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A machine learning based model for gray gas emissivity and absorptivity of H 2 O-CO 2 -CO-N 2 mixtures

Chen, W., Ren, T., & Zhao, C. (2023) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2023.108798 · Cited by 12

Automatic myeloblast segmentation in acute myeloid leukemia images based on adversarial feature learning

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Quantifying and communicating uncertain climate change hazards in participatory climate change adaptation processes

Müller, L., & Döll, P. (2024) · Geoscience Communication · doi:10.5194/gc-7-121-2024 · Cited by 11

Can AI Predict the Magnitude and Direction of Ortho-K Contact Lens Decentration to Limit Induced HOAs and Astigmatism?

Lin, W.-P., Wu, L.-Y., Li, W.-K., Lin, W.-R., Wu, R., White, L., Abass, R., Alanazi, R., Towler, J., Davies, J., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13185420 · Cited by 13

Explaining Normal Quantile-Quantile Plots Through Animation: The Water-Filling Analogy

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A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Yang, S., Ghosh, K., Sakaie, K., Sahoo, S. S., Carr, S. J. A., & Tatsuoka, C. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00492 · Cited by 12

Data Visualizations and Human Resource Management: The State of Science and Practice

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CECAV-DNN: Collective Ensemble Comparison and Visualization using Deep Neural Networks

He, W., Wang, J., Guo, H., Shen, H., & Peterka, T. (2020) · Visual Informatics · doi:10.1016/j.visinf.2020.04.004 · Cited by 11

Biomass hydrodeoxygenation catalysts innovation from atomistic activity predictors

Morteo‐Flores, F., Engel, J., & Roldán, A. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0056 · Cited by 13

Real curve: Identifying and quantifying the real environmental effects on migration in China

Zhao, Z., Pan, J., & Lei, P. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.108348 · Cited by 12

On the single and multiple associations of COVID-19 post-acute sequelae: 6-month prospective cohort study

Jiménez-Rodríguez, B. M., Fernández, J. G., Urbina, E. M. R., Romero-Ortiz, A. D., García‐Flores, P., Puertas, M. I. S., Martín-López, M. J., Milena, G. L., Fabregas, R., & Morales-García, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-07433-8 · Cited by 15

Time-of-day bias in diurnal raptors in arid region of Rajasthan

Tiwari, G., Pandey, P., Kaul, R., Lee, H., & Singh, R. K. (2022) · Acta Ecologica Sinica · doi:10.1016/j.chnaes.2022.07.005 · Cited by 12

Improving ranking quality and fairness in Swiss-system chess tournaments

Sauer, P., Cseh, Á., & Lenzner, P. (2024) · Journal of Quantitative Analysis in Sports · doi:10.1515/jqas-2022-0090 · Cited by 12

Comparison between two statistical downscaling methods for summer daily rainfall in Chongqing, China

Fan, L., Yan, Z., Chen, D., & Fu, C. (2014) · International Journal of Climatology · doi:10.1002/joc.4246 · Cited by 12

Fraction of design space plots for evaluating ridge estimators in mixture experiments

Jang, D., & Anderson‐Cook, C. M. (2010) · Quality and Reliability Engineering International · doi:10.1002/qre.1104 · Cited by 12

The Culture-Transmission Motive in Immigrants: A World-Wide Internet Survey

Mchitarjan, I., & Reisenzein, R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141625 · Cited by 15

Individuals and their interactions in demand planning processes: an agent-based, computational testbed

Hauke, J., Lorscheid, I., & Meyer, M. (2017) · International Journal of Production Research · doi:10.1080/00207543.2017.1377356 · Cited by 12

What Is Six Hours Worth? The Impact of Lead Time on Tropical-Storm Preparation Decisions

Regnier, E. (2020) · Decision Analysis · doi:10.1287/deca.2019.0396 · Cited by 12

Don't Know What You Got: A Bayesian Hierarchical Model of Neuroticism And Nonresponse

Klingler, J. D., Hollibaugh, G. E., & Ramey, A. (2015) · Political Science Research and Methods · doi:10.1017/psrm.2016.50 · Cited by 9

A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques

Gabr, A. R., Roy, B., Kaloop, M. R., Kumar, D., Arisha, A., Shiha, M., Shwaly, S. A., Hu, J. W., & El-Badawy, S. M. (2021) · International Journal of Pavement Engineering · doi:10.1080/10298436.2021.1892109 · Cited by 12

