bluuebunny/crossref_metadata_2025 huggingface dataset:
ARC-Nucléart : le nucléaire au chevet du patrimoine
Authors: Terminet François
Published: October 2024

Le nucléaire ne se résume pas uniquement à la production d’électricité. Son champ d’action est en effet beaucoup plus vaste avec notamment les secteurs du médical, de l’agroalimentaire et, plus étonnant, le domaine culturel. C’est là qu’officie ARC-Nucléart, à la fois atelier de restauration et laboratoire de recherche pour la sauvegarde du patrimoine culturel.
Uncovering Multi-step Attacks with Threat Knowledge Graph Reasoning
Authors: Xiang Xiayu , Zeng Liyi , Feng Wenying , Gu Zhaoquan , Xie Yushun , Ma Changchang
Published: October 2024

The rapid advancement of information technologies has significantly intensified the focus on cyberspace security across various sectors. In this evolving landscape, attackers deploy many of techniques- including exploits, weakness identification, and complex multi-step attacks- to gain unauthorized access to systems. Conversely, defenders harness insights from a variety of sources to pinpoint potential threats. Prominent public cybersecurity databases such as the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK), Common Attack Pattern Enumeration and Classification (CAPEC), Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Platform Enumeration (CPE) provide extensive data on security entities and their interrelations, playing a pivotal role in enriching the understanding of cybersecurity challenges and assisting in comprehensive defensive analyses. However, the semantic cross-analysis of these databases, crucial for identifying obscure threat patterns, remains underexploited. In this study, we amalgamate data from these disparate sources into a cohesive threat knowledge graph and introduce a novel knowledge representation learning approach, A4CKGE (ATT&CK-CAPEC-CWE-CVE-CPE Knowledge Graph Embedding). This method utilizes advanced structural and textual analytics to predict interactions among security entities such as products, vulnerabilities, weaknesses, and multi-step attack sequences, employing complex attack templates generated through a Large Language Model (LLM). Our extensive experiments demonstrate that this approach significantly outperforms existing state-of-the-art methods in effectively predicting these relationships. The findings validate the efficacy of our threat knowledge graph in unveiling hidden connections, thereby highlighting its potential to strengthen cybersecurity defenses substantially.
Impfpflicht im Gesundheitswesen – „Der Drops ist gelutscht!“  Eine
qualitative Studie zu Wahrnehmungen und Dynamiken in Pflegeteams aus Sicht von
Pflegefachkräften
Authors: Stagge Maya , Rädel-Ablass Katharina , Roddewig Marion , Henne Melissa , Fehling Patrick , Polat Abdulillah , Schliz Klaus
Published: October 2024

Die explorative Querschnittsstudie analysiert die Auswirkungen der
COVID-19-Impfpflicht auf Wahrnehmungen und die Dynamik in Pflegeteams. Die 27
leitfadengestützten Interviews mit Pflegefachkräften enthüllen, dass der Druck
zur Impfung und die Einführung der Impfpflicht im Gesundheitswesen zu Spaltungen
und konfliktgeladenen Interaktionen zwischen geimpften und ungeimpften
Teammitgliedern führten. Diese Konflikte haben die Arbeitsqualität und das
Teambewusstsein negativ beeinträchtigt, was in erhöhten Kündigungsraten und
einer anhaltenden Belastung der Pflegeteams resultierte.
Some Thiazolopyrimidine Derivatives: Synthesis, DFT, Cytotoxicity, and Pharmacokinetics Modeling Study
Authors: Ramadan Sayed K. , Alzahrani Abdullah Y. A. , El-Helw Eman A. E.
Published: October 2024

A pyrimidinethione candidate carrying pyrazole and thiophene scaffolds was produced by a Biginelli cyclocondensation reaction of a pyrazolecarbaldehyde with pentan-2,4-dione and thiourea. To create some heteroannulated thiazolopyrimidines, the pyrimidinethione was subjected to cyclocondensation reactions with ethyl chloroacetate, 1,2-dibromoethane, chloroacetonitrile, and oxalyl chloride. A DFT simulation was performed for a frontier-orbital analysis to determine the molecular geometry. Among the products, 6-acetyl-7-methyl-5-[1-phen­yl-3-(2-thienyl)-1H-pyrazol-4-yl]-5H-[1,3]thiazolo[3,2-a]pyrimidine-2,3-dione displayed the highest softness and the lowest energy gap in the DFT calculations. Moreover, it had the highest electrophilicity index, suggesting possible biological impacts. The compounds obtained were evaluated against cell lines of breast adenocarcinoma (MCF7) and hepatocellular carcinoma (HepG2) as antiproliferative agents. A simulation of the molecular docking of our compounds with the epidermal growth factor receptor demonstrated the rationality of our design and identified the binding mode. A model pharmacokinetics analysis showed that the products have the expected and desirable drug-like and bioavailability properties.
Advances in the Light-Promoted Transformations of N-Heterocyclic Carbene Ligated Boryl Radicals
Authors: Xuan Jun , Xie Yang
Published: October 2024

