Data envelopment analysis using the binary-data

Purpose The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction. Design/methodology/approach In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed. Findings The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA. Originality/value The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

Accessing the diffracted wavefield by coherent subtraction

Diffractions have unique properties which are still rarely exploited in common practice. Aside from containing subwavelength information on the scattering geometry or indicating small-scale structural complexity, they provide superior illumination compared to reflections. While diffraction occurs arguably on all scales and in most realistic media, the respective signatures typically have low amplitudes and are likely to be masked by more prominent wavefield components. It has been widely observed that automated stacking acts as a directional filter favouring the most coherent arrivals. In contrast to other works, which commonly aim at steering the summation operator towards fainter contributions, we utilize this directional selection to coherently approximate the most dominant arrivals and subtract them from the data. Supported by additional filter functions which can be derived from wave front attributes gained during the stacking procedure, this strategy allows for a fully data-driven recovery of faint diffractions and makes them accessible for further processing. A complex single-channel field data example recorded in the Aegean sea near Santorini illustrates that the diffracted background wavefield is surprisingly rich and despite the absence of a high channel count can still be detected and characterized, suggesting a variety of applications in industry and academia.

Entropy Numbers of Certain Summation Operators

Given nonnegative real sequences and we study the generated summation operator regarded as a mapping from ℓ p (ℤ) to ℓ q (ℤ). We give necessary and sufficient conditions for the boundedness of S 𝑎, 𝑏 and prove optimal estimates for its entropy numbers relative to the summation properties of 𝑎 and 𝑏. Our results are applied to the investigation of the behaviour of as ε → 0, where is some nondecreasing sequence in [0, ∞) and (W(t)) t ≥ 0 denotes the Wiener process.

Three‐dimensional time‐slice migration

Three‐dimensional migration of zero‐offset data using a velocity varying with depth can be performed in one pass using Fourier transforms of time slices. The migration process is carried out entirely in the two‐dimensional spatial Fourier domain. The algorithm consecutively filters and adds time slices of the 3-D data volume in a way that is equivalent to summing energy over the diffraction surface of a point scatterer. The partial energy being distributed along a circle in a time slice is properly added in each summation step. Time‐slice migration is based on an integral solution of the acoustic wave equation known as the “Kirchhoff integral.” The wavelet shape in a 3-D data volume is preserved throughout the entire migration process. The frequency characteristics are maintained by summing weighted differences between time slices instead of summing the time slices themselves. Automatic weighting is achieved by time slicing at equal increments of diffraction radius. Tapering the summation operator reduces effects introduced by limiting the summation window. Time‐slice migration preserves the frequency content of a 3-D data volume during summation in a natural way. Since the migration scheme assumes a constant velocity within the entire time slice, it is a local process in time which migrates a 3-D data volume with a constant velocity or with a velocity which varies with depth. The migration algorithm is applied to numerical and physical model data. This method is especially suitable for a migration of a targeted subset of the 3-D data volume.

The standard summation operator, the Euler-Maclaurin sum formula, and the Laplace transformation

A proof is given of the Euler-Maclaurin sum formula, on a Banach space of differentiable vector-valued functions of bounded exponential growth, using the Laplace transformation. Some related summation formulae are proved by the same methods. Properties of the standard summation operator are proved, namely spectral properties and boundedness, continuity and differentiability results.

The Euler-Maclaurin sum formula for a closed derivation

An operator form of the Euler-Maclaurin sum formula is obtained, expressing the sum of the Euler-Maclaurin infinite series in a closed derivation, whose spectrum is compact, not equal to {0}, and does not have 0 as a clusterpoint, as the difference between a summation operator and an antiderivation which is the local inverse of the derivation.

The Euler–Maclaurin formula generated by a summation operator

SynopsisA closed summation operator, whose spectrum lies within a certain region, generates a derivation and antiderivation, and an Euler–Maclaurin sum formula among these three operators.