Dynamic Exponential Random Graph Model

Dynamic Exponential Random Graph Model (Temporal ERGM)

Hanneke, Fu & Xing; Krivitsky & Handcock

The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change.

Probabilistic graphical model (temporal)

Longitudinal binary adjacency matrices (network panel data)

Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.

Krivitsky, P. N., & Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B, 76(1), 29–46.

exponential-random-graph-model temporal-exponential-random-graph-model stochastic-block-model dynamic-stochastic-block-model temporal-network-analysis network-diffusion-analysis


Dynamic Factor Model

Diffusion Index Model
Large-Scale Factor Model
Approximate Factor Model
Dinamik Faktör Modeli

A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models.

Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162.


Dynamic Functional Connectivity

dFC
time-varying connectivity
sliding window connectivity

Dynamic Functional Connectivity (dFC) is an analytical framework that tracks changes in functional connectivity between brain regions over time, rather than averaging connectivity across an entire scanning session. Systematized by Hutchison and colleagues in 2013, dFC reveals how brain networks reorganize moment-to-moment, providing insights into transient brain states and cognitive flexibility.

Resting-state fMRI connectivity pipeline

Hutchison, R. M., Womelsdorf, T., Allen, E. A., et al. (2013). Dynamic functional connectivity: promise, problems, and perspectives. NeuroImage, 80, 360–378.

Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.


Dynamic Fuzzy Regression Discontinuity

Dynamic Fuzzy RDD
DFRD
Time-varying Fuzzy RD
Dynamic Fuzzy RD Design

Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)

Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatment effects across multiple post-treatment periods, not just at a single cross-sectional snapshot.

Panel or repeated cross-sections with a continuous running variable and time dimension

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.

Cellini, S. R., Ferreira, F., & Rothstein, J. (2010). The Value of School Facility Investments: Evidence from a Dynamic Regression Discontinuity Design. Quarterly Journal of Economics, 125(1), 215-261.


Dynamic Hamiltonian Monte Carlo

Dynamic Hamiltonian Monte Carlo (No-U-Turn Sampler)

Dynamic HMC
NUTS
No-U-Turn Sampler
adaptive HMC

Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing the need to hand-tune the most sensitive tuning parameter of standard HMC. It is the default sampler in Stan and PyMC and is suitable for continuous, differentiable posterior distributions of moderate to high dimension.

continuous parameters; any model with differentiable log-posterior

Hoffman, M. D. & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623.

Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press.

hamiltonian-monte-carlo metropolis-hastings gibbs-sampling variational-inference sequential-monte-carlo bayesian-regression


Dynamic Instrumental Variables

Dynamic Panel Instrumental Variables Estimation

Dynamic IV
Dynamic Panel IV
Arellano-Bond GMM
System GMM

Arellano & Bond (1991); extended by Blundell & Bond (1998)

Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differenced lagged outcome, it produces consistent causal estimates even when standard OLS or fixed-effects are biased by dynamic feedback.

Panel data with lagged outcomes and endogenous regressors

Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297.

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115-143.

instrumental-variables panel-data-instrumental-variables difference-in-differences panel-fixed-effects generalized-method-of-moments two-stage-least-squares


Dynamic Interrupted Time Series

Dynamic Interrupted Time Series Analysis

Dynamic ITS
ITS with lagged effects
time-varying ITS
flexible ITS

Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini

Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.

Regularly spaced time-series observations (aggregate or unit-level)

Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.

Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.

interrupted-time-series difference-in-differences dynamic-difference-in-differences event-study-design panel-event-study segmented-regression


Dynamic Inverse Probability Weighting

Dynamic Inverse Probability Weighting for Time-Varying Treatments

Dynamic IPW
Time-varying IPW
Longitudinal IPW
Sequential IPW

Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.

Longitudinal / panel data with time-varying treatments and covariates

Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.

Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.

marginal-structural-model inverse-probability-weighting dynamic-marginal-structural-model propensity-score-weighting doubly-robust-estimation dynamic-doubly-robust-estimation


Dynamic Light Scattering

DLS
photon correlation spectroscopy
particle size measurement

Dynamic Light Scattering (DLS), also known as Photon Correlation Spectroscopy (PCS), is an analytical technique for determining the size and size distribution of particles suspended in fluids by analyzing the time-dependent intensity fluctuations of scattered laser light. Developed by Robert Pecora in 1964, DLS exploits the Brownian motion of particles: smaller particles move faster, causing faster intensity fluctuations; larger particles move slower, causing slower fluctuations. By correlating intensity over time, particle size is deduced. DLS is rapid, non-destructive, and requires minimal sample volume, making it the standard technique for characterizing nanoparticles, proteins, colloids, and emulsions.

Pecora, R. (1964). Spectral distribution of scattered light from a suspension of particles. Physica, 30(11), 2055-2070.

Berne, B. J., & Pecora, R. (1976). Dynamic Light Scattering: With Applications to Chemistry, Biology, and Physics. John Wiley & Sons.

Bushell, G. C., Yan, Y. D., Woodfield, D., Raper, J., & Amal, R. (2002). On techniques for the measurement of the mass fractal dimension of aggregates. Advances in Colloid and Interface Science, 95(1), 1-50.


Dynamic Matching Estimator

Dynamic Matching Estimator for Sequential Treatment Effects

dynamic treatment matching
sequential matching estimator
dynamic selection-on-observables
DME

Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)

The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.

Nonparametric causal inference / matching

Panel or longitudinal data with repeated treatment decisions

Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.

Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.

matching-estimator propensity-score-matching dynamic-difference-in-differences marginal-structural-model inverse-probability-weighting panel-data-matching-estimator


Dynamic Mechanical Analysis

Dynamic Mechanical Analysis Viscoelastic Property Characterization

DMA
rheological analysis
viscoelastic testing

Dynamic mechanical analysis (DMA) measures the viscoelastic properties of materials—their elastic stiffness and viscous damping—by applying a sinusoidal stress or strain and measuring the phase lag and amplitude of the material's response. Developed from rheology principles in the 1960s and formalized by Ferry, Schwarzl, and others, DMA provides quantitative measures of how polymeric biomaterials respond to time-dependent and frequency-dependent mechanical stimuli. Key outputs include the storage modulus (elastic component), loss modulus (viscous component), and loss tangent (tan δ), which together characterize the material's mechanical behavior across temperature and frequency ranges.

Menard, K. P. (2008). Dynamic mechanical analysis: a practical introduction (2nd ed.). CRC Press.

Ferry, J. D. (1980). Viscoelastic properties of polymers (3rd ed.). John Wiley & Sons.

Park, S. J., Jin, F. L., & Lee, J. R. (2004). Thermal stability and dynamic mechanical properties of epoxy/BaSO4 nanocomposites. Polymer, 45(25), 8475-8483.

mooney-rivlin-tensile swelling-and-degradation gpc-sec electrospinning


Dynamic Metropolis-Hastings Algorithm

Dynamic Metropolis-Hastings Algorithm for Time-Varying Models

Dynamic MH
MH for state-space models
Metropolis-Hastings in dynamic models
time-varying parameter MH

1970 (algorithm); 1992 (dynamic application)

W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer

The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-and-accept moves, yielding full posterior distributions over trajectories rather than single filtered estimates.

Bayesian MCMC sampler for dynamic models

time series, longitudinal, state-space data

Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.

Carlin, B. P., Polson, N. G., & Stoffer, D. S. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association, 87(418), 493–500.

metropolis-hastings-algorithm gibbs-sampling dynamic-bayesian-inference kalman-filter particle-filter dynamic-gibbs-sampling


Dynamic Modularity Analysis

Dynamic Modularity Analysis (Temporal Community Structure Detection)

dynamic community structure analysis
temporal modularity optimization
evolving community detection
time-varying modularity

Mucha, P. J.; Porter, M. A.; and colleagues

Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.

Community detection on temporal networks

Time-stamped or snapshot-based network data (edge lists, adjacency matrices per time step)

Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.

modularity-analysis temporal-network-analysis community-detection temporal-community-detection dynamic-social-network-analysis multiplex-network-analysis


Dynamic Monte Carlo Simulation

DMC simulation
kinetic Monte Carlo
time-driven Monte Carlo
event-driven Monte Carlo

Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)

Dynamic Monte Carlo (DMC) simulation is a computational method that tracks the stochastic time evolution of a system by drawing random event sequences weighted by transition rates. Unlike static Monte Carlo sampling of equilibrium distributions, DMC explicitly advances a clock, making it suitable for kinetic, reaction, and time-dependent phenomena where the sequence and timing of events matter.

event-driven or time-stepped sequential data

Bortz, A. B., Kalos, M. H., & Lebowitz, J. L. (1975). A new algorithm for Monte Carlo simulation of Ising spin systems. Journal of Computational Physics, 17(1), 10–18.

Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25), 2340–2361.

sequential-monte-carlo markov-chain-monte-carlo particle-filter dynamic-bayesian-inference gibbs-sampling bootstrap-simulation


Dynamic PageRank

Dynamic PageRank (Temporal Extension of the PageRank Algorithm)

Temporal PageRank
time-aware PageRank
evolving PageRank
DPR

Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank

Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history, making it well-suited for web archives, citation streams, social media cascades, and any domain where link recency matters.

Temporal directed graphs (edge-timestamped networks)

Rozenshtein, P., & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Lecture Notes in Computer Science, 9853, 674–689. Springer.

Berberich, K., Vazirgiannis, M., & Weikum, G. (2007). Time-aware authority ranking. Internet Mathematics, 3(4), 407–429.

temporal-network-analysis eigenvector-centrality degree-centrality betweenness-centrality temporal-community-detection dynamic-community-detection


Dynamic Panel Data Model

panel data model with lagged dependent variable

Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)

The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy.

Balanced or unbalanced panel data (cross-sectional units observed over time)

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.

Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.

arellano-bond-gmm-estimator panel-data-analysis fixed-effects-model random-effects-model difference-gmm panel-fixed-effects-model


Dynamic Panel Event Study

dynamic event study
panel event-study regression
leads-and-lags event study
event-time panel design

Sun & Abraham (2021); Callaway & Sant'Anna (2021)

The dynamic panel event study is a quasi-experimental method that uses panel data to trace out how a treatment effect evolves over time — before and after a defining event — by estimating a flexible regression of leads and lags around the treatment date. It simultaneously tests for pre-existing parallel trends and maps the full dynamic profile of causal impact across multiple post-event periods.

Balanced or unbalanced panel (repeated observations of units over multiple time periods)

Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.

difference-in-differences event-study-design panel-event-study dynamic-difference-in-differences staggered-difference-in-differences panel-fixed-effects


Dynamic Particle Filter

Dynamic Particle Filter for Sequential State Estimation

dynamic sequential Monte Carlo
dynamic SMC
bootstrap particle filter
dynamic SIR filter

Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)

A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting.

Time-ordered observations from nonlinear / non-Gaussian dynamic systems

Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.

Gordon, N. J., Salmond, D. J. & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F – Radar and Signal Processing, 140(2), 107–113.

kalman-filter sequential-monte-carlo dynamic-bayesian-inference particle-filter dynamic-kalman-filter hidden-markov-model


Dynamic Programming

DP
Bellman's Principle of Optimality
Recursive Optimization
Dinamik Programlama

Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.

Exact combinatorial optimization via recursive decomposition

Bellman, R. (1957). Dynamic Programming. Princeton University Press.


Dynamic Propensity Score Matching

Dynamic Propensity Score Matching for Sequential Treatments

dynamic PSM
sequential propensity score matching
longitudinal propensity matching
DPSM

Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching

Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.

Longitudinal / panel data with time-varying treatment

Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.

Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.

propensity-score-matching propensity-score-weighting marginal-structural-model dynamic-difference-in-differences inverse-probability-weighting doubly-robust-estimation


Dynamic Sequential Monte Carlo

Dynamic SMC
SMC for dynamic models
sequential particle filter
dynamic particle sampler

Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models.

Sequential Monte Carlo sampler for dynamic settings

Sequential or time-ordered observations; state-space data

Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.

Doucet, A., de Freitas, N. & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer.

sequential-monte-carlo particle-filter kalman-filter hamiltonian-monte-carlo gibbs-sampling dynamic-bayesian-inference


Dynamic Stochastic Block Model

Dynamic Stochastic Block Model (Temporal Community Detection)

DSBM
dynamic SBM
time-varying stochastic block model
temporal block model

Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.

The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.

Temporal adjacency matrices / longitudinal edge lists

Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189.

Matias, C., & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.

stochastic-block-model temporal-network-analysis dynamic-community-detection exponential-random-graph-model modularity-analysis bayesian-stochastic-block-model


Dynamic Synthetic Control Method

Dynamic Synthetic Control Method for Multi-Period Treatment Evaluation

Dynamic SCM
Time-varying synthetic control
Multi-period synthetic control
DSC

Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others

The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.

Comparative case study / counterfactual estimation

Aggregate panel data (few treated units, multiple control units, multiple pre- and post-treatment periods)

Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.

Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic Difference-in-Differences. American Economic Review, 111(12), 4088-4118.

synthetic-control-method difference-in-differences dynamic-difference-in-differences event-study-design panel-data-synthetic-control-method counterfactual-impact-evaluation


Dynamic Time Warping

DTW
dynamic programming time warping
elastic distance

Dynamic Time Warping is a distance metric for comparing time series or sequential data that may vary in length or speed. Introduced by Hideki Sakoe and Seibi Chiba in 1978 for speech recognition, DTW measures the minimal cumulative distance needed to align two sequences using dynamic programming. Unlike fixed-distance metrics, DTW allows flexible time warping, making it ideal for sequences that are similar in shape but offset or scaled differently in time.

Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

Salvador, S., & Chan, P. (2007). FastDTW: Toward accurate dynamic time warping in linear time and space. KDD Explorations, 5(1), 70-86.


Dynamic Two-Mode Network Analysis

Dynamic Two-Mode (Bipartite) Network Analysis

Longitudinal affiliation network analysis

Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks)

Dynamic two-mode network analysis studies bipartite networks — structures with two distinct node types, such as actors and events or authors and papers — as they evolve over time. By tracking how memberships, affiliations, and co-participations change across temporal snapshots, it reveals the emergence, dissolution, and reorganization of collaborative or membership structures that static analysis would miss.

Time-stamped affiliation or membership data (actors and events/groups across time points)

Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 417–433). SAGE.

Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

two-mode-network-analysis temporal-network-analysis dynamic-community-detection multiplex-network-analysis social-network-analysis temporal-community-detection


Dynamic Variational Inference

Dynamic Variational Inference for Sequential Latent Variable Models

variational inference for state-space models

Bayer, Osendorfer, Krishnan and colleagues

Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO).

Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.

Bayer, J., & Osendorfer, C. (2014). Learning Stochastic Recurrent Networks. NIPS 2014 Workshop on Advances in Variational Inference.

variational-inference sequential-monte-carlo kalman-filter dynamic-bayesian-network particle-filter time-series-bayesian-inference


Nijmegen Questionnaire

Nijmegen Questionnaire for Dysfunctional Breathing

The Nijmegen Questionnaire is a 16-item self-report instrument designed to identify dysfunctional breathing patterns, particularly hyperventilation syndrome, in patients presenting with respiratory or non-respiratory symptoms. Developed by van Beveren and colleagues in the Netherlands in 1994, it provides rapid assessment of symptoms attributable to chronic hyperventilation: dizziness, chest tightness, muscle tension, paresthesias, and anxiety. The Nijmegen Questionnaire is widely used in respiratory physiology clinics, pulmonary rehabilitation programs, and psychosomatic medicine to detect dysfunctional breathing phenotypes that may masquerade as asthma, anxiety disorders, or cardiopulmonary disease.

Van Beveren, T. L., Fülöp, M., van Beek, H. G., & Zijlstra, F. J. (1994). Hyperventilation and panic panic attacks in a group of asthma patients. Respiration, 61(5), 282-287.

Higgs, F., Donovan, G., Opdam, H., & Tiller, J. (2013). Hyperventilation and dysfunctional breathing: a tool for assessment and retraining. Breathe, 9(4), 284-293.


Dysphagia Outcome and Severity Scale

dysphagia severity & functional swallowing outcomes

The Dysphagia Outcome and Severity Scale (DOSS) is a 7-point clinician-rated ordinal scale that measures the severity of swallowing dysfunction and functional swallowing outcomes across two dimensions: safety (penetration-aspiration risk) and efficiency (oral intake adequacy and diet level tolerance). Developed by O'Neil and colleagues in 1999, DOSS integrates clinical observation with videofluoroscopic findings to provide a standardized, functionally meaningful classification of swallowing status from normal to non-functional.

O'Neil, K. H., Purdy, M., Falk, J., & Gidas, L. (1999). The Dysphagia Outcome and Severity Scale. Dysphagia, 14(3), 139–145.

