Exploring non-medical prescribing for patients with mental illness: a scoping review by Bashayr A Alsaeed, Jason Hall, Richard N. Keers

“…Either nurse (44/63, 69.8%), pharmacist (16/63, 25.3%) or non-medical prescribing models featuring both professionals were exclusively studied (3/63, 4.7%). …”
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Piloting the Extension for Community Healthcare Outcomes (ECHO) Pediatric Oncology Telehealth Education Program in Western Kenya: Implementation Study by Tyler Severance, Gilbert Olbara, Festus Njuguna, Martha Kipng'etich, Sandra Lang'at, Maureen Kugo, Jesse Lemmen, Marjorie Treff, Patrick Loehrer, Terry Vik

“…MethodsSessions were freely available on Zoom twice monthly and featured an expert-led didactic topic followed by a learner-led, case-based discussion. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…However, the classification of shrublands proved challenging due to their mixed composition and unique spatial patterns, resulting in lower accuracies. Feature importance analysis demonstrated the value of the enhanced map composition, and applying the LandTrendr algorithm simplified the diachronic land use and land cover (LULC) classification and change analysis by supporting automatic training data collection. …”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…First, we preprocess the data by standardizing the features and handling missing values. Secondly, we build and train a DNN with multiple hidden layers to capture nonlinear relationships between educational factors and soft power scores. …”
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Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…We further found that machine learning models with satellite-based features alone also outperform Window Averaging baselines, while not needing clinical data at inference time. …”
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Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface‐Enhanced Raman Scattering by Xiaoyu Zhao, Yuxia Wang, Yuting Liu, Xinyi Chen, Mingyu Cheng, Yaxin Wang, Jiahong Wen, Renxian Gao, Kun Zhang, Fengyi Zhang, Rufei Cui, Yongjun Zhang, Zengyao Wang, Bin Ai

“…To address these limitations, a novel SERS platform based on gradient nanostructures is developed using shadow sphere lithography, enabling the acquisition of diverse spectral features from a single analyte concentration under identical conditions. …”
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RTF-SAW-YOLOv11: A Bolt Defect Detection Model for Power Transmission Lines Under Low-Light Conditions by Hao Zhang, Lin Gao, Yuxiang Gong, Huaguo Liu, Yongdan Zhu, Yu Yang

“…The model incorporates the Retinexformer (RTF) module for illumination compensation, noise suppression, and texture enhancement, and employs an improved YOLOv11-based SAW-YOLOv11 detector with Shallow Robust Feature Downsampling (SRFD) and Deep Robust Feature Downsampling (DRFD) to strengthen small-object feature extraction. …”
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Simplifying Two-Stage Object Detectors for On-Board Remote Sensing by Jaemin Kang, Hoeseok Yang, Hyungshin Kim

“…However, relying solely on a single feature degrades detection accuracy. To compensate for the accuracy drop, we modify the method of selecting positive anchors used in training. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…We constructed the ORaph8K dataset, containing 8,000 images of Oudemansiella raphanipes at different growth stages, used for training and validation. The OR-FCOS uses the MobileNetV3-Large backbone with an efficient multi-scale attention (EMA) module, improving feature extraction efficiency without sacrificing accuracy. …”
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El patrimoni educatiu, element d'una pedagògia cultural i ciutadana Educational Heritage: Culture and Citizenship in Education El patrimonio educativo, elemento de una pedagogía cu... by Isabel Carrillo, Eulàlia Collelldemont Pujadas, Pedro Luís Moreno

“…In addition, emphasis is placed on the fact that teaching museums are recasting their activities based on ethical approaches to education that give a central role to citizenship projects. …”
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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction by Muhammad Omar Cheema, Zia Mohy-Ud-Din, Azhar Imran, Fawad Salam Khan, Mahmood Basil A. Al-Rawi, Mohammed A. El-Meligy, Jahan Zeb Gul

“…By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). …”
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InvarNet: Molecular property prediction via rotation invariant graph neural networks by Danyan Chen, Gaoxiang Duan, Dengbao Miao, Xiaoying Zheng, Yongxin Zhu

“…This paper introduces InvarNet, a GNN-based model trained with a composite loss function that bypasses intricate data processing while maintaining molecular property invariance. …”
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BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction by Xueqin Xie, Changchun Wu, Yixuan Qi, Shanghua Liu, Jian Huang, Hao Lyu, Fuying Dao, Hao Lin

“…Results In this study, we introduce a fine-tuning strategy of large-scale pre-trained protein language models (PLMs) for ADPs prediction, enabling automated extraction of discriminative semantic representations. …”
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Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability by Sankar Murugesan, Jayaprakash Chinnadurai, Saravanan Srinivasan, Sandeep Kumar Mathivanan, Radha Raman Chandan, Usha Moorthy

“…The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. …”
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Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest by Jiang Yuan, Huailei Cheng, Lijun Sun, Yadong Cao, Ruikang Yang, Tian Jin, Mingchen Li

“…Thirdly, the RF model was optimized using TL with a feature enhancement strategy. Finally, the optimized model was validated using data from other regions not included in the initial training set. …”
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MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…A comprehensive dataset covering the entire strawberry growth cycle is constructed to serve as the foundation for model training and testing. An innovative lightweight convolutional neural network, named GrowthNet, is designed as the backbone of SGSNet, facilitating efficient feature extraction while significantly reducing model parameters and computational complexity. …”
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A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments by Rubina Riaz, Guangjie Han, Kamran Shaukat, Naimat Ullah Khan, Hongbo Zhu, Lei Wang

“…First, SMOTE interpolates new minority-class examples to roughly balance the dataset. Next, a WGAN is trained on this augmented data to refine and generate high-fidelity minority samples that preserve the complex non-linear feature distributions characteristic of IIoT data. …”
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FGA-Corn: an integrated system for precision pesticide application in center leaf areas using deep learning vision by Zhongqiang Song, Zhongqiang Song, Wenqiang Li, Xuehang Song, Shun Li

“…Building on the YOLOv8n framework, a more efficient GHG2S backbone generated by HGNetV2 enhanced with GhostConv and SimAM is proposed for feature extraction. The CM module integrated with Mixed Local Channel Attention is used for multi-scale feature fusion. …”
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FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields by Zishang Yang, Lele Wang, Chenxu Li, He Li

“…To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. …”
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