Development of a Vaping Machine for the Sampling of THC and CBD Aerosols Generated by Two Portable Dry Herb Cannabis Vaporisers by Laura Carrara, Christian Giroud, Nicolas Concha-Lozano

“…This determination requires a specific vaping machine operating under realistic puffing conditions. …”
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Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam by Nguyen Quoc Hung, Trinh Hoang Viet, Phuong Truong Viet, Ly Truong Thi Minh

Subjects: “…machine learning models…”
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Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…Comparing the shape indices’, the slope parameter (SLP) index outperformed the half-area parameter (HAP) index. …”
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Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model by Dan Li, Jiwei Qu, Delan Zhu, Zheyu Qin

“…The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. …”
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Existence and Solution of Optimal Initial Angle and Maximum Travel Angle of the High Speed Printing Machine Mechanism Output Member by Fan Wu, Chang Yong

“…The connotation of the mechanism dimension synthesis task of high speed printing machine is enriched and expanded. It has important theoretical value and practical engineering significance to design better and optimal dimension parameter mechanism.…”
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Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen by Sharazita Dyah Anggita, Ferian Fauzi Abdulloh

“…Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. …”
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Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. by Christopher Heylman, Rupsa Datta, Agua Sobrino, Steven George, Enrico Gratton

“…Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. …”
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Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm by Nikolaos Fountas, Angelos Koutsomichalis, John D. Kechagias, Nikolaos M. Vaxevanidis

“…Two basic machinability parameters are the surface roughness, closely associated with the functional and tribological performance of components, and the cutting forces acting on the tool. …”
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ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data by Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi

“…Abstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. …”
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ST-YOLO: A defect detection method for photovoltaic modules based on infrared thermal imaging and machine vision technology. by Hanfei Xie, Baoxi Yuan, Chengyu Hu, Yujie Gao, Feng Wang, Chunlan Wang, Yuqian Wang, Peng Chu

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Machine learning analysis of a Fano resonance based plasmonic refractive index sensor using U shaped resonators by Shiva Khani, Pejman Rezaei, Mohammad Rahmanimanesh

“…The presented sensor with machine learning behavior prediction ability can be utilized for RI sensing performance.…”
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An Overview of Deep Neural Networks for Few-Shot Learning by Juan Zhao, Lili Kong, Jiancheng Lv

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A dataset of the operating station heat rate for 806 Indian coal plant units using machine learningZenodo by Yifu Ding, Jansen Wong, Serena Patel, Guiyan Zang, Dharik Mallapragada

“…This study leverages existing databases to create a SHR dataset for 806 Indian coal plant units, utilizing machine learning (ML), and presents the most comprehensive coverage to date. …”
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Study on the influence of pressurized air jet on respiratory dust pollution in fully mechanized excavation face by Wei CHANG, Hao WANG, Chuangye XIN, Shouqing LU, Zhuang LIU

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Study on the Design of the Gear Pair and Flow Characteristics of Circular-Arc Gear Pumps by Geqiang Li, Yunda Liu, Weifeng Han, Donglin Li, Shuai Wang, Zhenchao Hao

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INCREASE IN THE WORKING LIFE OF SCREWS OF IMMERSION PUMPS BY VIBRATORY FINISHING OF THEIR SURFACES by M. U. Akhmedpashaev, M. M. Akhmedpashaev, Zh. B. Begov

“…It is established that since vibratory finishing is a method of hardening, kinematic and dynamic characteristics of the process are connected, as in other types of finishing, with characteristic indicators of machined surfaces. The task of developing methods for determining the vibratory finishing regime is complicated, firstly by the number of parameters that determine the process conditions being considerably greater than tumbling and other processing methods with relatively simple kinematics; secondly, due to the fact that all the mode parameters affect all surface quality characteristics in one way or another. …”
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A Comprehensive Benchmark Dataset for Sheet Metal Forming: Advancing Machine Learning and Surrogate Modelling in Pro-cess Simulations by Heinzelmann Pascal, Baum Sebastian, Riedmüller Kim Rouven, Liewald Mathias, Weyrich Michael

“…An example application demonstrates using the dataset to model the impact of material and process parameters on the forming limit diagram (FLD) of a deep-drawn part. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

“…This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. …”
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First Global Machine Learning Model to Predict the Rate of TEC Index (ROTI) Response to X‐Class Solar Flares by A. Mahmoudian, F. Ghorbali, M. Vazifehkhah Hafteh

“…A nonlinear response of the ionosphere associated with solar flare characteristics including rise/fall time and maximum amplitude is discussed. The first global machine learning (ML) model to predict solar flare impact on Earth's ionosphere through ROTI parameter is developed. …”
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Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique by Shohreh Jalali, Majid Baniadam, Morteza Maghrebi

“…Machine learning model including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boost (CatBoost), and Light Gradient-Boosting Machine (LightGBM) were employed to enhance predictive capabilities. …”
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