Identifying the Peak Flowering Dates of Winter Rapeseed with a NBYVI Index Using Sentinel-1/2 by Fazhe Wu, Peng Lu, Shengbo Chen, Yucheng Xu, Zibo Wang, Rui Dai, Shuya Zhang

“…The error ranges for predicting the peak flowering dates with the NDYI (traditional optical index) and the VV (crop morphological index) are generally 2–7 days and 2–6 days, respectively, while the error range for the NBYVI index is generally 0–4 days, demonstrating superior stability and accuracy compared to the NDYI and VV indices.…”
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Detection and Optimization of Photovoltaic Arrays’ Tilt Angles Using Remote Sensing Data by Niko Lukač, Sebastijan Seme, Klemen Sredenšek, Gorazd Štumberger, Domen Mongus, Borut Žalik, Marko Bizjak

“…Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. …”
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Multiphysics Feature-Based State-of-Energy Estimation for LiFePO4 Batteries Using Bidirectional Long Short-Term Memory and Particle Swarm-Optimized Kalman Filter by Zhengpu Wu, Xu He, Haisen Chen, Lu Lv, Jiuchun Jiang, Lujun Wang

“…State-of-energy (SOE) estimation helps to enhance the safety of battery operation and predict vehicle range. However, the voltage plateau of the LiFePO4 (LFP) battery presents a significant challenge for SOE estimation. …”
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Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing by Sérgio H. G. Silva, Marcelo C. Berardo, Lucas R. Rosado, Renata Andrade, Anita F. S. Teixeira, Mariene H. Duarte, Fernanda A. Bócoli, Marco A. C. Carneiro, Nilton Curi

“…The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. …”
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Selecting patients with sickle cell disease for gene addition or gene editing‐based therapeutic approaches: Report on behalf of a joint EHA Specialized Working Group and EBMT Hemog... by Lucia de Franceschi, Franco Locatelli, David Rees, Christian Chabannon, Jean‐Hugues Dalle, Stefano Rivella, Achille Iolascon, Stephan Lobitz, Miguel R. Abboud, Josu de la Fuente, Pagona Flevari, Emanuele Angelucci, Mariane de Montalembert

“…The course of the disease is highly variable, making it difficult to predict severity and response to therapy. Allogeneic hematopoietic stem cell transplantation potentially provides a cure with a relatively low rate of complications, but few patients have an HLA‐identical sibling. …”
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Numerical analysis of geometrical configuration effects on near-field shockwave propagation in CL-20 based underwater explosives by Ji-ping Chen, Jun Wang, Xian-pi Zhang, Yan-jie Zhao, Xing-xing Wu, Fang-zhou Zhu, Jun Yu

“…The numerical methodology employs an advanced compressible multiphase flow simulation framework incorporating high-order weighted essentially non-oscillatory schemes, complemented by rigorous validation procedures involving comparative analyses against benchmark experimental data and analytical predictions from gas dynamics theory. The computational framework implements algorithmic initiation modeling for detonation processes, coupled with a front-tracking methodology employing the modified ghost fluid method to resolve discontinuities at immiscible phase boundaries. …”
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Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network by Vijaya Gunturu, J. Kavitha, Swapna Thouti, N. K. Senthil Kumar, Kamal Poon, Ayman A. Alharbi, Amar Y. Jaffar, V. Saravanan

“…A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. …”
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Assessing Urban Land Parcel Dynamics Driven by Bus Rapid Transit (BRT) as an Exclusive Transit Route by Rana Tahir Mehmood, Muhammad Zaly Shah, Mehdi Moeinaddini, Muhammad Mashhood Arif, Ramine Chuhdary, Mufeeza Tahira

“…This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. …”
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Rooting depth projections of global plant functional types and driving factors analysis based on a hybrid modeling framework by Qinggong Han, Bo Liu, Yunuo Liu, Jielin Zhang, Yongxia Ding, Shouzhang Peng

“…In this study, we developed a hybrid model that combines random forest algorithm with ecosystem data simulated by LPJ-GUESS model and environmental variables to predict global rooting depth, based on 1,184 integrated rooting depth observations. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. …”
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Modelling current and future forest fire susceptibility in north-eastern Germany by K. H. Horn, K. H. Horn, S. Vulova, S. Vulova, H. Li, B. Kleinschmit

