bluuebunny/crossref_metadata_2025 huggingface dataset:
Alliance for Scientific Autism Intervention: System Components and Outcome Data from High-Quality Service Delivery Organizations
Authors: Townsend Dawn Buffington , Brothers Kevin J. , MacDuff Gregory S. , Freeman Amanda , Fry Christine , Rozenblat Eric , DeFeo Donna , Budzinska Anna , Ruta-Sominka Iwona , Birkan Binyamin , Hall Laura J. , Krantz Patricia J. , McClannahan Lynn E.
Published: December 2023

Promoting excellence in autism intervention is arguably more urgent than ever for the field of applied behavior analysis. To fulfill this objective, autism agencies must operate from validated program systems and do so with fidelity. Program components include, but are not limited to, staff training and evaluation of clinical skills, functional personnel roles designed to promote positive outcomes for those served, and professional staff-communication-skill repertoires. Data on client outcomes must be tied to implementation of core program variables; and, contingencies between the data on client outcomes and staff performance must exist. Furthermore, these contingencies must be yoked across members of the organization to ensure a sustainable and effective program model. Finally, data on consumer satisfaction must be collected and used to evaluate program components and agency practices. Members of the Alliance for Scientific Autism Intervention have implemented key program-wide systems based upon the work of McClannahan and Krantz Journal of Applied Behavior Analysis, 26, 589–596 (1993) for decades and across various agency cultures. Data collected by six independent educational agencies on client outcomes, program implementation, and consumer feedback for a 10-year time span demonstrate the sustainability of the model and support the importance of key organizational systems and the relationship between implementation of the model and high-quality outcomes for individuals with autism.
Examining the agriculture-induced environment curve hypothesis and pollution haven hypothesis in Rwanda: the role of renewable energy
Authors: Moise Minani Leon
Published: December 2023

Agriculture has a main impact on increasing the economy of most developing countries, and energy policies in fighting climate change have a vital role in strengthening economic growth. This paper studies the impact of agriculture, trade openness and green energy on CO2 emissions in Rwanda during 1990–2022. Phillips- Peron and Augmented Dickey fuller unit root test affirmed the stationary of data, and autoregressive distributed lag (ARDL)-bound testing confirmed a long-run relationship among variables. The study employed an ARDL approach.

Findings from long-run elasticities showed that 1% increase in agriculture productivity increased carbon dioxide emissions by 1.94%; 1% increase in trade openness increased carbon dioxide emissions by 0.16%; whereas 1% increase in renewable energy consumption and GDP per capita decreased carbon dioxide emissions by 2.71% and 2%, respectively. The result confirmed the induced environment curve and the pollution haven hypothesis in Rwanda. Moreover, green energy consumption and GDP per capita decreases CO2 emissions eventually, which supports the validity of environment Kuznets curve in Rwanda.

The findings recommend that an increase in renewable energy usage and trade reforms accompanied by strong environmental policies to reduce CO2 emissions will develop the agriculture sector and regional economy.

Graphical Abstract
Re-examination of the effect of Reynolds number on local scour around a pipeline under steady current
Authors: Liang Bingchen , Liu Mingming , Zhang Qin
Published: December 2023

Herein, a two-dimensional numerical model was established to examine the effect of Reynolds number on local scour around a pipeline under steady current by solving the Naiver-Stokes equation with shear stress turbulence (SST) k-ω closure. Suspended load and bedload transport rates were considered in the model. The numerical results revealed that the Reynolds number can considerably affect the local scour around the pipeline. However, earlier works often ignored the effect of Reynolds number on local scour. An empirical equation for the variations in the maximum scour depth with Reynolds number was presented based on present study.
Knowledge, Perceptions, and Utilization of Generics and Biosimilars in Latin America and the Caribbean: A Scoping Review
Authors: Aguilera Bernardo , Peña Sebastián , Morales Juan Pablo
Published: December 2023

We conducted a scoping review to map and critically examine the knowledge, perceptions and utilization of generics and biosimilars, among physicians, pharmacists, patients, the general population, and other stakeholders from LAC.
INTRODUCTION: Rethinking Pharmaceutical Policies in Latin America and the Caribbean: An Overview
Authors: Rama Martín , Vargas Verónica
Published: December 2023

The demographic and epidemiological transitions are driving pharmaceutical expenditures up in Latin American and the Caribbean, with much of the cost falling on households. The domestic development and manufacturing of bio-similars could make medicines more affordable.
Multivariable reference centiles for maximum grip strength in childhood to young adults
Authors: Duran Ibrahim , Wloka Kim Ramona , Martakis Kyriakos , Spiess Karoline , Alexy Ute , Schoenau Eckhard
Published: December 2023

Objectives

Maximum grip strength (mGS) is a useful predictor of health-related outcomes in children and adults. The aim of the study was to generate sex- and age-adjusted reference centiles for mGS for children, adolescents and young adults, while adjusting for body height and body mass index (BMI).

