modeling indicated that LoR, dialect exposure, and proficiency, as well as reduced anxiety when speaking dialect, predicted differences in interpersonal varietal behavior. That said, a visual-quantitative analysis revealed critical thresholds concerning when, within these predictors, changes in sociolinguistic behavior manifested, indicating a rapid change in sociolinguistic development at the inter-individual level and ultimately facilitating initial insights as to how advanced one needs to be in order to engage in sociolinguistic variation in the Austro-Bavarian naturalistic context.