Organoboron compounds are integral to modern life, with extensive applications in synthesis, materials science, medicine, and various other domains closely linked to human endeavors. NHC-BH3, noted for its stability, ease of synthesis, and high reactivity as a boryl radical precursor, has emerged as a key focus in boryl radical chemistry. Recently, the visible-light-induced single electron transfer (SET) and hydrogen atom transfer (HAT) processes have garnered considerable interest, presenting innovative strategies for generating boryl radicals from NHC-BH3. In the context of this review, our focus is on the synthesis of C–B and X–B bonds under visible light irradiation, facilitated by NHC-BH3. Furthermore, we explored the role of NHC-BH3 as a hydrogen donor or halogen atom transfer reagent in the construction of C–C bonds.

1 Introduction

2 Hydroboration

3 Borylation

4 Construction of X–B Bonds (X = N, O, S)

5 Halogen Atom Transfer Reagent and Hydrogen Donor

6 Conclusion
The Effect of Noise and Reverberation on Spatial Perception in Sequential Bilateral Cochlear Implant Users
Authors: Kara Halide Çetin , Kara Eyyup , Ataş Ahmet
Published: October 2024

Abstract Background Spatial orientation is an executive function which includes vital activities and auditory organization according to daily bodily movements, directionality, and environmental information. It is directly linked to the vision and hearing and
used throughout life, building complex relationships with these systems, based on learning. Purpose Our purpose in our study is to try to see the effects of noise and reverberation on the users by comparing the localization and auditory performances of the cochlear implant
(CI) user individuals in a silent, noisy environment and reverberation. Research Design All subjects were subjected to immitancemetric/audiological tests, language development test (TIFALDI, Receptive/Expressive Language Test score 7 years and above), localization determination
in noise, and localization determination test in reverberation. Study sample: In our study, 18 female and 16 male bilateral CI users with profound sensorineural hearing loss were included. The age range of subjects was 8 years 4 months and 10 years 11 months. Data Collection
and Analysis Data from subjects were collected prospectively. Data analysis was analyzed with SPSS 21 program. Results It was observed that the subjects did not have difficulty in determining the direction in silent condition, but they had a significant difficulty
in localizing the 135-, 225-, and 315-degree angles especially when the noise was signal-to-noise ratio (SNR) ‐10 dB and the reverberation was 06 and 09 second (p ≤ 0.005). Subjects' performances were significantly altered in sequential implanted users both when the
SNR was changed and in the presence of reverberation (p < 0.05). Conclusion As a result of our study, it is thought that individuals with hearing loss will experience intense difficulties, especially in noisy and reverberant environments such as schools,
and using assistive listening devices in these environmental conditions will contribute positively to their academic development.
Frequency and Clinical Outcomes of Respiratory Infections in Children with Cancer Presenting with Febrile Illness
Authors: Srinivasan Arathi , Sekar Ramya Manur , Chandy Sara , Sundaram Balasubramanian
Published: October 2024

Objectives Fever is a known complication in pediatric cancer patients when on chemotherapy for which prompt initiation of empiric antibiotics is the single most important life-saving intervention. Nearly two-thirds of all children are treated without identifying the source of fever. Molecular diagnostics can improve management of febrile episodes and reduce unnecessary antibiotic use. The purpose of our study was to evaluate the pathogenic etiology of febrile episodes and analyze their clinical characteristics.

Materials and Methods We conducted a prospective observational study at our tertiary care institution from January 2019 to March 2020, to identify the etiology of febrile episodes with or without neutropenia in pediatric cancer patients and to study their clinical outcomes.

Results Forty febrile episodes were observed among 27 patients over a period of 15 months. The mean age group was 5 years. In 28 febrile episodes without a focus (70%), a respiratory organism (virus, bacteria, or coinfection) was detected. Rhinovirus was the most common single respiratory isolate (47.36%), followed by Streptococcus pneumoniae (21.05%) and six episodes had multiple viral isolates (21.42%). There was no prolonged hospitalization, need for intensive care unit or oxygen requirement, or mortality. The most common antibiotic used in empiric management was piperacillin–tazobactam. Aminoglycosides were added when there was a clinical suspicion of resistant organism.