Kuipers, P., & Daniels, S. K. (2000). Dysphagia Management in Stroke Patients. Current Treatment Options in Neurology, 2(5), 529–536.

Pauloski, B. R., Logemann, J. A., Colangelo, L. A., et al. (2001). Surgical Variables Affecting Functional Outcomes in Oropharyngeal Myocutaneous Flap Reconstruction. Head & Neck, 23(3), 175–185.


Borg Dyspnea Scale

Borg Rating of Perceived Exertion Dyspnea Scale (Borg RPE-D)

symptom severity and perceived exertion rating scale

The Borg Rating of Perceived Exertion (RPE) Scale is a simple 0–10 (or original 6–20) numerical rating scale that quantifies a patient's subjective perception of dyspnea or general effort during activity or exercise testing. Developed by Swedish psychophysicist Gunnar Borg in the 1970s–1980s, the Borg Scale is ubiquitous in cardiopulmonary medicine, rehabilitation, and exercise physiology for monitoring symptom severity, guiding exercise intensity, assessing treatment response, and ensuring patient safety during testing and rehabilitation.

Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine & Science in Sports & Exercise, 14(5), 377–381.

Borg, G. (1998). Borg's Rating of Perceived Exertion and Pain Scales. Human Kinetics.

new-york-heart-association-class seattle-angina-questionnaire minnesota-heart-failure duke-activity-status-index


E-Government Adoption Scale

The E-Government Adoption Scale (EGAS) measures citizens' willingness to adopt and use digital government services (e-permits, e-tax, e-voting, e-tourism information services, online licensing) based on Technology Acceptance Model principles (Venkatesh & Davis, 2000) extended to government contexts (Belanger et al., 2005). It operationalizes key adoption drivers: perceived usefulness, ease of use, trust in government, security concerns, and technical support. Essential for government agencies, tourism authorities, and public service digital transformation initiatives seeking to understand and overcome citizen barriers to e-service adoption.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Belanger, F., Carter, L., & Casper, J. (2005). Citizen adoption of e-government services. Proceedings of the 38th Hawaii International Conference on System Sciences, 109c.

Alomari, M., Sandhu, K., & Woods, P. (2012). Exploring citizen perceptions of barriers and drivers influencing the adoption of e-government services. Journal of Cases on Information Technology, 14(4), 36-48.

Oliveira, T., Martins, R., Sarker, S., & Thomas, M. (2016). Information and communication technology adoption across the global south. Journal of Global Information Management, 24(2), 1-22.

citizen-satisfaction-survey public-service-motivation-scale perceived-value-scale-tourism tourist-satisfaction-scale destination-image-scale


E-S-QUAL Electronic Service Quality Scale

A. Parasuraman, Valerie A. Zeithaml, Anantharanthan Malhotra

E-S-QUAL is a 22-item scale developed by Parasuraman, Zeithaml, and Malhotra (2005) to measure service quality in electronic commerce and digital service environments. Adapting the foundational SERVQUAL dimensions to online contexts, E-S-QUAL assesses four core dimensions: Efficiency (ability to complete transactions quickly), Fulfillment (accurate order fulfillment and on-time delivery), System Availability (website uptime and technical performance), and Privacy (security of customer data and transactions). The scale captures both service delivery (how the website functions) and service recovery (how problems are handled).

Multi-dimensional electronic service quality scale

Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality. Journal of Service Research, 7(3), 213-233.

Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service Quality Delivery Through Web Sites: A Critical Review of Extant Knowledge. Journal of the Academy of Marketing Science, 30(4), 362-379.

servqual servperf customer-satisfaction-index e-commerce-satisfaction


Early Childhood Environment Rating Scale, Third Edition

Thelma Harms, Richard Clifford, Debby Cryer

The ECERS-3 is a comprehensive observational rating scale assessing the quality of early childhood education and care environments for preschool-age children (ages 3–5 years, or ages 2.5–5.5 years). Developed by Harms, Clifford, and Cryer (2015), it evaluates classroom environment, materials, interactions, and practices across 35 items grouped into six domains: Space and Furnishings, Personal Care Routines, Language and Literacy, Learning Activities, Interaction, and Program Structure. The ECERS-3 is the most widely used instrument for assessing early childhood program quality and has become the standard for child care licensing, accreditation, and quality improvement research.

Harms, T., Clifford, R. M., & Cryer, D. (2015). Early Childhood Environment Rating Scale (ECERS-3) (3rd ed.). Teachers College Press.

Harms, T., Cryer, D., & Clifford, R. M. (2006). Infant/Toddler Environment Rating Scale, Revised Edition (ITERS-R). Teachers College Press.


Early Warning Score

Early Warning Score for Patient Deterioration Detection

EWS
NEWS
National Early Warning Score
Rapid Response System

Vital sign monitoring and acute deterioration detection

Royal College of Physicians and multiple researchers (Smith, Prytherch, et al.)

The Early Warning Score (EWS), most commonly known as the National Early Warning Score (NEWS) in the UK, is a standardized tool for identifying acutely unwell patients at risk of deterioration. Developed by the Royal College of Physicians and validated through research by Smith, Prytherch, and colleagues, NEWS combines vital sign measurements and supplemental oxygen use to generate a composite score. High NEWS scores trigger escalated care responses, enabling early intervention before critical illness develops.

Smith, G. B., Prytherch, D. R., Schmidt, P. E., & Featherstone, P. I. (2008). Should early warning systems be based on single data points or on trends? Resuscitation, 81(4), 424-426.

Royal College of Physicians. (2017). National Early Warning Score (NEWS) 2: standardising the assessment of acute illness severity in the NHS. Royal College of Physicians, London.

nursing-sensitive-indicators patient-fall-risk-assessment cam-delirium-screening medication-reconciliation


EASI

The EASI is a structured, clinician-administered tool for assessing the extent and intensity of atopic dermatitis across the body. Developed by Hanifin and colleagues in 2001, it divides the body into four regions with weighted area factors, ensuring proportional contribution to total score. EASI has become the primary objective severity measure in atopic dermatitis clinical trials and is recommended by international regulatory authorities (FDA, EMA).

Hanifin JM, Thurston M, Omoto M, et al. The eczema area and severity index (EASI): assessment of reliability in atopic dermatitis. Experimental Dermatology. 2001;10(1):11-18.

Stalder JF, Taïeb A. Therapeutic classification of atopic dermatitis. Arch Dermatol. 2003;139(9):1221-1222.


Eating Attitudes Test

Eating disorder symptom screening and severity

The EAT-26 is a 26-item self-report questionnaire designed to assess core attitudes and behaviors characteristic of eating disorders, particularly anorexia nervosa and bulimia nervosa. Developed by Garner and Garfinkel in 1979 and abbreviated to 26 items in 1982, it is widely used for screening eating disorders in community and clinical settings, and for monitoring treatment response. The EAT-26 measures restrictive eating attitudes, food preoccupation, and weight/shape concerns, with three subscales reflecting the multifaceted nature of eating disorder psychopathology.

Garner, D. M., Olmsted, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The eating attitudes test: Psychometric features and clinical correlates. Psychological Medicine, 12(4), 871–878.

Garner, D. M., & Garfinkel, P. E. (1979). The Eating Attitudes Test: An index of the symptoms of anorexia nervosa. Psychological Medicine, 9(2), 273–279.

Mintz, L. B., & O'Halloran, M. S. (2000). The Eating Attitudes Test: Validation with DSM-IV eating disorders. Journal of Personality Assessment, 74(3), 489–503.


Echo State Network

Echo State Network (Reservoir Computing)

Liquid State Machine (related formulation)

An Echo State Network (ESN) is a type of recurrent neural network introduced by Herbert Jaeger and Harald Haas in 2004 that exploits a large, randomly connected, fixed recurrent layer — the reservoir — to project input signals into a high-dimensional nonlinear space. Only the linear output weights are trained, typically via ridge regression, making ESNs computationally inexpensive yet highly expressive for temporal and chaotic time-series modeling tasks.

Recurrent neural network with fixed random reservoir

Linear (only output weights are trained)

Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78–80.


ECLAT

ECLAT (Equivalence Class Clustering and Bottom-up Lattice Traversal)

Eclat algorithm
vertical association mining
tidset intersection mining
ECLAT sık örüntü madenciliği

ECLAT, introduced by Mohammed Zaki in 2000, mines frequent itemsets using a vertical data representation: instead of scanning transactions, it stores for each item the set of transaction IDs (a tidset) that contain it, and computes the support of any itemset by intersecting tidsets. This depth-first, intersection-based approach is fast and memory-efficient, an alternative to Apriori's horizontal scans and FP-Growth's tree.

