bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracture prediction of CFRP laminates subjected to CW laser heating and pre-tensile loads based on ANN
Authors: Wang Jiawei , Lin Chongyu , Feng Guobin , Li Bin , Wu Lixiong , Wei Chenghua , Lv Yuwei , Cheng Jiajun 
Published: January 2022

For the application of composite materials in a complex thermal and mechanical environment, we developed a standard artificial neural network (ANN) model for the fracture prediction of carbon fiber-reinforced polymer (CFRP) laminates under continuous wave laser heating and pre-tensile loads. A substantial amount of data was collected through experimentation and from published references, which were converted into 12 800 binary-classification-type input/output data pairs before being used for model training. Different numbers of hidden neurons were evaluated to determine the optimal architecture of the model, while the “early stopping” and “dropout” methods were used to improve its robustness. The trained ANN model functions as a binary classifier that can predict the fracture probability of CFRP laminates after a certain period of laser irradiation. Subsequently, another 14 sets of experimentally collected data were used for ANN model testing. The correct prediction rate of the model reached 86%, which was higher than two other machine learning models (k-nearest neighbors and random forest models) under the same conditions. As the failure behavior of CFRP laminates has a certain degree of randomness, the fracture probabilities predicted by the ANN model have more practical values than the specific fracture times predicted by existing theories. Results indicate that it is feasible to apply the ANN method to predict the failure behavior of composite materials with discrete mechanical properties in complex thermal environments, and this study provides useful guidance for the engineering applications of composite materials in complex thermal and mechanical environments.
Bacillus anthracis
induces NLRP3 inflammasome activation and caspase-8–mediated apoptosis of macrophages to promote lethal anthrax
Authors: Van Hauwermeiren Filip , Van Opdenbosch Nina , Van Gorp Hanne , de Vasconcelos Nathalia , van Loo Geert , Vandenabeele Peter , Kanneganti Thirumala-Devi , Lamkanfi Mohamed 
Published: January 2022

Significance

Anthrax is a deadly infection caused by exposure to
Bacillus anthracis
bacteria. Anthrax lethal toxin (LeTx) has long been recognized as a major determinant of lethal anthrax, which paralyzes the host’s immune defenses by killing off macrophages. Despite the importance of macrophage cytotoxicity in anthrax pathogenesis, the signaling pathways underlying cell death of
B. anthracis
-infected macrophages are poorly understood. This study shows that infection with live
B. anthracis
or LeTx intoxication sensitizes macrophages to TNF-dependent NLRP3 inflammasome activation and caspase-8–mediated apoptosis. Moreover, caspase-8–mediated apoptosis is shown to promote anthrax-associated lethality in vivo. Collectively, the study establishes TNF- and RIPK1 kinase activity–dependent NLRP3 inflammasome activation and macrophage apoptosis as key host–pathogen mechanisms in lethal anthrax.
A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm
Authors: Chen Yun , Lu Yao , Ma Xiangyuan , Xu Yuesheng 
Published: January 2022

Abstract
The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting ill-posed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the superiority of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with the quadratic regularization, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.
Fabrication, modeling, and characterization of soft twisting electrothermal actuators with directly printed oblique heater
Authors: Cao Yang , Dong Jingyan 
Published: January 2022