Conclusion Around 70% of febrile episodes without a focus or documented infection in cancer children had a respiratory pathogen identified in nasopharyngeal swab. Molecular diagnostics greatly enhances diagnostic sensitivity and thereby individualizes the management of febrile illness in these children.
Realization of a hybrid multi-qubit quantum phase gate in circuit QED
Authors: Zhang Yu , Su Qiping , Kang Yihao , Zheng Wen , Li Shaoxiong , Yang Chuiping , Yu Yang
Published: October 2024

We propose to implement a hybrid multi-qubit quantum phase gate based on a setup comprising multiple microwave cavities coupled to a common superconducting transmon qutrit in the circuit quantum electrodynamics. The function of this hybrid quantum phase gate is that a phase related to the total number of cavities in non-vacuum state will be introduced when the transmon qubit is in an excited state. Furthermore, we propose an application, quantum voting machine, with this hybrid quantum gate. This scheme is scalable and simple to operate, requiring just a single step and necessitating only the reading of quantum states of one target qubit. While for quantum voting machine, it ensures the verifiability of the voting results through the measurement of the phase information of the target qubit. Additionally, the anonymity of the voters is ensured as the voting outcome is solely tied to the total number of affirmative votes. Numerical simulations indicate the feasibility of this hybrid quantum gate and quantum voting machine within the current quantum technology.
Revealing the dynamics of stagnant rings of third-grade fluid film with heat transfer in the presence of surface tension
Authors: Alaidrous Amel , Siddiqa Ayesha , Ashraf Hameed
Published: October 2024

In many engineering applications, including coating and lubrication operations, analyzing the temperature behavior of thin film flows on a vertically upward-moving tube is crucial to improving predictive models. This paper examines a steady third-grade fluid film flow with a surface tension gradient on a vertical tube. The mechanisms responsible for the fluid motion are upward tube motion, gravity, and surface tension gradient. This analysis focuses on heat transfer and stagnant ring dynamics. The formulated highly nonlinear ordinary differential equations are solved using the Adomian decomposition method. The conditions for stagnant rings and uniform film thickness are attained and discussed. The inverse capillary number C, Stokes number St, Deborah number De, and Brinkman number Br emerged as flow control parameters. The temperature of the fluid film rises with an increase in the C, St, De, and Br, whereas it decreases with an increase in thermal diffusion rate. The radius of stagnant rings tends to shrink by the increase in C, St, and De. When the value of De is high, third-grade fluid behaves like solids; only free drainage happens with smaller radius stagnant rings and high temperatures. A comparison between Newtonian and third-grade fluids regarding surface tension, velocity, temperature, stationary rings, and fluid film thickness is also provided.
Can new treatment garadacimab prevent attacks in patients with hereditary angioedema?
Authors: Craig Timothy
Published: October 2024

This video article presents the results of the pivotal phase III (VANGUARD) study investigating the efficacy and safety of garadacimab in patients with hereditary angioedema.
Survey on the distribution of medical imaging frequencies and dose levels for CT examinations in a comprehensive hospital in Shanghai
Authors: Yang Fanqiaochu , Qu Liangyong , Yao Jie , Zhou Zhijun , Gao Linfeng
Published: October 2024

With the continuous advancement and clinical application of CT technology, the increasing collective dose burden from CT scans and associated potential health risks have become significant concerns in radiation protection. Current research increasingly focuses on the cumulative effective dose (CED) resulting from multiple CT scans, often revealing patients with high CEDs, even exceeding 100 mSv. However, reports on CEDs from multiple CT scans in China are scarce. Therefore, we investigated the distribution of CT scan frequencies and CEDs at a comprehensive hospital in Shanghai, examining data from 1 October 2022, to 30 April 2024, sourced from the hospital’s radiology information system. The effective dose (E) was estimated using conversion factors k and DLP values from Radiation Dose Structured Reports (RDSR). We assessed the number of CT examinations conducted per patient and evaluated the CED over 1.6 years. During this period, 112 339 CT examinations were performed. Significant differences in CT examination frequencies were observed across different age groups and examination regions (P < 0.01). A total of 78.43% of patients underwent only one CT examination in 1.6 years, while 0.03% had more than 10 examinations, with a maximum of 15. Of the patients, 67.78% (76,142 individuals) received a CED less than 10 mSv, 0.05% (53 patients) received a CED over 50 mSv, and one patient exceeded 100 mSv. In conclusion, this study underscored the necessity of monitoring patients with high CT examination frequencies and CEDs, highlighting the importance of justification and optimization in medical radiation protection.
STGRL: SNN based two-stage geomagnetic road localization method
Authors: Luo Qinghua , Yu Mutong , Yan Xiaozhen , Liu Boyuan , Su Yuhao , Yang Longxin , Wang Lan
Published: October 2024