Frequent-itemset mining algorithm (vertical format)

Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.


EFKS

environmental knowledge and literacy assessment

The Ecological Footprint Knowledge Scale (EFKS) measures individuals' understanding of the ecological footprint concept—how much land and resources one's consumption requires—and knowledge of personal and global footprint impacts. Developed from the ecological footprint framework (Wackernagel & Rees, 1996), the EFKS assesses both conceptual comprehension (what is an ecological footprint?) and applied knowledge (how to estimate footprint, what factors affect it). The scale is critical for evaluating environmental education effectiveness, understanding why some individuals adopt sustainable consumption despite high footprint knowledge gaps, and identifying knowledge barriers that block behavior change.

Knowledge-based self-report and comprehension scale

Wackernagel, M., & Rees, W. E. (1996). Our ecological footprint: Reducing human impact on the earth. New Society Publishers.

Venetis, E., & Tsuchihashi, K. (2014). Awareness and understanding of the ecological footprint concept among university students. International Journal of Sustainability in Higher Education, 15(4), 405–416.


Ecological Footprint

EFA
Ecological Footprint Analysis
Biocapacity Accounting
Ekolojik Ayak İzi

Ecological Footprint Accounting (EFA) is a resource accounting framework that measures how much biologically productive land and water area a human population requires to produce the resources it consumes and to absorb the waste it generates. Introduced by Mathis Wackernagel and William Rees in 1996, it compares human demand on nature against Earth's regenerative capacity, expressed in standardized global hectares (gha).

Wackernagel, M., & Rees, W. (1996). Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers.


Ecological Study

aggregate study
correlational study
ecological correlation study
population-level study

19th century (Snow 1854); formalised mid-20th century

Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues

An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease.

Aggregate / grouped population-level data (rates, proportions, means)

Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81.

cross-sectional-epidemiological-study cohort-study case-control-study dose-response-analysis meta-analytic-ecological-study spatial-analysis


Economic Dispatch

Economic Dispatch (ED) is the process of optimally allocating power output among committed generators to meet demand at minimum fuel cost. Introduced by Kirchmayer in 1958, ED is a fundamental real-time optimization problem solved every few minutes in power system operations. Unlike Unit Commitment (which decides generator on/off), ED assumes generators are already committed and focuses on splitting load most economically. ED's rapid feedback enables efficient real-time power plant operations.

Continuous optimization for allocating power output among committed generators

Kirchmayer, L. K. (1958). Economic Operation of Power Systems. Wiley & Sons.

Horton, G. K. (1970). Techniques for the economic dispatch of generation. IEEE Transactions on Power Apparatus and Systems, 89(5), 893-901.

Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.


Economic Order Quantity

Wilson EOQ Model
Harris-Wilson Model
Optimal Lot Size Model
Ekonomik Sipariş Miktarı

The Economic Order Quantity (EOQ) is a classic deterministic inventory model that identifies the order quantity minimizing the sum of annual ordering and holding costs. Introduced by Ford W. Harris in 1913 and later popularized by R. H. Wilson, EOQ assumes constant demand, fixed cost parameters, and instantaneous replenishment. It remains the foundational benchmark for inventory management in manufacturing, retail, and supply chain contexts where demand is relatively stable and costs are well-characterized.

Deterministic inventory optimization model

Constant demand rate, fixed ordering and holding costs

Output: Optimal order quantity minimizing total annual inventory cost

Harris, F. W. (1913/1990). How many parts to make at once. Operations Research, 38(6), 947–950 (reprint).


Ecosystem Services Valuation

Ecosystem Services Valuation and Assessment

ESV
Natural capital accounting
Environmental valuation

Robert Costanza, Rudolf de Groot, and team

Ecosystem Services Valuation (ESV) is a framework pioneered by Costanza and colleagues (1997) that assigns economic value to the benefits nature provides to humanity—from pollination and water purification to climate regulation and cultural enjoyment. Formalized in the Millennium Ecosystem Assessment (2005) and The Economics of Ecosystems and Biodiversity (TEEB 2010), ESV bridges ecology and economics to make the invisible value of ecosystems visible to policymakers and markets.

Costanza, R., d'Arge, R., de Groot, R., Farberk, S., Grasso, M., Hannon, B., ... & van den Belt, M. (1997). The value of the world's ecosystem services and natural capital. Nature, 387(6630), 253-260.

Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Synthesis. Washington, DC: Island Press.

TEEB (2010). The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature. A synthesis of the approach, conclusions and recommendations of TEEB.


Ecotoxicological Testing

Assessment of Toxicity to Aquatic and Terrestrial Organisms

toxicity testing
aquatic bioassay
ecotoxicity assessment
organism exposure testing

Environmental toxicology and hazard assessment

Ecotoxicological testing is a suite of standardized laboratory and field methods to assess the toxicity of chemical substances to aquatic and terrestrial organisms (fish, invertebrates, algae, plants, soil fauna). Developed by regulatory agencies (OECD, EPA, EMEA) since the 1970s, these tests measure lethal concentration (LC50, EC50) and sublethal endpoints (growth, reproduction, behavior) under controlled conditions. Ecotoxicological data support chemical hazard classification, environmental risk assessment, and regulatory approval of new substances.

experimental measurement and analysis pipeline

OECD. (2011). Test Guidelines for Chemicals. OECD Publishing.

US Environmental Protection Agency. (2002). Aquatic Toxicity Test Methods. EPA 600/4-90/027.

Newman, M. C. (1998). Fundamentals of Ecotoxicology. CRC Press.


EDAS

EDAS (Evaluation Based on Distance from Average Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Eddy Covariance

Eddy covariance
EC flux
Eddy correlation
Direct flux measurement

The eddy covariance method is a direct, micrometeorological technique that measures turbulent fluxes of momentum, heat, water vapor, and CO2 by computing the covariance between high-frequency fluctuations of wind velocity and scalar properties (temperature, humidity, concentration). It is the gold standard for measuring ecosystem-atmosphere exchanges and validating model parameterizations.

Baldocchi, D. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide fluxes of ecosystems: past, present and future. Global Change Biology, 9(4), 479-492.

Foken, T. (2006). The energy balance closure problem: An overview. Ecological Applications, 18(6), 1351-1367.


EDE-Q

Eating Disorder Examination - Questionnaire

The EDE-Q is a 28-item self-report questionnaire derived from the gold-standard Eating Disorder Examination (EDE) interview. Developed by Fairburn and Beglin in 1993, it measures the cognitive, behavioural, and attitudinal features of eating disorders. It is widely used in both research and clinical screening because it captures the core psychopathology of eating disorders without requiring a trained interviewer.

Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self-report questionnaire? International Journal of Eating Disorders, 16(4), 363–370.

Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beumont, P. J. V. (2004). Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behaviour Research and Therapy, 42(5), 551–567.

Luce, K. H., & Crowther, J. H. (2007). The reliability of the Eating Disorder Examination-Questionnaire International (EDE-QI). International Journal of Eating Disorders, 40(6), 549–552.

scoff-questionnaire three-factor-eating-questionnaire body-shape-questionnaire binge-eating-scale yale-food-addiction-scale


Edinburgh Postnatal Depression Scale

Edinburgh Postnatal Depression Scale (EPDS)

EPDS
Edinburgh Postnatal Depression Scale

The Edinburgh Postnatal Depression Scale is a 10-item self-report screening questionnaire developed by John Cox, Jeni Holden, and Ruth Sagovsky in 1987 to identify postnatal depression in new mothers. Published in the British Journal of Psychiatry, the EPDS specifically addresses depressive symptoms common in the postpartum period, avoiding items that might confound with normal pregnancy or postpartum adjustment (e.g., sleep disturbance from infant care). It is widely endorsed by obstetric and midwifery organizations, freely available, and used globally as the standard for perinatal depression screening.

Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786.

Eberhard-Gran, M., Eskild, A., & Tambs, K. (2001). Review of validation studies of the Edinburgh Postnatal Depression Scale. Acta Psychiatrica Scandinavica, 104(4), 243–249.

