Blade Element Momentum Theory

Blade Element Momentum Theory for Rotors

BEM theory
rotor performance prediction
actuator disk method

Blade element momentum theory (BEM) is a fundamental method for analyzing rotor performance by combining blade element aerodynamics with momentum conservation. Developed initially by Froude and refined by Glauert and Leishman, BEM decomposes a rotor into radial blade elements, computes local aerodynamic forces, and sums contributions to predict total thrust, torque, power, and efficiency. BEM is standard for helicopter, wind turbine, and propeller design.

Froude, W. (1889). On the elementary relation between pitch, slip, and propulsive efficiency. Transactions of the Institution of Naval Architects, 30, 94–103.

Glauert, H. (1935). The Elements of Aerofoil and Airscrew Theory. Cambridge University Press.

Leishman, J. G. (2006). Principles of Helicopter Aerodynamics (2nd ed.). Cambridge University Press.


Bland-Altman Analysis

Bland-Altman plot
limits of agreement analysis
method agreement analysis
Bland-Altman Uyum Analizi

The Bland-Altman analysis is a graphical and statistical technique for assessing agreement between two measurement methods applied to the same subjects. Introduced by J. Martin Bland and Douglas G. Altman in their landmark 1986 Lancet paper, it plots the difference between the two methods against their mean for each subject, and derives the bias (mean difference) along with limits of agreement (LoA) that capture 95% of differences in the population.

Graphical and statistical method comparison

Limits of Agreement (mean bias ± 1.96 SD)

Bland, J.M. & Altman, D.G. (1986). Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement. Lancet, 327(8476), 307–310.

Giavarina, D. (2015). Understanding Bland Altman Analysis. Biochemia Medica, 25(2), 141–151.

icc-intraclass-correlation cohens-kappa paired-t-test equivalence-test-tost pearson-correlation


Blind Source Separation

BSS
Blind Signal Separation
Independent Component Analysis
ICA

Blind Source Separation (BSS) is a signal processing technique that recovers original signals from their unknown mixture without detailed knowledge of the mixing process. Through the framework of Independent Component Analysis (ICA), BSS recovers statistically independent source signals using only the assumption that sources are independent and non-Gaussian. First formalized by Pierre Comon in 1994, BSS has become essential for applications from audio separation to biomedical signal analysis.

Comon, P. (1994). Independent Component Analysis, a New Concept? Signal Processing, 36(3), 287–314.

Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. John Wiley & Sons.

short-time-fourier-transform power-spectral-density adaptive-lms-filter wiener-filter


Blob Detection

Connected component analysis
Region-based detection

Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiring a priori knowledge of their size.

Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.

Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. European Conference on Computer Vision (ECCV), 430–443.

harris-corner-detection scale-space-theory watershed-segmentation contour-analysis image-morphology


Block Bootstrap

Block Bootstrap (Moving Block and Stationary Bootstrap)

blok bootstrap (moving block / stationary)

Künsch (moving block, 1989); Politis & Romano (stationary, 1994)

Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).

Block resampling (moving or stationary blocks)

Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.

Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313.

bootstrap-inference permutation-test jackknife quantile-regression ols-regression


Blockchain Consensus

consensus algorithm
PoW
PoS
distributed consensus

Blockchain consensus mechanisms are distributed protocols that enable a network of untrusted nodes to agree on the correct state of a ledger without a central authority. Introduced with Bitcoin in 2008, consensus mechanisms like Proof of Work and Proof of Stake ensure that modifications to the blockchain cannot be made unilaterally by any participant. Consensus mechanisms are fundamental to cryptocurrency and blockchain applications, making them resistant to tampering and censorship.

Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf

Buterin, V. (2014). A next-generation smart contract and decentralized application platform. Ethereum White Paper.


Blocked AB Design

Blocked AB Single-Subject Experimental Design

blocked AB single-case design
randomized block AB design
AB design with blocking
blocked baseline-treatment design

1970s–1980s (systematic development of blocked randomization in single-case research)

Based on Fisher's randomized block principle (1926) applied to single-case AB designs

The Blocked AB Design applies the logic of randomized block experimental design to the classic single-subject AB framework. Observation sessions are organized into blocks — matched sets of time points or contextual units — and the assignment of baseline (A) and treatment (B) phases is randomized within each block. This controls for nuisance time-based variability while preserving the interpretive simplicity of the fundamental two-phase single-case structure.

Single-subject experimental design with blocking

Repeated measures (time-series observations per participant)

Edgington, E., & Onghena, P. (2007). Randomization Tests (4th ed.). Chapman and Hall/CRC.

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.

ab-design aba-design abab-design multiple-baseline-design blocked-randomized-controlled-trial single-subject-experimental-design


Blocked A/B Test

Blocked A/B Test (Block-Randomized Split Test)

block-randomized A/B test
stratified A/B test
blocked split test
block-design A/B experiment

1926 (blocking principle); 2000s–2010s (online A/B testing application)

R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners

A blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test.

Randomized controlled experiment with variance reduction

Categorical (group assignment) and continuous or binary outcome metrics

Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

ab-test blocked-randomized-controlled-trial factorial-ab-test multi-arm-experiment blocked-factorial-experiment randomized-controlled-trial


Blocked ABA Design

Blocked withdrawal design
ABA design with blocking
Blocked reversal single-subject design

1960s–1970s (ABA baseline); blocking extension developed through applied behavior analysis literature

ABA reversal logic: Wolf, Risley & Baer (1960s); blocking integration draws on Fisher's randomized block principles applied within single-case methodology

The Blocked ABA Design is a single-subject experimental approach that combines the classic ABA reversal logic (baseline, intervention, withdrawal) with block-based session organization to control for time-related or contextual nuisance variation. By grouping observation sessions into blocks — such as days, weeks, or settings — and ensuring phase transitions align to block boundaries, the design isolates the effect of an intervention on an individual participant's repeated behavior measures more rigorously than an unblocked ABA.

Single-subject experimental design with nuisance control

Repeated measures of a target behavior across time (continuous or interval-recorded behavioral data)

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.

aba-design abab-design multiple-baseline-design blocked-abab-design blocked-ab-design single-subject-experimental-design


Blocked Full Factorial Experiment

Blocked Full Factorial Experimental Design

blocked full factorial design
full factorial with blocking
complete factorial blocked design
BFF design

1935 (Fisher); systematized through 20th-century DOE literature

R. A. Fisher (blocking principle); full factorial DOE tradition

A blocked full factorial experiment tests every combination of all factor levels while grouping experimental runs into homogeneous blocks to isolate a known nuisance variable. This design preserves the power to detect all main effects and interactions of the factors of interest while preventing batch-to-batch, day-to-day, or machine-to-machine variability from inflating experimental error.

Continuous or categorical outcome measures from controlled experiments

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.

full-factorial-experiment fractional-factorial-experiment factorial-experiment blocked-randomized-controlled-trial blocked-fractional-factorial-experiment randomized-complete-block-design


Blocked Laboratory Experiment

Randomized Block Design in Laboratory Experiments

blocked lab experiment
laboratory randomized block design
RBD laboratory study
blocked within-lab experiment

A blocked laboratory experiment is a controlled laboratory study in which experimental units are grouped into homogeneous blocks before treatment assignment, and treatments are then randomly assigned within each block. Blocking removes the influence of a known nuisance variable — such as participant batch, equipment run, or testing day — from the error term, increasing the precision of treatment comparisons without expanding sample size.

Controlled experimental design with blocking

Continuous or categorical outcome measurements from controlled lab settings

laboratory-experiment randomized-controlled-trial blocked-randomized-controlled-trial factorial-experiment latin-square-design within-subjects-design


Blocked Multiple Baseline Design

Blocked Multiple Baseline Single-Subject Experimental Design

blocked multiple baseline across subjects

1968 (multiple baseline foundation); blocking variant codified 1980s–1990s

Baer, Wolf & Risley (multiple baseline); blocking extension developed in applied behavior analysis literature

A blocked multiple baseline design is a single-subject experimental approach that combines the logic of the multiple baseline design with blocking — the systematic grouping of participants, behaviors, or settings into matched sets — to reduce extraneous variability and strengthen causal inference. The intervention is introduced in a staggered sequence across baselines within each block, demonstrating experimental control through replication within and across blocks.

Repeated measures of individual behavior or performance (continuous observation data)

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.


Blocked Natural Experiment

stratified natural experiment
block-stratified quasi-experiment
natural experiment with blocking

Blocking: 1935; natural experiments as formal causal framework: 1990s–2000s

Combines Fisher's blocking principle (1935) with natural experiment methodology formalized by Angrist and Pischke (2009)

A blocked natural experiment is a quasi-experimental design that exploits naturally occurring, researcher-uncontrolled variation in treatment assignment while pre-stratifying (blocking) units on key observed covariates. Blocking absorbs between-stratum variance, improves statistical precision, and strengthens the plausibility of the as-if-random assumption within each block. The design draws on Fisher's blocking principle and the natural experiment tradition in economics and epidemiology.

Observational or administrative data with naturally occurring treatment variation, with known stratifying covariates

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

natural-experiment blocked-randomized-controlled-trial difference-in-differences regression-discontinuity-design instrumental-variable quasi-experimental-design


Blocked Pretest-Posttest Experimental Design

Randomized Block Pretest-Posttest Experimental Design

randomized block pre-post control group design

1935 (blocking, Fisher); 1963 (pretest-posttest + blocking synthesis, Campbell & Stanley)

Donald T. Campbell & Julian C. Stanley (systematized); blocking technique from Ronald A. Fisher

The blocked pretest-posttest experimental design combines blocking — grouping participants into homogeneous strata before randomization — with pre- and post-intervention measurement. Blocking controls for known sources of variability (e.g., baseline ability, gender, site), while the pretest-posttest structure quantifies change scores directly. Together, they reduce error variance and increase statistical power compared to a simple pretest-posttest design, making this approach well suited to educational, clinical, and behavioral intervention studies.

