Unveiling the drivers of climate change: the impact of economic indicators, renewable energy consumption and human development through a panel ardl approach by Abhinav Pal, Zamurd Ali, Shagufta Tariq Khan, Chandan Kumar Tiwari, Mohd. Abass Bhat

“…The methodology consists of unit root tests to assess staionarity followed by cointegration and error correction analysis. Findings show GDP and renewable energy use significantly impact short-run emissions while industrialization and HDI have minimal impact. …”
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Tripability Analysis of Casing Strings in Directional Wells Using the Continuous Beam-Column and Buckling Theory by Gang Wang, Gang Liu, Kai Wang, Yichen Li, Yinan Hu

“…Through finite element method verification, the prediction was in line with the actual casing running operation and the improved model has the smallest prediction error, i.e., 6.58%, compared with the existing models. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…Comparison with random forest, support vector machine, and neural network models showed that the BO-GBDT model achieved the highest accuracy and generalization, with an average relative error of 2.61%. This was 35.56%, 37.41%, and 32.03% lower than the random forest, support vector machine, and neural network models, respectively. …”
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Wireless Sensor Networks for Urban Development: A Study of Applications, Challenges, and Performance Metrics by Sheeja Rani S., Raafat Aburukba, Khaled El Fakih

“…Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods.…”
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Solar Wind Data Assimilation in an Operational Context: Use of Near‐Real‐Time Data and the Forecast Value of an L5 Monitor by Harriet Turner, Matthew Lang, Mathew Owens, Andy Smith, Pete Riley, Mike Marsh, Siegfried Gonzi

“…This suggests that an operational solar wind DA scheme would provide significant forecast improvement, with reduction in the mean absolute error of solar wind speed around 46% over forecasts without DA. …”
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A conceptual digital twin framework for supply chain recovery and resilience by Oluwagbenga Victor Ogunsoto, Jessica Olivares-Aguila, Waguih ElMaraghy

“…The MLPNN monitors the mean squared error (MSE) and disruptive inputs throughout training and validation, revealing consistent MSE reduction over recovery periods. …”
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Effects of mindfulness meditation on human impulsivity: a systematic review and meta-analysis by Eric W. Tsang, Junling Gao, Chi Ngai Lo, Nicholas T. Trapp, Aaron D. Boes, Hinhung Sik

“…Neurophysiological findings showed a significant reduction in event-related potential amplitudes related to impulsivity, including N200, Error-Related Negativity (ERN), and P300 components (Cohen’s d = −0.65, 95% CI [−1.01, −0.08]). …”
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Participant motivators and perceptions of risks and benefits in adult Phase 2/3 COVID-19 vaccine clinical trials by Bettina Wunderlich, Brittany Feijoo, Rupali Limaye, Lawrence H. Moulton, Kawsar Talaat

“…While limited by low response rate and recall error, this study suggests that in future emergency situations, messaging should be prioritized to improve understanding of trials and vaccines.…”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…Comparative analysis reveals that downscaling provided a $$28.5\%$$ 28.5 % improvement in accuracy and an $$89.3\%$$ 89.3 % reduction in mean absolute percentage error (MAPE) over non-downscaled data which has been proven to be statistically significant using the Wilcoxon signed rank test, illustrating the substantial advantages of employing downscaling for effective DENV forecasting. …”
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Evaluation of Hydropower Generation and Reservoir Operation under Climate Change from Kesem Reservoir, Ethiopia by Kinfe Bereda Mirani, Mesfin Amaru Ayele, Tarun Kumar Lohani, Tigistu Yisihak Ukumo

“…Bias, root mean square error, and coefficient of variation were used to check the accuracy of projected rainfall. …”
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A Piezoelectric Micromachined Ultrasonic Transducer-Based Bone Conduction Microphone System for Enhancing Speech Recognition Accuracy by Chongbin Liu, Xiangyang Wang, Jianbiao Xiao, Jun Zhou, Guoqiang Wu

“…Compared with the original noisy speech, the enhanced speech achieves a character error rate reduction of over 20%, approaching the speech recognition accuracy of clean speech. …”
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An Advanced Generative AI-Based Anomaly Detection in IEC61850-Based Communication Messages in Smart Grids by Aydin Zaboli, Yong-Hwa Kim, Junho Hong

