EARTHQUAKE SWARMS AS EVIDENCE OF VOLCANO-TECTONIC PROCESSES OF THE SLOWEST SPREADING GAKKEL RIDGE IN THE ARCTIC by А. N. Morozov, N. V. Vaganova, Y. A. Mikhailova, Е. R. Morozova, I. V. Starkov

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Analysis of the dynamics of changes in factor load models for predicting failures of a spacecraft by Kraev Konstantin, Zaitsev Pavel, Kazakovtsev Vladimir

“…The structure of the methodology for preparing data and creating factor load models is described. …”
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Simulation and Prediction of Spring Snow Cover in Northern Hemisphere by CMIP6 Model by Xulei WANG, Hui SUN, Hui GUO, Chula SA, Fanhao MENG, Min LUO

“…As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×106 km2 and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.…”
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A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti. by Daniel A M Villela, Claudia T Codeço, Felipe Figueiredo, Gabriela A Garcia, Rafael Maciel-de-Freitas, Claudio J Struchiner

“…We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. …”
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Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra by Lin Yang, Haibo Yuan, Bowen Huang, Ruoyi Zhang, Timothy C. Beers, Kai Xiao, Shuai Xu, Yang Huang, Maosheng Xiang, Meng Zhang, Jinming Zhang

“…This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy.…”
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Deep‐Learning Based Causal Inference: A Feasibility Study Based on Three Years of Tectonic‐Climate Data From Moxa Geodynamic Observatory by Wasim Ahmad, Valentin Kasburg, Nina Kukowski, Maha Shadaydeh, Joachim Denzler

“…We propose to use the theoretically well‐established Knockoffs framework to generate in‐distribution, uncorrelated copies of the original data as interventional variables and test the model invariance for causal discovery. …”
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Release Control of Biologically Active Substances from Simulation Model of Silicone Liners by S. V. Gribanova, I. L. Udyanskaya, V. G. Yankova, T. K. Slonskaya, N. B. Epshtein, A. A. Zhukova, O. N. Plakhotnaya, V. N. Kuzina

“…The release of the impregnated additives, as well as prolonged stable releasing effect, were confirmed. The data obtained during the experiment allows saying with confidence that the impregnated preparation (symdiol and bisabolol) releases from a SD even upon short and low-intensity mechanical contact with skin, which produces stable bacteriostatic effect on a wide range of microorganisms. …”
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The potential habitat of Phlomoides rotata in Tibet was based on an optimized MaxEnt model by Jun-Wei Wang, Jun-Wei Wang, Min Xu, Norbu Ngawang, Yonghao Chen, Ngawang Bonjor, Xiaoyan Jia, Zhefei Zeng, Zhefei Zeng, La Qiong, La Qiong

“…However, overharvesting and climate change have progressively reduced its distribution range, threatening its survival.MethodsThis study employed an optimized MaxEnt model, integrating field survey data and multiple environmental variables, to predict and analyze the potential suitable distribution of P. rotata in Tibet.ResultsThe model achieved high predictive accuracy, with Ture skill statistic (TSS) = 0.87 and Cohen’s Kappa Coefficient (Kappa) = 0.81. …”
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The Effects of Spatial Resolution Variability of Digital Elevation Models on Flood Hazard Analysis by Hasan Özdemir, Abdullah Akbaş

“…While the model time and average computational errors from low resolution to high resolution increased, the water extent and the spatial distribution of the hazard classes produced for people and buildings decreased. …”
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Spatiotemporal characteristics of vegetation cover change in the Central Yunnan urban agglomeration from 2000 to 2020 based on Landsat data and its driving factors by Rong Wang, Xue Ding, Bangjin Yi, Jinliang Wang

“…The average FVC is 0.496 with a growth rate of 0.0024 per year, and it exhibits a distribution pattern of higher values in the west and lower values in the east. (2) In terms of percentage distribution, areas with low FVC and high FVC account for a relatively high proportion. …”
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Modeling Electrochemical Impedance Spectroscopy Using Time-Dependent Finite Element Method by Yawar Abbas, Laura van Smeden, Alwin R. M. Verschueren, Marcel A. G. Zevenbergen, Jos F. M. Oudenhoven

