Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…Results show that the DT-based intelligent conveyor system reduces coal spillage to less than 100 g/min, decreases blade wear rate by 8.99%, cuts actual power consumption by 8.61%, with Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) in sweep force prediction being 3.2748% and 0.017 respectively. …”
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Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive by Darwin P. Quezada Gaibor, Lucie Klus, Roman Klus, Elena Simona Lohan, Jari Nurmi, Mikko Valkama, Joaquin Huerta, Joaquin Torres-Sospedra

“…This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. …”
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Leveraging Machine Learning Regression Algorithms to Predict Mechanical Properties of Evaporitic Rocks From Their Physical Attributes by Ayham Zaitouny, Hasan Arman, Anusuya Krishnan, Alaa Ahmed, Ahmed Gad

“…Nine ML models were trained (80:20 data split) and validated using R-squared (R2), mean absolute error (MAE), and root-mean square error (RMSE). …”
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HBIM reconstruction in cultural heritage: the case study of the Casina Cenci Giustiniani, Rome by Luca Gugliermetti, Federico Cinquepalmi, Lorenzo Villani

“…HBIM is an extension of Building Information Modelling (BIM) specifically designed for the documentation, management, and conservation of historic and heritage buildings, utilizing parametric 3D models enriched with both geometric and historical data. HBIM enables the integration of detailed survey data and archival research to create intelligent digital representations that support restoration, maintenance, and analysis of existing architectural heritage. …”
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Uncertainties of Annual Suspended Sediment Transport Estimates Driven by Temporal Variability by Aron Slabon, Thomas Hoffmann

“…Since low‐frequency data sets rely on sediment rating curves, we performed a sensitivity analysis of the rating parameters a, b, and ε. …”
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Simulation of spectral bands of the MERIS sensor to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region by Fernando Bezerra Lopes, Evlyn Márcia Leão de Moraes Novo, Cláudio Clemente Faria Barbosa, Eunice Maia de Andrade, Rafael Damiati Ferreira

“…The use of remote sensing data is important, since it allows evaluation of dynamic problems in aquatic systems, such as the eutrophication of bodies of water and suspended sediment. …”
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Prediction of post-irradiation swelling rate of 316L stainless steel based on Variational Autoencoders and interpretable machine learning by Chengcheng Liu, Hang Su

“…By comparing various machine learning models, it was found that the Extreme Trees Regression (ETR) model performed best on the test set, achieving an R2 of 0.79 and a Root Mean Square Error (RMSE) of 1.65 %. Although it demonstrated strong generalization capabilities, the limited data volume restricted its predictive accuracy. …”
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Groundwater Level Forecasting using a Deep Learning Convolutional Neural Network (CNN) Model: Seidan-Farooq Study Area by Mohammad Javad Aghaei, Hamed Ketabchi

“…Additionally, error analysis and model accuracy on a seasonal and monthly basis indicate that the second scenario outperforms the first. …”
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Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning by Frank Rhein, Timo Sehn, Michael A. R. Meier

“…Using the DS obtained from 1H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.069 in DS on test data, demonstrating higher accuracy compared to the manual evaluation based on peak integration. …”
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Turbidity and suspended sediment relationship based on sediment composition and particle size distribution by Jongmin Kim, Siyoon Kwon, Sewoong Chung, Young Do Kim

“…These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1–18%. …”
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Psychosocial factors of binge-eating: a meta-analytical study by A. A. Lifintseva, U. U. Novikova, T. A. Karavaeva, M. V. Fomicheva

“…As additional procedures, an I2- test was conducted to assess the homogeneity of the studies, a publication bias analysis to level the error of unpublished data, and a «trim-and-fill» test. …”
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Statistical inference for the generalized exponential distribution using ordered lower k-record ranked set sampling with random sample sizes by Haidy A. Newer

“…The efficacy of the proposed sampling method is further validated through its application to real-world medical datasets, showcasing its practical utility in enhancing statistical inferences for lifetime data analysis. The key findings highlight that ordered moving extremes lower k-record ranked set sampling effectively balances efficiency and accuracy, making it particularly well-suited for reliability studies and survival analysis.…”
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Service Communication On Unisba Student Satisfaction During The Covid-19 Pandemic by Tresna Wiwitan, Mohammad Subur Drajat, Nurrahmawati Nurrahmawati

“…The sampling technique that the authors used was the stratified probability one. The data analysis that we used was simple linear regression to see the influence of people, process, and technology aspects on Unisba student satisfaction.  …”
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Effect of proprioception training on Pain and knee joint position sense of athletes with Genuvalgum by Mohammad Rahim Amiri, Masoud Golpayegani, Fariba Moradi Vastgani, Mohammad Mirghasemi

“…The pre-test and post-test to detect the joint position (PFPPS) and the knee proprioception were measured via digital pictures using Auto-CAD software. Data analysis software SPSS V. 16 using paired t-test and one-way analysis of variance. …”
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Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. by Jinhao Yang, Junwen Cao, Mingyu Fang

“…The framework operates through three rigorously designed stages: (1)Data preprocessing involving kinematics feature extraction, (2)Driving style recognition utilizing acceleration variation rate and average time headway combined with K-Means++ traffic density clustering and K-neighbor Gaussian mixture model (K-GMM) analysis to classify driving behaviors into conservative, moderate, and radical categories, and (3)Personalized trajectory prediction employing a multi-level neural architecture with dedicated sub-networks for distinct driving styles. …”
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Determinants of environmental pollution: Evidence from Indonesia by Vu Ngoc Xuan

“…This study explores the nexus between electricity consumption, fossil fuel use, renewable energy adoption, population growth, GDP, and environmental pollution in Indonesia, incorporating open innovation dynamics as a novel dimension of analysis. The study uncovers significant long-term relationships between the variables using time series data from 2000 to 2023 and applying econometric techniques such as cointegration analysis, Granger causality tests, and Vector Error Correction Models (VECM). …”
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Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports by Kuo-Wei Wu, Wen-Fang Wu, Chung-Chih Liao, Wei-Ann Lin

“…We follow an identification method similar to fault tree analysis (FTA) with the help of the driving reliability and error analysis method (DREAM 3.0) and the Haddon matrix to find the potential key accident factors from all disengagement data. …”
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Humanoid Walking Robot: Modeling, Inverse Dynamics, and Gain Scheduling Control by Elvedin Kljuno, Robert L. Williams

“…This analysis is accompanied by a comparison with experimental data. …”
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Mapping probabilities of extreme continental water storage changes from space gravimetry by J. Kusche, A. Eicker, E. Forootan, A. Springer, L. Longuevergne

“…However, a simulation experiment reveals that differences observed by GRACE are statistically significant, and further error analysis suggests that by around the year 2020, it will be possible to detect temporal changes in the frequency of extreme total fluxes (i.e., combined effects of mainly precipitation and floods) for at least 10–20% of the continental area, assuming that we have a continuation of GRACE by its follow‐up GRACE Follow‐On (GRACE‐FO) mission.…”
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Application of Response Surface Methodology and Central Composite Inscribed Design for Modeling and Optimization of Marble Surface Quality by Sümeyra Cevheroğlu Çıra, Ahmet Dağ, Askeri Karakuş

“…Mathematical models describing responses were produced using experimental datasets, and analysis of variance (ANOVA) was used to assess the fit of the models generated with the experimental data. …”
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