bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proof of universality in one-dimensional few-body systems including anisotropic interactions
Authors: Happ Lucas , Efremov Maxim A 
Published: November 2021

Abstract
We provide an analytical proof of universality for bound states in one-dimensional systems of two and three particles, valid for short-range interactions with negative or vanishing integral over space. The proof is performed in the limit of weak pair-interactions and covers both binding energies and wave functions. Moreover, in this limit the results are formally shown to converge to the respective ones found in the case of the zero-range contact interaction.
Multifunctional metasurfaces integrating near-field display and 3D holography
Authors: Du Wenjuan , Lou Zhilang , Chen Xuesong , Chen Long , Tang Dongliang 
Published: November 2021

Abstract
Metasurfaces have versatile manipulation capabilities in the optical field and provide the possibility of building a compact optical device with various complex functions. They have been regarded as ideal candidates to construct a miniaturized optical system with high density and multi-channel information. In this work, reflective all-metallic multifunctional metasurfaces consisting of aluminum nanorods are designed by simultaneously realizing the near-filed display and three-dimensional (3D) holography. Specifically, in the proposed design, each nanorod acts as a complex amplitude modulator to provide continuous amplitude control and binary phase control. By carefully optimizing the orientations of nanorods, a multifunctional metasurface can be designed to display a near-field grayscale pattern and far-field 3D images simultaneously. Numerical results by a full-wave simulation validate the good performance of the proposed design. The proposed method could provide greater degree of freedom to designs of lightweight devices, which could be employed in optical applications, such as virtual or augmented reality displays and anti-counterfeiting technology.
Analysis of current-in-plane giant magnetoresistance using Co2FeAl0.5Si0.5 half-metallic Heusler alloy
Authors: Fathoni Kresna B , Sakuraba Yuya , Miura Yoshio , Sasaki Taisuke , Nakatani Tomoya , Hono Kazuhiro 
Published: November 2021

Abstract
There has been renewed interest in current-in-plane giant magnetoresistance (CIP-GMR) devices for high-sensitivity magnetic sensors. However, further improvement in magnetoresistance (MR) ratio is necessary to achieve sufficient magnetic field sensitivity. Use of a half-metallic Co-based Heusler alloy ferromagnetic (FM) layer has been demonstrated to be effective in enhancing GMR in the configuration with current perpendicular to the plane; however, only small MR ratios are obtained in the CIP configuration. To understand the origin of the disappointingly low MR in the CIP configuration when using Heusler alloy FM layers, we investigated the magnetotransport properties of CIP-GMR devices using half-metallic Co2FeAl0.5Si0.5 (CFAS) Heusler alloy and conventional CoFe alloy as the FM layers in combination with Ag or Cu as a nonmagnetic (NM) spacer layer. Regardless of the high lattice and electronic band matching at the CFAS/Ag interface, CFAS/Ag CIP spin valves (SVs) show a MR ratio of only 1.2% at room temperature, which is much smaller than those of reference CoFe/Cu and CoFe/Ag SVs (21.6% and 8.4%, respectively). Current density distribution simulations suggest that large current shunting occurs in the Ag layer due to the significant resistivity gap between CFAS and Ag, which limits the generation of highly spin-polarized current from the CFAS layer, resulting in the very small MR ratios. To enhance the MR ratio in CIP-GMR using half-metallic materials, resistivity matching between FM layers and the NM layer is required, in addition to the high electronic band match that has been considered, as a key factor to obtain a high MR ratio in CIP-GMR devices.
Non-adiabatic dynamics of the entanglement entropy in a symmetry-breaking Haldane insulator
Authors: Xu Junjun 
Published: November 2021

