bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments
Authors: Liu Jin , Li Zan , Gao Yue , Miao Qiguang , Yang Li , Wang Danyang , Qi Peihan 
Published: October 2024

Abstract
The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.
A proportional fusion adaptation algorithm to reduce noise in sEMG signals of the lower limb
Authors: Li Hao , Song Yongxian , Chen Quan , Li Yuanyuan , Zhang Qi 
Published: October 2024

Abstract
To address issues related to unknown noise pollution and the inadequate performance of single adaptive noise reduction algorithms in measuring lower limb surface electromyography (sEMG) signals, this paper proposes an algorithm that combines the fast convergence speed and strong noise reduction capability of recursive least-squares (RLS) with the low computational complexity of the normalized least-mean-square (NLMS) and improved proportionate NLMS (IPNLMS). Through the proportional fusion of RLS with NLMS and RLS with IPNLMS, the proposed algorithm greatly improves convergence speed, stability, and noise reduction performance while effectively reducing computational complexity. Moreover, considering the influence of the initial value of weights on the noise reduction performance during the updating process of the adaptive noise reduction algorithm, a weight initial value setting (WIS) module is proposed to optimize the initial value of the weights by the known amount of data. Based on 50 independent experiments, an adaptive noise reduction algorithm and WIS module were used to reduce the unknown noise in the lower limb sEMG signals, which was generated by white Gaussian noise, power line interference, or hybrid noise interfered by an unknown environment. The noise reduction performance was evaluated by using the average value of the signal-to-noise ratio (SNR), the root-mean-square error, and R-square. Compared with the RLS, NLMS, and IPNLMS algorithms for noise reduction of vastus lateralis, rectus femoris, and biceps femoris signals containing unknown noise, the SNR of RLS-NLMS, RLS-IPNLMS, WIS-RLS-NLMS, and WIS-RLS-IPNLMS is improved by an average of [7.92%, 55.54%, 55.63%], [7.45%, 54.90%, 54.99%], [19.70%, 72.38%, 72.71%], [19.32%, 71.84%, 72.19%]. The simulation results verify that the proportional fusion adaptive noise reduction algorithm and the WIS module effectively accelerate the convergence speed, enhance the noise reduction capability, and reduce the computational complexity.
Novel modified weights and cosine similarity based maximum marginal projection and its application in fault diagnosis
Authors: Wang Yongqiang , Wang Wei , Zhao Ruili , Chang Kaile , Duan Qixin 
Published: October 2024

Abstract
Facing the problem that the data generated in industrial processes have few labeled samples and the local manifold learning dimensionality reduction method ignores the local spatial structure of sample points and the distance relationship in constructing different weights. To solve the above problems, this paper presents a novel modified weights and cosine similarity based maximum marginal projection named MCMMP. In MCMMP, cosine similarity is used to consider the space feature of sample points, which enhances the performance of dimensionality reduction. The new modified weights are applied to measure the between-class and the within-class sample points, which enhance the divisibility of sample points. After MCMMP dimensionality reduction, the classifier is used to classify the dimensionality reduction sample points. Finally, the proposed new method is used in two cases Tennessee Eastman Process (TEP) and Three-phase Flow Facility (TFF) to test the fault diagnosis performance. The results of the simulation process indicated that the new fault diagnosis method based on MCMMP, compared with other related diagnosis methods, has good performance.
An aero–engine remaining useful life prediction model based on clustering analysis and the improved GRU–TCN
Authors: Chen Fudan , Yu Yang , Li Yuanjiang 
Published: October 2024

Abstract
Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA–DRGRU–TTCN, aimed at accurately predicting the RUL of engines. Firstly, a density–based spatial clustering of applications with noise algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by Jensen–Shannon divergence. Secondly, the deep connectivity of the residual gated recurrent unit (GRU) module is designed to extract deeper degradation features, and an improved time–based mean square error loss function based on the first degradation time point is applied to temporal convolutional networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C–MAPSS dataset show that the proposed method achieves better performance compared to existing state–of–the–art methods.
Doxorubicin-loaded methoxy-intercalated kaolinite as a repackaging of doxorubicin for an enhanced breast cancer treatment: in vitro and in vivo investigation
Authors: Sayed Fatma Al-Zahraa , Mohamed Ayman S , Fahmy Heba Mohamed 
Published: October 2024

