bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of N2O plasma treatment on the performance and stability of high indium content InGaZnO thin-film transistors
Authors: Nurmamat Patigul , Li Rui , Yang Linyu , Mamat Mamatrishat , Abliz Ablat 
Published: December 2024

Abstract
This study investigates the effects of N2O plasma treatment on the electrical performance and stability of In(2%)GaZnO thin-film transistors (TFTs). The experimental results show that with increasing N2O plasma treatment time, the electrical performance of In(2%)GaZnO TFTs deteriorated, and the threshold voltage (V
th) is positively shifted. Moreover, the optimized In(2%)GaZnO:N2O (4 min) TFTs exhibited the best electrical performance, which include a μ
FE of 22.5 cm2 Vs−1, a small SS value of 0.31 V decade−1, a small V
th of 0.3 V, a low D
it value of 8.5 × 1011 cm−2 eV−1, and a high I
on/I
off of 107. Moreover, a small V
th shift (0.4, 0.6, −0.5 and −0.7 V) in In(2%)GaZnO:N2O (4 min) TFT was observed under the gate bias and light illumination test. X-ray photoelectron spectroscopy and low-frequency noise analysis indicate that this enhanced performance is attributed to the appropriate N2O plasma treatment that can reduce the oxygen vacancy, interface trap density, and other bulk/surface defects due to the formation of metal–nitrogen bonds (especially Ga–N) and N–O bonds. Over all, the N2O plasma treatment simple method has provided a novel approach to achieve high performance and highly stable oxide In(2%)GaZnO TFTs for applications in flat panel displays.
LIDD-YOLO: a lightweight industrial defect detection network
Authors: Luo Shen , Xu Yuanping , Zhang Chaolong , Jin Jin , Kong Chao , Xu Zhijie , Guo Benjun , Tang Dan , Cao Yanlong 
Published: December 2024

Abstract
Surface defect detection is crucial in industrial production, and due to the conveyor speed, real-time detection requires 30–60 frames per second (FPS), which exceeds the capability of most existing methods. This demand for high FPS has driven the need for lightweight detection models. Despite significant advancements in deep learning-based detection that have enabled single-stage models such as the you only look once (YOLO) series to achieve relatively fast detection, existing methods still face challenges in detecting multi-scale defects and tiny defects on complex surfaces while maintaining detection speed. This study proposes a lightweight single-stage detection model called lightweight industrial defect detection network with improved YOLO architecture (LIDD-YOLO) for high-precision and real-time industrial defect detection. Firstly, we propose the large separable kernel spatial pyramid pooling (SPP) module, which is a SPP structure with a separable large kernel attention mechanism, significantly improving the detection rate of multi-scale defects and enhancing the detection rate of small target defects. Secondly, we improved the Backbone and Neck structure of YOLOv8n with dual convolutional (Dual Conv) kernel convolution and enhanced the faster implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module in the Neck structure with ghost convolution and decoupled fully connected (DFC) attention, reducing the computational and parameter overhead of the model while ensuring detection accuracy. Experimental results on the NEU-DET steel defect datasets and printed circuit board (PCB) defect datasets demonstrate that compared to YOLOv8n, LIDD-YOLO improves the recognition rate of multi-scale defects and small target defects while meeting lightweight requirements. LIDD-YOLO achieves a 3.2% increase in mean average precision (mAP) on the NEU-DET steel defect dataset, reaching 79.5%, and a 2.6% increase in mAP on the small target PCB defect dataset, reaching 93.3%. Moreover, it reduces the parameter count by 20.0% and floating point operations by 15.5%, further meeting the requirements for lightweight and high-precision industrial defect detection models.
ResNet50-3Cur-HGCN: a novel multimodal hybrid curvature space approach to bearing fault diagnosis
Authors: Yi Cancan , Wang Yuchen , Liu Yang , Xiao Han 
Published: December 2024