Baker, N. N., Williams, S. R., & Murray, L. (2003). Sensitivity and specificity of the Edinburgh Postnatal Depression Scale administered at 5 weeks postpartum. Journal of the Royal Society of Medicine, 96(2), 89–92.

phq-9 bdi-ii patient-global-impression-change quick-inventory-depressive


Editorial and Commentary

Editorial and Commentary (Peer-Reviewed Opinions on Research and Practice)

editorial commentary opinion perspective viewpoint

Academic journals (19th century formalization)

An editorial or commentary is a peer-reviewed opinion article in an academic journal, typically authored by experts to interpret, contextualize, or critique recent research findings or practice issues. Editorials are usually commissioned by journal editors; commentaries may be solicited or submitted unsolicited. Unlike research articles based on empirical data, editorials and commentaries are evidence-grounded opinions—authors synthesize literature, interpret findings, and offer perspectives on implications. These contributions are indexed in major databases and citable, establishing them as legitimate scholarly publications. Editorials and commentaries carry prestige, particularly when published in high-impact journals, and position authors as thought leaders in their fields.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.

Committee on Publication Ethics (2023). Guidelines for Editors and Commentators. https://publicationethics.org

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.

original-research-article letter-to-editor opinion-leadership academic-debate


EFS

The Edmonton Frail Scale (EFS) is a comprehensive, nine-domain assessment tool developed by Rolfson and colleagues in 2006 to systematically evaluate frailty across multiple physiological and functional dimensions in older adults. Combining clinical judgment with objective testing, the EFS assesses cognition, general health status, functional independence, social support, medication use, nutrition, mood, continence, and functional performance, providing a multidimensional frailty profile. It is widely used in geriatric clinics, acute care settings, and research to characterize the nature and severity of frailty.

Rolfson, D. B., Majumdar, S. R., Tsuyuki, R. T., Tahir, A., & Srivastava, S. (2006). Validity and reliability of the Edmonton Frail Scale. Age Ageing, 35(5), 526-529.

Moorhouse, P., & Rockwood, K. (2012). Frailty and its quantification. J Gerontol A Biol Sci Med Sci, 67(7), 712-717.

Hilmer, S. N., Perera, V., Mitchell, S., et al. (2009). The assessment of frailty in older persons. Aging Health, 5(3), 417-432.

frail-scale short-physical-performance-battery tinetti-balance-assessment cognitive-telephone-screening life-space-assessment


ESAS

The Edmonton Symptom Assessment System is a rapid, validated 9-item tool that assesses the severity of common symptoms in cancer and palliative care patients: pain, tiredness, nausea, depression, anxiety, drowsiness, appetite loss, general well-being, and shortness of breath. Developed by Bruera and colleagues at the University of Alberta in 1991, the ESAS has become the standard symptom-screening instrument in oncology clinics, palliative care units, and end-of-life care settings worldwide, enabling efficient symptom prioritization and management escalation.

Patient self-report multisymptom palliative care scale

Bruera, E., Kuehn, N., Miller, M. J., Selmser, P., & Macmillan, K. (1991). The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care, 7(2), 6–9.

Chang, V. T., Hwang, S. S., & Kasimis, B. (2000). Longitudinal documentation of cancer pain: a pilot chronic disease management model. Cancer, 88(12), 2892–2900.

distress-thermometer brief-fatigue-inventory memorial-symptom-assessment-scale fact-g piper-fatigue-scale


eDNA Metabarcoding

eDNA
metabarcoding
DNA metabarcoding
genetic monitoring

Environmental DNA (eDNA) metabarcoding detects and identifies species present in environmental samples (water, soil, air) by sequencing short DNA fragments released by organisms. Developed by Taberlet and colleagues (2012), this approach has revolutionized biodiversity monitoring: species can be surveyed without capture, observation, or complex sampling designs. Metabarcoding sequences millions of DNA fragments, identifies reads taxonomically, and assigns them to species. The method is non-invasive, rapid, and cost-effective, enabling large-scale biodiversity surveys and early detection of cryptic or rare species.

species detection and community assessment

Taberlet, P., Coissac, E., Hajibabaei, M., & Rieseberg, L. H. (2012). Environmental DNA. Molecular Ecology, 21(8), 1789-1793.

Deakin, G., Pettitt-Wade, H., & Waldick, R. C. (2016). Environmental DNA metabarcoding: A review of the application to fish biodiversity assessment in temperate freshwaters. Environmental DNA, 1(1), 4-14.

Ficetola, G. F., Miaud, C., Pompanon, F., & Taberlet, P. (2008). Species detection using environmental DNA from water samples. Biology Letters, 4(4), 423-425.

species-accumulation distance-sampling bioaccumulation-model functional-diversity


EDSS

Kurtzke Expanded Disability Status Scale

The EDSS is the most widely used clinical disability rating scale in multiple sclerosis research and practice. Developed by John Kurtzke in 1983, it provides a 0-10 ordinal scale capturing disease severity across eight neurological functional systems and functional status. The EDSS remains the primary endpoint in MS clinical trials and longitudinal cohort studies, with decades of prognostic and comparative data worldwide.

Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444-1452.

updrs nihss msfc rivermead-mobility-index


Educational Action Research

EAR
practitioner research
teacher action research
classroom action research

1940s (Lewin); educational context developed 1970s–1980s

Kurt Lewin (action research foundations); Lawrence Stenhouse and John Elliott (educational adaptation)

Educational action research is a cyclical, practitioner-led inquiry method in which educators systematically investigate a problem or opportunity in their own classroom or school, implement a change, observe its effects, and reflect on findings to guide the next cycle. Rooted in Kurt Lewin's action research framework and developed for educational contexts by Lawrence Stenhouse and John Elliott, it bridges the gap between educational theory and classroom practice by making teachers agents of rigorous inquiry.

Participatory qualitative research design

Observation notes, reflective journals, interviews, documents, student work samples

Elliott, J. (1991). Action Research for Educational Change. Open University Press.

Kemmis, S., McTaggart, R., & Nixon, R. (2014). The Action Research Planner: Doing Critical Participatory Action Research. Springer.

design-based-research classroom-observation lesson-study participatory-action-research program-evaluation ethnography


EFA for Scale Development

Açımlayıcı Faktör Analizi — Ölçek Geliştirme (EFA)

1904 (foundational); contemporary scale-development practice from 1990s onward

Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s)

Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Originating with Spearman's (1904) factor theory and formalised for applied scale construction by Costello and Osborne (2005) and Fabrigar and colleagues (1999), this variant imposes a stricter sample requirement (n ≥ 100, subject-to-item ratio ≥ 5) and a higher loading threshold (≥ 0.40) than general EFA, and it treats the recovered factor structure as a draft to be subsequently validated by confirmatory analysis.

Factor loadings, factor structure, communalities

Costello, A. B. & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.

exploratory-factor-analysis confirmatory-factor-analysis cronbach-alpha sem pca


Effect size analysis

effect magnitude estimation
standardized effect measure
practical significance analysis
ES analysis

1969 (first edition); 1988 (definitive second edition)

Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations.

Continuous, categorical, or ranked outcomes

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863.

power-analysis independent-samples-t-test one-way-anova meta-analysis confidence-interval-estimation roc-analysis


Effect Size

Effect Size: Quantifying the Magnitude of Research Findings

ES
Cohen's d
standardized effect
practical significance

Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.

Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge.

Lakens, D. (2013). Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and ANOVAs. Frontiers in Psychology, 4, 863.

p-value-significance confidence-interval statistical-power type-i-type-ii-error


Effective Field Theory

EFT
effective theory
operator product expansion

Effective Field Theory (EFT) is a general framework for studying physics at low energies in terms of the relevant degrees of freedom, without requiring complete knowledge of high-energy physics. By expanding in powers of energy, EFT provides model-independent parameterizations of new physics effects and systematic methods for computing precision predictions of the Standard Model.

Weinberg, S. (1979). Baryon and lepton nonconserving processes. Physical Review Letters, 43(21), 1566.

Buchmuller, W., & Wyler, D. (1986). Effective Lagrangian analysis of new interactions and flavor conservation. Nuclear Physics B, 268(3-4), 621–653.

Grojean, C., et al. (2017). New approaches to electroweak symmetry breaking. Reviews of Modern Physics, 71(3), 735.


Effectiveness-NTU Method

Effectiveness-Number of Transfer Units Method for Heat Exchangers

The Effectiveness-NTU method is an alternative approach to heat exchanger analysis that measures thermal performance relative to the theoretical maximum possible heat transfer. It is particularly powerful for design problems where outlet temperatures are unknown. The method uses effectiveness (ratio of actual to maximum possible heat transfer) and NTU (Number of Transfer Units, a dimensionless parameter related to overall heat transfer area) to characterize heat exchanger performance.

Kays, W. M., & London, A. L. (1984). Compact Heat Exchangers (3rd ed.). McGraw-Hill.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.


