STUDY ON ENERGY ABSORPTION CHARACTERISTIC OF SINUSOIDAL BELLOWS FILLED EMULSION ABSORBER by ZHANG JianZhuo, ZHANG WanJiu, PAN YiShan, GUO Hao, WANG ShuWen

“…The pre⁃folded energy absorber is the only energy absorber structure used in rock burst mine at present.It has excellent energy absorption characteristics,but there are some problems in the buckling deformation process such as large load fluctuation and spark due to friction.In view of the above situation,a sinusoidal bellows(reduced diameter round pipe)filled with emulsion was designed to form a solid⁃liquid coupling body energy absorption component(composite component).The effects of three factors,namely wall thickness of the bellows,amplitude of the busbar and diameter of the liquid outlet,on the energy absorption characteristics of the energy absorption component were studied.The prediction model of average load,load fluctuation coefficient and specific energy absorption was established.The smoothed partiole hydrodynamics(SPH)particle algorithm of Abaqus finite element software was used for fluid⁃structure coupling analysis of the model.The structural parameters of energy absorption components were optimized by response surface method and the optimization results were obtained.The results show that when the wall thickness of corrugated pipe is 3.959 mm,the amplitude of busbar is 3.721 mm,and the diameter of outlet is 22.161 mm,the energy absorption characteristics of the member are the best.The average load is 438.684 kN,the load fluctuation coefficient is 1.178,and the specific energy absorption is 12.123 kJ/kg.Through comparison and verification,the results have high reliability,which provides a superior energy absorption component for the energy absorption link of anti⁃impact support equipment.…”
Get full text

Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

“…The inversion loss due to flow prediction error and the two regularizations play dominant roles in the initial and final stages, respectively. …”
Get full text

Research on Sugar Concentration Sensing Based on Real-Time Polarization and Interaction Effects by Qiong Gong, Tongxiao Lyu, Song Ye, Xinqiang Wang

“…The addition of the interaction effect model significantly improves the prediction accuracy, with an RMSE of 0.36934 and a relative error within ±5%. …”
Get full text

Evaluation of inflammatory bowel disease-related sleep disorders based on an interpretable machine learning approach: a multicenter study in China by Jiayi Sun, Junhai Zhen, Chuan Liu, Changqing Jiang, Jie Shi, Kaichun Wu, Weiguo Dong

“…Conclusion: The construction of risk assessment models using ML has high clinical value in the prediction of IBD-related sleep disorders, and the efficacy of its application suggests it can serve as a promising evaluation tool in clinical work.…”
Get full text

Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting. by Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola

“…The Migrative Armadillo Optimization (MAO) algorithm effectively optimizes the hyperparameters of the model. …”
Get full text

Optimization of a Heavy-Duty Hydrogen-Fueled Internal Combustion Engine Injector for Optimum Performance and Emission Level by Murat Ozkara, Mehmet Zafer Gul

“…This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. …”
Get full text

A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland by Charlotte Poussin, Pascal Peduzzi, Bruno Chatenoux, Gregory Giuliani

“…Remote sensing technologies have emerged as key tools for monitoring snow cover, providing critical data for climate models, hazard prediction, and resource planning. In Switzerland, snow cover is monitored using ground-based measurements, remote sensing, and climate models, with datasets from satellites like Landsat and Sentinel-2 offering valuable insights despite challenges such as cloud obstruction. …”
Get full text

Construction of Industrial Economic Management Decision Model Based on Three Parameter Interval Grey Numbers by Pao-Ching Lin, Tzu-Jung Wu, Jui-Chan Huang

“…In addition, through comparative experiments with other decision models, this model has shown superiority in improving profit margins, increasing sales, reducing unit costs, and reducing prediction errors. Therefore, the proposed industrial management decision model can effectively handle uncertainty factors, enhance the scientific and effective nature of industrial economic management decisions, and provide more accurate and reliable solutions for industrial enterprises.…”
Get full text

Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. …”
Get full text

Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design by Fang-Fang Chua, Tek-Yong Lim, Bushra Tajuddin, Amarilis Putri Yanuarifiani

“…SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. …”
Get full text

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework by Mohammad Javad Aliabadi, Masoud Radmehr

“…The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. …”
Get full text

Study on Tooth Surface Roughness Modeling of Form Grinding Considering the Change of the Local Grinding Condition by Chen Xiaoqi, Tang Cheng, Liao Xianggui, Zhou Weihua, Tang Jinyuan

“…The results show that the prediction model of the surface roughness of formed grinding teeth presented in this study agrees with the experimental results. …”
Get full text

Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement by Jie Fang, Wenwu Li, Dunchu Chen

“…Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. …”
Get full text

Simulation-Based Design and Machine Learning Optimization of a Novel Liquid Cooling System for Radio Frequency Coils in Magnetic Hyperthermia by Serhat Ilgaz Yöner, Alpay Özcan

“…The performance index was also developed and modeled using Gaussian Process Regression, enabling rapid performance prediction without requiring extensive numerical studies. …”
Get full text

A multivariate model incorporating subharmonic measurements for evaluating vocal roughness by Itsuki Kitayama, Kiyohito Hosokawa, Shinobu Iwaki, Misao Yoshida, Akira Miyauchi, Kenji Aruga, Takanari Kawabe, Toshihiro Kishikawa, Hidenori Tanaka, Takeshi Tsuda, Takashi Sato, Yukinori Takenaka, Makoto Ogawa, Hidenori Inohara

“…Furthermore, we introduce the acoustic roughness index (ARI), a predictive acoustic model that integrates these parameters with existing acoustic parameters. …”
Get full text

RL-Based Vibration-Aware Path Planning for Mobile Robots’ Health and Safety by Sathian Pookkuttath, Braulio Felix Gomez, Mohan Rajesh Elara

“…Terrain roughness is classified into four levels using IMU sensor data, achieving average prediction accuracy of 97% with a 1D CNN model. A vibration cost map is created by assigning vibration costs to each predicted class on a 2D occupancy grid, incorporating obstacles, vibration-prone areas, and the robot’s pose for navigation. …”
Get full text

Machine learning approach for identifying and forecasting streamflow droughts in data limited basins of South Korea using threshold levels by Young-Ho Seo, Jang Hyun Sung, Byung-Sik Kim, Junehyeong Park

“…We utilized the XGBoost algorithm, integrating comprehensive meteorological data to enhance the accuracy and reliability of the drought predictions. …”
Get full text

Modélisation pluie-débit en région tropicale humide : application des réseaux de neurones sur quatre stations hydrométriques du Bandama Blanc (Bada, Marabadiassa, Tortiya et Bou) s... by Yao Blaise Koffi

“…To achieve this goal, two Multilayer Perceptrons trained with the backpropagation algorithm of error have been built. The first model was used only in simulation and the second in simulation and prediction. …”
Get full text

Identification of an HLA-A2-restricted epitope peptide derived from hypoxia-inducible protein 2 (HIG2). by Sachiko Yoshimura, Takuya Tsunoda, Ryuji Osawa, Makiko Harada, Tomohisa Watanabe, Tetsuro Hikichi, Masahiro Katsuda, Motoki Miyazawa, Masaji Tani, Makoto Iwahashi, Kazuyoshi Takeda, Toyomasa Katagiri, Yusuke Nakamura, Hiroki Yamaue

“…Among several candidate peptides predicted by the HLA-binding prediction algorithm, HIG2-9-4 peptide (VLNLYLLGV) was able to effectively induce peptide-specific cytotoxic T lymphocytes (CTLs). …”
Get full text

An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province by Relebogile Langa, Michael Nthabiseng Moeti, Thabiso Maubane

“…A machine learning precipitation prediction algorithm optimizes water usage. The paper also describes a system with multiple sensors that detect soil parameters, and automatically irrigate land based on soil moisture by switching the motor on/off. …”
Get full text