bluuebunny/crossref_metadata_2025_split huggingface dataset:
DFT combined to Boltzmann transport theory for optoelectronic and thermoelectric properties investigations for Se (4 & 8 (at.%)) doped Li2SnS3
Authors: Aamer Muhammad , Azam Sikander , Jawad Muhammad , Kanwal Sidra , Khan M Tahir , Mahmood Asif , Khan Wilayat , Rahman Amin Ur 
Published: April 2024

Abstract
Cubic Li2SnS3 emerges as a noteworthy ionic conductor and a viable electrode material for lithium secondary batteries. Its application extends to solar cell technologies, owing to its commendable optoelectronic properties and high-power conversion efficiency. In this study, we present density functional theory (DFT)-based first principles calculations for Li2SnS3-xSex (x = 0, 4, and 8% (atomic percent (at.%)) utilizing the modified Becke Johnson (mBJ) approximations, proposing a compelling alternative. Our investigation reveals significant optical absorption in the ultraviolet region for Li2SnS3-xSex (x = 0, 4, and 8%), accompanied by modest effective mass and indirect band gaps of 2.18 eV for the pristine material. Conversely, doped materials exhibit direct band gaps, with values of 2.113 eV for 4% and 2.026 eV for 8%. Furthermore, the calculated thermoelectric power factor underscores the potential and efficacy of Li2SnS3-xSex in thermoelectric energy devices. The findings not only highlight the material’s promise for solar applications but also underscore its candidacy as a novel solid-state electrolyte for lithium-ion batteries. This stems from its robust thermal stability and notable lithium-ion conductivity, positioning Li2SnS3 as a compelling candidate for advanced energy storage technologies.
Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model
Authors: Yan Shaohui , Jiang Jiawei , Zhang Yuyan , Zheng Bian , Zhan Hanbing , Jiang Defeng 
Published: April 2024

Abstract
The transmission of information between neurons is accomplished in living organisms through synapses. The memristor is an electronic component that simulates the tunability of the strength of biological synaptic connections in artificial neural networks. This article constructs a novel type of locally active memristor and verifies by nonlinear theoretical analysis, locally active analysis and circuit simulation. The designed memristor is simulated as a biological autapse of Hindmarsh-Rose(HR) neuron to obtain the improved HR neuron model of memristive autapse, and the Hamilton energy is obtained according to Helmholtz theorem. By varying the external forcing current and the memristive autapse strength, this article analyses the changes of the Hamilton energy and explores its self-excited and hidden firing behavior. The analog circuit simulation and digital circuit implementation of the HR model confirm the consistency between the mathematical model and the actual behavior, which can advance the field of neuroscience and artificial intelligence.
Master symmetries of non-linear systems
Authors: Serwa Nitin 
Published: April 2024

Abstract
We explore new symmetries in two-component third-order Burgers’ type systems in (1+1)-dimension using Wang’s

O

-scheme. We also find a master symmetry for a (2+1)-dimensional Davey-Stewartson type system. These results shed light on the behavior of these equations and help us understand their integrability properties. Our approach offers a practical method for identifying symmetries, contributing to the study of integrable systems in mathematics and physics.
Giant goos–hanchen shift with heigh reflection base on guided mode resonance in trapezoidal hBN/dielectric metasurface
Authors: Zhao Yue , Jin Gao , Zhou Sheng , Zhang Qiang , Fu Shu Fang , Wang Xiang Guang , Wang Xuan Zhang 
Published: April 2024

Abstract
hBN is a natural hyperbolic van der Waals material that can enhance light–matter interactions. In this work, the maximum Goos-Hanchen(GH) shift has a magnitude of −4680 μm with high reflection using the central beam method in the trapezoidal dielectric/hBN grating (TDG) metasurface (MS) where the beam waist

w

0

≥
4
λ
.

Analysis of the electromagnetic field distribution indicated that the amplified GH shift was a result of guided mode resonances excited in the waveguide dielectric layer. The frequency and magnitude of GH shifts can be effectively controlled by adjusting the height of the trapezoidal hBN, as well as by manipulating the widths of the top and bottom dielectric layers, the period of the TDG structure, and the height of the multi-layered structure. Moreover, an evaluation of the structure-sensing properties based on the GH shift was conducted. The enhanced and controllable GH shift exhibited by the TDG MS presents promising prospects for applications in optical sensors, optical switches, and optoelectronic detectors.
Unveiling the pressure-driven metal–semiconductor–metal transition in the doped TiS2
Authors: Chen 陈 Jiajun 佳骏 , Lv 吕 Xindeng 心邓 , Li 李 Simin 思敏 , Dan 但 Yaqian 雅倩 , Huang 黄 Yanping 艳萍 , Cui 崔 Tian 田 
Published: April 2024