EfficientNet

EfficientNet
compound scaling CNN
EfficientNet-B0 through B7
EfficientNetV2

EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.

Compound-scaled convolutional neural network architecture

Image classification, feature extraction, transfer learning

phi (phi >= 0, controls total resource budget)

Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.

Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press.

resnet vgg inception mobilenet convolutional-neural-network transfer-learning neural-architecture-search


Effort-Reward Imbalance Scale

Effort-Reward Imbalance (ERI) Questionnaire

The Effort-Reward Imbalance (ERI) Scale is an occupational stress assessment tool based on a reciprocal model of work stress. Developed by Johannes Siegrist in 1996, the ERI measures the degree to which employees experience imbalance between their job efforts (demands, overcommitment) and job rewards (income, recognition, career prospects, security). The instrument is grounded in social reciprocity theory and has strong evidence linking high imbalance to cardiovascular disease, depression, and burnout.

Siegrist, J., Starke, D., Chandola, T., Peter, I., Marmot, M., Theorell, T., ... & Fuhrer, R. (2004). The measurement of effort-reward imbalance at work: European comparisons. Social Science & Medicine, 58(8), 1483-1499.

Siegrist, J. (1996). Adverse health effects of high-effort/low-reward conditions. Journal of Occupational Health Psychology, 1(1), 27-41.

copenhagen-burnout-inventory oldenburg-burnout-inventory areas-of-worklife-scale recovery-experience-questionnaire job-demands-control-support-model


EGARCH model

Exponential Generalized Autoregressive Conditional Heteroscedasticity Model

Exponential GARCH
EGARCH
Nelson EGARCH
log-GARCH

The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

arch-model tgarch-model dcc-garch-model garch-model arima-model vector-autoregression


EGARCH

Exponential Generalised Autoregressive Conditional Heteroskedasticity

exponential GARCH
Nelson's EGARCH
asymmetric GARCH
EGARCH — Üstel GARCH

EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.

Conditional volatility model (asymmetric GARCH variant)

Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.

Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778.


Ego Network Analysis

Ego Network Analysis (Personal Network Analysis)

Ego Ağı Analizi (Personal Network Analysis)

1992 (Burt); foundational measurement formalised by Marsden 2002

Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)

Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence.

Descriptive / relational network analysis

Ego (focal actor) and their immediate alters

Network size, density, constraint index (Burt), brokerage roles, structural holes

Survey-based name generator or observed contact lists

10 egos recommended; fewer than 20 limits interpretation

Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press.

Marsden, P.V. (2002). Egocentric and Sociocentric Measures of Network Centrality. Social Networks, 24(4), 407-422.

centrality-analysis community-detection exponential-random-graph network-diffusion link-prediction temporal-network-analysis


eHealth Literacy Scale

The eHealth Literacy Scale measures individuals' ability to seek, find, understand, and appraise health information from electronic sources and apply that knowledge to health decision-making. Developed by Norman and Skinner in 2006, it assesses functional, communicative, and critical digital health literacy competencies essential for navigating modern health technologies and online medical information.

Norman, G. R., & Skinner, H. A. (2006). eHEALS: The eHealth Literacy Scale. Journal of Medical Internet Research, 8(4), e27.


Eigenvector Centrality

Eigenvector Centrality (Bonacich Power Centrality)

eigenvector centrality
EC
Bonacich centrality
power centrality

Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.

Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.

degree-centrality betweenness-centrality closeness-centrality pagerank social-network-analysis modularity-analysis


Ekman Transport

Ekman transport is the net volume flux of water driven by wind stress balanced with Coriolis force in the surface boundary layer. Derived by Vagn Walfrid Ekman in 1905 from the principle that wind stress is transmitted through the water column in a spiral pattern, Ekman transport is responsible for coastal upwelling and important oceanographic transports. The theory links surface wind patterns directly to ocean circulation.

Ekman, V. W. (1905). On the influence of the Earth's rotation on ocean currents. Arkiv for Matematik, Astronomi och Fysik, 2(11), 1-52.

Cushman-Roisin, B., & Beckers, J.-M. (2011). Introduction to Geophysical Fluid Dynamics: Physical and Numerical Aspects. Academic Press.


Elastic Net Regression

elastic net
EN regression
L1+L2 regularized regression
combined lasso-ridge regression

Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.

Continuous outcome, continuous or categorical predictors

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.

lasso-regression ridge-regression ols-regression regularized-logistic-regression quantile-regression robust-regression


Elastic Net

Elastic Net Regresyon
elastic net regression
ElasticNet
L1/L2 regularized regression

Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.

Regularized linear regression (L1 + L2 penalty)

Prediction & explanation with variable selection

Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.

lasso-regression ridge-regression linear-regression logistic-regression random-forest


Elastohydrodynamic Lubrication

Elastohydrodynamic Lubrication (EHL) Theory and Film Formation

EHL
Elastohydrodynamic film
Hydrodynamic lubrication

Elastohydrodynamic lubrication (EHL) is the regime of fluid film lubrication in which elastic deformation of the surfaces plays a crucial role in maintaining a fluid layer between sliding or rolling surfaces. In applications like roller bearings and gears, the contact pressure is extremely high, causing the lubricant viscosity to increase dramatically and the surfaces to deform elastically. EHL theory, developed rigorously by Dowson and Higginson, predicts the film thickness, pressure distribution, and friction in these heavily loaded contacts.

Mathematical model for lubricated contacts

Dowson, D., & Higginson, G. R. (1977). Elastohydrodynamic Lubrication: The Fundamentals of Roller and Gear Lubrication. Pergamon Press.

Hamrock, B. J., Schmid, S. R., & Jacobson, B. O. (1994). Fundamentals of Fluid Film Lubrication (2nd ed.). Marcel Dekker.

Masjedi, M., & Khonsari, M. M. (2014). Film thickness and asperity load formulas for elastohydrodynamic lubrication of rollers. Tribology International, 81, 1-14.

griffith-fracture-mechanics modal-analysis tolerance-stack-up design-for-manufacturing-and-assembly


Elbow Method

The Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.

Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267-276.

silhouette-score gap-statistic calinski-harabasz-index davies-bouldin-index inertia


E-Learning Satisfaction Scale

The E-Learning Satisfaction Scale measures learner satisfaction with online educational experiences across multiple dimensions including platform quality, instructor effectiveness, course content, peer interaction, and technical support. Developed through research by Bolliger, Halupa, Chi, and others studying online higher education, the scale helps institutions assess course quality, predict learner retention, and identify improvement opportunities in digital education delivery.

Chi, T., & Kilduff, P. P. (2011). Understanding consumer perceived value of casual online games. New Media & Society, 13(6), 954-971.

Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(2), 175-189.

tam-questionnaire online-trust-scale social-media-engagement-scale technology-readiness-index


ELECTRE-I

ELECTRE-I (ELECTRE I — ELimination Et Choix Traduisant la REalité I (kernel / choice)) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking — concordance/discordance with kernel extraction

Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). RIRO — Revue d'Informatique et de Recherche Opérationnelle

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


ELECTRE-II

ELECTRE-II (ELECTRE II — ELimination Et Choix Traduisant la REalité II (complete ranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B., Bertier, P. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking with strong/weak concordance thresholds (complete preorder)

Roy, B., Bertier, P. (1973). La méthode ELECTRE II: une application au media-planning. Operational Research '72 (Proceedings IFORS), North-Holland


ELECTRE-III

ELECTRE-III (ELECTRE III — ELimination Et Choix Traduisant la REalité III (pseudo-criteria, fuzzy outranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy outranking with indifference/preference/veto thresholds (complete preorder)

Roy, B. (1978). ELECTRE III: Un algorithme de classement fondé sur une représentation floue des préférences en présence de critères multiples. Cahiers du CERO


ELECTRE-IV

ELECTRE-IV (ELECTRE IV — ELimination Et Choix Traduisant la REalité IV (no criterion weights)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B., Hugonnard, J. C. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight-free outranking with pseudo-criteria (complete preorder)

Roy, B., Hugonnard, J. C. (1982). Ranking of suburban line extension projects on the Paris metro system by a multicriteria method. Transportation Research Part A


ELECTRE-TRI-B

ELECTRE-TRI-B (ELECTRE TRI-B — Sorting with central reference profiles (boundary variant)) is a sorting multi-criteria decision-making (MCDM) method introduced by Yu, W. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking sorting — central (boundary) reference profiles

Yu, W. (1992). ELECTRE TRI — Aspects méthodologiques et manuel d'utilisation. LAMSADE Cahier 74, Université Paris-Dauphine