Continuous or ordinal outcome measures collected at pretest and posttest

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage.

pretest-posttest-experimental-design randomized-controlled-trial blocked-randomized-controlled-trial factorial-experiment control-group-experimental-design solomon-four-group-design


Blocked Randomized Controlled Trial

blocked RCT
block-randomized trial
stratified block randomization trial
permuted block randomization

1920s (Fisher's blocking principle); applied to RCTs from the 1940s onward

R. A. Fisher (blocking principle); systematic RCT application by Bradford Hill and later Pocock, Friedman et al.

A blocked randomized controlled trial (blocked RCT) uses permuted-block randomization to ensure that treatment groups remain balanced in size — and optionally in key characteristics — throughout recruitment. Within each block of fixed or randomly varied size, all treatment allocations are present in equal numbers, so imbalance cannot accumulate even if the trial is stopped early. This makes blocked RCTs the standard randomization approach in clinical and behavioral intervention research.

Continuous, binary, or ordinal outcome data from randomized participants

Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.

Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley.


Blocked Solomon Four-Group Design

Blocked Solomon Four-Group Experimental Design

Blocked S4G
randomized blocked Solomon design
Solomon four-group with blocking

1949 (base); blocking extension applied in behavioral and social sciences from mid-20th century onward

Richard L. Solomon (base design, 1949); blocking integrated from classical experimental design tradition (Fisher, 1935)

The blocked Solomon four-group design combines Solomon's classic four-group structure — which disentangles pretest sensitization effects from treatment effects — with blocking on a known nuisance variable. Participants are first grouped into homogeneous blocks (e.g., by baseline ability, gender, or site), then randomly assigned within each block to one of four conditions: pretested treatment, pretested control, unpretested treatment, and unpretested control. This structure simultaneously controls for maturation, pretest reactivity, and block-level variance, making it one of the strongest quasi-controlled experimental frameworks available.

Continuous or categorical outcome measures; pretest and posttest scores

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications.

solomon-four-group-design pretest-posttest-experimental-design blocked-randomized-controlled-trial factorial-experiment randomized-controlled-trial control-group-experimental-design


Blood Gas Analysis in Veterinary Medicine

Arterial and Venous Blood Gas Analysis and Acid-Base Assessment in Veterinary Medicine

acid-base assessment
blood gas testing
respiratory assessment

Clinical pathology and emergency medicine

Blood gas analysis is a systematic laboratory method for measuring partial pressures of oxygen and carbon dioxide, pH, bicarbonate, and electrolytes in arterial or venous blood. Formalized in veterinary medicine since the 1960s-1970s, it provides critical real-time assessment of respiratory function, metabolic status, and acid-base balance, enabling rapid diagnosis and monitoring of severely ill animals and guiding intensive care management.

DiBartola, S. P. (2012). Fluid, Electrolyte, and Acid-Base Disorders in Small Animal Practice (4th ed.). St. Louis, MO: Elsevier Saunders.

Hopper, K., Haskins, S. C. (2015). Updates on the diagnosis and management of severe acidemia in dogs and cats. Veterinary Clinics of North America: Small Animal Practice, 45(5), 961-973.

Constable, P. D., Hinchcliff, K. W., Done, S. H., Grünberg, W. (2016). Veterinary Medicine: A Textbook of the Diseases of Cattle, Horses, Sheep, Pigs, and Goats (11th ed.). Edinburgh: Saunders.


BMP Release

Bone Morphogenetic Protein Release Kinetics Assay

BMP release kinetics
BMP elution profile
growth factor release assay

The bone morphogenetic protein (BMP) release assay measures the kinetics and amount of BMP elution from a biomaterial carrier over time. BMP-2, BMP-6, BMP-7, and BMP-9 are potent osteoinductive growth factors discovered by Marshall Urist in 1965 that trigger bone and cartilage formation. When loaded into scaffolds, hydrogels, or implants, BMPs must be released in a controlled manner to maximize biological effect while minimizing systemic exposure. The release assay quantifies how much BMP is present in the surrounding medium at defined timepoints, enabling optimization of carrier materials and release profiles for bone regeneration and fracture healing applications.

Urist, M. R. (1965). Bone: formation by autoinduction. Science, 150(3698), 893-899.

Reddi, A. H. (2001). Interplay between bone morphogenetic proteins and cognate binding proteins in bone and cartilage development: noggin, gremlin, and chordin. Arthritis Research, 3(1), 1-5.

Garrison, K. R., Donell, S., Reuben, A., et al. (2010). Clinical effectiveness and cost-effectiveness of bone morphogenetic proteins in the management of fractures of long bones: a systematic review. The Journal of Bone and Joint Surgery, 89(4), 477-490.

alizarin-red-staining picrosirius-red-staining electrospinning dynamic-mechanical-analysis


BN-ELECTRE-I

BN-ELECTRE-I (Bipolar Neutrosophic ELECTRE-I) is a electre family multi-criteria decision-making (MCDM) method introduced by Akram, M., Shumaiza, Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Bipolar neutrosophic outranking — concordance/discordance dominance

Akram, M., Shumaiza, Smarandache, F. (2018). Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I. Axioms


BN-TOPSIS

BN-TOPSIS (Bipolar Neutrosophic TOPSIS) is a topsis family multi-criteria decision-making (MCDM) method introduced by Akram, M., Shumaiza, Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Bipolar neutrosophic extension — revised closeness degree via inferior ratio


Body Condition Score for Cattle

Body Condition Scoring and Assessment in Cattle

BCS assessment
condition scoring
cattle body evaluation

Body condition scoring (BCS) is a systematic visual assessment of cattle body fat reserves and nutritional status. Formalized by dairy nutritionists in the 1980s-1990s (Edmonson and Ferguson), BCS integrates palpation of bony landmarks and observation of body shape to quantify energy stores on a standardized scale. The method is essential for nutritional management, health monitoring, and reproductive performance in dairy and beef cattle.

Ferguson, J. D., Galligan, D. T., & Thomsen, N. (1994). Principal descriptors of body condition score in Holstein dairy cattle. Journal of Dairy Science, 77(9), 2695-2703.

Edmonson, A. J., Lean, I. J., Weaver, L. D., Farver, T., & Webster, G. (1989). Body condition scoring chart for Holstein dairy cattle. Journal of Dairy Science, 72(1), 68-78.

Roche, J. R., Friggens, N. C., Kay, J. K., Fisher, M. W., Stafford, K. J., & Berry, D. P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science, 92(12), 5769-5801.


Body Condition Score for Dogs and Cats

Systematic Body Condition Scoring Assessment in Canine and Feline Patients

body condition assessment
weight assessment
obesity screening

Body condition scoring is a systematic clinical assessment method for evaluating a dog's or cat's body fat and muscle mass relative to ideal standards. Developed and standardized by Purina and veterinary nutrition experts in the 1990s-2000s, it provides objective evaluation of nutritional status, guides dietary management, and identifies obesity and malnutrition as contributors to disease. Body condition scoring is fundamental to preventive medicine and geriatric care in small animal practice.

Purina PetCare Company. (2006). Body Condition Score Charts for Dogs and Cats. Retrieved from Purina ProPlan Veterinary Diets resource center.

German, A. J., Holden, S. L., Meurisse, B., et al. (2018). Long-term weight loss is effective and improves quality of life in obese dogs. Journal of Veterinary Internal Medicine, 32(4), 1201-1212.

Scarlett, J. M., Donoghue, S. (2012). Associations between body condition and disease in cats. Journal of the American Veterinary Medical Association, 214(11), 1644-1649.


Body Condition Scoring

Body Condition Scoring System for Livestock

BCS
body condition assessment
nutritional status evaluation

Body Condition Scoring (BCS) is a semi-quantitative visual and palpation assessment method used to evaluate the nutritional status and adipose tissue reserves of livestock, particularly dairy cattle, beef cattle, and small ruminants. Developed systematically in the 1980s, BCS provides a practical, non-invasive tool for herd management, reproductive performance prediction, and feed efficiency optimization.

Ferguson, J. D., Galligan, D. T., & Thomsen, N. (2004). Principal descriptors of body condition score in Holstein cows. Journal of Dairy Science, 87(12), 3793-3806.

Bewley, J. M., Peacock, A. M., Lewis, O., Boyce, R. E., Roberts, D. J., Coffey, M. P., & Kenyon, S. J. (2008). Evaluating the prevalence of lameness in dairy cattle. Journal of Dairy Science, 91(2), 907-920.

Green, M. J., Macrae, A., & Green, L. (2014). Predicting dairy cow body condition score from features of herbage during grazing. Livestock Science, 160, 47-53.


BIDQ

BIDQ
Body Dysmorphic Disorder Questionnaire
Appearance Concern Screen

body dysmorphic disorder and appearance concerns

The BIDQ is a brief self-report questionnaire screening for body dysmorphic disorder (BDD), a disorder characterized by preoccupation with a perceived defect in appearance and repetitive behaviours (mirror checking, grooming, comparing with others). Developed by Castle and colleagues, the BIDQ focuses on the core diagnostic features of BDD: appearance concern, functional impairment, and repetitive behaviours. It is used in clinical, cosmetic dermatology, and research settings to identify individuals who may have BDD.

Mancuso, S. G., Knoesen, N. P., & Castle, D. J. (2010). The Dysmorphic Concern Questionnaire: A screening measure for body dysmorphic disorder. Australian & New Zealand Journal of Psychiatry, 44(6), 535–542.

Grant, J. E., Kim, S. W., & Eckert, E. D. (2002). Body dysmorphic disorder in patients with body image concerns visiting cosmetic dermatologists. Journal of Psychiatric Research, 36(6), 379–385.

