AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Ablation results revealed that incorporating the ADown module reduced the baseline's parameters by 18.6%, although it led to a slight decrease in recognition accuracy and recall. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. …”
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Effectiveness of low-dose aspirin in reducing the risk of preeclampsia in women with chronic hypertension: an integrative literature review by Prisca Asiimwe, Grace Nambozi, Laura Brennaman, Joseph Ngonzi, Gad Ruzaaza

“…Previous research has mainly focused on the dosage and gestational timing, yet the timing the drug is taken and adherence are also important aspects in evaluating drug efficacy. Therefore, an appraisal of the combination of these parameters was necessary. …”
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Computationally Selected Multivalent HIV-1 Subtype C Vaccine Protects Against Heterologous SHIV Challenge by Dieter Mielke, Marina Tuyishime, Natasha S. Kelkar, Yunfei Wang, Robert Parks, Sampa Santra, Wes Rountree, LaTonya D. Williams, Tiffany Peters, Nathan Eisel, Sheetal Sawant, Lu Zhang, Derrick Goodman, Shalini Jha, Adam Zalaquett, Pratamesh Ramasubramanian, Sherry Stanfield-Oakley, Gary Matyas, Zoltan Beck, Mangala Rao, Julie Ake, Thomas N. Denny, David C. Montefiori, Margaret E. Ackerman, Lawrence Corey, Georgia D. Tomaras, Bette T. Korber, Barton F. Haynes, Xiaoying Shen, Guido Ferrari

“…Evaluation of the immunogenicity of each vaccine regimen at the time of challenge demonstrated that different gp120 combination boosts elicited similar high magnitudes of gp120 and breadth of V1V2-binding antibodies, as well as strong Fc-mediated immune responses. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4). Conclusions The FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes.…”
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Reliability assessment and small signal analysis of the enhanced switched impedance inverter with low input current ripple by Mohsen Hasan Babayi Nozadian, Ebrahim Babaei, Seyed Hossein Hosseini

“…A comparative study evaluates the enhanced SII against conventional Z-source inverters, considering key parameters such as boost factor, component voltage and current ratings, and reliability. …”
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Color Change Stability Using Different Bleaching Gels and Light Sources: An in Vitro Study by Abolfazl Bazari Jamkhaneh, Zohreh Moradi, Sina Sadeghabadi, Pooya Soltanzadeh, Nasim Chiniforush

“…The samples were randomly divided into 5 bleaching groups: Snow L [containing 40% hydrogen peroxide (HP) and 20% carbamide peroxide] with 980nm diode laser, White Smile (containing 32% HP) with LED (Monitex), Boost (containing 40% HP) with 980nm diode laser, Boost (containing 40% HP) with LED (Monitex), and Boost without activation. …”
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AI-Driven Spatial Data Analysis of Groundwater Level and Gravimetric Data in Roorkee Region, India by H. Sarkar, C. S. P. Ojha, S. K. Ghosh

“…The primary goal is to improve local water resource management and encourage responsible water usage. The study evaluates the use of K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM), XG Boost and Polynomial regression models, using two groups of input parameters. …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
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Impact of the non-ideal condition in the analysis of high voltage gain switched impedance inverter with cost perspectives by Mohsen Hasan Babayi Nozadian, Ebrahim Babaei, Seyed Hossein Hosseini

“…A comprehensive comparison is conducted, evaluating various parameters such as voltage and current stress, boost factor, and efficiency. …”
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A Blend of natural phytobiotics enhances growth performance, feed efficiency, and the immuno-health status of fingerlings of Nile tilapia (Oreochromis niloticus) by Shreifa G. Mabrouk, Asmaa M. El-Nokrashy, Nahla A. Ebied, Bahaa H. Abdella, Mohamed M. Zayed, Sayed M. Aboleila, Radi A. Mohamed

“…Background: Numerous natural phytobiotic products are uusedas feed additives to enhance fish performance, quality, and immunity. Aim: This study evaluated the effect of a natural phytobiotics mixture (Syrena Boost "SB") on growth performance, intestine health, immune-oxidative status, and hemato-biochemical parameters of fingerlings (Oreochromis niloticus). …”
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Design of a Controller for Supercapacitor’s Bidirectional High-Gain Interleaved Converter by Jessica C. A. Sousa, Thiago M. Soares, Jonathan M. Tabora, Hugo G. Lott

“…The converter’s operation in both buck and boost modes is described, detailing its operating stages, design parameters, and component sizing. …”
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A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA by Bhagya M. Patil, Vishwanath Burkpalli

“…The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. …”
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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery by E. Z. Golukhova, M. A. Keren, T. V. Zavalikhina, N. I. Bulaeva, D. S. Akatov, I. Yu. Sigaev, K. B. Yakhyaeva, D. A. Kolesnikov

“…Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…Regression metrics, such as mean MAE, R2, Adjusted R2, RMSE, MinE, and MaxE are employed to evaluate the efficacy of the models. The results suggest that the stacking model, which integrates CatBoost and Random Forest (RF) as base models and Polynomial Regression (PR) as the meta-model, achieves an R2 of 0.9846, an adjusted R2 of 0.9842, MAE of 11.20 and an RMSE of 22.747 on the testing dataset. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. …”
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Development of a Conceptual Model for the Information and Control System of an Autonomous Underwater Vehicle for Solving Problems in the Mineral and Raw Materials Complex by Dmitry Pervukhin, Dmitry Kotov, Vyacheslav Trushnikov

“…This model was built using correlation analysis and expert evaluations to identify critical parameters affecting AUV efficiency and reliability. …”
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Extraction and chemical composition of Tetraclinis articulata (Vahl) mast oil: Oil's value as a natural antioxidant and environmentally benign inhibitor of mild steel corrosion in... by Elouadi Yassir, Benhiba Fouad, El Faydy Mohammed, Elmsellem Hicham, Bendaif Hajar, Hafez Baraa, Hachmi Azeddine, Arabi Mourad, Zarrouk Abdelkader

“…The results hint that the oil's effectiveness boosts with concentration, reaching 89.4% at 0.8 g/L. …”
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Enhancing Photovoltaic System Efficiency Through Fuzzy Logic-Based Maximum Power Point Tracking by N. A. Yusri, S. N. H. M. Tarmizi, M. I. Zakaria, S. F. M. Hussein, A. R. A. A. Emhemed

“…The MPPT process involves the analysis of system parameters, including current error and the rate of change of errors, which serve as the input to the fuzzy logic system. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

“…Abstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. …”
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