Big Chemical Encyclopedia

Chemical substances, components, reactions, process design ...

Articles Figures Tables About

Bias

Measurement error has two general components, a random error and a systematic error (Paulson, 2003). Random error is an unexplainable fluctuation in the data for which the researcher cannot identify a specific cause and, therefore, cannot be controlled. Systematic error, or bias, is an error that is not the consequence of chance alone. In addition, systematic error, unlike random fluctuation, has a direction and magnitude. [Pg.14]

Researchers cannot will themselves to take a purely objective perspective toward research, even if they think they can. Researchers have personal desires, needs, wants, and fears that will unconsciously come into play by filtering, to some degree, the research, particularly when interpreting the data s meaning (Polkinghome, 1983). In addition, shared, cultural values of the scientific research community bias researchers interpretations with preset expectations (Searle, 1995). Therefore, the belief of researchers that they are without bias is particularly dangerous (Varela and Shear, 1999). [Pg.14]

Knowing the human predisposition to bias, it is important to collect data using methods for randomization and blinding. It is also helpful for researchers continually to hone their minds toward strengthening three important characteristics  [Pg.14]


We will see that superseding the functional fi(p ) in the form of Gibbs measure (4) ensures the linearity of equation (1), simplifies the iteration procedure, and naturally provides the support of any expected feature in the image. The price for this is, that the a priori information is introduced in more biased, but quite natural form. [Pg.115]

It takes time to train a film interpreter. In addition, human interpretation of weld quality based on film radiography is very subjective, inconsistent, labour intensive, and sometimes biased. It is thus desirable to develop some forms of computer-aided systems. [Pg.181]

Figure Bl.6.1 Equipotential surfaces have the shape of lenses in tlie field between two cylinders biased at different voltages. The focusing properties of the electron optical lens are specified by focal points located at focal lengthsandy2, measured relative to the principal planes, The two principal rays emanating... Figure Bl.6.1 Equipotential surfaces have the shape of lenses in tlie field between two cylinders biased at different voltages. The focusing properties of the electron optical lens are specified by focal points located at focal lengthsandy2, measured relative to the principal planes, The two principal rays emanating...
An electron prisin , known as an analyser or monochromator, is created by tlie field between the plates of a capacitor. The plates may be planar, simple curved, spherical, or toroidal as shown in Figure Bl.6.2. The trajectory of an electron entering the gap between the plates is curved as the electron is attracted to the positively biased (iimer) plate and... [Pg.1310]

Katzenellenbogen N and Grischkowsky D 1991 Efficient generation of 380 fs pulses of THz radiation by ultrafast laser pulse excitation of a biased metal-semiconductor interface Appl. Phys. Lett. 58 222-4... [Pg.1991]

It is usefiil to write down here the basic fomuilae for sampling with an additional weight fimction applied, sometimes called non-Boltzmaim or umbrella sampling, and for sampling when the selection of trial moves is done in a biased way, i.e., the a matrix is not syimnetrical. [Pg.2258]

The biased-sampling approach may be considerably generalized, to allow the construction of MC moves step-by-step, with each step depending on the success or failure of the last. Such a procedure is biased, but it is then possible to correct for the bias (by considering the possible reverse moves). The technique has dramatically speeded up polymer simulations, and is capable of wider application. [Pg.2265]

The idea may be illustrated by considering first a method for increasing the acceptance rate of moves (but at the expense of trying, and discarding, several other possible moves). Having picked an atom to move, calculate the new trial interaction energy for a range of trial positions t = 1.. . k. Pick the actual attempted move from this set, with a probability proportional to the Boltzmann factor. This biases the move selection. [Pg.2265]

The expense is justified, however, when tackling polymer chains, where reconstruction of an entire chain is expressed as a succession of atomic moves of this kind [121]. The first atom is placed at random the second selected nearby (one bond length away), the third placed near the second, and so on. Each placement of an atom is given a greater chance of success by selecting from multiple locations, as just described. Biasing factors are calculated for the whole multi-atom move, forward and reverse, and used as before in the Metropolis prescription. For fiirther details see [122, 123. 124. 125]. A nice example of this teclmique is the study [126. 127] of the distribution of linear and branched chain alkanes in zeolites. [Pg.2266]

