The effect of partial substitution of nb for v on austenite grain size and hardness of a tool steel

In the present work, the composition of H- 13 tool steel was modified with respect to the vanadium content to include an addition of niobium to obtain improved austenite grain size control without altering the tempering response of the steel. Optical metallography and transmission electron microscopy (TEM) observation of extraction replicas showed that the new steel had a smaller austenite grain size and a finer carbide size distribution than the vanadium steel, which indicates a higher potential toughness.     

Analysis and optimization of weakly coupled thermoelastoplastic systems with applications to weldment design

A systematic approach for the design of weakly coupled thermoelastoplastic systems is presented. The Newton–Raphson iteration method is used in the solution process so that analytic design sensitivity formulations may be efficiently derived via the direct differentiation technique. The derived formulations are suitable for finite element implementations. Analysis and sensitivity analysis capabilities are combined with numerical optimization to form an optimum design algorithm. To demonstrate the algorithm, we optimally design a weldment with respect to manufacturing and service life aspects.     

Time‐dependent heat transfer coefficient of a wall

A time‐dependent coefficient of heat transfer is proposed for the computation of thermal power required, so that a room temperature reaches a desired value within a given time. A mathematical formulation of the room heating transient phenomenon is constructed in a dimensionless form. Using an integral approximate solution an analytical expression for this coefficient is provided and it is verified by diagrams adopted by DIN 4701. Copyright © 2003 John Wiley & Sons, Ltd.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Autoimmune diabetes induced by the beta-cell toxin STZ. Immunity to the 60-kDa heat shock protein and to insulin

Administered at a suitably low dose, the toxin streptozotocin (STZ) can trigger an autoimmune process leading to destruction of the beta-cells of the pancreatic islets. In this study, we examined specific immunological reactions in mice before and during the development of STZ-induced autoimmune diabetes. We now report that the development of spontaneous autoantibodies to insulin can serve as a marker of susceptibility to a low dose of STZ. Susceptible male mice of the C57BL/KsJ strain manifested such anti-insulin antibodies, and resistant female mice did not. Administration of a low dose of STZ (five daily doses each of 30 mg/kg) induced transient hyperglycemia approximately 20-30 days later, which temporarily remitted but was followed by intractable diabetes approximately 2.5 months later. The diabetogenic process triggered by the low dose of STZ was associated with an increase in the level of anti-insulin antibodies bearing the Dana and Micha (DM) idiotype, later followed by the appearance of anti-idiotypic antibodies that peaked before the onset of diabetes. Antibodies and T-cells reactive to hsp60 (heat shock protein) were triggered by the low-dose STZ administration and persisted throughout the period that preceded clinical diabetes. T-cells reactive to the p277 peptide of hsp60 were also observed. Finally, active immunization to hsp60 caused transient hyperglycemia by itself and also aggravated the hyperglycemia induced by low-dose STZ. Thus, autoantibodies to insulin can indicate susceptibility to a toxic trigger of diabetes, and a low dose of a toxin can activate the insulin and hsp60 autoimmunity that has been detected previously in the spontaneous autoimmune diabetes of NOD strain mice.     

New orientations for bulk-acoustic-wave devices

Several new materials/orientations for which strong BAW (bulk acoustic wave) response is predicted are presented. These new materials/orientations have much lower insertion losses and therefore present the possibility of superior BAW devices. The results also point out some correspondence between pseudo-SAW and bulk acoustic waves. The pseudo-SAW waves cannot theoretically exist in a semiinfinite piezoelectric half-space. The presence of a pseudo-SAW pole with high coupling coefficient (related to the residue of the pole) indicates a strong bulk wave being launched into the medium.     

An optimal algorithm for the boundary of a cell in a union of rays

In this paper we study a cell of the subdivision induced by a union ofn half-lines (or rays) in the plane. We present two results. The first one is a novel proof of theO(n) bound on the number of edges of the boundary of such a cell, which is essentially of methodological interest. The second is an algorithm for constructing the boundary of any cell, which runs in optimal (n logn) time. A by-product of our results are the notions of skeleton and of skeletal order, which may be of interest in their own right.This work was partly supported by CEE ESPRIT Project P-940, by the Ecole Normale Supérieure, Paris, and by NSF Grant ECS-84-10902.This work was done in part while this author was visiting the Ecole Normale Supérieure, Paris, France.     

Exporting water wave optimization concepts to modified simulated annealing algorithm for size optimization of truss structures with natural frequency constraints

This paper proposes an improved version of a recently proposed modified simulated annealing algorithm (MSAA) named as an improved MSAA (I-MSAA) to tackle the size optimization of truss structures with frequency constraint. This kind of problem is problematic because its feasible region is non-convex while the boundaries are highly non-linear. The main motivation is to improve the exploitative behavior of MSAA, taking concept from water wave optimization metaheuristic (WWO). An interesting concept of WWO is its breaking operation. Thirty functions extracted from the CEC2014 test suite and four benchmark truss optimization problems with frequency constraints are explored for the validity of the proposed algorithm. Numerical results indicate that I-MSAA is more reliable, stable and efficient than those found by other existing metaheuristics in the literature.

     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...