Strong Amplitude Modulation of Hard-band X-Ray Quasiperiodic Oscillation with Soft-band Flux in RE J1034+396 by Ruisong Xia, Hao Liu, Yongquan Xue

“…They are highly correlated, with an average cross-correlation function (CCF) peak coefficient of 0.61 and a lag of approximately 3 ks. …”
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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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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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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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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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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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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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ESTIMATING THE DEMAND FOR RAILWAY FREIGHT TRANSPORTATION: A CASE STUDY IN KAZAKHSTAN by Madiyar SULTANBEK, Nazdana ADILOVA, Aleksander SŁADKOWSKI, Arnur KARIBAYEV

“…Qualitative methods rely on judgments and opinions, while quantitative methods utilize historical data or identify causal relationships between variables. Overall, the present study highlights the critical role of demand forecasting in the railway freight transportation industry and its impact on efficient planning and decision-making processes.…”
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Effect of design and kinematic parameters on energy requirement in inclined screw mixer by V. F. Khlystunov, S. V. Braginets, A. S. Alferov, M. V. Chernutskiy

“…The establishment of functional dependences between parameters and modes enables to design power-efficient equipment for the on-farm feed production.Materials and Methods. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Using autism spectrum disorder (ASD) diagnosed child RGB images data, researchers evaluated six prevalent deep learning structures: DenseNet201, ResNet152, VGG16, VGG19, MobileNetV2, and EfficientNet-B0. ResNet152 reached the highest accuracy rate of 89% when functioning independently. …”
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In vitro study of macro and micro nutrients decline in MS medium on growth and development levels of Acacia tortilis (Forssk.) Hayne. by Seyed Mohammad Hosseini Nasr, Marziyeh Valizadeh, Seyed Ali Razavi

“…Acacia tortilis is one of the valuable species growing in the southern regions of Iran, which is highly considerable for a variety of reasons such as stabilization of quicksand, medicinal uses and ecological functions. Low germination capacity of seeds, hard rooting of cuttings, the value of aesthetics and multiple medicinal uses highlight the importance of further research to explore methods for easy and efficient propagation of this species in vitro. …”
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Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques by Randa Jabeur, Alaa Alaerjan

“…These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and lifespan of each sensor node, thereby improving the overall performance of the WSN. …”
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Engineering students' perceptions and actual use of AI-based math tools for solving mathematical problems by Kimberly F. Garcia, Ardvin Kester S. Ong, Ma. Janice J. Gumasing, Cesar Romeo V. Delos Reyes

“…The objective of this study was to evaluate the variables that influenced the engineering students' perceptions of adoption and actual academic use of AI-based math tools as academic assistance. …”
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Importance of ice algae versus phytoplankton in the diet of megabenthic organisms under contrasting sea ice conditions (Canadian Arctic): a dual biochemical approach (SIA and HBIs) by Thibaud Combaz, Thibaud Combaz, Ursula Witte, Thomas A. Brown, Philippe Archambault

“…Overall, organisms responded rapidly and efficiently to this pulse of fresh organic matter but trends in resource utilisation (quality and quantity) were observed based on feeding strategy. …”
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Mild Cognitive Impairment and Cardiometabolic Risk Factors in Bosnian and Herzegovinian Patients with Heart Failure by Jasna Ibrahimović-Pašić, Orhan Lepara, Amela Dervišević, Nermina Babić, Nesina Avdagić, Amina Valjevac, Asija Začiragić

“…Associations between categorical variables and correlation coefficients were assessed by the Chi-square and Spearman test, respectively. …”
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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…Dehui City in Jilin Province, China, was selected as the case study area, where multidimensional data including vegetation indices, ecological function parameters, and environmental variables were collected, covering seven key growth stages of rice. …”
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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. …”
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