bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on civil aircraft fault diagnosis method based on selective integration
Authors: Ren Chenhui , Ma Linnlong , Wan Junlong , Huang Tianrui 
Published: May 2024

Abstract
Based on the research of the existing methods, this paper applies the performance parameters of the base learner and proposes the overall performance metrics of the base learner subset based on the diversity measurement method, the diagnostic accuracy of the base learner, and the running time. The performance metric as the fitness function of the genetic algorithm establishes the selection method of the subset of the base learner; the deep neural network is used to learn the complex nonlinear relationship between the base learners and a selective integration framework based on the stacking method is proposed to improve the overall diagnostic performance of integration model. Then, the data from engine operation to failure are used to verify the method performance proposed in this paper. The comparison results find that the method has a higher fault diagnosis accuracy and robustness. Finally, the method is streamlined to make it more applicable and suitable for engineering problems.
An efficient aerodynamic optimization method based on approximate gradient analysis
Authors: Liu Miao , Qian Ruizhan 
Published: May 2024

Abstract
With the rapid development of the computer capability and the continuous improvement of CFD (Computational Fluid Dynamics) technology, optimization design has gradually become a powerful and reliable means to solve the aerodynamic design problems. Among all kinds of optimization methods, gradient-based optimization algorithm can find the local optimal solution quickly, which is widely used in engineering problems. However, the computation of gradient is very expensive when an engineering problem involves large number of design variables. In this paper, an optimization method based on approximate gradient analysis is introduced, where the one-dimensional linear searches during the optimization process are evaluated with high-fidelity flow field analyses, while the gradients are evaluated with low-fidelity flow field analyses, leading to a significant saving in the computational cost of high-fidelity analysis. This method is demonstrated using a numerical example and RAE2822 airfoil optimization, and it is finally applied to the aerodynamic optimization of a wing-body configuration, which obtains good optimization results.
Prediction of maintenance hours based on maintenance scenarios
Authors: Chang Ningning 
Published: May 2024

Abstract
Maintenance time is one of the important parameters that affect maintenance costs, and is a factor in evaluating whether civil aircraft can be repaired quickly and economically, comprehensively reflecting the level of maintainability design. The study in this article is based on the analysis of maintenance scenarios across the entire system by identifying typical maintenance modes and their decomposition actions. At the same time, the concept of degree division and recognition principles are introduced to establish a maintenance time prediction table. The aim is to scientifically and reasonably predict maintenance time during the aircraft design stage, identify improved designs that can reduce maintenance costs, reduce the consumption of maintenance resources, improve maintenance efficiency, and ultimately achieve the goal of reducing the entire life cycle cost of the aircraft. Finally, a case study of maintenance time for a certain aircraft scheduled maintenance scenario was conducted using the prediction table, the main factors affecting maintenance time are analyzed, and directions for optimizing design are proposed.
Time cooperative guidance law based on neural network
Authors: Su Shi , Nan Ying 
Published: May 2024

Abstract
In response to the issue of coordinated guidance for vehicle groups, a neural network-based time-to-go prediction method has been designed. The influence of the motion state of both the vehicle and the target on the prediction results has been considered in this method. Compared with the classical method, the prediction error of the moving target is obviously reduced. A guidance law with a time synergy coefficient is proposed. This method adjusts the flight trajectory of the vehicles by the time-to-go error, ensuring that the cluster of vehicle arrive at the target simultaneously. According to the simulation results, the prediction error of the neural network is less than 0.4 s, and the maximum coordination error is 0.2 s, which achieves the effect of cooperative interception.
ARMA time series prediction model for fault detection of launch vehicle
Authors: Yang Tengyue , Wang Haiying , Ma Guorong 
Published: May 2024

Abstract
Spaceflight is a high-risk activity, especially for launch vehicles, and the losses caused by launch failures are considerable. Real-time fault detection is a prerequisite to ensure that future launch vehicles can detect and isolate faults in a timely manner, and ultimately reduce the hazard of fault. In this article, a time series prediction model of axial overload and three-axis angular rate is established using ARMA (Auto Regressive Moving Average) time series prediction model, and the residuals of the prediction model are analyzed and combined with the fault determination decision strategy to determine whether the system has a fault, and a fault tree can be built to further locate the fault location.
Dynamic multi-objective evolutionary algorithm based on decomposition with hybrid prediction
Authors: Zhao Shenjia , Zhang Hairui , Lyu Rui 
Published: May 2024