Geomagnetic navigation is a widely used positioning method capable of correcting the cumulative errors of odometers and inertial navigation systems, thereby ensuring long-distance positioning for vehicles in GPS-denied environments. However, common geomagnetic road navigation algorithms are susceptible to measurement noise, which hinder improvements in positioning efficiency and accuracy. To address this issue, this paper proposes a Siamese Neural Network (SNN) based two-stage geomagnetic road localization method. First, attitude angle information is combined with geomagnetic scalar and vector value to establish geomagnetic reference database to increase the feature dimensions of geomagnetic matching. Then, we use the Random Forest algorithm to perform a coarse matching of the data sequence to determine the current road, balancing the increased computational load resulting from the addition of feature dimensions. Finally, to further reduce the impact of random noise, this paper employs the SNN algorithm based on Transformer Encoder for fine matching of the data sequence. Experiments show that compared to existing methods, the average absolute positioning error of our algorithm has been reduced from 32.36 m to 4.07 m, and the increase in computational load is kept within an acceptable range.
Differential evolution-based time domain decomposition method for multi-impact vibration signals of reciprocating machinery
Authors: Dou Quanli , Song Yedong , Zhang Zhenjing , Zhang Jinjie
Published: October 2024

To solve the problem of extracting the impact component from the complex time-domain vibration signal of reciprocating machinery vibration signals, a differential evolution (DE)-based time domain decomposition method is proposed to achieve adaptive extraction of impact components. The method establishes new decomposition window containing three adjustment parameters to adapt to multiple forms of impact components. Furthermore, with the optimization objectives of minimizing reconstruction loss, amplitude moment loss, and similarity loss, a decomposition parameter optimization algorithm based on DE is established to achieve the optimization process of decomposition parameters. The results of processing simulated and actual vibration signals of diesel engines show that the new method can adaptively and accurately identify the impact component and impact time center in the vibration component, with a signal reconstruction loss of less than 2.5% and a decomposition time of only 54.1 s.
Large scale analogue experiments in rock mechanics: new experimental facility, 3D-printed material and insertion of force transducers for stresses measurement
Authors: Faure-Catteloin Delphine , Deck Olivier , Bonnelye Audrey , Abi-Aad Emilio , Fenneteau Baptiste , Gaillard Laura , Conin Marianne
Published: October 2024

The aim of this paper is to present a new experimental device that was designed to perform analogue rock mechanics experiments with 3D-printed material. The device consists in the creation of two rectangular excavations (mining tunnels) in slices of 3D sand printed material. This is a true-triaxial test device that allows us to study the mechanical behaviour of reduced scale models of underground structures. The experimental protocol entails the horizontal loading of the specimen, followed by the excavation of two galleries while the specimen is maintained under a constant load. The results of the test carried out in DiMiTri show a generally logical evolution of the sensors with an increase in vertical stresses induced by the presence of excavations. This paper discusses the upcoming challenges in terms of instrumentation that will be encountered, together with the new opportunities for future research.
Optimization of the memristor fabrication based on graph convolutional network
Authors: Wu Qiyuan , Jia Han , Tang Wenchao , Dongale Tukaram D , Cai Hongling , Wu Xiaoshan
Published: October 2024

In this article, a machine learning model for accurately predicting the performance of unknown memristors is constructed by employing a graph convolutional network approach. Thickness and elemental composition are used to transform memristors into graph-structured data. This model exhibits high accuracy and, based on extensive training with a certain type of memristor data, can be applied to novel memristors and give rapid predictions of the performance with only a small-batch sample reported in the literature, showing the potential for excellent transfer learning. This model is also applied to predict the performance of halide memristors, which have received less attention in current research, and it is indeed that a halide perovskite memristor with potential high switching ratio is predicted.
Generalized kudryashov and extended auxiliary equation methods for novel solitons solutions to (1+1)-dimensional doubly dispersive equation of murnaghan’s rod
Authors: Adnan Mirza , Ahmed Naveed , Rani Mehwish , Mohsin Bandar Bin
Published: October 2024

In this study, the Generalized Kudryashov method and the Extended Auxiliary Equations method are employed to investigate the strongly nonlinear (1+1)-dimensional Doubly Dispersive equation model of inhomogeneous Murnaghan’s rod for developing novel soliton solutions. These symbolic methods are famous for solving various problems involving nonlinear partial differential equations. The study finds novel solitons like hyperbolic, exponential, rational, and trigonometric. Moreover, 2D, 3D, and contour plots under tunable parameters depict graphical representations of the solutions that furnish dynamic wave behavior and insights into the material’s elastic properties under strain and stress.
Deformable bodies in two dimensions and the gauge aspect of bottle flip
Authors: Hasan Arryan , Mohd Arif
Published: October 2024