Methods

A retrospective analysis of longitudinal data from children and young adults participating in the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study (single center, open cohort study) from 2004 to 2022 was conducted. To generate sex-, age-, height- and BMI-adjusted reference centiles, a new algorithm combining multiple linear regression and the LMS method was conducted.

Results

Overall, 3325 measurements of mGS of 465 females and 511 males were eligible. The mean age at measurement of females was 12.6 ± 3.9 years, mean age of males was 12.4 ± 4.7 years. The median of number of repeated measurements per individual was 3 (range 1–8). The mGS was significantly (p < 0.001) correlated to body height and BMI (r = 0.303–0.432). Additional reference centiles for the change of z-scores of mGS were generated for children and young adults from 8 to 20 years.

Conclusions

We proposed to evaluate mGS in children, adolescents and young adults with the presented reference centiles adjusted to sex, age, height and BMI. The method presented may also be applicable to other biological variables that depend more than just on sex and age. For the first time, also reference centiles to assess the change of mGS in repeated measurements were presented.
Click chemistry in the development of PROTACs
Authors: Yang Ce , Tripathi Ravi , Wang Binghe
Published: December 2023

This review discusses leveraging click chemistry to address unmet needs in PROTAC development.
Tailoring the subshell and electronic structure of an atomically precise AuAg alloy nanocluster
Authors: Deng Guocheng , Ki Taeyoung , Liu Xiaolin , Chen Yuping , Lee Kangjae , Yoo Seungwoo , Tang Qing , Bootharaju Megalamane S. , Hyeon Taeghwan
Published: December 2023

We synthesized and characterized an alkynyl-protected Au34Ag27 nanocluster, revealing a metallic core subshell structural difference with one missing Ag atom compared to Au34Ag28. This leads to modulated electronic structure and optical properties.
Sensing platform for the highly sensitive detection of catechol based on composite coupling with conductive Ni3(HITP)2 and nanosilvers
Authors: Xu Yuandong , Ben Yingying , Sun Lili , Su Jishan , Guo Hui , Zhou Rongjia , Wei Yaqing , Wei Yajun , Lu Yongjuan , Sun Yizhan , Zhang Xia
Published: December 2023

The electrooxidation of catechol is a phenol-to-keton process controlled by diffusion requiring more ΔG.
Improved eco-friendly CsSn0.5Ge0.5I3 perovskite photovoltaic efficiency beyond 20% with SMe-TATPyr hole-transporting layer
Authors: Mohammed Mustafa K. A. , Al-Gazally Moaed E. , Khaleel Omar A. , Al-Mousoi Ali K. , Jeddoa Zuhair Mohammed Ali , Majdi Hasan Sh. , Jabir Majid S. , Hossain M. Khalid , Hatshan Mohammad Rafe , Rahman Md. Ferdous , Dastan Davoud
Published: December 2023

Perovskites composed of inorganic cesium (Cs) halide provide a route to thermally resistant solar cells.
High-valent nonheme Fe(iv)O/Ru(iv)O complexes catalyze C–H activation reactivity and hydrogen tunneling: a comparative DFT investigation
Authors: Katoch Akanksha , Mandal Debasish
Published: December 2023

A comprehensive density functional theory investigation has been presented towards the comparison of the C–H activation reactivity between high-valent iron-oxo and ruthenium-oxo complexes.
Anti-thermal quenching of luminescence in Y2W3O12:Yb3+/RE3+ (RE = Er/Ho/Tm) and its temperature sensing application
Authors: Zhang Yuhong , Cai Wentong , Liu Jian , Zhang Ziyi , Sun Bo , Liu Hang
Published: December 2023

Herein, a series of Y2W3O12:10%Yb3+/x%RE3+ (RE = Er/Ho/Tm) phosphors is prepared via a solid-state reaction.
Boron diamide derivatives containing N–N and N–P molecular fragments
Authors: Major Christopher J. , Chen Shi-Ming , Stephan Douglas W.
Published: December 2023

Cationic and neutral boron–diamide species with N2 and N–P molecular fragments are prepared via use of hydrazine or derivatization of B(NHR) species.
Structure sensitivity of the electrochemical hydrogenation of cis,cis-muconic acid to hexenedioic acid and adipic acid
Authors: Patel Deep M. , Prabhu Prathamesh T. , Gupta Geet , Dell'Anna Marco Nazareno , Kling Samantha , Nguyen Huy T. , Tessonnier Jean-Philippe , Roling Luke T.
Published: December 2023

The electrochemical hydrogenation of cis,cis-muconic acid yields adipic acid on palladium terrace sites, while unsaturated hexenedioic acids are formed on undercoordinated step sites.
Wavefront-enhanced laser-induced breakdown spectroscopy (WELIBS) utilizing a crystalline silicon wafer for a flat-top IR laser beam
Authors: Abdel-Harith Mohamed , El-Saeid Raghda Hosny , Abdelazeem Rania M. , Abdel-Salam Zienab , Elhassan Asmaa
Published: December 2023

