Dynamic relationships between energy use economic growth foreign investment and environmental pollution in South Korea from 1990 to 2023 by Vu Ngoc Xuan

“…The findings underscore the need for targeted policy interventions to promote green investment and reduce fossil dependency. …”
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Exploring Economic Dynamics and Urbanization in the Context of Environmental Impact: A Study on Capital Flows, Investment Trends, and Urban Growth by Sandip Sarker

“…Leveraging time series data from 1990 to 2021, the research employs robust econometric models, including Ordinary Least Squares (OLS) regression and Error Correction Model (ECM), to disentangle the short-term and long-term influences of these factors on environmental degradation. …”
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Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation by Jacopo Amidei, Rubén Nieto, Andreas Kaltenbrunner, Jose Gregorio Ferreira De Sá, Mayte Serrat, Klara Albajes

“…GPT-4 was comparable in terms of agreement with experts (with a weighted percentage agreement higher than 0.95), correlations with standardized measurements (for example in the range of 0.43 and 0.49 between the Revised Fibromyalgia Impact Questionnaire and GTP-4 with temperatures 1), and low error rates (root mean squared error of 1.20 for severity and 1.44 for disability). …”
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Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…Results demonstrate that the Transformer-GWO and Transformer-WOA models outperform conventional models such as GARCH by 70–90% across standard evaluation metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). By bridging state-of-the-art deep learning architectures with robust optimization strategies, this study contributes a scalable and interpretable solution for agricultural price forecasting. …”
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Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon by Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Luis Augusto Morais, Elton Gino Santos, Sebastião Rogerio da S. Neto, Vanderson Sampaio, Patricia Takako Endo

“…The results demonstrate that the RF model consistently outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values in most cases, such as in cluster 02 of the state of Acre, with RMSE of 0.00203 and MAE of 0.00133. …”
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A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta by Nurtiti Sunusi, Ankaz As Sikib, Sumanta Pasari

“…Forecasting accuracy results for SO₂ and CO in Jakarta, based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), showed excellent performance, underscoring the efficacy of the CLARA-FTSMC hybrid approach in predicting air pollution levels. • The CLARA-FTSMC hybrid method demonstrates high effectiveness in analyzing large datasets, addressing the limitations of previous hybrid clustering fuzzy time series methods. • The number of fuzzy time series partitions is optimally determined based on clustering results obtained through the gap statistic approach, ensuring robust partitioning. • The forecasting accuracy of the CLARA-FTSMC hybrid method, evaluated using MAE and RMSE, showed excellent performance in predicting daily air pollution levels of SO₂ and CO in Jakarta.…”
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Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa. by Simon J O'Hanlon, Hannah C Slater, Robert A Cheke, Boakye A Boatin, Luc E Coffeng, Sébastien D S Pion, Michel Boussinesq, Honorat G M Zouré, Wilma A Stolk, María-Gloria Basáñez

“…The mean Pearson's correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. …”
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A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers by Pablo Andrés Buestán Andrade, Pedro Esteban Carrión Zamora, Anthony Eduardo Chamba Lara, Juan Pablo Pazmiño Piedra

“…Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). …”
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Assessment of executive functions of poisoning following suicide by Nastaran Eizadi-Mood

“…However, in the commission error component, the mean scores were higher in the patients with recurrent suicide. …”
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Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques by Roberto Molowny-Horas, Saeed Harati-Asl, Liliana Perez

“…We conclude that, for prediction purposes, error metrics and components of the confusion table were most helpful in understanding the ability and limitations of Mountain Pine Beetle predictive maps. …”
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Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria by Sebastian Bauhoff, John Tucker, Arpita Biswas

“…Model performance decreased in the time between the data on which the model was trained and the data to which it was applied (test data). The exclusion error of the algorithmic approach was about 9.1% lower than the exclusion error of the geographical approach. …”
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A WebGL Serious Game for Practicing English Conversations in Public Places Using Speech Recognition by Rickman Roedavan, Iis Kunia Nurhayati, Kemas Muslim Lhaksmana, Muhammad Yudhi Rezaldi, Esa Prakasa, Sritenaya Geovani Putri

