bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synergy of semiempirical models and machine learning in computational chemistry
Authors: Fedik Nikita , Nebgen Benjamin , Lubbers Nicholas , Barros Kipton , Kulichenko Maksim , Li Ying Wai , Zubatyuk Roman , Messerly Richard , Isayev Olexandr , Tretiak Sergei 
Published: September 2023

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.
A numerical-tensorial “hybrid” nuclear motion Hamiltonian and dipole moment operator for spectra calculation of polyatomic nonrigid molecules
Authors: Rey Michaël , Viglaska Dominika , Egorov Oleg , Nikitin Andrei V. 
Published: September 2023

The analysis and modeling of high-resolution spectra of nonrigid molecules require a specific Hamiltonian and group-theoretical formulation that differs significantly from that of more familiar rigid systems. Within the framework of Hougen–Bunker–Johns (HBJ) theory, this paper is devoted to the construction of a nonrigid Hamiltonian based on a suitable combination of numerical calculations for the nonrigid part in conjunction with the irreducible tensor operator method for the rigid part. For the first time, a variational calculation from ab initio potential energy surfaces is performed using the HBJ kinetic energy operator built from vibrational, large-amplitude motion, and rotational tensor operators expressed in terms of curvilinear and normal coordinates. Group theory for nonrigid molecules plays a central role in the characterization of the overall tunneling splittings and is discussed in the present approach. The construction of the dipole moment operator is also examined. Validation tests consisting of a careful convergence study of the energy levels as well as a comparison of results obtained from independent computer codes are given for the nonrigid molecules CH2, CH3, NH3, and H2O2. This work paves the way for the modeling of high-resolution spectra of larger nonrigid systems.
Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio &gt;104
Authors: Yang Fengxia , Wei Wenbin , Dong Xiaofei , Zhao Yun , Chen Jiangtao , Chen Jianbiao , Zhang Xuqiang , Li Yan 
Published: September 2023

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation &lt;0.08/0.02 V), high On/Off ratio (&gt;104), and long retention time (&gt;104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470–655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.
Communicating medical information with Aboriginal patients: lessons learned from GPs and GP registrars in Aboriginal primary health care
Authors: Ghamrawi Wissam , Benson Jill , Kennedy Emma 
Published: September 2023

Background Aboriginal culture stands as the oldest continuous culture in the world. It gives paramount importance to a harmonious balance between personal connections to the body, spirit, and mind, as well as collective relationships with family, land, and community, integral to the wellbeing of Aboriginal people. However, obstacles can emerge for patients due to language barriers, cultural differences, or a historical lack of trust in the healthcare system. The establishment of Aboriginal Community Controlled Health Organisations (ACCHOs) has undoubtedly improved the healthcare experience for Aboriginal patients, yet there is limited research on the specific approaches utilised by general practitioners (GPs) working in these clinics. Methods Twelve semi-structured interviews were conducted with two groups of GPs working in Aboriginal health. Each GP was presented with three scenarios and asked questions related to each scenario. Braun and Clarke’s method of thematic analysis was applied to transcribed interviews. Results Patient-doctor relationship, health literacy, and engagement with the health system emerged as key factors influencing communication with Aboriginal patients. Experienced GPs, despite differing clinical backgrounds, shared concise yet similar ideas to their less experienced counterparts. Notably, experienced GPs prioritised non-medical conversations and mindful body language, emphasising the importance of building strong patient relationships over other consultation aspects. Conclusions This research provides initial insights for GPs in Aboriginal health, comparing experienced GPs with more than 10 years experience to novices. However, further research involving Aboriginal patients is needed to validate GP strategies and understand their significance from the patients’ perspective.
The physics of time
Authors: Wang Renqiang , Li Heng , Yang Bo 
Published: September 2023

Abstract
According to the Temporal Focus Hypothesis, people’s orientations of sagittal spatiotemporal mappings are conditioned by their characteristic patterns of attention to the past and/or future. While a growing body of research has investigated how a variety of psychological, social, and environmental factors associated with temporal focus shape implicit space-time mappings, little is known about whether the degree of entropy in the visual context influences spatial conceptions of time. Based on the findings that high-entropy images invoke a past-focused mindset and low-entropy images invoke a future-focused mindset, the current work explores how entropy impacts people’s temporal focus and mental representations of time. In Study 1 involving a self-report measure of temporal focus, we found that while high-entropy images increased Chinese students’ attention to the past and led to more past-in-front responses, low-entropy images increased Chinese students’ attention to the future and led to more future-in-front responses. Using both self-reported measures and other-report ratings of temporal focus, Study 2 conceptually replicated the findings of Study 1 in a more diverse population. Considered together, these results bolster support for the Temporal Focus Hypothesis that entropy triggers corresponding changes in temporal focus and in mental sagittal space-time mappings.
Search for postmerger gravitational waves from binary neutron star mergers using a matched-filtering statistic
Authors: Królak Andrzej , Jaranowski Piotr , Bejger Michał , Ciecieląg Paweł , Dorosh Orest , Pisarski Andrzej 
Published: September 2023