Abstract
Conventional theories expect that materials under pressure exhibit expanded valence and conduction bands, leading to increased electrical conductivity. Here, we report the electrical properties of the doped 1T-TiS2 under high pressure by electrical resistance investigations, synchrotron x-ray diffraction, Raman scattering and theoretical calculations. Up to 70 GPa, an unusual metal–semiconductor–metal transition occurs. Our first-principles calculations suggest that the observed anti-Wilson transition from metal to semiconductor at 17 GPa is due to the electron localization induced by the intercalated Ti atoms. This electron localization is attributed to the strengthened coupling between the doped Ti atoms and S atoms, and the Anderson localization arising from the disordered intercalation. At pressures exceeding 30.5 GPa, the doped TiS2 undergoes a re-metallization transition initiated by a crystal structure phase transition. We assign the most probable space group as P212121. Our findings suggest that materials probably will eventually undergo the Wilson transition when subjected to sufficient pressure.
Physical information-enhanced graph neural network for predicting phase separation
Authors: Zhang 张 Yaqiang 亚强 , Wang 王 Xuwen 煦文 , Wang 王 Yanan 雅楠 , Zheng 郑 Wen 文 
Published: April 2024

Abstract
Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here, we present a physical information-enhanced graph neural network (PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers. The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.
Anomalous bond lengthening in compressed magnetic doped semiconductor Ba(Zn0.95Mn0.05)2As2
Authors: Sun Fei , Peng Yi , Zhao Guoqiang , Wang Xiancheng , Deng Zheng , Jin Changqing 
Published: April 2024

Abstract
Applying pressure has been evidenced as an effective method to control the properties of semiconductors, owing to its capability to modify the band configuration around Fermi energy. Correspondingly, structural evolutions under external pressures are required to analyze the mechanisms. Herein high-pressure structure of a magnetic doped semiconductor Ba(Zn0.95Mn0.05)2As2 is studied with combination of in-situ synchrotron X-ray diffractions and diamond anvil cells. The materials become ferromagnetic with Curie temperature of 105 K after further 20% K doping. The title material undergoes an isostructural phase transition at around 19 GPa. Below the transition pressure, it is remarkable to find lengthening of Zn/Mn−As bond within Zn/MnAs layers, since chemical bonds are generally shortened with applying pressures. Accompanied with the bond stretch, interlayer As−As distances become shorter and the As−As dimers form after the phase transition. With further compression, Zn/Mn−As bond becomes shortened due to the recovery of isotropic compression on the Zn/MnAs layers.
Improvement of Ga2O3 vertical Schottky barrier diode by constructing NiO/Ga2O3 heterojunction
Authors: Ji Xueqiang , Wang Jinjin , Qi Song , Liang Yijie , Hu Shengrun , Zheng Haochen , Zhang Sai , Yue Jianying , Qi Xiaohui , Li Shan , Liu Zeng , Shu Lei , Tang Weihua , Li Peigang 
Published: April 2024

Abstract
The high critical electric field strength of Ga2O3 enables higher operating voltages and reduced switching losses in power electronic devices. Suitable Schottky metals and epitaxial films are essential for further enhancing device performance. In this work, the fabrication of vertical Ga2O3 barrier diodes with three different barrier metals was carried out on an n–-Ga2O3 homogeneous epitaxial film deposited on an n+-β-Ga2O3 substrate by metal−organic chemical vapor deposition, excluding the use of edge terminals. The ideal factor, barrier height, specific on-resistance, and breakdown voltage characteristics of all devices were investigated at room temperature. In addition, the vertical Ga2O3 barrier diodes achieve a higher breakdown voltage and exhibit a reverse leakage as low as 4.82 ×10−8 A/cm2 by constructing a NiO/Ga2O3 heterojunction. Therefore, Ga2O3 power detailed investigations into Schottky barrier metal and NiO/Ga2O3 heterojunction of Ga2O3 homogeneous epitaxial films are of great research potential in high-efficiency, high-power, and high-reliability applications.
Countermeasure against blinding attack for single-photon detectors in quantum key distribution
Authors: Jiang Lianjun , Li Dongdong , Fang Yuqiang , Zhao Meisheng , Liu Ming , Xie Zhilin , Zhao Yukang , Tang Yanlin , Jiang Wei , Fang Houlin , Ma Rui , Cheng Lei , Yang Weifeng , Han Songtao , Tang Shibiao 
Published: April 2024