“…The proposed ToD model demonstrates significant advantages over the human-in-the-loop (HITL) approach, particularly in error rate, adaptability, and scalability. Specifically, compared to HITL, the ToD model achieves a reduction in false positives (FPs) of up to 20% and enhances the accuracy of AD by up to 17.5%, resulting in a general accuracy of 97.5%. …”
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Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction by Xingxing Ma, Hongnian Liu, Zhen Peng

“…Throughout the initial 24-h period, compared with the control experiment, the results of WRF-Chem_DA_BC (WRF-Chem_DA, WRF-Chem_BC) showed an improvement in terms of root-mean-square error, with reduction proportions varying from 38.90 % to 48.86 % (18.88 % to 32.44 %, 30.10 % to 46.08 %). …”
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Reconstruction of Electrochemical Impedance Spectroscopy from Time-Domain Pulses of a 3.7 kWh Lithium-Ion Battery Module by Manuel Kasper, Manuel Moertelmaier, Hartmut Popp, Ferry Kienberger, Nawfal Al-Zubaidi R-Smith

“…The experimental EIS data were corrected by a multistep calibration workflow in a frequency range from 50 mHz to 1 kHz, achieving error corrections of up to 80% at 1 kHz. The reconstructed impedances from the pulse test data are in good agreement with the EIS experiments in a broad frequency range, delivering relevant electrochemical information including the ohmic resistance and dynamic time constants of a battery module and its corresponding submodules. …”
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Effects of aerobic training on brachial artery flow-mediated dilation in healthy adults: a meta-analysis of inter-individual response differences in randomized controlled trials by Armin H. Paravlic, Kristina Drole

“…The standard deviation of change scores in the intervention and control groups suggests that most of the variation in the observed change from pre-to-post intervention is due to other factors (e.g., measurement error, biological variability etc.) unrelated to the intervention itself. …”
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Variable Structure Depth Controller for Energy Savings in an Underwater Device: Proof of Stability by João Bravo Pinto, João Falcão Carneiro, Fernando Gomes de Almeida, Nuno A. Cruz

“…Specifically, the stability proof is based on a novel analysis of the system’s trajectory in the net buoyancy force-versus-depth error plane. The findings were applied to a depth-controlled sensor platform previously developed by the authors, using a well-established system model and considering physical constraints. …”
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Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network by Azizollah Khormali, Soroush Ahmadi, Aleksandr Nikolaevich Aleksandrov

“…The ANN model using the Levenberg-Marquardt (LM) algorithm showed superior performance with the lowest mean squared error (MSE) for training, testing, and all data sets. …”
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Applying the FAIR Principles to Open Educational Resources: A Semantic Similarity Approach to Improve Resource Discovery by Farzaneh Sadeghi, Auriol Degbelo, Carsten Kessler, Reza Zolnouri

“…Notably, even with limited training data, fine-tuning the Sentence-BERT model significantly improved performance, resulting in a 16% reduction in mean squared error compared to the base model. …”
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Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference by Xiwen Chen, Huayu Li, Rahul Amin, Abolfazl Razi

“…Extensive experiments verify the superiority of our scalable method over the most commonly used data selection methods, including GreeDi, Greedymax, random selection, and stratified sampling by a substantial gain of at least 12% reduction in Relative Diversity Error (RDE). This enhanced diversity translates to a substantial improvement in the performance of various downstream learning tasks, including multi-level classification (2%-4% gain in accuracy), object detection (2% gain in mAP), and multiple-instance learning (1.3% gain in AUC).…”
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An enhanced deep learning approach for speaker diarization using TitaNet, MarbelNet and time delay network by Muzamil Ahmed, Riad Alharbey, Ali Daud, Hikmat Ullah Khan, Javeria Nawal, Ghazia Arshad

“…We evaluate the proposed approach on two standard datasets of VoxConverse and VoxCeleb, comparing clustering-based methods with the proposed Neuro-TM Diarizer using three metrics: Diarization Error Rate (DER), false alarm rate, and missed detection rate. …”
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