A Hermite DRK interpolation-based collocation method for the analyses of Bernoulli–Euler beams and Kirchhoff–Love plates

A Hermite differential reproducing kernel (DRK) interpolation-based collocation method is developed for solving fourth-order differential equations where the field variable and its first-order derivatives are regarded as the primary variables. The novelty of this method is that we construct a set of differential reproducing conditions to determine the shape functions of derivatives of the Hermite DRK interpolation, without directly differentiating it. In addition, the shape function of this interpolation at each sampling node is separated into a primitive function possessing Kronecker delta properties and an enrichment function constituting reproducing conditions, so that the nodal interpolation properties are satisfied for the field variable and its first-order derivatives. A weighted least-squares collocation method based on this interpolation is developed for the static analyses of classical beams and plates with fully simple and clamped supports, in which its accuracy and convergence rate are examined, and some guidance for using this method is suggested.     

A state space differential reproducing kernel method for the 3D analysis of FGM sandwich circular hollow cylinders with combinations of simply-supported and clamped edges

A state space differential reproducing kernel (DRK) method is developed for the three-dimensional (3D) analysis of functionally graded material (FGM) sandwich circular hollow cylinders with combinations of simply-supported and clamped edges and under sinusoidally (or uniformly) distributed loads. The strong formulation of this 3D elasticity problem is derived on the basis of the Reissner mixed variational theorem (RMVT), which consists of the Euler–Lagrange equations of this problem and its associated boundary conditions. The primary field variables are expanded as the single Fourier series in the circumferential coordinate, then interpolated in the axial coordinate using the early proposed DRK interpolation functions, and finally the state space equations of this problem are obtained, which represent a system of ordinary differential equations in the thickness coordinate. The present state space DRK solutions can then be obtained by means of the transfer matrix method. In the illustrative examples, three different edge conditions, the simple-simple (SS), simple-clamped (SC), and clamped–clamped (CC) edges, are considered, and the accuracy and convergence of this method are examined by comparing their solutions with the exact 3D ones available in the literature and the solutions using the ANSYS commercial software.     

Multiregion Boundary Element Analysis in Geomechanics Including Body Forces

A multiregion boundary element method (BEM) is developed for the plane elasticity analysis in geomechanics including body forces. The boundary element scheme considers the zoned nonhomogeneous material property in the realistic soil media, and the body-force term in the scheme is transformed from domain to boundary integrals. A geomechanics problem of footing founded on a horizontal layer considering self weight is analyzed. The multiregion BEM solution is compared with other available solutions in the literature.     

AI and Global Science and Technology Assessment

Strength in science & technology (S&T) is the foundation of a nation's economic power, so an effective, automated means of continually assessing this strength is critical to understanding a country's economic status. Six essays on global S&T assessment present various research frameworks, computational methods, issues, and results relative to knowledge mapping, scientometrics, information visualization, digital libraries, and multilingual knowledge management.     

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy.     

VCO phase-noise improvement by gate-finger configuration of 0.13-/spl mu/m CMOS transistors

Using a standard logic process, 0.13-/spl mu/m RF CMOS devices with multifinger gate structure have been fabricated. The flicker noise and minimum noise figure characteristics have been investigated with different gate layout splits, where the device parasitic resistance is the determining factor in this issue. The stripe-shaped gate configuration demonstrates better noise performance, due to the reduction of device gate resistance. In addition, the MOS varactors designed with different gate layouts were used in a 5.2-GHz voltage-controlled oscillator (VCO) design, where the VCO with the stripe-shaped (2 /spl mu/m /spl times/ 36 fingers) gate varactor improved about 6 dB in phase-noise performance at 100-kHz offset frequency than that of square-shaped (8 /spl mu/m /spl times/ 9 fingers) gate varactor.     

