Beyond Spirometry: Linking Wasted Ventilation to Exertional Dyspnea in the Initial Stages of COPD by J. Alberto Neder, Giles Santyr, Brandon Zanette, Miranda Kirby, Marina Pourafkari, Matthew D. James, Sandra G. Vincent, Carrie Ferguson, Chu-Yi Wang, Nicolle J. Domnik, Devin B. Phillips, Janos Porszasz, William W. Stringer, Denis E. O’Donnell

“…Pulmonary function tests estimating the extension of the wasted ventilation and selected cardiopulmonary exercise testing variables can, therefore, shed unique light on the genesis of patients’ out-of-proportion dyspnea. …”
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Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive D... by Duc Nam Nguyen, Thanh-Phong Dao, Ngoc Le Chau, Van Anh Dang

“…The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. …”
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Artificial Neural Network (ANN) Modeling of Plasma and Ultrasound-assisted Air Drying of Cumin Seeds by M. Namjoo, M. Moradi, M. A. Nematollahi, H. Golbakhshi

“…Therefore, the wavelet-based neural network (WNN), the multilayer perceptron neural network (MLPNN), and the radial-basis function neural network (RBFNN), as three well-known artificial neural networks models, were used to map the inputs and output data and the results were compared with the Multiple Quadratic Regression (MQR) analysis. …”
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Research on optimization strategies of university ideological and political parenting models under the empowerment of digital intelligence by Fan Lv

“…It constructs the no-incentive, cost-sharing, and collaborative cooperation models, respectively, and obtains the optimal trajectories for the degree of effort, the subsidy coefficient, the optimal benefit function, and the digital and intelligent technology stock. …”
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Assessment of Intrarenal Artery Doppler Resistive Index in Normotensive Patients and Patients with Essential Hypertension by Obinna E. Abonyi, Uloma B. Nwogu, Vitus K. Ika, Julius A. Agbo, Beatrice U. Maduka, Sharonrose O. Nwadike

“…The renal resistive index (RRI) is a useful tool for assessing kidney function in hypertensive patients, but population-specific data are scarce. …”
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Development of Sub-channel Program and Multi-objective Optimization Method Research for Printed Circuit Heat Exchanger by CHEN Liqin, HUO Honglei, XIE Renyao, YANG Yi

“…The results of sensitivity analysis show that as the flow channel width and height of the cold side flow channel increase, the sensitivity of the objective function to the variables continues to decrease, the optimization effect gradually becomes worse, and there are optimal design points, which are 4 mm and 1 mm respectively. …”
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Fuzzy Controller Design Optimized by the Grasshopper Algorithm to Realize Maximum Power in Photovoltaic Systems by Kamran Safaei, Aida Esmaeilian-Marnani, Hossein Emami, Amir Hossein Zaeri

“…In this paper, the ISE evaluation index is employed as the cost function of algorithm to verify the obtained results. …”
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Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery Using Deep Transfer Learning and Active Contour Loss by Miguel Chicchon, Francisco James Leon Trujillo, Ivan Sipiran, Ricardo Madrid

“…We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. …”
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Providing a Roadmap for the Adaption of Agricultural Sector to Water Scarcity Conditions (Case Study: Selected Crops of Tajan Basin, Iran) by H. Fouladi, H. Amirnejad, S. Shirzadi Laskookalayeh

“…For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. …”
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Ridge regression and its applications in genetic studies. by M Arashi, M Roozbeh, N A Hamzah, M Gasparini

“…On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. …”
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System Modeling to Optimize the Production Quantity of Men's and Women's Jackets and Achieve Maximum Profit: A Case Study at Konveksi ABC Cikarang Using Linear Programming by Wisnu Aji

“…The objective function is designed to maximize profit, and the model is solved using IBM ILOG CPLEX software. …”
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Creating Balance on Bandwidth Consumption using Network Coding in Wireless Sensor Networks by Ehsan Kharati

“…For this purpose, we need to calculate the derivative of the Lagrangian function relative to its variables, in order to determine the condition as a multi-excited multi-equation device. …”
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Slow and Quick Flow Models Explain the Temporal Dynamics of Daily Salinity in Streams by Thomas G. Westfall, Tim J. Peterson, Anna Lintern, Andrew W. Western

“…Compared to the simple C‐Q equation, this equation explained the temporal dynamics with an average increase of 0.17 in the Nash‐Sutcliffe efficiency coefficient. Global parameter estimation gave an objective estimate of baseflow with a plausible baseflow index ranging between 0.05 and 0.40 across catchments. …”
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Tranexamic acid impairs plasmin generation on human mesenchymal stem cells and derived membrane microvesicles, halting pericellular proteolysis by Ramy Abou Rjeily, Christina Mrad, Fatiha Z. El-Ghazouani, Florence Toti, Audrey Cras, Audrey Cras, Eduardo Angles-Cano

“…Cells thus obtained were seeded in multi-well plates and treated with different concentrations of plasminogen and pro-urokinase in the presence or absence of variable amounts of tranexamic acid. We measured MVs formation and phenotypical changes occurring on MSCs. …”
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Factors affecting the design of the management dashboard framework with an emphasis on the components of human resources productivity by Hassan Barmaki, Farshad Faezy Razi, Ehtesham Rashidi

“…Using productivity as a standard is useful for calculating efficiency, and can guide efforts to allocate resources optimally. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Therefore, Na+, Cl+, Na%, CO3 −, and SO4 2− were used as input variables (independent variables), and EC, TDS, and SAR were used as output variables (dependent variables). …”
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Stochastic Analysis of Nonlinear Vehicle-Track Coupled Dynamic System and Its Application in Vehicle Operation Safety Evaluation by Fu-Shan Liu, Zhi-Ping Zeng, Wei-Dong Wang, Abdulmumin Ahmed Shuaibu

“…The nonlinear coupled vehicle-track dynamic system is used to accurately describe the wheel-rail contact state. The stochastic function-spectral representation is used to simulate the random track irregularity in the time domain for the first time; consequently, the frequency components in the irregularity are preserved and random variables are reduced. …”
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Changes in Race Performance During the Underwater Phases of a 200 m Bi-Fins Race Simulation After Application of Respiratory Muscle Training—A Case Study in the Current World Recor... by Tomáš Michalica, Jakub Březina, Marek Polach, Dennis-Peter Born, Jiří Mališ, Zbyněk Svozil, Eva Kociánová

“…Maximal athletic performance can be limited by various factors, including restricted respiratory function. These limitations can be mitigated through targeted respiratory muscle training, as supported by numerous studies. …”
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Impedance-based sensitivity analysis for enhanced dynamic stability in DC off-grid hydrogen production systems by Chenguang Zhang, Zhongjian Kang, Bin Li, Hongyang Zhang, Di Zhu

“…DC off-grid hydrogen production systems, employing source–hydrogen interface converters to achieve efficient renewable energy conversion and electrolysis coupling, represent a core technological solution for green hydrogen generation. …”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
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