Abstract
Quantum key distribution (QKD), rooted in quantum mechanics, offers information-theoretic security. However, practical systems open security threats due to imperfections, notably bright-light blinding attacks targeting single-photon detectors. Here, we propose a concise, robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks. Our strategy uses a dual approach: detecting the bias current of the avalanche photodiode (APD) to defend against continuous-wave blinding attacks, and monitoring the avalanche amplitude to protect against pulsed blinding attacks. By integrating these two branches, the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts, significantly enhancing the resilience of QKD systems against various bright-light blinding attacks. This method fortifies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.
Biochemical and immunomodulatory insights of extracellular matrix from decellularized human whole cervix: recellularization and in vivo ECM remodeling interplay
Authors: Ojha Atul Kumar , Rajasekaran Ragavi , Hansda Arman Kunwar , Choudhury Priyanka , Biswas Asmita , Sharma Sunita , Chaudhuri Prithwis Pal , Dogra Nantu , Goswami Ritobrata , Chaudhury Koel , Dhara Santanu 
Published: April 2024

Abstract
Extracellular matrix (ECM) rich whole organ bio-scaffolds, preserving structural integrity and essential growth factors, has potential towards regeneration and reconstruction. Women with cervical anomalies or trauma can benefit from clinical cervicovaginal repair using constructs rich in site specific ECM. In this study, complete human cervix decellularization was achieved using a modified perfusion-based stir bench top decellularization method. This was followed by physico-chemical processes including perfusion of ionic agents, enzymatic treatment and washing using detergent solutions for a duration of 10–12 d. Histopathological analysis, as well as DNA quantification confirmed the efficacy of the decellularization process. Tissue ultrastructure integrity was preserved and the same was validated via scanning electron microscopy and transmission electron microscopy studies. Biochemical analysis and structural characterizations like Fourier transform infrared, Raman spectroscopy of decellularized tissues demonstrated preservation of important proteins, crucial growth factors, collagen, and glycosaminoglycans. In vitro studies, using THP-1 and human umbilical vein endothelial cell (HUVEC) cells, demonstrated macrophage polarization from M1 to M2 and vascular functional genes enhancement, respectively, when treated with decellularized human cervical matrix (DHCp). Crosslinked DHC scaffolds were recellularized with site specific human cervical epithelial cells and HUVEC, showing non-cytotoxic cell viability and enhanced proliferation. Furthermore, DHC scaffolds showed immunomodulatory effects in vivo on small rodent model via upregulation of M2 macrophage genes as compared to decellularized rat cervix matrix scaffolds (DRC). DHC scaffolds underwent neo-vascularization followed by ECM remodeling with enhanced tissue integration.
Dynamic trust security approach for edge computing-based mobile IoT devices using artificial intelligence
Authors: Jedidi Ahmed 
Published: April 2024

Abstract
The Industrial Internet of Things (IIoT) encompasses various applications such as smart cities, healthcare, and agriculture, which rely on a multitude of interconnected devices. These applications demand specific performance requirements, including low latency and sufficient bandwidth. Edge computing has emerged as a promising solution to address the challenges associated with data flow processing. Particularly, in scenarios like smart cities, where a massive number of IIoT devices are deployed, edge computing serves as the primary data processing layer. However, the limited resources of edge computing, compared to cloud computing, pose significant security concerns for IIoT applications. In fact, this paper proposes a novel security system to secure the relation between the IIoT devices and edge computing. Dynamic Trust Security Approach (DTSA) leverages Artificial Intelligence to analyze, evaluate and enhance the security and privacy aspects of the IIoT devices’ interaction with edge computing. DTSA improve the security level of the IIoT system and improve the performance with more than 45% compare to the traditional method.
Distilling particle knowledge for fast reconstruction at high-energy physics experiments
Authors: Bal A , Brandes T , Iemmi F , Klute M , Maier B , Mikuni V , Årrestad T K 
Published: April 2024

Abstract
Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources, in particular on edge devices. In this article, we consider proton-proton collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is a DNN that is trained to learn about the provenance of particles, as provided by the soft labels that are the GNN outputs, to predict whether or not a particle originates from the primary interaction vertex. The results indicate that for this problem, which is one of the main challenges at the HL-LHC, there is minimal loss during the transfer of knowledge to the small student network, while improving significantly the computational resource needs compared to the teacher. This is demonstrated for the distilled student network on a CPU, as well as for a quantized and pruned student network deployed on an field programmable gate array. Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. Such an approach can become essential at the HL-LHC experiments, e.g. to comply with the resource budget of their trigger stages.
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
Authors: Sedykh Alexandr , Podapaka Maninadh , Sagingalieva Asel , Pinto Karan , Pflitsch Markus , Melnikov Alexey 
Published: April 2024

Abstract
Finding the distribution of the velocities and pressures of a fluid by solving the Navier–Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and in design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks (PINNs) are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum PINN (HQPINN) that simulates laminar fluid flow in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a PINN, resulting in a 21% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular HQPINN, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.
Functionalization of silver nanoparticles coating cotton fabrics through hydrothermal synthesis for improved antimicrobial properties
Authors: Tooklang Pises , Audtarat Sasiporn , Chaisen Konkanok , Thepsiri Jaruwan , Chingsungnoen Artit , Jittabut Pongsak , Dasri Thananchai 
Published: April 2024

