Modeling the Geomagnetic Response to the September 2017 Space Weather Event Over Fennoscandia Using the Space Weather Modeling Framework: Studying the Impacts of Spatial Resolution by A. P. Dimmock, D. T. Welling, L. Rosenqvist, C. Forsyth, M. P. Freeman, I. J. Rae, A. Viljanen, E. Vandegriff, R. J. Boynton, M. A. Balikhin, E. Yordanova

“…Abstract We must be able to predict and mitigate against geomagnetically induced current (GIC) effects to minimize socio‐economic impacts. …”
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Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of... by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven, Rebecca Retzlaff

“…Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy).…”
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Monitoring Space Weather: Using Automated, Accurate Neural Network Based Whistler Segmentation for Whistler Inversion by Bálint Ármin Pataki, János Lichtenberger, Mark Clilverd, Gergely Máthé, Péter Steinbach, Szilárd Pásztor, Lilla Murár‐Juhász, Dávid Koronczay, Orsolya Ferencz, István Csabai

“…Testing the proposed method against a manually, and semi‐manually segmented whistler data set achieved <10% relative electron density prediction error for 80% of the segmented whistler traces, while for the L shell, the relative error is <5% for 90% of the cases. …”
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Do land models miss key soil hydrological processes controlling soil moisture memory? by M. A. Farmani, A. Behrangi, A. Behrangi, A. Gupta, A. Tavakoly, A. Tavakoly, M. Geheran, G.-Y. Niu

“…We suggest that LSMs for use in seasonal-to-subseasonal climate prediction should, at least, adopt the Van Genuchten hydraulic scheme.…”
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Theoretical impact of a bedside decision-making tool on antibiotic use for suspected neonatal healthcare-associated infection: an observational study by Lizel Georgi Lloyd, Mirjam Maria van Weissenbruch, Adrie Bekker, Cecilia Ferreyra, Birgitta Gleeson, Angela Dramowski

“…The antibiotic decision-making tool included an infection prediction score (NeoHoP), and a point-of-care C-reactive protein test (CRP) performed at HAI diagnosis and 24 h later. …”
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Habitat Quality Dynamics in Urumqi over the Last Two Decades: Evidence of Land Use and Land Cover Changes by Siying Chen, Ümüt Halik, Lei Shi, Wentao Fu, Lu Gan, Martin Welp

“…The simulation and prediction results suggest that from 2020 to 2035, habitat degradation will be mitigated under Ecological Protection scenarios, while the decline in habitat quality will be most pronounced under Business-As-Usual scenarios. …”
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Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective by Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

“…Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. …”
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Structural, electronic, mechanical, optical and magnetic properties of RhNbZ (Z = Li, Si, As) Half-Heusler compounds: a first-principles study by Adem Beriso Bejo, Megersa Wodajo Shura, Kumneger Tadele, Mesfin Asfaw Afrassa, Fekadu Tolessa Maremi

“…From magnetic property calculations, RhNbSi is predicted to be nonmagnetic material but RhNbLi and RhNbAs have magnetic nature.…”
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Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…A regression model for predicting image quality was developed using a convolutional neural network (CNN). …”
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Improving drug repositioning with negative data labeling using large language models by Milan Picard, Mickael Leclercq, Antoine Bodein, Marie Pier Scott-Boyer, Olivier Perin, Arnaud Droit

“…This approach showed remarkable improvement in predictive accuracy on independent test sets with a Matthews Correlation Coefficient of 0.76 (± 0.33) compared to 0.55 (± 0.15) and 0.48 (± 0.18) for two commonly used PU learning approaches. …”
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The clinical features and outcomes of diabetes patients infected with COVID-19: a systematic review and meta-analysis comprising 192,693 patients by Kai Liu, Shu Liu, Ting-ting Xu, Hong Qiao

“…Our findings will help identify elevated risk factors in diabetics, which will benefit early prediction.…”
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Geomagnetically Induced Current Model Validation From New Zealand's South Island by T. Divett, D. H. Mac Manus, G. S. Richardson, C. D. Beggan, C. J. Rodger, M. Ingham, E. Clarke, A. W. P. Thomson, M. Dalzell, Y. Obana

“…This is because of the limited range of frequencies for which the thin‐sheet model is valid and severely limits the usefulness of this modeling approach for accurate prediction of peak GICs.…”
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The evolving roles of geophysics in environmental assessment, monitoring, and management of landfill leachate contaminant plumes: An overview by Joseph Omeiza Alao

“…Future research should focus on developing advanced data interpretation techniques, integrating geophysical data with other environmental monitoring data, and exploring the use of artificial intelligence for automated analysis and prediction of LLP behaviour, which ultimately aiding the environmental decision-makers in addressing ecological challenges and designing/managing landfill sites.…”
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Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management by César de Oliveira Ferreira Silva, Rodrigo Lilla Manzione, Epitácio Pedro da Silva Neto, Ulisses Alencar Bezerra, John Elton Cunha

“…In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. …”
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Two previously undescribed triterpenoid saponins from the roots and rhizomes of Caulophyllum robustum Maxim by Cong-Yu Zhang, Yu-Mei Wang, Peng-Cheng Qiu, Jia-Yu Feng, Bing-Wen Wang, Yu Cao, Yi-Sha Wei, Yi-Tong Zhou, Hai-Feng Tang, Yun-Yang Lu, Qian Zhang

“…By utilizing the SEA platform for target prediction, a common tumor related target CD81 was identified. …”
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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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Fibroblasts in rheumatoid arthritis: novel roles in joint inflammation and beyond by Elpida Neofotistou-Themeli, Elpida Neofotistou-Themeli, Panagiota Goutakoli, Panagiota Goutakoli, Theodoros Chanis, Theodoros Chanis, Maria Semitekolou, Maria Semitekolou, Eirini Sevdali, Eirini Sevdali, Prodromos Sidiropoulos, Prodromos Sidiropoulos

“…Although our knowledge of ILD has progressed, significant gaps in both basic and clinical science remain, posing hurdles to efficient diagnosis, prediction of disease course and its effective treatment. …”
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Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach by Penghao Tian, Bingkun Yu, Hailun Ye, Xianghui Xue, Jianfei Wu, Tingdi Chen

“…Abstract The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. …”
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Systematic Coronary Risk Evaluation 2 (SCORE2), arterial stiffness, and subclinical coronary atherosclerosis in a population-based study by Susanna Strömberg, Andreas Stomby, Jan Engvall, Carl Johan Östgren

“…This knowledge can be useful in primary care, where SCORE2 is frequently used as a risk prediction tool. The modest overlap between arterial stiffness and coronary calcification suggests that CACS and PWV describe different types of vascular damage.…”
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Lulu Filterized Lin’s correlative Theil-Sen regression-based fully connected deep multilayer perceptive neural network for eye gaze pattern recognition by Rathi K., Srinivasan K.

“…Explained result illustrates which LFLCTR-FCDMPNN method improves the accuracy of gaze pattern recognition and decreases the time consumption than the conventional prediction methods. Using the Synthes Eyes dataset, it turned out that the FPR of the suggested LFLCTR-FCDMPNN was 63% higher than existing.…”
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