The water-bottle flip demonstration, that has received quite a viewership on media, involves tossing a bottle filled (up to some fraction of its volume) with water up in the air while giving it a transverse spin in such a way that it lands perfectly upright on its base. A mathematical description of the actual dynamics of the bottle flip is bound to be very complicated due to the highly non-linear motion of the fluid. A model that is amenable to an analytical treatment has been proposed in the literature. Conservation of angular momentum in the center of mass frame allows the angular velocity of the bottle to change, and in particular it becomes very low right before the landing. This is thought to be the essential feature needed for an upright landing of the bottle. In this paper we study a mathematical formalism that would be useful to improve upon the analysis of this model. We point out that the proper mathematical setting to analyze such systems is to model the content of the bottle as a deformable body. This brings out the gauge dependence of the angular velocity and shows that it can even go to zero without violating the conservation of angular momentum. The mechanism is well studied in literature under the heading of the falling cat problem. Indeed, from our point of view the bottle-flip system is a two dimensional analog of the falling cat problem (modulo the self-control of the cat).
Advancing nonlinear problem solutions: Newton-like iterative techniques for varied convergence orders using homotopy perturbation
Authors: Raza Muhammad , Toheed Daanish , Rehman Najma Abdul
Published: October 2024

Iterative methods are crucial in numerical analysis for approximating solutions to nonlinear problems encountered in various fields, such as biomathematics, thermodynamics, chemical engineering, and fluid mechanics. This paper introduces innovative iterative methods of convergence orders three and six to eight, developed using the homotopy perturbation technique (HPT). We address the limitations of traditional derivative-based methods by incorporating divided differences, resulting in derivative-free schemes with optimal convergence properties. We present four newly designed algorithms (HPM1, HPM2, HPM3, and HPM4) and provide a comprehensive convergence analysis. Numerical simulations and basins of attraction demonstrate the superior performance of the proposed methods compared to existing methods of the same order. The proposed methods exhibit faster convergence and larger basins of attraction, making them highly effective for solving nonlinear equations. Our new numerical methods rely on approximations and iterative processes, with each small step bringing us closer to the exact solution. Exploring these numerical methods encourages us to apply mathematics to solve challenging problems in physics, engineering, finance, and more.
Cosmic strings from Tribrid inflation
Authors: Antusch Stefan , Trailović Katarina
Published: October 2024

Tribrid inflation is a class of supersymmetric inflation models where the scalar component of a matter superfield, or a D-flat direction of matter fields, drives inflation. Similar to Hybrid inflation, the end of inflation is reached when a “waterfall field”, which was stabilized during inflation at a field value where the scalar potential features a large vacuum energy, starts rapidly rolling towards its minimum where a symmetry group G is spontaneously broken. In contrast to standard supersymmetric Hybrid inflation, where the inflaton is a gauge singlet, in Tribrid inflation it can be a gauge non-singlet, which, via its vacuum expectation value, already breaks the gauge symmetry. This raises the question whether topological defects can still form after inflation in this class of models, and if so, which types of defects are generated. We investigate this question systematically in realisations of Tribrid inflation where G = U(1) and we analyse under which conditions cosmic strings form. We find that in the considered cases where domain walls form, these are only temporary and do not invalidate the model realisations. We also discuss how our results can be used to analyse models of Tribrid inflation associated with the final step of SO(10) breaking, where cosmic strings can be metastable and provide a promising explanation of the recent PTA results hinting at a stochastic gravitational wave background at nanohertz frequencies.
Optical image watermarking based on orbital angular momentum holography
Authors: Zhu 朱 Jialong 家龙 , Ji 季 Jiaying 佳滢 , Wang 王 Le 乐 , Zhao 赵 Shengmei 生妹
Published: October 2024

We propose an optical image watermarking scheme based on orbital angular momentum (OAM) holography. Multiple topological charges (TCs, l) of OAM, as multiple cryptographic sub-keys, are embedded into the host image along with the watermark information. Moreover, the Arnold transformation is employed to further enhance the security and the scrambling time (m) is also served as another cryptographic key. The watermark image is embedded into the host image by using the discrete wavelet transformation (DWT) and singular value decomposition (SVD) methods. Importantly, the interference image is utilized to further enhance security. The imperceptibility of our proposed method is analyzed by using the peak signal-to-noise ratio (PSNR) and the histogram of the watermarked host image. To demonstrate robustness, a series of attack tests, including Gaussian noise, Poisson noise, salt-and-pepper noise, JPEG compression, Gaussian low-pass filtering, cropping, and rotation, are conducted. The experimental results show that our proposed method has advanced security, imperceptibility, and robustness, making it a promising option for optical image watermarking applications.
Spin fluctuations and orbital-selective superconductivity in Ba2CuO4−y: A FLEX study
Authors: Zheng 郑 Pei-Jun 裴俊 , Quan 全 Ya-Min 亚民 , Zou 邹 Liang-Jian 良剑
Published: October 2024

