Research on the Fine Control of the Influence of Pipe-Jacking Parameter Deviation on Surrounding Stratum Deformation by Tianlong Zhang, Guoqing Chen, Ping Lu, Dongqing Nie

“…Based on the Zhuyuan–Bailonggang sewage interconnection pipe project in Shanghai, the ABAQUS finite element software was used in numerical simulations to study the fine control of stratum disturbances caused by pipe jacking parameter deviation in soft soil areas. Combining the simulation results with onsite measured data, the Peck formula was used to predict surface settlement. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. …”
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SuperKEKB positron beam tuning using machine learning by Natsui Takuya

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Kinematics and Workspace Analysis for Articulated Arm Coordinate Measuring Machine by Zhou Aiguo, Zhou Fei, Lv Gang, Ge Weiliang

“…The kinematics and workspace for articulated arm coordinate measuring machine（AACMM）is the basis for structure optimization and precision analysis.First,the kinematics theory model of AACMM is established by using D-H method and tri-dimension kinematics simulation is implemented based on the secondary development of SolidWorks to verify whether the kinematics model is correct.Then the AACMM workspace is analyzed and simulated by Monte Carlo method and the cloud picture is obtained.Finally,the volume of workspace is calculated more accurately by the stratifying method and boundary extension,a basis for the parameter optimization is provided.The simulation results show sthat the workspace of AACMM is distributed uniformly and no cavity and it meets the measurement requirement.…”
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A Perception-Based Method for the Noise Control of Construction Machines by Eleonora CARLETTI

“…During operation, construction machines generate high noise levels which can adversely affect the health and the job performance of operators. …”
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Why Machine Learning Models Systematically Underestimate Extreme Values by Yuan-Sen Ting

“…A persistent challenge in astronomical machine learning is a systematic bias where predictions compress the dynamic range of true values---high values are consistently predicted too low while low values are predicted too high. …”
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Tunnelling in urban areas by EPB machines: technical evaluation of the system by Cardu Marilena, Oreste Pierpaolo

“…Thefundamental point in analysing technical aspects regarding an earth pressure balance (<br />EPB) machine concerned storing the main excavation parameter values; having collected and organised such data, statistical methods were used for processing it, the instantaneous velocities attained were empirically estimated and idle times were evaluated. …”
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Deep Learning Model of Image Classification Using Machine Learning by Qing Lv, Suzhen Zhang, Yuechun Wang

“…Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. …”
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Accelerated predictions of the sublimation enthalpy of organic materials with machine learning by Yifan Liu, Huan Tran, Chaofan Huang, Beatriz G. delRio, V. Roshan Joseph, Mark Losego, Rampi Ramprasad

“…Abstract The sublimation enthalpy, ΔHsub, is a key thermodynamic parameter governing the phase transformation of a substance between its solid and gas phases. …”
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Aerodynamics-guided machine learning for design optimization of electric vehicles by Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira

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Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC by Ernesto Arganda, Marcela Carena, Martín de los Rios, Andres D. Perez, Duncan Rocha, Rosa M. Sandá Seoane, Carlos E. M. Wagner

“…We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.…”
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Inverse imaging with elastic waves driven by unsupervised machine learning by Liyou Luo, Yaxi Shen, Jiawei Xi, Yabin Jin, Daniel Torrent, Jensen Li

“…After confirming the uniqueness, this latent representation, through independent component analysis, is then converted into independent components each corresponding to one physical parameter to be recovered. The practical application is demonstrated by applying the trained machine learning model to the experimental data measured from 3D-printed samples, with high accuracy. …”
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Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines by Yang Yu, Zhiwei Su, Junhao Zhang, Jinglong Li, Wu Qin

“…The vibration of the belt drive system in fresh wolfberry sorting machines significantly impacts the sorting efficiency of wolfberries. …”
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Machine learning algorithm for the rapid and accurate detection of Plasmodium falciparum by Mr Andrew Hill

“…The high prevalence of malaria in under-developed regions requires highly precise and computationally efficient models to achieve rapid and accurate diagnosis, which in turn has the potential to be developed into a smartphone app. Methods: Machine learning algorithms (MLA) consisting of a family of tiny (3,911 to 100,000 parameters) hybrid convolutional neural network / encoder-decoder models were developed which output a both a label {Parasite, Normal} and a confidence. …”
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Quantitative Assessment of Cooling Methods for Electrical Machines in Aircraft Drives by Karunakar Reddy Konda, Jonas Franzki, Dikshant Sharma, Paul Konig, Akilan Mathiazhagan, Markus Henke, Klaus Hoschler

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Investigation of Titanium Alloy Cutting Dynamics in Thin-Layer Machining by Anna Zawada-Tomkiewicz, Emilia Zeuschner, Dariusz Tomkiewicz

“…Manufacturing in modern industrial sectors involves the machining of components where the undeformed chip thickness inevitably decreases to values comparable to the tool edge radius. …”
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A machine learning approach to population pharmacokinetic modelling automation by Sam Richardson, Itziar Irurzun Arana, Andrzej Nowojewski, Diansong Zhou, Jacob Leander, Weifeng Tang, Richard Dearden, Megan Gibbs

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Chronic Kidney Disease Prediction Based On Machine Learning Algorithms by Kethineni Likitha., Nithinchandra, Kumar Narendra, Sk Sajida Sultana.

“…This paper analyses the suitability of several machine learning algorithms for CKD prediction in consideration of health-related parameters like age, blood pressure, and blood glucose levels. …”
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Machine learning-driven optimization of arsenic phytoextraction using amendments by Huading Shi, Yunxian Yan, Zhaoyang Han, Liang Wang, Guanghui Guo, Jun Yang

“…Using %IncMSE to quantify parameter contribution, we found that the biomass of P. vittata had a greater influence than the As concentration. …”
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Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit by Uzair Sajjad, Farzana Bibi, Imtiyaz Hussain, Naseem Abbas, Muhammad Sultan, Hafiz Muhammad Asfahan, Muhammad Aleem, Wei-Mon Yan

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