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Energy consumption prediction in water treatment plants using deep learning with data augmentation

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Understanding the use of lambda expressions in Java

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Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding

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An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon

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State of the Art of Visual Analytics for eXplainable Deep Learning

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Artificial neural network hyperparameters optimization for predicting the thermal conductivity of MXene/graphene nanofluids

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Discovering community patterns in open-source: a systematic approach and its evaluation

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Projected Changes in Increased Drought Risks Over South Asia Under a Warmer Climate

Ullah, I., Ma, X., Asfaw, T. G., Yin, J., Iyakaremye, V., Saleem, F., Xing, Y., Azam, K., & Syed, S. (2022) · Earth s Future · doi:10.1029/2022ef002830 · Cited by 75

Ultra Deep Sequencing of a Baculovirus Population Reveals Widespread Genomic Variations

Chateigner, A., Bézier, A., Labrousse, C., Jiolle, D., Barbe, V., & Herniou, E. A. (2015) · Viruses · doi:10.3390/v7072788 · Cited by 72

Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks

Cheng, F., Cai, W., Zhang, X., Liao, H., & Cui, C. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110795 · Cited by 75

Lowering greenhouse gas emissions in the built environment by combining ground source heat pumps, photovoltaics and battery storage

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Influence of demand patterns on the optimal orientation of photovoltaic systems

Litjens, G. B. M. A., Worrell, E., & van Sark, W. G. J. H. M. (2017) · Solar Energy · doi:10.1016/j.solener.2017.07.006 · Cited by 73

Spatiotemporal characteristics of meteorological drought variability and trends (1981–2020) over South Asia and the associated large-scale circulation patterns

Ullah, I., Ma, X., Yin, J., Omer, A., Habtemicheal, B. A., Saleem, F., Iyakaremye, V., Syed, S., Arshad, M., & Liu, M. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06443-6 · Cited by 73

A family of null models to distinguish between environmental filtering and biotic interactions in functional diversity patterns

Chalmandrier, L., Münkemüller, T., Gallien, L., de Bello, F., Mazel, F., Lavergne, S., & Thuiller, W. (2013) · Journal of Vegetation Science · doi:10.1111/jvs.12031 · Cited by 73

Hi-Fi Multi-Speaker English TTS Dataset

Bakhturina, E., Lavrukhin, V., Ginsburg, B., & Zhang, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1599 · Cited by 82

The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient

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Comparing measures of democracy: statistical properties, convergence, and interchangeability

Vaccaro, A. (2021) · European Political Science · doi:10.1057/s41304-021-00328-8 · Cited by 71

Recent advances and challenges in uncertainty visualization: a survey

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Incorporating chitin nanocrystal yields stronger soy protein gel: Insights into linear and nonlinear rheological behaviors by oscillatory shear tests

Zhang, Q., Jiang, L., & Sui, X. (2022) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2022.108177 · Cited by 71

Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure–Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients

Banerjee, A., & Roy, K. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00155 · Cited by 71

A multidimensional empirical study on refactoring activity

Tsantalis, N., Guana, V., Stroulia, E., & Hindle, A. (2013) · Conference of the Centre for Advanced Studies on Collaborative Research · Cited by 71

Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions

Matzke, D. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00918 · Cited by 71

An Individual's Rate of Forgetting Is Stable Over Time but Differs Across Materials

Sense, F., Behrens, F., Meijer, R. R., & van Rijn, H. (2016) · Topics in Cognitive Science · doi:10.1111/tops.12183 · Cited by 80

Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China

Tan, X., & Shao, D. (2016) · International Journal of Climatology · doi:10.1002/joc.4794 · Cited by 71

Pair Copula Constructions for Insurance Experience Rating

Shi, P., & Yang, L. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1330692 · Cited by 68

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

Seyler, S. L., Kumar, A., Thorpe, M. F., & Beckstein, O. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004568 · Cited by 72

Machine learning for recognizing minerals from multispectral data

Jahoda, P., Drozdovskiy, I., Payler, S. J., Turchi, L., Bessone, L., & Sauro, F. (2020) · The Analyst · doi:10.1039/d0an01483d · Cited by 73

Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques

Indraratna, B., Armaghani, D. J., Correia, A. G., Hunt, H., & Ngo, T. (2022) · Transportation Geotechnics · doi:10.1016/j.trgeo.2022.100895 · Cited by 70

Ecogeography of ploidy variation in cultivated potato (Solanum sect. Petota)

Spooner, D. M., Гавриленко, Т. А., Jansky, S. H., Ovchinnikova, A. B., Krylova, E. A., Knapp, S., & Simon, R. (2010) · American Journal of Botany · doi:10.3732/ajb.1000277 · Cited by 70

