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Journal articles on the topic 'Network-hardening'

Author: Grafiati

Published: 29 June 2021

Last updated: 29 July 2025

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1

Zhao, Chao, Huiqiang Wang, Junyu Lin, Hongwu Lv, and Yushu Zhang. "A Generation Method of Network Security Hardening Strategy Based on Attack Graphs." International Journal of Web Services Research 12, no. 1 (2015): 45–61. http://dx.doi.org/10.4018/ijwsr.2015010104.

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Analyzing attack graphs can provide network security hardening strategies for administrators. Concerning the problems of high time complexity and costly hardening strategies in previous methods, a method for generating low cost network security hardening strategies is proposed based on attack graphs. The authors' method assesses risks of attack paths according to path length and the common vulnerability scoring system, limits search scope with a threshold to reduce the time complexity, and lowers cost of hardening strategies by using a heuristic algorithm. The experimental results show that th

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2

Pan, Zhong Feng, Gui Cheng Wang, Chong Lue Hua, and Hong Jie Pei. "Research and Development of LM Neural Network Prediction System for Grind-Hardening." Key Engineering Materials 416 (September 2009): 248–52. http://dx.doi.org/10.4028/www.scientific.net/kem.416.248.

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An improved neural network based on L-M algorithm has been applied to the prediction of the grind-hardening parameters against to the slow convergence rate of conventional BP neural network. And the the neural network model for grind-hardening is established. The neural network prediction system for grind-hardening process has been developed based on L-M algorithm. The functions of system is analyzed, particularly and some pivotal technology to realize the system are put forward.

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3

Manzanares, Antonio Izquierdo. "Hardening Network Infrastructure: Not Suitable for Everyone." IEEE Distributed Systems Online 8, no. 10 (2007): 4. http://dx.doi.org/10.1109/mdso.2007.4384584.

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4

Wang, Lingyu, Steven Noel, and Sushil Jajodia. "Minimum-cost network hardening using attack graphs." Computer Communications 29, no. 18 (2006): 3812–24. http://dx.doi.org/10.1016/j.comcom.2006.06.018.

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5

Borbor, Daniel, Lingyu Wang, Sushil Jajodia, and Anoop Singhal. "Surviving unpatchable vulnerabilities through heterogeneous network hardening options." Journal of Computer Security 26, no. 6 (2018): 761–89. http://dx.doi.org/10.3233/jcs-171106.

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6

Myung, David, Wongun Koh, Jungmin Ko, et al. "Biomimetic strain hardening in interpenetrating polymer network hydrogels." Polymer 48, no. 18 (2007): 5376–87. http://dx.doi.org/10.1016/j.polymer.2007.06.070.

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7

Wu, Xiao Ling, and Fei Ren. "Research on the Prediction Model of Laser Surface Hardening Index on Cylinder Liner Based on RBF." Advanced Materials Research 148-149 (October 2010): 215–18. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.215.

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Based on the data obtained from the perpendicular experiment of laser surface hardening on HT150 cylinder liner of a certain motor engine, a Radial Basis Function (RBF) neural network prediction model of laser surface hardening index about HT150 cylinder liner is established by Matlab neural network toolbox in this paper. The parameters of input layer are affirmed by analyzing influential factors of the hardened index ,and the best form of the network is affirmed by selecting suitable spread in function-newrb( ),and as a result, the prediction accuracy and the adaptability of the network are i

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8

Yang, Tung Sheng, and Huai Shiun Lu. "Predictions of Springback of Strain-Hardening Material in U-Shaped Bending Process." Key Engineering Materials 419-420 (October 2009): 481–84. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.481.

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This study applies the finite element method (FEM) in conjunction with an abductive network to predict springback of different strain-hardening material in U-shaped bending process.Springback is investigated for different material parameters, such as strength coefficient of material, strain-hardening exponent and Young’s modulus, by finite element analysis during U-shaped bending process. The abductive network is then applied to synthesize the data sets obtained from the numerical simulations. Prediction results of the springback of different strain-hardening material in U-shaped bending proce

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9

Zhang, Hong, Zhaoguang Ma, Da Kang, and Min Yang. "A Beam Hardening Artifact Correction Method for CT Images Based on VGG Feature Extraction Networks." Sensors 25, no. 7 (2025): 2088. https://doi.org/10.3390/s25072088.