Abstract
Soft electrothermal actuators have drawn extensive attention in recent years for their promising applications in biomimetic and biomedical areas. Most soft electrothermal actuators reported so far demonstrated uniform bending deformation, due to the deposition based fabrication of the conductive heater layer from nanomaterial-based solutions, which generally provides uniform heating capacity and uniform bending deformation. In this paper, a soft electrothermal actuator that can provide twisting deformation was designed and fabricated. A metallic microfilament heater of the soft twisting actuator was directly printed using electrohydrodynamic (EHD) printing, and embedded between two structural layers, a polyimide film and a polydimethylsiloxane layer, with distinct thermal expansion properties. Assisted by the direct patterning capabilities of EHD printing, a skewed heater pattern was designed and printed. This skewed heater pattern not only produces a skewed parallelogram-shaped temperature field, but also changes the stiffness anisotropy of the actuator, leading to twisting deformation with coupled bending. A theoretical kinematic model was built for the twisting actuator to describe its twisting deformation under different actuation effects. Based on that model, influence of design parameters on the twisting angle and motion trajectory of the twisting actuator were studied and validated by experiments. Finite element analysis was utilized for the thermal and deformation analysis of the actuator. The fabricated twisting actuator was characterized on its heating and twisting performance at different supply voltages. Using three twisting actuators, a soft gripper was designed and fabricated to implement pick-and-place operations of delicate objects.
Improved dielectric properties and energy-storage densities of BaTiO3-doped PVDF composites by heat treatment and surface modification of BaTiO3
Authors: Sang Xiaodong , Li Xingjia , Zhang Dandan , Zhang Xiuli , Wang Huiping , Li Saisai 
Published: January 2022

Abstract
In this paper, the dielectric properties of barium titanate/polyvinylidene fluoride (BT/PVDF) composites are investigated. The doped BT particles were prepared using a simple heat treatment. It was found that 1000 °C was the optimal temperature for the doped BT particles to improve the dielectric properties of the BT/PVDF composites. Furthermore, we also found that the breakdown strength of the BT/PVDF composites could be significantly enhanced when the surface of the doped BT particles were pre-modified with phthalic acid or KH550. In particular, the BT/PVDF composites doped with KH550-modified BT particles had a maximum energy-storage density of 4.08 J cm−3, which is 81.33% higher than that of BT/PVDF composites doped with BT particles and without any treatment. Therefore, we can conclude that heat treatment and surface modification of doped BT particles could become a new approach to enhance the energy-storage performance of BT/PVDF composites, and which has good application prospects in the field of dielectric energy-storage materials.
Quantum dynamics research in India: a perspective
Authors: Dutta Amit , Sengupta Krishnendu 
Published: January 2022

Abstract
Comprehending out-of-equilibrium properties of quantum many-body systems is still an emergent area of recent research. The upsurge in this area is motivated by tremendous progress in experimental studies, the key platforms being ultracold atoms and trapped ion systems. There has been a significant contribution from India to this vibrant field. This special issue which includes both review articles and original research papers highlights some of these contributions.
Improving accuracy of cavitation severity recognition in axial piston pumps by denoising time–frequency images
Authors: Chao Qun , Wei Xiaoliang , Lei Junbo , Tao Jianfeng , Liu Chengliang 
Published: January 2022

Abstract
The vibration signal is a good indicator of cavitation in axial piston pumps. Some vibration-based machine learning methods have been developed for recognizing pump cavitation. However, their fault diagnostic performance is often unsatisfactory in industrial applications due to the sensitivity of the vibration signal to noise. In this paper, we present an intelligent method for recognizing the cavitation severity of an axial piston pump in a noisy environment. First, we adopt short-time Fourier transformation to convert the raw vibration data into spectrograms that act as input images of a modified LeNet-5 convolutional neural network (CNN). Second, we propose a denoising method for the converted spectrograms based on frequency spectrum characteristics. Finally, we verify the proposed method on the dataset from a test rig of a high-speed axial piston pump. The experimental results indicate that the denoising method significantly improves the diagnostic performance of the CNN model in a noisy environment. For example, using the denoising method, the accuracy rate forcavitation recognition increases from 0.52 to 0.92 at a signal-to-noise ratio of 4 dB.
A two-stage method for real-time baseline drift compensation in gas sensors
Authors: Zhang Chao , Wang Wen , Pan Yong , Cheng Lina , Zhai Shoupei , Gao Xu 
Published: January 2022

Abstract
Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.
A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes
Authors: Mishra Rohit , Singh Bhagat 
Published: January 2022