Castle, D. J., Rossell, S. L., & Cecil, D. (2006). Concealment of delusions and obsessions in body dysmorphic disorder. Australian & New Zealand Journal of Psychiatry, 40(1), 19–22.


Body Sensations Questionnaire

The Body Sensations Questionnaire (BSQ) is a 17-item self-report measure that assesses the degree to which respondents fear common bodily sensations associated with panic and anxiety (e.g., heart palpitations, dizziness, trembling). Developed by Chambless and colleagues in 1984, the BSQ captures a specific form of anxiety sensitivity—fear of interoceptive cues. It is widely used in clinical and research assessment of panic disorder, agoraphobia, and other anxiety conditions.

Chambless, D. L., Caputo, G. C., Bright, P., & Gallagher, R. (1984). Assessment of fear in agoraphobics: The Body Sensations Questionnaire and the Agoraphobia Cognitions Questionnaire. Journal of Consulting and Clinical Psychology, 52(6), 1090–1097.


BSQ

Body Shape Questionnaire Revised (BSQ-R)

Peter Cooper, Melanie Taylor, Zafra Cooper, Christopher Fairburn

The BSQ is a self-report questionnaire measuring preoccupation with and dissatisfaction about body shape. Originally developed by Cooper and colleagues in 1987, the full version contains 34 items; shorter versions (BSQ-16, BSQ-8) are also widely used. The BSQ was designed to assess body shape concern as a core psychopathological feature of eating disorders and is widely used in eating disorder assessment, body image research, and epidemiological studies.

Cooper, P. J., Taylor, M. J., Cooper, Z., & Fairburn, C. G. (1987). The development and validation of the Body Shape Questionnaire. International Journal of Eating Disorders, 6(4), 485–494.

Evans, C., & Dolan, B. (1993). Body Shape Questionnaire: Derivation of shortened 'alternate forms' and further data on reliability and validity. International Journal of Eating Disorders, 13(3), 315–321.

Welch, E., Lagerström, M., & Ghaderi, A. (2012). Body Shape Questionnaire: Psychometric properties of the short version (BSQ-8C) and norms from the general Swedish population. Body Image, 9(1), 15–21.

ede-q scoff-questionnaire three-factor-eating-questionnaire binge-eating-scale body-image-disturbance-questionnaire


BWIS

Janell Mond, Phillipa J. Hay (body image in eating disorders); David Frederick (body satisfaction)

Body image satisfaction and dissatisfaction are important psychological constructs measured through multiple instruments, with no single standardized 'Body Weight Image and Satisfaction Scale,' but rather several validated measures of body dissatisfaction (e.g., EDE-Q body dissatisfaction items, Figure Rating Scale, Body Shape Questionnaire). These instruments assess the degree to which individuals are satisfied with their body weight and shape, a key psychological outcome in nutrition, eating disorder, and weight management research. Body dissatisfaction is strongly associated with disordered eating, poor mental health, and reduced quality of life.

Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beumont, P. J. (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.

Frederick, D. A., Buchanan, G., Sadeghi-Azar, S., et al. (2016). Desiring the muscular ideal: Men's body satisfaction in the United States, Ukraine, and Ghana. Psychology of Men & Masculinity, 9(6), 351-365.

weight-bias-internalization-scale dutch-eating-behavior-questionnaire intuitive-eating-scale nutrition-self-efficacy-scale mini-nutritional-assessment


Bond Work Index

The Bond Work Index, introduced by Fred C. Bond in 1952, is an empirical parameter that characterizes the resistance of an ore to grinding in a tumbling mill. It is defined as the kilowatt-hours per short ton (kWh/st) of electrical energy required to reduce a coarse ore from theoretically infinite size to 80% passing 100 micrometers. The Bond Index is foundational in mineral processing plant design and cost estimation worldwide.

Empirical method for grinding energy estimation

Bond, F. C. (1952). The third theory of comminution. Transactions of the American Institute of Mining and Metallurgical Engineers, 193, 484-494.

Napier, J. A. L., & Rowland, C. A. (2005). Optimizing comminution circuit design and operation for improved mineral processing. Society for Mining, Metallurgy & Exploration.


Bone Density Assessment in Dentistry

Alveolar Bone Density and Quality Evaluation

bone quality assessment
trabecular pattern analysis
bone density classification

1985 (classification); modern CBCT 2000s+

Lekholm and Zarb (bone quality classification); Hounsfield units standardization

Bone density assessment in dentistry evaluates the quantity and quality of alveolar bone supporting teeth or serving as an implant site. Assessment integrates radiographic imaging (panoramic radiographs, periapical films, and cone-beam computed tomography) and clinical examination to classify bone density into four categories (Type I to IV) and to quantify bone loss. Accurate bone density assessment is critical for implant planning, predicting implant success, and adjusting surgical and loading protocols to account for bone quality variations.

Lekholm, U., & Zarb, G. A. (1985). Patient selection and preparation. In Brånemark, P.-I., et al. (Eds.), Tissue-integrated prostheses: Osseointegration in clinical dentistry. Quintessence Publishing, 199-209.

Turkyilmaz, I., Tözüm, T. F., & Tumer, C. (2007). Bone density assessments of dental implant sites using computerized tomography. Journal of Oral Implantology, 33(6), 335-343.

Meijer, H. J., Steen, W. H., & Bosman, F. (1992). Standardized radiographs of alveolar bone: effects on bone density, bone loss, and abutment tooth angulation. Clinical Oral Implants Research, 3(2), 100-108.

bitewing-radiography dental-implant-stability-rfa orthodontic-cephalometry periodontal-probing


Bonferroni Correction

Bonferroni Family-Wise Error Rate Correction

alpha correction for multiple comparisons

Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn

The Bonferroni correction is a conservative, universally applicable method for controlling the family-wise error rate (FWER) when conducting multiple simultaneous hypothesis tests. Grounded in Bonferroni's 1936 probability inequality and formalized for multiple comparisons by Olive Jean Dunn in 1961, the procedure divides the target significance level α by the number of tests m, ensuring that the probability of making even one false rejection across the entire family of tests does not exceed α.

Family-wise error rate (FWER) correction

Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.

Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56(293), 52–64.

Miller, R. G. (1981). Simultaneous Statistical Inference (2nd ed.). Springer-Verlag.

holm-correction benjamini-hochberg-procedure sidak-correction tukey-hsd-test scheffe-test one-way-anova independent-t-test


BONFERRONI-MEAN

BONFERRONI-MEAN (Bonferroni Mean (BM)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Bonferroni, C. in 1950. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Interrelationship-based aggregation — captures pairwise interactions

Bonferroni, C. (1950). Sulle medie multiple di potenze. Bollettino dell'Unione Matematica Italiana


Bonus-Malus System

Experience Rating in Automobile Insurance

A Bonus-Malus System (BMS) is an actuarial experience-rating mechanism used primarily in automobile insurance to adjust individual policyholders' premiums based on their personal claim history. Policyholders who remain claim-free receive premium discounts (bonus), while those who file claims are penalised with surcharges (malus). The framework was comprehensively formalised and analysed by Jean Lemaire in his landmark 1995 monograph, which remains the definitive reference for the design and evaluation of such systems worldwide.

Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers.


Book Chapter

Book Chapter (Contributed Chapter in Edited Academic Volume)

chapter
edited volume contribution
book contribution
invited chapter

Publishing tradition (18th century onward)

A book chapter is an original scholarly contribution comprising a single chapter within an edited academic volume (book). Unlike journal articles (independent publications in a periodical), book chapters are integrated parts of a larger work edited by one or more scholars. Book chapters allow greater length (5,000–15,000 words typical) and flexible formats compared to journal articles. Edited volumes are common in humanities, social sciences, and professional fields; some disciplines (especially sciences) emphasize single-authored or journal-edited books over edited volumes. Book chapters provide substantial scholarly venues for specialized research, methodological essays, and interdisciplinary synthesis.

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

University of Chicago Press (2017). The Chicago Manual of Style (17th ed.). University of Chicago Press.

Routledge Publishing Guidelines for Book Chapters. https://www.routledge.com/authors

original-research-article literature-review-article book-authorship scholarly-communication


Boolean Search Operators

Boolean Logic Operators for Database Searching

1847 (Boolean algebra); 1960s (database applications)

George Boole and IT information retrieval practitioners

Boolean search operators are logical functions—AND, OR, NOT, and parentheses—used to combine and filter search terms in bibliographic databases, library catalogs, and search engines. Named after mathematician George Boole (1815–1864), Boolean logic has been applied to information retrieval since the 1960s. These operators allow researchers to construct complex, precise searches that retrieve only articles meeting specific combinations of criteria, dramatically improving search efficiency and reducing irrelevant results.

Wilkinson, M. D., Sansone, S. A., Vandervalk, B., & Rocca-Serra, P. (2011). Evaluating information retrieval systems: a guide for researchers. Expert Review of Molecular Diagnostics, 11(2), 181–190.

Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.

Sampson, M., Barrowman, N. J., Moher, D., & Klassen, T. P. (2008). Should meta-analysts search Embase in addition to Medline? Journal of Clinical Epidemiology, 56(10), 943–955.


Boosting Ensemble

Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.

Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.

Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.

adaboost gradient-boosting bagging-ensemble majority-voting


Boosting

Boosting (Ensemble of Sequentially Weighted Weak Learners)

AdaBoost
gradient boosting
iterative reweighting ensemble
sequential ensemble

Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.

Sequential ensemble (iterative reweighting)

Labeled tabular data (classification; regression extensions exist)

Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.

random-forest gradient-boosting xgboost voting-ensemble decision-tree bagging


Bootstrap DEA

Bootstrapped DEA
DEA Bootstrap Inference
Simar-Wilson Bootstrap
Bootstrap Sınır Analizi

Bootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers.