Abstract
The proposed dynamic multi-objective evolutionary algorithm, DMOEA/D-HP, addresses temporal variations in both the Pareto Front (PF) and Pareto Set (PS) for dynamic multi-objective optimization problems (DMOPs). Utilizing a hybrid prediction approach, the algorithm adapts to the dynamic nature of the problem. The population is divided into three segments for prediction: individuals with a distance greater than a threshold in PS for central prediction, those with a distance less than a threshold in PS for differential evolutionary prediction, and the remaining individuals for cross-mutation to maintain diversity. To assess DMOEA/D-HP’s effectiveness, it is compared with three advanced algorithms in DMOP by using the DF test set. Experimental results demonstrate that DMOEA/D-HP outperforms in terms of distribution and convergence when solving DMOPs.
Research on collaborative localization technology of UAVs based on INS and UWB
Authors: Xu Shuai , Li Qun , Wu Lianghua , Jiang Shuming , Jiang Ning , Li Chao 
Published: May 2024

Abstract
The trend of miniaturization and low-cost development in cluster systems restricts the ability of all UAVs within the cluster system to be equipped with expensive high-precision navigation equipment. Relying solely on their low-precision INS (inertial navigation system) will cause navigation errors to quickly diverge. To improve the navigation accuracy of UAVs, a distributed collaborative localization method combining leader and wingman is proposed, and the corresponding mathematical model is derived. The wingman obtains the position information and distance information of the leader through UWB (Ultra-Wide Band) devices and then applies the Kalman filter algorithm to fuse it with its inertial navigation solution information, thereby improving the positioning accuracy of the wingman. On this basis, to address the issue of height channel divergence when the number of leaders decreases, it is proposed to add a barometer and maintain collaborative positioning accuracy by introducing height information assistance. Finally, the feasibility of this method was verified through mathematical simulation, and the simulation results show that compared with inertial navigation, the collaborative navigation and positioning method is more suitable for long-endurance navigation.
The FASER detector
Authors: Abreu Henso , Mansour Elham Amin , Antel Claire , Ariga Akitaka , Ariga Tomoko , Bernlochner Florian , Boeckh Tobias , Boyd Jamie , Brenner Lydia , Cadoux Franck , Casper David W. , Cavanagh Charlotte , Chen Xin , Coccaro Andrea , Crespo-Lopez Olivier , Débieux Stéphane , Dmitrievsky Sergey , D'Onofrio Monica , Dougherty Liam , Dozen Candan , Ezzat Abdallah , Favre Yannick , Fellers Deion , Feng Jonathan L. , Ferrere Didier , Galantay Edward Karl , Gall Jonathan , Gamberini Enrico , Gibson Stephen , Gonzalez-Sevilla Sergio , Gornushkin Yuri , Gwilliam Carl , Hayakawa Daiki , Hsu Shih-Chieh , Hu Zhen , Iacobucci Giuseppe , Inada Tomohiro , Jakobsen Sune , Johnson Eliott , Kajomovitz Enrique , Kawahara Hiroaki , Kling Felix , Kose Umut , Kotitsa Rafaella , Krusse Jesse , Kuehn Susanne , Lefebvre Helena , Levinson Lorne , Li Ke , Liu Jinfeng , Magliocca Chiara , Martinelli Fulvio , McFayden Josh , Meehan Sam , Milanesio Matteo , Miura Manato , Mladenov Dimitar , Moretti Théo , Munker Magdalena , Nakamura Mitsuhiro , Nakano Toshiyuki , Nessi Marzio , Neuhaus Friedemann , Nevay Laurie , Osborne John , Otono Hidetoshi , Pandini Carlo , Pang Hao , Paolozzi Lorenzo , Petersen Brian , Pietropaolo Francesco , Prim Markus , Queitsch-Maitland Michaela , Resnati Filippo , Rizzi Chiara , Rokujo Hiroki , Ruiz-Choliz Elisa , Salfeld-Nebgen Jakob , Sanchez Galan Francisco , Sato Osamu , Scampoli Paola , Schmieden Kristof , Schott Matthias , Sfyrla Anna , Shively Savannah , Sipos Roland , Spencer John , Takubo Yosuke , Tarannum Noshin , Theiner Ondrej , Thonet Pierre , Torrence Eric , Tufanli Serhan , Vasina Svetlana , Vendeuvre Camille , Vormwald Benedikt , Wang Di , Zambito Stefano , Zhang Gang ,   FASER Collaboration
Published: May 2024