“…Model accuracy ranged between 69 % (<span class="inline-formula">RF_test</span>) and 71 % (leave one year out, LOYO), showing a moderately high model reliability for predicting FFS. The model results underscore the importance of anthropogenic parameters and vegetation parameters in modelling FFS on a regional level. …”
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Preoperative predictors of thrombosis in children with congenital heart disease by V. A. Lastovka, R. F. Tepaev, O. B. Gordeeva, A. R. Bidzhiev, A. A. Chotchaeva

“…The literature describes in detail the disturbances in the hemostasis system observed after cardiac surgeries performed in conditions of artificial circulation, but there are no indicators that could predict the risk of thrombosis in children. The use of parameters of plasma hemostasis as predictors of thrombosis in the algorithm for diagnosing hemocoagulation disorders in children of the first year of life undergoing surgery for congenital heart defects has been substantiated. …”
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Measurement of snowpack density, grain size, and black carbon concentration using time-domain diffuse optics by Connor Andrew Henley, Colin Richard Meyer, Jacob Ian Chalif, Joseph Lee Hollmann, Ramesh Raskar

“…We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. …”
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Trauma, personality structure and psychopathology: a cartography of psychodynamic constructs by Jürgen Fuchshuber, Victor Blüml, Nestor Kapusta, Henriette Löffler-Stastka, Johanna Alexopoulos, Elisa Renner, Hugo Senra, Human-Friedrich Unterrainer

“…A cross-sectional network between personality organization [IPO-16], hypomentalizing [RFQ-6], attachment [ECR-RD8]), primary affective traits [BANPS-GL], depression, anxiety and somatization symptoms [BSI-18], addiction [WHO-ASSIST] and childhood trauma [CTQ] was estimated via the EBICglasso and relimp algorithm. Regularized partial correlation edge weights, node centrality, predictability, bridge centrality, relative importance and stability coefficients were analyzed. …”
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A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients by Andreea Istrate, Radu Constantinescu, Lithicka Anandavel, Shraddha Rajeshkumar Tandel, Simon Dye, Charlotte Dye

“…Computed tomography (CT) is the imaging method of choice for evaluating the canine nasal cavity, being invaluable in determining disease extent, guiding sampling, and planning treatment. While predictions of pathology type can be made, there is significant overlap between CT changes noted in neoplastic, inflammatory, and infectious nasal disease. …”
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Transabdominal ultrasound for the characterization and follow-up of cystic pancreatic lesions by Julian Seelig, Maria Heni, Max Seitzinger, Kien Vu Trung, Jürgen Feisthammel, Marcus Hollenbach, Robert Henker, Albrecht Hoffmeister, Jonas Rosendahl, Valentin Blank, Thomas Karlas

“…The final multivariate model (BMI, age, CPL diameter) yielded an 85% accuracy in predicting CPL detectability by TAUS. TAUS could be a cost-effective and patient-friendly imaging method for the surveillance of CPL in selected patients.…”
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Design and validation of a CFD model for energy-efficient Ni-Co-Mn cathode calcination process by Jaeseop Jo, Minyoung Hwang, Jungeui Lee, Joo Hyun Park

“…The primary objective is to quantitatively predict and optimize energy consumption in high-temperature solid-state synthesis processes. …”
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Brain structural features with functional priori to classify Parkinson’s disease and multiple system atrophy using diagnostic MRI by Kai Zhou, Jie Li, Rui Huang, Jiali Yu, Rong Li, Wei Liao, Fengmei Lu, Xiaofei Hu, Huafu Chen, Qing Gao

“…The positive and negative impacts of various features predicting PD and MSA were clarified, with larger fourth ventricular and smaller brainstem volumes being most significant. …”
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Filamentary Hierarchies and Superbubbles. I. Characterizing Filament Properties across a Simulated Spiral Galaxy by Rachel Pillsworth, Erica Roscoe, Ralph E. Pudritz, Eric W. Koch

“…The fragmentation of magnetized filaments on 200 pc scales or less occurs when they exceed an average critical line mass, as predicted by theory. On larger scales, however, kiloparsec filaments form out of the cold neutral medium, and fragmentation follows local variations in the critical line mass along spiral arms or at the boundaries of superbubbles.…”
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Real-Time Interpretation Model of Reservoir Characteristics While Underbalanced Drilling Based on UKF by Miao He, Yihang Zhang, Mingbiao Xu, Jun Li, Jianjian Song

“…Benefits from this model are seen in accurate bottomhole pressure and reservoir characteristic predictions, which are of major importance for safety and economic reasons during UBD and follow-up completion operations.…”
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