The impact of integrated genomic surveillance on non-typhoidal Salmonella infection in Australia: an ecological studyResearch in context by Son Nghiem, Nhung Mai, My Tran, Danielle M. Cribb, Liliana Bulfone, Patiyan Andersson, Alireza Zahedi, Tuyet Hoang, Tehzeeb Zulfiqar, Angeline Ferdinand, Katie Glass, Martyn D. Kirk, Vitali Sintchenko, Amy V. Jennison, Benjamin P. Howden, Emily Lancsar

“…Results of a dynamic specification were slightly higher, with a 12.7% reduction in NTS cases after WGS. The estimated effects increased to 17.5% when a multi-period DiD model with a double machine learning estimator was applied. …”
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Research Status and Prospects of Distribution Network Planning Technology Based on Artificial Intelligence by LI Jingru, LI Hongjun, MA Liang, JIANG Shigong, MU Chaoxu, SI Chenyi

“…It proposes potential solutions in technical research， such as graph learning， transfer learning， multimodal fusion， enhanced interpretability， and human-machine hybrid intelligence enhancement. …”
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Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement by Gabrielle Hoyer, Kenneth T. Gao, Felix G. Gassert, Johanna Luitjens, Fei Jiang, Sharmila Majumdar, Valentina Pedoia

“…We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. …”
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From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers by Emmanuel A. Onsay, Jomar F. Rabajante

“…By training and testing datasets, this work proposes new metrics and illustrates the effectiveness of machine learning in predicting poverty. Lastly, the results provide various localities with customized policy targeting tools for poverty alleviation. …”
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Impact of climate change on snow avalanche activity in the Swiss Alps by S. Mayer, M. Hendrick, A. Michel, B. Richter, J. Schweizer, H. Wernli, A. van Herwijnen

“…We assess avalanche activity by employing machine learning models trained with observed records of avalanches. …”
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Sleep Fragmentation as a Diagnostic Biomarker of Traumatic Brain Injury by Grant S. Mannino, Christian R. Baumann, Mark R. Opp, Rachel K. Rowe

“…Drawing on empirical findings from a mouse model of diffuse TBI, we show that summary measures of sleep fragmentation and duration can reliably distinguish injured from uninjured animals using dimensionality reduction and machine learning techniques. …”
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Enhancing precision flood mapping: Pahang's vulnerability unveiled. by Tahmina Afrose Keya, Siventhiran S Balakrishnan, Maheswaran Solayappan, Saravana Selvan Dheena Dhayalan, Sreeramanan Subramaniam, Low Jun An, Anthony Leela, Kevin Fernandez, Prahan Kumar, A Lokeshmaran, Abhijit Vinodrao Boratne, Mohd Tajuddin Abdullah

“…The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). …”
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An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysi... by Debo He, Zhixin Dong, Bo Zhu

“…In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. …”
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Adaptive Handover Management in High-Mobility Networks for Smart Cities by Yahya S. Junejo, Faisal K. Shaikh, Bhawani S. Chowdhry, Waleed Ejaz

“…Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios.…”
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Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. …”
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…The feature fusion CNN model based on spectra and images can achieve good classification of the four strains, which outperforms the other three traditional machine learning algorithm models. …”
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Evaluating cognitive decline detection in aging populations with single-channel EEG features based on two studies and meta-analysis by Lior Molcho, Neta B. Maimon, Talya Zeimer, Ofir Chibotero, Sarit Rabinowicz, Vered Armoni, Noa Bar On, Nathan Intrator, Ady Sasson

“…The study highlights the feasibility of using a single-channel EEG device combined with advanced machine learning models, offering a practical and accessible method for evaluating cognitive function and identifying individuals at risk in various settings.…”
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Applications of Probabilistic Forecasting in Demand Response by María Carmen Ruiz-Abellón, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Antonio Gabaldón

“…The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. …”
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Multidimensional bioinformatics analysis of chondrosarcoma subtypes and TGF-β signaling networks using big data approaches by Shengke Li, Junteng Chen, Fuping He, Maosheng Wang, Jun Liu, Hui Xie

“…Pseudotime analysis charted differentiation trajectories, while machine learning models evaluated the classification accuracy of gene expression patterns. …”
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ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends by Hiren Dave, Vinay Vakharia, Hitesh Panchal, Md Irfanul Haque Siddiqui, Dan Dobrotă

“…The experiments were carried out at a fixed engine load of 80 % and three different engine speeds of 1600 rpm, 2000 rpm and 2400 rpm respectively. Two machine learning (ML) models: Artificial Neural Network (ANN) and Multilayer Extreme Learning Machine (MELM) were developed to predict the key engine characteristics such as brake specific fuel consumption (BSFC), maximum cylinder pressure (Pmax), smoke emissions and nitrogen oxides (NOx) emissions. …”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

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A comprehensive review of bibliometric and methodological approaches in flood mitigation studies: Current trends and future directions by Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi

“…Furthermore, it highlights the growing diversity of approaches, with increasing interest in machine learning algorithms and combined methods. …”
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Integrating AI and Multi-objective Optimization for Enhanced Microgrid Energy Management Using Quadratic Programming by Agrawal Priyanka, Thethi H. Pal, Mohammad Q., Gupta Navya, Asha V., Reddy K. Jyothsna

“…The simulated machine learning possesses good generalisation capacity and an excellent learning structure. …”
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Tropical Cyclone Size Prediction and Development of An Error Correction Method by Guo Ruichen, Xu Jing, Wang Yuqing

“…Based on this relationship, a machine learning model, XGBoost, is used to develop an R17 size correction scheme that incorporate initial and forecast intensity, inner-core and outer-core sizes, and initial errors as predictors to estimate and correct model-predicted size errors. …”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…The study evaluates the GA-PSO-enhanced Optimized Deep Neural Network (ODNN) against traditional DNNs and state-of-the-art machine learning methods on multiple real-world energy forecasting tasks. …”
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