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The Co-evolution of Organizational and Network Structure: The Role of Multilevel Mixing and Closure Mechanisms

Amati, V., Lomi, A., Mascia, D., & Pallotti, F. (2019) · Organizational Research Methods · doi:10.1177/1094428119857469 · Cited by 26

Urban lake spatial openness and relationship with neighboring land prices: Exploratory geovisual analytics for essential policy insights

Zuo, Z., Li, J., Luo, X., Li, C., & Zhang, L. (2020) · Land Use Policy · doi:10.1016/j.landusepol.2020.104479 · Cited by 26

The Impact of Focus and Context Visualization Techniques on Depth Perception in Optical See-Through Head-Mounted Displays

Gomez, A. M., Weiß, J., Keller, A., Eck, U., Roth, D., & Navab, N. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3079849 · Cited by 26

Adversarially Learning Disentangled Speech Representations for Robust Multi-Factor Voice Conversion

Wang, J., Li, J., Zhao, X., Wu, Z., Kang, S., & Meng, H. M. L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1990 · Cited by 31

On Multi-Objective Minimum Spanning Tree Problem under Uncertain Paradigm

Majumder, S., Barma, P. S., Biswas, A., Banerjee, P., Mandal, B. K., Kar, S., & Ziemba, P. (2022) · Symmetry · doi:10.3390/sym14010106 · Cited by 26

Inferring the number of floors for residential buildings

Roy, E., Pronk, M., Agugiaro, G., & Ledoux, H. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2160454 · Cited by 26

MORGAN: a modeling recommender system based on graph kernel

Sipio, C. D., Rocco, J. D., di Ruscio, D., & Nguyen, P. (2023) · Journal of Software and Systems Modeling · doi:10.1007/s10270-023-01102-8 · Cited by 30

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Alizamir, M., Kazemi, Z., Kazemi, Z., Kermani, M., Kim, S., Heddam, S., Kişi, Ö., & Chung, I.-M. (2023) · Water · doi:10.3390/w15132453 · Cited by 26

Ecological and human health risk from exposure to contaminated sediments in a tropical river impacted by gold mining in Colombia

Córdoba-Tovar, L., Marrugo‐Negrete, J., Barón, P. A. R., & Dı́ez, S. (2023) · Environmental Research · doi:10.1016/j.envres.2023.116759 · Cited by 26

Boxplots for grouped and clustered data in toxicology

Pallmann, P., & Hothorn, L. A. (2015) · Archives of Toxicology · doi:10.1007/s00204-015-1608-4 · Cited by 26

Spatial variability of emergence, splash, surge, and submergence in wave‐exposed rocky‐shore ecosystems

Mislan, K. A. S., Blanchette, C. A., Broitman, B. R., & Washburn, L. (2011) · Limnology and Oceanography · doi:10.4319/lo.2011.56.3.0857 · Cited by 26

Experimental evidence that predator range expansion modifies alpine stream community structure

Khamis, K., Brown, L. E., Hannah, D. M., & Milner, A. M. (2014) · Freshwater Science · doi:10.1086/679484 · Cited by 26

Predictors of the variability in neuromuscular block duration following succinylcholine

Dell‐Kuster, S., Levano, S., Burkhart, C. S., Lelais, F., Zemp, A., Schobinger, E., Hampl, K. F., Kindler, C. H., & Girard, T. (2015) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000000308 · Cited by 26

Spatiotemporal variability of annual and seasonal rainfall time series in Ho Chi Minh city, Vietnam

Phuong, D. N. D., Linh, V. T., Nhat, T. T., Dung, H. M., & Lợi, N. K. (2018) · Journal of Water and Climate Change · doi:10.2166/wcc.2018.115 · Cited by 26

Strengthening Forest Fire Management in India

Forest and Climate Change Ministry of Environment, & Bank, W. (2018) · doi:10.1596/30013 · Cited by 28

Pay to play? Economic constraints and participation in extracurricular activities

Hjalmarsson, S. (2022) · European Sociological Review · doi:10.1093/esr/jcac061 · Cited by 25

Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

Westerholt, R., Steiger, E., Resch, B., & Zipf, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162360 · Cited by 25

Extraction and analysis of microscopic traffic data in disordered heterogeneous traffic conditions