Abstract
Doxorubicin (Dox) is one of the most common wide-spectrum chemotherapeutics. However, its efficacy is limited due to off-target accumulation and selectivity issues. In this study, we compared the anti-cancer effect and biocompatibility of KaoliniteMeOH-Dox (KMD), a Dox repackaging, to Dox monotherapy. The formulation was extensively tested using transmission electron microscopy, dynamic light scattering, zeta potential, Fourier transform infrared, x-ray diffraction, and in vitro drug release. The MTT assay measured MCF-7 cell growth inhibition in vitro. In vivo testing involved 20 naïve mice and 40 Ehrlich solid tumor-inoculated mice. The tumor size was monitored for 18 d. In all experimental groups, tumor and cardiac tissues were evaluated for cytotoxicity and genotoxicity by addressing oxidative stress, histopathology, and comet assay. We found that KMD has many advantages in terms of size, charge, shape, high loading efficiency (90.16%), and pH-dependent release. The MTT assay showed that the formulation outperformed Dox in growth inhibition and selectivity. In vivo, research showed that KMD suppressed tumors by 86.075% compared to 60.379% for free Dox. Histological analysis showed that KMD reduced tumor size, metastasis, and carcinogenic oxidative stress and inflammation in mice without harming naive mice. Based on the obtained data, the KMD formulation holds promise for breast cancer treatment and warrants further investigation.
The effect of intra- and inter-fractional motion on target coverage and margins in proton therapy for uveal melanoma
Authors: Björkman Daniel , Via Riccardo , Lomax Antony , De Prado Maria , Baroni Guido , Weber D C , Hrbacek Jan 
Published: October 2024

Abstract

Introduction. This study aims to assess the effective lateral margin requirements for target coverage in ocular proton therapy (OPT), considering the unique challenges posed by eye motion and hypofractionation. It specifically addresses the previously unaccounted-for uncertainty contribution of intra-fractional motion, in conjunction with setup uncertainties, on dosimetric determination of lateral margin requirements. Method. The methodology integrates dose calculations from the in-house developed treatment planning system OCULARIS with measured intra-fractional motion, patient models from EyePlan and Monte Carlo (MC) sampling of setup uncertainties. The study is conducted on 16 uveal melanoma patients previously treated in the OPTIS2 treatment room at the Paul Scherrer Institute (PSI). Result. The retrospective simulation analysis highlights a significant impact of non-systematic factors on lateral margin requirements in OPT. Simulations indicate that reducing the 2.5 mm clinical lateral margin, represented by a 2.1 mm margin in this work, would have resulted in inadequate target coverage for two patients, revealing a greater impact of non-systematic factors on lateral margin requirements. Conclusions. This work characterizes intra-fractional motion in 16 OPT patients and identifies limitations of clinical margin selection protocols for OPT applications. A novel framework was introduced to assess margin sufficiency for target coverage. The findings suggest that prior research underestimated non-systematic factors and overestimated systematic contributions to lateral margin components. This re-evaluation highlights the critical need to prioritize the management of non-systematic uncertainty contributions in OPT.
Investigation of optical soliton solutions for the cubic-quartic derivative nonlinear Schrödinger equation using advanced integration techniques
Authors: El-Horbaty Mahmoud , Gepreel Khaled A , Yildirim Yakup 
Published: October 2024

Abstract
This paper aims to investigate optical soliton solutions in the context of the cubic-quartic derivative nonlinear Schrödinger equation with differential group delay, incorporating perturbation terms for the first time. Motivated by the need to better understand soliton dynamics in advanced optical communication systems, we employ three integration techniques: the direct algebraic approach, Kudryashov’s method with an addendum, and the unified Riccati equation expansion method. Our study reveals that, by appropriately selecting parameter values, the resulting solutions include Jacobi elliptic functions that describe straddle solitons, bright, dark, and singular solitons. We also identify the conditions under which these soliton pulses can exist. Furthermore, we provide numerical simulations to illustrate these solutions under specific parameter settings, highlighting their potential applications in optical fiber systems.
Unlocking high-efficiency solar energy: optimizing P3HT/Si hybrid solar cells through numerical simulation
Authors: A K Sijo , Krishnan Akhil , Sapna P , Tom Emmanuel 
Published: October 2024