Abstract
In recent decades, a lot of work has been done to mitigate self-excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rates (MRRs). In the present work, experimentally-acquired acoustic signals in milling operations have been computed using a modified spline-based local mean decomposition technique in order to cite tool chatter features. Further, three artificial neural network training algorithms; resilient propagation, conjugate gradient-based and Levenberg–Marquardt algorithms, and two activation functions; hyperbolic tangent sigmoid and log sigmoid, have been used to train the acquired chatter vibration and MRR data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and MRR. Finally, multi-objective particle swarm optimization has been invoked to optimize the developed prediction models to obtain the most favourable range of input parameters pertaining to stable milling with higher productivity.
Investigation of the ejected mass during high-intensity laser solid interaction for improved plasma mirror generation
Authors: Indorf G F H , Scott G G , Ennen M A , Forestier-Colleoni P , Haddock D , Hawkes S J , Scaife L , Bourgeois N , Symes D R , Thornton C , Andreev A A , Teubner U , Neely D 
Published: January 2022

Abstract
The interaction of very intense and ultrashort laser pulses with solid targets is a topic that has attracted a large amount of interest in science and applications. This interest is boosted by the large progress made in the development of high repetition rate, high-power laser systems. With the significant increase in average power, there is concern about how to deal with ablated debris that may lead to contamination and damage during interaction experiments with solid targets. This issue is also highly relevant in experiments that include plasma mirrors. These are often employed to increase the contrast ratio of the intense laser pulse to unwanted laser pre-pulses from the amplifier chain and/or the background of amplified spontaneous emission. For this reason, the present work investigates the mass ejected from the target into vacuum for different conditions, particularly those present when plasma mirrors are introduced. The total amount of ablated mass can be reduced by making use of a temporally controlled plasma expansion that enhances the plasma mirror reflectivity. In this way, high intensity laser interaction experiments can be carried out with efficient and clean plasma mirrors significantly reducing the degradation of the laser optics and plasma diagnostics placed near the interaction.
Commercial gigahertz-class NMR magnets
Authors: Wikus Patrick , Frantz Wolfgang , Kümmerle Rainer , Vonlanthen Patrik 
Published: January 2022

Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a wide-spread analytical technique which is used in a large range of different fields, such as quality control, food analysis, material science and structural biology. In the widest sense, NMR is an analytical technique to determine the structure of molecules. At the time of writing this manuscript, commercial NMR spectrometers with a proton resonance frequency ⩾900 MHz are only available from Bruker. In 2019, Bruker installed the first 1.1 GHz (25.8 T) NMR spectrometer at the St. Jude Children Research Hospital in Memphis, Tennessee, followed by the installation of the first 1.2 GHz (28.2 T) NMR spectrometer at the University of Florence in Italy in 2020. These were the first commercial NMR spectrometers operating at magnetic fields in excess of what can be achieved with conventional low temperature superconductors, and which depend on high temperature superconductors to generate the required magnetic field. In this paper, the requirements on commercial NMR magnets are discussed and the history of high-field NMR magnets is reviewed. Bruker’s R&D program for 1.1 and 1.2 GHz NMR magnets and spectrometers will be described, and some of the key properties of these first commercial NMR magnets with high-temperature superconductors are reported.
Brownian non-Gaussian polymer diffusion and queuing theory in the mean-field limit
Authors: Nampoothiri Sankaran , Orlandini Enzo , Seno Flavio , Baldovin Fulvio 
Published: January 2022

Abstract
We link the Brownian non-Gaussian diffusion of a polymer center of mass (CM) to a microscopic cause: the polymerization/depolymerization phenomenon occurring when the polymer is in contact with a monomer chemostat. The anomalous behavior is triggered by the polymer critical point, separating the dilute and the dense phase in the grand canonical ensemble. In the mean-field limit we establish contact with queuing theory and show that the kurtosis of the polymer CM diverges alike a response function when the system becomes critical, a result which holds for general polymer dynamics (Zimm, Rouse, reptation). Both the equilibrium and nonequilibrium behaviors are solved exactly as a reference study for novel stochastic modeling and experimental setup.
Influence of an indefinite causal order on an Otto heat engine
Authors: Zhao Jifei , Xu Youyang 
Published: January 2022

