Coal gangue image recognition model based on improved U−Net and top coal caving control by Yong YUAN, Zhenghan QIN, Yongqi XIA, Rang WU, Libao LI, Yong LI

“…An advanced U−Net−based coal gangue segmentation model has been developed, incorporating Feature Pyramid Networks (FPN) and Atrous Spatial Pyramid Pooling (ASPP). …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Deep learning architectures, including convolutional and recurrent neural networks, generative adversarial networks, and variational autoencoders, proved instrumental in multiepitope vaccine design and adaptive clinical trial simulations. …”
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Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model by Youfu He, Youfu He, Youfu He, Yu Zhou, Yu Zhou, Yu Qian, Jingjie Liu, Jinyan Zhang, Debin Liu, Qiang Wu, Qiang Wu

“…CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. …”
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Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections by S.M. Rowe, E. Zhang, S.M. Godden, A.K. Vasquez, D.V. Nydam

“…Machine learning (ML) algorithms evaluated were logistic regression, decision tree, random forest, light gradient-boosting machine, naive Bayes, and neural networks. …”
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Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, Hee-Cheol Kim

“…Our study culminates in the development of an automated system for robust pet (cat) activity analysis using artificial intelligence techniques, featuring a 1D-CNN-based approach. In this experimental research, the 1D-CNN approach is evaluated using training and validation sets. …”
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Exploring Individuals’ Views and Feedback on a Nutritional Screening Mobile App: Qualitative Focus Group Study by Debra Jones, Anne Marie Sowerbutts, Sorrel Burden

“…Participants were recruited consecutively and United Kingdom–wide using advertisements through emails, newsletters, and on social media across appropriate local and national networks. Participants had the opportunity to look at the app on their phones before giving feedback and an on-screen demonstration of the app was provided during the focus group. …”
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LYN and CYBB are pivotal immune and inflammatory genes as diagnostic biomarkers in recurrent spontaneous abortion by Zhuna Wu, Qiuya Lin, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Qirong Shi, Yumin Ke

“…Protein-protein interaction (PPI) networks were utilized to explore the connections between various DIIRGs. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Various clinical, demographic, and laboratory variables were analyzed using univariate and multivariate logistic regression, complemented by LASSO regression for feature selection. Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. …”
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Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine by Xulong Duan, Rana Waqar Aslam, Syed Ali Asad Naqvi, Dmitry E. Kucher, Zohaib Afzal, Danish Raza, Rana Muhammad Zulqarnain, Yahia Said

“…By resolving sensor inconsistencies and enhancing reliability in complex environments, this work underscores the broader relevance of multisensor fusion for drought vulnerability assessments, where land-sea interactions demand integrated sensor networks and machine learning to mitigate ecological and climatic risks.…”
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Unveiling Hidden Aspects of Intermediary Organizations in Innovation: A Systematic Review through the Lens of Industry Taxonomy by Mostafa Mohseni kiasari, ‌Javad Soltanzadeh, Amirhossein Azizi Hasanabadi, Hamed Talebi

“…Objective In the innovation system, actors are involved in a complex situation of formal and informal networks, both within and among subgroups. The unsteady interactions between the actors make the role of intermediaries important. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…For FCN-RF Semantic Segmentation, street view images are processed by fully convolutional networks to quantify 10 spatial metrics, validated against human-scored safety perceptions via random forest-based adversarial training; for XGBoost-SHAP Interpretability Framework, the nonlinear relationships between 12 street environment indicators and emotional indices are modeled through extreme gradient boosting, with SHapley additive explanations (SHAP) decoding feature contributions and interaction effects. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. …”
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Introduction by Xin Li

“…However, other residents, especially those who are deprived (e.g. with low-incomes, unemployed or age-related diseases), might feel disrupted if they are highly dependent on their neighbourhoods in various ways (e.g. closeness to job opportunities, cheap rent, and social networks) (Day and Cervero, 2010; Fried, 1963). In addition, some residents may feel increasingly ambivalent facing forced relocation as they may have both positive and negative experiences in their neighbourhoods which might make it difficult to evaluate the negative and positive influences of urban redevelopment and forced relocation before they actually relocate. …”
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Residents’ Perceptions of Impending Forced Relocation in Urban China by Xin Li

“…From a people-place interaction point of view, this might be contradicting earlier research which emphasizes the more ‘romantic’ side of people-place interactions, such as place attachment and its related components (e.g. neighbourhood-based social networks and mutual help), that contribute to relocatees’ willingness to stay in their neighbourhoods when facing neighbourhood redevelopment and demolition (Fried, 1963; Manzo et al., 2008). …”
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