Simulation study of cutting fluid flow field in kerf of fine diameter diamond wire saw by Jiahu CHEN, Peiqi GE

“…Firstly, according to the actual situation of the diamond wire saw cutting process, a 3D simulation geometric model is established based on the liquid supply mode, where cutting fluid flows along the sawing wire and is brought into the sawing area by the motion of the sawing wire. …”
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Understanding the Magnetic Field and Plasma-β along Umbral Fan Loops Traced Using 3 Minute Slow Waves by Ananya Rawat, Girjesh Gupta, Tom Van Doorsselaere, S. Krishna Prasad, Robertus Erdélyi

“…Plasma- β is an important fundamental physical quantity in solar plasma physics, which determines the dominating process in the solar atmosphere, i.e., magnetic or thermodynamic processes. …”
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Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities by Samuel Moveh, Emmanuel Alejandro Merchán-Cruz, Maher Abuhussain, Yakubu Aminu Dodo, Saleh Alhumaid, Ali Hussain Alhamami

“…The evaluation included a comparison with the baseline models (ARIMA, LSTM, GRU) and robustness testing. …”
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Inflight Parameter Identification and Icing Location Detection of the Aircraft: The Time-Varying Case by Yiqun Dong, Jianliang Ai

“…In particular, ice accumulation is modeled as a continuous process, and the effect of the ice upon aircraft dynamics is to be accreted with time. …”
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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators by Xiaowei Huang, Xuhua Shi, Peiyao Wang, Hongzan Xu, Xiaojun Tang, Gaoran Zhang

“…Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator’s motion reference control signal sequences for the same duration. …”
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Active deception defense method based on dynamic camouflage network by Shuo WANG, Jianhua WANG, Qingqi PEI, Guangming TANG, Yang WANG, Xiaohu LIU

“…In view of the problem that the existing honeypots often fail to resist the penetration attack due to the lack of confidentiality,an active deception defense method based on dynamic camouflage network (DCN) was presented.The definition of DCN was given firstly,and then the attacker-defender scenario of active deception based on DCN was described.Next,the interaction process of the attacker-defender scenario was modeled by using a signaling game,whose equilibrium can guide the selection of optimal deception strategy.Furthermore,to quantify the payoffs accurately,the two-layer threat penetration graph (TLTPG) was introduced.Finally,the solution for game equilibrium was designed,through which pure strategy and mixed strategy could be calculated simultaneously.The experimental results show that,based on the dynamic camouflage network,the perfect Bayesian equilibrium can provide effective guidance for the defender to implement the optimal defense strategy and maximize the benefits of the defender.In addition,the characteristics and rules of active deception defense DCN-based are summarized.…”
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谐波传动中柔轮轮齿对柔轮变形的影响分析 by 储著金, 谭晶

“…In traditional research of harmonic gear drive,the method used for solving the harmonic gear transmission conjugate tooth profile is generally based on the theory of envelope to solve conjugate tooth profile.The influence of the flexspline tooth isn’t considered in the process of formula derivation with the method,so the result gained is not accurate.Using UG software,the 3Dmodels of the elliptical cam wave generator,the cup-shaped flexspline and the simplify ring are built,the models are imported to the ANSYS finite element software by Parasolid format and analyzed.Using surface-surface contact element to solve the contacting problem between the flexspline and the wave generator is simulated.The displacement curve and elastic strain curve of the nodes located on neutral layer of the gear ring and the simplify ring is obtained.The influence of flexspline tooth on flexspline deformation is analyzed.A great reference value for solving harmonic gear tooth is provided.…”
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Thermal analysis and experimental design of PVDF-trfe-based pyroelectric sensors for infrared applications by Mohamed Behloul, Essaid Bousbiat, Dalila Yamani, Riyadh Bouddou, Nour Eddine Derguini, Ayodeji Olalekan Salau, Djamel Kendil, Sarra Adiche

“…The sensor architecture comprises a multilayer structure consisting of a PVDF-TrFE active layer, a 2 μm air gap for thermal insulation, and a silicon substrate. …”
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Supervisory Configuration of Deep Learning Networks for Plant Stress Detection and Synthetic Dataset Generation by J. Renan Velazquez-Gonzalez, Madain Perez-Patricio, J. A. de Jesus Osuna-Coutino, Jorge Luis Camas-Anzueto, Abiel Aguilar-Gonzalez, N. A. Morales-Navarro, Carlos A. Hernandez-Gutierrez

“…First, we collected a small set of Internet images of the stressed crops. Second, the process involves final layer training of the image generation model by introducing a new node into the network. …”
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Variabilities of dissolved inorganic carbon along chain-shaped karst river-reservoir continuum: The control of cascade damming by Xinyu Kang, Tao Zhang, Jianhong Li, Jinke Chen, Peng Chen, Xinlu Su, Yaru Jiang, Wenqing Feng, Junbing Pu

“…In stratified reservoirs, surface DIC decreases from aquatic photosynthesis, while bottom layer accumulated DIC as “storage” in summer dominated by OM decomposition and release in winter. …”
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An Evaluation Method for Distribution Network Operation Based on Multi-source Data Deep Fusion by Junfeng QIAO, Aihua ZHOU, Lin PENG, Yiqing WANG, Xiaofeng SHEN, Sen PAN, Pei YANG, Chenhong HUANG

