An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction by Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak

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Processamento de sementes de Moringa oleifera utilizando-se diferentes equipamentos para obtenção de solução coagulante Processing of Moringa oleifera seeds using different equipme... by Camila C. Arantes, Tulio A. P. Ribeiro, José E. S. Paterniani

“…Using a blender and a mixer the reduction rates of turbidity and apparent color were the lowest. …”
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Quantum natural language processing and its applications in bioinformatics: a comprehensive review of methodologies, concepts, and future directions by Gundala Pallavi, Rangarajan Prasanna Kumar

Subjects: “…quantum natural language processing…”
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Predicting Optimum Moisture Content by the individual and hybrid approach of machine learning by Yinghui Yang, Yahui Dai, Qunting Yang

“…This study aims to develop an advanced framework utilizing the Gaussian Process Regression (GPR) model for predicting OMC. …”
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POSITIVE CONFIRMED PREDICTION OF COVID-19 IN EAST JAVA USING COUNT TIME SERIES BASED DOUBLE POISSON INAR(p) PROCESS by A'yunin Sofro, Framitha Septian Subiantoro, Khusnia Nurul Khikmah

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Unlocking early academic skills: children’s cognitive processes, learning skills, and parental beliefs and behaviors predicting children’s language and math skills by Anne-Mai Meesak, Dmitri Rozgonjuk, Tiia Õun, Eve Kikas, Eve Kikas

“…IntroductionThis study explored the emerging academic skills of five-year-old Estonian children, focusing on cognitive processes, learning skills, and parental beliefs and behaviors. …”
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Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models by Daniel Fernando Figueiredo Spengler, Paulo Carteri Coradi, Dágila Melo Rodrigues, Izabela Cristina de Oliveira, Dalmo Paim de Oliveira, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…Therefore, the aim was to assess and predict the physical properties (moisture content, seed mass, length, thickness, width, volume, apparent specific mass, projected area, sphericity, mean diameter, circular area, circularity, drag coefficient), and physicochemical quality (crude protein, ash content, and acidity index) of Jatobá-do-Cerrado seeds under different processing conditions with pulp, without pulp (scarification), without pulp (fermented), and storage conditions at 10 and 23 °C over six months. …”
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Early warning study of field station process safety based on VMD-CNN-LSTM-self-attention for natural gas load prediction by Wei Zhao, Bilin Shao, Ning Tian, Wen Zhang, Xue Zhao, Shuqiang Wu

“…Abstract As a high-risk production unit, natural gas supply enterprises are increasingly recognizing the need to enhance production safety management. Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. …”
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Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops by Christian Unigarro, Jorge Hernandez, Hector Florez

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Investigation of the Effect of Tool Rotation Rate in EDM Drilling of Ultrafine Grain Tungsten Carbide Using Predictive Machine Learning by Sai Dutta Gattu, Lucas Pardo Bernardi, Jiwang Yan

“…Classification models trained on raw and processed electrical signal features achieved over 88% accuracy and 90% recall. …”
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Optimization of Laser-Induced Hybrid Hardening Process Based on Response Surface Methodology and WOA-BP Neural Network by Qunli Zhang, Jianan Ling, Zhijun Chen, Guolong Wu, Zexin Yu, Yangfan Wang, Jun Zhou, Jianhua Yao

“…This study uses Box–Behnken design (BBD) experiments to analyze key process parameters and develops response surface methodology (RSM) and whale-optimization-algorithm-optimized back-propagation neural network (WOA-BPNN) models for prediction and optimization. …”
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LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks by Xue Mi, Shaohao Li, Zheng Ye, Zhu Dai, Bo Ding, Bo Sun, Yang Shen, Yang Shen, Zhongdang Xiao, Zhongdang Xiao

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MED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notes by Awais Ahmed, Xiaoyang Zeng, Rui Xi, Mengshu Hou, Syed Attique Shah

“…The core of our framework lies in developing specialized prompts, which act as guiding instructions for the models during the prediction process. MED-Prompt develops prompts that help models interpret and extract medical information from clinical corpus. …”
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Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data by Natchanon Santasup, Parichat Theanjumpol, Choochad Santasup, Sila Kittiwachana, Nipon Mawan, Nuttapon Khongdee

“…The acquisition process started by air-drying the soil and sieving it through 2.0 mm and 0.5 mm mesh. …”
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Ideological orientation and extremism detection in online social networking sites: A systematic review by Kamalakkannan Ravi, Jiann-Shiun Yuan

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GOLF: A Singing Voice Synthesiser with Glottal Flow Wavetables and LPC Filters by Chin-Yun Yu, György Fazekas

Subjects: “…differentiable digital signal processing…”
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Flash Flood Hazard Mapping Using Analytical Hierarchy Process (AHP) and GIS Application: In the Barzan Area of Iraqi Kurdistan Region by Basil Younus Mustafa, Mohammed Dler Faisal, DE WILDE Roeland, Ali R. Ahmed, Mohamed Moafak Arbili

Subjects: “…Disaster Risk Reduction (DRR), Flood prone areas, Flood hazard Index, GIS analysis, Analytical Hierarchy Process, Sensitivity analysis.…”
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Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector by Inés Rodríguez-Muñoz-de-Baena, María Coronado-Vaca, Esther Vaquero-Lafuente

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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

Subjects: “…protein structure prediction…”
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A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger by Gregor M. Essert, Marko Tesanovic, Sonja Berensmeier, Isabell Hagemann, Peter Schwan

“…As part of the Design of Experiments (DoE), statistical models increased efficiency somewhat and are still state of the art; however, the capability to predict process conditions is limited due to their nature as interpolating models. …”
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