Abstract
FASER, the ForwArd Search ExpeRiment, is an experiment
dedicated to searching for light, extremely weakly-interacting
particles at CERN's Large Hadron Collider (LHC). Such particles
may be produced in the very forward direction of the LHC's
high-energy collisions and then decay to visible particles inside
the FASER detector, which is placed 480 m downstream of the ATLAS
interaction point, aligned with the beam collisions axis. FASER also
includes a sub-detector, FASERν, designed to detect neutrinos
produced in the LHC collisions and to study their properties. In
this paper, each component of the FASER detector is described in
detail, as well as the installation of the experiment system and its
commissioning using cosmic-rays collected in September 2021 and
during the LHC pilot beam test carried out in October 2021. FASER
has successfully started taking LHC collision data in 2022, and will
run throughout LHC Run 3.
On the solutions of the local Zamolodchikov tetrahedron equation
Authors: Chirkov M , Konstantinou-Rizos S 
Published: May 2024

AbstractWe study the solutions of the local Zamolodhcikov tetrahedron equation in the form of correspondences derived by3×3matrices with free variables. We present all the associated generators of 4-simplex maps satisfying the local tetrahedron equation. Moreover, we demonstrate that, from some of our solutions, we can recover the 4-simplex extensions of Kashaev–Korepanov–Sergeev and Hirota type tetrahedron maps. Finally, we construct several novel 4-simplex maps.
Fabrication and in vitro biological performance of a double-layered nanoparticles-microarc oxidation composite coating on titanium for dental implant application
Authors: Cheng Yicheng , Ma Rui , Kong Xiangwei , Qiao Youbei , Jiang Tao , Wu Jiang 
Published: May 2024

Abstract
Sufficient residual alveolar bone volume plays an important role in the success rate and service life of dental implants. However, alveolar bone deficiency is a common clinical phenomenon, and the alveolar bone would be further absorbed by peri-implant infection. Therefore, it is highly desirable to promote peri-implant alveolar bone regeneration and inhibit alveolar bone resorption when the alveolar bone mass is insufficient. For this purpose, a pH-sensitive double-layered nanoparticles-microarc oxidation (MAO) composite coating was fabricated on titanium for dental implant application in this study. The pH-sensitive double-layered nanoparticles were prepared by a poly(L-lactic acid) inner layer and a chitosan outer shell, containing stromal-cell derived factor-1, recombinant human bone morphogenetic protein 2 and osteoprotegerin. The composite coating was fabricated on MAO coating by cross-linking the pH-sensitive double-layered nanoparticles with gelatin. The surface morphology of the composite coating showed that the pH-sensitive double-layered nanoparticles were well distributed and tightly cross-linked in the pores of MAO coating. The composite coating could sustain release the three drugs for more than 30 days. With decreasing pH, the release of osteoprotegerin from the composite coating increased (p < 0.05). In vitro biological studies suggested that the composite coating exhibited no cytotoxicity, and can recruit bone marrow-derived mesenchymal stem cells (BMSCs), promote BMSC differentiation into osteoblasts, and inhibit osteoclast generation. Moreover, with decreasing pH, the inhibitory effect on osteoclast generation was enhanced (p < 0.05). It can be concluded that the fabricated composite coating, which can promote bone regeneration and inhibit bone resorption, has the potential to be applied on the surface of dental implant, especially when the residual alveolar bone is in poor condition.
Current status of Pb-free PSCs and infer the highest achievable PCE via numerical modeling, and optimization of novel structure FAMASnGeI3 based PSCs
Authors: Hamzah Hairul Mardiah , Miah Md Helal , Hossen Md Jakir , Soin Norhayati Binti , Abdul Wahab Yasmin , Khandaker Mayeen Uddin , Aminul Islam Mohammad 
Published: May 2024

Abstract
In this study, we have extensively investigated an eco-friendly perovskite-based solar cell via a theoretical approach and numerical simulation. For that purpose, firstly, we have conducted a brief literature review to decipher the status of Pb-free perovskite solar cells (PSCs). The literature review reflects that the research on Pb-free PSCs is going on with immense interest. By acquiring inspiration from the literature review, we have designed a Pb-free hybrid PSC with a structure of FTO/CdS/FAMASnGeI3/NiO/Ag utilizing SCAPS-1D software. The structure has been optimized by fine-tuning the thickness, and defect density of the light-absorbing layer and the thickness, donor density, and band gap of ETL. The optimized structure of Pb-free perovskite material displays promising results with PCE of 30.05 %, Voc of 0.964 V, fill factor of 82.35%, and current density of 27.77 mA cm−2. In addition, we have also found that the temperature and light intensity play a crucial role in the performance of the proposed PSC. We have found quantum efficiency as high as 98% for 360 nm thickness of the perovskite absorber layer. Finally, from the dark J–V analysis, lower current density (6.73×10−8 mA cm−2), less series resistance (2.8×10−5 Ωcm2) as well as high shunt resistance (11412.67 Ωcm2) were witnessed, which is expectable for a promising solar cell. Our inclusive exploration unveiled the fact that the suggested novel architecture (FTO/CdS/FAMASnGeI3/NiO/Ag) can be considered an exceptional design for PSCs with greater efficiency and practical suitability.
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
Authors: Correia P M M , Cruzeiro B , Dias J , Encarnação P M C C , Ribeiro F M , Rodrigues C A , Silva A L M 
Published: May 2024

