A fuzzy model and algorithm to handle subjectivity in life cycle costing based decision-making.

A life cycle costing (LCC) algorithm that can effectively deal with judgmental assessments of input parameters is proposed. This algorithm is based on the fuzzy set theory and interval mathematics. The development of the algorithm is motivated by the need to handle in a systematic and a more objective way the imprecision in these subjective assessments. Three major issues were considered in the development of the algorithm. First, an appropriate mathematical framework for representing subjective imprecision was identified. Then, the original LCC closed-form equation was reformulated so that uncertainties in all input parameters can be modelled in an effective and convenient manner. Finally, the formulated model was implemented in the form of an efficient computational algorithm. The algorithm handles a number of alternatives with imprecise input data and ranks them automatically. The solution of a selected example problem is included to clarify the theory of the model

Phase transitions in the Shastry-Sutherland lattice

Two recently developed theoretical approaches are applied to the Shastry-Sutherland lattice, varying the ratio $J'/J$ between the couplings on the square lattice and on the oblique bonds. A self-consistent perturbation, starting from either Ising or plaquette bond singlets, supports the existence of an intermediate phase between the dimer phase and the Ising phase. This existence is confirmed by the results of a renormalized excitonic method. This method, which satisfactorily reproduces the singlet triplet gap in the dimer phase, confirms the existence of a gapped phase in the interval $0.66<J'/J<0.86$Comment: Submited for publication in Phys. Rev.

Theoretical studies of the phase transition in the anisotropic 2-D square spin lattice

The phase transition occurring in a square 2-D spin lattice governed by an anisotropic Heisenberg Hamiltonian has been studied according to two recently proposed methods. The first one, the Dressed Cluster Method, provides excellent evaluations of the cohesive energy, the discontinuity of its derivative around the critical (isotropic) value of the anisotropy parameter confirms the first-order character of the phase transition. Nevertheless the method introduces two distinct reference functions (either N\'eel or XY) which may in principle force the discontinuity. The Real Space Renormalization Group with Effective Interactions does not reach the same numerical accuracy but it does not introduce a reference function and the phase transition appears qualitatively as due to the existence of two domains, with specific fixed points. The method confirms the dependence of the spin gap on the anisotropy parameter occurring in the Heisenberg-Ising domain

Endoscopic ultrasound-guided tissue acquisition of pancreatic masses

Endoscopic ultrasound (EUS) has assumed an increasing role in the management of pancreaticobiliary disease over the past 2 decades but its impact is particularly evident in the management of pancreatic masses. EUS helps improve patients′ outcomes by enhancing tumor detection and staging while providing safe and reliable tissue diagnosis. This review provides an evidence-based approach to the use of EUS for the diagnosis of pancreatic cancer, its staging, and for the determination of resectability compared to other imaging modalities. We will focus on techniques specific to obtaining tissue from solid pancreatic masses and will review best practices in EUS-guided tissue acquisition

Condition-Based Monitoring on High-Precision Gearbox for Robotic Applications

This work presents a theoretical and experimental study regarding defect detection in a robotic gearbox using vibration signals in both cyclostationary and noncyclostationary conditions. The existing work focuses on inferring the health of the robot during operation with little regard toward the defective element of the components. This article illustrates the detection of specific element damage of a robotic gearbox during a robotic cycle based on domain knowledge and presents a novel data-driven method for asset health. This starts by studying the robotic gearbox, specifically its kinematics as a planetary 2-stage reduction gearbox to acquire the knowledge of the rotations of each component. The signals acquired from a test bench with four sensors undergo different acquisition methods and signal processing techniques to correlate the elements' frequencies. The work shows the detection of the artificially created defects from the acquired vibration data, verifying the kinematic methodology and identifying the root cause of failure of such gearboxes. A novel resampling method, Binning, is presented and compared with the traditional signal processing techniques. Binning combined with Principal Component Analysis (PCA) as a data-driven method to infer the state of the gearbox is presented, tested, and validated. This work presents methods as a step toward automatized predictive maintenance on robots in industrial applications

A renormalized excitonic method in terms of block excitations. Application to spin lattices

Dividing the lattice into blocks with singlet ground state and knowing the exact low energy spectrum of the blocks and of dimers (or trimers) of blocks, it is possible to approach the lowest part of the lattice spectrum through an excitonic type effective model. The potentialities of the method are illustrated on the 1-D frustrated chain and the 1/5-depleted square and the plaquette 2-D lattices. The method correctly locates the phase transitions between gapped and non-gapped phases.Comment: Submitted for publication in Phys. Rev.