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In X-ray industrial computed tomography (ICT) imaging, beam hardening artifacts significantly degrade the quality of reconstructed images, leading to cupping effects, ring artifacts, and reduced contrast resolution. These issues are particularly severe in high-density and irregularly shaped aerospace components, where accurate defect detection is critical. To mitigate beam hardening artifacts, this paper proposes a correction method based on the VGG16 feature extraction network. Continuous convolutional layers automatically extract relevant features of beam hardening artifacts, establish a non

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Liang, Ruijun, Zhiqiang Wang, Shuying Yang, and Weifang Chen. "Study on hardness prediction and parameter optimization for carburizing and quenching: an approach based on FEM, ANN and GA." Materials Research Express 8, no. 11 (2021): 116501. http://dx.doi.org/10.1088/2053-1591/ac3279.

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Abstract A proper hardening depth is critical to the load-bearing capacity of a part, and heat treatment, including carburizing and quenching, can highly determine the hardness distribution in the part’s surface after manufacturing. This paper proposes a ‘hardness prediction and parameter optimization’ approach that deploys the finite element method (FEM), the artificial neural network (ANN), and the Genetic Algorithm (GA), to describe the relationships between the carburizing/quenching parameters and the hardening depths and conversely to determine the optimized parameters for a given hardeni

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11

Lambiase, F., A. M. Di Ilio, and A. Paoletti. "Prediction of Laser Hardening by Means of Neural Network." Procedia CIRP 12 (2013): 181–86. http://dx.doi.org/10.1016/j.procir.2013.09.032.

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12

Bouzid, Mehdi, and Emanuela Del Gado. "Network Topology in Soft Gels: Hardening and Softening Materials." Langmuir 34, no. 3 (2017): 773–81. http://dx.doi.org/10.1021/acs.langmuir.7b02944.

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13

Wang, Chunyan, Guicheng Wang, and Chungen Shen. "Analysis and Prediction of Grind-Hardening Surface Roughness Based on Response Surface Methodology-BP Neural Network." Applied Sciences 12, no. 24 (2022): 12680. http://dx.doi.org/10.3390/app122412680.

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Surface morphology and surface roughness are very important properties used to assess the quality of grind-hardening surfaces. In this study, grind-hardening tests for 42CrMo steel were designed using the response surface methodology to reveal the surface morphological characteristics of the grind-hardening surface and the effects of grinding parameters on its roughness. The results showed considerable grinding damage in both the cutting-in and cutting-out areas of the grind-hardened surface, while the middle area was more stable. More specifically, the cutting-in area showed much bonding and

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14

Nimpaiboon, Adun, Sureerut Amnuaypornsri, and Jitladda T. Sakdapipanich. "OBSTRUCTION OF STORAGE HARDENING IN NR BY USING POLAR CHEMICALS." Rubber Chemistry and Technology 89, no. 2 (2016): 358–68. http://dx.doi.org/10.5254/rct.16.84825.

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ABSTRACT The effect of the polar chemicals phenol, diethylene glycol, and hydroxylamine hydrochloride on obstruction of storage hardening in NR was examined. A decrease in gel content and molecular weight of NR was observed after addition of diethylene glycol and hydroxylamine hydrochloride, whereas no significant change was observed after addition of phenol. The storage hardening behavior of NR containing these polar chemicals was investigated by accelerated storage with phosphorus pentoxide. The gel content, Mooney viscosity, and Wallace plasticity values of NR containing phenol increased ob

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15

Tan, Zhaoqiang, Zijun Qin, Qing Zhang, Yong Liu, and Feng Liu. "Prediction and Process Analysis of Tensile Properties of Sinter-Hardened Alloy Steel by Artificial Neural Network." Metals 12, no. 3 (2022): 381. http://dx.doi.org/10.3390/met12030381.

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Sinter-hardening is an emerging powder metallurgy process by which the consolidation of powder and the hardening of dense bulk samples are integrated into one step. In this study, to understand the complex effects of sinter-hardening parameters on the properties of the Fe-Cr-Ni (Cu)-C alloy, an artificial neural network (ANN) with the topology of a nonlinear multi-layered perceptron was designed to predict the ultimate tensile strength and elongation, considering parameters including chemical composition, sintering temperature, and cooling rate. The predictability of the ANN was verified by ex

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Kemp, R., G. A. Cottrell, H. K. D. H. Bhadeshia, G. R. Odette, T. Yamamoto, and H. Kishimoto. "Neural-network analysis of irradiation hardening in low-activation steels." Journal of Nuclear Materials 348, no. 3 (2006): 311–28. http://dx.doi.org/10.1016/j.jnucmat.2005.09.022.

