Data driven healthcare insurance system using machine learning and blockchain technologies by Irum Matloob, Shoab Khan, Bushra Bashir, Rukaiya Rukaiya, Javed Ali Khan, Hessa Alfraihi

“…The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. …”
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Evaluation of Four Semi‐Mechanistic Models for Predicting Glycemic Control With a Glucagon Receptor Antagonist in People With Type 2 Diabetes by Austin Yue Feng Tan, Karen Schneck, Parag Garhyan, Eric Chun Yong Chan, Lai San Tham

“…Metrics for predictive performance included: (a) mean change from baseline HbA1c (ΔHbA1c) at Week 24 between observed and simulated values; (b) mean prediction error (MPE) for bias; and (c) root mean squared error (RMSE) for precision. …”
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Cómo Iniciar el Proceso de Mercadeo en la Empresa by Gerardo Lee Rojas

“…Sin un plan de mercadeo, la posibilidad decometer errores es altísima. En muchos casos, la desesperaciónque causa el avance de la competencia y lareducción del nivel de ventas, provocan que la empresase desenfoque, cometiendo el error de operar en camposque no estaban contemplados en su estrategia comercialbásica, y, por tanto, en los que no tiene suficiente habilidadcompetitiva. …”
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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms by Sawsan Akram Hassan, Mahir M. Hason, Ammar N. Hanoon, Ali A. Abdulhameed

“…In terms of absolute relative error (ARE), the dataset proves that the suggested model has both the greatest (ARE 5 %) and worst (ARE > 15 %) frequencies. …”
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Clinical Outcome Scores Post Medial Unicompartmental Knee Arthroplasty: A Comparison of the MAKO Robotic Arm versus the Oxford Conventional Approach by Tan CMP, Shih SSW, Ravichandran V, Quah ESH, Kunnasegaran R

“…Robotic-assisted UKA offers more accurate implant positioning, soft tissue balancing, improved lower limb alignment, and a reduction in surgical error. There are few studies studying functional outcomes post robotic-assisted UKA. …”
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Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index. by Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman

“…A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.…”
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Validity of pulse oximetry measures for heart rate and oxygen saturation during profound hypoxia in normobaric simulated extreme altitudes. by Harald Vikne, Jon Arild Kjeserud, Willy Westgaard, Ruben Baalsrud Westlie, Jan Ivar Kåsin, Jon Ingulf Medbø, Terje Gjøvaag, Nils Henrik Holmedahl

“…All pulse oximeters increased the bias or the variability for SpO2 significantly by reductions in oxygen saturation, while no systematic differences were found for HR.…”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…The experimental outcomes highlight significant advancements in convergence rates and error reduction, particularly in complex models like 3-layer-wrapped Bidirectional Long Short-Term Memory (3wBiLSTM). …”
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Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…This result shows a significant improvement over the case without regularization, with an increase of 0.2 in $$R^2$$ and a reduction of 0.26 eV/atom in energy prediction error, and is even slightly better than the fully supervised case for traditional regression method based on the Mie-Gr $$\ddot{\text {u}}$$ neisen equation. …”
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Effect of concentric exercise-induced fatigue on proprioception, motor control and performance of the upper limb in handball players. by Stelios Hadjisavvas, Michalis A Efstathiou, Irene-Chrysovalanto Themistocleous, Katerina Daskalaki, Paraskevi Malliou, Christoforos D Giannaki, Jeremy Lewis, Manos Stefanakis

“…In addition, there was a significant reduction in isometric strength (ASH test) in the I position of the right hand (p = 0.010) and all positions of the left hand (p<0.05). …”
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Fundamental of constant electrode melting rate control for electroslag fusion welding by Haoran Xu, Baokuan Li, Xuechi Huang, Yu Wang, Yanchun Lou, Zhongqiu Liu

“…Slag regulation mechanism stabilizes slag pool depth (18.93 % reduction during stabilization) but reduces horizontal melting depth (10.42 % decrease at 42.4 kg/min). …”
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Dynamic Aggregation and Augmentation for Low-Resource Machine Translation Using Federated Fine-Tuning of Pretrained Transformer Models by Emmanuel Agyei, Xiaoling Zhang, Ama Bonuah Quaye, Victor Adeyi Odeh, Joseph Roger Arhin

“…A thorough evaluation of translation quality is provided by the use of SPBLEU, ROUGE (ROUGE-1, ROUGE-2, and ROUGE-L) measures, and Word Error Rate (WER) metrics. A pretrained mT5 model is used to set baseline performance, which acts as a standard for the optimized model. …”
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High-Level Synthesis: Productivity, Performance, and Software Constraints by Yun Liang, Kyle Rupnow, Yinan Li, Dongbo Min, Minh N. Do, Deming Chen

“…Such tools reduce design effort: high-level descriptions are more compact and less error prone. HLS tools promise hardware development abstracted from software designer knowledge of the implementation platform. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…Among these, XGB-GWO demonstrated the highest accuracy, with R² improving to 0.991 and MSE decreasing significantly from 83.95 to 14.42, resulting in an 82.82% error reduction. SHAP, PDP, and ICE analyses identified superplasticizer dosage, curing days, and coarse aggregate as the most influential parameters affecting compressive strength (CS). …”
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IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

“…SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>32.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> across various subsets under diverse solar conditions. …”
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Opioid prescriptions following behavioral health training among primary care providers by Shutong Huo, Tim A. Bruckner, Abhery Das, Glen L. Xiong, David Marcovitz, Ariel B. Neikrug, Robert McCarron

“…Conclusion In a large Southern California healthcare system, the TNT training program preceded a reduction in primary care providers’ prescription rates of opioids. …”
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Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach by Dong Zhan, Ai-Qing Tian, Shao-Quan Ni

“…Experimental validation demonstrates that, compared to the Snow Goose Algorithm (SGA), the IBM-Dy-SGA achieves a 10%–30% reduction in average fitness values and a 20% improvement in convergence speed. …”
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Chromolaena odorata and male fertility: evidence from the testes and epididymis of Wistar rats by Eguono Raphael Uwejigho, Felix O Enemali, Victor Oghenekparobo Emojevwe, Jesuyomike Marvellous Oluremi, Israel Olorunsola Adeyomoye

“…Results were described as the mean ± standard error of means (S.E.M.). One-way analysis of variance  (at a 95% confidence level) was used to compare the means of the control group and experimental groups using GraphPad Prism (version 10.2.0). …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Results The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R2 compared to the best-performing IDW variant (RS-IDW). …”
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Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method by Y. Zhu, Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, C. Geiß, S. Fatichi

“…Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. …”
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