A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.…”
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pH-Sensitive TRPC5 Is Differentially Expressed in Various Common Skin Tumors by Lara Hopmann, Judith Heider, Dennis Niebel, Katja Evert, Florian Zeman, Christoph M. Hammers, Tobias Ettl, Christoph Brochhausen, Stephan Schreml

“…Transient receptor potential classical or cation channels (TRPCs) are integral to tumor biology, particularly in maintaining Ca²⁺ homeostasis within cancer cells. …”
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Evaluating livelihood resilience in the Qinghai-Tibet Plateau's pastoral communities: Insights from an entropy-TOPSIS and geospatial analysis approach by Tong Li, Lizhen Cui, Zhihong Xu, Xiaoyong Cui, Yanfen Wang

“…To address the gap in evaluating herders' livelihood resilience with a comprehensive methodology, this research harnessed survey data from 758 pastoralists within the Three River Headwater Region (TRHR) on the Qinghai-Tibet Plateau (QTP). …”
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phylospatial: An R package for spatial phylogenetic analysis with quantitative community data by Matthew M. Kling

“…However, existing software packages for analysing spatial phylogenetic diversity (PD) patterns only provide full support for binary (presence–absence) community data. A major gap exists in support for occurrence probabilities (such as from species distribution models), a common and valuable data type that captures spatial uncertainty and habitat suitability gradients. …”
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Sustainable Geothermal Energy: A Review of Challenges and Opportunities in Deep Wells and Shallow Heat Pumps for Transitioning Professionals by Tawfik Elshehabi, Mohammad Alfehaid

“…This review bridges the current knowledge gap by addressing challenges and opportunities for engineers and scientists, especially those transitioning from other professions. …”
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The Coupling Coordination Relationship Between Urbanization and Ecosystem Health in the Yellow River Basin: A Spatial Heterogeneity Perspective by Shanshan Guo, Junchang Huang, Xiaotong Xie, Xintian Guo, Yinghong Wang, Ling Li

“…The findings provide refined and differentiated decision-making references for effectively narrowing the gap in coordinated development in the YRB.…”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…A range of ML models—linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)—were trained and validated using experimental data from five different alloys under various impact conditions. …”
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Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors by Jiangsong Jiang, Chunhong Xin, Sifei Wu, Wenbing Chen, Hui Li, Zhaolun Ran

“…This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. …”
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Investigating Spatial Heterogeneity Patterns and Coupling Coordination Effects of the Cultural Ecosystem Service Supply and Demand: A Case Study of Taiyuan City, China by Xin Huang, Cheng Li, Jie Zhao, Shuang Chen, Minghui Gao, Haodong Liu

“…These outcomes address methodological gaps in coupled social–ecological system research while informing practical spatial governance strategies.…”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps.…”
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Establishment of a low-temperature immersion method for extracting high-activity and high-purity mitochondria from Syntrichia caninervis Mitt. by Wenting Huo, Xiaohua Lin, Mengyu Gao, Xiang Shi, Hongbin Li, Lu Zhuo

“…This study pioneered the use of low-temperature immersion combined with differential centrifugation and discontinuous percoll density gradient centrifugation to isolate mitochondria from Syntrichia caninervis, a model desiccation-tolerant moss. …”
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Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age by Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang, Chenjie Xu

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A Transformer-LSTM-SVR hybrid model for AI-driven emotional optimization in NEV embedded interior systems by Zongming Liu, Xuhui Chen, Xinan Liang, Zhicheng Sun, Fengqi Yang, Wenwen Ou, Linwei Li, Xiayan Qin

“…Current research on emotion optimization in new energy vehicle interiors lacks efficient nonlinear modeling methods. To address this gap, a hybrid Transformer-LSTM-SVR model is proposed, which significantly enhances emotion prediction accuracy by integrating attention mechanisms and temporal modeling. …”
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Assessing groundwater drought in Iran using GRACE data and machine learning by Ali Kashani, Hamid R. Safavi

“…Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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Encapsulation Process and Dynamic Characterization of SiC Half-Bridge Power Module: Electro-Thermal Co-Design and Experimental Validation by Kaida Cai, Jing Xiao, Xingwei Su, Qiuhui Tang, Huayuan Deng

“…To address the research gap in collaborative validation between electro-thermal coupling models and process reliability, this paper proposes a closed-loop methodology of “design-simulation-process-validation”. …”
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Navigating Ecological–Economic Interactions: Spatiotemporal Dynamics and Drivers in the Lower Reaches of the Jinsha River by Zhongyun Ni, Yinbing Zhao, Jingjing Liu, Yongjun Li, Xiaojiang Xia, Yang Zhang

“…The lower reaches of the Jinsha River, serving as a vital ecological barrier in southwestern China and playing a crucial role in advancing targeted poverty alleviation efforts, remain underexplored in terms of the coupling between ecological and economic development, creating a gap in understanding the region’s sustainable development potential. …”
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Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters by Loukas Katikas, Sofia Reizopoulou, Paraskevi Drakopoulou, Vassiliki Vassilopoulou

“…., the Common Fisheries Policy) and more integrated policies (e.g., marine spatial planning). …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. …”
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Deep learning analysis of long COVID and vaccine impact in low- and middle-income countries (LMICs): development of a risk calculator in a multicentric study by Ahmed Shaheen, Nour Shaheen, Long COVID Collaboration Study Group in the LMICs, Sheikh Shoib, Sheikh Shoib, Fahimeh Saeed, Mudathiru Buhari, Vishal Bharmauria, Vishal Bharmauria, Oliver Flouty

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