Carbon emission prediction method of steel plants based on long short-term memory network by Fengyun LI, Zehui DOU, Peng LI, Wei GUO

“…As the second largest carbon emitter in China, iron and steel enterprises have great potential for carbon emission reduction.In order to facilitate the supervision and control of carbon emissions by relevant departments, carbon emission prediction research is carried out.Taking a steelmaking plant as the research object, firstly, the carbon dioxide emissions in the steelmaking process were analyzed, and 10 energy substances that caused carbon emissions were determined.The basic energy data of the steelmaking plant from 2001 to 2023 were collected, and the carbon emissions were calculated from the basic energy data according to the carbon emission accounting method.Secondly, based on the long short-term memory network to predict the carbon emissions in the next 7 years, the training error and test error were close to 0.01, and the actual error was 1 323 307.46 tons of carbon dioxide.Then, the Mann-Kendall trend test was used to evaluate the overall carbon emission trend of the steelmaking plant.Finally, some reasonable suggestions were put forward for steelmaking plants in order to actively respond to the goal of low-carbon environmental protection.…”
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Calculation of Correction Factors for Vickers’ Hardness Measurements on a Non-Planar Surface by S. G. Sandomirski, A. L. Val’ko, S. P. Rudenko

“…Simplification of calculation of K coefficient and decrease of calculation error in comparison with the recommended in the regulatory documents obtaining of K value by linear interpolation relative to two adjacent table values are shown.The reduction of the calculation error in comparison with the calculation recommended in the regulatory documents occurred because of the reason that when calculating by the developed formulas, the error in the value of the calculated for a specific value of d/D coefficient K is averaged over all n values of d/D given in the table of GOST for a given surface. …”
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Mid-Term Optimal Scheduling of Hydro-Wind-Solar Systems Addressing Extreme Drought and Renewable Energy Forecast by CHEN Cong, LIU Benxi, WANG Haidong, WANG Wenbin

“…This paper proposed a flexibility regulation capability assessment method for hydropower systems to address low output in mid-term extreme forecast errors of renewable energy under extreme drought conditions. …”
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Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> error, which measures the proportion of variance explained by the model, achieved an impressive value of 0.93. …”
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Spatially and temporally correlated channel estimation and detection for comparator network-aided MIMO receivers with 1-bit ADCs by Luiz Sampaio, Lukas T. N. Landau

“…Abstract The low-resolution aware linear minimum mean squared error (LRA-LMMSE) channel estimator, designed for low-resolution MIMO receivers, achieves a notable reduction in mean squared error by incorporating a comparator network. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation.…”
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TransformerPayne: Enhancing Spectral Emulation Accuracy and Data Efficiency by Capturing Long-range Correlations by Tomasz Różański, Yuan-Sen Ting, Maja Jabłońska

“…The newly introduced TransformerPayne emulator outperformed all other tested models, achieving a mean absolute error (MAE) of approximately 0.15% when trained on the full grid. …”
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A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments by Abdullah AlHayan, Jalal Al-Muhtadi

“…These results represent a substantial improvement over standalone deep learning models (standalone LSTM FNR = 0.302, FPR = 0.185) and compare favorably to state-of-the-art benchmarks reported in the literature, particularly in minimizing critical detection errors. The findings indicate that the proposed hybrid STL-LSTM framework presents a robust and viable solution for high-stakes IoT network security, effectively balancing high detection accuracy with exceptionally low error rates, making it well-suited for real-time deployment in protecting critical IoT infrastructure.…”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. …”
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Prevalence and Causes of Blindness and Vision Impairment in the State of Qatar: Results of a Population-Based Cross-Sectional Study by Mohammed AlThani, Mariam Abdulmalik, Samya AlAbdulla, Kholoud AlMotawaa, Halla Algadi, Muhammad Rabiu, Ian McCormick, Shadi AlAshwal

“…VI was mainly caused by DR, cataract, and uncorrected refractive error. Further reduction in vision loss can be achieved with early detection and improved access using innovation and technology. …”
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A Hardware Architecture of a Dynamic Ranking Packet Scheduler for Programmable Network Devices by Mostafa Elbediwy, Bill Pontikakis, Jean-Pierre David, Yvon Savaria

“…Additionally, the DR-PIFO offers a significant reduction in the relative flow completion time (FCT) errors, exhibiting a minimum of 30&#x0025; less error compared to the previously proposed models when implementing various scheduling policies with workloads collected from data centers. …”
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Modeling residential property prices in emerging climate-responsive urban markets: a hybrid modeling framework for Baidoa City-Somalia by Mohamed Ibrahim Nor, Shuaib Nour Hussein

“…Compared to the baseline linear hedonic regression model, the ANN achieved approximately a 20% reduction in mean squared error (MSE), with performance improvements validated through 5-fold cross-validation and supported by 95% confidence intervals. …”
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A Deep Learning Enabled Chest X-Ray Abnormality Detection Model for Radiology Assistance. by Ssempeebwa, Phillip, Ainembabazi, Patience

“…This difficulty persists even among experienced radiologists, leading to potential errors and delays in diagnosis (Pang et al., 2021). …”
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Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction by Xueyi SHANG, Yong CHEN, Jie CHEN, Linlin CHEN, Yuanyuan PU

“…Additionally, the noise reduction and P-wave arrival time picking performance was evaluated by the total cost function of P-wave arrival picking errors. …”
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Diagnosis of Dry Eye Syndrome During the Preparing Patients for Cataracts Phacoemulsification. Literature Review by V. N. Trubilin, E. G. Polunina, A. V. Trubilin, V. V. Kurenkov, M. E. Konovalov, V. S. Zakatianskii

“…The increasing patient demands for quality of life after surgery necessitate optimization of approaches in the field of surgical intervention, reduction of the frequency of refractive errors and correction of tactics of postoperative interventions. …”
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Experimental Investigation of Motion Control of a Closed-Kinematic Chain Robot Manipulator Using Synchronization Sliding Mode Method with Time Delay Estimation by Tu T. C. Duong, Charles C. Nguyen, Thien Duc Tran

“…The experimental results confirm that the control scheme performed much better than other existing control schemes in terms of synchronization and control performance, achieving a reduction in maximum tracking errors of up to 81% as compared to other existing control schemes. …”
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Resonant Elements for Tunable Reflectarray Antenna Design by M. Y. Ismail, M. Inam

“…The results demonstrate a reduction in the phase errors offering an increased static linear phase range of 225° which allows to improve the bandwidth performance of single layer reflectarray antenna. …”
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INVARIAN AUTOMATIC CONTROL SYSTEM, USING THE INTERMEDIATE-FREQUENCY SIGNALS OF HEAT POWER PARAMETERS by G. T. Kulakov, A. N. Kukhorenko, I. M. Golinko

“…And that is why it leads to the further reduction of maximal dynamic regulation error in processing of external disturbance by consumption of steam, and this allows to improve the quality of control.…”
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Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels by Qiwei Ji, Huabing Wu

“…Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. …”
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0.003°/h bias instability of honeycomb disk resonator gyroscope achieved by mode reversal combined mode deflection control method by Liangqian Chen, Qingsong Li, Tongqiao Miao, Peng Wang, Xuhui Zhang, Yang Zhang, Xuezhong Wu, Dingbang Xiao

“…This paper incorporates electrode machining error and capacitance detection nonlinear error into the gyroscope model, resulting in a more comprehensive bias output model. …”
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