Feynman graph generation and calculations in the Hopf algebra of Feynman graphs

Two programs for the computation of perturbative expansions of quantum field theory amplitudes are provided. feyngen can be used to generate Feynman graphs for Yang–Mills, QED and φ^k${\phi }^k$ theories. Using dedicated graph theoretic tools feyngen can generate graphs of comparatively high loop orders. feyncop implements the Hopf algebra of those Feynman graphs which incorporates the renormalization procedure necessary to calculate finite results in perturbation theory of the underlying quantum field theory. feyngen is validated by comparison to explicit calculations of zero dimensional quantum field theories and feyncop is validated using a combinatorial identity on the Hopf algebra of graphs.     

A resource broker with an efficient network information model on grid environments

This paper describes a resource broker whose main function is to match available resources to user needs. The resource broker provides a uniform interface for accessing available and appropriate resources via user credentials. We also focus on providing approximate measurement models for network-related information using NWS for future scheduling and benchmarking. We first propose a network measurement model for gathering network-related information (including bandwidth, latency, forecasting, error rates, etc.) without generating excessive system overhead. Second, we constructed a grid platform using Globus Toolkit that integrates the resources of five schools in Taichung integrated grid environment resources (TIGER). The resource broker runs on top of TIGER. Therefore, it provides security and current information about available resources and serves as a link to the diverse systems available in the Grid.

A reputation-driven scheduler for autonomic and sustainable resource sharing in Grid computing

The obstacle for the Grid to be prevalent is the difficulty in using, configuring and maintaining it, which needs excessive IT knowledge, workload, and human intervention. At the same time, inter-operation amongst Grids is on track. To be the core of Grid systems, the resource management must be autonomic and inter-operational to be sustainable for future Grid computing. For this purpose, we introduce HOURS, a reputation-driven economic framework for Grid resource management. HOURS is designed to tackle the difficulty of automatic rescheduling, self-protection, incentives, heterogeneous resource sharing, reservation, and SLA in Grid computing. In this paper, we focus on designing a reputation-based resource scheduler, and use emulation to test its performance with real job traces and node failure traces. To describe the HOURS framework completely, a preliminary multiple-currency-based economic model is also introduced in this paper, with which future extension and improvement can be easily integrated into the framework. The results demonstrate that our scheduler can reduce the job failure rate significantly, and the average number of job resubmissions, which is the most important metric in this paper that affects the system performance and resource utilization from the perspective of users, can be reduced from 3.82 to 0.70 compared to simple sequence resource selection.     

A dual K-Na selective Prussian blue nanotubes sensor

A strategy for dual sensing of Na⁺ and K⁺ ions using Prussian blue nanotubes via selective inter/deintercalation of K⁺ ion and competitive inhibition by Na⁺ ion, is reported. The analytical signal is derived from the cyclic voltammetry cathodic peak position E_pc of Prussian blue nanotubes. Na⁺ and K⁺ levels in a sample solution can be determined conveniently using one Prussian blue nanotubes sensor. In addition, this versatile method can be applied for the analysis of single type of either Na⁺ or K⁺ ions. The dual-ion sensor response towards Na⁺ and K⁺ can be described using a model based on the competitive inhibition effects of Na⁺ on K⁺ inter/deintercalation in Prussian blue nanotubes. Successful application of the Prussian blue nanotubes sensor for Na⁺ and K⁺ determination is demonstrated in artificial saliva.     

Automatically wrapping legacy software into services: A grid case study

The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing for solving large-scale data and computationally intensive problems. Peer-to-peer (P2P) systems have emerged as an infrastructure enabling technologies for enhanced scalability and reliability in file sharing and content distribution. It is envisioned that P2P enabled service-oriented grid systems would virtualize various resources as services with high scalability and reliability. Many legacy software resources exist nowadays, but making them grid aware services for effective resource sharing has become an issue of vital importance. This paper presents GSLab, a toolkit for automatically wrapping legacy software into services that can be published, discovered and reused in grid environments. GSLab employs Sun Grid Engine (SGE) to enhance its performance in execution of wrapped services. Using GSLab, we have automatically wrapped a legacy computer animation rendering code written in C as a service that can be discovered and accessed in a SGE environment. The evaluation results show that the performance of GSLab improves with an increase in the number of computing nodes involved.