Intelligent image prefetching for supporting radiologists' primary reading: a decision-rule inductive learning approach

The expanded role of radiology in clinical medicine and its emerging digital practice have made patient-image management a growing concern for health-care organizations. A fundamental aspect of patient-image management is to provide a radiologist with convenient access to prior images relevant to his or her reading of a recently taken radiological examination. For confirmation or evaluation purposes, radiologists often reference relevant prior images of the same patient when interpreting the images of a current examination. To alleviate the time and physical requirements on radiologists, many health-care organizations have taken a prefetching strategy for meeting their patient-image reference needs. Radiologists' patient-image reference knowledge understandably may exhibit subtle individual variations and dynamically evolves over time, thus making the artificial intelligence-based inductive learning approach appealing. Central to patient-image prefetching is a knowledge base of which knowledge elements need continual update and individual customization. In this study, we extended a decision rule induction technique (i.e., CN2 algorithm) to address the challenging characteristics of the targeted learning. We experimentally evaluated the extended algorithm using the learning performances achieved by backpropagation neural network as benchmarks. Overall, our evaluation results suggest that the extended algorithm exhibited satisfactory learning effectiveness and, at the same time, showed desirable noise tolerance, immunity to missing data, and robustness in relation to limited training data.     

Molecular Verification of Rule-Based Systems Based on DNA Computation

Various graphic techniques have been developed to analyze structural errors in rule-based systems that utilize inference (propositional) logic rules. Four typical errors in rule-based systems are: redundancy (numerous rule sets resulting in the same conclusion); circularity (a rule leading back to itself); incompleteness (deadends or a rule set conclusion leading to unreachable goals); and inconsistency (rules conflicting with each other). This study presents a new DNA-based computing algorithm mainly based upon Adleman's DNA operations. It can be used to detect such errors. There are three phases to this molecular solution: rule-to-DNA transformation design, solution space generation, and rule verification. We first encode individual rules using relatively short DNA strands, and then generate all possible rule paths by the directed joining of such short strands to form longer strands. We then conduct the verification algorithm to detect errors. The potential of applying this proposed DNA computation algorithm to rule verification is promising given the operational time complexity of O(n*q), in which n denotes the number of fact clauses in the rule base and q is the number of rules with longest inference chain.     

Analyzing User-Perceived Dependability and Performance Characteristics of Voting Algorithms for Managing Replicated Data

User-perceived dependability and performance metrics are very different from conventional ones in that the dependability and performance properties must be assessed from the perspective of users accessing the system. In this paper, we develop techniques based on stochastic Petri nets (SPN) to analyze user-perceived dependability and performance properties of quorum-based algorithms for managing replicated data. A feature of the techniques developed in the paper is that no assumption is made regarding the interconnection topology, the number of replicas, or the quorum definition used by the replicated system, thus making it applicable to a wide class of quorum-based algorithms. We illustrate this technique by comparing conventional and user-perceived metrics in majority voting algorithms. Our analysis shows that when the user-perceiveness is taken into consideration, the effect of increasing the network connectivity and number of replicas on the availability and dependability properties perceived by users is very different from that under conventional metrics. Thus, unlike conventional metrics, user-perceived metrics allow a tradeoff to be exploited between the hardware invested, i.e., higher network connectivity and number of replicas, and the performance and dependability properties perceived by users.     

Optimization of material composition to minimize the thermal stresses induced in FGM plates with temperature-dependent material properties

A hybrid genetic algorithm with the complex method is developed for the optimization of the material composition of a multi-layered functionally graded material plate with temperature-dependent material properties in order to minimize the thermal stresses induced in the plate when it is subjected to steady-state thermal loads. In the formulation, the plate is artificially divided into an n _l -layered plate, and a weak-form-based finite layer method is developed to obtain the displacement and stress components induced in the n _l -layered plate using the Reissner mixed variational theorem. Two thermal conditions, namely the specified temperature and heat convection conditions, imposed on the top and bottom surfaces of the plate are considered. The through-thickness distributions of the volume fractions of the constituents are assumed as certain specific/non-specific function distributions, such as power-law, sigmoid, layerwise step and layerwise linear function distributions, and the effective material properties of the plate are estimated using the Mori–Tanaka scheme. Comparisons with regard to the minimization for the peak values of the stress ratios induced in the FGM plates with various optimal material compositions are conducted.