Abstract
In this paper, we present a new method to search for a short, a few tens of millis long, postmerger gravitational-wave (GW) signal following the merger of two neutron stars. Such a signal could follow the event GW170817 observed by Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo detectors. Our method is based on a matched filtering statistic and an approximate template of the postmerger signal in the form of a damped sinusoid. We test and validate our method using postmerger numerical simulations from the CoRe database. We find no evidence of the short postmerger signal in the LIGO data following the GW170817 event and we obtain upper limits. For short postmerger signals investigated, our best upper limit on the root sum square of the GW strain emitted from 1.15 kHz to 4 kHz is

h

rss

50
%

=
1.8
×

10

−
22

/

Hz

at 50% detection efficiency. The distance corresponding to this best upper limit is 4.64 Mpc.
Statics and dynamics of an underwater electrostatic curved electrode actuator with rough surfaces
Authors: Lake-Speers Melinda A , Preetham Burugupally Sindhu , Hoelzle David J 
Published: September 2023

Abstract
Here, we present a model, design, static and dynamic testing, and analysis of an electrostatic curved electrode actuator in deionized water. The actuator is integrated within a microfluidic device designed for high throughput cell sorting. The actuator shifts the bifurcation point of a Y-shaped microfluidic channel to simultaneously increase the width of one channel while decreasing the width of another channel, thus changing the bias in hydrodynamic resistance between outlet channels. The actuator is modeled as a clamped-roller beam and the static displacement is calculated based on Rayleigh–Ritz energy methods. The model accounts for oxide growth and surface roughness that occurs during fabrication. We observe that modeling a rough contact surface improves the maximum displacement prediction to within less than 20% error from the experimental value. Additionally, the model predicts a release voltage within less than 8% error of the experimental value. We also present dynamic experiments to test the actuator displacement at frequencies from 1 to 4096 Hz and show that the actuator achieves large displacements (

>

8 µm) at high frequencies (

>

100 Hz).
Dual-wavelength dielectric metasurface for full-space light manipulations
Authors: Li Hualin , Yue Wenjing , Gao Song 
Published: September 2023

Abstract
Dielectric metasurfaces have been credited for their salient capability in manipulating visible light with high efficiency, yet their operations are usually specified to the transmission space with limited functionality at a specified single wavelength. In this work, we show that dielectric metasurface can also be considered as an effective platform to independently control transmitted and reflected light wavefronts with high efficiency at two distinct wavelengths. In specific, silicon meta-atoms allowing high transmission and reflection of orthogonally linear-polarized light at wavelengths of 690 nm and 750 nm along with independent 2π phase modulations are successfully established. The first demonstrated dual-functional metasurface can transform incident plane wave into focused point beam spot and focused vortex beam in transmission and reflection spaces, respectively. The spatial multiplexing strategy is further considered to enrich the functionality diversity of a single meta-device, on which two off-axis light focusing phenomena and focused vortex beams carrying opposite topological charges in either transmission or reflection spaces with reduced crosstalk are successfully witnessed. Lastly, the metasurface enabling broadband anomalous deflection further demonstrates that the device’s efficiency can be further improved with densely packed meta-atoms. Our study provides a new thought in light control, which can be further considered in the development of multi-wavelength and full-space meta-devices.
Predicting mechanical properties of CO2 hydrates: machine learning insights from molecular dynamics simulations
Authors: Zhang Yu , Song Zixuan , Lin Yanwen , Shi Qiao , Hao Yongchao , Fu Yuequn , Wu Jianyang , Zhang Zhisen 
Published: September 2023

