A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys by Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu

“…The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. …”
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Spectral enhancement of PlanetScope using Sentinel-2 images to estimate soybean yield and seed composition by Supria Sarkar, Vasit Sagan, Sourav Bhadra, Felix B. Fritschi

“…The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. …”
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AI-enabled Barilai–Borwein–Blinder–Oaxaca–Bernoulli Deep Classifier for Enhanced Crop Yield Prediction by Rajesh Kumar Dhanaraj, Nithya Rekha Sivakumar, Firoz Khan, Mahmoud Ahmad Al-Khasawneh

“…Abstract This article explores the integration of advanced Artificial Intelligence (AI) enabled deep learning methods with accurate crop yield prediction. …”
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MySTOCKS: Multi-Modal Yield eSTimation System of in-prOmotion Commercial Key-ProductS by Cettina Giaconia, Aziz Chamas

“…The proposed system, named the “Multi-modal yield eSTimation System of in-prOmotion Commercial Key-ProductS” (MySTOCKS) platform, is a sophisticated multi-modal yield estimation system designed to optimize inventory forecasting for the agrifood and large-scale retail sectors, particularly during promotional periods. …”
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Can intraoperative improvement of radial endobronchial ultrasound imaging enhance the diagnostic yield in peripheral pulmonary lesions? by Kazuki Nishida, Takayasu Ito, Shingo Iwano, Shotaro Okachi, Shota Nakamura, Basile Chrétien, Toyofumi Fengshi Chen-Yoshikawa, Makoto Ishii

“…We evaluated whether intraoperative probe repositioning improves R-EBUS imaging and affects diagnostic yield and safety of EBUS-guided sampling for PPLs. …”
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Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration by Shaofeng Tan, Jie Pei, Yaopeng Zou, Huajun Fang, Tianxing Wang, Jianxi Huang

“…Multi-modal information, including 2D/3D spectral indices, textural features, temperature data, and canopy structural attributes, was derived and integrated for rice yield prediction using ensemble Machine Learning (ML) models. …”
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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

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Adaptive Echo State Network for crop yield prediction incorporating Fall Armyworm dynamicsMendeley Data by Mulima Chibuye, Jackson Phiri, Phillip Nkunika

“…This curve quantifies yield losses at different pest pressures. During prediction, we apply this learned penalty to the raw ESN output, adjusting yield estimates to account for pest damage without altering the original ESN model. …”
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Expediting Manufacturing Launch Using Integrated Data With AI/ML Analytic Solutions by Helen Haiying Yu, Scott Martin, Laurenz van der Meer, Edward Yang, Sherry Lee, Wanting Xiong, Vaishnavi Reddipalli, Richard Burch, Tomonori Honda, Jeffrey David

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Developing Assessment for Learning-Oriented Worksheets to Improve Student Learning Outcomes in Thermochemistry by Adib Al Aisy, Muchlis Muchlis

“…Abstract:  This study aims to develop assessment-for-learning-oriented worksheets suitable for improving students' learning outcomes in thermochemistry. …”
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The effectiveness of extensive reading on EFL learners’ vocabulary learning: incidental versus intentional learning by Teng, Feng

“…The research found that (a) both the two instructional methods resulted in significant gains in learners’ receptive and productive vocabulary knowledge, but the combination of the incidental and intentional learning instruction yields greater vocabulary gains; (b) around 60% of receptive vocabulary is understood productively; and (c) students’ vocabulary size plays a decisive role in acquiring the receptive and productive aspect of vocabulary knowledge…”
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Effects of Flipped Learning on Language Learning Outcomes: A Meta-Analysis investigating Moderators by Hsieh-Jun Chen, Cheng-Huan Chen, Wen-Chi Vivian Wu

“…Treatment duration, school location, and level of education were influential moderators affecting language learning results, while the other three were not. Flipped learning, particularly when implemented for over 6 weeks, consistently yields superior learning outcomes across different educational contexts. …”
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Research on the Liquor Yield Prediction Model Integrating Hybrid Kernel Support Vector Regression and Dung Beetle Optimizer by Qinwen Deng, Yibo Xu, Qiang Han, Suyi Zhang, Xianguo Tuo

“…To enhance the efficiency of liquor production and reduce subjective errors caused by manual operations, this study explores the relationship between production process parameters and yield to achieve accurate yield prediction. Based on actual liquor production data, a Multi Kernel Support Vector Regression (MKSVR) prediction model is established using the Stacking model fusion method to improve the model’s generalization ability. …”
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UAV-Based LiDAR and Multispectral Imaging for Estimating Dry Bean Plant Height, Lodging and Seed Yield by Shubham Subrot Panigrahi, Keshav D. Singh, Parthiba Balasubramanian, Hongquan Wang, Manoj Natarajan, Prabahar Ravichandran

“…At the same time, three MSI-derived data were used to estimate seed yield. Classification- and regression-based machine learning models were used to estimate key agronomic traits using both LiDAR and MSI-based crop features. …”
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Trend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024) by Jiahui Xu, Jiahui Xu, Jiahui Xu, Yalong Song, Yalong Song, Yalong Song, ZhaoYu Rui, ZhaoYu Rui, Zhao Zhang, Zhao Zhang, Can Hu, Can Hu, Can Hu, Long Wang, Long Wang, Long Wang, Wentao Li, Wentao Li, Wentao Li, Jianfei Xing, Jianfei Xing, Jianfei Xing, Xufeng Wang, Xufeng Wang, Xufeng Wang

“…Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. …”
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Solution for crop classification in regions with limited labeled samples: deep learning and transfer learning by Hengbin Wang, Yu Yao, Zijing Ye, Wanqiu Chang, Junyi Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang

“…In this study, we propose two new solutions that leverage the feature representation capabilities of deep learning and the sample reuse potential of transfer learning to solve the limited label problem. …”
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The Use of Solite Kids Interactive Learning Media to Improve Fiqh Learning Outcomes in Madrasah by Izzah Nur Hudzriyah Hasan, Imam Syafi’i, Muhammad Fahmi, Laili Mas Ulliyah Hasan

“…The use of technology-based learning media, such as Android applications, offers a solution for creating a more interactive and effective learning environment. …”
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Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning by Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri

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Developing e-learning-based remedial learning videos on function for senior high schools by Rahmah Johar, Amna Sri Rizeky, Mailizar

“…The results revealed that the e-learning-based remedial video demonstrated strong validity, with expert assessments yielding a score of 86.19% (very valid). …”
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Machine Learning and Deep Learning Models for Dengue Diagnosis Prediction: A Systematic Review by Daniel Cristobal Andrade Girón, William Joel Marín Rodriguez, Flor de María Lioo-Jordan, Jose Luis Ausejo Sánchez

“…In response to this crisis, there has been a notable increase in research employing machine learning and deep learning algorithms to anticipate diagnosis in patients with suspected dengue. …”
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