“…Developed in COMSOL Multiphysics, the model incorporates ion transport, electric field distribution, Stern layer effects, and electrode sheet resistance, governed by the Poisson and Nernst–Planck equations. …”
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Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction by Jae sung Shin, Jun Ma, Mao Makara, Nak-Jun Sung, Seong Jun Choi, Sung yeup Kim, Min Hong

“…Additionally, all models faced challenges predicting class 2 (borderline hearing levels) due to overlapping data distributions. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Objectives] To enhance water quality prediction accuracy, this study aims to address the following challenges: (1) traditional prediction methods often rely on simple, elementary decomposition techniques, limiting their ability to extract meaningful data features. (2) Single models and basic optimization algorithms result in low prediction accuracy. (3) Most approaches fail to leverage the advantages of different networks to analyze components of varying complexity, leading to inefficient model utilization. (4) Few studies incorporate error correction after prediction. …”
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Reactive Transport Modelling of Elevated Dissolved Sulphide Concentrations in Sedimentary Basin Rocks by Mingliang Xie, Danyang Su, Kerry T. B. MacQuarrie, K. Ulrich Mayer

“…Two-dimensional reactive transport modelling was undertaken to provide potential explanations for the presence and distribution of sulphidic waters in sedimentary basins, partially constrained by field data from the Michigan Basin underlying Southern Ontario, Canada. …”
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Prevalence and Mortality of Heart Failure Stages in a Free‐Living Older Adult Population: Data From the Brazilian Longitudinal Study of Adult Health by Altair I. Heidemann, Angela B. S. Santos, Marcio S. Bittencourt, Antonio L. P. Ribeiro, Luis E. Rohde, Paulo A. Lotufo, Bruce B. Duncan, Murilo Foppa

“…Of these, 504 (21%) participants were considered at low risk for developing HF (stage 0). Prevalence of HF was 1026 (44%) for stage A (at risk for HF), 557 (24%) for stage B (pre‐HF), and 269 (11%) for stage C (symptomatic HF), with different distribution between sexes (P<0.001). …”
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NSPLformer: exploration of non-stationary progressively learning model for time series prediction by Sun Jiaxing, Li Yanhui, Zhao Yuying

“…This design is based on two core components: (1)Low-cost non-stationary attention mechanism, which restores intrinsic non-stationary information to time-dependent relationships at a lower computational cost by approximating the distinguishable attention learned in the original sequence.; (2) dual-data-stream Progressively learning, which designs an auxiliary output stream to improve information aggregation mechanisms, enabling the model to learn residuals of supervised signals layer by layer.The proposed model outperforms the mainstream Tranformer with an average improvement of 5.3% on multiple datasets, which provides theoretical support for the analysis of non-stationary engineering data.…”
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Quantitative Inversion of Martian Hydrous Minerals Based on LSTM-1DCNN Model by Xinbao Liu, Ming Jin, Xiangnan Liu, Zhiming Yang, Zengqian Hou, Xiaozhong Ding

“…By incorporating deep learning into traditional mixing models, this study identifies the distribution of various low-abundance hydrous minerals, enhancing the accuracy of Martian hydrous mineral inversion. …”
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Modelling the incidence of Plasmodium vivax and Plasmodium falciparum malaria in Afghanistan 2006-2009. by Victor A Alegana, Jim A Wright, Sami M Nahzat, Waqar Butt, Amad W Sediqi, Naeem Habib, Robert W Snow, Peter M Atkinson, Abdisalan M Noor

“…<h4>Conclusion</h4>This study showed how routine data can be combined with household survey data to model malaria incidence. …”
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A Satellite Drift Scan Approach for Establishing the Pointing Model of Radio Telescopes by Ye Yuan, Zhi-Yong Liu, Na Wang, Hao Yan

“…Given the diverse drift directions of satellites, the obtained pointing data encompass a wide range of directions. Therefore, this method is expected to be capable of establishing a pointing model based on scanning data from arbitrary directions. …”
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Diffusion Model With Gradient Descent Module Guiding Reconstruction for Single-Pixel Imaging by Chen Huang, Qiurong Yan, Jinwei Yan, Yi Li, Xiaolong Luo, Hui Wang

“…The preliminary reconstruction serves as prior information to iteratively constrain the diffusion model, allowing it to generate target images consistent with the training data distribution. …”
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