Abstract
Understanding the mechanical properties of CO2 hydrate is crucial for its diverse sustainable applications such as CO2 geostorage and natural gas hydrate mining. In this work, classic molecular dynamics (MD) simulations are employed to explore the mechanical characteristics of CO2 hydrate with varying occupancy rates and occupancy distributions of guest molecules. It is revealed that the mechanical properties, including maximum stress, critical strain, and Young’s modulus, are not only affected by the cage occupancy rate in both large 51262 and small 512 cages, but also by the distribution of guest molecules within the cages. Specifically, the presence of vacancies in the 51262 large cages significantly impacts the overall mechanical stability compared to 512 small cages. Furthermore, four distinct machine learning (ML) models trained using MD results are developed to predict the mechanical properties of CO2 hydrate with different cage occupancy rates and cage occupancy distributions. Through analyzing ML results, as-developed ML models highlight the importance of the distribution of guest molecules within the cages, as crucial contributor to the overall mechanical stability of CO2 hydrate. This study contributes new knowledge to the field by providing insights into the mechanical properties of CO2 hydrates and their dependence on cage occupancy rates and cage occupancy distributions. The findings have implications for the sustainable applications of CO2 hydrate, and as-developed ML models offer a practical framework for predicting the mechanical properties of CO2 hydrate in different scenarios.
Applicability of ‘Toolkit for Safety Assessment’ tool to interventional radiology using probabilistic risk assessment techniques
Authors: Raso A L , Lopes R , Leyton F , Neves E , Ribeiro E J , Vasconcelos V , Nogueira M S 
Published: September 2023

Abstract
Interventional radiology brings extensive benefits to patients. Nevertheless, certain procedures may result in high doses of radiation, leading to health risks to occupationally exposed individuals (OEIs). Therefore, a more comprehensive risk analysis is essential to ensuring safety and minimising radiation exposures for all OEIs. The Toolkit for Safety Assessment (TOKSA) tool performs risk assessments based on the concepts described in ‘General Safety Requirements’ Part 3 (Radiation Protection and Safety of Radiation Sources: International Basic Safety Standards) and Part 4 (Safety Assessment for Facilities and Activities). This tool was developed based on the ‘Ibero-American Forum of Radiological and Nuclear Regulatory Agencies’ risk models and can promote the use of the risk assessment processes by OEIs. The aim of this study was to experimentally analyse the applicability of the TOKSA tool in interventional radiology with the use/support of probabilistic risk assessment techniques. The results were used to reduce the risks associated with a hemodynamics room in a hospital in Belo Horizonte, Brazil.
An SVD-based turntable calibration method with multiple reference planes segmented from point clouds
Authors: Yang Bo , Wang Zhengtuo , Xu Yuetong , Hu Songyu , Fu Jianzhong 
Published: September 2023

Abstract
Turntables play an essential role in various industrial areas due to their ability to improve the degree of freedom of workpieces or tools. In applications where positional accuracy of the objects is required, the accuracy of the system parameters of the turntable is critical for the applications to work properly. This paper proposes a new approach to calibrating the turntable system. First, we propose an optimized RANSAC (random sample consensus) algorithm to obtain high-accuracy reference planes from multiple frames of point clouds grabbed from the reference object placed on a turntable. Second, an singular value decomposition-based two-step method is proposed to calculate the rotation axis vector and the center position of the turntable based on the reference planes extracted by the modified RANSAC algorithm. Finally, several experiments are performed to verify the proposed methods. Compared with the existing methods, our approach gets the analytical results and tries to correct the rotation angles. The experimental results show that the proposed method considerably improves calibration accuracy and efficiency without complex devices.
A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter
Authors: Wang Ke , Wu Panlong , Li Xingxiu , Xie Wenhan , He Shan 
Published: September 2023

Abstract
Considering that existing robust filtering algorithms rely on the selection of initial values of degree of freedom (DOF) parameters in outlier interference environments and cannot effectively cope with unknown non-stationary heavy-tailed measurement noises (HMN), a Gaussian–Pearson type VII (PTV) adaptive mixture distribution-based outlier-robust Kalman filter (GPTVMAKF) is proposed. In order to determine whether the current measurement is a normal value or an outlier, a judgment factor subject to the Beta-Bernoulli distribution is introduced. PTV distribution is used to model HMN caused by outliers, and two Gamma distributions are used to model the two different DOF parameters, which can make the PTV distribution have the adaptive adjustment ability. By introducing the inverse Wishart distribution as the prior distribution of the measurement noise covariance, which is adaptively estimated to cope with the unknown time-varying measurement noises. The state and parameters are jointly estimated by variational Bayesian. Finally, the simulation experiments verify that the proposed GPTVMAKF can obtain more accurate state estimation than existing filters in the environments with varying degrees of HMN and unknown non-stationary HMN.
Fault diagnosis of coal mills based on a dynamic model and deep belief network
Authors: Weiming Yin , Yefa Hu , Guoping Ding , Kai Yang , Xuefei Chen , Xifei Cao 
Published: September 2023