For a multicomponent system, it is possible to simulate at constant pressure rather than constant volume, as separation into phases of different compositions is still allowed. The method allows one to study straightforwardly phase equilibria in confined systems such as pores [166]. Configuration-biased MC methods can be used in combination with the Gibbs ensemble. An impressive demonstration of this has been the detennination by Siepmaim et al [167] and Smit et al [168] of liquid-vapour coexistence curves for n-alkane chain molecules as long as 48 atoms. [Pg.2269]

Osborne M A, Balasubramanian S, Furey W S and Klenerman D 1998 Optically biased diffusion of single molecules studied by confocal fluorescence microscopy J. Chem. Phys. B 102 3160-7... [Pg.2510]

Chiu D T and Zare R N 1996 Biased diffusion, optical trapping and manipulation of single molecules in solution J. Am. Chem. Soc. 118 6512-13... [Pg.2510]

In addition to their practical importance, colloidal suspensions have received much attention from chemists and physicists alike. This is an interesting research area in its own right, and it is an important aspect of what is referred to as soft condensed matter physics. This contribution is written from such a perspective, and although a balanced account is aimed for, it is inevitably biased by the author s research interests. References to the original literature are included, but within the scope of this contribution only a fraction of the vast amount of literature on colloidal suspensions can be mentioned. [Pg.2667]

Other techniques to detennine the corrosion rate use instead of DC biasing, an AC approach (electrochemical impedance spectroscopy). From the impedance spectra, the polarization resistance (R ) of the system can be detennined. The polarization resistance is indirectly proportional to j. An advantage of an AC method is given by the fact that a small AC amplitude applied to a sample at the corrosion potential essentially does not remove the system from equilibrium. [Pg.2720]

Light is generated in semiconductors in the process of radiative recombination. In a direct semiconductor, minority carrier population created by injection in a forward biased p-n junction can recombine radiatively, generating photons with energy about equal to E. The recombination process is spontaneous, individual electron-hole recombination events are random and not related to each other. This process is the basis of LEDs [36]. [Pg.2890]

The bipolar junction transistor (BIT) consists of tliree layers doped n-p-n or p-n-p tliat constitute tire emitter, base and collector, respectively. This stmcture can be considered as two back-to-back p-n junctions. Under nonnal operation, tire emitter-base junction is forward biased to inject minority carriers into tire base region. For example, tire n type emitter injects electrons into a p type base. The electrons in tire base, now minority carriers, diffuse tlirough tire base layer. The base-collector junction is reverse biased and its electric field sweeps tire carriers diffusing tlirough tlie base into tlie collector. The BIT operates by transport of minority carriers, but botli electrons and holes contribute to tlie overall current. [Pg.2891]

A band diagram of a biased n-p-n BIT is shown in figure C2.16.8. Under forward bias, electrons are injected from tlie n type emitter, giving rise to tlie current 7. flowing into tlie p type base. Some of tlie carriers injected into tlie base recombine in tlie base or at tlie surface. This results in a reduction of tlie base current by 7, tlie lost recombination current, and tlie base current becomes 7g = At tlie same time, holes are injected from tlie... [Pg.2891]

Figure C2.16.9. Schematic cross-section and biasing of a metai-oxide-semiconductor transistor. A unifonn conducting channei is induced between source (S) and drain (D) for > V. Voitage is appiied between the gate (G) and the source. Part (A) shows the channei for - V the transistor acts as a triode. The source-... Figure C2.16.9. Schematic cross-section and biasing of a metai-oxide-semiconductor transistor. A unifonn conducting channei is induced between source (S) and drain (D) for > V. Voitage is appiied between the gate (G) and the source. Part (A) shows the channei for - V the transistor acts as a triode. The source-...
The biasing function is applied to spread the range of configurations sampled such that the trajectory contains configurations appropriate to both the initial and final states. For the creation or deletion of atoms a softcore interaction function may be used. The standard Lennard-Jones (LJ) function used to model van der Waals interactions between atoms is strongly repulsive at short distances and contains a singularity at r = 0. This precludes two atoms from occupying the same position. A so-called softcore potential in contrast approaches a finite value at short distances. This removes the sin-... [Pg.154]