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17

Rahmat Novrianda Dasmen and Muhammad Reihan Pratama. "Implementation of Hardening for Optimization of Wireless Local Area Network Security." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 13, no. 1 (2025): 1–10. https://doi.org/10.33558/piksel.v13i1.9957.

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Computer networks use two main methods for data transmission, namely wired and wireless networks or what is known as a Wireless Local Area Network (WLAN). In WLAN networks, the security standard usually used is the WiFi Protected Access 2 Pre-Shared Key or WPA2-PSK protocol, which utilizes SSID and password. Despite using security mechanisms such as WPA2-PSK, criminal activities such as intrusion into the network still occur. Therefore, it is necessary to improve the network security system to ensure that the WLAN network is more secure and can minimize potential risks to users. This study aim

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18

Veretelnik, Oleg, Mykola M. Tkachuk, Serhii Kravchenko, et al. "RESEARCH AND EXPERIMENTAL STUDIES OF STRESS-STRAIN STATE OF DISCRETE-CONTINUAL HARDENED MACHINE PARTS." Bulletin of the National Technical University «KhPI» Series: Engineering and CAD, no. 2 (December 30, 2021): 5–21. http://dx.doi.org/10.20998/2079-0775.2021.2.03.

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Research and experimental studies of stress-strain state of discrete-continual hardened machine parts are presented in this work. This hardening method is distinguished by incorporation of numerous hard spots into the surface layer of one of the bodies. Meanwhile the other part is covered by a continuous corundum layer. Correspondingly, a network of microchannels for lubricant is formed between the bodies. Furthermore the contact loads are intensified in the vicinity of the harder material in the discrete zones. As a result the strength and durability of the loaded parts is increased. The tech

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19

Weiss, Stephanie, Regina Seidl, Waltraud Kessler, Rudolf W. Kessler, Edith M. Zikulnig-Rusch, and Andreas Kandelbauer. "Unravelling the Phases of Melamine Formaldehyde Resin Cure by Infrared Spectroscopy (FTIR) and Multivariate Curve Resolution (MCR)." Polymers 12, no. 11 (2020): 2569. http://dx.doi.org/10.3390/polym12112569.

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Here, we study resin cure and network formation of solid melamine formaldehyde pre-polymer over a large temperature range via dynamic temperature curing profiles. Real-time infrared spectroscopy is used to analyze the chemical changes during network formation and network hardening. By applying chemometrics (multivariate curve resolution, MCR), the essential chemical functionalities that constitute the network at a given stage of curing are mathematically extracted and tracked over time. The three spectral components identified by MCR were methylol-rich, ether linkages-rich and methylene linkag

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20

Waheed, Faisal, and Maaruf Ali. "Hardening CISCO Devices based on Cryptography and Security Protocols - Part II: Implementation and Evaluation." Annals of Emerging Technologies in Computing 2, no. 4 (2018): 11–27. http://dx.doi.org/10.33166/aetic.2018.04.002.

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This second part covers the implementation, testing, critical evaluation, conclusion and further study. It concentrates on the actual implementation details of hardening of network devices by referring to the hardware and software components, device operating system’s features, management controls, access-list restrictions, operational configurations and critically making sure that the data and credentials are not stored or transferred in ‘plaintext’ over the network by detailed testing and evaluation. It investigates the commands used to enable cryptography and network protocols based on encr

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21

Библик, Ирина Валентиновна. "НЕЙРОСЕТЕВАЯ МОДЕЛЬ ДЛЯ ОЦЕНКИ ВЛИЯНИЯ КАЧЕСТВА ПОВЕРХНОСТНОГО СЛОЯ НА УСТАЛОСТНУЮ ПРОЧНОСТЬ ДЕТАЛЕЙ ГТД". Aerospace technic and technology, № 8 (31 серпня 2019): 85–89. http://dx.doi.org/10.32620/aktt.2019.8.13.