Abstract
As the significant ancillary equipment of coal-fired power plants, coal mills are the key to ensuring the steady operation of boilers. In this study, a fault diagnosis model was proposed on the basis of a dynamic model of a coal mill and deep belief network (DBN). First, a dynamic coal mill model that considered the joint influence of drying, ventilation and grinding forces was established. Parameters in the model were identified by designing a two-phase optimization method based on the genetic algorithm. Then, this model was used for simulating the common faults of coal mills under a variety of operating conditions and obtaining extensive data. On this basis, the DBN fault diagnosis model was established and the combination of parameters was optimized by use of an orthogonal experiment. Finally, the validity of the model was verified by using the actual operation data of the coal mill. Compared with the dynamic models built in previous studies, that constructed in this paper can significantly improve the capability to simulate and analyze the coal mill. The convergence rate of the designed two-phase optimization method was improved. The experimental results show that the proposed method of coal mill fault diagnosis based on the dynamic model and DBN has an accuracy of 95%, which proves that this method has excellent application potential.
Spatially resolved chemiluminescence imaging of multiple combustion species using a single high-speed camera
Authors: James Ashley M , Agrawal Ajay K 
Published: September 2023

Abstract
Simultaneous chemiluminescence (CL) imaging of multiple chemical species in unsteady and/or turbulent combustion environments requires multiple high-speed camera/intensifier systems which can be cost prohibitive. This study presents a novel optical configuration for simultaneous, spatially resolved CL imaging of two species using a single high-speed camera. The superiority of the technique over existing approaches is demonstrated via a series of experiments in a methane fueled McKenna burner operated at steady and unsteady conditions. Results demonstrate capabilities of the present system to provide spatially resolved measurements of hydroxyl (OH*) and methylidyne (CH*) species without parallax or path length errors in different flame configurations.
In situ construction of N-doped Ti3C2T
x
confined worm-like Fe2O3 nanoparticles by Fe–O–Ti bonding for LIBs anode with superior cycle performance
Authors: Jiang Wei , Zhang Zhen , Yang Kai , Zhou Jun , Hu Changjian , Pan Limei , Li Qian , Yang Jian 
Published: September 2023

Abstract
The development of Fe2O3 as lithium-ion batteries (LIBs) anode is greatly restricted by its poor electronic conductivity and structural stability. To solve these issues, this work presents in situ construction of three-dimensional crumpled Fe2O3@N-Ti3C2T
x
composite by solvothermal-freeze-drying process, in which wormlike Fe2O3 nanoparticles (10–50 nm) in situ nucleated and grew on the surface of N-doped Ti3C2T
x
nanosheets with Fe–O–Ti bonding. As a conductive matrix, N-doping endows Ti3C2T
x
with more active sites and higher electron transfer efficiency. Meanwhile, Fe–O–Ti bonding enhances the stability of the Fe2O3/N-Ti3C2T
x
interface and also acts as a pathway for electron transmission. With a large specific surface area (114.72 m2 g−1), the three-dimensional crumpled structure of Fe2O3@N-Ti3C2T
x
facilitates the charge diffusion kinetics and enables easier exposure of the active sites. Consequently, Fe2O3@N-Ti3C2T
x
composite exhibits outstanding electrochemical performance as anode for LIBs, a reversible capacity of 870.2 mAh g−1 after 500 cycles at 0.5 A g−1, 1129 mAh g−1 after 280 cycles at 0.2 A g−1 and 777.6 mAh g−1 after 330 cycles at 1 A g−1.
A pedagogical approach to wavelength division multiplexing measurements using 3D printing and Arduino
Authors: Rana Ankush , Bhatnagar Shivansh , Garg Sagar , Garg Amit 
Published: September 2023

Abstract
We have successfully performed wavelength division multiplexing (WDM) measurements using traditional as well as innovative approaches. The results obtained are accurate and precise. Both the approaches add to the conceptual understanding of WDM for the students. The innovative approach allows students to develop the WDM experimental setup at a very low cost by using the Arduino Board. It reduces the hardware dependence of the costly instruments like function generators as well as digital storage oscilloscope besides facilitating the carrying out of experiment using RGB LED and single optical fibre. It can be used as a good classroom demonstration to explain this difficult concept. The 3D design and printing of connectors for the RGB LED, and the optical rod has made the system very robust as one can easily couple, multiplex and demultiplex the optical signals. It is to the best of our knowledge that these studies have not been reported earlier.