Recommendation of RILEM Technical Committee 250-CSM: Test method for Textile Reinforced Mortar to substrate bond characterization

Textile Reinforced Mortar (TRM), also known as Fabric Reinforced Mortar or Fabric Reinforced Cementitious Matrix, composites are an emerging technology for the external repair and strengthening of existing structures. For most applications, the effectiveness of the TRM reinforcement relies on its bond performance. This recommendation identifies the best practice to characterize the bond behaviour of TRM. A shear bond test method is proposed to determine the peak axial stress (associated with the maximum load that can be transferred from the structural member to the externally bonded TRM reinforcement), the stress–slip relationship and the failure mode that controls the TRM-to-substrate load transfer capacity. Guidelines on specimen manufacturing, experimental setup, test execution, and determination of test results are provided.     

Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid resources are divided into computational resources and network resources. Computational tasks and transmission tasks of a workflow are distinguished. We present a model of the problem, and an algorithm for finding feasible resource allocations. A numerical example is included, showing the importance of the resource allocation phase on a grid. Some conclusions and directions for future research are given.     

Low temperature co-fired ceramic (LTCC)-based biosensor for continuous glucose monitoring

This article presents design, fabrication and testing of a miniature ceramic-based biosensor which is destined for continuous glucose monitoring. It is fabricated using well known LTCC (low temperature co-fired ceramics) technology. The biosensor consists of a microreaction chamber, three thick-film electrodes and a microdialysis tube. The detection process is based on oxidation of glucose by molecular oxygen in the presence of enzyme - glucose oxidase, GOx. One of the reaction products is a hydrogen peroxide which is detected amperometrically during its oxidation at a working electrode. Computational fluid dynamics (CFD) analysis was used for optimization of the biosensor geometry and considerations of the influence of operating conditions (flow rate) on mass transfer (diffusion) of the glucose within the microreaction chamber. Tests of the developed LTCC-based biosensor indicate its linear response to glucose concentration up to 9 mM with a relatively high sensitivity of about 147 nA/mM. Moreover, the properties of the presented ceramic biosensor are compared with properties of a similar device made in silicon/glass and in Perspex^®.     

Consistent Sets of Secondary Structures in Proteins

Ab initio predictions of secondary structures in proteins have to combine local predictions, based on short fragments of the protein sequence, with consistency restrictions, as not all locally plausible predictions may be simultaneously true. We use the fact that secondary structures are patterns of hydrogen bonds and that a single residue can participate in hydrogen bonds of at most one secondary structure. Consistency of fixed-sized pieces of secondary structures is the easiest to approximate and we formalize it as 1-2 matching problem. Consistency of entire secondary structures is a version of set packing. We also investigate how to form a simple problem if we add the requirement that the secondary structure and the loops that connect them fit together in a metric space. Every problem that we investigated is MAX-SNP hard and it has a constant factor approximation. Computational experience suggests that in biological instances, we can find nearly optimal solutions using heuristics.     

Self-optimizing generalized adaptive notch filters — comparison of three optimization strategies

The paper provides comparison of three different approaches to on-line tuning of generalized adaptive notch filters (GANFs) — the algorithms used for identification/tracking of quasi-periodically varying dynamic systems. Tuning is needed to adjust adaptation gains, which control tracking performance of GANF algorithms, to the unknown and/or time time-varying rate of system nonstationarity. Two out of three compared approaches are classical solutions — the first one incorporates sequential optimization of adaptation gains while the second one is based on the concept of parallel estimation. The main contribution of the paper is that it suggests the third way — it shows that the best results can be achieved when both approaches mentioned above are combined in a judicious way. Such joint sequential/parallel optimization preserves advantages of both treatments: adaptiveness (sequential approach) and robustness to abrupt changes (parallel approach). Additionally the paper shows how, using the concept of surrogate outputs, one can extend the proposed single-frequency algorithm to the multiple frequencies case, without falling into the complexity trap known as the “curse of dimensionality”.     

Accelerating scientific computations with mixed precision algorithms

On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. The approach presented here can apply not only to conventional processors but also to other technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the STI Cell BE processor. Results on modern processor architectures and the STI Cell BE are presented.

Program summary

Program title: ITER-REFCatalogue identifier: AECO_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AECO_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 7211No. of bytes in distributed program, including test data, etc.: 41 862Distribution format: tar.gzProgramming language: FORTRAN 77Computer: desktop, serverOperating system: Unix/LinuxRAM: 512 MbytesClassification: 4.8External routines: BLAS (optional)Nature of problem: On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution.Solution method: Mixed precision algorithms stem from the observation that, in many cases, a single precision solution of a problem can be refined to the point where double precision accuracy is achieved. A common approach to the solution of linear systems, either dense or sparse, is to perform the LU factorization of the coefficient matrix using Gaussian elimination. First, the coefficient matrix A is factored into the product of a lower triangular matrix L and an upper triangular matrix U. Partial row pivoting is in general used to improve numerical stability resulting in a factorization PA=LU, where P is a permutation matrix. The solution for the system is achieved by first solving Ly=Pb (forward substitution) and then solving Ux=y (backward substitution). Due to round-off errors, the computed solution, x, carries a numerical error magnified by the condition number of the coefficient matrix A. In order to improve the computed solution, an iterative process can be applied, which produces a correction to the computed solution at each iteration, which then yields the method that is commonly known as the iterative refinement algorithm. Provided that the system is not too ill-conditioned, the algorithm produces a solution correct to the working precision.Running time: seconds/minutes     

Speeding up global optimization with the help of intelligent supervisors

It is shown in the paper that Developmental Genetic Programming is an efficient tool for evolutionary development of intelligent supervisors that solve an extension of Resource-Constrained Project Scheduling Problem. The extension assumes that resources are only partially available. It also assumes that renewable resources affect the project cost. The cost should be as low as possible and a deadline of the project must be met. This is apparent with regard to software houses and building enterprises. Computational experiments showed that supervisors find solutions of the problem much faster than other genetic approaches. A specific property of the supervisor is that it has various strategies of allocating the resources to the tasks. The supervisor uses the strategies in order to develop a procedure for producing the best schedule for the whole project. The analysis of the evolutionary process was performed and experimental results were compared with the optimal ones obtained by means of the exhaustive search method.     

3D‐vision based detection,localization, and sizing of broccoli heads in the field

This paper describes a 3D vision system for robotic harvesting of broccoli using low‐cost RGB‐D sensors, which was developed and evaluated using sensory data collected under real‐world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground‐truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross‐validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low‐cost 3D imaging for commercial broccoli harvesting.     

Using association rules to assess purchase probability in online stores

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formulate some predictions for the online store, e.g. that a logged user who has viewed only traditional, printed books, has been staying in the store from 10 to 25 min, and has opened between 30 and 75 pages, will decide to confirm a purchase with the probability of more than 92 %.

     

A Monge-Ampère enhancement for semi-Lagrangian methods

Demanding the compatibility of semi-Lagrangian trajectory schemes with the fundamental Euler expansion formula leads to the Monge-Ampère (MA) nonlinear second-order partial differential equation. Given standard estimates of the departure points of flow trajectories, solving the associated MA problem provides a corrected solution satisfying a discrete Lagrangian form of the mass continuity equation to round-off error. The impact of the MA enhancement is discussed in two diverse limits of fluid dynamics applications: passive tracer advection in a steady cellular flow and in fully developed turbulence. Improvements of the overall accuracy of simulations depend on the problem and can be substantial.