Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method by Tzu-Chia Chen, Hasan Sh. Majdi, Aras Masood Ismael, Jamshid Pouresmi, Danial Ahangari, Saja Mohammed Noori

“…In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. …”
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Optimization Method for Reliability–Redundancy Allocation Problem in Large Hybrid Binary Systems by Florin Leon, Petru Cașcaval

“…The proposed method uses a zero–one integer programming formulation that applies log-transformed reliability functions and binary decision variables to represent subsystem configurations. …”
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ТНЕ WORLD OIL MARKET: THE SEARCH FOR BALANCE IN THE NEW "OIL" REALITY by T. A. Malova, V. I. Sisoeva

“…The proposed approach allows toreveal the substance of the transformation of the world oil market, to assess the changes in the oil market with the development of rhenium in terms of efficiency and functioning of the mechanism, the prospects of price volatility in the oil market. …”
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Characterizing the Growth Kinetics of Oyster Mushroom Through Substrate Supplementation and Environmental Control by Khurram Shahzad, Hamza Aslam, Shahid Hussain

“…Conclusion: Future work should explore additional nutrient additives, environmental variables, and the mushrooms’ bioactive compounds to further improve cultivation efficiency and nutritional value. …”
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Energy management of microgrid coalitions considering renewable energy prediction based on machine learning algorithms by Negar Dehghani Mahmoudabadi, Mehran Khalaj, Davood Jafari, Ali Taghizadeh Herat, Parisa Mousavi Ahranjani

“…First, a systematic management framework is established by defining the functions of various management units throughout the multi-microgrid system. …”
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Characterizing the Growth Kinetics of Oyster Mushroom Through Substrate Supplementation and Environmental Control by Khurram Shahzad, Hamza Aslam, Shahid Hussain

“…Conclusion: Future work should explore additional nutrient additives, environmental variables, and the mushrooms’ bioactive compounds to further improve cultivation efficiency and nutritional value. …”
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Impact of Low-dose Imaging on PET Quantitative Accuracy and Image Quality by SU Xuesong, GENG Jianhua, ZHENG Rong, WANG Xuejuan

“…Subsequently, the recovery coefficient (RC), contrast recovery coefficient (CRC), contrast-to-noise ratio (CNR), percent background variability (PBV), background coefficient of variation (BCV), and residual error (RE) of the lung insert at different image planes were calculated within the phantom. …”

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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Efectos de los factores de edificabilidad sobre la productividad laboral de moldajes de vigas en fundaciones The effects of buildability factors on formwork labor productivity of g... by Abdulaziz M Jarkas

“…Therefore, the objective of this research is to investigate and quantify the effects and relative influence of the following buildability factors on formwork labor efficiency of this activity: (a) variability of beam sizes; (b) beam sizes; and (c) number of joints formed at beams intersections. …”
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Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network by Qiang Guo, Yunpeng Zhuang, Houzhuo Xu, Wei Li, Haitao Li, Zhidong Wu

“…In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. …”
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Chemical magnetic field-assisted batch polishing method of 316L stainless steel by Fukang Yin, Chenchun Shi, Zikang Huang, Ke Wu, Jinbang Li, Chunjin Wang

“…This work is capable of providing valuable reference undoubtedly for batch precision polishing of 316L stainless steel and other significant functional materials.…”
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Reverse Active Modification Method of Tooth Profile for RV Reducer Cycloidal Gear by Xiaotao An, Tianxing Li, Chunrong Xing, Guofeng Wang, Meng Tian

“…></graphic></alternatives></inline-formula> of the parabolic modification method are taken as the modification variables of the tooth profile, and the mathematical model of the reverse modification of the tooth profile is established with the minimum transmission error as the objective function. …”
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Words of Mouth on Destination Images and Intentions to Revisit

“…These results highlight the critical function of EWOM in reinforcing positive destination perceptions and encouraging repeat visits. …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

“…The mean squared error is assumed as the loss function which stipulates several restrictions: deviatoric character of internal variables, compliance with the flow rule, the differentiation of elastic and plastic steps and the associativity of the flow rule. …”
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Two-Echelon Location-Routing Problem with Fuzzy Demand of Rural E-Commerce Logistics by Xiaojuan LU, Jianjun WANG, Shuai WU, Shiyu ZHENG, Qian LIU

“…Considering fuzzy demand, government subsidies and simultaneous delivery, the objective function aims to maximise the profit of enterprises considering government subsidies. …”
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Sensory profiles in older adults with orthopedic conditions during quiet stance: a cross-sectional study by Marine Brika, France Mourey, Alexandre Kubicki

“…Methods Fifty-one older adults (76.9 ± 7.6 years) were divided into 2 Functional Groups (FG-/FG+) according to a composite score that included 3 variables (gait speed, grip strength and fear of falling). …”
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Advancing the modified face name associative memory exam in cognitive aging research: insights into connectomic correlates and task reliability by Yilin Liu, Yilin Liu, Mark H. Sundman, Mark H. Sundman, Dalen Hinderaker, Allison Yu-Chin Chen, Allison Yu-Chin Chen, Jacob M. Green, Jacob M. Green, Lisbeth G. Haaheim, Lisbeth G. Haaheim, Hannah M. Siu, Hannah M. Siu, Catherine Jezerc, Kaitlyn Lai, Carol Chen, Parker Guss, Ying-hui Chou, Ying-hui Chou, Ying-hui Chou

“…Experiment 2 revealed significant associations between mFNAME performance and network properties like global efficiency, local efficiency, and system segregation in the default mode network (DMN) and medial temporal network (MTN). …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

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Spatial dependencies between large-scale brain networks. by Robert Leech, Gregory Scott, Robin Carhart-Harris, Federico Turkheimer, Simon D Taylor-Robinson, David J Sharp

“…Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…In this work, we show two machine learning approaches – one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features – to better classify thermostable scFv variants from sequence. …”
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