Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The model introduces a dynamic causal weight adjustment mechanism to adaptively optimize the causal relationship intensity between variables and adopts a hierarchical multilevel feature extraction strategy to effectively capture complex spatiotemporal dependencies, thereby enhancing prediction accuracy and model interpretability. …”
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MentalAId: an improved DenseNet model to assist scalable psychosis assessment by Muxi Li, Farong Liu, Fei Du, Guolin Hong, Qing Hu, Zhi-Liang Ji, Pan You

“…MentalAId learned subtle variations in 49 routine clinical hematological tests and two demographic variables (sex and age) across 28,746 individuals spanning four distinct cohorts: psychotic inpatients (n = 9,271), non-psychotic inpatients with various diseases (n = 14,508), healthy controls (n = 1,826), and drug-naïve first-episode psychosis (FEP) patients (n = 3,141). …”
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Role of Biological Age in the Determination of Long‐Term Cause‐Specific Death Following Percutaneous Coronary Interventions by Mandeep Singh, Paul A. Friedman, Rajiv Gulati, Abdallah El Sabbagh, Bradley R. Lewis, Amrit Kanwar, Claire E. Raphael, Mohammed A. Al‐Hijji, Zachi I. Attia, Atta Behfar, James L. Kirkland

“…A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The workflow involves identifying optimal predictors from land surface model (LSM) outputs, meteorological variables and climatic indices using a novel Bayesian network (BN) technique for raster-based TWSA simulations. …”
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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Literature analysis reveals that, early studies primarily used demographic and clinical variables, while recent efforts integrate genetic, lifestyle, and environmental predictors. …”
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Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations by Alireza Hayati, Mohammad Reza Abdol Homayuni, Reza Sadeghi, Hassan Asadigandomani, Mohammad Dashtkoohi, Sajad Eslami, Mohammad Soleimani

“…<b>Methods</b>: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. …”
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Simulation and Identification of the Habitat of Antarctic Krill Based on Vessel Position Data and Integrated Species Distribution Model: A Case Study of Pumping-Suction Beam Trawl... by Heng Zhang, Yuyan Sun, Hanji Zhu, Delong Xiang, Jianhua Wang, Famou Zhang, Sisi Huang, Yang Li

“…Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. …”
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Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory by Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery

“…During the model development stage, the optimal variables were determined successfully via heatmaps for precise assessment of ETo in both stations. …”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…The results showed that the improved model could maintain a high detection accuracy in complex and variable scenes, with mAP50 and mAP50:95 increasing by 1.7% and 1.2%, respectively, compared to the original model. …”
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A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping by Xu Zhang, Xin Liu, Xiang Zhang, Aminjon Gulakhmadov, Jiefeng Wu, Xihui Gu, Won-Ho Nam, Panda Rabindra Kumar, Veber Afonso Figueiredo Costa, Mahlatse Kganyago, Berhanu Keno Terfa, Wenying Du, Chao Wang, Peng Wang, Jing Yuan, Nengcheng Chen

“…First, the ESTARFM spatiotemporal fusion algorithm was applied to combine high-resolution MODIS data with long-term GLASS data, generating daily SM driving variables (NDVI and LST) at a 1-km resolution. Subsequently, the LightGBM downscaling algorithm was used to reduce the spatial resolution of GLEAM SSM and RZSM data from 0.25&#x00B0; to daily 1-km resolution. …”
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A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data by David M. Morris, Chengjia Wang, Giorgos Papanastasiou, Calum D. Gray, Wei Xu, Samuel Sjöström, Sammy Badr, Julien Paccou, Scott IK Semple, Tom MacGillivray, William P. Cawthorn

“…Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. …”
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Comparison of Surgical Outcomes between Endovenous Laser Ablation and Conventional Surgery in Patients with Lower Limb Varicose Veins: A Prospective Interventional Study by Mannam Viswateja, Deepak R Chavan, Vijaya Patil, Vikram U Sindagikar

“…Introduction: Dilated, convoluted, subcutaneous veins measuring more than 3 mm in diameter when measured while upright and exhibiting reflux are called varicose veins. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. …”
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Scalable recurrence graph network for stratifying RhoB texture dynamics in rectal cancer biopsies by Tuan D. Pham

“…RhoB, a key biomarker assessed via immunohistochemistry, is crucial in predicting responses to radiotherapy (RT), but variability in staining techniques and tumor heterogeneity often complicate these assessments. …”
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Improving Hand Pose Recognition Using Localization and Zoom Normalizations over MediaPipe Landmarks by Miguel Ángel Remiro, Manuel Gil-Martín, Rubén San-Segundo

“…This can be mitigated by employing MediaPipe to facilitate the efficient extraction of representative landmarks from static images combined with the use of Convolutional Neural Networks. Extracting these landmarks from the hands mitigates the impact of lighting variability or the presence of complex backgrounds. …”
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A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons by K. M. R. Hunt, K. M. R. Hunt, S. P. Harrison

“…<p>We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of models: dense neural networks (“regional models”) and convolutional neural networks (CNNs). The regional models are trained individually on seven regional rainfall datasets, and while they capture decadal-scale variability and significant droughts, they underestimate inter-annual variability. …”
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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

“…Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. …”
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Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. by Yingjing Yan, Defu Zhang

“…As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. …”
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