Nonparametric efficiency estimation with bootstrap inference

Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.


Bootstrap Inference

bootstrap
bootstrap resampling
nonparametric bootstrap
Bootstrap Çıkarımı

Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.

Empirical sampling distribution from resampling with replacement

Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.

Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC Press.

permutation-test jackknife winsorized-estimation trimmed-mean-test robust-correlation


Bootstrap Simulation with Missing Data

Bootstrap Simulation with Missing Data Handling

bootstrap with missing data
bootstrap imputation simulation
resampling under missingness
bootstrap MI

Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others

Bootstrap simulation with missing data combines resampling-based variance estimation with principled handling of incomplete observations. Rather than deleting cases or assuming complete data, the method integrates imputation or weighting directly into the bootstrap loop, propagating the additional uncertainty due to missingness into the final standard errors and confidence intervals.

Any data type with partially observed values (MCAR, MAR, MNAR)

Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC.

Little, R. J. A. & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). Wiley.


Bootstrap Simulation

Bootstrap Simulation (Bootstrap Resampling)

Önyükleme Simülasyonu (Bootstrap Resampling)

Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.

1000 minimum; 10,000 for confidence intervals

BCa (bias-corrected accelerated) for skewed distributions

Required for dependent (time-series) data

Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap Methods and their Application. Cambridge University Press.

monte-carlo-simulation permutation-test jackknife-estimation variance-reduction-mc bayesian-inference


Borda Count Aggregation

Borda count is a preference aggregation method that combines ranked predictions from multiple classifiers by assigning points based on ranking position. Each classifier ranks the possible outcomes, and each class receives points inversely proportional to its rank position. The class with the highest total score is selected. Originally proposed by French mathematician Jean-Charles de Borda in 1781, this method has been adapted for ensemble learning to aggregate soft predictions and rank-ordered outputs.

Borda, J. C. de (1781). Mémoire sur les élections au scrutin. Histoire de l'Académie Royale des Sciences.

Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613-622.

majority-voting weighted-voting stacked-generalization ensemble-averaging


BORDA

BORDA (Borda Count — Positional scoring rule for rank aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Borda, J.-C. de in 1900. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Borda, J.-C. de (1900). Mémoire sur les Élections au Scrutin. Histoire de l'Académie Royale des Sciences, Paris


Borderline Symptom List

Borderline personality disorder symptom severity

The BSL-95 is a 95-item self-report questionnaire designed to measure the severity of borderline personality disorder (BPD) symptoms across nine subscales: affect dysregulation, distrust, self-harming behaviors, suicide risk, identity disturbance, negative relationships, and dissociation. Developed by Bohus and colleagues in 2007, it provides comprehensive assessment of the multifaceted psychopathology of BPD. A brief 23-item version (BSL-23) has also been validated for rapid assessment. The BSL is sensitive to treatment effects and widely used in BPD research and clinical monitoring.

Bohus, M., Kleindienst, N., Limberger, M. F., Stieglitz, R. D., Domsalla, M. E., Chapman, A. L., ... & Wolf, M. (2009). The short version of the Borderline Symptom List (BSL-23): Development and initial data on psychometric properties. Psychopathology, 42(1), 32–39.

Stiglmayr, C. E., Ebner-Priemer, U. W., Bretz, J., Behm, R., Mohammadi, B., Schlottke, P. F., & Bohus, M. (2008). Dissociative symptoms are positively related to stress in borderline personality disorder. Acta Psychiatrica Scandinavica, 117(4), 278–288.

Kleindienst, N., Limberger, M. F., Ebner-Priemer, U. W., Keibel, A., & Bohus, M. (2011). Prospective prediction of suicide attempts within a 6-month follow-up period in female patients with borderline personality disorder: Findings from the Berlin Affective Psychosis Study. Acta Psychiatrica Scandinavica, 123(1), 61–70.


Born-Oppenheimer Approximation

The Born-Oppenheimer (BO) Approximation is a foundational assumption in molecular quantum mechanics that nuclei can be treated as fixed while solving for electrons, and vice versa. Introduced by Born and Oppenheimer in 1927, this separation reduces the complex many-body electronic-nuclear problem to a sequence of simpler problems, enabling nearly all molecular calculations.

Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484.

Longuet-Higgins, H. C. (1975). The intersection of potential energy surfaces in polyatomic molecules. Proceedings of the Royal Society A, 344, 147–156.

Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry. Dover Publications.


Boston Aphasia Severity Rating Scale

BDAE
Boston Aphasia Rating Scale
Boston Aphasia Severity

aphasia linguistic severity & classification

The Boston Diagnostic Aphasia Examination Severity Rating Scale (BDAE-SRS) is the gold-standard clinician-administered assessment of aphasia severity and type in adults following stroke or acquired brain injury. Developed by Goodglass, Kaplan, and colleagues (2001, third edition), BDAE provides comprehensive evaluation of language across 18 domains (auditory comprehension, oral expression, naming, repetition, reading, writing) and yields both an overall severity rating (0–5 scale) and a detailed profile classifying aphasia syndrome (Broca's, Wernicke's, conduction, global, etc.). BDAE is foundational to aphasia diagnosis, prognosis, and treatment planning.

Goodglass, H., Kaplan, E., & Barresi, B. (2001). The Boston Diagnostic Aphasia Examination–Third Edition (BDAE-3). Philadelphia: Lippincott Williams & Wilkins.

Kertesz, A. (1982). Western Aphasia Battery. New York: Grune & Stratton.

Scarpa, M. C., Colombo, M., Agosta, F., Volonté, M. A., & Filippi, M. (2009). Longitudinal Neuroimaging and Neuropsychological Changes in Primary Progressive Aphasia. Neurology, 72(8), 1705–1711.


Boundary Element Method

The Boundary Element Method (BEM) is a numerical technique that solves partial differential equations by transforming them into boundary integral equations, requiring discretization only of the problem boundary rather than the entire domain. Developed systematically by Carlos Brebbia in the late 1970s, BEM offers significant advantages for infinite or semi-infinite domains, stress concentration analysis, and problems with high aspect ratios. It is especially valuable in geotechnical engineering, acoustics, and materials characterization.

Brebbia, C. A. (1978). The Boundary Element Method for Engineers. Pentech Press.

Gatmiri, B., & Kamalian, M. (2008). Advances in Boundary Element Techniques. WIT Press.

Paris, F., & Cañas, J. (2012). Boundary element method: Fundamentals and applications. Oxford University Press.


Boundary Layer Theory

BL theory
Prandtl boundary layer
viscous layer

Boundary Layer Theory is the analytical and approximate framework for understanding viscous flow near solid surfaces, pioneered by Ludwig Prandtl in 1904. The central insight is that at high Reynolds numbers, viscous effects are confined to a thin layer near walls (the boundary layer), while the flow outside remains essentially inviscid. This separation enables powerful approximations: the boundary layer equations reduce the full Navier-Stokes to a parabolic system solvable via streamwise marching, yielding analytical or semi-analytical solutions for many practical cases. Boundary layer theory remains fundamental to aerodynamics, hydrodynamics, and heat transfer.

Analytical framework and approximation method

Prandtl, L. (1904). Über Flüssigkeitsbewegung bei sehr kleiner Reibung. In Verhandlungen des 3. Internationalen Mathematiker-Kongresses in Heidelberg (pp. 484-491). Teubner.

Blasius, H. (1908). Grenzschichten in Flüssigkeiten mit kleiner Reibung. Zeitschrift für Mathematik und Physik, 56, 1-37.

Schlichting, H., & Gersten, K. (2000). Boundary-Layer Theory (8th ed.). Springer-Verlag.

reynolds-averaged-navier-stokes large-eddy-simulation direct-numerical-simulation detached-eddy-simulation smoothed-particle-hydrodynamics


Boussinesq Approximation

Boussinesq Approximation for Natural Convection

The Boussinesq Approximation simplifies the governing equations for natural convection by treating density as constant except in the buoyancy term. This approximation is valid when temperature variations produce small density changes and allows researchers to solve coupled heat-fluid flow problems without solving the full, nonlinear compressibility equations. The Boussinesq Approximation is fundamental to analyzing buoyancy-driven flows in buildings, enclosures, and geophysical applications.

Boussinesq, J. (1903). Théorie Analytique de la Chaleur. Gauthier-Villars.

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


Box-Behnken Design

BBD
Box-Behnken
Box-Behnken RSM design
three-level incomplete factorial design

The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test.

Response surface design (incomplete three-level factorial)

Continuous quantitative factor levels and continuous response variable

Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.

central-composite-design full-factorial-design fractional-factorial-design response-surface-methodology taguchi-method design-of-experiments


Braden Scale

Braden Scale for Predicting Pressure Ulcer Risk

Braden Pressure Ulcer Risk Assessment Scale

The Braden Scale is a standardized risk assessment instrument used in nursing to identify hospitalized patients at risk of developing pressure ulcers. Developed by Barbara Braden and Nancy Bergstrom in 1987, it remains one of the most widely adopted tools in clinical practice for pressure ulcer prevention. The scale combines assessment of intrinsic patient risk factors with extrinsic environmental factors.

Braden, B., & Bergstrom, N. (1987). A conceptual schema for the study of the etiology of pressure sores. Rehabilitation Nursing, 12(1), 8-12.