Abstract
We study the non-adiabatic dynamics of a typical symmetry-protected topological (SPT) phase-the Haldane insulator (HI) phase with broken bond-centered inversion. By continuously breaking the middle chain, we find the gap closes at a critical point in the deep HI regime with a change of particle number partition of the left or right system. The adiabatic evolution fails at this critical point and we show how to predict the dynamics of the entanglement entropy near this point using a two-level model. These results show that one can find a critical regime where the entanglement measurement is relatively robust against perturbation that breaks the protecting symmetries in the HI. This is in contrast to the common belief that the SPT phases are fragile without the protecting symmetries.
An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise
Authors: He Shan , Wu Panlong , Yun Peng , Li Xingxiu , Li Jimin 
Published: November 2021

Abstract
In practical applications, the range rate used in a target tracking algorithm will further increase the strong nonlinearity between the measurement and the state, and the unknown correlation coefficient of measurement noise between the range and the range rate will cause suboptimal gain of the filter, which will seriously affect the performance of the filter. In this paper, an expectation maximization (EM)-based sequential modified unbiased converted measurement Kalman filter is proposed for target tracking with an unknown correlation coefficient of measurement noise between the range and the range rate. Firstly, a pseudo measurement is constructed by multiplying the range and the range rate to reduce the strong nonlinearity. The mean and covariance of converted errors are subsequently derived using modified unbiased converted measurement (MUCM) to weaken the error caused by the linearization of the measurement equation, which will effectively improve the dynamic accuracy of target tracking. Then, the converted errors of the position and the pseudo measurement are decorrelated by Cholesky factorization to facilitate the subsequent identification of the correlation coefficient, and the posterior probability distribution of the state is obtained using sequential filtering within the Bayesian framework. Finally, the EM is introduced in the updating procedure of the pseudo measurement to estimate both the target state and the correlation coefficient. The target tracking scenario with an unknown correlation coefficient is built to demonstrate the validity and feasibility of the proposed algorithm. Simultaneously, the results of the normalized error squared validate the consistency of the MUCM.
Physically unclonable security patterns created by electrospinning, and authenticated by two-step validation method
Authors: Taşcıoğlu Didem , Atçı Arda , Ünlütürk Seçil Sevim , Özçelik Serdar 
Published: November 2021

Abstract
Counterfeiting is a growing economic and social problem. For anticounterfeiting, random and inimitable droplet/fiber patterns were created by the electrospinning method as security tags that are detectable under UV light but invisible in daylight. To check the authenticity of the original security patterns created; images were collected with a simple smartphone microscope and a database of the recorded original patterns was created. The originality of the random patterns was checked by comparing them with the patterns recorded in the database. In addition, the spectral signature of the patterns in the droplet/fiber network was obtained with a simple and hand-held spectrometer. Thus, by reading the spectral signature from the pattern, the spectral information of the photoluminescent nanoparticles was verified and thus a second-step verification was established. In this way, anticounterfeiting technology that combines ink formula, unclonable security pattern creation and two-level verification is developed.
Magnetically induced self-assembly DNAzyme electrochemical biosensor based on gold-modified α-Fe2O3/Fe3O4 heterogeneous nanoparticles for sensitive detection of Ni2+
Authors: Yu Lulu , Liu Min , Zhang Yanling , Ni Yun , Wu Shaobo , Liu Ruijiang 
Published: November 2021

Abstract
A magnetically induced self-assembly DNAzyme electrochemical biosensor based on gold-modified α-Fe2O3/Fe3O4 heterogeneous nanoparticles was successfully fabricated to detect Nickel(II) (Ni2+). The phase composition and magnetic properties of α-Fe2O3/Fe3O4 heterogeneous nanoparticles controllably prepared by the citric acid (CA) sol–gel method were investigated in detail. The α-Fe2O3/Fe3O4 heterogeneous nanoparticles were modified by using trisodium citrate as reducing agent, and the magnetically induced self-assembly α-Fe2O3/Fe3O4–Au nanocomposites were obtained. The designed Ni2+-dependent DNAzyme consisted of the catalytic chain modified with the thiol group (S1-SH) and the substrate chain modified with methylene blue (S2-MB). The MGCE/α-Fe2O3/Fe3O4–Au/S1/BSA/S2 electrochemical sensing platform was constructed and differential pulse voltammetry was applied for electrochemical detection. Under the optimum experimental parameters, the detection range of the biosensor was 100 pM–10 μM (R
2 = 0.9978) with the limit of detection of 55 pM. The biosensor had high selectivity, acceptable stability, and reproducibility (RSD = 4.03%).
Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network
Authors: Dai Xianjin , Lei Yang , Wang Tonghe , Zhou Jun , Rudra Soumon , McDonald Mark , Curran Walter J , Liu Tian , Yang Xiaofeng 
Published: November 2021