Abstract
As a consequence of its great load-bearing capacity, low friction loss, and low vibration and impact, bearings are employed extensively in a variety of applications. In response to the current issues in the process of fault signal identification using graph neural networks (GNNs), such as the insufficient expression of node data types, the inadequate exploration of feature information carried by the graph structure, and the singularity of mathematical space operations on graph data, this paper proposes a residual network-50-heterogeneous graph convolutional neural network (ResNet50-HGCN) model based on the combination of ResNet50 and HGCN. The model is trained in a threefold mixed curvature (3 Curvature, 3Cur) space environment for semi-supervised learning, aiming to achieve accurate classification of bearing fault signals. Specifically, first, the time-domain signals and the components of the time–frequency map obtained from the wavelet synchrosqueezing transform are used as the bimodal node information for HGCN. Then, the graph network is merged in the 3Cur space for training and weighted validation, obtaining the predicted category labels of the model under the 3Cur configuration space. Finally, experimental data analysis is conducted, sparse confusion matrices for predicted categories are drawn, and four types of accuracy-related evaluation metrics are calculated. The experimental results show that the proposed ResNet50-3Cur-HGCN classification model outperforms other models in the experiment, achieving an accuracy of 97.71%, which verifies the method’s beneficial effects with regard to precision and efficiency. It also provides a good methodological reference for bearing fault diagnosis approaches centered on GNNs.
Evaluation of impact energy in composites using acoustic emission sensing technique
Authors: Ai Li , Mesaric Tanner , Flowers Sydney , Houck Sydney , Widawsky Joshua , Ziehl Paul 
Published: December 2024

Abstract
A major challenge faced by composite materials is impact, which can result in unexpected damage and degradation. Impact events can cause significant structural damage that may not be immediately visible, leading to a reduction in the material’s mechanical properties and overall performance. This paper presents an impact assessment method using acoustic emission (AE) sensing technology. The primary goal of this approach is to determine the extent of impact damage on composite components by analyzing AE signals produced under operating stress conditions. An advanced algorithm is proposed to predict the probability that the damage falls into various damage categories, providing a detailed assessment of the material’s condition. For validation, a series of composite panels were manufactured and subjected to controlled impact and subsequent compression after impact tests. The AE signals gathered during these tests provided initial validation for the proposed method, showcasing its potential for accurately assessing impact damage. The results demonstrate that AE sensing technology, combined with advanced algorithmic analysis, has the potential to be an effective tool for monitoring the integrity of composite materials and ensuring their reliability in critical applications.
Unveiling the phases of bulk ZrTe5 through magnetotransport phenomena
Authors: Shih Pi-Ju , Yang Cheng-Hsueh , Liao Pin-Chi , Lin Wei-Chen , Chen Fa-Hua , Chen Jeng-Chung , Cao Limin , Chuang Chiashain , Liang Chi-Te 
Published: December 2024

Abstract
We present a straightforward method which may greatly simplify and lower the threshold for determining the phase of the relatively enigmatic quantum material—ZrTe5. In this study, without directly probing the band structure, we identify the topological phase of the three-dimensional (3D) bulk ZrTe5 crystal solely through low-temperature electrical and magnetotransport measurements. A two-dimensional (2D) weak antilocalization (WAL) effect was observed in our bulk ZrTe5 crystal, along with clear Shubnikov–de Haas oscillations. The large prefactor

α

derived from WAL analyses indicates the presence of multiple conducting channels in the bulk ZrTe5 crystal, where each channel is associated with individual 2D ZrTe5 layers. It is the large

α

value provides insights into the topological Dirac semimetal phase inherent to our ZrTe5 crystal. Additionally, we analyze the pronounced linear magnetoresistance and saturation behavior under a perpendicular magnetic field. Our results suggest that bulk ZrTe5 crystals, which exhibit unique layered transport features, serve as a promising platform for further research in quantum phases and transitions in 3D quantum systems.
Validation of the generalized stochastic microdosimetric model (GSM