The complexity of achievement and maintenance problems in agent-based systems

We completely classify the computational complexity of the basic achievement and maintenance agent design problems in bounded environments when these problems are parameterized by the number of environment states and the number of agent actions. The different problems are P-complete, NP-complete, co-NP-complete or PSPACE-complete (when they are not trivial). We also consider alternative achievement and maintenance agent design problems by allowing longer runs in environments (that is, our environments are bounded but the bounds are more liberal than was the case previously). Again, we obtain a complete classification but so that the different problems are DEXPTIME-complete, NEXPTIME-complete, co-NEXPTIME-complete or NEXPSPACE-complete (when they are not trivial).     

Four-arc approximation to ellipses: The best in general

So far, the four-arc approximations to an ellipse E are made under the condition that the major and minor axes keep strictly unchanged. In general, however, this condition is unnecessary. Then the fitting can be further improved. Considering a representative quadrant of E, we first draw two auxiliary circular arcs tangent to E at the axes but having a gap ε at their boundary, such that the small arc S lies outside the large arc L. Meanwhile the extreme errors of S and L w.r.t. E are ε and −ε respectively. Giving the radii of S and L a decrement −ε/2 and an increment ε/2 brings them to join smoothly. Thus, reducing the overall error to minimum, an analytic solution in implicit form is derived.     

MPaaS: Mobility prediction as a service in telecom cloud

Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.     

Learning complex action models with quantifiers and logical implications

Automated planning requires action models described using languages such as the Planning Domain Definition Language (PDDL) as input, but building action models from scratch is a very difficult and time-consuming task, even for experts. This is because it is difficult to formally describe all conditions and changes, reflected in the preconditions and effects of action models. In the past, there have been algorithms that can automatically learn simple action models from plan traces. However, there are many cases in the real world where we need more complicated expressions based on universal and existential quantifiers, as well as logical implications in action models to precisely describe the underlying mechanisms of the actions. Such complex action models cannot be learned using many previous algorithms. In this article, we present a novel algorithm called LAMP (Learning Action Models from Plan traces), to learn action models with quantifiers and logical implications from a set of observed plan traces with only partially observed intermediate state information. The LAMP algorithm generates candidate formulas that are passed to a Markov Logic Network (MLN) for selecting the most likely subsets of candidate formulas. The selected subset of formulas is then transformed into learned action models, which can then be tweaked by domain experts to arrive at the final models. We evaluate our approach in four planning domains to demonstrate that LAMP is effective in learning complex action models. We also analyze the human effort saved by using LAMP in helping to create action models through a user study. Finally, we apply LAMP to a real-world application domain for software requirement engineering to help the engineers acquire software requirements and show that LAMP can indeed help experts a great deal in real-world knowledge-engineering applications.     

Novel carbazole/anthracene hybrids for efficient blue organic light-emitting diodes

Two novel carbazole/anthracene hybrided molecules, namely 2-(anthracen-9-yl)-9-ethyl-9H-carbazole (AnCz) and 2,7-di(anthracen-9-yl)-9-ethyl-9H-carbazole (2AnCz), were designed and synthesized via palladium catalyzed coupling reaction. The anthracene was attached either at the 2-site (AnCz) or at both 2,7-sites (2AnCz) of the central carbazole core to tune the conjugation state and the optoelectronic properties of the resultant molecules. Both of them show good solubility in common organic solvents. They also possess relatively high HOMO levels (−5.39 eV, −5.40 eV) that would facilitate efficient hole injection and be favorable for high power efficiencies when used in organic light-emitting devices (OLEDs). AnCz and 2AnCz were used as non-doped emitter to fabricate OLEDs by vacuum evaporation. Good performance was achieved with maximum luminance efficiency of 2.61 cd A⁻¹ and CIE coordinates of (0.15, 0.12) for AnCz, and 9.52 cd A⁻¹ and (0.22, 0.37) for 2AnCz.     