On passing through a thin crystalline silicon wafer, the profile of an infrared laser beam is changed from a quasi-Gaussian to a flat-top one, improving the LIBS technique's analytical performance. That is the proposed (WELIBS) Wavefront-enhanced laser-induced breakdown spectroscopy approach.
A vibrating capillary for ultrasound rotation manipulation of zebrafish larvae
Authors: Zhang Zhiyuan , Cao Yilin , Caviglia Sara , Agrawal Prajwal , Neuhauss Stephan C. F. , Ahmed Daniel
Published: December 2023

We proposed a capillary acoustofluidic rotation system that offers rapid trapping, high-speed rotation, multi-angle imaging, 3D model reconstruction, and more of zebrafish larvae.
Light-driven nanomotors with reciprocating motion and high controllability based on interference techniques
Authors: Mohammadnezhad Mohammadbagher , Saeed Salah Raza , Abdulkareem Sarkew Salah , Hassanzadeh Abdollah
Published: December 2023

Interference of three pairs of mutually perpendicular Gaussian beams (top left image); stretching or compressing DNA with dynamic optical lattice (images on the right); slice plots of normalized potential energy at three orthogonal planes and the corresponding optical forces (bottom left image).
Conjugated D–π–A photovoltaic polymers containing thieno[3,2-b]thiophene π-bridge
Authors: Zhou Bingjie , Dai Tingting , Zhou Jialing , Chen You , Geng Yanfang , Lei Peng , Zheng Guoqiang , Zeng Qingdao , Zhou Erjun
Published: December 2023

We review the development of TT-bridged D–π–A copolymers and emphasize the role played by TT-bridge on tuning polymer properties and improving device performance.
Atlantoaxial Instability with Persistent Second Intersegmental Artery
Authors: Diyora Batuk , Wankhade Ravi , Devani Kavin , Purandare Anup , Palave Prakash , Gawali Sagar
Published: December 2023

Understanding the anatomy of the vertebral artery is essential while manipulating the craniovertebral joint during surgery. Its anomalous course in congenital atlantoaxial dislocation makes it more vulnerable to injury. Preoperative dedicated computed tomography (CT) angiography helps identify the artery's position and plan for surgical procedure. A 13-year-boy presented with neck pain and spastic quadriparesis for 1 year. Radiological imaging of the craniovertebral junction revealed atlantoaxial instability with basilar invagination. His CT angiography of neck and brain vessels revealed an anomalous course of the vertebral artery due to a persistent second intersegment artery. He underwent posterior atlantoaxial fixation after mobilization of the vertebral artery. His clinical condition significantly improved after surgery. We report a case of management of an atlanto axial dislocation with persistent second intersegment artery and describe the role of vertebral artery mobilization during surgery.
Probabilistic Markov chain modeling of photonic crystal surface emitting lasers
Authors: Liu Jingzhao , Gao Yunyun , Ivanov Pavlo , Harvey Paul , Hogg Richard
Published: December 2023

Probabilistic Markov chain modeling of photonic crystal surface emitting lasers (PCSELs) is reported. This simulation links the scattering parameters of the photonic crystal (PC) and device level losses of the PCSEL. The criteria for the conversion of the numerical model and agreement with data from the literature are discussed. We then explore the effect of increasing PC coupling coefficients and boundary mirror reflectivity on the in-plane power loss from the PCSEL. The effect of spatially varying the boundary reflectivity on the near-field is also explored.
Localization operator and Weyl transform on reduced Heisenberg group with multidimensional center
Authors: Dasgupta Aparajita , Nayak Santosh Kumar
Published: December 2023

In this article, we study two different types of operators, the localization operator and Weyl transform, on the reduced Heisenberg group with multidimensional centre G. The group G is a quotient group of non-isotropic Heisenberg group with multidimensional centre Hm by its centre subgroup. Firstly, we define the localization operator using a wavelet transform on G and obtain the product formula for the localization operators. Next, we define the Weyl transform associated to the Wigner transform on G with the operator-valued symbol. Finally, we have shown that the Weyl transform is not only a bounded operator but also a compact operator when the operator-valued symbol is in Lp, 1 ≤ p ≤ 2, and it is an unbounded operator when p > 2.
Atomic layer deposition of magnetic thin films: Basic processes, engineering efforts, and road forward
Authors: Jussila Topias , Philip Anish , Tripathi Tripurari , Nielsch Kornelius , Karppinen Maarit
Published: December 2023

Atomic layer deposition (ALD) is known as a key enabler of the continuous advances in device engineering for microelectronics. For instance, the state-of-the-art transistor technology depends entirely on ALD-grown high-κ materials. Another application branch where ALD could potentially play a similar important role in future is the magnetic thin film devices. Spin-based devices based on high-quality magnetic thin films are anticipated to provide high-efficiency operations with low power consumption. The strict quality demands the magnetic thin films must fulfill in the next-generation applications form the strong bases for the efforts to implement ALD in this application area. In this first comprehensive review on the topic, our aim is to provide an insightful account of the ALD processes so far developed for magnetic materials and to highlight the application-relevant magnetic properties of the thus fabricated thin films. Moreover, we discuss the various innovative engineering efforts made toward different multi-layered and nanostructured composite materials and complex architectures uniquely enabled by the sophisticated self-terminated film-growth mechanism of ALD. The review is finished with a brief outlook toward the future prospects and challenges in the field.
Tunneling induced transparency and slow light in an asymmetric double quantum dot molecule—Metal nanoparticle hybrid
Authors: Kosionis Spyridon G. , Paspalakis Emmanuel
Published: December 2023