Amrutsamanvar, R., Muthurajan, B., & Vanajakshi, L. (2019) · Transportation Letters · doi:10.1080/19427867.2019.1695563 · Cited by 25

A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

He, C., Wang, R., & Chen, X. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00395 · Cited by 26

Spatially distributed simulations of the effect of snow on mass balance and flooding of Antarctic sea ice

Wever, N., Leonard, K., Maksym, T., White, S., Proksch, M., & Lenaerts, J. T. M. (2021) · Journal of Glaciology · doi:10.1017/jog.2021.54 · Cited by 25

Quantifying the Link Between the Detrital Zircon Record and Tectonic Settings

Jian, D., Williams, S. E., Yu, S., & Zhao, G. (2022) · Journal of Geophysical Research Solid Earth · doi:10.1029/2022jb024606 · Cited by 25

Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties

Zhou, S., Lei, Y., Zhang, Z., Luo, X., Aladejare, A. E., & Ozoji, T. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2313569 · Cited by 25

Classifying genotype by environment interactions for targeted germplasm deployment with a focus on Eucalyptus

Brawner, J. T., Lee, D. J., Meder, R., Almeida, A. C., & Dieters, M. J. (2013) · Euphytica · doi:10.1007/s10681-013-0892-4 · Cited by 25

Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

Zhang, J., Zhang, Z., & Tao, F. (2017) · International Journal of Disaster Risk Science · doi:10.1007/s13753-017-0115-z · Cited by 25

Which estimator to measure local governments’ cost efficiency? The case of Spanish municipalities

Narbón‐Perpiñá, I., Balaguer-Coll, M. T., Petrović, M., & Tortosa‐Ausina, E. (2019) · SERIEs · doi:10.1007/s13209-019-0194-8 · Cited by 25

Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting

Graf, R., Kolerski, T., & Zhu, S. (2022) · Resources · doi:10.3390/resources11020012 · Cited by 25

Reduced folate carrier 80A→G polymorphism, plasma folate, and risk of placental abruption

Ananth, C. V., Peltier, M. R., Moore, D. F., Kinzler, W. L., Leclerc, D., Rozen, R. R., & for the New Jersey-Placental Abruption Study Investigators (2008) · Human Genetics · doi:10.1007/s00439-008-0531-7 · Cited by 25

Optimal Baseflow Separation Through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters

Mei, Y., Wang, D., Zhu, J., Tang, G., Cai, C., Shen, X., Hong, Y., & Zhang, X. (2024) · Water Resources Research · doi:10.1029/2023wr036386 · Cited by 24

Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments

Lan, T., Li, T., Zhang, H., Wu, J., Chen, Y. D., & Xu, C. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-903-2025 · Cited by 24

A Division of Labor in the Recruitment and Topological Organization of a Bacterial Morphogenic Complex

Caccamo, P. D., Jacq, M., VanNieuwenhze, M. S., & Brun, Y. V. (2020) · Current Biology · doi:10.1016/j.cub.2020.07.063 · Cited by 24

Household storage, surplus and supra-household storage in prehistoric and protohistoric societies of the Western Mediterranean

Prats, G., Antolín, F., & Alonso, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238237 · Cited by 24

Assessment of Risk Factors and Outcomes of Severe Ehrlichiosis Infection

Kuriakose, K., Pettit, A. C., Schmitz, J. E., Moncayo, A. C., & Bloch, K. C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.25577 · Cited by 24

Feasibility of adaptive thermal comfort for energy savings in cooling and heating: A study on Europe and the Mediterranean basin

Bienvenido-Huertas, D., Pulido-Arcas, J. A., Rubio-Bellido, C., & Pérez-Fargallo, A. (2021) · Urban Climate · doi:10.1016/j.uclim.2021.100807 · Cited by 24

The role of protected intersections in improving bicycle safety and driver right-turning behavior

Deliali, K., Christofa, E., & Knodler, M. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106295 · Cited by 24

Factors affecting precipitation formation and precipitation susceptibility of marine stratocumulus with variable above- and below-cloud aerosol concentrations over the Southeast Atlantic

Gupta, S., McFarquhar, G. M., O'Brien, J. R., Poellot, M. R., Delene, D. J., Miller, R. M., & Griswold, J. D. S. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-2769-2022 · Cited by 24