Abstract
Magnetic resonance imaging (MRI) allows accurate and reliable organ delineation for many disease sites in radiation therapy because MRI is able to offer superb soft-tissue contrast. Manual organ-at-risk delineation is labor-intensive and time-consuming. This study aims to develop a deep-learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy. A novel regional convolutional neural network (R-CNN) architecture, namely, mask scoring R-CNN, has been developed in this study. In the proposed model, a deep attention feature pyramid network is used as a backbone to extract the coarse features given by MRI, followed by feature refinement using R-CNN. The final segmentation is obtained through mask and mask scoring networks taking those refined feature maps as input. With the mask scoring mechanism incorporated into conventional mask supervision, the classification error can be highly minimized in conventional mask R-CNN architecture. A cohort of 60 HN cancer patients receiving external beam radiation therapy was used for experimental validation. Five-fold cross-validation was performed for the assessment of our proposed method. The Dice similarity coefficients of brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord were 0.89 ± 0.06, 0.68 ± 0.14/0.68 ± 0.18, 0.89 ± 0.07/0.89 ± 0.05, 0.90 ± 0.07, 0.67 ± 0.18/0.67 ± 0.10, 0.82 ± 0.10, 0.61 ± 0.14, 0.67 ± 0.11/0.68 ± 0.11, 0.92 ± 0.07, 0.85 ± 0.06/0.86 ± 0.05, 0.80 ± 0.13, and 0.77 ± 0.15, respectively. After the model training, all OARs can be segmented within 1 min.
A comprehensive analytical model for ambipolar transport in nano-dimensional VOPc/p-6P OHJFET for applications in organic electronics
Authors: Katiyar Suleshma , Verma Neha , Jogi Jyotika 
Published: November 2021

Abstract
This paper presents a compact analytical DC model for high mobility VOPc (vanadyl pthalocyanine)/p-6P (para-sexiphenyl) ambipolar organic heterojunction field-effect transistor (OHJFET). The proposed model accounts for both unipolar and ambipolar regimes of VOPc/p-6P ambipolar OHJFET by considering spatial charge carrier density in the channel. The model incorporates subthreshold conduction phenomenon in addition to describing beyond threshold transport. The model is extended to describe ambipolar regime occurring in subthreshold region at low drain to source voltage, V
DS. Device characteristics and various parameters obtained are presented and are further used to model recombination zone and channel potential profile. Results obtained, are compared with available experimental data and a good match is observed.
Crystalline anisotropy of β-Ga2O3 thin films on a c-plane GaN template and a sapphire substrate
Authors: Ma Xiaocui , Xu Rui , Mei Yang , Ying Leiying , Zhang Baoping , Long Hao 
Published: November 2021

Abstract
In this work, crystalline anisotropy of heteroepitaxial (

2
ˉ

01

) β-Ga2O3 films on a c-plane sapphire substrate and a GaN template was investigated using x-ray diffraction. The (

2
ˉ

01

) ω-scan broadening of β-Ga2O3 on GaN exhibited six-fold rotational symmetric anisotropy along different azimuths, with maxima along the [010] direction and minima along the [102] direction, respectively. However, in β-Ga2O3 on sapphire, it was nearly isotropic. Smaller lattice mismatch between β-Ga2O3 and GaN were taken into account to explain the discrepancy, which also explained the better quality of β-Ga2O3 deposited on GaN. Our results present a new viewpoint of the crystallographic anisotropy of (