We investigate the optical properties appearing in a nanostructure that is composed of an asymmetric double semiconductor quantum dot (SQD) molecule and a metal nanoparticle (MNP). The profile of the total linear absorption spectrum is proportional to the SQD contribution, while the MNP contribution is important. The profile of the doublet of resonances detected on the total linear absorption spectrum creates a transparency window. The doublet is asymmetric for small SQD-MNP distances and has a narrow peak and a wide peak. The width of the transparency window is increased, either with the enhancement of the rate at which the electron tunneling effect takes place within the double SQD molecule or with the decrease of the distance that separates the SQD molecule from the center of the MNP. The steep slope detected on the linear dispersion spectrum for frequencies laying within the transparency window owes its presence to the tunneling induced transparency and leads to slow light production. The corresponding value of the slow down factor is maximized for low values of the electron tunneling rate as well as for low center-to-center distances between the components of the hybrid nanostructure.
Mechanism study on particle deposition and clogging characteristics in film cooling hole
Authors: Li Bingran , Liu Cunliang , Li Lin , Li Jiebo , Xu Weijiang
Published: December 2023

The present study is focused on the problems of gas–solid two-phase flow transport in the film cooling hole that cause film flow obstruction and cooling failure. To study the unsteady development process of the deposition layer in the film hole, a simulation method combining computational fluid dynamics and the discrete element method was used, and a film hole flow model was established. The effect of gas phase and solid phase characteristics on clogging and deposition in the film hole was studied. The following conclusions are drawn: The inlet/outlet pressure ratio is inversely proportional to the clogging degree of the film hole. The inlet/outlet pressure determines the deposition behavior by affecting the initial momentum and drag force of particles. In the Stokes number range of 1.58–14.26, the deposition in the film hole first increases and then decreases. There is a Stokes number with the most severe clogging. The Stokes number determines the deposition pattern by affecting the relative magnitudes of the drag force and interaction forces of particles. The particle surface energy is positively correlated with film hole clogging. The particle surface energy determines the stability of the deposition layer by influencing the strength of the force chain network.
Three-dimensional numerical study of two drops impacting on a heated solid surface by smoothed particle hydrodynamics
Authors: Sohag Md. M. A. , Zhang Wei , Yang Xiufeng
Published: December 2023

This paper presents a numerical study of a pair of water drops simultaneously and non-simultaneously impacting on a heated surface using smoothed particle hydrodynamics (SPH). The present SPH method is validated qualitatively and quantitively with available experiment results for the impact of single, simultaneous, and non-simultaneous drops on the solid surface. Numerical simulations are performed at the Weber number in the range of 20–117, surface temperature in the range of 25–250 °C, and pressure in the range of 1–20 bar. In the simulations, the coalescence, breakup, and evaporation of the drops are considered. After the collision of the two drops, the hydrodynamic behavior of the uprising sheet height and spreading areas are investigated by considering the horizontal and vertical distances between the two drops, Weber number, surface temperature, and elevated pressure. The numerical results indicate that the Weber number and horizontal distance significantly influence the height of the rising sheet and the spreading area. Conversely, the vertical spacing does not affect the rising sheet height or spreading area. The drop rebound height increases with the wall temperature in the film boiling regime for high boiling point liquids at atmospheric pressure. The effect of ambient pressure on drop rebound height is investigated for simultaneous and non-simultaneous impacts. According to the numerical results, the pressure increase causes a decrease in droplet rebound height.
Research on breakup length and atomization characteristics of the swirl liquid sheet in perforation disintegration mode
Authors: Liu Yang , Liu Guo , Xu Hui , Chen Long , Qin Suwen , Chao Yaojie , Zhang Huiqiang
Published: December 2023

Pressure swirl nozzles usually operate in aerospace or aviation engines by discharging a swirl liquid sheet. Understanding the disintegration characteristics of the swirl liquid sheet is beneficial to control the combustion instability. In this study, a swirl liquid sheet was injected into the atmosphere. The whole breakup process was numerically simulated by Gerris, an open-source code that anticipates gas–liquid interface using the volume of fluid approach. With the increase in Reynolds number, there were three distinct disintegration modes including rim mode, perforation mode, and wave mode. Then, a perforation disintegration model (PDM) was proposed to predict the droplet size of the perforation disintegration mode. The droplet sizes predicted by PDM are consistent with the numerical results with an average error of 11.09%. A breakup length model (BLM) was also proposed for the swirl liquid sheet using energy conservation. The breakup length results of BLM are in good agreement with the numerical simulation results with an average error of 10.97%. Moreover, with the increase in the liquid surface tension coefficient, the droplet size of the swirl liquid sheet atomization gradually increases. With the increase in liquid density, the droplet size gradually decreases, but the trend of decrease is not obvious.
Hydrogen-iodine scattering. I. Development of an accurate spin–orbit coupled diabatic potential energy model
Authors: Weike Nicole , Viel Alexandra , Eisfeld Wolfgang
Published: December 2023