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An approach based on using the neural networks has been developed for predicting the fatigue strength of gas turbine engine (GTE) parts at the manufacturing stage. The neural network model implemented in the Delphi programming environment consists of an input layer containing four elements, one hidden layer with four neurons, and an output layer with one element. The output parameter of the neural network is fatigue strength, and the input parameters are determined by the technological process of manufacturing parts. These are the surface roughness, the degree, and depth of strain hardening an

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Chen, Xin, Yudong Xie, Liangzhou Huo, et al. "Implementation of Highly Reliable Convolutional Neural Network with Low Overhead on Field-Programmable Gate Array." Electronics 13, no. 5 (2024): 879. http://dx.doi.org/10.3390/electronics13050879.

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Due to the advantages of parallel architecture and low power consumption, a field-programmable gate array (FPGA) is typically utilized as the hardware for convolutional neural network (CNN) accelerators. However, SRAM-based FPGA devices are extremely susceptible to single-event upsets (SEUs) induced by space radiation. In this paper, a fault tolerance analysis and fault injection experiments are applied to a CNN accelerator, and the overall results show that SEUs occurring in a control unit (CTRL) lead to the highest system error rate, which is over 70%. After that, a hybrid hardening strategy

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23

Guan, Chun Ping, and Hong Ping Jin. "Determination of Residual Stress and Strain-Hardening Exponent Using Artificial Neural Networks." Advanced Materials Research 472-475 (February 2012): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.332.

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Through dimensional analysis of indentation parameters in this study, we propose an artificial neural network (ANN) model to extract the residual stress and strain-hardening exponent based on spherical indentation. The relationships between indentation parameters and the residual stress and material properties are numerically calibrated through training and validation of the ANN model. They enable the direct mapping of the characteristics of the indentation parameters to the residual stress and the elastic-plastic material properties. The proposed ANN model can be used to quickly and effective

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Kundu, Arkadeep, and Soumya K. Ghosh. "A multi-objective search strategy to select optimal network hardening measures." International Journal of Decision Support Systems 1, no. 1 (2015): 130. http://dx.doi.org/10.1504/ijdss.2015.067283.

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25

Jun-chun, MA, WANG Yong-jun, SUN Ji-yin, and CHEN Shan. "A Minimum Cost of Network Hardening Model Based on Attack Graphs." Procedia Engineering 15 (2011): 3227–33. http://dx.doi.org/10.1016/j.proeng.2011.08.606.

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26

Theocaris, Pericles S., and P. D. Panagiotopoulos. "Generalised hardening plasticity approximated via anisotropic elasticity: A neural network approach." Computer Methods in Applied Mechanics and Engineering 125, no. 1-4 (1995): 123–39. http://dx.doi.org/10.1016/0045-7825(94)00769-j.

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27

Paidy, Pavan. "Hardening AWS Infrastructure after Capital One: IAM, S3, and Network Security." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING 7, no. 2 (2019): 126–41. https://doi.org/10.70589/jrtcse.2019.2.10.

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28

Xu, Wei Feng, Jin He Liu, Dao Lun Chen, Guo Hong Luan, and Jun Shan Yao. "Tensile Properties and Strain Hardening Behavior of a Friction Stir Welded AA2219 Al Alloy." Advanced Materials Research 291-294 (July 2011): 833–40. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.833.

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Microstructures, tensile properties and work hardening behavior of friction stir welded (FSWed) AA2219-T62 aluminum alloy (in its one-third bottom slice of a 20 mm thick plate) were evaluated at different strain rates. While the yield strength was lower in the FSWed joint than in the base metal, the ultimate tensile strength of the FSWed joint approached that of the base metal. In particular the FSW resulted in a significant improvement in the ductility of the alloy due to the prevention of premature failure caused by intergranular cracking along the second-phase boundary related to the presen

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Yang, Tung Sheng, S. Q. Lee, J. Y. Li, and C. Y. Liu. "Prediction of Surface Parameters for Strain Hardening Material of Asperity Flattening in Metal Forming." Materials Science Forum 697-698 (September 2011): 470–73. http://dx.doi.org/10.4028/www.scientific.net/msf.697-698.470.

