Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

“…Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. …”
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Federated learning for digital twin applications: a privacy-preserving and low-latency approach by Jie Li, Dong Wang

“…The digital twin (DT) concept has recently gained widespread application for mapping the state of physical entities, enabling real-time analysis, prediction, and optimization, thereby enhancing the management and control of physical systems. …”
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Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic acc... by Hanneke F M Rhodius-Meester, Ingrid S van Maurik, Juha Koikkalainen, Antti Tolonen, Kristian S Frederiksen, Steen G Hasselbalch, Hilkka Soininen, Sanna-Kaisa Herukka, Anne M Remes, Charlotte E Teunissen, Frederik Barkhof, Yolande A L Pijnenburg, Philip Scheltens, Jyrki Lötjönen, Wiesje M van der Flier

“…Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.…”
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Evaluating Radio Occultation (RO) Constellation Designs Using Observing System Simulation Experiments (OSSEs) for Ionospheric Specification by Nicholas Dietrich, Tomoko Matsuo, Chi‐Yen Lin, Brandon diLorenzo, Charles Chien‐Hung Lin, Tzu‐Wei Fang

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Optimization of ATIG Weld Based on a Swarm Intelligence Approach: Application to the Design of Welding in Selected Manufacturing Processes by Kamel Touileb, Sahbi Boubaker

“…Based on the numerical observations, linear and nonlinear models for describing the effect of the thermophysical parameters on the weld characteristics were tuned using a particle swarm optimization algorithm. While the linear model provided good prediction accuracy, the nonlinear exponential model outperformed the linear one for the depth yielding a mean absolute percentage error of 17%, a coefficient of determination of 0.8266, and a root mean square error of 0.9665 mm. …”
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Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…By integrating the Mars-smallCNN feature extraction network with a Kalman filtering-based trajectory prediction module, the system attains 58.3% Multiple Object Tracking Accuracy (MOTA) and 62.1% Identity F1 Score (IDF1) in dense multi-object scenarios, representing an improvement of approximately 9.8 percentage points over the conventional YOLO+DeepSORT method. …”
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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

“…The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. …”
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Grapes leaf disease dataset for precision agricultureMendeley Data by Madhuri Dharrao, Nilima Zade, R. Kamatchi, Rakesh Sonawane, Rabinder Henry, Deepak Dharrao

“…This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. …”
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Analysis of Temperature Changes, Vegetation, Evapotranspiration, and Drought Based on Modeling of Landsat 5 and 8 Satellite Imagery and Meteorological Indicators by Mitra Sadat Fathifard, Abbas Rahdan, Mahsa Tohidfar

“…Evapotranspiration was then calculated using MODIS data and the Torrent-White method, incorporating the vegetation growth coefficient, and compared with evapotranspiration values derived from the SEBAL algorithm. Additionally, drought prediction was conducted using the Standardized Precipitation Index (SPI) (1-month, 3-month, 6-month, and 12-month) for the period from 2015 to 2044. …”
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Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder. by Caroline C McGrouther, Aaditya V Rangan, Arianna Di Florio, Jeremy A Elman, Nicholas J Schork, John Kelsoe, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium

“…By investigating the subgroup's bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluster can be leveraged to eliminate noise from our GWAS analyses and improve risk prediction. This improvement is particularly notable when using only a relatively small subset of the available SNPs, implying improved SNP replication. …”
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Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. Experimental evaluations on a dataset of 450 FSTS test videos demonstrate the high performance of <sc>FRAVM</sc>, with detection accuracy, F1 score, and ROC-AUC achieving 88.64%, 88.58%, and 98.86%, respectively. …”
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Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study by Yan Jiang, Canyang Jiang, Bin Shi, You Wu, Shuli Xing, Hao Liang, Jianping Huang, Xiaohong Huang, Li Huang, Lisong Lin

“…To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR). …”
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Biomarker and clinical data–based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal st... by Eva María Arriero-País, María Auxiliadora Bajo-Rubio, Roberto Arrojo-García, Pilar Sandoval, Guadalupe Tirma González-Mateo, Patricia Albar-Vizcaíno, Gloria del Peso-Gilsanz, Marta Ossorio-González, Pedro Majano, Manuel López-Cabrera, Gloria del Peso-Gilsanz

“…Linear discriminant analysis (LDA) discerns among transfer to haemodialysis or death, predicts whether the cause of PD end is ultrafiltration failure (UFF) or cardiovascular disease (CVD) and anticipates the type of CVD (receiver operating characteristic curve under the area&gt;0.71).Discussion Our combination of longitudinal PD datasets, attribute shrinkage and gold-standard algorithms with overfitting testing and class imbalance ensures robust predictions in PD. …”
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Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU by Wei Liu, Zihui Lin, Yuan Hu, Aodong Tian, Xintai Yuan, Jens Wickert

“…Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy and robustness, especially in complex terrains and extreme weather. …”
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Vision-Guided Maritime UAV Rescue System with Optimized GPS Path Planning and Dual-Target Tracking by Suli Wang, Yang Zhao, Chang Zhou, Xiaodong Ma, Zijun Jiao, Zesheng Zhou, Xiaolu Liu, Tianhai Peng, Changxing Shao

“…Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. …”
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Challenges, Difficulties, and Delayed Diagnosis of Multiple Myeloma by Tugba Zorlu, Merve Apaydin Kayer, Nazik Okumus, Turgay Ulaş, Mehmet Sinan Dal, Fevzi Altuntas

“…<b>Conclusions:</b> Multimodal tools for early diagnosis of MM. Educational campaigns to raise awareness of the disease, algorithms dedicated to routine care and novel technologies, including AI and big data analytics, and new biomarkers may serve this purpose, as well as genomic approaches to the premalignant MGUS stage.…”
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Plasma cfDNA multi-omic biomarkers profiling for detection and stratification of gastric carcinoma by Shiyi Song, Xiuli Zhang, Pin Cui, Weihuang He, Jiyuan Zhou, Shubing Wang, Yong Xiong, Shu Xu, Xiaohui Lin, Guozeng Huang, Xiaohua Tan, Qinglong Xu, Yongling Liu, Qingqun Li, Kehua Yuan, Mingji Feng, Hanming Lai, Hui Yang, Shaorong Zhang

“…And these biomarkers were extracted from WGS data to build machine learning algorithm based classifiers, prediction models, to discriminate GC patients from healthy individuals, achieving extremely high precision of sensitivity at 94.87% and specificity at 99.35%. …”
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Rapid discrimination and quantification of chemotypes in Perillae folium using FT-NIR spectroscopy and GC–MS combined with chemometrics by Dai-xin Yu, Cheng Qu, Jia-yi Xu, Jia-yu Lu, Di-di Wu, Qi-nan Wu

“…Furthermore, the main components of perilla ketone and isoegomaketone in PF were successfully quantified using partial least squares regression models, with prediction to deviation values of 3.76 and 2.59, respectively. …”
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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…The aim was to assess the potential presence of predictive factors for favorable or unfavorable responses to neoadjuvant chemoradiotherapy, thereby optimizing treatment management and improving personalized clinical decision-making. …”
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