Bergstrom, N., Braden, B. J., Laguzza, A., & Holman, V. (1987). The Braden Scale for predicting pressure sore risk. Nursing Research, 36(4), 205-210.

norton-scale patient-fall-risk-assessment wound-assessment-bates-jensen nursing-sensitive-indicators


Bradley-Terry Model

Bradley-Terry Model for Paired Comparisons

BT Model
Bradley-Terry-Luce Model
Paired Comparison Model
İkili Karşılaştırma Modeli

The Bradley-Terry model is a probabilistic model for paired comparisons that assigns a latent strength parameter to each item and predicts the probability that one item beats another in a head-to-head contest. Introduced by Ralph A. Bradley and Milton E. Terry in 1952, it provides a principled statistical framework for ranking items from pairwise preference data, including incomplete comparison designs where not every pair is directly observed.

Output: Latent strength parameters and win probabilities

Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345.


Branch and Bound

B&B
Land-Doig Algorithm
Implicit Enumeration
Dal ve Sınır

Branch and Bound is a systematic exact algorithm for combinatorial and integer optimization problems, introduced by Ailsa Land and Alison Doig in 1960. It organizes the search space as a tree of subproblems, uses relaxation-derived upper bounds to prune branches that cannot improve the best known solution, and guarantees finding a globally optimal integer solution. It is the backbone of modern mixed-integer programming solvers used in operations research, logistics, scheduling, and engineering design.

Exact combinatorial optimization algorithm

NP-hard in worst case; exponential tree size

Land, A. H., & Doig, A. G. (1960). An automatic method of solving discrete programming problems. Econometrica, 28(3), 497–520.


Brand Equity Measurement

Brand Valuation
Brand Strength Assessment

Brand Equity Measurement is a comprehensive framework developed by David Aaker in 1991 for quantifying and assessing the value that a brand name adds to a product or service. It provides organizations with methods to understand how consumers perceive their brand across multiple dimensions, enabling better strategic decision-making and resource allocation.

Aaker, D. A. (1991). Managing Brand Equity. Free Press.

Keller, K. L., & Lehmann, D. R. (2008). Assessing Long-Term Brand Effects. Journal of Brand Management, 15(5), 359-371.

Yoo, B., & Donthu, N. (2000). Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale. Journal of Business Research, 52(1), 1-14.

net-promoter-score customer-lifetime-value customer-journey-mapping advertising-effectiveness-study market-segmentation-analysis


Brand Equity Scale

Customer-Based Brand Equity
Brand Perception Scale

David A. Aaker, Boonghee Yoo, Naveen Donthu, Sungho Lee

The Brand Equity Scale (BES) measures customer-based brand equity through perceived quality, brand loyalty, brand associations, and brand awareness. Developed by Yoo, Donthu, and Lee (2000), building on Aaker's foundational brand equity framework (1991), the BES operationalizes brand equity as the differential effect of brand knowledge on consumer response to marketing activities. The scale enables organizations to quantify the value customers attach to their brand and diagnose which equity dimensions require strategic investment.

Aaker, D. A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. Free Press.

Yoo, B., Donthu, N., & Lee, S. (2000). An Examination of Selected Marketing Mix Elements and Brand Equity. Journal of the Academy of Marketing Science, 28(2), 195-211.


BRAUERS-DOMINANCE

BRAUERS-DOMINANCE (Brauers & Zavadskas (2014) Dominance Theory — used in MultiMOORA aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pareto-dominance based aggregation (absolute/general/transitive/equal)

Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları

borda condorcet copeland dodgson topsis vikor ahp


Bray-Curtis Dissimilarity

Bray-Curtis index
Sorensen-Bray-Curtis
percentage difference

Bray-Curtis dissimilarity is a quantitative measure of compositional difference between two samples, widely used in ecology and community analysis. Introduced by John Bray and John T. Curtis in 1957 for comparing forest communities, this index ranges from 0 (identical composition) to 1 (completely different). It is sensitive to abundance differences and is particularly effective for abundance data such as species counts, microbial populations, or preference intensities.

Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecological Monographs, 27(4), 325-349.

Sorensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter, 5, 1-34.


Brayton Cycle

Brayton Cycle for Gas Turbine Power Generation

The Brayton Cycle (also called Joule Cycle) describes the thermodynamic process in gas turbines and jet engines. It consists of four processes: isentropic compression in a compressor, isobaric combustion (heat addition), isentropic expansion in a turbine, and isobaric heat rejection. The Brayton Cycle is the foundation for analyzing aircraft propulsion, ground-based power generation, and simple-cycle gas turbine plants.

Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2014). Fundamentals of Engineering Thermodynamics (8th ed.). Wiley.

Cohen, H., Rogers, G. F. C., & Saravanamuttoo, H. I. H. (1996). Gas Turbine Theory (4th ed.). Longman.


Breakdown Point Analysis

breakdown point
finite-sample breakdown point
robustness breakdown analysis
Bozunma Noktası Analizi

Breakdown point analysis quantifies the fraction of outliers an estimator can tolerate before it produces meaningless results. Formalised by Hampel (1971) and Donoho and Huber (1983), it is the standard tool for comparing the robustness of competing estimators.

Donoho, D. L. & Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann (pp. 157-184). Wadsworth.

Hampel, F. R. (1971). A General Qualitative Definition of Robustness. Annals of Mathematical Statistics, 42(6), 1887-1896.

ols-regression quantile-regression robust-discriminant-analysis heteroscedasticity-robust-se bootstrap-inference


Breastfeeding Self-Efficacy Scale

BSES-SF
BSES
Breastfeeding Confidence Scale

The Breastfeeding Self-Efficacy Scale—Short Form (BSES-SF) is a 14-item self-report instrument designed to measure maternal confidence in breastfeeding ability. Developed by Cheryl Dennis in 2003 and grounded in Albert Bandura's self-efficacy theory, the BSES-SF identifies mothers at risk for breastfeeding cessation due to low confidence. Low breastfeeding self-efficacy is a robust predictor of early discontinuation and is modifiable through targeted lactation support.

Dennis, C. L. (2003). The Breastfeeding Self-Efficacy Scale: psychometric assessment of the short form. Journal of Obstetric, Gynecologic & Neonatal Nursing, 32(6), 734-744.

Dennis, C. L. (1999). Theoretical underpinnings of breastfeeding confidence: a self-efficacy framework. Journal of Human Lactation, 15(3), 195-201.


BCS

Multiple international authors (cardiopulmonary collaboration)

The BCS is a brief, symptom-focused assessment tool measuring the frequency and severity of three cardinal respiratory symptoms: breathlessness (dyspnea), cough, and sputum production. Developed in cardiopulmonary research as a pragmatic measure of disease burden in chronic heart failure and chronic obstructive pulmonary disease, the BCS provides rapid, patient-centered tracking of respiratory symptom trajectories. Unlike comprehensive quality-of-life questionnaires, the BCS concentrates solely on symptom phenotype, making it ideal for routine monitoring and longitudinal disease surveillance in busy clinical settings.

Rohrmann, S., Anker, S. D., Coats, A. J., Hildebrandt, P., & Köhler, F. (2007). Prognostic relevance of respiratory symptoms in patients with systolic left ventricular dysfunction. American Heart Journal, 153(1), 42-50.

Pittman, L. M., Nyberg, P. W., Paulin, P. F., & Hollinsworth, K. P. (2007). Breathlessness, Cough, and Sputum Scale (BCS): A simple measure of respiratory symptoms. Respiratory Medicine, 101(9), 1954-1962.


Breitung Test

Breitung Nonparametric Panel Unit-Root Test

The Breitung test, introduced by Jörg Breitung in 2000, is a nonparametric panel unit-root test designed to assess whether all cross-sectional units in a balanced panel share a common unit root. Unlike competing first-generation tests, it avoids bias-correction terms that depend on lag selection or kernel bandwidth estimation, thereby preserving local power under a homogeneous alternative. It is widely used in macroeconometrics and finance when the researcher suspects cross-sectional homogeneity in the autoregressive structure.

All panel units contain a unit root (common autoregressive parameter equals one)

No lag-length or kernel bandwidth selection required

Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Econometrics, 15, 161–177.


Breusch-Godfrey Test

Breusch-Godfrey LM Test for Serial Correlation

BG test
LM test for autocorrelation
Breusch-Godfrey serial correlation test
Breusch-Godfrey otokorelasyon testi

The Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing.

Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301.

Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(31), 334–355.


Breusch-Pagan Test

Breusch-Pagan Test for Heteroskedasticity

Lagrange multiplier test for heteroskedasticity

The Breusch-Pagan test, introduced by Trevor Breusch and Adrian Pagan in 1979, is a Lagrange-multiplier test for heteroskedasticity — the condition where the variance of a regression's errors changes with the explanatory variables. It works by regressing the squared OLS residuals on candidate variables and checking whether they explain any of the residual variation, signalling that the constant-variance assumption is violated.

Homoskedastic errors (constant variance)

Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294.

Koenker, R. (1981). A note on studentizing a test for heteroscedasticity. Journal of Econometrics, 17(1), 107–112.


BAM

The BAM is a 17-item self-report instrument designed to provide rapid, multimodal assessment of substance use, craving, risk factors, protective factors, and psychosocial functioning in individuals receiving addiction treatment. Developed by Cacciola and colleagues in 2013, it serves as an efficient outcome monitoring tool for tracking treatment progress, identifying relapse warning signs, and guiding therapeutic adjustments. The BAM is useful in treatment settings where frequent assessment of multiple domains is needed to optimize care.

Cacciola, J. S., Alterman, A. I., Drapkin, M. L., & Valadez, C. (2013). Development and initial validation of the Brief Addiction Monitor (BAM). Journal of Substance Abuse Treatment, 44(3), 256–263.


Brief Fatigue Inventory

The Brief Fatigue Inventory is a 9-item patient self-report instrument specifically designed for rapid, repeated assessment of cancer-related fatigue severity and its functional impact. Developed by Mendoza, Cleeland, and colleagues at M.D. Anderson Cancer Center in 1999, the BFI is optimized for use in busy oncology clinics, allowing comprehensive fatigue profiling in 2–3 minutes without sacrificing clinical validity.