2

) over a broad range of LET and particle beam type: a unique model for accurate description of (therapy relevant) radiation qualities
Authors: Bordieri Giulio , Missiaggia Marta , Cartechini Giorgio , Battestini Marco , Bronk Lawrence , Guan Fada , Grosshans David , Rai Priyamvada , Scifoni Emanuele , La Tessa Chiara , Lattanzi Gianluca , Cordoni Francesco G 
Published: December 2024

Abstract

Objective. The present work shows the first extensive validation of the generalized stochastic microdosimetric model (GSM2). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from

1

keV

μ

m

−
1

up to more than

300

keV

μ

m

−
1

. Currently, the existing mechanistic radiation biophysical models show some limitations in describing cell killing without the addition of ad hoc corrections, especially in the high-LET regime, where the overkill effect is observed. Approach. The experimental irradiation conditions have been accurately reproduced with Monte Carlo simulations using the GEANT4-based TOPAS computational toolkit. We show the main and unique features of GSM

2

, i.e. how it can predict the biological response by considering the full information on the stochasticity of radiation through the microdosimetric spectrum, which is supposed to be the best descriptor of radiation quality. Main results. Well-matching results for different biological endpoints with the natural presence of the overkill effect fully display the predictive power of GSM2. Significance. This study shows the complete generality and flexibility of GSM2 and its ability to successfully predict the cell survival probability from very different particle radiation fields. Consequently, we demonstrate the dependence of the relative biological effectiveness on the whole microdosimetric spectrum, which fully includes the stochasticity inherently given by radiation-matter interaction.
A comprehensive dual energy method for CBCT metal artifact reduction
Authors: Ge Weiwei , Liu Zihao , Cui Hehe , Yuan Xiaogang , Yang Yidong 
Published: December 2024

Abstract

Objective. A major limitation in cone beam CT (CBCT) application is the presence of metal artifacts when scanning metal-embedded objects or high attenuation materials. This study aims to develop a dual-energy based method for effective metal artifact reduction. Approach. The proposed method comprised three steps. Initially, the virtual monoenergetic (VM) projections were generated by combining high- and low-energy projections to mitigate metal artifacts caused by the beam hardening effect. Subsequently, the normalized metal artifact reduction (NMAR) projections were created using the VM projections through the NMAR method. Then, the NMAR CBCT was produced by reintegrating metal into the CBCT reconstructed from NMAR projections. Finally, the iterative reconstruction was employed to obtain the final CBCT, utilizing VM projections and the NMAR CBCT as the initial input. Validation of the proposed method was achieved through Monte Carlo (MC) simulations on digital dental and abdominal phantoms, and CBCT scanning on CIRS Model 062M head and body phantoms. The structural similarity index measurement (SSIM) and the root mean square error (RMSE) calculated within a metal-containing ROI were employed for image quality evaluation. Main Results. Both the MC simulation and phantom scanning demonstrated that the proposed method was superior to the frequency split metal artifact reduction (FSMAR) method in mitigating artifacts and preserving anatomic details around metal. Averaged over four phantoms, the SSIM was enhanced from 99.48% with FSMAR to 99.86% with our proposed method, and the RMSE was reduced from 93.62 HU to 70.75 HU. Furthermore, the proposed method could be implemented with less than two minutes after GPU acceleration. Significance. The proposed dual-energy based metal artifact correction method effectively corrects metal artifacts and preserves tissue details surrounding the metal region by leveraging the strengths of VM, projection interpolation and iterative reconstruction techniques. It has strong potential of clinical implementation due to the superior performance in image quality and process efficiency.
A statistically-based continuum anisotropic constitutive model of fiber-reinforced shape memory hydrogel composites
Authors: Gao Zhaoguo , Hu Yunqiang , Sun Jian , Jia Fei , Liu Yanju , Leng Jinsong 
Published: December 2024