The scattering of H by I is a prototypical model system for light-heavy scattering in which relativistic coupling effects must be taken into account. Scattering calculations depend strongly on the accuracy of the potential energy surface (PES) model. The methodology to obtain such an accurate PES model suitable for scattering calculations is presented, which includes spin–orbit (SO) coupling within the Effective Relativistic Coupling by Asymptotic Representation (ERCAR) approach. In this approach, the SO coupling is determined only for the atomic states of the heavy atom, and the geometry dependence of the SO effect is accounted for by a diabatization with respect to asymptotic states. The accuracy of the full model, composed of a Coulomb part and the SO model, is achieved in the following ways. For the SO model, the extended ERCAR approach is applied, which accounts for both intra-state and inter-state SO coupling, and an extended number of diabatic states are included. The corresponding coupling constants for the SO operator are obtained from experiments, which are more accurate than computed values. In the Coulomb Hamiltonian model, special attention is paid to the long range behavior and accurate c6 dispersion coefficients. The flexibility and accuracy of this Coulomb model are achieved by combining partial models for three different regions. These are merged via artificial neural networks, which also refine the model further. In this way, an extremely accurate PES model for hydrogen iodide is obtained, suitable for accurate scattering calculations.
Magneto-plasmonic scattering by a disk-shaped particle made of an artificial dielectric
Authors: R Tuz Vladimir , Evlyukhin Andrey B
Published: December 2023

The main features of artificial dielectrics are high anisotropy and controllable heterogeneity, as well as adjustable values of their synthesized material parameters. In this work, we numerically study the scattering features of a disk-shaped particle made of an artificial dielectric (finely stratified structure, FSS) that is composed of magnetic and semiconductor constituents influenced by an external static magnetic field. The tensor-valued permittivity and permeability of the FSS are derived involving the effective medium theory. Due to a specific composition of the FSS, the material properties of the disk simultaneously acquire electric and magnetic gyrotropy, which depends on the proportion of the semiconductor and magnetic components included in the FSS. It is supposed that the ferromagnetic and plasma resonances of the constitutive materials are closely spaced. In particular, we examine the electric and magnetic dipole contributions to the scattering and absorption cross-sections obtained in the framework of the multipole decomposition method while accounting for the polarizability and magnetization induced in the particle by the field of incoming radiation. By varying the proportion of components of the artificial dielectric, we demonstrate the magneto-plasmonic functionality of the particle. Our presentation generalizes and complements several known solutions obtained separately for either magnetic or dielectric anisotropic particles. This approach can be used to study magneto-optical effects in metamaterials and metasurfaces composed of an ensemble of gyroelectric and gyromagnetic particles that is important for both plasmonic and photonic applications.
Exploiting phase transitions in catalysis: reaction of
CO2
and
H2
on doped
VO2
-polymorphs
Authors: Stahl Berenike , Bredow Thomas
Published: December 2023

V

O
2

is well known for its reversible transition between two phases with tetragonal rutile and monoclinic structure. In a previous theoretical study (Stahl and Bredow 2022 ChemPhysChem
23 e202200131) we showed that the adsorption energy of CO is different on surfaces of the two Mo-stabilized polymorphs. This can be exploited to promote catalytic reactions by removing CO from the catalyst surface. As proof-of-principle, we investigated the hydrogenation reaction of

CO

2

. For this purpose, the adsorption energies of

CO

2

and possible intermediates and products

H
2

O

, HCOOH,

H
2

C
O

and CO were calculated. Significant differences were found for the reaction energies of the hydrogenation of

CO

2

to formic acid and formaldehyde on the two polymorphs. This shows that it is in principle possible to alter the reaction thermodynamics by applying reaction conditions which stabilize a particular polymorph. In order to investigate the influence of the polymorph on kinetic properties, the reactions barriers of a step-wise reaction of

C

O
2

+
2

H
2

→

H
2

C
O
+

H
2

O

was calculated using the nudged elastic band method.