“…The methodology includes system analysis using Word Error Rate (WER) and Average Word Error Rate per Sentence (Avg WER) to evaluate speech recognition accuracy. …”
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Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials by Junxian Zhu, Jialiang Li, A. Mark Richards, Mark Y. Chan, Bee-Choo Tai

“…We compare the proposed method (IPW_Dnew) with intention-to-treat (ITT), per protocol (PP), instrumental variable (IV), and IPW method via a simulation study and real-life data from the JOBS II intervention trial and the IMMACULATE trial. Results Simulation results demonstrate that the proposed method performs better than other methods in terms of bias, coverage, mean squared error, power and Type I error under various scenarios, particularly in situations with selection bias and partial compliance. …”
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Machine learning insights into scapular stabilization for alleviating shoulder pain in college students by Omar M. Mabrouk, Doaa A. Abdel Hady, Tarek Abd El-Hafeez

“…Among the optimization techniques, scikit-optimize demonstrated the best performance, resulting in a mean squared error of 0.0085, a mean absolute error of 0.0712, and an impressive R2 score of 0.8501. …”
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An optimization method for hemi-diaphragm measurement of dynamic chest X-ray radiography during respiration based on graphics and diaphragm motion consistency criterion by Yingjian Yang, Jie Zheng, Peng Guo, Tianqi Wu, Qi Gao, Yong Li, Chengcheng Liu, Yang Liu, Yingwei Guo, Huai Chen

“…Lastly, these abnormal hemi-diaphragms are optimized based on the consistency criterion of diaphragm motion.ResultsResults show that the proposed optimization method can effectively measure the hemi-diaphragm, even in the presence of the inapparent cardiophrenic angle caused by abnormal deformations of the lung field morphology during respiration, reducing the mean error by 49.050 pixels (49.050 × 417 μm = 20,453.85 μm).DiscussionTherefore, the proposed optimization method may become an effective tool for precision healthcare to find the pattern of diaphragm movement during respiratory interventions.…”
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Assessing Environmental Policy Impact through the Ecological Footprint: The Case of Türkiye by Sacit Sarı

“…Annual per capita data obtained from the Global Footprint Network are analyzed using two-unit root testing methodologies: the conventional Augmented Dickey-Fuller (ADF) test and the more robust RALS-LM unit root test, which accounts for structural breaks and non-normal error distributions. The findings reveal that both the EF and its largest component, the CF, are stationary, suggesting that past environmental policy interventions have yielded transitory effects. …”
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BASE-6 psychological adjustment scale validation in the context of Indonesian adolescents by Ersa Lanang Sanjaya, Reynalda Fildzah Dessyrianti, Fibia Tista Avanti

“…This validated instrument is expected to contribute to future research and inform interventions designed to promote adolescent mental health. …”
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Regional Uncertainty Analysis in the Air–Sea CO2 Flux by L. Gloege, M. D. Eisaman

“…Abstract Accurate quantification of the ocean carbon sink and its associated uncertainty is critical for guiding international policy efforts and the accurate monitoring, reporting, and verification of marine carbon dioxide removal interventions. Here we use error propagation to break down the uncertainty in air–sea CO2 flux into three primary sources: the gas transfer velocity kw, the solubility K0, and the difference in partial pressure of CO2 ΔpCO2 between the ocean and atmosphere. …”
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Measurement tools and outcome measures used in transitional patient safety; a systematic review. by Marije A van Melle, Henk F van Stel, Judith M Poldervaart, Niek J de Wit, Dorien L M Zwart

“…In the validated measurement tools, the overall quality of content and structural validity was acceptable; other COSMIN criteria, such as reliability, measurement error and responsiveness, were mostly poor or not reported. …”
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AI-Integrated AR as an Intelligent Companion for Industrial Workers: A Systematic Review by Steven Yoo, Sakib Reza, Hamid Tarashiyoun, Akhil Ajikumar, Mohsen Moghaddam

“…Yet, a less understood question is: How can AR systems, coupled with artificial intelligence (AI) capabilities, adaptively tailor instructions and feedback interventions to the specific needs of users, their cognitive states, and levels of expertise during task execution? …”
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