Recently discovered Ba2CuO4−y
provides new perspectives to the study of high-temperature superconductivity. Whereas, little is known about the spin dynamics of this material. In this work, we employ the fluctuation exchange (FLEX) approximation within the framework of spin-fluctuation mediated superconductivity to examine the behavior of the spin fluctuations of a two-orbital Hubbard model for Ba2CuO4−y
. Our calculations reveal an extraordinary spin resonance mode coupled to the superconducting state in the hole-underdoped regime. Furthermore, we confirm that the coupling between the electrons and this resonance mode can lead to a dip-like feature in the electronic spectrum as a feedback effect. In the hole-overdoped regime, by incorporating self energy into our calculations, we obtain orbital-dependent renormalizations and show how these self-energy effects can lead to the detailed gap structures and the orbital-selective superconductivity, which could not be obtained in a previous study using random phase approximation (RPA). This research may shed new light on searching for unconventional superconductors with higher transition temperatures.
Enhanced mechanical squeezing in an optomechanical system via backward stimulated Brillouin scattering
Authors: Chen 陈 Shan-Shan 珊珊 , Xie 谢 Yi-Long 亦龙 , Zhang 张 Jing-Jing 京京 , Zhang 张 Na-Na 娜娜 , Guo 郭 Yong-Rui 永瑞 , Yang 杨 Huan 桓 , Ma 马 Yong 勇
Published: October 2024

We investigate theoretically the enhancement of mechanical squeezing in a multimode optomechanical system by introducing a coherent phonon–photon interaction via the backward stimulated Brillouin scattering (BSBS) process. The coherent photon–phonon interaction where two optical modes couple to a Brillouin acoustic mode with a large decay rate provides an extra channel for the cooling of a Duffing mechanical oscillator. The squeezing degree and the robustness to the thermal noises of the Duffing mechanical mode can be enhanced greatly. When the Duffing nonlinearity is weak, the squeezing degree of the mechanical mode in the presence of BSBS can be improved by more than one order of magnitude compared with that in the absence of BSBS. Our scheme may be extended to other quantum systems to study novel quantum effects.
Heliospheric Effect on Solar Activity Parameters during Maximum Phase of Solar Cycle 24 (2012–2015)
Authors: Singh P. R. , Kushwaha Upendra Kr. Singh , Singh A. K. , Pant T. K.
Published: October 2024

The time series of daily data on solar activity proxies, namely the sunspot number (SSN), sunspot area (SSA), solar radio flux (F10.7), modified coronal index (MCI), solar flare index (FI), and cosmic ray intensity (CRI), were analyzed to understand the solar activity modulations and short-term periodicities therein. Rieger-type and other short-term periods include the solar rotational period that covers the maximum activity phase period (maximum phase of solar cycle 24). The wavelet power spectra and Periodogram of SSN, SSA, F10.7, MCI, FI, and CRI exhibited a significant short-term period. The heliospheric effects exist for a particular period (∼27 days) and they are related to the solar activity phenomena. The cross-correlation coefficients and time lags between the CRI and solar activity parameters were estimated to be ∼200, 46, 281, 39, and 47 days for SSN, SSA, F10.7, MCI, and FI respectively during the time series 2012–2015 (maximum phase of solar cycle 24).
Six lectures on linearized neural networks
Authors: Misiakiewicz Theodor , Montanari Andrea
Published: October 2024

This tutorial examines what can be learnt about the behavior of multi-layer neural networks from the analysis of linear models. While there are important gaps between neural networks and their linear counterparts, many useful lessons can be learnt by studying the latter.

A few preliminary remarks, before diving into the math:

•

We will not assume specific background in machine learning, let alone neural networks. On the other hand, we will assume some graduate-level mathematics, in particular probability theory (however, we will refer to the literature for complete proofs.)

•

Some of the notations that are used throughout the text will be summarized in appendix A.

•

We will keep bibliographic references in the main text to a minimum. A short guide to the literature is given in appendix B.
Kernels, data & physics
Authors: Cagnetta Francesco , Oliveira Deborah , Sabanayagam Mahalakshmi , Tsilivis Nikolaos , Kempe Julia
Published: October 2024

Lecture notes from the course given by Professor Julia Kempe at the summer school ‘Statistical physics of Machine Learning’ in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.
Les Houches lectures on deep learning at large and infinite width*
Authors: Bahri Yasaman , Hanin Boris , Brossollet Antonin , Erba Vittorio , Keup Christian , Pacelli Rosalba , Simon James B
Published: October 2024

These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include the various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks, connections between trained deep neural networks, linear models, kernels and Gaussian processes that arise in the infinite-width limit, and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.
Computational complexity of deep learning: fundamental limitations and empirical phenomena
Authors: Barak Boaz , Carrell Annabelle , Favero Alessandro , Li Weiyu , Stephan Ludovic , Zlokapa Alexander
Published: October 2024

This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ratio to statistical query complexity and the Franz–Parisi criterion, as well as the various relationships between those criteria. We also present a few aspects of the study of deep learning, from both a theoretical and empirical point of view.
Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching
Authors: Li Guang , Liu Jifang , Chang Zhiqiang , Luo Jing , Wang Yanzhao
Published: October 2024

The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.
Research on Power Grid Load Forecasting Method Based on Support Vector Machine
Authors: Chen Rong , Zheng Yan
Published: October 2024