Salicylaldehyde based colorimetric and “turn on” fluorescent sensors for fluoride anion sensing employing hydrogen bonding

Two salicylaldehyde based colorimetric and fluorescent chemosensors 1 and 2 were developed. Both receptors 1 and 2 showed unique selectivity for the fluoride anions over other anions in DMSO solution. [TBA] OH and ¹H NMR titration experiments revealed that the F⁻-induced colorimetric and “turn on” fluorescence response were driven by hydrogen bonding interaction between the OH protons and F⁻.     

Enhanced chemosensing of ammonia based on the novel molecular semiconductor-doped insulator (MSDI) heterojunctions

A series of new molecular semiconductor-doped insulator (MSDI) heterojunctions as conductimetric transducers to NH₃ sensing were fabricated based on a novel semiconducting molecular material, an amphiphilic tris(phthalocyaninato) rare earth triple-decker complex, Eu₂[Pc(15C5)₄]₂[Pc(OC₁₀H₂₁)₈], quasi-Langmuir-Shäfer (QLS) film, as a top-layer, and vacuum-deposited and cast film of CuPc as well as copper tetra-tert-butyl phthalocyanine (CuTTBPc) QLS film as a sub-layer, named as MSDIs 1, 2 and 3, respectively. MSDIs 1-3 and respective sub-layers prepared from three different methods were characterized by X-ray diffraction, electronic absorption spectra and current-voltage (I-V) measurements. Depending on the sub-layer film-forming method used, α-phase CuPc film structure, β-phase CuPc crystallites and H-type aggregates of CuTTBPc have been obtained, respectively. An increasing sensitivity to NH₃ at varied concentrations in the range of 15-800 ppm, follows the order MSDI 2 < MSDI 3 < MSDI 1, revealing the effect of sub-layer film structures on sensing performance of the MSDIs. In particular, the time-dependent current plot of the MSDI 1, with α-phase CuPc film as a sub-layer, clearly shows an excellent separation of the different ammonia concentration levels and nearly complete reversibility and reproducibility even at room temperature, which is unique among the phthalocyanine-based ammonia sensors thus far reported in the literature. This provides a general method to improve sensor response of organic heterojunctions by controlling and tuning the film structure of sub-layer with appropriate fabrication techniques. On the other hand, the enhanced sensitivity, stability and reproducible response of the MSDI 1 heterostructure in comparison with the respective single-layer films have also been obtained. A judicious combination of materials and molecular architectures has led to enhanced sensing properties of the MSDI 1, in which control at the molecular level can be achieved.     

Geometry-based semantic ID for persistent and interoperable reference in feature-based parametric modeling

In current feature-based parametric design systems, the reusability principle is not fully supported as it was expected. Unpredictability and ambiguity of models often happen during design modification within one system as well as among different systems. This reference deficiency significantly reduces the power of feature-based parametric modeling, where geometry re-evaluation generates unexpected shapes. In this paper, a sufficient condition of B-Rep variance based on geometry continuity in parametric complex Euclidean (P^pC³) space is proposed. Shape and relation parameters are differentiated in P^pC³, thus parametric family can be defined. A semantic id scheme based on continuity of geometry is developed to solve the problem of naming persistency and to improve interoperability of CAD feature modeling. Hierarchical namespaces localize entity creation and identification. All geometric and topological entities are referred uniformly based on surface ids, and topology semantics is retained in id itself.     

Geometric properties of partial least squares for process monitoring

Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input X, while principal component analysis (PCA) produces unsupervised decomposition of input X. In this paper, the effect of output Y on the X-space decomposition in PLS is analyzed and geometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric way for the purpose of process monitoring. A numerical example and a case study are given to illustrate the analysis results.     

