Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model by Zhaoqi Tang, Yi Wang, Wen Chen

“…This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. …”
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High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 by Xining Zhao, Jichao Wang, Yelu Ding, Xiaodong Gao, Changjian Li, Hongwei Huang, Xuerui Gao

“…The research methodology involved four key steps: (1) Enhancing the sample dataset using phenological indices and the Dynamic Time Warping (DTW) algorithm; (2) Identifying crop planting intensity based on phenological growth curves; (3) Developing independent random forest classifiers tailored to agricultural climate zones; and (4) Constructing an optimal feature subset for crop classification. …”
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Assessing distortion in carbon fiber woven fabrics based on machine vision by Shiyue Li, Quanzhou Yao, Lin Ye

“…This work proposes a machine vision method to locate defective areas, identify defects, and describe fiber tow distribution patterns. A back-lighting imaging system was designed to minimize surface reflection interference, enabling high-quality fabric image acquisition. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…The model successfully captures nonlinear and time-varying characteristics in concrete dam deformation processes, showing high consistency with measured deformation patterns and demonstrating excellent engineering practicality. …”
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Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review by Omnia Nabil, Nahid Afia, T Ismail

“…Despite the advances, the availability and quality of datasets remain a significant limitation in developing robust models. Future research directions are identified, including the need for improved feature engineering, architecture optimization, and model interpretability. …”
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Early Diagnosis of Alzheimer Disease from Mri Using Deep Learning Models by Apparna Allada, R. Bhavani, Kavitha Chaduvula, R. Priya

“…In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. …”
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SYNOVIAL MEMBRANE CHANGES IN PATIENTS WITH RHEUMATOID ARTHRITIS REVEALED DURING ARTHROSCOPY by E. B. Komarova

“…Exudative or proliferative processes accompanied by joint destruction may predominate in the synovial membrane (SM) in different stages of rheumatoid arthritis (RA). The features of SM changes should be considered in the early diagnosis of RA and in the development of combination treatment for this disease.Objective: to study arthroscopic SM changes in patients with different RA durations and different blood anti-cyclic citrullinated peptide (anti-CCP) antibody levels.Subjects and methods. 37 patients with RA underwent arthroscopy with an arthroscope of a 2.4-mm diameter and 30° angle (Karl Storz GmbH, Germany). …”
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Efficient Classification of Pomegranate Diseases Using Deep Learning Models and Interactive Visualization by Mishra Gaurav, Nagaonkar Jagruti, Parmar Harsh, Joshi Gaurav

“…These models accurately detect complicated disease patterns, allowing for timely intervention to reduce crop losses and increase agricultural production. …”
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The Extraction of <i>Torreya grandis</i> Growing Areas Using a Spatial–Spectral Fused Attention Network and Multitemporal Sentinel-2 Images: A Case Study of the Kuaiji Mountain Reg... by Yanyan Lyu, Yong Wang, Xiaoling Shen

“…This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. Following minimum redundancy maximum relevance (mRMR) feature selection, a spatial–spectral fused attention network (SSFAN) was developed to extract the distribution of <i>T. grandis</i> in the Kuaiji Mountain area and to analyze the influence of topographic factors. …”
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Transgenic overexpression of LARGE induces α-dystroglycan hyperglycosylation in skeletal and cardiac muscle. by Martin Brockington, Silvia Torelli, Paul S Sharp, Ke Liu, Sebahattin Cirak, Susan C Brown, Dominic J Wells, Francesco Muntoni

“…Further detailed muscle physiological analysis demonstrated a loss of force in response to eccentric exercise in the older, but not in the younger mice, suggesting this deficit developed over time. However this remained a subclinical feature as no pathology was observed in older mice in any muscles including the diaphragm, which is sensitive to mechanical load-induced damage.…”
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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. …”
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Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. by Claudia Giambartolomei, Damjan Vukcevic, Eric E Schadt, Lude Franke, Aroon D Hingorani, Chris Wallace, Vincent Plagnol

“…In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. …”
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mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data. by Hannes Doerfler, Xiaoliang Sun, Lei Wang, Doris Engelmeier, David Lyon, Wolfram Weckwerth

“…Pathways are extracted directly from the data and putative novel structures can be identified. The detected m/z features can be mapped on a van Krevelen diagram according to their H/C and O/C ratios for pattern recognition and to visualize oxidative processes and biochemical transformations. …”
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Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data by Pei-Chun Lin, Kuan-Yen Chen, Jenhung Wang

“…Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely comprehensive and precise dataset that captures spatiotemporal traffic patterns and environmental conditions across the national highway network. …”
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Using 3D observations with high spatio-temporal resolution to calibrate and evaluate a process-focused cellular automaton model of soil erosion by water by A. Eltner, D. Favis-Mortlock, O. Grothum, M. Neumann, T. Laburda, P. Kavka

“…</p> <p>These results underscore the need for more nuanced evaluation of erosion models, e.g. by incorporating spatial-pattern comparison techniques to provide a deeper understanding of the model's capabilities. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

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GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification by Ramitha Vimala, Saharsh Mehrotra, Satish Kumar, Pooja Kamat, Arunkumar Bongale, Ketan Kotecha

“…The GradCAM-PestDetNet methodology utilizes object detection models like YOLOv8m, YOLOv8s and YOLOv8n, alongside transfer learning techniques such as VGG16, ResNet50, EfficientNetB0, MobileNetV2, InceptionV3 and DenseNet121 for feature extraction. Additionally, Vision Transformers (ViT) and Swim Transformers were explored for their ability to process complex data patterns. …”
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MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.…”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

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