2
ˉ

01

) β-Ga2O3 thin films.
Shape optimization of magnetorheological damper piston based on parametric curve for damping force augmentation
Authors: Liu Gaoyu , Gao Fei , Liao Wei-Hsin 
Published: November 2021

Abstract
Making full use of the magnetically controllable rheological properties of magnetorheological (MR) fluid, MR actuators have been applied in many engineering fields. To adapt to different application scenarios, parameters of MR actuators often need to be optimized. Previous MR actuator optimization was focused on finding optimal combinations of geometric dimensions and physical parameters that meet certain requirements. The parts with optimized dimensions were still in regular shape, which might not bring optimal damping performance. Therefore, in this paper, shape optimization of MR damper piston based on parametric curve is performed for the first time. First, the regional magnetic saturation problem in the previous prototype is stated. Then, the MR damper with normal piston is simulated as a reference. Later, Bezier curve, one of the typical parametric curves, is used to form the piston with optimized parameters, and the MR damper with optimized piston is also simulated. Finally, prototypes of the MR dampers with normal and optimized pistons are fabricated and tested. Compared with the MR damper with normal piston, the one with optimized piston has larger field dependent force and total damping force under relatively large current, with about 52% and 24% maximum increasing percentage, respectively. The controllable force range of the MR damper with optimized piston is also larger than that with normal piston.
Superconductivity in doped Weyl semimetal Mo0.9Ir0.1Te2 with broken inversion symmetry
Authors: Mandal Manasi , Patra Chandan , Kataria Anshu , Paul Suvodeep , Saha Surajit , Singh R P 
Published: November 2021

Abstract
This work presents the emergence of superconductivity in Ir—doped Weyl semimetal T
d
—MoTe2 with broken inversion symmetry. Chiral anomaly induced planar Hall effect and anisotropic magneto-resistance confirm the topological semimetallic nature of Mo

1
−
x

Ir
x
Te2. Observation of weak anisotropic, moderately coupled type-II superconductivity in T
d
-Mo

1
−
x

Ir
x
Te2 makes it a promising candidate for topological superconductor.
Line-shape broadening of an autoionizing state in helium at high XUV intensity
Authors: Aufleger Lennart , Friebel Patrick , Rupprecht Patrick , Magunia Alexander , Ding Thomas , Rebholz Marc , Hartmann Maximilian , Ott Christian , Pfeifer Thomas 
Published: November 2021

Abstract
We study the interaction of intense extreme ultraviolet (XUV) light with the 2s2p doubly excited state in helium. In addition to previously understood energy-level and phase shifts, high XUV intensities may lead to other absorption-line-shape distortions. Here, we report on experimental transient-absorption spectroscopy results on the 2s2p line-width modification in helium in intense stochastic XUV fields. A few-level-model simulation is realized to investigate the origins of this effect. We find that the line-shape broadening is connected to the strong coupling of the ground state to the 2s2p doubly excited state which is embedded in the ionization continuum. As the broadening takes place for intensities lower than for other strong-coupling processes, e.g. observed asymmetry changes of the absorption profile, this signature can be identified already in an intermediate intensity regime. These findings are in general relevant for resonant inner-shell transitions in nonlinear experiments with XUV and x-ray photon energies at high intensity.
A thermodynamically consistent Markovian master equation beyond the secular approximation
Authors: Potts Patrick P , Kalaee Alex Arash Sand , Wacker Andreas 
Published: November 2021

Abstract
Markovian master equations provide a versatile tool for describing open quantum systems when memory effects of the environment may be neglected. As these equations are of an approximate nature, they often do not respect the laws of thermodynamics when no secular approximation is performed in their derivation. Here we introduce a Markovian master equation that is thermodynamically consistent and provides an accurate description whenever memory effects can be neglected. The thermodynamic consistency is obtained through a rescaled Hamiltonian for the thermodynamic bookkeeping, exploiting the fact that a Markovian description implies a limited resolution for heat. Our results enable a thermodynamically consistent description of a variety of systems where the secular approximation breaks down.
Alkali-metal(Li, Na, and K)-adsorbed MoSi2N4 monolayer: an investigation of its outstanding electronic, optical, and photocatalytic properties
Authors: Sun Zhiyuan , Xu Jing , Mwankemwa Nsajigwa , Yang Wenxing , Wu Xianwen , Yi Zao , Chen Shanjun , Zhang Weibin 
Published: November 2021