V

O
2

was found to reduce the reaction barriers compared to the gas phase. Additionally, the minimum energy path of the bulk phase transition of undoped

V

O
2

was calculated using the distinguished reaction coordinate method. A catalytic cycle exploiting the phase transition is proposed based on the theoretical results.
Thermodynamic properties and switching dynamics of perpendicular shape anisotropy MRAM
Authors: Lack Wayne , Jenkins Sarah , Meo Andrea , Chantrell Roy W , McKenna Keith M , Evans Richard F L
Published: December 2023

The power consumption of modern random access memory (RAM) has been a motivation for the development of low-power non-volatile magnetic RAM (MRAM). Based on a CoFeB/MgO magnetic tunnel junction, MRAM must satisfy high thermal stability and a low writing current while being scaled down to a sub-20 nm size to compete with the densities of current RAM technology. A recent development has been to exploit perpendicular shape anisotropy along the easy axis by creating tower structures, with the free layers’ thickness (along the easy axis) being larger than its width. Here we use an atomistic model to explore the temperature dependent properties of thin cylindrical MRAM towers of 5 nm diameter while scaling down the free layer from 48 to 8 nm thick. We find thermal fluctuations are a significant driving force for the switching mechanism at operational temperatures by analysing the switching field distribution from hysteresis data. We find that a reduction of the free layer thickness below 18 nm rapidly loses shape anisotropy, and consequently stability, even at 0 K. Additionally, there is a change in the switching mechanism as the free layer is reduced to 8 nm. Coherent rotation is observed for the 8 nm free layer, while all taller towers demonstrate incoherent rotation via a propagated domain wall.
Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method
Authors: Li Xueyi , Su Kaiyu , Li Daiyou , He Qiushi , Xie Zhijie , Kong Xiangwei
Published: December 2023

Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. This study aims to enhance the practicality of bearing fault diagnosis to meet real-world engineering requirements. In real industrial environments, the continuously changing operating conditions such as equipment speed and load pose challenges in collecting data for bearing fault diagnosis, as it is challenging to gather data for all operational conditions. This paper proposes a transfer learning approach for bearing fault diagnosis based on adaptive batch normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the Case Western Reserve University dataset and Northeast Forestry University (NEFU) dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.
Synergistic antibacterial attributes of copper-doped polydopamine nanoparticles: an insight into photothermal enhanced antibacterial efficacy
Authors: Hu Zhiqiong , Wu Kexian , Lin Jiahong , Tan Xiaoqian , Jiang Xinyuan , Xiao Yuhang , Xiang Lanxin , Yang Shuang , Zhang Maolan , Xu Wenfeng , Chen Peixing
Published: December 2023

Antibiotic-resistant bacteria and associated infectious diseases pose a grave threat to human health. The antibacterial activity of metal nanoparticles has been extensively utilized in several biomedical applications, showing that they can effectively inhibit the growth of various bacteria. In this research, copper-doped polydopamine nanoparticles (Cu@PDA NPs) were synthesized through an economical process employing deionized water and ethanol as a solvent. By harnessing the high photothermal conversion efficiency of polydopamine nanoparticles (PDA NPs) and the inherent antibacterial attributes of copper ions, we engineered nanoparticles with enhanced antibacterial characteristics. Cu@PDA NPs exhibited a rougher surface and a higher zeta potential in comparison to PDA NPs, and both demonstrated remarkable photothermal conversion efficiency. Comprehensive antibacterial evaluations substantiated the superior efficacy of Cu@PDA NPs attributable to their copper content. These readily prepared nano-antibacterial materials exhibit substantial potential in infection prevention and treatment, owing to their synergistic combination of photothermal and spectral antibacterial features.
Global existence and singularity formation for the generalized Constantin–Lax–Majda equation with dissipation: the real line vs. periodic domains
Authors: Ambrose David M , Lushnikov Pavel M , Siegel Michael , Silantyev Denis A
Published: December 2023

The question of global existence versus finite-time singularity formation is considered for the generalized Constantin–Lax–Majda equation with dissipation

−

Λ
σ

, where

Λ

σ

ˆ

=

|

k

|

σ

, both for the problem on the circle

x
∈
[
−
π
,
π
]

and the real line. In the periodic geometry, two complementary approaches are used to prove global-in-time existence of solutions for

σ
⩾
1

and all real values of an advection parameter a when the data is small. We also derive new analytical solutions in both geometries when a = 0, and on the real line when

a
=
1

/

2

, for various values of σ. These solutions exhibit self-similar finite-time singularity formation, and the similarity exponents and conditions for singularity formation are fully characterized. We revisit an analytical solution on the real line due to Schochet for a = 0 and σ = 2, and reinterpret it terms of self-similar finite-time collapse. The analytical solutions on the real line allow finite-time singularity formation for arbitrarily small data, even for values of σ that are greater than or equal to one, thereby illustrating a critical difference between the problems on the real line and the circle. The analysis is complemented by accurate numerical simulations, which are able to track the formation and motion of singularities in the complex plane. The computations validate and build upon the analytical theory.
Intensive vision-guided network for radiology report generation
Authors: Zheng Fudan , Li Mengfei , Wang Ying , Yu Weijiang , Wang Ruixuan , Chen Zhiguang , Xiao Nong , Lu Yutong
Published: December 2023