ELECTRE-TRI-C

ELECTRE-TRI-C (ELECTRE Tri with Central Reference Profiles) is a sorting multi-criteria decision-making (MCDM) method introduced by Almeida-Dias, J. Figueira, J. R. Roy, B. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking-based sorting using central characteristic reference profiles

Almeida-Dias, J., Figueira, J. R., Roy, B. (2010). ELECTRE Tri-C: A multiple criteria sorting method based on characteristic reference actions. European Journal of Operational Research


ELECTRE-TRI

ELECTRE-TRI (Outranking-Based Sorting Method with Boundary Profiles (also ELECTRE TRI-B)) is a sorting multi-criteria decision-making (MCDM) method introduced by Yu, W. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Yu, W. (1992). ELECTRE TRI: aspects méthodologiques et manuel d'utilisation. Document du LAMSADE 74, Université Paris-Dauphine


ELECTRE

ELECTRE (ELECTRE I — ELimination Et Choix Traduisant la REalité) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Revue Française d'Informatique et de Recherche Opérationnelle


Electrical Resistivity Tomography

Electrical Resistivity Tomography (ERT) is an active-source geophysical method that maps the spatial distribution of electrical resistivity in the subsurface by injecting current between two electrodes and measuring potential differences across an array of receiver electrodes. Advanced as a practical technique by Loke and Barker in 1996, ERT has become standard for hydrogeological, environmental, and structural characterization due to its sensitivity to fluid saturation and salt content.

Active source resistivity mapping and subsurface imaging

Loke, M. H., & Barker, R. D. (1996). Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophysical Prospecting, 44(1), 131-152.

Telford, W. M., Geldart, L. P., & Sheriff, R. E. (1990). Applied geophysics (2nd ed.). Cambridge University Press.


Electrochemical Impedance Spectroscopy

Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for characterizing electrochemical systems by applying a small AC voltage over a range of frequencies and measuring the resulting current response. Developed in the late 1960s, EIS reveals the frequency-dependent resistance and capacitance of interfaces, allowing researchers to separate charge transfer kinetics, diffusion, and ohmic losses. It is widely used in battery research, corrosion studies, fuel cells, and biosensors.

AC impedance measurement and analysis technique

Barsoukov, E., & Macdonald, J. R. (2005). Impedance Spectroscopy: Theory, Experiment, and Applications (2nd ed.). John Wiley & Sons.

Orazem, M. E., & Tribollet, B. (2008). Electrochemical Impedance Spectroscopy. John Wiley & Sons.

Lasia, A. (2014). Electrochemical Impedance Spectroscopy and its Applications. Springer.


Electrofishing

Electrofishing Fish Capture and Survey Method

electroshocking
electric netting
fish stunner

Electrofishing is a bioelectrical sampling technique in which electric current is applied to water to stun fish temporarily, allowing their capture for identification, measurement, and return to the stream. Developed in the 1950s and refined continuously, electrofishing is the standard method for inventorying fish communities in streams and small rivers, providing unbiased population estimates and species composition data.

Paukert, C. P., & Willis, D. W. (2001). Electrofishing: sampling fish in small streams with respect to fish size, species, and rarity. Journal of Freshwater Ecology, 16(1), 11-23.

Lucas, M. C., & Mercer, T. (1997). Electrofishing and point abundance sampling for stream fish surveys: sensitivity to habitat and species differences. Journal of Fish Biology, 37(3), 433-443.

Simonson, T. D., Lyons, J., & Klosiewski, P. D. (1994). Quantifying fish habitat in streams: substrate, flow, cover, and macrophytes. North American Journal of Fisheries Management, 14(3), 490-500.


Electromechanical Delay

Electromechanical Delay and Muscle Activation Latency

EMD
electromechanical lag
neural delay
activation delay

Electromechanical delay (EMD) is the time interval between electrical muscle activation (detected via electromyography) and the first detectable mechanical force output. Introduced by Cavanagh and Komi (1979), EMD reflects the physiological lag inherent in converting neural input into mechanical work. This delay arises from several sources: time for the action potential to propagate, time for calcium release, time for cross-bridge cycling to begin, and elastic recoil of muscle-tendon structures. EMD is typically 30-100 milliseconds in skeletal muscle and varies with muscle group, contraction type, and training status. Understanding EMD is important for explaining performance in rapid movements and for assessing neuromuscular function.

Cavanagh, P. R., & Komi, P. V. (1979). Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiologica Scandinavica, 106(3), 241-248.

Zhou, S. (2000). Acute neuromuscular adaptations to strength training in untrained men. Journal of Applied Physiology, 88(4), 1215-1222.

Maffiuletti, N. A., Aagaard, P., Blazevich, A. J., Folland, J., & Tillin, N. (2016). Rate of force development: physiological and methodological considerations. European Journal of Applied Physiology, 116(6), 1091-1116.

rate-of-force-development isokinetic-dynamometry counter-movement-jump reactive-strength-index electromechanical-delay


Clinical Electromyography

Electromyography (EMG) and Nerve Conduction Studies

Electromyography (EMG) and nerve conduction studies (NCS) are electrodiagnostic tests measuring electrical activity in muscles and nerves, providing objective data on neuromuscular function. These tests identify pathology in motor neurons, peripheral nerves, neuromuscular junctions, and muscles, helping clinicians diagnose conditions like peripheral neuropathy, myopathy, radiculopathy, and motor neuron disease when clinical examination is inconclusive.

Daube, J. R., & Rubin, D. I. (2009). Clinical neurophysiology (3rd ed.). Oxford University Press.

Preston, D. C., & Shapiro, B. E. (2021). Electromyography and neuromuscular disorders (4th ed.). Elsevier.


Electron Paramagnetic Resonance

Electron Paramagnetic Resonance Spectroscopy

EPR spectroscopy
ESR
electron spin resonance

Electron Paramagnetic Resonance (EPR), also called Electron Spin Resonance (ESR), is a spectroscopic technique that detects and characterizes unpaired electrons in molecules and materials. Discovered by Zavoiskii in 1945, EPR measures the absorption of microwave radiation by paramagnetic species in a magnetic field, providing information about electron spin states, local electronic environment, and molecular dynamics.

Zavoiskii, E. K. (1945). Paramagnetic relaxation of liquid solutions for perpendicular fields. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 15(6), 378-380.

Aasa, R., & Vänngård, T. (1975). Parameter hyperfine interactions in high-spin ferric complexes with applications to biochemical electron paramagnetic resonance. The Journal of Magnetic Resonance, 19(3), 308-315.


Electron Spin Resonance Dating

ESR dating
electron paramagnetic resonance dating
EPR dating

Electron spin resonance (ESR) dating is a chronometric method that determines the age of bones, teeth, mollusk shells, and sediments by measuring accumulated radiation-induced unpaired electrons. Developed by Michael Aitken in the 1980s, ESR detects free radicals trapped in mineral crystal structures. Unlike luminescence techniques that require heating or light exposure, ESR directly measures paramagnetic defects, making it particularly valuable for dating dental and skeletal remains that are inaccessible to other methods.

Grün, R. (1989). Electron spin resonance (ESR) dating. Quaternary International, 1, 65-109.

Blackwell, B., & Schwarcz, H. P. (1992). ESR dating of tooth enamel: A review of the state of the art. Nuclear Tracks and Radiation Measurements, 20(2), 231-246.

Shlukov, I., & Aitken, M. J. (1990). Studies of ESR dose response in tooth enamel. Radiation Measurements, 19(3-4), 275-283.


Electronic Nose

An electronic nose (e-nose) is an instrumental analytical device that mimics the mammalian olfactory system to detect and identify volatile organic compounds (odors) in food products. Developed by Persaud and Dodd in 1982, e-noses use arrays of non-selective chemical sensors combined with pattern recognition algorithms to create electronic signatures of food aromas, enabling objective, rapid quality assessment and shelf-life prediction.

Persaud, K., & Dodd, G. (1982). Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299(5881), 352-355.

Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: Electronic noses. Analytica Chimica Acta, 638(2), 159-171.


Electropalatography

Electropalatography (EPG) is an instrumental method for measuring tongue-to-palate contact during speech by using a specially designed artificial palate fitted with an array of sensors. Developed by William John Hardcastle in the 1970s, EPG provides detailed real-time visualization of articulation and has applications in phonetic research, speech pathology assessment, and biofeedback training. The method enables precise documentation of articulatory patterns across languages and is especially valuable for analyzing consonants that require palatal contact.