We then require that the exact trajectory will have a weight of at least i, which will make its sampling possible in a search biased by the weight. [Pg.274]


See other pages where Bias is mentioned: [Pg.1217]    [Pg.1248]    [Pg.1309]    [Pg.1310]    [Pg.1312]    [Pg.1561]    [Pg.2256]    [Pg.2258]    [Pg.2258]    [Pg.2260]    [Pg.2264]    [Pg.2265]    [Pg.2265]    [Pg.2266]    [Pg.2268]    [Pg.2806]    [Pg.2861]    [Pg.2890]    [Pg.2894]    [Pg.18]    [Pg.42]    [Pg.153]    [Pg.154]    [Pg.154]    [Pg.155]    [Pg.156]    [Pg.159]    [Pg.159]    [Pg.159]    [Pg.160]    [Pg.108]   
See also in sourсe #XX -- [ Pg.261 ]

See also in sourсe #XX -- [ Pg.19 ]

See also in sourсe #XX -- [ Pg.141 , Pg.144 , Pg.175 ]




SEARCH



A Few Words about Hindsight Bias and Examples

Absolute bias

Accidental bias

Allocation bias

Analogue bias

Analysis bias

Analytical bias

Anchoring bias

Anthropic bias

Applied Bias Photon-to-Current Efficiency

Artificial bias potential

Attributional bias

Attributional bias example

Author’s bias

Availability Bias

Avermectin Bia

BIA with Voltammetric Detection

BIAS TYRE

Beware of bias

Bias Enhanced Plasma CVD

Bias Point Detail

Bias Point Detail results

Bias Voltage Controlled System

Bias algorithm

Bias and

Bias and precision

Bias and variance analysis

Bias binding

Bias circuit

Bias correction

Bias current

Bias current, switching

Bias cycling

Bias enhanced nucleation

Bias error

Bias example calculation

Bias exchange metadynamics

Bias extension test

Bias feedforward

Bias in Risk Evaluation

Bias in assays

Bias laboratory

Bias limit

Bias mass

Bias maximum

Bias method

Bias neurons

Bias neurons, neural networks

Bias node

Bias nucleation process

Bias operator

Bias platform

Bias pressure cooker device

Bias restraint

Bias significant

Bias strength

Bias stress

Bias tape

Bias testing, mechanical sampling

Bias tires

Bias treatment

Bias uncorrected

Bias update

Bias vector

Bias vector force

Bias versus Variance Tradeoff

Bias voltage

Bias weave

Bias yams

Bias, concept

Bias, definition

Bias, least

Bias, minimization methodology

Bias, model

Bias, pulsed mode

Bias, statistical

Bias, statistical sources

Bias, user, incorporation

Bias-Stress Instability and Hysteresis

Bias-corrected standard error

Bias-free

Bias-stress effect

Bias-stress trapping

Biases of data selection

Biases of data use

Biomolecular interaction analysis (BIA

Bootstrap bias estimates from

Cathodic bias

Chain configurational bias moves

Charge bias

Classification bias

Clinical decision making, bias

Clinical trials bias problems

Codon bias

Codon bias detection

Cognitive bias

Cognitive bias behavior

Cognitive bias in manufacturing

Commission bias

Common-method bias

Competition bias

Conceptual bias

Configuration bias

Configurational bias

Configurational bias Monte Carlo applications

Configurational bias Monte Carlo simulations

Configurational bias method

Configurational-bias Monte Carlo CBMC)