Mendoza, T. R., Wang, X. S., Cleeland, C. S., et al. (1999). The rapid assessment of fatigue severity in cancer patients: use of the Brief Fatigue Inventory. Cancer, 85(5), 1186–1196.

Cleeland, C. S., Mendoza, T. R., Wang, X. S., et al. (2000). Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory. Cancer, 89(7), 1634–1646.

piper-fatigue-scale cancer-fatigue-scale multidimensional-fatigue-inventory edmonton-symptom-assessment fact-g


Brief Pain Inventory

The Brief Pain Inventory (BPI) is a concise, validated self-report instrument developed by Cleeland and Ryan beginning in 1994 to measure the severity and functional impact of pain in patients with cancer and chronic pain conditions. The BPI-Short Form comprises 11 items assessing pain severity and interference with daily activities, enabling rapid multidimensional pain assessment across diverse clinical populations.

Pain severity and interference measurement

Cleeland, C. S., & Ryan, K. M. (1994). Pain assessment: global use of the Brief Pain Inventory. Annals of the Academy of Medicine Singapore, 23(2), 129-138.

Mendoza, T. R., Mayne, T., Rublee, D., & Cleeland, C. (2006). Rapid assessment of dyspnea in cancer patients: usefulness of a single-item screening question. Cancer, 100(4), 879-885.

Keller, S., Bann, C. M., Dodd, S. L., Schein, J., Mendoza, T. R., & Cleeland, C. S. (2004). Validity of the Brief Pain Inventory as a measure of neuropathic pain. Journal of Pain, 5(2), 133-137.


Brief Psychiatric Rating Scale

Rapid psychotic symptom severity screening

The BPRS is an 18-item clinician-administered scale for rapid assessment of psychiatric symptom severity in psychotic and other major psychiatric disorders. Developed by Overall and Gorham in 1962, it remains widely used in clinical settings and research trials due to its brevity (administration 15–20 minutes), broad symptom coverage (psychotic, mood, and behavioral symptoms), and robust psychometric properties. The BPRS is particularly valued in acute psychiatry, inpatient units, and longitudinal monitoring where quick, repeated assessments are needed.

Overall, J. E., & Gorham, D. R. (1962). The Brief Psychiatric Rating Scale. Psychological Reports, 10(3), 799–812.

Ventura, J., Green, M. F., Shaner, A., & Liberman, R. P. (1993). Training and quality assurance with the Brief Psychiatric Rating Scale: 'The drift busters'. International Journal of Methods in Psychiatric Research, 3(4), 221–244.

Andreasen, N. C., Carpenter, W. T., Kane, J. M., Lasser, R. A., Marder, S. R., & Weinberger, D. R. (1988). Remission in schizophrenia: Proposed criteria and rationale for consensus. American Journal of Psychiatry, 162(3), 441–449.


Brief RCOPE

Kenneth I. Pargament, Bruce W. Smith, Harold G. Koenig, & Lennon Perez

The Brief RCOPE, developed by Pargament and colleagues (1998), is a 14-item measure that distinguishes between positive and negative religious coping strategies that individuals employ when facing major life stressors. Derived from the longer 105-item RCOPE, the Brief RCOPE captures how people use faith, prayer, spiritual reframing, and community support to manage illness, loss, and adversity, while also identifying religiously-based distress responses (e.g., spiritual anger, perception of abandonment by God). It has become a standard measure in health psychology, particularly in research on coping with serious illness, grief, and trauma.

Pargament, K. I., Smith, B. W., Koenig, H. G., & Perez, L. (1998). Patterns of positive and negative religious coping with major life stressors. Journal for the Scientific Study of Religion, 37(4), 710–724.

Pargament, K. I., Feuille, J., & Burdzy, D. (2011). The Brief RCOPE: Current psychometric status of a short measure of religious coping. Religions, 2(1), 51–76.


Brier Score

The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.

Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.

Murphy, A. H. (1973). A new vector partition of the probability score. Journal of Applied Meteorology, 12(4), 595-600.

log-loss accuracy calibration mean-absolute-error


Brix Measurement

Soluble Solids Content Analysis via Brix Measurement

soluble solids measurement
sugar content analysis
refractometry

Brix measurement quantifies the dissolved solids (primarily sugars) in fruit juice using refractometry, a non-destructive optical technique. Introduced by Carl Zeiss in the 19th century and standardized by AOAC, it is the universal industry standard for assessing fruit ripeness and quality in horticulture and postharvest processing.

AOAC International. (2005). Official Methods of Analysis of AOAC International (18th ed.). AOAC International.

Anthon, G. E., & Barrett, D. M. (2015). Determination of vitamins, minerals, and phytochemicals in vegetable-based drinks and dietary supplements: A review and analysis of analytical methods. Journal of Agricultural and Food Chemistry, 63(29), 6495–6511.

ripeness-index fruit-color-analysis cold-storage-protocol postharvest-storage-simulation


Positive Emotions Scale

Positive Emotions Scale (based on Broaden-and-Build Theory)

The Positive Emotions Scale measures the frequency or intensity of positive emotions experienced by individuals. Drawing on Fredrickson's Broaden-and-Build Theory of positive emotions, this scale operationalizes the understanding that positive emotional states (joy, contentment, interest, gratitude, serenity) have cognitive and behavioral consequences—they broaden attention and thinking, building psychological, intellectual, and social resources. Common instruments include the Positive Affect subscale of the PANAS (Positive and Negative Affect Schedule) and other positive emotion inventories assessing the range and intensity of positive feelings.

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The Broaden-and-Build Theory of positive emotions. American Psychologist, 56(3), 218–226.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.

who-5-wellbeing-index flourishing-scale subjective-wellbeing-scale perma-scale


Brunner-Munzel Test

Brunner-Munzel Nonparametric Behrens-Fisher Test

Brunner-Munzel Testi
generalized Wilcoxon test
nonparametric Behrens-Fisher test
probabilistic index test

The Brunner-Munzel test is a nonparametric two-sample hypothesis test that estimates the probabilistic superiority index P(X < Y) — the probability that a randomly selected observation from one group exceeds a randomly selected observation from the other. Introduced by Brunner and Munzel in 2000 as a solution to the nonparametric Behrens-Fisher problem, it remains valid even when the two groups have unequal variances or differently shaped distributions, making it a robust alternative to the Mann-Whitney U test in heteroscedastic settings.

P(X < Y) — probabilistic superiority index

Brunner, E. & Munzel, U. (2000). The Nonparametric Behrens-Fisher Problem: Asymptotic Theory and a Small-Sample Approximation. Biometrical Journal, 42(1), 17–25.

Neubert, K. & Brunner, E. (2007). A studentized permutation test for the nonparametric Behrens-Fisher problem. Computational Statistics & Data Analysis, 51(10), 5192–5204.

Brunner, E., Bathke, A. C., & Konietschke, F. (2019). Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs. Springer.

mann-whitney-u wilcoxon-signed-rank welch-t-test mood-median-test fligner-killeen-test kruskal-wallis


Budget Impact Analysis

BIA
financial impact assessment
budget consequence analysis

Sullivan, Mauskopf, and colleagues (ISPOR task force)

Budget impact analysis estimates the financial consequences (net costs or savings) of implementing a new health technology in a specific healthcare system or population over a short time horizon (typically 1–5 years). Distinct from cost-effectiveness analysis (which compares health outcomes per dollar), BIA answers a budgetary question: 'If we adopt this new drug/device, how much will it cost our health system next year?' Widely used by hospital procurement committees, insurance formularies, and government health budgets to assess financial feasibility and reimbursement decision.

Sullivan, S. D., Mauskopf, J. A., Augustovski, F., et al. (2014). Budget Impact Analysis—Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value in Health, 17(1), 5-14.

Klok, R. M., Brouwers, J. R., Postma, M. J., et al. (2005). Budget-impact analysis: a systematic literature review of implementation studies. The Annals of Pharmacotherapy, 39(3), 518-526.

Canadian Agency for Drugs and Technologies in Health (CADTH). (2017). Guidelines for the Economic Evaluation of Health Technologies: Canada (4th ed.). Ottawa: CADTH.

cost-effectiveness-analysis cost-benefit-analysis markov-model-health-economics decision-analytic-modeling quality-adjusted-life-year


Building Energy Performance Simulation

Building Energy Performance Simulation and Analysis

energy simulation
building thermal modeling
annual energy consumption analysis

Building Energy Performance Simulation is a computational method for predicting how much energy a building consumes for heating, cooling, lighting, and equipment operation under specified weather and occupancy conditions. Pioneered by researchers like Joe Clarke and Drury Crawley in the 1990s, it has become essential for design optimization, compliance demonstration, and operational planning.

dynamic thermal and energy simulation method

Crawley, D. B., Hand, J. W., Kummert, M., Griffith, B. T. (2008). Contrasting the Capabilities of Building Energy Performance Simulation Programs. Building and Environment, 43(4), 661-673.

Fumo, N. (2014). A Review on the Basics of Building Energy Estimation. Renewable and Sustainable Energy Reviews, 31, 53-60.

Clarke, J. A. (1993). Energy Simulation in Building Design. Butterworth-Heinemann, Oxford.


Bulk Aerodynamic Flux

Bulk Aerodynamic Flux Calculation Method

Bulk aerodynamic approach
Bulk flux parametrization
Aerodynamic bulk method

The bulk aerodynamic method estimates surface energy and momentum fluxes from standard meteorological observations. Rather than measuring turbulent fluxes directly, it parameterizes them using measurements of wind speed, temperature, and moisture at a reference height (typically 10 m) and surface conditions, multiplied by empirically derived drag and transfer coefficients.