A Maple package for improved global mapping forecast

We present a Maple implementation of the well known global approach to time series analysis and some further developments designed to improve the computational efficiency of the forecasting capabilities of the approach. This global approach can be summarized as being a reconstruction of the phase space, based on a time ordered series of data obtained from the system. After that, using the reconstructed vectors, a portion of this space is used to produce a mapping, a polynomial fitting, through a minimization procedure, that represents the system and can be employed to forecast further entries for the series. In the present implementation, we introduce a set of commands, tools, in order to perform all these tasks. For example, the command VecTS deals mainly with the reconstruction of the vector in the phase space. The command GfiTS deals with producing the minimization and the fitting. ForecasTS uses all these and produces the prediction of the next entries. For the non-standard algorithms, we here present two commands: IforecasTS and NiforecasTS that, respectively deal with the one-step and the N$N$-step forecasting. Finally, we introduce two further tools to aid the forecasting. The commands GfiTS and AnalysTS, basically, perform an analysis of the behavior of each portion of a series regarding the settings used on the commands just mentioned above.     

The values of college students in business simulation game: A means-end chain approach

Business simulation games (BSGs) enable students to practice making decisions in a virtual environment, accumulate experience in application of strategies, and train themselves in modes of decision-making. This study examines the value sought by players of BSG. In this study, a means-end chain (MEC) model was adopted as the basis, and ladder method soft laddering was used to conduct in-depth interviews with students who had experience in using BSGs. The chain concept of “attribute-consequence-value” was used to understand students’ value cognition structures. Content analysis was used to analyze the attributes-consequences-values for BSGs players, then converted into a Hierarchical Value Map (HVM). The results showed that students consider teamwork and market diversity as the most important attributes, and the consequences of a cooperative approach and market diversity are emotional exchange and multi-thinking, with the ultimate value brought to users by exchanges between teams and constant thinking being interpersonal relationships and a sense of accomplishment.     

A full-wave solver of the Maxwell's equations in 3D cold plasmas

A new solver for Maxwell's equations in three-dimensional (3D) plasma configurations is presented. The new code LEMan (Low-frequency ElectroMagnetic wave propagation) determines a global solution of the wave equation in a realistic stellarator geometry at low frequencies. The code is aimed at the applications with relatively small computational resources and is very efficient in the Alfvén frequency range. In the present work, the cold plasma model is implemented. Finite elements are applied for the radial discretization and the spectral representation is used for the poloidal and toroidal angles. Special care is taken to avoid the numerical pollution of the spectrum as well as to ensure the energy conservation. The numerical scheme and the convergence properties are discussed. Several benchmarks and results in different geometries are presented.     

Design and implementation of a workflow-based resource broker with information system on computational grids

The grid is a promising infrastructure that can allow scientists and engineers to access resources among geographically distributed environments. Grid computing is a new technology which focuses on aggregating resources (e.g., processor cycles, disk storage, and contents) from a large-scale computing platform. Making grid computing a reality requires a resource broker to manage and monitor available resources. This paper presents a workflow-based resource broker whose main functions are matching available resources with user requests and considering network information statuses during matchmaking in computational grids. The resource broker provides a graphic user interface for accessing available and the appropriate resources via user credentials. This broker uses the Ganglia and NWS tools to monitor resource status and network-related information, respectively. Then we propose a history-based execution time estimation model to predict the execution time of parallel applications, according to previous execution results. The experimental results show that our model can accurately predict the execution time of embarrassingly parallel applications. We also report on using the Globus Toolkit to construct a grid platform called the TIGER project that integrates resources distributed across five universities in Taichung city, Taiwan, where the resource broker was developed.

FINE: A Fully Informed aNd Efficient communication-induced checkpointing protocol for distributed systems

Communication-Induced Checkpointing (CIC) protocols are classified into two categories in the literature: Index-based and Model-based. In this paper, we discuss two data structures being used in these two kinds of CIC protocols, and their different roles in helping the checkpointing algorithms to enforce Z-cycle Free (ZCF) property. Then, we present our Fully Informed aNd Efficient (FINE) communication-induced checkpointing algorithm, which not only has less checkpointing overhead than the well-known Fully Informed (FI) CIC protocol proposed by Helary et al. but also has less message overhead. Performance evaluation indicates that our protocol performs better than many of the other existing CIC protocols.