In order to improve the accuracy of power load forecasting, a power load forecasting model with the support vector machine (SVM) was proposed in the existing load forecasting methods. Firstly, establish the basic analysis on the SVM theoretical foundation. Through the establishment of SVM prediction model, the real power data is sent into the support vector machine prediction model for learning and forecasting once it has been processed. Think about the electricity grid load change rule states, our enterprises accurately understand the local power grid operation and scheduling measures by estimating the electricity uses. Meanwhile, it optimizes the power grid management through careful scheduling means, prevent excessive consumption of resources and large energy consumption. To achieve the prediction effect, this paper builds the prediction model and analyses the data with the MATLAB. In the experiment, the real value is compared with the anticipated value. Through the analysis of more than 8000 load data, the accuracy of the training set is 96.184%, and the accuracy of the test set is 96.6134%. The outcomes demonstrate the great accuracy of the SVM prediction model in predicting electricity load.
Research on Digital Twin-Oriented UAV Data Chain Communication
Authors: Chen Zhang , Liu Lijun , Zhang Hui , Dai Xueya , Fu Yanfang
Published: October 2024

In this paper, for the problems of low utilization of link resources, poor flexibility, poor adaptability, and low accuracy and difficulty in verification and assessment of traditional network modelling technology in Link-16 data chain time slot allocation research, digital twin technology is introduced to combine digital twin with data chain modelling to build a digital twin-oriented data chain verification platform. And through the OPNET simulation platform, the UAV data chain simulation model of multiple terrain scenarios is established. The influence of terrain on UAV communication is explored, focusing on the irregular terrain model, including the consideration of electromagnetic wave propagation and multipath effect, and the data chain performance indexes under different terrain conditions are compared through simulation. It is found that marine terrain has a significant impact on latency and communication, while mountainous terrain results in higher latency due to complex terrain structure and vegetation cover. Digital twin oriented UAV data chain modelling is characterized by high accuracy and low latency. Therefore, the research in this paper can provide guidance for designing a more reliable and efficient Link-16 data chain communication system and provide substantial insights for military communication network planning.
Face Recognition with Siamese Networks
Authors: Niu Yuxin , Wang Zhongsheng
Published: October 2024

Face recognition method is a common problem in modern Internet life. In this paper, face recognition will be designed based on Siamese network. The traditional methods have some problems, such as being greatly affected by environmental factors, insufficient processing of individual differences, lack of robustness and adaptability. However, the existing Siamese network based method has better recognition effect. In this paper, a face recognition technology based on Siamese network will be proposed. In this method, the features of two face images are extracted by the same neural network, and then these feature vectors are input into a distance metric model to calculate the similarity or distance between them, which is used to compare the similarity between faces. This method reduces the training parameters of the model, improves the utilization of parameters, and the average accuracy can reach 98%. Contrasted with traditional face recognition techniques, it offers enhanced accuracy and resilience, delivering favourable outcomes in real-world applications.
Fatigue Driving Detection Based on Facial Multi-Information Feature Fusion
Authors: Qu Wenjing , Wang Zhongsheng
Published: October 2024

Driver fatigue driving risk detection is one of the important areas of road traffic safety research in China. According to statistics, more than 40% of China’s traffic accidents every year are related to driver fatigue. In order to reduce this risk, this paper proposes a new method based on facial multi-information feature fusion to realize real-time fatigue driving detection. The method includes using the camera to capture the driver’s face image in real time, combining the face recognition in Dlib library and the detection of 68 key points, and implementing the face detection through OpenCV. Further, the opening and closing states of eyes and mouth were analyzed based on EAR and MAR algorithms, and a reasonable threshold was set to judge fatigue behavior. The HPE head pose estimation method is used to judge the fatigue state by considering the pitch Angle and roll Angle information. Finally, the improved rough set theory comprehensive evaluation method is used to carry out decision fusion processing on the feature results, and intuitively display the system detection results, which is convenient to evaluate the fatigue state of the current driver. The experimental results show that the multi-feature fusion detection method has good real-time performance, the accuracy rate is 94.55%, and the fatigue state can be detected quickly and accurately under various driving attitudes.
UAV Vehicle Detection System Based on YOLOv8
Authors: Chang Hao , Wang Zhongsheng
Published: October 2024