Emergence of continents above sea‐level influences sediment melt composition

Liebmann, J., Spencer, C. J., Kirkland, C. L., Bucholz, C. E., He, X., Santosh, M., Xia, X. P., Martin, L. A. J., & Evans, N. J. (2021) · Terra Nova · doi:10.1111/ter.12531 · Cited by 12

Phosphorus status and saturation in soils that drain into the Peel Inlet and Harvey Estuary of Western Australia

Weaver, D. M., & Summers, R. (2021) · Soil Research · doi:10.1071/sr20259 · Cited by 12

Adherence to Antiseizure vs Other Medications Among US Medicare Beneficiaries With and Without Epilepsy.

Terman, S. W., Kerr, W. T., Aubert, C. E., Hill, C. E., Marcum, Z. A., & Burke, J. F. (2022) · doi:10.48350/162440 · Cited by 12

Data. Information and Knowledge Visualization for Frequent Patterns

Hoi, C. S. H., Leung, C. K.-S., & Pazdor, A. G. M. (2022) · International Conference on Information Visualisation (IV) · doi:10.1109/iv56949.2022.00045 · Cited by 12

Classification of normal and abnormal colonic motility based on cross‐correlations of pancolonic manometry data

Wiklendt, L., Mohammed, S. D., Scott, S. M., & Dinning, P. G. (2013) · Neurogastroenterology & Motility · doi:10.1111/nmo.12077 · Cited by 12

Embedding and extraction of knowledge in tree ensemble classifiers

Huang, W., Zhao, X., & Huang, X. (2021) · Machine Learning · doi:10.1007/s10994-021-06068-6 · Cited by 14

Land-use Change in the Central Highlands of Vietnam

Müller, D. (2003) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-1825 · Cited by 12

A Modern Reconstruction of Richard Carrington’s Observations (1853–1861)

Bhattacharya, S., Teague, E. T. H., Fay, S., Lefèvre, L., Jansen, M., & Clette, F. (2021) · Solar Physics · doi:10.1007/s11207-021-01864-8 · Cited by 11

Automatically deriving JavaScript static analyzers from specifications using Meta-level static analysis

Park, J., An, S., & Ryu, S. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549097 · Cited by 11

Explainable machine learning to uncover hydrogen diffusion mechanism in clinopyroxene

Li, A., Wu, S., Chen, H., Du, Z., & Xia, Q. (2023) · Chemical Geology · doi:10.1016/j.chemgeo.2023.121776 · Cited by 11

Global Sensitivity Analysis for Segmented Inverse Uncertainty Quantification in the Safety Analysis of Nuclear Power Plants

Maio, F. D., Coscia, T. M., Pedroni, N., Bersano, A., Mascari, F., & Zio, E. (2024) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2024.110791 · Cited by 11

Network analysis of cross-income-level collaboration on multiple myeloma in sub-Saharan Africa

Yang, K., Benkwitz-Bedford, S., Cazier, J., & Spill, F. (2025) · npj Health Systems · doi:10.1038/s44401-024-00003-2 · Cited by 11

Heterogeneous zonal impacts of climate change on a wide hyperendemic area of human and animal fascioliasis assessed within a One Health action for prevention and control

Cuervo, P., Bargues, M. D., Artigas, P., Buchón, P., Angles, R., & Mas‐Coma, S. (2025) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0012820 · Cited by 11

Φ -OTDR Event Recognition System Based on Reconstructed Signal Feature Space

Chen, J., Shi, Y., Wei, C., & Yu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3558470 · Cited by 11

Autonomous Trajectory Planning for External Ventricular Drain Placement

Beckett, J. S., Gaonkar, B., Babayan, D., Mathew, J., McArthur, D. A., Salamon, N., Martin, N. A., Yang, I., & Macyszyn, L. J. (2018) · Operative Neurosurgery · doi:10.1093/ons/opx285 · Cited by 11

Mapping the Research Landscape of Agricultural Sciences

Devyatkin, D. A., Nechaeva, E., Suvorov, R., & Tikhomirov, I. A. (2018) · Foresight-Russia · doi:10.17323/2500-2597.2018.1.69.78 · Cited by 11

Visual Analysis of Multi-Parameter Distributions Across Ensembles of 3D Fields

Kumpf, A., Stumpfegger, J., Hartl, P., & Westermann, R. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3061925 · Cited by 11

