SOLVING MULTI-CRITERIA ALLOCATION PROBLEMS: A DECISION SUPPORT SYSTEM APPROACH

MCADSS is a multi-criteria allocation decision support system for assisting in the task of partitioning a set of individuals into groups. Based upon multiple criteria, MCADSSâs goal is to maximize the diversity of members within groups, while minimizing the average differences between groups. (The project may be viewed from several perspectives: as a multi-criteria decision making problem, as a "reverse" clustering problem, or as a personnel assignment problem). The system is currently being used to allocate MBA students into sections and study teams at INSEAD, a leading European business school. This paper describes the rationale for MCADSS, design criteria, system methodology, and application results. It also suggests how the approach outlined here might be used for further applications.Information Systems Working Papers Serie

Matter-Antimatter Asymmetry in the Large Hadron Collider

The matter-antimatter asymmetry is one of the greatest challenges in the modern physics. The universe including this paper and even the reader him(her)self seems to be built up of ordinary matter only. Theoretically, the well-known Sakharov's conditions remain the solid framework explaining the circumstances that matter became dominant against the antimatter while the universe cools down and/or expands. On the other hand, the standard model for elementary particles apparently prevents at least two conditions out of them. In this work, we introduce a systematic study of the antiparticle-to-particle ratios measured in various $NN$ and $AA$ collisions over the last three decades. It is obvious that the available experimental facilities turn to be able to perform nuclear collisions, in which the matter-antimatter asymmetry raises from $\sim 0$ at AGS to $\sim 100$ at LHC. Assuming that the final state of hadronization in the nuclear collisions takes place along the freezeout line, which is defined by a constant entropy density, various antiparticle-to-particle ratios are studied in framework of the hadron resonance gas (HRG) model. Implementing modified phase space and distribution function in the grand-canonical ensemble and taking into account the experimental acceptance, the ratios of antiparticle-to-particle over the whole range of center-of-mass-energies are very well reproduced by the HRG model. Furthermore, the antiproton-to-proton ratios measured by ALICE in $pp$ collisions is also very well described by the HRG model. It is likely to conclude that the LHC heavy-ion program will produce the same particle ratios as the $pp$ program implying the dynamics and evolution of the system would not depend on the initial conditions. The ratios of bosons and baryons get very close to unity indicating that the matter-antimatter asymmetry nearly vanishes at LHC.Comment: 9 pages, 5 eps-figures, revtex4-styl

Fluctuations of Particle Yield Ratios in Heavy-Ion Collisions

We study the dynamical fluctuations of various particle yield ratios at different incident energies. Assuming that the particle production yields in the hydronic final state are due to equilibrium chemical processes ($\gamma=1$), the experimental results available so far are compared with the hadron resonance gas model (HRG) taking into account the limited momentum acceptance in heavy-ion collisions experiments. Degenerated light and conserved strange quarks are presumed at all incident energies. At the SPS energies, the HRG with $\gamma=1$ provides a good description for the measured dynamical fluctuations in $(K^++K^-)/(\pi^++\pi^-)$. To reproduce the RHIC results, $\gamma$ should be larger than one. We also studied the dynamical fluctuations of $(p+\bar{p})/(\pi^++\pi^-)$. It is obvious that the energy-dependence of these dynamical fluctuations is non-monotonic.Comment: 8 pages, 2 eps figures and 1 tabl

SOLVING MULTI-CRITERIA ALLOCATION PROBLEMS: A DECISION SUPPORT SYSTEM APPROACH

MCADSS is a multi-criteria allocation decision support system for assisting in the task of partitioning a set of individuals into groups. Based upon multiple criteria, MCADSSâs goal is to maximize the diversity of members within groups, while minimizing the average differences between groups. (The project may be viewed from several perspectives: as a multi-criteria decision making problem, as a "reverse" clustering problem, or as a personnel assignment problem). The system is currently being used to allocate MBA students into sections and study teams at INSEAD, a leading European business school. This paper describes the rationale for MCADSS, design criteria, system methodology, and application results. It also suggests how the approach outlined here might be used for further applications.Information Systems Working Papers Serie

Urinary and serum neutrophil gelatinase-associated lipocalin as a biomarker in Egyptian systemic lupus erythematosus patients: Relation to lupus nephritis and disease activity