Configurational-bias Monte Carlo Gibbs ensemble

Configurational-bias Monte Carlo method

Confirmation bias

Continuum Configuration Bias

Continuum Configurational Bias Monte Carlo

Control of bias

Convexity bias

Corporate Bias

Correcting Parameter Estimates for Statistical Bias

Current bias voltage relation

Current-bias potential curves

Current-bias relation

DC-bias

Data selection biases

De Bias

Dependence of Bias Stress on Operating Conditions Lifetime Predictions

Detection bias

Detector bias supply

Diastereofacial bias

Dynamic bias

Elevation bias

Emotional bias

Evaluating Systematic Biases

Exchange bias

Exclusion bias

Experimenter bias

Exponential Estimator - Issues with Sampling Error and Bias

Extended continuum configurational bias

Extended continuum configurational bias Carlos

External bias

External bias potential

Extrapolation bias

Fabric bias

Fabric bias-woven

Facial bias

Feedback Bias

Field-effect transistors gate bias

Fixed bias

Fixed bias calculations

Force-bias Monte Carlo method

Force-bias Monte Carlo simulation

Force-bias displacements

Forward bias

Gate bias

Gender bias

Head-tail bias

Hidden bias

Hindsight bias

In-group bias

Inclusion bias

Information bias

Information processing biases

Instrument bias

Instrument bias range

Instrument bias repeatability

Instrument bias resolution

Instrumental mass bias

Interpretation bias

Isotope mass bias

Langevin equation force bias

Laser mass bias

Lead-time bias

Length bias

Ligand bias

Linear mass bias model

Magnetic exchange bias

Mask bias

Mass Bias Correction

Mass Bias in MC-ICP-MS

Mass bias correction models

Mean bias

Measurement bias

Measurement of Information Bias

Meta-analysis publication bias

Methodology to minimize bias in open-label trials

Minimum detectable bias

Momentum bias

Monte Carlo configurational bias

Monte Carlo force-bias

Motivational bias

Mutational bias

Negative bias

Non-response bias

Number bias

Observational bias

Observational selection bias

Observer Bias

Omission bias

Optimism bias

Orbital bias

Organ system bias

Orientational bias

Overconfidence bias

PH bias

PLS model for assessing common method bias

Pattern size bias

Perfect sampling bias

Persistent bias

Platinum electrode cathodic bias

Positive bias

Positivity bias

Potential bias

Potential self-bias

Precision, Bias and Accuracy

Pressure Bias

Projection bias

Proportional controller bias

Publication bias

Pulsed bias

Random bias

Rational bias

Recall bias

Recency bias

Receptor bias

Receptor bias chemokine system

Reduce Bias Errors

Reko, Bias Pablo

Relative bias

Relative bias calculations

Relaxation time bias field effects

Representative Bias

Responsivity reverse bias

Reverse bias

Reversed bias

Reversible Bias Stress

Sample bias

Sampling bias

Sampling bias, systematic

Scale factor bias

Schottky diodes, bias annealing

Selection bias

Self-bias

Self-bias negative

Self-bias positive

Self-serving bias

Semiconductors forward bias

Semiconductors reverse bias

Sensory bias

Sex-ratio bias

Side bias

Significance publication bias

Skill 1.4 Understanding procedures for collecting and interpreting data to minimize bias

Spectrum bias

Statistical error and bias

Statistics allocation bias

Statistics assessment bias

Statistics bias prevention

Statistics measurement bias

Statistics selection bias

Status quo bias

Steric bias

Stimulus bias

Stochastic resonance bias field

Stochastic resonance, bias field effects

Strand bias mutations

Strategy bias parameter

Sub-sampling bias

Substrate bias

Substrate bias surface characteristics

Substrate bias voltage

Substrate spatial bias

Sunk cost bias

Systematic Errors and Biases

Systematic biases

Systematics of Mass Bias Correction Models

Temperature-humidity-bias

Temperature-humidity-bias testing

Test bias

Testing biases

The Configurational Bias Monte Carlo Method

The Junction at Equilibrium (Zero Bias)

The Problem of Bias

The bias potential

The self-serving bias

Three Recurrent Biases in Relation to Human Error

Tires bias-belted

Tissue bias

Transistor Bias Point Detail

Trigger bias

Tunnel-Diodes and Catalytic Bias

Twist sense bias

Typical bias

User bias

Value bias-free

Various types of bias

Vitamin Bia

Voltage bias condition

Washington bias

Weights and biases

White Light Bias IPCE Experiment

Wishful thinking bias

Zeolite adsorption, simulations configurational-bias Monte Carlo

Zeolites configurational-bias Monte Carlo

Zero-bias anomaly

Zero-bias conductance peak

© 2024 chempedia.info