Abstract
This study numerically investigates the optimization of P3HT/Si hybrid solar cells for maximum efficiency via numerical simulation using SCAPS ID. The analysis identifies P3HT layer thickness and Si donor density as critical parameters, with optimal values of 20 nm and 1017 cm−3, respectively. Exceeding these values leads to decreased efficiency due to reduced hole mobility, increased recombination, and enhanced absorption loss. Furthermore, minimizing defect density in Si is crucial for achieving high efficiency. The optical properties of the P3HT/Si hybrid thin film were investigated using the transfer matrix method (TMM), yielding exceptional transmittance (94.5%), low reflectance (4.5%), and minimal absorption (1.0%) at a wavelength of 0.5876 μm. This highlights the material’s potential for efficient and transparent photovoltaic devices. By optimizing these parameters, a maximum efficiency of ∼24% was achieved, demonstrating the potential for high-performance hybrid solar cells. This work underscores the significance of numerical analysis in designing efficient hybrid solar cells, paving the way for the development of low-cost, high-performance devices via spin coating, a groundbreaking advancement in solar energy harvesting.
Monitoring kiloelectron-volt emission variability in partially observed nuclear events through distance weighted censored control charts
Authors: Nisar Shumaila , Raza Syed Muhammad Muslim , Albalawi Olayan , Alharthi Aiedh Mrisi , Mahmood Haider 
Published: October 2024

Abstract
The variation in the lifespan of particles is a crucial parameter in nuclear physics and is essential for identification purposes. Modern particle detectors excel at recognizing individual radioactive nuclei arrivals and their subsequent decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radio active process. This study focuses on the meticulous surveillance of the standard deviation in keV emission from partially observed events within the domain of nuclear physics.The utilization of censored control charts allows for the effective management of incomplete data, enabling researchers to make informed decisions despite potential limitations in observation. To accomplish this, the methodology employs the statistical approach known as Distance Weighted Mean based Standard Deviation (DWMS) integrated with the application of censored control charts. We propose a DWMS based exponentially weighted moving (DWMS-E) control chart for monitoring kiloelectron-volt E(keV) data. The proposed charts is developed for Weibull lifetimes with type-I censored data. The goal is to find changes in the mean of Weibull lifetimes with known and estimated parameter conditions. The performance of the proposed DWMS-E chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on E(keV) related to the alpha decays of 177 Lutetium isotope is also discussed.
Investigation of periodic heat transfer on the transient thermal characteristics of fully wet moving semi-spherical porous fin composed of functionally graded material
Authors: Pavan Kumar P L , Gireesha B J , Venkatesh P 
Published: October 2024

Abstract
This study investigates the thermal behaviour of a fully wet, moving semi-spherical porous fin made of linear Functionally Graded Material (FGM). This investigation examines the fin response to convective-radiative heat transfer under periodic variations in base temperature across three different FGM scenarios: Homogeneous material (HM), Functionally Graded Material I (FGM I) and Functionally Graded Material II (FGM II). The resultant nonlinear partial differential equation is accurately solved using the Finite Difference Method (FDM) and outcomes are benchmarked against existing literature. This research pioneers an investigation into the effects of periodic heat transfer on the detailed thermal profiles of FGM fin, an area not been explored in existing literature. It significantly enhances understanding of the influence of oscillatory base temperatures on thermal management within FGMs, uncovering pivotal insights into the interaction between material grading, amplitude of temperature oscillation and their collective effects on thermal efficiency. Additionally, this research assesses the influence of variables such as the amplitude of input temperature, frequency of oscillation, thermal conductivity grading parameter and others on temperature distribution along the fin length and over dimensionless time. Notably, periodic heat transfer induces a dynamically wavy thermal profile in the fin over time, attributable to oscillating base temperatures. Moreover, the analysis demonstrates that FGM II fin exhibit the most significant temperature distribution, followed by FGM I and HM. The fin heat transfer rate is profoundly influenced by the amplitude of input temperature and the thermal conductivity grading parameter. These insights are pivotal for optimizing fin designs in critical applications, including electronics cooling, HVAC systems, automotive engine cooling and solar energy collection, substantially improving upon traditional designs.
Non-linear dynamics and control of COVID-19 in india revisited : evidence of synergistic, antagonistic and threshold effects
Authors: Yogita  , Adlakha Neeru 
Published: October 2024

