Collaborative Planning for Distribution Network and PV-ESS in the Marketization Environment by Ping RAO, Yue XIANG, Haotian YAO, Youbo LIU, Junyong LIU

“…Firstly, the benefit coupling model for the subjects in the planning layer was constructed considering photovoltaic prediction errors and agency costs based on the agency transaction mode. …”
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Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang, Robert Kallenbach

“…We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. …”
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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. …”
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Catatonia in anti-NMDA receptor encephalitis: a case series and approach to improve outcomes with electroconvulsive therapy by F. Gabriela Kraiter, Dakota T. May, Ryan D. Slauer, Nandini Abburi, Christopher Eckstein, Suma Shah, Jonathan R. Komisar, Jacob P. Feigal

“…Patients received an average of 29.9 ECT treatments in total.Conclusions Based on clinical prediction scores, this cohort had better than expected functional outcomes. …”
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Device Modeling Based on Cost-Sensitive Densely Connected Deep Neural Networks by Xiaoying Tang, Zhiqiang Li, Lang Zeng, Hongwei Zhou, Xiaoxu Cheng, Zhenjie Yao

“…By incorporating cost-sensitive learning methods, the proposed model focus more on the critical data that determines device characteristics, leading to accurate prediction of key device characteristics under various parameters. …”
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Vulnerability of peatland fires in bengkalis regency during the ENSO El nino phase using a machine learning approach by Fauziah, Lilik B. Prasetyo, Nonon Saribanon, Nur Hayati

“…The best model is RF that was able to predict peatland fires in Bengkalis Regency effectively, with achieving an AUC value of 0.972. …”
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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network by Qi Yang, Dequan Zhang, Chongxin Liu, Le Xu, Shaobo Li, Xiaochun Zheng, Li Chen

“…The results showed that particle swarm optimization-back propagation (PSO-BP) neural network had the best prediction effect, with 100 % prediction accuracy in both the training and test sets. …”
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Intelligent Methods of Operational Response to Accidents in Urban Water Supply Systems Based on LSTM Neural Network Models by Aliaksey A. Kapanski, Nadezeya V. Hruntovich, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin, Nikita V. Martyushev

“…The objective of this study was to evaluate the effectiveness of LSTM models for pressure prediction tasks. To acquire real-time pressure data, an information system based on Internet of Things (IoT) technology using the MQTT protocol was proposed. …”
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A deep learning approach to real-time Markov modeling of ion channel gating by Efthymios Oikonomou, Yannick Juli, Rajkumar Reddy Kolan, Linda Kern, Thomas Gruber, Christian Alzheimer, Patrick Krauss, Andreas Maier, Tobias Huth

“…In addition, we propose a method to evaluate the goodness of a predicted model by re-simulating the prediction. Finally, we tested the algorithm with data recorded on a patch-clamp setup. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective by Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

“…Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. …”
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Creating prognostic systems for cancer patients: A demonstration using breast cancer by Mathew T. Hueman, Huan Wang, Charles Q. Yang, Li Sheng, Donald E. Henson, Arnold M. Schwartz, Dechang Chen

“…In the approach, an unsupervised learning algorithm was used to create dendrograms and the C‐index was used to cut dendrograms to generate prognostic groups. …”
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Identification of mesoscale eddies based on improved YOLOv8 model: a case study in the South China Sea by Jianhao Gao, Jianhao Gao, Jianhao Gao, Feng Zhou, Feng Zhou, Feng Zhou, Di Tian, Di Tian, Muping Zhou, Muping Zhou, Hailong Guo

“…Accurate identification of mesoscale eddies is crucial for a deeper understanding of ocean internal dynamics, the development of marine resources, and the prediction of changes in the marine environment. This study utilizes Absolute Dynamic Topography (ADT) data provided by AVISO and the YOLOv8 algorithm model to investigate the identification of mesoscale eddies in the South China Sea (SCS). …”
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Compensation for vibration signal power variance when changing equipment speed mode by D. A. Kechik

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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…In MACE prediction, our model outperformed the traditional score-based models, with 8.2% higher AUC than Systematic COronary Risk Evaluation (SCORE), 3.5% for SCORE 2 and 7.1% for the Framingham Risk Score (with p value<0.05 for all three comparisons).Conclusions Our algorithm estimates the 5-year risk of MACE based on retinal images, while explicitly presenting which risk factors should be checked and intervened. …”
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Flood Routing of Tigris River in Baiji Station and Makhoul Dam Reservoir under Supposed Operation of the Dam by Ghassan Shalan Nida Al-Shahry, Sabbar Abdullah Saleh

“…Two mathematical models were used for flood routing purpose, the first is a relationship of discharge-level to predict the level in Baiji station depended on outflow from gates, and the second is the relationship of storage-level to predict the level in the reservoir depended on storage volume when the leave of the flood wave, an algorithm and flow chart were developed to describe and explain the steps of the flood routing program, which can be modified and applied for any dam reservoir in the world, and it is used in current study, also to calculate inflow discharges then inflow volumes, either outflow discharges may assumed for eleven operating scenarios (at 11 supposed levels to receive the flood wave) and predict the equivalent level in Baiji, and the change in storage, and then the accumulated volume and the equivalent level in reservoir, when the end of flood wave. …”
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Enablers and barriers to community pharmacists' readiness to implement deprescribing of inappropriate medications for older adults in Qatar. by Marwa Elshazly, Sondus Jawad, Ayesha Ahmed, Hager ElGeed, Kazeem Babatunde Yusuff

“…The majority of the community pharmacists were females (54.5%), within the age range of 20-40 years (88.0%), had BSc / BPharm as the highest educational qualification (70.5%), were full-time employee (97.0%). …”
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Methodology and results of creating 3D models of historical and cultural objects using a smartphone equipped with a LiDAR scanner by I. Kovalchuk, V. Yukhymyuk, V. Bogdanets

“…Godlin, located in the auditorium No. 53 and in the lobby on the third floor of the educational building No. 2 of the NUBiP of Ukraine, was chosen. …”
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MAJORITY DECODER OF CYCLIC CODE WITH TWO-STAGE ESTIMATION OF RELIABILITY OF INFORMATION by A. I. Korolev, V. K. Konopelko, N. A. Salas, E. G. Makeichik, K. I. Pirogov

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Time series forecasting of infant mortality rate in India using Bayesian ARIMA models by Anuj Singh, Tripti Tripathi, Rakesh Ranjan, Abhay K. Tiwari

“…Forecasts based on this model predict a steady decline in IMR from 2024 to 2033. …”
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