Abstract

Introduction. The positioning of γ ray interactions in positron emission tomography (PET) detectors is commonly made through the evaluation of the Anger logic flood histograms. machine learning techniques, leveraging features extracted from signal waveform, have demonstrated successful applications in addressing various challenges in PET instrumentation. Aim. This paper evaluates the use of artificial neural networks (NN) for γ ray interaction positioning in pixelated scintillators coupled to a multiplexed array of silicon photomultipliers (SiPM). Methods. An array of 16 Cerium doped Lutetium-based (LYSO) crystal pixels (cross-section 2 × 2 mm2) coupled to 16 SiPM (S13360-1350) were used for the experimental setup. Data from each of the 16 LYSO pixels was recorded, a total of 160000 events. The detectors were irradiated by 511 keV annihilation γ rays from a Sodium-22 (22Na) source. Another LYSO crystal was used for electronic collimation. Features extracted from the signal waveform were used to train the model. Two models were tested: i) single multiple-class neural network (mcNN), with 16 possible outputs followed by a softmax and ii) 16 binary classification neural networks (bNN), each one specialized in identifying events occurred in each position. Results. Both NN models showed a mean positioning accuracy above 85% on the evaluation dataset, although the mcNN is faster to train. Discussion The method’s accuracy is affected by the introduction of misclassified events that interacted in the neighbour’s crystals and were misclassified during the dataset acquisition. Electronic collimation reduces this effect, however results could be improved using a more complex acquisition setup, such as a light-sharing configuration. Conclusions The methods comparison showed that mcNN and bNN can surpass the Anger logic, showing the feasibility of using these models in positioning procedures of future multiplexed detector systems in a linear configuration.
Real-time edge computing design for physiological signal analysis and classification
Authors: Suppiah Ravi , Noori Kim , Abidi Khalid , Sharma Anurag 
Published: May 2024

Abstract
Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction, and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of an ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.
Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses
Authors: Sokol M , Baker C , Baker M , Joshi R P 
Published: May 2024

Abstract
Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann–Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.
Turning CO2 into sustainable graphene: a comprehensive review of recent synthesis techniques and developments
Authors: Sorayani Bafqi Mohammad Sajad , Aliyeva Nargiz , Baskan-Bayrak Havva , Dogan Semih , Saner Okan Burcu 
Published: May 2024

Abstract
The synthesis of graphene through environmentally friendly and efficient methods has posed a persistent challenge, prompting extensive research in recent years to access sustainable sources and attain high quality graphene competing with the one obtained from graphite ores. Addressing this challenge becomes even more intricate when aiming to convert captured CO2 into graphene structures, encountering hurdles stemming from the inherent stability of the CO2 molecule and its steadfast transformation. Together with CO2, there is a great potential to create carbon source by using natural biomass, cellulosic plant sources and industrial wastes. This comprehensive review delves into the recent synthesis techniques and developments, exploring both direct and indirect pathways for the integration of CO2 that strive to overcome the complexities associated with transforming CO2 into graphene. The review critically analyzes CO2 capturing mechanisms designed for air, ocean, and alternative sources, outlining the progress made in harnessing captured CO2 as a feedstock for graphene production by evaluating captured CO2 values. This review consolidates the recent advancements by providing a roadmap for future research directions in the sustainable synthesis of graphene from captured CO2 in the pursuit of a greener, circular economy.
The Prophet Between Philosopher and Poet
Authors: Rosenthal Michael A 
Published: May 2024

Abstract
Schlegel describes the prophet as both a philosopher and a poet. This chapter adopts Spinoza’s own method reading of Scripture to help us understand what Spinoza meant by prophecy and then how it was read, or might have been read, by later thinkers like Schlegel. The chapter presents three models of prophecy. The first is what Spinoza himself explicitly says about prophecy in his Theological-Political Treatise, namely that it is the product of the imagination and only coincides at points with what reason teaches us. The second is how prophecy was read creatively in light of the Romantic reading of Spinoza’s Ethics. Finally, the third is how the Spinozistic idea of the prophet might have been interpreted by Romantics like Schlegel beyond the rationalistic framework, with the prophet understood as the paragon of an anti-foundationalist philosopher.