Design of real-time transmission system for underwater panoramic camera based on RTSP. by Wenhui Wang, Yongqi Li, Rufei He

“…In the current transmission system application process, the interference noise present in the collected original image will increase the average reprojection error of the image; And some systems have complex data collection processes, requiring the deployment of multiple sensors, which increases data transmission time. …”
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Design and parameter identification of the wireless torque test system for hybrid vehicles by LI Jie, ZHOU Yijian, GENG Chong, PANG Jinlu

“…Road tests were conducted to obtain the real vehicle torque and speed data. System identification method was employed for the iterative analysis of the test data, yielding a 6th-order identification value and establishing a torque identification model.ResultsThe calibration linearity of the test system reaches 0.703%, demonstrating small linear error and high accuracy. …”
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Numerical Modeling of Tsunamis Generated by Subaerial, Partially Submerged, and Submarine Landslides by Tomoyuki Takabatake, Ryosei Takemoto

“…Using the existing two-dimensional experimental data and Open-source Fields Operation and Manipulation (OpenFOAM) software, this study performs a comprehensive comparative analysis of three types of landslide-generated tsunamis (subaerial, partially submerged, and submarine). …”
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Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks by Jeongho Han, Seoro Lee

“…This study aims to estimate rainfall erosivity (RE) in North Korea using automated machine learning (AutoML), with a particular focus on regional soil erosion risks. North Korean data were sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 dataset, while South Korean data were obtained from the Korea Meteorological Administration. …”
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Approaches, Relevant Topics, and Internal Method for Uncertainty Evaluation in Predictions of Thermal-Hydraulic System Codes by Alessandro Petruzzi, Francesco D'Auria

“…Namely, the propagations of code input error and calculation output error constitute the keywords for identifying the methods of current interest for industrial applications, while the adjoint sensitivity-analysis procedure and the global adjoint sensitivity-analysis procedure, extended to performing uncertainty evaluation in conjunction with concepts from data adjustment and assimilation, constitute the innovative approach. …”
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Factors influencing parental role satisfaction among Korea fathers of young children in the COVID-19 endemic era by Eun Ju Choi, Sun Jung Park

“…Descriptive statistics, t-test, ANOVA (Analysis of Variance), correlation analysis and multiple regression were used to analyze the collected data. …”
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Cost Estimation of Construction Projects under Risk and Cost-Influencing Factors by Mohammad Sheikhalishahi, Mahshid Yadegari, Emran Eshgheelahi

“…ANN is selected as the preferred method for project cost estimation with the minimum MAPE (Mean Absolute Percentage Error). Sensitivity analysis is performed to demonstrate the applicability of DEA in identifying the influential factors. …”
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Artificial neural network algorithm for time dependent radiative Casson fluid flow with couple stresses through a microchannel by Pradeep Kumar, Felicita Almeida, Qasem Al-Mdallal

“…The regression analysis and plotfit demonstrate a high degree of concordance between the data points for training, testing, and validation, with an approximate correlation coefficient ≈1. …”
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Simulation of Navigation Receiver for Ultra-Small Satellite by A. A. Spiridonov, D. V. Ushakov, V. A. Saechnikov

“…Radio visibility intervals for GPS and GLONASS satellites were calculated and optimal conditions for the cold start of the navigation receiver with a relative speed limit (Vr < 500 m/s) for 1 hour of operation both in separate and in joint operation on both systems were determined.To test the verification methods of the experimental data of the СubeBel-1 satellite, the operation of the navigation receiver of the Nsight satellite was studied according to the received telemetry from the beginning of its flight until the moment it entered stable operation.It is shown that the telemetry data of the navigation receiver at the testing stage had a significant error. …”
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A hybrid long short-term memory with generalized additive model and post-hoc explainable artificial intelligence with causal inference for air pollutants prediction in Kimberley, S... by Israel Edem Agbehadji, Ibidun Christiana Obagbuwa

“…Meteorological and air pollutant statistical records were leveraged from a Hantam (Karoo) air monitoring station in South Africa, and through a random sampling approach, synthetic data were generated for the city of Kimberley. The model was evaluated with the mean squared error (MSE), root mean squared error (RMSE) and mean absolute error (MAE) for different time-steps. …”
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An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm by Peipei Yang, Zhidong Chen, Wen Tang, Zongyang Liu, Bingrui He

“…The methodology involves the following steps: (1) Collecting historical ultra-short-term power generation data from photovoltaic systems, where outlier detection and data cleaning are performed using horizontal processing methods; (2) Applying Pearson correlation analysis to identify key meteorological factors significantly influencing power output as feature inputs; (3) Developing an Adaptive Sparrow Search Algorithm (ASSA) by dynamically adjusting the quantities of discoverers and followers in traditional SSA; (4) Optimizing LSTM network parameters through ASSA to enhance prediction accuracy. …”
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Grain Yield Prediction Based on the Improved Unbiased Grey Markov Model by Wu Yuan, Zhou Rui, Yu Bao, Huang Xiang, Li Bo

“…The original grain output data for Chongqing from 2000 to 2022 were used for the validation analysis to compare the prediction accuracies of the four models. …”
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Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor by Shiliang Zhou, Menghan Tang, Jun Wu, Chunru Ke

“…Before generating the training sample set, principal component analysis is employed to reduce dimensionality and eliminate a substantial amount of redundant data. …”
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Two-steps variance calibrated estimators with linear and non-linear constraints for mailed surveys with non-response by Ahmed Audu, Maggie Aphane

“…The results of the error analysis revealed that the members of the proposed class of estimator are robust and efficient.…”
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THE PROFILE OF CREATIVE THINKING PROCESS: PROSPECTIVE MATHEMATICS TEACHERS by Gunawan Gunawan, Ferry Ferdianto, Fauzi Mulyatna, Reni Untarti

“…Data analysis uses data reduction techniques, presentation, and conclusion drawn. …”
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Application of Distributed Acoustic Sensing for Active Near-Surface Seismic Monitoring by Eslam Roshdy, Mariusz Majdański, Szymon Długosz, Artur Marciniak, Paweł Popielski

“…For example, the multichannel analysis of surface waves (MASW) using DAS data clearly identifies S-wave velocities down to 13 m with an RMS error of 3.26%, compared to an RMS error of 6.2% for geophone data. …”
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…The Tikhonov model demonstrates high accuracy with R2 values of 0.973 for training data and 0.958 for testing data, showcasing strong generalization capabilities, while maintaining low error rates, as evidenced by RMSE and MAE values of 4.62 and 3.65 for training data, and 5.04 and 4.21 for testing data, respectively. …”
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Lattice-Based CP-ABE Access Control for SDS Constraint with Lazy Assignment of Attributes and Attribute Revocation by Ting Guo, Abdugeni Abduxkur, Nurmamat Helil

“…This paper proposes a revocable CP-ABE scheme on the lattice, based on ring learning with error (R-LWE) problem, to enforce access control constraints on user access to such data objects. …”
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Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility by Bafren K. Raoof, Ali Rabia, Usama Alameedy, Pshtiwan Shakor, Moses Karakouzian

“…The future forecast is compared with an outcome of a previous physical model that integrates wells and reservoir properties to simulate gas production using regressions and forecasts based on empirical and theoretical relationships. Regression analysis ensures alignment between historical data and model predictions, forming a baseline for hybrid model performance evaluation. …”
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Comparison of Deep Learning and Gradient Boosting: ANN Versus XGBoost for Climate‐Based Dengue Prediction in Bangladesh by Arman Hossain Chowdhury

“…Exploratory data analysis, as well as ANN and XGBoost models, were performed to analyze the data. …”
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