AbstractBackgroundNeutrophil gelatinase-associated lipocalin (NGAL) is an excellent structural biomarker for the early diagnosis of acute kidney injury, prognosis, dialysis requirement and mortality in several common clinical scenarios.Aim of the workThe aim of this work is to detect the levels of both urinary and serum NGAL in SLE patients with and without lupus nephritis (LN) and to correlate their levels with renal biopsy class and disease activity.Patients and methodsThe study included 35 SLE patients; 22 with LN and 13 without as well as 30 matched controls. The SLE Disease Activity Index (SLEDAI) was assessed and the renal biopsy class determined. Urinary and serum levels of NGAL were assessed by ELISA.ResultsThe 35 patients had a median age of 30years and disease duration of 4years. They were 31 females and 4 males. The SLE patients had an elevated urinary NGAL (UNGAL) (median 19ng/ml, IQR 8–87) as compared to controls (median 2ng/ml, IQR 1–18.3) (p<0.006). Levels of UNGAL were higher in patients with LN than those without (p<0.023). In patients with LN, serum levels of NGAL were not significantly different from controls (p=0.6). The UNGAL level significantly correlated with the renal score of SLEDAI (r=0.54, p=0.001) but serum NGAL level did not (r=0.25, p=0.15). UNGAL significantly correlated with grade III and IV of renal biopsy (r=0.67, p=0.009). The sensitivity of UNGAL levels for the diagnosis of LN was 85.7%, with a specificity of 80%.ConclusionUrinary NGAL is a sensitive marker of proliferative nephritis in SLE and disease activity

Is there still any Tc mystery in lattice QCD? Results with physical masses in the continuum limit III

The present paper concludes our investigations on the QCD cross-over transition temperatures with 2+1 staggered flavours and one-link stout improvement. We extend our previous two studies [Phys. Lett. B643 (2006) 46, JHEP 0906:088 (2009)] by choosing even finer lattices ($N_t$=16) and we work again with physical quark masses. The new results on this broad cross-over are in complete agreement with our earlier ones. We compare our findings with the published results of the hotQCD collaboration. All these results are confronted with the predictions of the Hadron Resonance Gas model and Chiral Perturbation Theory for temperatures below the transition region. Our results can be reproduced by using the physical spectrum in these analytic calculations. The findings of the hotQCD collaboration can be recovered by using a distorted spectrum which takes into account lattice discretization artifacts and heavier than physical quark masses. This analysis provides a simple explanation for the observed discrepancy in the transition temperatures between our and the hotQCD collaborations.Comment: 25 pages, 10 figures and 3 table

Bulk viscous cosmology with causal transport theory

We consider cosmological scenarios originating from a single imperfect fluid with bulk viscosity and apply Eckart's and both the full and the truncated M\"uller-Israel-Stewart's theories as descriptions of the non-equilibrium processes. Our principal objective is to investigate if the dynamical properties of Dark Matter and Dark Energy can be described by a single viscous fluid and how such description changes when a causal theory (M\"uller-Israel-Stewart's, both in its full and truncated forms) is taken into account instead of Eckart's non-causal theory. To this purpose, we find numerical solutions for the gravitational potential and compare its behaviour with the corresponding LambdaCDM case. Eckart's and the full causal theory seem to be disfavoured, whereas the truncated theory leads to results similar to those of the LambdaCDM model for a bulk viscous speed in the interval 10^{-11} << c_b^2 < 10^{-8}. Tentatively relating such value to a square propagation velocity of the order of T/m of perturbations in a non-relativistic gas of particles with mass m at the epoch of matter-radiation equality, this may be compatible with a mass range 0.1 GeV < m << 100 GeV.Comment: 23 pages, 7 figure

An energy-aware service composition algorithm for multiple cloud-based IoT applications

There has been a shift in research towards the convergence of the Internet-of-Things (IoT) and cloud computing paradigms motivated by the need for IoT applications to leverage the unique characteristics of the cloud. IoT acts as an enabler to interconnect intelligent and self-configurable nodes “things” to establish an efficient and dynamic platform for communication and collaboration. IoT is becoming a major source of big data, contributing huge amounts of streamed information from a large number of interconnected nodes, which have to be stored, processed, and presented in an efficient, and easily interpretable form. Cloud computing can enable IoT to have the privilege of a virtual resources utilization infrastructure, which integrates storage devices, visualization platforms, resource monitoring, analytical tools, and client delivery. Given the number of things connected and the amount of data generated, a key challenge is the energy efficient composition and interoperability of heterogeneous things integrated with cloud resources and scattered across the globe, in order to create an on-demand energy efficient cloud based IoT application. In many cases, when a single service is not enough to complete the business requirement; a composition of web services is carried out. These composed web services are expected to collaborate towards a common goal with large amount of data exchange and various other operations. Massive data sets have to be exchanged between several geographically distributed and scattered services. The movement of mass data between services influences the whole application process in terms of energy consumption. One way to significantly reduce this massive data exchange is to use fewer services for a composition, which need to be created to complete a business requirement. Integrating fewer services can result in a reduction in data interchange, which in return helps in reducing the energy consumption and carbon footprint. This paper develops a novel multi-cloud IoT service composition algorithm called (E2C2) that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements. A formal user requirements translation and transformation modelling and analysis is adopted for the proposed algorithm. The algorithm was evaluated against four established service composition algorithms in multiple cloud environments (All clouds, Base cloud, Smart cloud, and COM2), with the results demonstrating the superior performance of our approach