The profitability of online loans: A competing risks analysis on default and prepayment

Li, Z., Li, A., Bellotti, A. G., & Yao, X. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.08.013 · Cited by 24

Morphological profiling by high-throughput single-cell biophysical fractometry

Zhang, Z., Lee, K. C. M., Siu, D. M. D., Lo, M. C. K., Lai, Q. T. K., Lam, E. Y. M., & Tsia, K. K. (2023) · Communications Biology · doi:10.1038/s42003-023-04839-6 · Cited by 24

A Potential Role for Plasma Uric Acid in the Endothelial Pathology of Plasmodium falciparum malaria

Mita-Mendoza, N. K., van de Hoef, D. L., Lopera-Mesa, T. M., Doumbia, S., Konate, D. S., Doumbouya, M., Gu, W., Anderson, J. M., Santos‐Argumedo, L., Rodrı́guez, A., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054481 · Cited by 24

Racial Differences in the Association Between Luminal Master Regulator Gene Expression Levels and Breast Cancer Survival

Byun, J. S., Singhal, S. K., Park, S., Yi, D. I., Yan, T., Caban, A., Jones, A. C., Mukhopadhyay, P., Gil, S. M., Hewitt, S. M., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0875 · Cited by 24

Empirical Relationship between Chemical Structure and Redox Properties: Mathematical Expressions Connecting Structural Features to Energies of Frontier Orbitals and Redox Potentials for Organic Molecules

Tagade, P., Adiga, S. P., Park, M., Pandian, S., Hariharan, K. S., & Kolake, S. M. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b03577 · Cited by 23

Effects of the Kampo medicine Yokukansan for perioperative anxiety and postoperative pain in women undergoing breast surgery: A randomized, controlled trial

Tanaka, M., Tanaka, T., Takamatsu, M., Shibue, C., Imao, Y., Ando, T., Baba, H., & Kamiya, Y. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260524 · Cited by 21

Meteotsunamis in Orography‐Free, Flat Bathymetry and Warming Climate Conditions

Denamiel, C., Tojčić, I., & Vilibić, I. (2022) · Journal of Geophysical Research Oceans · doi:10.1029/2021jc017386 · Cited by 23

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Xu, D., Bisht, G., Sargsyan, K., Liao, C., & Leung, L.-Y. R. (2022) · Geoscientific model development · doi:10.5194/gmd-15-5021-2022 · Cited by 24

Vivax malaria in Duffy-negative patients shows invariably low asexual parasitaemia: implication towards malaria control in Ethiopia

Abate, A., Bouyssou, I., Mabilotte, S., Doderer-Lang, C., Dembélé, L., Ménard, D., & Golassa, L. (2022) · Malaria Journal · doi:10.1186/s12936-022-04250-2 · Cited by 23

Social Entrepreneurship, Complex Thinking, and Entrepreneurial Self-Efficacy: Correlational Study in a Sample of Mexican Students

Parra, J. C. V., Alonso-Galicia, P. E., Cruz-Sandoval, M. A., Suárez-Brito, P., & Carlos-Arroyo, M. (2023) · Administrative Sciences · doi:10.3390/admsci13040104 · Cited by 26

Seismic reliability-based assessment and design optimization of shape memory alloy bars in concrete bridge piers

Zhou, L., & Alam, M. S. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119226 · Cited by 23

Proteomics approaches for the analysis of enriched microbial subpopulations and visualization of complex functional information

Bernhardt, J., Michalik, S., Wollscheid, B., Völker, U., & Schmidt, F. (2012) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.10.009 · Cited by 23

Establishment and characterization of a new human myxoid liposarcoma cell line (DL-221) with the FUS-DDIT3 translocation

de Graaff, M. A., Yu, J., Beird, H. C., Ingram, D. R., Nguyen, T. P. T., Liu, J. J., Bolshakov, S. V., Szuhai, K., Åman, P., Torres, K. E., et al. (2016) · Laboratory Investigation · doi:10.1038/labinvest.2016.64 · Cited by 22

Eye Gaze-based Early Intent Prediction Utilizing CNN-LSTM

Koochaki, F., & Najafizadeh, L. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857054 · Cited by 24

v‐plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions

Blumenschein, M., Debbeler, L. J., Lages, N. C., Renner, B., Keim, D. A., & El‐Assady, M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14002 · Cited by 23

