RUL prediction method based on cross-view hybrid network model by Ai Yandi, Fang Dong, Tian Zhiping, Yan Kaiyang

“…To this end, this paper designs a RUL prediction framework based on a cross-view hybrid network model (CVHNet). …”
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Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction by Jingye Zhang, Ke Sun, Xiaoming Han, Ning Mao

“…Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. …”
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Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins by Qiang Yu, Liguang Jiang, Raphael Schneider, Yi Zheng, Junguo Liu

“…The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. …”
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A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka by Sadia Afrin, Mohammad Maksimul Islam, Tanvir Ahmed

“…Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. …”
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A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models by J. C. Vogeler, W. B. Cohen

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External validation of risk prediction models for post-stroke mortality in Berlin by Jessica L Rohmann, Tobias Kurth, Heinrich J Audebert, Marco Piccininni, Lukas Reitzle

“…We aimed to assess the performance of two prediction models for post-stroke mortality in Berlin, Germany.Design We used data from the Berlin-SPecific Acute Treatment in Ischaemic or hAemorrhagic stroke with Long-term follow-up (B-SPATIAL) registry.Setting Multicentre stroke registry in Berlin, Germany.Participants Adult patients admitted within 6 hours after symptom onset and with a 10th revision of the International Classification of Diseases discharge diagnosis of ischaemic stroke, haemorrhagic stroke or transient ischaemic attack at one of 15 hospitals with stroke units between 1 January 2016 and 31 January 2021.Primary outcome measures We evaluated calibration (calibration-in-the-large, intercept, slope and plot) and discrimination performance (c-statistic) of Bray et al’s 30-day mortality and Smith et al’s in-hospital mortality prediction models. …”
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Spatial multilevel modelling male partners’ influence on women’s modern contraceptive use: a study in Angola and Zambia by Kabeya Clement Mulamba

“…This paper discusses the application of spatial multilevel modelling, which incorporates two levels of information based on the nature of the data available. …”
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A novel cancer-associated membrane signature predicts prognosis and therapeutic response for lung adenocarcinoma by Biao Tu, Jun Wu, Wei Zhang, Haitao Tang, Tenghui Dai, Bingfeng Xie

“…A distinct LUAD-enriched epithelial cluster (Epi_c0) exhibiting hypoxic and EMT signatures was identified. 35 cancer-specific membrane proteins were defined, several of which, including TSPAN8, BACE2, and COX16, showed strong spatial localization within the tumor regions. LCaMPS, a 9-membrane gene-based prognostic model, stratified patient prognosis and predicted 5- and 10-year survival rates with high accuracy. …”
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Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan–Guizhou Plateau by Shanshan Xu, Kun Yang, Xiaolu Zhou, Jie Zhang, Chenghao Yang, Yi Luo

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Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study by Jingzhe Guo, Ce Liu, Fang Liu, Erkai Zhou, Runxue Ma, Ling Zhang, Bin Luo

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An Improved YOLOv5 Model for Lithographic Hotspot Detection by Mu Lin, Wenjing He, Jiale Liu, Fencheng Li, Jun Luo, Yijiang Shen

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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

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BUILDING PREDICTIVE MODELS TO ASSESS DEGRADATION OF SOIL ORGANIC MATTER OVER TIME USING REMOTE SENSING DATA by Abdulsalam Aljumaily, Ammar Kashmolaa

“…The results of the study showed the possibility of applying predictive models to Satellite data for a particular area and for previous years to give results with high spatial accuracy (R2 = 0.9581). …”
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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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Modeling inter-city population flows: a Deep Radiation model with multisource geographic features by Jingjing Liu, Lei Xu, Le Ma, Chao Wang, Nengcheng Chen

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Impact of Multiscale Heterogeneous Sediments and Boundary Conditions on Dispersivity Spatial Variations by Ziqi Ma, Linlin Qi, Zhenxue Dai, Zhijie Yang, Yue Ma, Dayong Wang, Kenneth C. Carroll, Mohamad Reza Soltanian

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Evaluation of CMIP Earth System Models on Root Biomass Simulation by Ke ZHOU, Youqi SU, Yu ZHANG, Minhong SONG, Tongwen WU, Linfeng YANG, Xizhao WANG, Tianya LI

“…Roots play an important role in the carbon cycle of ecosystems.The Earth System Models （ESMs） of the Coupled Model Intercomparison Project （CMIP） have been widely used to simulate and predict root biomass.In this paper， the spatial distribution， dynamic change， and comparison with observed values of root biomass of 14 ESMs simulated historical experiments were analyzed.The results showed that： （1） The global distribution of multi-year average from 1850 to 2005 of root biomass indicated that 7 ESMs had maximum or minimum values， while the rest ESMs showed that root biomass was higher in the middle and high latitudes of the equator and the northern hemisphere.The root biomass distribution at different latitudes showed that 40°S is also one of the high value areas； （2） The simulation results from 1995 to 2005 are compared with the site observation data （1990 -2010） in different climatic zones by using SS index， the result showed deviations are relatively large but the simulation effect of temperate zone is slightly better than frigid zone and tropical zone.On the global scale， BCC-CSM2-MR is the best simulated ESM； （3） In the Qinghai-Xizang Plateau， ESMs can reasonably simulate the seasonal variation over the years 1850 to 2005 of root biomass， but the relationship between interannual variation of the simulated root biomass and the meteorological factors （temperature， precipitation） by different ESMs is different.In order to conduct more precise and in-depth research and analysis， in addition to improving the model， it is also necessary to improve the collection of observational data.…”
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IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION by Б.І. Кузнецов, Т.Б. Нікітіна, І.В. Бовдуй, К.В. Чуніхін, В.В. Коломієць, Б.Б. Кобилянський

“…Both vector game solution calculated based on particles multi-swarm optimization (PMSO) algorithms from Pareto optimal solutions taking into account binary preference relations. Prediction model and location of compensating units in spherical coordinates as well as multipole harmonic coefficients of dipoles, quadrupoles and octupoles are calculated during prediction and control of uncertain microsatellite MC. …”
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Movement Patterns and Habitat Suitability of Translocated Sumatran Tigers (Panthera tigris sumatrae) by Farhan Reza Pahlevi, Lilik Budi Prasetyo, Dolly Priatna

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Spatial and Temporal Changes in Nutrient Source Contribution in a Lowland Catchment Within the Baltic Sea Region Under Climate Change Scenarios by Damian Bojanowski, Paulina Orlińska‐Woźniak, Paweł Wilk, Ewa Jakusik, Ewa Szalińska

“…To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. …”
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