Abstract
Various researchers have investigated the COVID-19 pandemic dynamics since 2020 focusing on various aspects of the epidemics. However, the non-linear dynamics of this epidemic is still not well understood. India having the largest population in the world with wide cultural, genomics, and immunity diversity is being considered a suitable case for present investigation to generate the crucial information of non-linear phenomena during the first two waves of the epidemics. An SIRD model is suggested, integrating varying transmission, recovery, and mortality rates over time to explore the nonlinear behavior of the epidemic in India. The time-dependent transmission, recovery, and mortality are calculated using the finite difference method with the aid of actual data released by the Indian government, for the periods before and during the lockdown phases. The novel results generated from the model show evidence of synergistic, antagonistic, and threshold effects and the role of various measures in controlling these epidemic trends.
The number of spanning trees in K

n
-chain and ring graphs
Authors: Cheng Sujing , Ge Jun 
Published: October 2024

Abstract
The problem of counting spanning trees of graphs or networks is a fundamental and crucial area of research in combinatorics, while has numerous important applications in statistical physics, network theory and theoretical computer science. Very recently, Kosar, Zaman, Ali and Ullah obtained a nice formula on the number of spanning trees of a K
5-chain network

K

5

ℓ

constructed by connecting ℓcopys of complete graphs K
5. They made extensive use of matrix theory and spectral graph theory, especially the normalized Laplacian of graphs. In this paper, by using a rather simple and more physical treatment (the mesh-star transformation in electrical network) without any linear algebra, we generalize their result to K

n
-chain graphs and K

n
-ring graphs. The results show that there is a simple relation between the number of spanning trees of the K

n
-chain graph

L

n

ℓ

and the K

n
-ring graph

C

n

ℓ

. We also calculate the corresponding tree entropy (or so called ‘the asymptotic growth constant’) and find that the tree entropy of the corresponding K

n
-chain graphs and K

n
-ring graphs are totally the same.
Study of structural, magnetic, and physical properties in nano cobalt ferrite by computational analysis
Authors: Boussaida B , Masrour R , Tamboli Qudsiya Y , Patange Sunil M , Zakde Kranti R 
Published: October 2024

Abstract
Using the DFT+U method, we investigated the structural, magnetic, electronic, mechanical, thermal, optical, thermoelectric, and thermodynamic properties of the CoFe2O4 system. The GGA approximation was employed to calculate the exchange–correlation potential. The CoFe2O4 has a metallic bond and is ductile, as determined by Poisson’s and Pugh’s ratio mathematical calculations. Transport properties such as the figure of merit, thermal (k/τ) and electrical (σ/τ) conductivities, power factor, and Seebeck coefficient were computed to explain the thermal behavior of this spinel. Additionally, optical parameters like energy loss L(ω), absorption coefficient α(ω), dielectric constants ε(ω), optical conductivity σ(ω), reflectivity R(ω), extinction coefficient k(ω), and refractive index n(ω) are investigated. Lastly, we study how the magnetic phase transitions, magnetization, critical temperature, and specific heat capacity of this material are affected by an external magnetic field at constant pressure. Around the critical temperature, magnetic entropy and relative cooling capacity for various external magnetic fields are obtained.
Quantum pairwise-parallel mismatch attack on Kyber
Authors: Wang Yaru , Liu Jianmei 
Published: October 2024

Abstract
Quantum algorithm uses the quantum parallel method to calculate, which can better solve the encryption and decryption problems in cryptography and secure communication. This paper proposeS a quantum pairwise-parallel mismatch attack on Kyber using the quantum binary search method. We first give quantum search methods for finding the secret key and show that our method can be applied to Kyber. Then, According to the proposed quantum search method, we compute the number of queries required and computational complexity for recovering the full key on Kyber. Compared with the existing results, our improved attack significantly reduces the number of queries and computational complexity.
Gas jet target with controllable density via throat diameter of conical nozzle
Authors: Zuo Zebin , Yang Yang , Chen Guanglong , Cao Yunjiu 
Published: October 2024

Abstract
A supersonic gas jet has been a special target in the ultraintense laser interaction field due to its controllable atomic density distribution. This work investigates the spatial atomic density distribution in argon gas jets ejected from conical nozzles with different throat diameters. Both experiment and simulation results show that the atomic density and its distribution can be controlled by changing the throat diameter of the conical nozzle. The quantitative dependence of atomic density on the throat diameter under different backing pressures is obtained. It also agrees with that from the one-dimensional gas dynamics model. However, it is noted that for a large throat diameter at a high gas backing pressure, a radial saddle-shaped atomic density profile is demonstrated experimentally within a few millimeters away from the nozzle outlet. The results are helpful to optimize the density profile in gas-jet targets and to understand the effect of the throat diameter of the conical nozzle on cluster size in Hagena scaling law.