Objective. Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis. Second, when generating reports, they overlook context reasoning with multi-modal information and focus on pure textual optimization utilizing retrieval-based methods. We aim to address these two issues by proposing a model that better simulates clinicians perspectives and generates more accurate reports. Approach. Given the above limitation in feature extraction, we propose a globally-intensive attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. GIA aims to learn three types of vision perception: depth view, space view, and pixel view. On the other hand, to address the above problem in report generation, we explore how to involve multi-modal signals to generate precisely matched reports, i.e. how to integrate previously predicted words with region-aware visual content in next word prediction. Specifically, we design a visual knowledge-guided decoder (VKGD), which can adaptively consider how much the model needs to rely on visual information and previously predicted text to assist next word prediction. Hence, our final intensive vision-guided network framework includes a GIA-guided visual encoder and the VKGD. Main results. Experiments on two commonly-used datasets IU X-RAY and MIMIC-CXR demonstrate the superior ability of our method compared with other state-of-the-art approaches. Significance. Our model explores the potential of simulating clinicians perspectives and automatically generates more accurate reports, which promotes the exploration of medical automation and intelligence.
Measurements of the noise power spectrum for digital x-ray imaging devices
Authors: Kim Dong Sik
Published: December 2023

Objective. The noise characteristics of digital x-ray imaging devices are determined by contributions such as photon noise, electronic noise, and fixed pattern noise, and can be evaluated from measuring the noise power spectrum (NPS), which is the power spectral density of the noise. Hence, accurately measuring NPS is important in developing detectors for acquiring low-noise digital x-ray images. To make accurate measurements, it is necessary to understand NPS, identify problems that may arise, and know how to process the obtained x-ray images. Approach. The primitive concept of NPS is first introduced with a periodogram-based estimate and its bias and variance are discussed. In measuring NPS based on the IEC62220 standards, various issues, such as the fixed pattern noise, high-precision estimates, and lag corrections, are summarized with simulation examples. Main results. High-precision estimates can be provided for an appropriate number of samples extracted from x-ray images while compromising spectral resolution. Depending on medical imaging systems, by eliminating the influence of fixed pattern noise, NPS, which represents only photon and electronic noise, can be efficiently measured. For NPS measurements in dynamic detectors, an appropriate lag correction technique can be selected depending on the emitted x-rays and image acquisition process. Significance. Various issues in measuring NPS are reviewed and summarized for accurately evaluating the noise performance of digital x-ray imaging devices.
A step-by-step simulation code for estimating yields of water radiolysis species based on electron track-structure mode in the PHITS code
Authors: Matsuya Yusuke , Yoshii Yuji , Kusumoto Tamon , Akamatsu Ken , Hirata Yuho , Sato Tatsuhiko , Kai Takeshi
Published: December 2023

Objective. Time-dependent yields of chemical products resulting from water radiolysis play a great role in evaluating DNA damage response after exposure to ionizing radiation. Particle and Heavy Ion Transport code System (PHITS) is a general-purpose Monte Carlo simulation code for radiation transport, which simulates atomic interactions originating from discrete energy levels of ionizations and electronic excitations as well as molecular excitations as physical stages. However, no chemical code for simulating water radiolysis products exists in the PHITS package. Approach. Here, we developed a chemical simulation code dedicated to the PHITS code, hereafter called PHITS-Chem code, which enables the calculation of the G values of water radiolysis species (•OH, eaq
−, H2, H2O2 etc) by electron beams. Main results. The estimated G values during 1 μs are in agreement with the experimental ones and other simulations. This PHITS-Chem code also simulates the radiolysis in the presence of OH radical scavengers, such as tris(hydroxymethyl)aminomethane and dimethyl sulfoxide. Thank to this feature, the contributions of direct and indirect effects on DNA damage induction under various scavenging capacities can be analyzed. Significance. This chemical code coupled with PHITS could contribute to elucidating the mechanism of radiation effects by connecting physical, physicochemical, and chemical processes.
A sewer disease grading method based on entropy power theory
Authors: Chen Shuai , Li Ce , Han Qin , Qin Tongzhen
Published: December 2023

Nowadays, urban sewers are becoming more and more dense, and a large number of pipes are inevitably suffering from various kinds of diseases due to their old age and long exposure to a wet environment, which creates various hidden dangers for people’s property safety. The identification of the type and degree of damage to sewers is still mainly manual, which requires a lot of accumulated experience to determine the level of damage and is time-consuming and laborious. To address these problems, we collected and constructed a sewer disease dataset named sewerdata, which contains over 300K images labeled by professionals and contains three common disease categories: crack, corrosion, and misalignment. Meanwhile, a sewer disease grading method based on entropy power theory is proposed. The method designs three disease grading indicators for the characteristics of sewer diseases, and its experimental results show that the detection accuracy is significantly improved compared with the traditional detection method. A disease grading method based on entropy power theory proposed in this paper aims to provide a new scientific research idea in the field of sewer disease grading and detection.
Research on super resolution angle measurement based on esprit algorithm
Authors: Zhao Wensha
Published: December 2023