Hardcastle, W. J. (1989). Electropalatography and its clinical applications. In W. J. Hardcastle & A. Marchal (Eds.), Speech Production and Speech Modelling. Dordrecht: Kluwer.

Articulate Instruments Ltd. (2012). Electropalatography (EPG): Technical and clinical documentation. Edinburgh: Articulate Instruments.

Katz, W. F., & Bharadwaj, S. V. (2000). Acoustic and electropalatographic (EPG) analysis of connected speech. Journal of Speech, Language, and Hearing Research, 43(2), 429-441.


Electroretinography

Electroretinographic Recording of Retinal Function

ERG
retinal recording
functional assessment

Electroretinography (ERG) is an electrophysiological technique that records the electrical activity generated by the retina in response to light stimulation. By measuring the amplitude and timing of the resulting potential, ERG provides objective assessment of retinal photoreceptor and bipolar cell function independent of the animal's ability to see. It is essential for diagnosing inherited retinal dystrophies, assessing retinal toxicity, and monitoring disease progression in both clinical and veterinary ophthalmology.

Marmor, M. F., Fulton, A. B., Holder, G. E., Miyake, Y., Brigell, M., & Bach, M. (2009). ISCEV Standard for full-field clinical electroretinography. Documenta Ophthalmologica, 118(1), 69-77.

Preising, M. N., & Lorenz, B. (2012). Electroretinography: standardization of the white flash and flicker test as recommended by the international standardization committee. Documenta Ophthalmologica, 125(1), 67-72.

Ofri, R. (2015). Veterinary Ophthalmology (5th ed.). Wiley-Blackwell.


Electrospinning

electrospun fiber production
electrostatic fiber spinning

Electrospinning is an electrostatic fiber fabrication process that uses a high electric field to draw polymer solutions or melts into nanoscale fibers. Developed by Anton Formhals in the 1930s and refined by researchers including Darrell Reneker in the 1990s, the technique has become foundational to biomaterials engineering, enabling the creation of porous scaffolds for tissue engineering and drug delivery systems.

Formhals, A. (1934). Process and apparatus for preparing artificial threads. U.S. Patent 1,975,504.

Doshi, J., & Reneker, D. H. (1995). Electrospinning process and applications of electrospun fibers. Journal of Electrostatics, 35(2-3), 151-160.

Huang, Z. M., Zhang, Y. Z., Kotaki, M., & Ramakrishna, S. (2003). A review on polymer nanofibers by electrospinning and their applications in nanocomposites. Composites Science and Technology, 63(15), 2223-2253.

contact-angle-goniometry dynamic-mechanical-analysis swelling-and-degradation decellularization


Electrowinning

Electrowinning for Metal Extraction and Purification

Electrowinning is an electrochemical process that extracts and refines metals from dilute leaching solutions by passing electric current through an electrolytic cell. Metal ions migrate to the cathode (negative electrode) and are reduced to pure metal, while impurities remain in solution. This process is essential for copper, zinc, cobalt, nickel, and gold refining, producing metals of exceptional purity.

Electrochemical metal extraction and purification

Habashi, F. (2011). Electrometallurgy: principles, processes and materials. Metallurgical Transactions, 29(7), 1569-1589.

Sinclair, D. B., & Sinclair, R. J. (2005). A practical guide to the electrometallurgy of copper. AusIMM Bulletin, 4(2), 28-35.


Enzyme-Linked Immunosorbent Assay (ELISA)

enzyme immunoassay
EIA
solid-phase enzyme immunoassay
ELISA test

Eva Engvall and Peter Perlmann; independent parallel development by Anton Schuurs and Bauke van Weemen

ELISA is a plate-based immunoassay technique that detects and quantifies proteins, antibodies, antigens, hormones, and other analytes in biological samples. Widely used in veterinary science, medicine, and food safety, it exploits the specificity of antibody–antigen binding coupled to an enzyme-driven colorimetric signal to deliver sensitive, reproducible measurements across large sample batches.

Absorbance readings (optical density); serum, plasma, urine, tissue homogenate

Engvall, E., & Perlmann, P. (1971). Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin G. Immunochemistry, 8(9), 871–874.

Crowther, J. R. (2001). The ELISA Guidebook. Humana Press.

western-blot pcr immunofluorescence radioimmunoassay lateral-flow-assay flow-cytometry


Ellingham Diagram

Gibbs Free Energy Diagram
High-Temperature Reduction Diagram

The Ellingham Diagram, introduced by Harold Ellingham in 1944, is a graphical representation of the Gibbs free energy change for oxide formation and reduction as a function of temperature. It is an essential tool for predicting the thermodynamic feasibility of ore reduction and selecting appropriate reducing agents and temperatures for smelting and roasting operations.

Gibbs free energy diagram for high-temperature reactions

Ellingham, H. J. T. (1944). Reducibility of oxides and sulfides. Journal of the Society of Chemical Industry, 63(5), 125-160.

Richardson, F. D. (2007). Physical chemistry of melts in metallurgy (Vol. 2). Academic Press.


Elliptic Curve Cryptography

Elliptic Curve Cryptography (ECC) is a public-key cryptosystem based on the algebraic structure of elliptic curves over finite fields. Proposed independently by Neal Koblitz and Victor Miller in 1985, ECC offers equivalent security to RSA with much smaller key sizes. Modern cryptography increasingly favors ECC for its efficiency: a 256-bit ECC key provides security comparable to a 2048-bit RSA key, making it ideal for constrained environments and high-performance systems.

Miller, V. S. (1985). Use of Elliptic Curves in Cryptography. In Proceedings of the Advances in Cryptology - CRYPTO 1985, LNCS 218, pp. 417-426.

Koblitz, N. (1987). Elliptic Curve Cryptosystems. Mathematics of Computation, 48(177), 203-209.


Elo Rating

Elo Rating System
Elo Chess Rating
Elo Skill Rating
Elo Derecelendirme Sistemi

The Elo Rating System is a pairwise comparison-based ranking method developed by Hungarian-American physicist and chess master Arpad Elo and formally published in 1978. Originally designed to assess the relative skill levels of chess players, it assigns each competitor a numerical rating that rises or falls after each encounter based on the expected versus actual outcome. The system assumes that player performance follows a logistic distribution, enabling probabilistic predictions of match results and continuous rating refinement over time.

Elo, A. E. (1978). The Rating of Chessplayers, Past and Present. Arco Publishing.


eLORETA

Exact LORETA
eLORETA source reconstruction

Exact Low-Resolution Electromagnetic Tomography (eLORETA) is a non-parametric solution to the inverse problem in EEG and MEG source localization. Developed by Roberto D. Pascual-Marqui in 2002, eLORETA reconstructs three-dimensional maps of electrical brain activity from scalp electrode recordings, offering zero localization error under ideal noise-free conditions.

Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology, 24(S-D), 5–12.

Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65.


EM Algorithm

EM
Expectation-Maximization
Maximum Likelihood via Incomplete Data
BM Algoritması

The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.


Emax Model

Emax Pharmacodynamic Dose-Response Model

Maximum Effect Model
Hyperbolic Emax Model
Sigmoidal Emax Model
Emax Farmakodynamik Modeli

The Emax model is a nonlinear pharmacodynamic model that describes the relationship between drug concentration and biological effect. Introduced by Holford and Sheiner in 1981, it characterizes dose-response curves using three fundamental parameters: the maximum achievable effect (Emax), the concentration producing half-maximal effect (EC50), and an optional baseline effect (E0). It remains the standard framework in clinical pharmacology and drug development for quantifying pharmacodynamic dose-response relationships.

Nonlinear dose-response regression model

Nonlinear least squares or maximum likelihood

EC50 (concentration producing 50% of Emax)

Holford, N. H. G., & Sheiner, L. B. (1981). Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clinical Pharmacokinetics, 6(6), 429–453.


Embedded Case-Focused Mixed Methods

Embedded Case-Focused Mixed Methods Design

embedded case-study mixed methods
case-centered embedded MMR
nested case mixed methods
embedded within-case mixed design

Creswell & Plano Clark (embedded design); Yin (case-study framework)

Embedded case-focused mixed methods design combines a case-study unit of analysis with an embedded mixed methods structure, nesting one smaller data strand — typically qualitative — within a dominant primary strand — typically quantitative — all organized around one or more bounded cases. This design enables researchers to answer within-case questions at multiple levels, capturing both statistical patterns and rich contextual meaning for a specific case or set of cases.

Both quantitative and qualitative data; one strand nested within a case-study structure

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). Sage.