Abstract
Deposition of silver nanoparticles (AgNPs) on the surfaces of cotton fabric (CF) is an approach for producing antimicrobial textile products. CF-AgNPs composites were fabricated ex situ using a simple hydrothermal synthesis of AgNPs, while the fabric was surface modified using a plasma to improve adhesion prior to coating. The morphology and chemical composition of CF-AgNPs nanocomposites were examined using UV–vis spectrophotometry (UV–vis), x-ray diffraction (XRD), transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM). Study results show uniform deposition of AgNPs on cotton fabrics. The CF-AgNPs composite material exhibits excellent laundering durability and antimicrobial performance against Gram-positive, Staphylococcus aureus and Gram-negative, Escherichia coli pathogens. These findings indicate that the developed fabric can be used in a wide range of biomedical applications, health care and various packaging systems.
Defining the Ideal Reduction Mammaplasty: A Crowdsourcing Study
Authors: Zibitt Meira , Shauly Orr , Ash Makenna , Menon Ambika , Gould Daniel J , Faulkner Heather , Losken Albert 
Published: April 2024

Abstract

Background
Reduction mammaplasty relieves macromastia symptoms while improving breast aesthetics, although the ideal breast aesthetically has been shown to differ culturally in previous crowdsourcing studies. Better understanding these differences can aid in setting postoperative expectations.

Objectives
The aim of this study was to characterize the ideal reduction mammaplasty according to demographics such as gender, ethnicity, socioeconomic status, and education.

Methods
A crowdsourcing platform was utilized to collect 10,169 deidentified responses. Users completed 1 of 3 surveys, either a preoperative, postoperative, or preoperative and postoperative paired survey. The preoperative and postoperative surveys addressed 10 breast measurements, including upper breast slope, projection proportion, nipple position, breast width, and breast fullness. The paired preoperative and postoperative survey assessed the nipple-areola complex (NAC), chest fit, symmetry improvement, and scarring.

Results
Preoperative images were rated more aesthetic than postoperative images. This was consistent across all demographics evaluated. Females, African Americans, Asians, participants ages 55+, and participants with no high school degree or a graduate degree found the most improvement in breast symmetry (P = .001, P = .002, P = .027, P &lt; .001, P = .01). Male and Hispanic participants were most likely to see no change in symmetry (P = .008, P = .04), and South Asian participants found breasts less symmetric postoperatively (P &lt; .001). There were significant demographic differences in aesthetic ratings of the NAC, scarring, and breast fit.

Conclusions
Perceived breast aesthetics after reduction mammaplasty vary significantly across demographics, including gender, ethnicity, age, socioeconomic status, and educational achievement. Surgeons should consider demographics when planning each patient's reduction mammaplasty.

Level of Evidence: 5
Automated 3D Perioral Landmark Detection Using High-Resolution Network: Artificial Intelligence–based Anthropometric Analysis
Authors: Yang Yuyan , Zhang Mengyuan , An Yicheng , Huang Qi , Shi Yongwang , Jin Lin , Zeng Ang , Long Xiao , Yu Nanze , Wang Xiaojun 
Published: April 2024

Abstract

Background
Three-dimensional facial stereophotogrammetry, a convenient, noninvasive and highly reliable evaluation tool, has in recent years shown great potential in plastic surgery for preoperative planning and evaluating treatment efficacy. However, it requires manual identification of facial landmarks by trained evaluators to obtain anthropometric data, which takes much time and effort. Automatic 3D facial landmark localization has the potential to facilitate fast data acquisition and eliminate evaluator error.

Objectives
The aim of this work was to describe a novel deep-learning method based on dimension transformation and key-point detection for automated 3D perioral landmark annotation.

Methods
After transforming a 3D facial model into 2D images, High-Resolution Network is implemented for key-point detection. The 2D coordinates of key points are then mapped back to the 3D model using mathematical methods to obtain the 3D landmark coordinates. This program was trained with 120 facial models and validated in 50 facial models.

Results
Our approach achieved a satisfactory mean [standard deviation] accuracy of 1.30 [0.68] mm error in landmark detection with a mean processing time of 5.2 [0.21] seconds per model. Subsequent analysis based on these landmarks showed mean errors of 0.87 [1.02] mm for linear measurements and 5.62° [6.61°] for angular measurements.

Conclusions
This automated 3D perioral landmarking method could serve as an effective tool that enables fast and accurate anthropometric analysis of lip morphology for plastic surgery and aesthetic procedures.