“…Based on the results, the distribution network operation evaluation index system is designed and a distribution network operation comprehensive evaluation model based on multi-layer weight constraints is constructed to obtain the distribution network comprehensive evaluation results. …”
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Bathymetry-constrained warm-mode melt estimates derived from analysing oceanic gateways in Antarctica by L. Nicola, L. Nicola, R. Reese, R. Reese, M. Kreuzer, M. Kreuzer, T. Albrecht, T. Albrecht, R. Winkelmann, R. Winkelmann, R. Winkelmann

“…We use the ice-shelf cavity model PICO to estimate an upper bound of melt rate changes in case offshore, intermediate-layer warm water masses gain access to the cavities. …”
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Detailed Radial Scale Height Profile of Dust Grains as Probed by Dust Self-scattering in HL Tau by Haifeng Yang, Ian W. Stephens, Zhe-Yu Daniel Lin, Manuel Fernández-López, Zhi-Yun Li, Leslie W. Looney, Rachel Harrison

“…The vertical settling of dust grains in a circumstellar disk, characterized by their scale height, is a pivotal process in the formation of planets. This study offers in-depth analysis and modeling of the radial scale height profile of dust grains in the HL Tau system, leveraging high-resolution polarization observations. …”
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Perancangan Sistem Pendukung Keputusan Untuk Prediksi Penerima Beasiswa Menggunakan Metode Neural Network Backpropagation by Ade Pujianto, Kusrini Kusrini, Andi Sunyoto

“…To solve the problem the complex neural network requires many neurons or so-called layers. One method of multi layer neural network is backpropagation that is able to optimize the weight of neurons and solve complex problems. …”
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Study on the Disturbed Mechanical Behavior and Energy Evolution Characteristics of Deep Roof Rock Considering Spatio-Temporal Effects by WANG man, ZHANG Yang, XIE Jing, NIU Zehua, DENG Huchao, YANG Bengao, ZHAO Lijuan, D ING Xiaogang, GAO Mingzhong

“…This model generalizes the disturbance stress path generated by the multi-working face mining of coal seam group in the longitudinal dimension as a cyclic loading and unloading process, the increase of the disturbance stress concentration factor caused by the reduction of the distance between the coal seams is reflected in the stress path as the increase of the peak stress of cyclic loading and unloading. …”
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Advanced attention-driven deep learning architectures for multi-depth soil temperature prediction by Safwan Mohammed, Sana Arshad, Akasairi Ocwa, Main Al-Dalahmeh, Ashraf ALDabbas, Muhammad Manhal Alzoubi, Attila Vad, Endre Harsányi

“…The wavelet analysis revealed a high coherence of 0.8 to 1.0 between maximum and minimum temperature (Tmax & Tmin) and multi depth soil temperature from 2003 to 2007. Among deep learning models, proposed LSTM with attention layer outperformed GRU, CNN, and Transformer with highest prediction accuracy of R² = 0.951, RMSE = 1.809, and MSE = 3.273 during testing stage. …”
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Feasibility study of single-image super-resolution scanning system based on deep learning for pathological diagnosis of oral epithelial dysplasia by Zhaochen Liu, Zhaochen Liu, Peiyan Wang, Peiyan Wang, Nian Deng, Hui Zhang, Hui Zhang, Fangjie Xin, Xiaofei Yu, Xiaofei Yu, Mujie Yuan, Mujie Yuan, Qiyue Yu, Yuhao Tang, Keke Dou, Jie Zhao, Bing He, Jing Deng, Jing Deng

“…This was done to evaluate the feasibility of the super-resolution digital slide-scanning system, which is based on deep learning, for the pathological diagnosis of OED. The DS30R scanner processes an entire slide in a single layer within 0.25 min, occupying 0.35GB of storage. …”
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Simulation and optimization of heat transfer in underground in-situ shale upgrading by LI Yuan, ZHONG Anhai, QIAN Qin, YU Denglang, ZHANG Yuzhe, ZHONG Yanlei, WANG Dan

“…A three-dimensional fracture-network heat transfer model was established. Combined with the finite element fluid simulation technology， the heat transfer law of the fracture-network was systematically simulated and analyzed， and the influence law of fracturing， gas injection volume， gas injection temperature， rock layer heating methods， and insulation on the heating effect of in-situ upgrading was explored. …”
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The 500 keV electron irradiation effect on free-standing reduced graphene oxide film by ZHEN Xiaojuan, HUANG Yifan, FENG Zhanzu, LUO Dan, GONG Chengshi, JIANG Kai, PAN Duolong, YANG Shengsheng

“…FLUKA simulation results demonstrate that the 500 keV energy electron can penetrate the entire rGOF with deposited energy per unit depth of ±35 eV.ConclusionsIt can be concluded that the change in resistance is caused by defects, structural disturbances, oxygen-containing functional groups and C/O atomic ratios whilst the thermal stability is improved and the graphene layer spacing is larger after electron irradiation.…”
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Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information by Joshua W. Joseph, Evan L. Leventhal, Anne V. Grossestreuer, Matthew L. Wong, Loren J. Joseph, Larry A. Nathanson, Michael W. Donnino, Noémie Elhadad, Leon D. Sanchez

“…Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519–0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671–0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802–0.804), 2‐layer neural network with structured data with AUC 0.811 (95% CI = 0.807–0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818–0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849–0.852). …”
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