Abstract
The quantum effect plays an important role in quantum thermodynamics, and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention. Based on two trapped ions, we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of an Otto engine through reservoir engineering. Then, we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach one. When the power is its maximum, the efficiency is also much higher than that of a normal Otto heat engine. This enhancement may originate from the non-equilibrium of the reservoir and the measurement on the control qubit.
Concept design of the megawatt power level gyrotron stabilized by a low-power signal for DEMO project
Authors: Denisov Gregory , Kuftin Andrey , Manuilov Vladimir , Chirkov Alexey , Popov Leonid , Zapevalov Vladimir , Zuev Andrey , Sedov Anton , Zheleznov Ilya , Glyavin Mikhail 
Published: January 2022

Abstract
The specific features of the main components of the new powerful 230 GHz/80 kV/40 A gyrotron aimed to use in the future control fusion facility DEMO are described. The gyrotron design provides a stable output power generation of more than 1 MW using a superconducting magnet with a moderate size warm bore. Furthermore, the new original quasi-optical converter providing the gyrotron operation in three possible regimes—two free oscillation regimes with co-rotating TE33,13 or counter-rotating TE33,−13 mode, and the regime with frequency locking by the stable input signal—is suggested and optimized. The Gaussian content in the output wave-beam in all above-mentioned regimes is about 98%.
Efficient learning of discrete graphical models*
Authors: Vuffray Marc , Misra Sidhant , Lokhov Andrey Y 
Published: January 2022

Abstract
Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for which the maximum likelihood approach is intractable. In this work, we provide the first sample-efficient method based on the interaction screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis. We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models. Importantly, our bounds make explicit distinction between parameters that are proper to the model and priors used as an input to the algorithm. Finally, we show that the interaction screening framework includes all models previously considered in the literature as special cases, and for which our analysis shows a systematic improvement in sample complexity.
Spectrum of localized states in fermionic chains with defect and adiabatic charge pumping
Authors: Ares Filiberto , Esteve José G , Falceto Fernando 
Published: January 2022

Abstract
In this paper, we study the localized states of a generic quadratic fermionic chain with finite-range couplings and an inhomogeneity in the hopping (defect) that breaks translational invariance. When the hopping of the defect vanishes, which represents an open chain, we obtain a simple bulk-edge correspondence: the zero-energy modes localized at the ends of the chain are related to the roots of a polynomial determined by the couplings of the Hamiltonian of the bulk. From this result, we define an index that characterizes the different topological phases of the system and can be easily computed by counting the roots of the polynomial. As the defect is turned on and varied adiabatically, the zero-energy modes may cross the energy gap and connect the valence and conduction bands. We analyze the robustness of the connection between bands against perturbations of the Hamiltonian. The pumping of states from one band to the other allows the creation of particle–hole pairs in the bulk. An important ingredient for our analysis is the transformation of the Hamiltonian under the standard discrete symmetries, C, P, T, as well as a fourth one, peculiar to our system, that is related to the existence of a gap and localized states.
Galaxy evolution in different environments along redshift within the local universe z < 0.8
Authors: Yoifoi Ponlawat , Kriwattanawong Wichean 
Published: January 2022

Abstract
This study presents the evolution of the galaxies in different matter density along redshift within the local universe. A sample of 702,352 galaxies was collected from the Sloan Digital Sky Survey (SDSS). Under the limitation of the spectroscopic data, the appropriate photometric redshift was used to represent the spectroscopic redshift in the range of 0.0 ≤
z
≤ 0.8. Number density of galaxies, galaxy’s colors, and star formation activities are considered to describe the evolution of galaxies. In summary, the number density is not clearly different although the Dec and RA of the sky areas are disparate, but it steeply declines along the redshift direction. Considering the number density together with galaxies’ Hα emission line from spectroscopic data, we find that both equivalent of hydrogen alpha and Hα flux tend to decrease along the redshift, similar to the decreasing trend of the number density. Furthermore, the galaxy color trend is found to be redder as a function of the redshift for the magnitude range of -19 ≤
M

g

≤ -17. It implies that the overview of the star formation activity of the fainter galaxies at the lower redshift tend to show higher than the ones at higher redshift.