Genomic and transcriptional analysis of genes containing fibrinogen and IgSF domains in the schistosome vector Biomphalaria glabrata, with emphasis on the differential responses of snails susceptible or resistant to Schistosoma mansoni

Lu, L., Loker, E. S., Adema, C. M., Zhang, S.-M., & Bu, L. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008780 · Cited by 23

Larch Cellulose Shows Significantly Depleted Hydrogen Isotope Values With Respect to Evergreen Conifers in Contrast to Oxygen and Carbon Isotopes

Arosio, T., Ziehmer, M. M., Nicolussi, K., Schlüchter, C., & Leuenberger, M. (2020) · Frontiers in Earth Science · doi:10.3389/feart.2020.523073 · Cited by 24

High-throughput analysis of lung immune cells in a combined murine model of agriculture dust-triggered airway inflammation with rheumatoid arthritis

Gaurav, R., Mikuls, T. R., Thiele, G. M., Nelson, A. J., Niu, M., Guda, C., Eudy, J. D., Barry, A., Wyatt, T. A., Romberger, D. J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0240707 · Cited by 24

Winter Euro‐Atlantic Climate Modes: Future Scenarios From a CMIP6 Multi‐Model Ensemble

Cusinato, E., Rubino, A., & Zanchettin, D. (2021) · Geophysical Research Letters · doi:10.1029/2021gl094532 · Cited by 23

Self-Segregation or Deliberation? Blog Readership, Participation and Polarization in American PoliticsPreprint

Farrell, H. J., Lawrence, E., & Sides, J. M. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1151490 · Cited by 23

Explainable machine learning for enhancing predictive accuracy of cutting forces in hard turning processes

Dorbane, A., Harrou, F., Sun, Y., Makhfi, S., & Habak, M. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14470-2 · Cited by 22

A Secure Bank Loan Prediction System by Bridging Differential Privacy and Explainable Machine Learning

Hossain, M. M., Mamun, M., Munir, A., Rahman, M. M., & Chowdhury, S. H. (2025) · Electronics · doi:10.3390/electronics14081691 · Cited by 22

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

Idkowiak, J., Dehairs, J., Schwarzerová, J., Olešová, D., Truong, J. X. M., Kvasnička, A., Eftychiou, M., Cools, R., Spotbeen, X., Jirásko, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63751-1 · Cited by 32

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Maharjan, G. R., Hoffmann, H., Webber, H. A., Srivastava, A. K., Weihermüller, L., Villa, A., Coucheney, E., Lewan, E., Trombi, G., Moriondo, M., et al. (2018) · European Journal of Agronomy · doi:10.1016/j.eja.2018.11.001 · Cited by 22

Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

Ghimire, G. R., Jadidoleslam, N., Krajewski, W. F., & Tsonis, A. A. (2020) · Frontiers in Water · doi:10.3389/frwa.2020.00017 · Cited by 22

3D source tracking and error detection in HDR using two independent scintillator dosimetry systems

Rosales, H. M. L., Johansen, J. G., Kertzscher, G., Tanderup, K., Beaulieu, L., & Beddar, S. (2020) · Medical Physics · doi:10.1002/mp.14607 · Cited by 22

Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation

Barik, R., Minutoli, M., Halappanavar, M. M., Tallent, N. R., & Kalyanaraman, A. (2020) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc50251.2020.00031 · Cited by 22

Show or suppress? Managing input uncertainty in machine learning model explanations

Wang, D., Zhang, W., & Lim, B. Y. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103456 · Cited by 24

The q–q Boxplot

Rodu, J. S., & Kafadar, K. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.1938586 · Cited by 22

Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches

Li, T., Lan, T., Zhang, H., Sun, J., Xu, C., & Chen, Y. D. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131535 · Cited by 22

Short-term cooling, drying, and deceleration of an ice-rich rock glacier

Bast, A., Kenner, R., & Phillips, M. (2024) · The Cryosphere · doi:10.5194/tc-18-3141-2024 · Cited by 22

Enhancing Statistical Education in Chemistry and STEAM Using JAMOVI. Part 1: Descriptive Statistics and Comparing Independent Groups

de Souza, R. S., Sequeira, C. A., & Borges, E. M. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00563 · Cited by 22