Object Manipulation in Virtual Reality Under Increasing Levels of Translational Gain

Wilson, G., McGill, M., Jamieson, M., Williamson, J., & Brewster, S. A. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173673 · Cited by 70

Anthropogenic and atmospheric variability intensifies flash drought episodes in South Asia

Ullah, I., Mukherjee, S., Syed, S., Mishra, A. K., Ayugi, B. O., & Aadhar, S. (2024) · Communications Earth & Environment · doi:10.1038/s43247-024-01390-y · Cited by 72

Multi-Charts for Comparative 3D Ensemble Visualization

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NeSSM: A Next-Generation Sequencing Simulator for Metagenomics

Jia, B., Xuan, L., Cai, K., Hu, Z., Ma, L., & Wei, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075448 · Cited by 66

Identifying periods of susceptibility to the impact of phthalates on children's cognitive abilities

Li, N., Papandonatos, G. D., Calafat, A. M., Yolton, K. A., Lanphear, B. P., Chen, A., & Braun, J. M. (2019) · Environmental Research · doi:10.1016/j.envres.2019.03.009 · Cited by 72

Analyzing the fine structure of distributions

Thrun, M. C., Gehlert, T., & Ultsch, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238835 · Cited by 68

An exploratory study on exception handling bugs in Java programs

Ebert, F., Castor, F., & Serebrenik, A. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.04.066 · Cited by 67

Reassess thetTest: Interact with All Your Data via ANOVA

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Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data

Thrun, M. C., & Ultsch, A. (2020) · Journal of Classification · doi:10.1007/s00357-020-09373-2 · Cited by 65

Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models

Alsehli, M., Basem, A., Jasim, D. J., Mausam, K., Alshamrani, A., Sultan, A. J., Kassim, M. B., Rajab, H., Musa, V. A., & Maleki, H. (2024) · Fuel · doi:10.1016/j.fuel.2024.132431 · Cited by 67

Bigger, Faster?

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Facial expressions in response to a highly surprising event exceeding the field of vision: a test of Darwin's theory of surprise

Schützwohl, A., & Reisenzein, R. (2012) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2012.04.003 · Cited by 65

Cross-National Response Styles in International Educational Assessments: Evidence from PISA 2006

Buckley, J. (2009) · Cited by 66

Looks Good To Me: Visualizations As Sanity Checks

Correll, M., Li, M., Kindlmann, G., & Scheidegger, C. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864907 · Cited by 72

Programming with ChatGPT: How far can we go?

Bucaioni, A., Ekedahl, H., Helander, V., & Nguyen, P. T. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100526 · Cited by 64

Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution

Li, C., Olave, M., Hou, Y., Qin, G., Schneider, R., Gao, Z., Tu, X., Wang, X., Qi, F., Nater, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21379-x · Cited by 68

Is eastern Mongolia drying? A long‐term perspective of a multidecadal trend

Davi, N., Pederson, N., Leland, C., Nachin, B., Suran, B., & Jacoby, G. C. (2012) · Water Resources Research · doi:10.1029/2012wr011834 · Cited by 63

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

Mukherjee, S., & Nateghi, R. (2018) · Risk Analysis · doi:10.1111/risa.13192 · Cited by 61

Plasma Uric Acid Levels Correlate with Inflammation and Disease Severity in Malian Children with Plasmodium falciparum Malaria

Lopera-Mesa, T. M., Mita-Mendoza, N. K., van de Hoef, D. L., Doumbia, S., Konate, D. S., Doumbouya, M., Gu, W., Traoré, K., Diakité, S. A. S., Remaley, A. T., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046424 · Cited by 62

Multi-fidelity neural network-based aerodynamic optimization framework for propeller design in electric aircraft

Wu, X., Zuo, Z., Ma, L., & Zhang, W. (2024) · Aerospace Science and Technology · doi:10.1016/j.ast.2024.108963 · Cited by 61

Modelling the distribution of chickens, ducks, and geese in China

Prosser, D. J., Wu, J., Ellis, E. C., Gale, F., Boeckel, T. P. V., Wint, W., Robinson, T., Xiao, X., & Gilbert, M. (2011) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2011.04.002 · Cited by 61

Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

Matzke, D., Ly, A., Selker, R., Weeda, W. D., Scheibehenne, B., Lee, M. D., & Wagenmakers, E. (2017) · Collabra Psychology · doi:10.1525/collabra.78 · Cited by 59

More on the Best Evolutionary Rate for Phylogenetic Analysis

Klopfstein, S., Massingham, T., & Goldman, N. (2017) · Systematic Biology · doi:10.1093/sysbio/syx051 · Cited by 58

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

Karikari, B., Wang, Z., Zhou, Y., Yan, W., Feng, J., & Zhao, T. (2020) · BMC Plant Biology · doi:10.1186/s12870-020-02604-z · Cited by 60

The shifting boxplot. A boxplot based on essential summary statistics around the mean.