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This study applies the finite element method (FEM) in conjunction with an abductive network to predict the surface parameters for strain hardening material of asperity flattening in metal forming process. To verify the prediction of FEM simulation for surface parameters, the experimental data are compared with the results of current simulation. Contact area ratio, surface roughness, skewness and kurtosis are investigated for different process and material parameters, such as normal pressure, bulk strain rate, yielding stress, strength coefficient and strain hardening exponent of surface asperi

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Conrad, Nathaniel, Tynan Kennedy, Deborah K. Fygenson, and Omar A. Saleh. "Increasing valence pushes DNA nanostar networks to the isostatic point." Proceedings of the National Academy of Sciences 116, no. 15 (2019): 7238–43. http://dx.doi.org/10.1073/pnas.1819683116.

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The classic picture of soft material mechanics is that of rubber elasticity, in which material modulus is related to the entropic elasticity of flexible polymeric linkers. The rubber model, however, largely ignores the role of valence (i.e., the number of network chains emanating from a junction). Recent work predicts that valence, and particularly the Maxwell isostatic point, plays a key role in determining the mechanics of semiflexible polymer networks. Here, we report a series of experiments confirming the prominent role of valence in determining the mechanics of a model system. The system

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Wibowo, Dega Surono, Hepatika Zidny Ilmadina, Ardi Susanto Ardi, and Fariq Fadillah Gusti Insani. "Apache web server security with security hardening." Journal of Soft Computing Exploration 4, no. 4 (2023): 213–21. http://dx.doi.org/10.52465/joscex.v4i4.230.

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With the internet network, we can quickly get information very quickly. The information we get is not changed by people not authorized to access the system or platform. Apache is a web server often used to connect users with websites where the information is located. The more users there are, the more crimes there will be when attacking the web server by irresponsible people. Due to limited time for web administrators, to improve the security of the Apache web server, an intrusion detection system is needed that can help monitor network traffic and detect the type of attack that is occurring a

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Guo, Zhongzhong, Shangqi Yu, Jiazhi Fu, Kai Ma, and Rui Zhang. "Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things." PLOS ONE 17, no. 2 (2022): e0263755. http://dx.doi.org/10.1371/journal.pone.0263755.

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The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an

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Hsieh, Ming-Chang, Yu-Hao Tsao, Yu-Jane Sheng, and Heng-Kwong Tsao. "Microstructural Dynamics of Polymer Melts during Stretching: Radial Size Distribution." Polymers 15, no. 9 (2023): 2067. http://dx.doi.org/10.3390/polym15092067.

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The transient elongational viscosity ηe(t) of the polymer melt is known to exhibit strain hardening, which depends on the strain rate ε˙. This phenomenon was elucidated by the difference of chain stretching in the entanglement network between extension and shear. However, to date, the microscopic evolution of polymer melt has not been fully statistically analyzed. In this work, the radial size distributions P(Rg,t) of linear polymers are explored by dissipative particle dynamics during the stretching processes. In uniaxial extensional flow, it is observed that the mean radius of gyration R¯g(t

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Vidal, Leonhard Maria, Thekla Alpers, and Thomas Becker. "Structure Strengthening Phenomena of Gluten Matrices under Different Stress Types." Polymers 15, no. 23 (2023): 4491. http://dx.doi.org/10.3390/polym15234491.

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To predict the achievable product volume with respect to the gas retention capacity of the gluten matrix in wheat flour doughs, strain hardening evaluation is crucial. But assessing these structure hardening phenomena in wheat flour dough systems is still a challenging task. In this work, a simple shear method applied to kneaded dough samples was tested and compared to biaxial extension tests performed with a lubricated squeezing flow method. The comparability of shear-induced structure hardening with biaxial extension tests was shown. Structure hardening and breakdown after overload were visu

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Mishra, Akshansh, Vijaykumar S Jatti, Nitin K Khedkar, Rahul B. Dhabale, and Ashwini V Jatti. "Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process." Frattura ed Integrità Strutturale 17, no. 63 (2022): 234–45. http://dx.doi.org/10.3221/igf-esis.63.18.

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A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated

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Babič, M., P. Wangyao, B. Šter, D. Marinković, and Cristiano Fragassa. "Modelling the surface roughness of steel after laser hardening by using 2D visibility network, convolutional neural networks and genetic programming." FME Transactions 50, no. 3 (2022): 393–402. http://dx.doi.org/10.5937/fme2203393b.

