Behavior-Based branch prediction by dynamically clustering branch instructions

Conditional branches frequently exhibit similar behavior (bias, time-varying behavior,...), a property that can be used to improve branch prediction accuracy. Branch clustering constructs groups or clusters of branches with similar behavior and applies different branch prediction techniques to each branch cluster. We revisit the topic of branch clustering with the aim of generalizing branch clustering. We investigate several methods to measure cluster information, with the most effective the storage of information in the branch target buffer. Also, we investigate alternative methods of using the branch cluster identification in the branch predictor. By these improvements we arrive at a branch clustering technique that obtains higher accuracy than previous approaches presented in the literature for the gshare predictor. Furthermore, we evaluate our branch clustering technique in a wide range of predictors to show the general applicability of the method. Branch clustering improves the accuracy of the local history (PAg) predictor, the path-based perceptron and the PPM-like predictor, one of the 2004 CBP finalists.

Fast-track construction with slag cement concrete: Adiabatic strength development and strength prediction

The early-age strength development of concrete containing slag cement has been investigated to give guidance for its use in fast-track construction. Measurements of temperature rise under adiabatic conditions have shown that high levels of slag cement-for example, 70% of the total binder-are required to obtain a significant reduction in the peak temperature rise. Despite these temperature rises being lower than those for portland cement mixtures, however the early-age strength under adiabatic conditions of slag cement concrete can be as high as 250% of the strength of companion cubes cured at 20 degrees C (68 degrees F). The maturity and, hence, strength development were calculated from the adiabatic temperature histories based on several Maturity functions available in the literature. The predicted strength development with age was compared with the experimental results. Maturity functions that take into account the lower ultimate strengths obtained at elevated curing temperatures were found to be better at predicting the strength development.

Stiffness prediction for bolted moment-connections between cold-formed steel members

The authors have recently described a cold-formed steel portal framing system in which simple bolted moment-connections, formed through brackets, were used for the eaves and apex joints. Such connections, however, cannot be considered as rigid because of localised in-plane elongation of the bolt-holes caused by bearing against the bolt-shanks. To therefore predict the initial stiffness of such connections, it is necessary to know the initial bolt-hole elongation stiffness k(b). In this paper, a finite element-solid idealisation of a bolted lap joint in shear will be described that can be used to determine k(b); the results obtained are validated against experimental data. A beam idealisation of a cold-formed steel bolted moment-connection is then described, in which spring elements are used to idealise the rotational flexibility of the bolt-groups resulting from bolt-hole elongation: Using the value of k(b) in the beam idealisation, the deflections predicted are shown to be similar to those measured experimentally in laboratory tests conducted on the apex joint of a cold-formed steel portal frame. (C) 2003 Elsevier Ltd. All rights reserved.

High confidence prediction of essential genes in burkholderia cenocepacia

Essential genes are absolutely required for the survival of an organism. The identification of essential genes, besides being one of the most fundamental questions in biology, is also of interest for the emerging science of synthetic biology and for the development of novel antimicrobials. New antimicrobial therapies are desperately needed to treat multidrug-resistant pathogens, such as members of the Burkholderia cepacia complex.

P2Y(1) antagonists: Combining receptor-based modeling and QSAR for a quantitative prediction of the biological activity based on consensus scoring

P2Y(1) is an ADP-activated G protein-coupled receptor (GPCR). Its antagonists impede platelet aggregation in vivo and are potential antithrombotic agents. Combining ligand and structure-based modeling we generated a consensus model (LIST-CM) correlating antagonist structures with their potencies. We docked 45 antagonists into our rhodopsin-based human P2Y(1) homology model and calculated docking scores and free binding energies with the Linear Interaction Energy (LIE) method in continuum-solvent. The resulting alignment was also used to build QSAR based on CoMFA, CoMSIA, and molecular descriptors. To benefit from the strength of each technique and compensate for their limitations, we generated our LIST-CM with a PLS regression based on the predictions of each methodology. A test set featuring untested substituents was synthesized and assayed in inhibition of 2-MeSADP-stimulated PLC activity and in radioligand binding. LIST-CM outperformed internal and external predictivity of any individual model to predict accurately the potency of 75% of the test set.

Diagnosis of common bile duct stones by intravenous cholangiography:prediction by ultrasound and liver function tests compared with endoscopic retrograde cholangiography

Routine intravenous cholangiography using the safer contrast medium, meglumine iotroxate, may be a useful investigation prior to laparoscopic cholecystectomy for the detection of suspected common bile duct stones. We compared this with endoscopic cholangiography.

The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples

A study was undertaken to examine a range of sample preparation and near infrared reflectance spectroscopy (NIPS) methodologies, using undried samples, for predicting organic matter digestibility (OMD g kg(-1)) and ad libitum intake (g kg(-1) W-0.75) of grass silages. A total of eight sample preparation/NIRS scanning methods were examined involving three extents of silage comminution, two liquid extracts and scanning via either external probe (1100-2200 nm) or internal cell (1100-2500 nm). The spectral data (log 1/R) for each of the eight methods were examined by three regression techniques each with a range of data transformations. The 136 silages used in the study were obtained from farms across Northern Ireland, over a two year period, and had in vivo OMD (sheep) and ad libitum intake (cattle) determined under uniform conditions. In the comparisons of the eight sample preparation/scanning methods, and the differing mathematical treatments of the spectral data, the sample population was divided into calibration (n = 91) and validation (n = 45) sets. The standard error of performance (SEP) on the validation set was used in comparisons of prediction accuracy. Across all 8 sample preparation/scanning methods, the modified partial least squares (MPLS) technique, generally minimized SEP's for both OMD and intake. The accuracy of prediction also increased with degree of comminution of the forage and with scanning by internal cell rather than external probe. The system providing the lowest SEP used the MPLS regression technique on spectra from the finely milled material scanned through the internal cell. This resulted in SEP and R-2 (variance accounted for in validation set) values of 24 (g/kg OM) and 0.88 (OMD) and 5.37 (g/kg W-0.75) and 0.77 (intake) respectively. These data indicate that with appropriate techniques NIRS scanning of undried samples of grass silage can produce predictions of intake and digestibility with accuracies similar to those achieved previously using NIRS with dried samples. (C) 1998 Elsevier Science B.V.

An Integration Methodology for Automated Recurring Cost Prediction using Digital Manufacturing Technology

The need to account for the effect of design decisions on manufacture and the impact of manufacturing cost on the life cycle cost of any product are well established. In this context, digital design and manufacturing solutions have to be further developed to facilitate and automate the integration of cost as one of the major driver in the product life cycle management. This article is to present an integration methodology for implementing cost estimation capability within a digital manufacturing environment. A digital manufacturing structure of knowledge databases are set out and the ontology of assembly and part costing that is consistent with the structure is provided. Although the methodology is currently used for recurring cost prediction, it can be well applied to other functional developments, such as process planning. A prototype tool is developed to integrate both assembly time cost and parts manufacturing costs within the same digital environment. An industrial example is used to validate this approach.