Optimal design for parameter estimation in EEG problems in a 3D multilayered domain by H. T. Banks, D. Rubio, N. Saintier, M. I. Troparevsky

“…Many inverse problems consist in selecting best parameter values of a given mathematical model based on fits to measured data. These are usually formulated as optimization problems and the accuracy of their solutions depends not only on the chosen optimization scheme but also on the given data.We consider an electromagnetic interrogation problem, specifically one arising in an electroencephalography (EEG) problem, of finding optimal number and locations for sensors for source identification in a 3D unit sphere from data on its boundary. …”
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NAHP: Neutrosophic Hierarchical Analysis for Pest Prioritization in Plantain Cultivation by Marely del Rosario Cruz Felipe, Víctor Noe Sánchez Carreno, Lorenzo Cevallos-Torres

“…Through a hierarchical structure, the model assesses pest threats based on criteria such as economic damage, frequency of occurrence, and control difficulty. …”
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Leveraging LSTM and ensemble classifiers for enhanced food waste classification by Khalaf Alsalem

“…The study shows that ensemble models, particularly gradient boosting and SVM, perform well for waste classification tasks. …”
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Application of UAV Photogrammetry and Multispectral Image Analysis for Identifying Land Use and Vegetation Cover Succession in Former Mining Areas by Volker Reinprecht, Daniel Scott Kieffer

“…Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. …”
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Socio-Demographic Factors Influencing Adoption of Digital Technologies in Beekeeping by Karmańska Anna, Ziemba Ewa Wanda, Maruszewska Ewa Wanda, Jarka Sławomir

“…A Random Forest machine learning algorithm was employed to predict digital adoption, and SHAP values were applied to interpret the model’s output. The findings reveal that only 28.6% of beekeepers have adopted digital technologies. …”
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A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…Precise identification of water bodies in agricultural watersheds is crucial for irrigation, water resource management, and flood disaster prevention. …”
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U-MGA: A Multi-Module Unet Optimized with Multi-Scale Global Attention Mechanisms for Fine-Grained Segmentation of Cultivated Areas by Yun Chen, Yiheng Xie, Weiyuan Yao, Yu Zhang, Xinhong Wang, Yanli Yang, Lingli Tang

“…Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. …”
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Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks by Feng Han, Chongshi Gu

“…The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. …”
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Spatial Distribution of Exhalation Droplets in the Bus in Different Seasons by Wenzhu Duan, Dan Mei, Jiaqian Li, Zihan Liu, Mengfan Jia, Shanshan Hou

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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11 by Xinwu Du, Xiaoxuan Zhang, Tingting Li, Xiangyu Chen, Xiufang Yu, Heng Wang

“…The model aims to achieve efficient and accurate automatic multi-species apple identification while reducing computational resource consumption and facilitating real-time applications on low-power devices. …”
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From setbacks to success: lessons from the journey of RSV vaccine development by Victor M. Cnossen, Rogier P. van Leeuwen, Natalie I. Mazur, Charlotte Vernhes, Wouter ten Voorde, Jacobus Burggraaf, Saco J. de Visser, Meta Roestenberg, Ingrid M. C. Kamerling

“…Assessing protective efficacy early on would result in the most cost-efficient pathway and identification of target populations should be done as early as possible. …”
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A Method for Recognizing Dead Sea Bass Based on Improved YOLOv8n by Lizhen Zhang, Chong Xu, Sai Jiang, Mengxiang Zhu, Di Wu

“…Existing object detection algorithms, when applied to the task of detecting dead sea bass, often suffer from excessive model complexity, high computational cost, and reduced accuracy in the presence of occlusion. …”
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Influence of Leadership on Human–Artificial Intelligence Collaboration by Rodrigo Zárate-Torres, C. Fabiola Rey-Sarmiento, Julio César Acosta-Prado, Nelson Alfonso Gómez-Cruz, Dorys Yaneth Rodríguez Castro, José Camargo

“…The thematic analysis allowed the identification of conceptual patterns and research gaps; the model elaborated from the review shows that leadership has an ethical and strategic mediation in the HI-AI relationship in a hybrid space of cooperation, in which automated decisions are put in real context through human judgment and reasoning; ethical governance mechanisms emerge for systems supported by artificial intelligence; and finally, a balancing mechanism to algorithmic efficiency is established through cognitive adaptability. …”
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Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks by Yixin Xu, Yanli Sun, Yina Teng, Shanglai Liu, Shiyu Ji, Zhen Zou, Yang Yu

“…Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. …”
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Leveraging Sentinel-1/2 time series and deep learning for accurate forest tree species mapping by Jun Tan, Jing Li, Tianyue Ma, Xingguang Yan, Ziye Huo

“…Additionally, statistical and visualization methods were applied to SITS and model analysis. The results showed that longer time series tended to improve species identification and model confidence, with OA increasing from 0.496 (6–7 months) to 0.795 (1–12 months), macro-F1 from 0.384 to 0.779, and a significant improvement in predicted scores. …”
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Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. by Don Klinkenberg, Jantien A Backer, Xavier Didelot, Caroline Colijn, Jacco Wallinga

“…These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. …”
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Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images by Peter Sykora, Patrik Kamencay, Roberta Hlavata, Robert Hudec

“…In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. …”
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PolicySegNet: a policy-based reinforcement learning framework with pretrained embeddings and transformer decoder for joint brain tumors segmentation and classification in MRI by Vishv Patel, Vandana Patel, Aakash Shinde

“…It combines a pretrained SegFormer-B4 encoder (with a MiT backbone, originally trained on the ADE20K dataset) as a fixed feature extractor with a UNet-inspired decoder for segmentation and a lightweight classification head for tumor type identification. Unlike typical fine-tuning approaches, the SegFormer encoder remains frozen, enabling efficient training on limited domain-specific data. …”
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PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection by Wang Yin, Shen Lingxin, Li Maohuan, Sun Qianlai, Li Xiaosong

“…In this paper, we implemented a lightweight design for the model, and proposes four models of different sizes to be-sized models that are suitable for different detection scenarios.…”
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Screening Decommissioned Oil and Gas Pipeline Cleaners Using Big Data Analytics Methods by Rongguang Li, Junqi Zhao, Ling Sun, Long Jin, Sixun Chen, Lihui Zheng

“…Citric acid was used as a model cleaning agent, and cleaning efficiency was tested under varying temperature, agitation, and contact time. …”
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