The social shape of sperm: using an integrative machine-learning approach to examine sperm ultrastructure and collective motility

Hook, K. A., Yang, Q., Campanello, L., Losert, W., & Fisher, H. S. (2021) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2021.1553 · Cited by 11

Container material dictates stability of bacteriophage suspensions: Light scattering and infectivity measurements reveal mechanisms of infectious titre decay

O’Connell, L., Roupioz, Y., & Marcoux, P. R. (2022) · Journal of Applied Microbiology · doi:10.1111/jam.15581 · Cited by 11

Studying Early Decision Making with Progressive Bar Charts

Patil, A., Richer, G., Jermaine, C., Moritz, D., & Fekete, J. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209426 · Cited by 11

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Somadder, P. D., Hossain, M. A., Ahsan, A., Sultana, T., Soikot, S. H., Rahman, M. M., Ibrahim, S. M., Ahmed, K., & Bui, F. M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106630 · Cited by 11

Vestibulo-spatial navigation: pathways and sense of direction

Zachou, A., & Bronstein, A. M. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00422.2022 · Cited by 11

Implementation and parametric study of J-integral data reduction methods for the translaminar toughness of hierarchical thin-ply composites

Broggi, G., Cugnoni, J., & Michaud, V. J. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109169 · Cited by 11

Learning Semantic Representation on Visual Attribute Graph for Person Re-identification and Beyond

Tang, G., Gao, X., & Chen, Z. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3487044 · Cited by 11

Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Türkiye

Katipoğlu, O. M., & Sarıgöl, M. (2023) · Journal of Hydro-environment Research · doi:10.1016/j.jher.2023.07.004 · Cited by 11

Low-ionization structures in planetary nebulae – III. The statistical analysis of physico-chemical parameters and excitation mechanisms

Mari, M. B., Akras, S., & Gonçalves, D. R. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2256 · Cited by 11

Regulatory agilities impacting review timelines for Pfizer/BioNTech’s BNT162b2 mRNA COVID-19 vaccine: a retrospective study

Patel, P., Macdonald, J. C., Boobalan, J., Marsden, M., Rizzi, R., Zenon, M., Ren, J., Chu, H., Cappelleri, J. C., Roychoudhury, S., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1275817 · Cited by 11

SCAN: An Approach to Label and Relate Execution Trace Segments

Medini, S., Antoniol, G., Guéhéneuc, Y., Penta, M. D., & Tonella, P. (2012) · Working Conference on Reverse Engineering (WCRE) · doi:10.1109/wcre.2012.23 · Cited by 11

Modelling Class Uncertainty in the Geodemographic Output Area Classification

FISHER, P. F., & Tate, N. (2015) · Environment and Planning B Planning and Design · doi:10.1068/b130176p · Cited by 11

Probabilistic sensor data processing for robot localization on load-sensing floors

Rio, M., Colas, F., Andries, M., & Charpillet, F. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487654 · Cited by 11

Edaphic niches of metallophytes from southeastern Democratic Republic of Congo: Implications for post-mining restoration

Boisson, S., Monty, A., Lebrun, J., Séleck, M., & Mahy, G. (2016) · Journal for Nature Conservation · doi:10.1016/j.jnc.2016.06.002 · Cited by 11

Rapid Production of Virus Protein Microarray Using Protein Microarray Fabrication through Gene Synthesis (PAGES)

Qi, H., Zhou, H., Czajkowsky, D. M., Guo, S., Li, Y., Wang, N., Shi, Y., Lin, L., Wang, J., Wu, D., et al. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.064873 · Cited by 11

Accurate lattice-parameter determination from electron diffraction tomography data using two-dimensional diffraction vectors

Ångström, J., Chen, H., & Wan, W. (2018) · Journal of Applied Crystallography · doi:10.1107/s1600576718006635 · Cited by 11

Evolution of host plant use and diversification in a species complex of parasitic weevils (Coleoptera: Curculionidae)

Hernández‐Vera, G., Toševski, I., Caldara, R., & Emerson, B. C. (2019) · PeerJ · doi:10.7717/peerj.6625 · Cited by 11

The socio-economic planning of a community nurses programme in mountain areas: A Directional Distance Function approach

Falavigna, G., & Ippoliti, R. (2019) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2019.100770 · Cited by 11

Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode Networks

Wang, C., Butts, C. T., Hipp, J. R., & Lakon, C. M. (2020) · Sociological Methods & Research · doi:10.1177/0049124120914933 · Cited by 11

Influence of layer separation on the determination of stomach smooth muscle properties

Borsdorf, M., Böl, M., & Siebert, T. (2021) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-021-02568-5 · Cited by 11

Modelling hospital outcome: problems with endogeneity

Moran, J. L., Santamaria, J. D., & Duke, G. J. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01251-8 · Cited by 11

Radiographical Survey of Osteochondrodysplasia in Scottish Fold Cats caused by the TRPV4 gene variant

Rorden, C., Griswold, M. C., Moses, N., Berry, C. R., Keller, G. G., Rivas, R., Flores-Smith, H., Shaffer, L. G., & Malík, R. (2021) · Human Genetics · doi:10.1007/s00439-021-02337-5 · Cited by 11

Lab-Tec@Home: Technological Innovation in Control Engineering Education with Impact on Complex Thinking Competency

Sotelo, D., Parra, J. C. V., Cruz-Sandoval, M. A., & Sotelo, C. (2023) · Sustainability · doi:10.3390/su15097598 · Cited by 11

Non-stress-related factors associated with maternal corticotrophin-releasing hormone (CRH) concentration

Kramer, M. S., Lydon, J. E., Séguin, L., Goulet, L., Kahn, S. R., McNamara, H., Genest, J. J., Sharma, S., Meaney, M. J., Libman, M., et al. (2010) · Paediatric and Perinatal Epidemiology · doi:10.1111/j.1365-3016.2010.01127.x · Cited by 11

PSYCHOMETRIC ANALYSIS OF THE VISUAL FORM DISCRIMINATION TEST

Malina, A. C., Regan, T., Bowers, D. A., & Millis, S. R. (2001) · Perceptual and Motor Skills · doi:10.2466/pms.2001.92.2.449 · Cited by 11

Epigenetic re-wiring of breast cancer by pharmacological targeting of C-terminal binding protein

Byun, J. S., Park, S., Yi, D. I., Shin, J., Gil‐Hernández, S., Hewitt, S. M., Nicklaus, M. C., Peach, M. L., Guasch, L., Tang, B., et al. (2019) · Cell Death and Disease · doi:10.1038/s41419-019-1892-7 · Cited by 11

Molecular characterization of the permanent outer-inner membrane contact site of the mitochondrial genome segregation complex in trypanosomes

Stettler, P., Schimanski, B., Aeschlimann, S., & Schneider, A. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012635 · Cited by 10

The role of religion in adolescent mental health: faith as a moderator of the relationship between distrust and depression

Tsomokos, D. I., & Dunbar, R. I. M. (2024) · Religion, Brain & Behavior · doi:10.1080/2153599x.2023.2248230 · Cited by 10

Microphysical Properties of Generating Cells Over the Southern Ocean: Results From SOCRATES

Wang, Y., McFarquhar, G. M., Rauber, R. M., Zhao, C., Wu, W., Finlon, J. A., Stechman, D. M., Stith, J., Jensen, J. B., Schnaiter, M., et al. (2020) · Journal of Geophysical Research Atmospheres · doi:10.1029/2019jd032237 · Cited by 10

Designing a robust extreme gradient boosting model with SHAP-based interpretation for predicting carbonation depth in recycled aggregate concrete

Alizamir, M., Gholampour, A., Kim, S., Heddam, S., & Kim, J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11411-7 · Cited by 10

Proximal exploration of Venus volcanism with teams of autonomous buoyancy-controlled balloons

Rossi, F., da Silva, M. S., Krishnamoorthy, S., & Hook, J. V. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.03.003 · Cited by 10

A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images

Wu, T., Shen, P., Dai, J., Ma, Y., & Feng, Y. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0119 · Cited by 10

Measuring Tree Diameter Using LiDAR Equipped iPad: An Evaluation of ForestScanner and Arboreal Forest Applications

Howie, N. A., & Stefano, A. D. (2024) · Forest Science · doi:10.1093/forsci/fxae017 · Cited by 10

An intelligent approach: Integrating ChatGPT for experiment planning in biochar immobilization of soil cadmium

Yang, H., Wang, J., Mo, R., Hu, P., Liu, X., Liu, Y., Cui, J., & Xiao, Y. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.128170 · Cited by 10

Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks

Bånkestad, M., Dorst, K. M., Widmalm, G., & Rönnols, J. (2024) · RSC Advances · doi:10.1039/d4ra03428g · Cited by 10

Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation

Lackinger, A., Morichetta, A., & Dustdar, S. (2024) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose62363.2024.00011 · Cited by 10

Predicting working alliance in psychotherapy: A multi-modal machine learning approach

Doorn, K. A., Cicconet, M., Cohn, J. F., & Aafjes, M. (2025) · Psychotherapy Research · doi:10.1080/10503307.2024.2428702 · Cited by 11

Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems

Deantoni, J., Muñoz, P., Gomes, C. Â. G., Verbrugge, C., Mittal, R., Heinrich, R., Bellis, S., & Vallecillo, A. (2025) · at - Automatisierungstechnik · doi:10.1515/auto-2024-0036 · Cited by 11

A reliable hybrid extreme learning machine-metaheuristic framework for enhanced strength prediction of 3D-printed fiber-reinforced concrete

Alizamir, M., Kim, S., Ikram, R. M. A., Ahmed, K. O., Heddam, S., & Gholampour, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105715 · Cited by 10

Lymph node fibroblast phenotypes and immune crosstalk regulated by podoplanin activity

Makris, S., Hari‐Gupta, Y., Cantoral-Rebordinos, J. A., Martínez, V. G., Horsnell, H. L., Benjamin, A. C., Cinti, I., Jovancheva, M., Shewring, D., Nguyen, N., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115908 · Cited by 10

Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management

Alizamir, M., Ahmed, K. O., Heddam, S., Kim, S., & Lee, J. E. (2025) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2025.2541686 · Cited by 10

Object detection neural network improves Fourier ptychography reconstruction

Ströhl, F., Jadhav, S., Ahluwalia, B. S., Agarwal, K., & Prasad, D. K. (2020) · Optics Express · doi:10.1364/oe.409679 · Cited by 10

NoAS-DS: Neural optimal architecture search for detection of diverse DNA signals

Sivangi, K. B., Dasari, C. M., Amilpur, S., & Bhukya, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.009 · Cited by 11

Stress intensity factor prediction on offshore pipelines using surrogate modeling techniques

Kaloop, M. R., Samui, P., Kim, J.-J., Hu, J. W., & Ramzy, A. (2022) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2022.e01045 · Cited by 10

Video-Observed Therapy With a Notification System for Improving the Monitoring of Tuberculosis Treatment in Thailand: Usability Study

Kumwichar, P., Chongsuvivatwong, V., & Prappre, T. (2022) · JMIR Formative Research · doi:10.2196/35994 · Cited by 10

Comparative proteoinformatics revealed the essentials of SDS impact on HaCaT keratinocytes

Shkrigunov, T., Kisrieva, Y. S., Samenkova, N. F., Larina, O. B., Zgoda, V. G., Rusanov, A. L., Romashin, D. D., Luzgina, N. G., Karuzina, I. I., Лисица, А. В., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25934-4 · Cited by 10

Using fecal immmunochemical cartridges for gut microbiome analysis within a colorectal cancer screening program

Brezina, S., Borkovec, M., Baierl, A., Bastian, F., Futschik, A., Gabriel, P., Gasche, N., Gruenberger, T., Hallas, M., Jansen, C., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2176119 · Cited by 10

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Ke, L., Zhang, Z., Ma, Y., Xiao, Y. J., Wu, S., Wang, X., Liu, X., & Hu, J. (2023) · Journal of Visualized Experiments · doi:10.3791/65170 · Cited by 10

Fully automatic tracking of native glenohumeral kinematics from stereo-radiography

Burton, W. S., Crespo, I. R., Andreassen, T. E., Pryhoda, M. K., Jensen, A., Myers, C. A., Shelburne, K. B., Banks, S. A., & Rullkoetter, P. J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107189 · Cited by 10

Thermal performance of three horizontal ground heat exchanger systems: comparison of linear-loop, spiral-coil and slinky-coil arrangements

Hu, L., Rizvi, Z. H., Tobber, L., & Wuttke, F. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1188506 · Cited by 10

Comparative Analysis of Interactive Modalities for Intuitive Endovascular Interventions

Wu, D., Li, Z., Ansari, M. H. D., Ha, X. T., Ourak, M., Dankelman, J., Menciassi, A., Momi, E. D., & Poorten, E. V. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3362628 · Cited by 10

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