Abstract
Shape memory hydrogel composites (SMHCs) have been developed to enhance the performance of SMHs, serving as smart soft materials with superior shape and performance programmability. The establishment of a constitutive model is crucial for the utilization of SMHCs, yet research in this area remains limited. Herein, based on the transient network theory and the Fung-type elastic model, a micro-macroscopic anisotropic constitutive model for SMHCs incorporating double-network SMHs and the distribution of fibers was developed. Mechanical behaviors and shape memory effects of SMHCs were investigated with three different fiber distributions: unidirectional, planar uniform, and volumetric uniform. The efficacy of model was validated by implementing it into a user-defined materials subroutine. Various parameters of SMHCs, such as ambient temperature, instantaneous shear modulus of the network, and fiber stiffness, were examined for their effects on mechanical behaviors and shape memory effects. The proposed model can capture the shape memory effects of double network SMHCs. Simulation examples involving SMHC-based stents were provided to illustrate the practical implications of our work in SMHCs applications.
A novel body centered cubic 3D auxetic chiral geometry
Authors: Caporale Antonio Maria , Airoldi Alessandro , Novak Nejc 
Published: December 2024

Abstract
Auxetic materials and structures have a negative Poisson’s ratio (PR), which means that they expand under a longitudinal tensile loading and shrink under compressive load. This property gives them an increased shear resistance, damping and energy absorption capabilities. Chiral geometries are among the most effective auxetic structures for their ability to convert the axial loading in the rotation of their internal nodes. In the present work, a novel 3D chiral geometry is presented, and its response is investigated both experimentally and numerically. It is made of spheres placed following a Body Centered Cubic lattice; each one connected to the closest neighbors by eight ligaments. A sample was 3D-printed and mechanically tested in compression exhibiting an auxetic behavior in certain directions. A Finite Element model was then developed, successfully reproducing the outcome of the experimental tests, both in terms of force and PR. Then, a numerical testing campaign was performed on representative units constrained using periodic boundary conditions. This campaign allows to evaluate the mechanical properties of the structure as a function of its geometrical parameters. It shows to be able to exhibit an approach a PR of

−
1

in all the directions. Moreover, the relationship between the sphere’s rotation, PR and instability was investigated.
Research on the characteristics of accurate engineering mathematical model of photovoltaic cells based on grey relational analysis
Authors: Zhou Biying , Zhang Peng , Yin Guofu 
Published: December 2024

Abstract
Traditional photovoltaic (PV) models are characterized by excessive parameters, significant nonlinearity, and transcendental equations, making it challenging to simultaneously satisfy the speed and accuracy criteria for PV array engineering design. To improve traditional PV models and optimize parameters, a precise engineering mathematical model of PV cells is developed. At a temperature of 25 °C and a solar irradiance of 1000 W m−2, compared with actual measurement, the calculated results of the proposed model have an average relative error of 0.22% s. In 5 random settings, compared with the traditional Newton iteration method and the approximate fitting straight line method, this model has an average relative error of 0.23% and 1.37%, respectively. After sensitivity testing on the model parameters, the grey correlation degree method is used to analyze the impact correlation and maximum power point (MPP) of 6 factors, U

mpp
, T, I

mpp
, α, R

sh
, and S. The results show that the proposed engineering mathematical model of PV cells is responsive to different parameters. It effectively demonstrates electrical characteristics under different conditions and highlights the impact of each parameter on the MPP. The findings can provide a foundation for optimizing large-scale solar power generation system.
Design a chaotic circuit using tunable and power efficient memristor emulator circuit for image encryption
Authors: Kumari Usha , Yadav Rekha 
Published: December 2024

Abstract
In a non-linear dynamic system, chaos is a deterministic phenomenon that arises when the state vector trajectories become highly sensitive to the initial conditions, given certain criteria and periodic. The memristor, being the fourth essential component of a two-terminal device, has the potential to overcome the barrier for secure communication against eavesdroppers and manufacturing of duplicate chaotic transrecivers by the untrusted foundaries.It can close a gap among secure manufacturing and reliable communication because its resistance can be programmed by the designer not by the foundary. This property makes the communication system more efficient, reliable secure, and provides more security. In this study, the memristor and analog design of a chaotic transreviver is designed. The memristor model is designed using a Current mirror operational transconductance amplifier (OTA) and an analog multiplier with sinusoidal input having amplitude (V