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The surface characterization of materials after Robot Laser Hardening (RLH) is a technically demanding procedure. RLH is commonly used to harden parts, especially when subject to wear. By changing their surface properties, this treatment can offer several benefits such as lower costs for additional machining, no use of cooling agents or chemicals, high flexibility, local hardening, minimal deformation, high accuracy, and automated and integrated process in the production process. However, the surface roughness strongly depends on the heat treatment and parameters used in the process. This arti

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Yang, Tung Sheng, and Tsung Hsien Yang. "Predictions of Maximum Forging Load and Effective Stress for Strain-Hardening Material of near Net-Shape Helical Gear Forging." Applied Mechanics and Materials 284-287 (January 2013): 894–97. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.894.

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In this paper, the use of the finite element method in conjunction with abductive network is presented to predict the maximum forging force and effective stress for strain-hardening material during near net-shape helical forging. The maximum forging load and effective stress are influenced by the material properties such as yielding stress, strength coefficient and strain hardening exponent. A finite element method is used to investigate the clamping-type forging of helical gear. In order to verify the prediction of FEM simulation for forging load, the experimental data are compared with the r

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Łazarska, Małgorzata, Zbigniew Ranachowski, Janusz Musiał, Tomasz Tański, and Qingshan Jiang. "Identification of Phase Transformations in Alloy and Non-Alloy Steel During Austempering Using Acoustic Emission and Neural Network." Materials 18, no. 10 (2025): 2198. https://doi.org/10.3390/ma18102198.

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This research was carried out for selected alloy (bearing) and non-alloy (tool) steel. The steels were subjected to austempering. The hardening temperature range was from 100 °C to 180 °C. The use of acoustic emission in connection with the artificial neural network (ANN) enabled the analysis and identification of phase changes occurring in steels during austempering. Classification of acoustic emission events was carried out with the help of their energy values and with the use of an artificial neural network. On this basis, it was observed that in the process of isothermal hardening of steel

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Zrník, Jozef, Sergey V. Dobatkin, and Ondrej Stejskal. "Deformation Behaviour and Ultrafine Grained Structure Development in Steels with Different Carbon Content Subjected to Severe Plastic Deformation." Key Engineering Materials 345-346 (August 2007): 45–48. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.45.

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The article focuses on the results from recent experimental of severe plastic deformation of low carbon (LC) steel and medium carbon (MC) steel performed at increased temperatures. The grain refinement of ferrite respectively ferrite-pearlite structure is described. While LC steel was deformed by ECAP die (ε = 3) with a channel angle φ = 90° the ECAP severe deformation of MC steel was conducted with die channel angle of 120° (ε = 2.6 - 4). The high straining in LC steel resulted in extensively elongated ferrite grains with dense dislocation network and randomly recovered and polygonized struct

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Li, Fang, Yunxiang Long, Daxi Guo, et al. "Ion Irradiation Defects and Hardening in FeCrAl Alloy." Metals 12, no. 10 (2022): 1645. http://dx.doi.org/10.3390/met12101645.

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The self-ion irradiation experiments of FeCrAl and Y−FeCrAl alloys are carried out at 330 °C to 1–10 displacements per atom (dpa). The formation of dislocation loops in these alloys is investigated by transmission electron microscopy (TEM) and nano-indentation tests are used to assess the irradiation hardening. A large number of dislocation loops are formed after irradiation, and dislocation network gradually develops above 2.5 dpa. The average size of dislocation loops increases while the number density decreases when the dose was increased. In comparison to a/2<111> dislocation loops,

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Li, Changhong, Chenbo Yin, and Xingtian Xu. "Hybrid optimization assisted deep convolutional neural network for hardening prediction in steel." Journal of King Saud University - Science 33, no. 6 (2021): 101453. http://dx.doi.org/10.1016/j.jksus.2021.101453.

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Liu, Gu, Liu-ying Wang, Gui-ming Chen, and Shao-chun Hua. "Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network." Journal of Iron and Steel Research International 18, no. 12 (2011): 57–64. http://dx.doi.org/10.1016/s1006-706x(12)60010-7.

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Cai, Yulong, Ming Cai, Yanlai Wu, et al. "Evaluation and Mitigation of Weight-Related Single Event Upsets in a Convolutional Neural Network." Electronics 13, no. 7 (2024): 1296. http://dx.doi.org/10.3390/electronics13071296.