Large, W. G., & Pond, S. (1981). Open ocean momentum flux measurements in moderate to strong winds. Journal of Physical Oceanography, 11(3), 324-336.

Garratt, J. R. (1992). The atmospheric boundary layer. Cambridge University Press.


Bullwhip Effect

Bullwhip Effect in Supply Chain Management

The Bullwhip Effect is a phenomenon in supply chain management where small fluctuations in end-customer demand cause progressively larger fluctuations in orders as one moves upstream from retail to distributors to manufacturers to suppliers. First formally documented by Jay Forrester in his 1961 system dynamics work, and later popularized by Lee, Padmanabhan, and Whang in 1997, the effect reveals how information delays and ordering strategies amplify demand variability throughout supply chains, leading to excess inventory, inefficient production scheduling, and increased costs.

Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102.

Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.

inventory-routing aggregate-planning vendor-managed-inventory kanban material-requirements-planning


Burn Severity (dNBR)

Burn Severity Assessment using Normalized Burn Ratio

Burn severity is a quantitative measure of fire-induced changes in vegetation and soil, assessed using satellite-based spectral indices. The Normalized Burn Ratio (NBR) and its delta (dNBR) compare pre-fire and post-fire spectral reflectance in the near-infrared and shortwave-infrared bands to detect fire-caused vegetation damage and soil exposure. Developed by Key and Benson in 2006, dNBR has become the standard remote-sensing tool for rapid post-fire assessment and is used for emergency response, recovery planning, and ecological analysis.

Key, C. H., & Benson, N. C. (2006). Landscape Assessment (LA): Sampling and Analysis Methods. General Technical Report RMRS-GTR-164-CD, USDA Forest Service Rocky Mountain Research Station.

Parks, S. A., Holsinger, L. M., Miller, C., & Parisien, M. A. (2019). Wildland-urban interface in the western U.S.: Spatial patterns and demographic transitions over time. Journal of Geophysical Research: Biogeosciences, 124(3), 558–573.


Burnout Assessment Tool

Occupational stress and burnout assessment

The BAT is a brief 10-item self-report instrument measuring occupational burnout across three dimensions: exhaustion, cynicism, and reduced professional efficacy. Developed by Schaufeli and colleagues in 2020 as a more contemporary alternative to the widely used Maslach Burnout Inventory, the BAT aligns with the International Classification of Diseases (ICD-11) definition of burnout and emphasizes work-related exhaustion and reduced effectiveness. The scale is used for occupational health screening, research on workplace stress, and organizational assessment of employee wellbeing.

Schaufeli, W. B., De Witte, H., & Desart, S. (2020). Burnout Assessment Tool (BAT)—Development, validity, and reliability. International Journal of Environmental Research and Public Health, 17(5), 1797.

Maslach, C., Jackson, S. E., & Leiter, M. P. (2001). Maslach Burnout Inventory Manual (3rd ed.). Consulting Psychologists Press.


Butterworth Filter Design

Butterworth Infinite Impulse Response Filter Design

Butterworth IIR Design
Butterworth Lowpass Filter

The Butterworth filter is a type of signal processing filter designed to have the flattest possible frequency response in the passband while rolling off toward the stopband with a gentle slope. Introduced by Stephen Butterworth in 1930, it has become one of the most widely used filter designs in electrical engineering and digital signal processing due to its predictable and smooth frequency characteristics.

Infinite Impulse Response (IIR) filter design

Butterworth, S. (1930). On the Theory of Filter Amplifiers. Wireless Engineer and Experimental Wireless, 7, 536–541.

Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.

chebyshev-filter-design iir-filter-design wiener-filter matched-filter


Bayesian VAR

BVAR
Bayesian vector autoregression
Minnesota prior VAR
Bayesian VAR (BVAR)

Litterman (1986); Bańbura, Giannone & Reichlin (2010)

Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.

Bayesian posterior (Minnesota/Litterman prior)

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian Vector Auto Regressions. Journal of Applied Econometrics, 25(1), 71-92.

var-model favar stvar markov-switching ols-regression


BWM-BAYESIAN

BWM-BAYESIAN (Bayesian BWM — Probabilistic Group Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Mohammadi, M., Rezaei, J. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hierarchical Dirichlet posterior over weights via MCMC (JAGS) — group decision

Mohammadi, M., Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega

ahpsort aploco aras aroman artasi cobra cocoso codas


Best Worst Method with Sorting

Best Worst Method with Sorting (BWM-Sort)

BWM-Sort is a variant of the Best Worst Method introduced by Jafar Rezaei around 2015. It combines pairwise comparison of criteria with alternative sorting, enabling decision-makers to prioritize both evaluation dimensions and final ranked outcomes in a single integrated framework.

Pairwise comparison weighting with sorting

Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.

Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. European Journal of Operational Research, 255(2), 357-368.

bwm ahp-bocr swara-ii stratified-bwm non-linear-bwm lexicographic-bwm


BWM-ZNUMBER-GAME

BWM-ZNUMBER-GAME (BWM + Z-number + Zero-Sum Game — Best Worst Method weighting with Z-number payoff matrix and game-theoretic ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Adesina, K.A.; Yazdi, M.; Omidvar, M. (hybrid); Rezaei, J. 2015 (BWM); Zadeh, L.A. 2011 (Z-numbers); von Neumann 1928 + Nash 1950 (zero-sum game / Nash equilibrium) in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Game-theoretic ranking under Z-number uncertainty — BWM weights + Z-number payoff matrix + Nash equilibrium zero-sum game

Adesina, K. A., Yazdi, M., Omidvar, M. (2022). Emergency Decision Making Fuzzy-Expert Aided Disaster Management System. in: Yazdi M. (ed.), Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis, Studies in Fuzziness and Soft Computing, Vol. 414, Springer, Cham, pp. 139-150 (Chapter 6)


BWM

BWM (Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Rezaei, J. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise comparison (best-to-others + others-to-worst vectors), LP

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega


By-Production Technology DEA

Data Envelopment Analysis with By-Product Treatment (By-Production Technology DEA)

By-Production Technology DEA is a variant of Data Envelopment Analysis designed for production systems that generate both desirable outputs and undesirable by-products or emissions. Rather than ignoring or arbitrarily penalizing undesirable outputs, this method explicitly models them as joint products of the production process. It evaluates efficiency while accounting for the trade-off between desired production and environmental impact.

Non-parametric efficiency with undesirable outputs and by-products

Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400-410.

Färe, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126(2), 469-492.

dea crs-dea undesirable-output-dea slack-based-measure network-dea


CA-Markov

Cellular Automata-Markov Land-Use Change Model

CA-Markov model
cellular automata Markov
land-use change simulation
CA-Markov arazi kullanımı modeli

Cellular automata (Clarke) + Markov chain (Muller & Middleton)

CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.

Spatio-temporal land-use change simulation

Markov transition + CA spatial allocation

Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.

Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.

cellular-automata agent-based-modeling least-cost-path markov-switching-model


Caco-2 Permeability

Caco-2 assay
intestinal permeability
ADME screening

The Caco-2 assay is an in vitro model system using human colon carcinoma cell monolayers to screen drug intestinal permeability. Developed by Hidalgo and colleagues in 1989, Caco-2 cells differentiate into an epithelial barrier resembling intestinal mucosa, enabling rapid assessment of drug absorption potential and identification of transporter-mediated transport.

Hidalgo, I. J., Raub, T. J., & Borchardt, R. T. (1989). Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology, 96(3), 736-749.

Artursson, P. (1990). Epithelial transport of drugs in cell culture. I: A model for studying the passive diffusion of drugs over intestinal absorptive (Caco-2) cells. Journal of Pharmaceutical Sciences, 79(6), 476-482.


CADF Test

Cross-sectionally Augmented Dickey-Fuller (CADF) Test

Cross-Sectionally Augmented ADF
Panel CADF Test
Pesaran Panel Unit Root Test
CADF Birim Kök Testi

The Cross-sectionally Augmented Dickey-Fuller (CADF) test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed to handle cross-sectional dependence among panel units. Unlike first-generation panel unit-root tests that assume cross-sectional independence, the CADF test augments individual ADF regressions with cross-sectional averages of lagged levels and first differences, making it suitable for macro-panels and cross-country studies where common factors drive co-movement.

Panel unit-root test with cross-sectional augmentation

Non-standard; critical values tabulated by Pesaran (2007)

All panel units contain a unit root (non-stationarity)

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.


CAGE Questionnaire

CAGE
Cut-Annoyed-Guilty-Eye Opener
Alcohol Dependency Screen

The CAGE is a 4-item brief alcohol screening questionnaire developed by Ewing and colleagues in the 1970s. The acronym represents the four questions: Cut down, Annoyed, Guilty, Eye opener. Published in 1984, it has become one of the most widely used brief alcohol screens in medical practice due to its simplicity and historical validation.

Brief alcohol dependence screening questionnaire

Ewing, J. A. (1984). Detecting alcoholism: the CAGE questionnaire. JAMA, 252(14), 1905–1907.

Mayfield, D., McLeod, G., & Hall, P. (1974). The CAGE questionnaire: validation of a new alcoholism screening instrument. American Journal of Psychiatry, 131(10), 1121–1123.

U.S. Department of Health and Human Services. (2002). Alcohol Use Disorders: Drinking Levels Defined. National Institute on Alcohol Abuse and Alcoholism.

audit-alcohol audit-c whoqol-bref sf-36 promis


CAHPS Survey

Patient satisfaction and experience measurement

The Consumer Assessment of Healthcare Providers and Systems (CAHPS) is a family of evidence-based surveys developed by the Agency for Healthcare Research and Quality (AHRQ) beginning in 1995. It systematically measures patient experiences across diverse healthcare settings including hospitals, ambulatory clinics, and home health agencies, capturing dimensions critical to care quality from the patient perspective.