m
) and input frequency (f). Variations in input frequency, and amplitude have an impact on conductance and resistance state and the OTA parameter g

m
Also affects the V-I response. The research focuses on memristor tunability with a variation of the hysteresis curve using factors such as temperature, amplitude, load resistance, frequency, and incremental and decremental behavior and secure communication using a Chaotic circuit using memristor. The maximum operational frequency that exhibits a pintch hystresis curve is 100 kilohertz, and a power dissipation of 3.1 μW with noise 56 nV/Hz1/2 The results also show the chaotic system are sensitive with their secret key or starting conditions of integrator which is uses for the encryption approach.
Elzaki residual power series approach for semi-analytic approximation of time-fractional nonlinear equations occurring in magneto-acoustic waves
Authors: Kumar Sachit , Joshi Varun , Kapoor Mamta 
Published: December 2024

Abstract
The study of magneto-sound propagation in plasma, capillary gravity water waves, and the travel of long waves under the effect of gravity in shallow water are all important applications of the 5th-order Korteweg–de Vries equation. This research article uses the Elzaki residual power series method(ERPSM), an appropriate innovative technique, to explore time-fractional nonlinear 5th-order Korteweg–de Vries equations. Three instances of the time-fractional 5th-order Korteweg–de Vries equations in order to demonstrate the effectiveness and relevance of the proposed method has been examined. This work's originality is expressed by an analysis of the fractional behavior of the considered equations.There is a discernible difference in the solutions that are produced for various fractional orders, which may have distinct ramifications for further work. Readers can employ the hybrid approaches combining with our proposed scheme as a future study direction to get better results. Additionally, appropriate numerical simulations were setup to confirm the accuracy and dependability of the suggested strategy. The results collected indicate that the suggested approach is highly satisfying and looks at the intricate nonlinear problems that occur in innovation and science.
Exploring the stability of equilibria and Hopf-bifurcation in a tuberculosis model with delayed treatment
Authors: Das Saduri , Sarkar Tapan , Biswas Pankaj 
Published: December 2024

Abstract
Tuberculosis poses a worldwide threat, particularly to low and middle-income nations. The timely screening, identification, and management of TB become problematic in these regions due to resource constraints, inadequate education and awareness, stigma and discrimination, weak healthcare infrastructure, and concurrent health conditions. This delay may lead to postponed diagnosis and treatment. This work presents and examines a mathematical model for tuberculosis that incorporates exogenous reinfection, slow-fast progression, and delay in detection and treatment. More precisely, we examine a scenario where a certain proportion of people with active tuberculosis receive prompt diagnosis and treatment, while the rest experience a delay in detection and appropriate treatment. First, we establish the non-negativity and boundedness of the solutions, and discuss the criteria for the existence of different equilibria. Then, the local stability of the equilibria is analyzed. The threshold value of the time delay which decides the local stability of the disease-free equilibrium (DFE) is obtained. We observe that the model exhibits a Hopf-bifurcation when the time delay crosses a certain threshold value,

τ

0

*

. This reveals that the EEP is stable for

τ
<

τ

0

*

. However, as

τ
>

τ

0

*

, the EEP loses its stability via Hopf-bifurcation, leading to sustained periodic oscillations. Further, by applying center manifold theory, we analytically determine the direction and stability of the Hopf-bifurcation and validate the results numerically. It is observed that the Hopf-bifurcation is supercritical and the bifurcating periodic oscillations are stable. Global stability of the endemic equilibrium point (EEP) under certain conditions is established by choosing a suitable Lyapunov function. Additionally, the system also exhibits a Hopf-bifurcation in the absence of delay and numerical simulations are performed to determine the critical parameter values at which the bifurcation occurs. The impact of treatment delay is significant, as it not only contributes to the emergence of complex dynamical behavior but also significantly influences the stability properties of the system. This delay can lead to critical transitions, such as changes in equilibrium stability and the onset of oscillatory dynamics, depending on the interaction of system parameters.