Abstract
Single-layer MoSi2N4, a high-quality two-dimensional material, has recently been fabricated by chemical vapor deposition. Motivated by this latest experimental work, herein, we apply first principles calculations to investigate the electronic, optical, and photocatalytic properties of alkali-metal(Li, Na, and K)-adsorbed MoSi2N4 monolayer. The electronic structure analysis shows that pristine MoSi2N4 monolayer exhibits an indirect bandgap (E
g = 1.89 eV). By contrast, the bandgaps of one Li-, Na-, and K-adsorbed MoSi2N4 monolayer are 1.73 eV, 1.61 eV, and 1.75 eV, respectively. Moreover, the work function of MoSi2N4 monolayer (4.80 eV) is significantly reduced after the adsorption of alkali metal atoms. The work functions of one Li-, Na-, and K-adsorbed MoSi2N4 monolayer are 1.50 eV, 1.43 eV, and 2.03 eV, respectively. Then, optical investigations indicate that alkali metal adsorption processes substantially increase the visible light absorption range and coefficient of MoSi2N4 monolayer. Furthermore, based on redox potential variations after alkali metals are adsorbed, Li- and Na-adsorbed MoSi2N4 monolayers are more suitable for the water splitting photocatalytic process, and the Li-adsorbed case shows the highest potential application for CO2 reduction. In conclusion, alkali-metal-adsorbed MoSi2N4 monolayer exhibits promising applications as novel optoelectronic devices and photocatalytic materials due to its unique physical and chemical properties.
Neuro-symbolic A.I. for the smart city
Authors: Morel Gilles 
Published: November 2021

Abstract
Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for the smart city, and point the way towards a complete integration of the two technologies, compatible with standard software.
Cloud-based Hardware-in-the-Loop testing of building automation controllers
Authors: Maghnie M , Stegemerten F , Niewöhner A , Baranski M , Kümpel A , Müller D 
Published: November 2021

Abstract
This paper aims to fill the gap in knowledge about building control testing, thus encouraging control evaluation, to ultimately improve energy efficiency in building operation. A hardware controller and its digital twin are tested in real-time in a separate platform in the cloud, and the results are analyzed to determine what insights can tests on virtual controllers give about the behaviors of their real-life hardware counter-parts. Key performance indicators (KPIs) from literature are used to evaluate the control. It is shown that although the controllers differ in their control output time-series, their overall performance profile along the entire simulation run follows a similar pattern (KPI percent error varies between 3% and 8%). This implies that virtual controller testing can be a good indicator of hardware controller behavior.
Dimensionality reduction and clustering of time series for anomaly detection in a supermarket heating system
Authors: Salmina Lorenzo , Castello Roberto , Stoll Justine , Scartezzini Jean-Louis 
Published: November 2021

Abstract
A timely identification of an anomalous functioning of the energy system of an industrial building would increase the efficiency and the resilience of the energy infrastructure, beside reducing the economic wastage. This work has been inspired by the need of identifying, for a series of supermarket buildings in Switzerland, the failures happening in their heating systems across the years in an unsupervised and easy-to-visualize fashion for the building managers. The lack of any a-priori label differentiating between typical and anomalous behaviors calls for the usage of unsupervised machine learning methods to extract the relevant features to describe the system operations, to reduce the dimension of the feature space, and to cluster together similar patterns of operations. The method is validated on a standard supermarket building, where it successfully discriminates winter and summer operations from periods of refurbishment or malfunctioning of the heating system.
A Machine Learning approach to enhance indoor thermal comfort in a changing climate
Authors: Kramer Tobias , Garcia-Hansen Veronica , Nik Sara Omrani Vahid M. , Chen Dong 
Published: November 2021

Abstract
This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science & AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.