Agency for Healthcare Research and Quality (AHRQ). (1995). Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey Development Program.

Hargraves, J. L., Hays, R. D., & Cleary, P. D. (2003). Psychometric properties of the Consumer Assessment of Health Plans Study (CAHPS) 2.0 adult core survey. Health Services Research, 38(6), 1509-1527.

Burt, C. W., & Hing, E. (2005). Use of computerized clinical support systems in medical settings: United States, 2001-03. National Health Statistics Reports, 14(3), 1-8.


Calinski-Harabasz Index

Calinski-Harabasz Index (Variance Ratio Criterion)

variance ratio criterion
pseudo F-statistic
CH index

The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters.

Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.

davies-bouldin-index silhouette-score dunn-index gap-statistic inertia


Calorimeter Calibration

energy calibration
detector response
response function

Calorimeter calibration establishes the relationship between the measured energy deposited in a detector and the true energy of incident particles. Precise calibration is essential for physics measurements, Higgs boson properties, and new physics searches at colliders, requiring careful control of systematic uncertainties.

Aad, G., et al. (ATLAS Collaboration). (2012). Measurements of Higgs boson production. Physical Review Letters, 108(11), 111803.

Chatrchyan, S., et al. (CMS Collaboration). (2012). Observation of a new boson. Physics Letters B, 716(1), 30–61.

Aaltonen, T., et al. (CDF Collaboration). (2015). Measurement of jet energy scale. Physical Review D, 75(9), 092006.


CALPHAD

CALPHAD method
computational thermodynamics

CALPHAD (CALculation of PHAse Diagrams) is a computational method for predicting thermodynamic equilibrium properties and phase diagrams of multicomponent alloys. Pioneered by Larry Kaufman in 1970, CALPHAD combines experimental and computational data to assess thermodynamic properties of phases and subsequently predict equilibrium conditions. It is the standard methodology in physical metallurgy and materials design for alloy development, process optimization, and understanding phase stability.

Kaufman, L., & Bernstein, H. (1970). Computer Calculation of Phase Diagrams. Academic Press.

Saunders, N., Miodownik, A. P., & Schobel, R. (2016). CALPHAD (Calculation of Phase Diagrams): A Comprehensive Guide. Elsevier.

Lukas, H. L., Feucht, B., & Sundman, B. (2007). Computational Thermodynamics: The CALPHAD Method. Cambridge University Press.


CAM Assay

Chorioallantoic Membrane Assay Angiogenesis and Biocompatibility

chick embryo chorioallantoic membrane
angiogenesis assay
CAM angiogenesis model

The chorioallantoic membrane (CAM) assay is a well-established in vivo model for studying angiogenesis (new blood vessel formation) and evaluating the pro- or anti-angiogenic properties of biomaterials, drugs, and bioactive molecules. Developed by Judah Folkman in the 1970s, the assay uses the highly vascularized CAM of developing chick embryos as a platform for implanting test materials and observing vascular response. The CAM provides a transparent, immunologically naive microenvironment with rapid and reproducible neovascularization, making it ideal for screening angiogenic potential and assessing biomaterial biocompatibility.

Folkman, J. (1974). Tumor angiogenesis: therapeutic implications. New England Journal of Medicine, 285(21), 1182-1186.

Ribatti, D. (2016). The chick embryo chorioallantoic membrane (CAM) assay. Current Protocols in Immunology, 15, 12.1-12.13.

Norris, C. S., Griffith, O. W., & Reid, L. M. (2003). The chorioallantoic membrane (CAM) as a model for angiogenesis. In Antiangiogenic Agents in Cancer Therapy. Humana Press, pp. 463-477.

hemolysis-assay live-dead-assay scratch-wound-assay transwell-assay


CAM Delirium Screening

Confusion Assessment Method for Detecting Delirium

CAM
Confusion Assessment Method
Delirium Detection Tool

Mental status assessment and cognitive screening

The Confusion Assessment Method (CAM) is a widely validated diagnostic tool developed by Sharon K. Inouye and colleagues to detect delirium in hospitalized patients. Delirium is an acute change in mental status characterized by inattention, disorganized thinking, and altered consciousness that is often missed in clinical practice. The CAM provides a standardized, reproducible method for identifying delirium, which is associated with increased morbidity, mortality, and hospital costs.

Inouye, S. K., van Dyck, C. H., Alessi, C. A., Balkin, S., Siegal, A. P., & Horwitz, R. I. (1990). Clarifying confusion: The Confusion Assessment Method. A new method for detection of delirium. Annals of Internal Medicine, 113(12), 941-948.

Ely, E. W., Inouye, S. K., Bernard, G. R., et al. (2001). Delirium in mechanically ventilated patients: validity and reliability of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). JAMA, 286(21), 2703-2710.

medication-reconciliation nursing-sensitive-indicators early-warning-score braden-scale


CAM Use Questionnaire

Complementary and Alternative Medicine Use Questionnaire

The I-CAM-Q is a structured questionnaire designed to systematically assess the use of complementary and alternative medicine practices and practitioners. Developed by Quandt and colleagues in 2009, it provides comprehensive data on CAM utilization patterns, frequency, purposes, and perceived helpfulness across diverse populations.

Quandt, S. A., Ip, E. H., & Saldana, M. (2009). Integrative medicine use among immigrant Latino farm workers. Journal of Immigrant and Minority Health, 11(6), 498–506.

Eisenberg, D. M., Davis, R. B., Ettner, S. L., et al. (1998). Trends in alternative medicine use in the United States, 1990–1997. JAMA, 280(18), 1569–1575.

attitudes-cam-scale integrative-medicine-attitudes patient-satisfaction-cam holistic-caring-inventory


CAMELS Rating

Uniform Financial Institutions Rating System

US bank supervisory framework; Cole & Gunther

The CAMELS Rating System is a supervisory framework used by US bank regulators to evaluate the overall condition of financial institutions across six dimensions: Capital Adequacy, Asset Quality, Management, Earnings, Liquidity, and Sensitivity to Market Risk. Each component is scored on a scale of 1 (strong) to 5 (critically deficient), and a composite score is assigned based on examiner judgment. Developed in the US federal banking regulatory context, CAMELS emerged as the standard on-site examination tool and has since been adopted and adapted by regulators globally.

1 (strong) to 5 (critically deficient) per component and composite

Capital Adequacy, Asset Quality, Management, Earnings, Liquidity, Sensitivity to Market Risk

Cole, R. A., & Gunther, J. W. (1998). Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103–117.


Canberra Distance

Canberra metric
normalized Manhattan distance

Canberra distance is a weighted version of the Manhattan distance that normalizes differences by the sum of absolute values. Introduced by Geoffrey Lance and William Williams in 1967 as part of their work on clustering classification methods, this metric emphasizes differences in small values and is sensitive to changes in relative proportions. It is commonly used in taxonomy, ecology, decision-making, and any application where normalized relative differences matter.

Lance, G. N., & Williams, W. T. (1967). A general theory of classificatory sorting strategies. Computer Journal, 10(3), 271-277.

Cantrell, C. D. (1971). A review of taxonomic methods. Taxon, 20(2), 157-175.


Cancer Fatigue Scale

The Cancer Fatigue Scale is a 15-item disease-specific self-report instrument that comprehensively assesses three dimensions of cancer-related fatigue: physical, cognitive, and emotional. Developed by Takuo Okuyama and colleagues at the Japanese Foundation for Cancer Research and published in 2000, the CFS provides a brief yet multidimensional fatigue profile suitable for both clinical practice and research, with particular strength in non-English-speaking populations where it has been extensively validated.

Patient self-report three-dimensional fatigue scale

Okuyama, T., Akechi, T., Kugaya, A., et al. (2000). Development and validation of a cancer fatigue scale: a brief, three-dimensional, disease-specific instrument. J Pain Symptom Manage, 19(1), 5–14.

Okuyama, T., Akechi, T., Endo, C., et al. (2000). Validation of the Cancer Fatigue Scale (CFS) in gastric cancer patients. J Pain Symptom Manage, 19(4), 324–330.

brief-fatigue-inventory piper-fatigue-scale multidimensional-fatigue-inventory chalder-fatigue-scale edmonton-symptom-assessment


Cancer Worry Scale

The Cancer Worry Scale (CWS) is a brief 8-item instrument assessing the degree to which cancer-related worry interferes with daily functioning and emotional well-being. Developed by Lerman et al. in 1991, it quantifies cancer-related anxiety and distress—psychological burden distinct from symptom burden and functional impairment. It is widely used in cancer screening, treatment, and survivorship contexts to identify patients requiring psychological support.

Lerman, C., Trock, B., Rimer, B. K., Jepson, C., Brody, D., & Boyce, A. (1991). Psychological side effects of breast cancer screening. Health Psychol, 10(1), 259–267.

fact-lung fact-colorectal fact-prostate fact-ovarian eortc-qlq-lc13


Canny Edge Detection

The Canny edge detector, introduced by John Canny in 1986, is a multi-stage algorithm for identifying edges in digital images where significant intensity changes occur. Canny's method is optimal for step edges in additive Gaussian noise and remains the gold standard for edge detection in computer vision due to its mathematical elegance and practical effectiveness.

Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

Sobel, I., & Feldman, G. (1968). A 3x3 isotropic gradient operator for image processing. Pattern Recognition and Machine Intelligence, 271–272.

hough-transform contour-analysis image-morphology harris-corner-detection template-matching


Canonical Correlation Analysis

CCA
canonical variate analysis
canonical analysis
multiple canonical correlation

Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.

Multivariate linear dimension reduction and association

Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.

Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis (3rd ed.). Wiley.

Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.

principal-component-analysis factor-analysis multivariate-analysis-of-variance multiple-linear-regression partial-least-squares discriminant-analysis