Impact of Climate Change on the Thermoeconomic Performance of Binary-Cycle Geothermal Power Plants by Paolo Blecich, Igor Wolf, Tomislav Senčić, Igor Bonefačić

“…Different climate models from the most recent Climate Model Intercomparison Project (CMIP6) are compared against each other and against the observed temperature data. The predictive power of the CMIP6 climate models is evaluated using the root mean square error (<i>RMSE</i>) and the Kullback–Leibler (<i>KL</i>) criteria. …”
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An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf–Convolutional Neural Network (LDW-CNN) Model by Vedna Sharma, Surender Singh Samant, Tej Singh, Gusztáv Fekete

“…In the evolving healthcare landscape, recommender systems have gained significant importance due to their role in predicting and anticipating a wide range of health-related data for both patients and healthcare professionals. …”
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Evaluating post-traumatic growth among healthcare workers by Basant K. Puri, Anastasia Potoglou, Argyroula Kalaitzaki, Vasiliki Yotsidi, Maria Theodoratou

“…The confirmatory factor analysis of the PTGI confirmed its five-factor structure (χ2189 = 1233.642, p < 0.0001), root mean square error of approximation (RMSEA = 0.229, p < 0.0001), and internal consistency (Cronbach α = 0.971). …”
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Validation of a Commercially Available IMU-Based System Against an Optoelectronic System for Full-Body Motor Tasks by Giacomo Villa, Serena Cerfoglio, Alessandro Bonfiglio, Paolo Capodaglio, Manuela Galli, Veronica Cimolin

“…Fifteen healthy participants performed a battery of single- and multi-joint tasks while motion data were simultaneously recorded by both systems. …”
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3-D fracture network reconstruction and quantitative fractal analysis of subsurface rock fractures via integrated CT scanning and box-counting dimension methodology by Hongjian Wu, Xiangwei Kong, Xing Chen, Dianwei Guan

“…Second, this research is the first to develop a coupled poroelastic-fracture model incorporating Analytic Hierarchy Process (AHP)-based size deviation indices, achieving permeability prediction errors of ≤ 4.84 % compared to experimental data, substantially improving upon existing models' typical 8–12 % error margins. …”
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A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incidents by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. …”
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Investigation of effective conservation measures for the critically endangered White-bellied Heron Ardea insignis using an integrated population model and Bayesian population via... by Indra P. Acharja, Yuji Okahisa, M. Clay Green, Tshewang Lhendup, Sonam Tshering, Thinley Phuntsho, Tshering Tobgay, Lungten Lungten, Jigme Tshering, Samten Leki

“…This study sought to propose a measure to conserve the species by analyzing two decades of population, breeding, and mortality data using a Bayesian population viability analysis and integrated population model. …”
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

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Comparison of marker-less 2D image-based methods for infant pose estimation by Lennart Jahn, Sarah Flügge, Dajie Zhang, Luise Poustka, Sven Bölte, Florentin Wörgötter, Peter B. Marschik, Tomas Kulvicius

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Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil by Xiang Chen, Paula Moraga

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Wind speed forecasting approach using conformal prediction and feature importance selection by Cesar Vinicius Zuege, Stefano Frizzo Stefenon, Cristina Keiko Yamaguchi, Viviana Cocco Mariani, Gabriel Villarrubia Gonzalez, Leandro dos Santos Coelho

“…Taking into account a Beutenberg data set, Germany, the best model was Light Gradient Boosting Machine (LGBM)-VMD-SSA without partial fit, resulting in a root mean squared error (RMSE) criterion of 0.25031, coverage measure of 94.4%, and width measure of 1.008. …”
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GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…The GAT model exhibited less variability in its median error 0.22, 0.21, and 0.038, respectively, in each case. …”
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A review of UK publicly funded non-inferiority trials: is the design more inferior than it should be? by Nikki Totton, Steven Julious, Stephen Walters, Elizabeth Coates

“…Additionally, despite suggestions that it can increase the type 1 error in NI trials, the use of the intention-to-treat alone is the most common analysis population. …”
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Enhancing Medium-Term Load Forecasting Accuracy in Post-Pandemic Tropical Regions: A Comparative Analysis of Polynomial Regression, Split Polynomial Regression, and LSTM Networks by Agus Setiawan

“…The analysis found that LSTM outperformed the other methods, exhibiting the lowest error rates with Mean Absolute Percentage Error (MAPE) at 3.86% and Root Mean Squared Error (RMSE) at 1247.93. …”
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A systematic review and meta-analysis of cow-level factors affecting milk urea nitrogen and urinary nitrogen output under pasture-based diets by Mancoba C. Mangwe, Winston A. Mason, Charlotte B. Reed, Olivia K. Spaans, David Pacheco, Racheal H. Bryant

“…Multilevel, mixed meta-analysis regression techniques were used to analyze the data. …”
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Consistency Is Key: A Secondary Analysis of Wearable Motion Sensor Accuracy Measuring Knee Angles Across Activities of Daily Living Before and After Knee Arthroplasty by Robert C. Marchand, Kelly B. Taylor, Emily C. Kaczynski, Skye Richards, Jayson B. Hutchinson, Shayan Khodabakhsh, Ryan M. Chapman

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Decoding disease–specific ageing mechanisms through pathway-level epigenetic clock: insights from multi-cohort validationResearch in context by Pan Li, Jijun Zhu, Shenghan Wang, Haowen Zhuang, Shunjie Zhang, Zhongting Huang, Fuqiang Cai, Zhijian Song, Yuxin Liu, Weixin Liu, Sebastian Freidel, Sijia Wang, Emanuel Schwarz, Junfang Chen

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An artificial neural network approach to comparative aspects: A predictive analysis of magnetic dipole on the heat transfer of maxwell hybrid nano coolants flow in an inclined cyli... by J. Aruna, H. Niranjan

“…The ANN accurately predicts performance metrics for inclined and vertical cylinders validated through mean square error (MSE), regression analysis, and error distribution. …”
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Evaluation of accuracy and changing rainfall trends of gridded satellite precipitation products: a comparative analysis with ground observations over the Modjo catchment, central E... by Wakjira Takala Dibaba, Bereket Abera Bedada

“…The Sen Slope Estimator, Mann–Kendall test, and Innovative Trend Analysis (ITA) were used to assess the trends within the data series. …”
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Screening Elite Genotypes of Durum Wheat Based on Univariate and Multivariate Stability Models under Unpredictable Environments by Sharyar Sasani, Armin Saed-Moucheshi

“…Methods: To verify the feasibility of introducing new durum wheat varieties with high yields and stable performance in different environmental conditions, 18 genotypes of durum wheat along with two control varieties (Hana and Parsi) were studied in four crop years at the research station of the Agricultural Research and Training Center and The natural resources of Kermanshah Province (Islamabad West Agricultural Research Station) based on a completely randomized block design in three replications and four consecutive years from 2013 to 2016. After adjusting the data, composite variance analysis was performed considering year × genotype in relation to grain yield. …”
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