Robust versions of the Tukey boxplot with their application to detection of outliers

Shevlyakov, G. L., Клитон, А., Lakshminarayan, C. K., Smirnov, P. O., Ulanov, A., & Vassilieva, N. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638919 · Cited by 22

Direct Power Output Forecasts From Remote Sensing Image Processing

Larson, D. P., & Coimbra, C. F. M. (2018) · Journal of Solar Energy Engineering · doi:10.1115/1.4038983 · Cited by 22

Data-Driven Identification of the Reaction Network in Oxidative Coupling of the Methane Reaction via Experimental Data

Miyazato, I., Nishimura, S., Takahashi, L., Ohyama, J., & TAKAHASHI, K. G. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b03678 · Cited by 22

STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN

Chen, J., Xiao, Z., Xing, H., Dai, P., Luo, S., & Iqbal, M. A. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148789 · Cited by 21

Heterogeneity of the Endocannabinoid System Between Cerebral Cortex and Spinal Cord Oligodendrocytes

Moreno‐Luna, R., Esteban, P. F., Paniagua‐Torija, B., Arévalo‐Martín, Á., García‐Ovejero, D., & Molina‐Holgado, E. (2020) · Molecular Neurobiology · doi:10.1007/s12035-020-02148-1 · Cited by 22

Perceived Usefulness of a Social Robot Augmented Telehealth Platform by Therapists in the United States

Sobrepera, M. J., Lee, V., Garg, S., Mendonca, R. J., & Johnson, M. J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062349 · Cited by 22

The use of an active learning approach to teach metabolism to students of nutrition and dietetics

González‐Sancho, J. M., Sánchez‐Pacheco, A., Lasa, M., Molina, S., Vara, F., & del Peso, L. (2013) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.20684 · Cited by 22

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

Alizamir, M., Wang, M., Ikram, R. M. A., Kim, S., Ahmed, K. O., & Heddam, S. (2024) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2024.2422060 · Cited by 22

A robust and explainable deep learning model based on an LSTM-CNN framework for reliable FDOM prediction in water quality monitoring: Incorporating SHAP analysis for enhanced interpretability

Alizamir, M., Heddam, S., & Kim, S. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.107594 · Cited by 20

Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

Zampini, M. A., Buizza, G., Paganelli, C., Fontana, G., D’Ippolito, E., Valvo, F., Preda, L., & Baroni, G. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.12.003 · Cited by 21

Object-Based Six-Degrees-of-Freedom Rendering of Sound Scenes Captured with Multiple Ambisonic Receivers

McCormack, L., Politis, A., McKenzie, T. L., Hold, C., & Pulkki, V. (2022) · Journal of the Audio Engineering Society · doi:10.17743/jaes.2022.0010 · Cited by 21

What Makes the River Chief System in China Viable? Examples from the Huaihe River Basin

Zhang, Z., Xiong, C., Yang, Y., Liang, C., & Jiang, S. (2022) · Sustainability · doi:10.3390/su14106329 · Cited by 21

Individualized diagnosis of preclinical Alzheimer’s Disease using deep neural networks

Park, J., Jang, S., Gwak, J., Kim, B. C., Lee, J. J., Choi, K. Y., Lee, K. H., Jun, S. C., Jang, G., & Ahn, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118511 · Cited by 22

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

Buss, E., Aust, T., Wahby, M., Rabbel, T.-L., Kernbach, S., & Hamann, H. (2023) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/acbad2 · Cited by 20

Opportunities and challenges for self-experimentation in self-tracking

Karkar, R., Fogarty, J. A., Kientz, J. A., Munson, S. A., Vilardaga, R., & Zia, J. K. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2800949 · Cited by 21

Quantifying the morphology of flow patterns in landslide-affected and unaffected soils

Bogner, C., Bauer, F., y Widemann, B. T., Viñan, P., Balcázar, L., & Huwe, B. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2014.01.063 · Cited by 21

Current and Evolving Methods to Visualize Biological Data in Cancer Research

Chia, P. L., Gedye, C. A., Boutros, P. C., Wheatley‐Price, P., & John, T. (2016) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djw031 · Cited by 23

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

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