With the rapid development of communication, radar, sonar and many other fields, angle measurement has become a very important field in signal processing. Super resolution angle measurement, compared with the traditional angle measurement, has more advantages in accuracy, so it is widely used in practical production. Super resolution angle measurement includes MUSIC algorithm and ESPRIT algorithm. This paper firstly compares the MUSIC algorithm and ESPRIT algorithm, then elaborates the ESPRIT algorithm and deduces the process. In this paper, DOA estimation based on ESPRIT algorithm is carried out for three different number of input signal sources, and the errors are analysed according to different SNR (signal to noise ratio) values. Finally, based on the simulation results, the effectiveness of ESPRIT algorithm is evaluated.
CycleGAN-based image translation from MRI to CT scans
Authors: Cai Yingchao , Li Mengxiao , Liu Shiqi , Zhou Changhao
Published: December 2023

In recent years, the application of deep learning techniques to medical image analysis has shown promising results in improving diagnosis and treatment of diseases. One such technique is CycleGAN, a variant of Generative Adversarial Networks (GANs) that enables unpaired image-to-image translation. This paper presents a CycleGAN-based approach for transforming CT and MRI scans, which can provide doctors with more diagnostic information and assist in the prediction and diagnosis of tumors. Our experiments are based on brain scan images collected from the Kaggle dataset, with no paired information available. The generator and discriminator models of the CycleGAN are trained with the Adam optimizer and a cycle consistency loss weight (λ) of 10. The total training time is about 12 days, and the model is tested for 75 epochs with a fixed learning rate of 0.0002. The results demonstrate the effectiveness of the proposed method, achieving high-quality image translation from MRI to CT scans. The advantages of CycleGAN in medical image analysis include its ability to handle unpaired data, perform cross-domain image translation, ensure cycle consistency, and generate diverse outputs. Future work can further explore the use of CycleGAN for other medical image analysis tasks and investigate how to optimize the model performance.
The application of deep learning in computer vision: Facial emotion recognition based on convolutional neural network
Authors: Liu Weiting , Xia Minghong
Published: December 2023

Emotion recognition is an essential task in many fields, including affective computing, psychology, and human-computer interaction. The ability to accurately recognize emotions from facial expressions can help in the development of more personalized and responsive systems. The objective of this paper is to investigate the effectiveness of a Convolutional Neural Network (CNN) model in classifying emotions from facial expressions. We used the images classified into seven different categories from FER2013 dataset. The model is implemented using TensorFlow Keras, which consists of four CNN layers followed by two fully connected (FCN) layers and an output layer. In between each layer we use batch normalization, max pooling, and dropout to reduce calculation cost and prevent overfitting. Our model achieved an accuracy of 66.85% and a precision accuracy of 77.18% on the training set after 20 epochs. The recall and the F-1 score is 56.93% and 0.6545 respectively. This study demonstrates that the proposed CNN model can effectively recognize emotions in facial images. The availability of the FER2013 dataset provides researchers with an opportunity to further explore this area of research. The proposed model can be useful in various applications, such as affective computing, human-computer interaction, and psychology. Future work could involve tuning the hyperparameters of the model or using different datasets to explore the generalizability of the proposed model. Overall, our study provides insights into the effectiveness of CNNs for emotion recognition and highlights the potential for further research in this area.
Overview on reinforcement learning of multi-agent game
Authors: Zou Wenrui
Published: December 2023

Game intelligence is an emerging hot topic in the field of artificial intelligence in recent years, and multi-agent learning is a frontier topic in the field of the intelligent game, which has a huge development prospect in all fields. This paper introduces the origin of reinforcement learning (RL) from the law of effect in animal experimental psychology and the optimization theory of optimal control. Then, the author describes the systematic composition of multi-agent reinforcement learning (MARL), and summarizes the classification of its research methods. The existing problems of MARL are discussed from three aspects: non-stationarity of the environment, partial observability, and the dimensional explosion problem. Finally, an outlook on the future is given based on the current development status of MARL and the important and difficult issues in the research field.
CNN combined with attention module for facial expression recognition
Authors: Yang Tiancheng
Published: December 2023

In contemporary times, the proliferation of information technology has prompted an increased interest among scholars towards artificial intelligence, particularly its constituent algorithms, such as deep learning, multilayer perceptron and convolutional neural networks. Of specific interest is the analysis of facial expressions, which has emerged as a popular research topic. However, classifying facial expressions presents a significant challenge due to variations in expressions associated with different emotions, as well as similarities between the various emotions. The task of expression classification is further complicated by the abundance of facial features that must be considered. In this paper, the basic Convolutional Neural Network (CNN) model was first trained on the FRE-2013 face expression dataset and optimized by adding an attention module to learn and analyse the key features of faces, thereby improving the classification accuracy of the model. In short, this model achieved an accuracy of 71.65% and an F1-Score of 0.66 while the model without the attention module only achieved a model accuracy of 69.86% and an F1-Score of 0.65, an improvement of about 2%. The analysis demonstrated that by adding the attention mechanism, some important features e.g. the eyes and mouth, are given more weight, thus improving the classification accuracy.