To utilize the drone vehicle detection technology to observe the road traffic conditions in real-time, thereby improving the operational effectiveness and safety of the traffic system, this paper adopts a vehicle target detection algorithm based on YOLOv8. YOLOv8 is the latest iteration developed by Ultralytics, which has further improvements and enhancements compared to YOLOv5. In the backbone network, YOLOv8 employs a C2f structure with richer gradient flow, boosting feature selection capabilities, and enhancing target detection performance and accuracy; In the detection head, YOLOv8 utilizes a Decoupled Head design and uses an improved Anchor-Free method to reduce computational complexity while improving detection efficiency; In terms of loss function, YOLOv8 utilizes the The Task-Aligned Assigner positive sample allocation strategy, and adds the Distribution Focal Loss to improve the model’s generalization ability. This paper divides the vehicles data set into 5 types, trains it using a pre-training model, and optimizes the network parameters through multiple iterations to achieve better detection performance. The experimental results indicate that YOLOv8 reaches the mAP of over 97% on the experimental data set, which is an improvement of approximately 8% compared to YOLOv5, and achieves a good detection performance, and is highly practical.
Research on Image Colorization Based on Deep Learning
Authors: Li Ao , Wang Zhongsheng
Published: October 2024

Gray scale picture colorization, a research hotspot in the domain of computer vision, seeks to assign reasonable colours to every single pixel of a grayscale picture, enriching the visual information of the coloured picture. Neural networks take grayscale images as input and output colorized images. From a psychological perspective, colours can provide observers with a more pleasant perceptual experience. Old photos can also be coloured, which are important documentary resources for recording real history and restoring the social landscape of the time. Additionally, due to the limitations of imaging mechanisms, most medical images are grayscale. Therefore, research on grayscale image colorization is a task of significant importance. However, existing automatic image colorization systems have issues such as “blurry boundaries”, “colour overflow”, “inappropriate colour choices” and “incorrect colour regions”. This paper proposes a model combining adversarial neural networks and U-Net networks, optimizing them by incorporating global feature fusion modules, Pathway emphasis mechanisms and spatial focus mechanisms. The idea of Markov discriminator was added to the discriminator, and the loss function was optimized. The ultimate investigative conclusions demonstrate that the PSNR and SSIM of the suggested system are 24.45 and 0.941, accordingly, in relation to the preceding protocol, which are 3% and 5% higher than the previous algorithm, and the example has been ameliorated to some degree. And the loss function is modified, and the final algorithm can improve the effect of picture colouring, which encompasses a broad spectrum of application prospects.
Research on Pseudo-Label Improvement Based on Probabilistic Uncertainty
Authors: Yang Chuanyuan , Zhao Shifeng , Fu Yanfang
Published: October 2024

The pedestrian re-identification task explored in this study has strong application scenarios in real life. Generally, in actual scenarios, the pedestrian samples taken by the camera are unlabeled, so pseudo-labeling is used. However, low-quality image feature extraction and imperfect clustering will produce low-quality and incorrect clustering results, which will have a negative impact on the re-identification performance. This article proposes a dual extraction method (PLR). The core idea of this study is to explore the distribution differences and similarities between two data sets through the pseudo-label method, so that the model can learn the distribution in one data set and apply it to another data set and accurately identify the target person to be found. Therefore, this article proposes a dual refinement method based on probability uncertainty. PLR first removes unqualified clustering results through probability uncertainty, and then continues to refine the remaining ones. Experiments have shown that compared to newer methods, PLR is approximately 1% higher in most tasks.
Global Indoor Localization Method for AGV Based on Ultrasonic Asynchronous Distance Measurement
Authors: Yang Xin’gang , Song Xianchen , Du Runtian , Shu Botong , Yang Shuo , Han Xuzhao
Published: October 2024

In order to realize free path indoor navigation and meet requirements of low cost, an integrated kinematics model of AGV and orienting mechanism of ultrasonic transmitter is established, and a global localization scheme based on three times asynchronous correlated ultrasonic distance measurement and with small number of end-nodes is given. The experiments showed that maximum localization error is just only 67 mm when the AGV travelled at a speed of 0.5 m/s and performed distance measurement at long time intervals of 1 s, which can meet the positioning requirements of most workshop operations.
Three-Frame Random Phase-Shifting Algorithm Based on VU Decomposition Method and Ellipse Fitting
Authors: Li Ziwen , Du Hubing , Feng Leijie
Published: October 2024

To enhance the precision and robustness of the three-frame phase shift technique, a new three-frame random phase-shifting fringe pattern phase demodulation method that combines VU decomposition and ellipse fitting techniques without the need for pre-filtering is proposed. The proposed method first performs VU decomposition on the interferograms to establish two orthogonal components of the fringe pattern. Then, the ellipse fitting method is used to obtain the relevant ellipse coefficients, thereby achieving precise phase demodulation. This method does not require an accurate phase-shifting process, thereby relaxing the stringent requirements on the phase shifter. Compared with traditional algorithms, the proposed method performs better under conditions of non-uniform background intensity and modulation amplitude. Numerical simulation experiments demonstrate that the proposed method maintains good stability across 20 repeated tests. Within the SNR range of 30 to 50 dB, the RMSE of the suggested approach is approximately 0.006 rad. This proves the effectiveness of the algorithm and provides a new approach for the development of three-frame phase-shifting technology.