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Single Event Upsets (SEUs) are most likely to cause bit flips within the trained parameters of a convolutional neural network (CNN). Therefore, it is crucial to analyze and implement hardening techniques to enhance their reliability under radiation. In this paper, random fault injections into the weights of LeNet-5 were carried out in order to evaluate and propose strategies to improve the reliability of a CNN. According to the results of an SEU fault injection, the accuracy of the CNN can be classified into the following three categories: benign conditions, poor conditions, and critical condi

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Waheed, Faisal, and Maaruf Ali. "Hardening CISCO Devices based on Cryptography and Security Protocols - Part One: Background Theory." Annals of Emerging Technologies in Computing 2, no. 3 (2018): 27–44. http://dx.doi.org/10.33166/aetic.2018.03.004.

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Network Security is a vital part of any corporate and enterprise network. Network attacks greatly compromise not only the sensitive data of the consumers but also cause outages to these networks. Thus inadequately protected networks need to be “hardened”. The hardening of network devices refers to the hardware and software components, device operating system’s features, management controls, access-list restrictions, operational configurations and above all making sure that the data and credentials are not stored or transferred in ‘plaintext’ over the network. This article investigates the use

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45

Salvador, Daniel, Yoseli Acosta, Anna Zamora, and Manuel Castillo. "Rennet-Induced Casein Micelle Aggregation Models: A Review." Foods 11, no. 9 (2022): 1243. http://dx.doi.org/10.3390/foods11091243.

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Two phases are generally recognized in the enzymatic coagulation of milk: hydrolysis and aggregation, although nowadays more and more researchers consider the non-enzymatic phase to actually be a stage of gel formation made up of two sub-stages: micellar aggregation and hardening of the three-dimensional network of para-κ-casein. To evaluate this controversy, the main descriptive models have been reviewed. Most of them can only model micellar aggregation, without modeling the hardening stage. Some are not generalizable enough. However, more recent models have been proposed, applicable to a wid

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46

Salvador, Daniel, Yoseli Acosta, Anna Zamora, and Manuel Castillo. "Rennet-Induced Casein Micelle Aggregation Models: A Review." Foods 11, no. 9 (2022): 1243. http://dx.doi.org/10.3390/foods11091243.

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Abstract:

Two phases are generally recognized in the enzymatic coagulation of milk: hydrolysis and aggregation, although nowadays more and more researchers consider the non-enzymatic phase to actually be a stage of gel formation made up of two sub-stages: micellar aggregation and hardening of the three-dimensional network of para-κ-casein. To evaluate this controversy, the main descriptive models have been reviewed. Most of them can only model micellar aggregation, without modeling the hardening stage. Some are not generalizable enough. However, more recent models have been proposed, applicable to a wid

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47

Moraes, Izabel Cristina Freitas, and Loic Hilliou. "Viscoelastic Reversibility of Carrageenan Hydrogels under Large Amplitude Oscillatory Shear: Hybrid Carrageenans versus Blends." Gels 10, no. 8 (2024): 524. http://dx.doi.org/10.3390/gels10080524.

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The viscoelastic response of carrageenan hydrogels to large amplitude oscillatory shear (LAOS) has not received much attention in the literature in spite of its relevance in industrial application. A set of hybrid carrageenans with differing chemical compositions are gelled in the presence of KCl or NaCl, and their nonlinear viscoelastic responses are systematically compared with mixtures of kappa- and iota-carrageenans of equivalent kappa-carrageenan contents. Two categories of LAOS response are identified: strain softening and strain hardening gels. Strain softening gels show LAOS non-revers

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Geng, Jing, Yifan Yang, Sailong Zhang, Li Fan, Yunwei Cao, and Bo Shi. "Shear band network induced relaxation, hardening and uniform plastic deformation in metallic glass." Journal of Alloys and Compounds 1010 (January 2025): 177946. https://doi.org/10.1016/j.jallcom.2024.177946.

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IGARASHI, Hideki, and Yoji SHIBUTANI. "405 Strain-hardening with Self-organization of Dislocation Network Patterned by Cellular Automata." Proceedings of Conference of Kansai Branch 2001.76 (2001): _4–9_—_4–10_. http://dx.doi.org/10.1299/jsmekansai.2001.76._4-9_.

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OKIHARA, Koji, and Atsushi SAKUMA. "203 Representation of Hardening Coefficient by Neural Network learning Cyclic Stress-Strain Response." Proceedings of Conference of Chugoku-Shikoku Branch 2001.39 (2001): 45–46. http://dx.doi.org/10.1299/jsmecs.2001.39.45.

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