Marmolejo‐Ramos, F., & Tian, T. S. (2010) · International journal of psychological research · doi:10.21500/20112084.823 · Cited by 60

Fuzzy c -ordered medoids clustering for interval-valued data

D’Urso, P., & Łęski, J. M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.04.005 · Cited by 60

Better than Rician: modelling millimetre wave channels as two-wave with diffuse power

Zöchmann, E., Caban, S., Mecklenbräuker, C. F., Pratschner, S., Lerch, M., Schwarz, Š., & Rupp, M. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1336-6 · Cited by 60

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

Brede, B., Verrelst, J., Gastellu‐Etchegorry, J., Clevers, J. G. P. W., Goudzwaard, L., den Ouden, J., Verbesselt, J., & Herold, M. (2020) · Remote Sensing · doi:10.3390/rs12060915 · Cited by 59

Polar auxin transport is implicated in vessel differentiation and spatial patterning during secondary growth in Populus

Johnson, D., Eckart, P., Alsamadisi, N., Noble, H., Martin, C., & Spicer, R. (2018) · American Journal of Botany · doi:10.1002/ajb2.1035 · Cited by 58

Developing a novel proximity analysis approach for assessment of waste management cost efficiency in low population density regions

Richter, A., Ng, K. T. W., Karimi, N., & Chang, W. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102583 · Cited by 59

Waste disposal characteristics and data variability in a mid-sized Canadian city during COVID-19

Richter, A., Ng, K. T. W., Vu, H. L., & Kabir, G. (2021) · Waste Management · doi:10.1016/j.wasman.2021.01.004 · Cited by 59

A Low-Cost NLOS Identification and Mitigation Method for UWB Ranging in Static and Dynamic Environments

Dong, M. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3070311 · Cited by 59

Consumer Preference Segments for Plant-Based Foods: The Role of Product Category

Cardello, A. V., Llobell, F., Giacalone, D., Chheang, S. L., & Jaeger, S. R. (2022) · Foods · doi:10.3390/foods11193059 · Cited by 59

Identifying and Quantifying Heterogeneity in High Content Analysis: Application of Heterogeneity Indices to Drug Discovery

Gough, A., Chen, N., Shun, T. Y., Lezon, T. R., Boltz, R. C., Reese, C. E., Wagner, J., Vernetti, L. A., Grandis, J. R., Lee, A. V., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102678 · Cited by 61

Violin plots as visual tools in the meta-analysis of Single-Case Experimental Designs

Tanious, R., & Manolov, R. (2022) · Methodology · doi:10.5964/meth.9209 · Cited by 58

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells

Li, H., Zhong, Z., Li, L., Gao, R., Cui, J., Gao, T., Li, H., Lu, Y., Su, Z., & Li, H. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23886 · Cited by 58

SinaPlot: An Enhanced Chart for Simple and Truthful Representation of Single Observations Over Multiple Classes

Sidiropoulos, N., Sohi, S. H., Pedersen, T. L., Porse, B. T., Winther, O., Rapin, N., & Bagger, F. O. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1366914 · Cited by 58

Metaproteomics to unravel major microbial players in leaf litter and soil environments: C hallenges and perspectives

Becher, D., Bernhardt, J., Fuchs, S., & Riedel, K. (2013) · PROTEOMICS · doi:10.1002/pmic.201300095 · Cited by 60

The Economic Consequences of Divorce in Germany: What Has Changed since the Turn of the Millennium?

Bröckel, M., & Andreß, H. (2015) · Comparative Population Studies · doi:10.12765/cpos-2015-04 · Cited by 58

Importance of radiative transfer processes in urban climate models: a study based on the PALM 6.0 model system

Salim, M., Schubert, S., Resler, J., Krč, P., Maronga, B., Kanani-Sühring, F., Sühring, M., & Schneider, C. (2022) · Geoscientific model development · doi:10.5194/gmd-15-145-2022 · Cited by 57

Ovis: A Framework for Visual Analysisof Ocean Forecast Ensembles

Höllt, T., Magdy, A., Zhan, P., Chen, G., Gopalakrishnan, G. L., Hoteit, I., Hansen, C. D., & Hadwiger, M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2307892 · Cited by 57

Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources

Koo, H., Iwanaga, T., Croke, B., Jakeman, A. J., Yang, J., Wang, H., Sun, X., Lü, G., Li, X., Yu, G., et al. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104857 · Cited by 56

A quantitative comparison of regionalization methods

Aydin, O., Janikas, M. V., Assunção, R., & Lee, T. (2021) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2021.1905819 · Cited by 56

Good scientific practice in EEG and MEG research: Progress and perspectives

Niso, G., Krol, L. R., Combrisson, E., Dubarry, A., Elliott, M., Clément, F., Héjja‐Brichard, Y., Herbst, S. K., Jerbi, K., Ković, V., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119056 · Cited by 68

Effects of the Pacific‐Japan teleconnection pattern on tropical cyclone activity and extreme precipitation events over the Korean peninsula

Kim, J., Li, R. C., & Zhou, W. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jd017677 · Cited by 58

Endogenous Nutritive Support after Traumatic Brain Injury: Peripheral Lactate Production for Glucose Supply via Gluconeogenesis

Glenn, T. C., Martin, N. A., McArthur, D. L., Hovda, D. A., Vespa, P. M., Johnson, M. L., Horning, M. A., & Brooks, G. A. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2014.3482 · Cited by 56

Sex differences in ability tilt in the right tail of cognitive abilities: A 35-year examination

Wai, J., Hodges, J., & Makel, M. C. (2018) · Intelligence · doi:10.1016/j.intell.2018.02.003 · Cited by 56

Smed454 dataset: unravelling the transcriptome of Schmidtea mediterranea

Abril, J. F., Cebrià, F., Rodríguez-Esteban, G., Horn, T. L., Fraguas, S., Calvo, B., Bartscherer, K., & Saló, E. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-731 · Cited by 55

Adipose cells and tissues soften with lipid accumulation while in diabetes adipose tissue stiffens

Abuhattum, S., Kotzbeck, P., Schlüßler, R., Harger, A., de Schellenberger, A. A., Kim, K., Escolano, J.-C., Müller, T., Braun, J., Wabitsch, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-13324-9 · Cited by 57

Battle of centralized and decentralized urban stormwater networks: From redundancy perspective

Hesarkazzazi, S., Bakhshipour, A. E., Hajibabaei, M., Dittmer, U., Haghighi, A., & Sitzenfrei, R. (2022) · Water Research · doi:10.1016/j.watres.2022.118910 · Cited by 55

Geometric morphometrics for the taxonomy of 11 species of A nopheles ( N yssorhynchus ) mosquitoes

Jaramillo, N., Dujardin, J., Londoño, D. A. C., & Fonseca, I. (2014) · Medical and Veterinary Entomology · doi:10.1111/mve.12091 · Cited by 55

Multilevel modeling of insurance claims using copulas

Shi, P., Feng, X., & Boucher, J.-P. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas914 · Cited by 58

Polarized but illusory beliefs about tap and bottled water: A product- and consumer-oriented survey and blind tasting experiment

Debbeler, L. J., Gamp, M., Blumenschein, M., Keim, D. A., & Renner, B. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.06.190 · Cited by 55

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Hai, T., Salih, S. Q., Saggi, M. K., Dodangeh, E., Voyant, C., Al-Ansari, N. A., Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M., & Shahid, S. (2020) · IEEE Access · doi:10.1109/access.2020.2990439 · Cited by 55

Pseudo-global warming projections of extreme wave storms in complex coastal regions: the case of the Adriatic Sea

Denamiel, C., Pranić, P., Quentin, F., Mihanović, H., & Vilibić, I. (2020) · Climate Dynamics · doi:10.1007/s00382-020-05397-x · Cited by 56

Natural Variation in Decision-Making Behavior in Drosophila melanogaster

Miller, P. M., Saltz, J. B., Cochrane, V. A., Marcinkowski, C. M., Mobin, R., & Turner, T. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016436 · Cited by 54

Examining The Role of Smart TVs and VR HMDs in Synchronous At-a-Distance Media Consumption

Mcgill, M., Williamson, J., & Brewster, S. (2016) · ACM Trans. Comput. Hum. Interact. · doi:10.1145/2983530 · Cited by 54

Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study

Kumar, D., Roshni, T., Singh, A., Jha, M. K., & Samui, P. (2020) · Earth Science Informatics · doi:10.1007/s12145-020-00508-y · Cited by 54

RepliCueAuth: Validating the Use of a Lab-Based Virtual Reality Setup for Evaluating Authentication Systems

Mathis, F., Vaniea, K. E., & Khamis, M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445478 · Cited by 53

Robustness to Divergence Time Underestimation When Inferring Species Trees from Estimated Gene Trees

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