Ranking Iranian universities: an interpretative structural modeling approach

Ranking is widely considered to be an important tool for evaluating the performance, competitiveness, and success of academic institutions. An appropriate ranking system should evaluate the key missions of the higher education system in a way that helps to improve the leadership goals and activities carried out by the universities. Based on the concepts derived from the Iranian Higher Education Upstream Documents and Measures used internationally for university ranking, this study identifies 21 key measures that can be used in the ranking of Iranian universities. The measures are grouped into five categories: scientific infrastructure, scientific effectiveness, socio-cultural effectiveness, international interactions, and sustainability. Then, using the Interpretative Structural Modeling approach, the researchers develop a coherent rubric for establishing the ranking. The proposed conceptual model focuses primarily on the universities’ contribution to technological and scientific infrastructure, then secondarily on their contribution to scientific advancement and international interactions, and finally at a tertiary level on their socio-cultural effectiveness and sustainability.     

Novel dental nanocomposites: fabrication and investigation of their physicochemical,mechanical and biological properties

The aim of this study was to investigate biological, physicochemical and mechanical characteristics of a series of novel dental restorative nanocomposites that comprise dendritic methacrylate end-caped monomers, triethylene glycol dimethacrylate (TEGDMA; as diluting monomer) and modified silica nanoparticles (\(\hbox {M-SiO}_{2}\); as inorganic filler). The cytotoxicity effects of the monomers and fabricated nanocomposites were examined against NIH3T3 cells (the standard fibroblast cell line) through MTT and trypan blue cell viability tests, respectively. The antibacterial activities of the monomers were evaluated against Lactobacillus plantarum by standard agar disk diffusion approach. The mechanical properties (flexural strength (FS) and compressive strength (CS)) as well as some physicochemical characteristics such as water sorption (WS), sol fraction (SF) and double bond conversion (DC) were also investigated, and compared with corresponding characteristics of 3M Filtek Z250 as a reference. Thus, the fabricated nanocomposites have potential as dental restorative materials mainly due to their suitable biological, physicochemical and mechanical properties.     

BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER

In this article, the brain emotional learning-based pattern recognizer (BELPR) is proposed to solve multiple input–multiple output classification and chaotic time series prediction problems. BELPR is based on an extended computational model of the human brain limbic system that consists of an emotional stimuli processor. The BELPR is model free and learns the patterns in a supervised manner and evaluates the output(s) using the activation function tansig. In the numerical studies, various comparisons are made between BELPR and a multilayer perceptron (MLP) with a back-propagation learning algorithm. The methods are tested to classify 12 UCI (University of California, Irvine) machine learning data sets and to predict activity indices of the Earth's magnetosphere. The main features of BELPR are higher accuracy, decreased time and spatial complexity, and faster training.     

A Neural Basis Computational Model of Emotional Brain for Online Visual Object Recognition

In this study, we propose a novel visual object recognizer inspired by the human brain’s emotional learning. In the proposed computational model, the visual information is transferred through the ventral visual pathway to the amygdala, which is responsible for emotional visual stimuli. In the model, the orbitofrontal cortex (OFC) evaluates the amygdala response and tries to prevent inappropriate answers. The proposed visual recognizer is based on threshold logic units defined on the neural models of the amygdala and the OFC. According to the experimental results, the presented model learns the visual patterns quickly and shows higher performance than the brain emotional learning-based pattern recognizer (BRLPR) and multilayer perceptron (MLP) with Levenberg–Marquardt backpropagation (BPG) learning algorithm, in which the adaptive neurofuzzy inference system (ANFIS) cannot be trained because of the curse of dimensionality. The main features of the proposed model are the lower time and spatial complexity. Hence, it can be utilized in real-time visual object recognition.     

Is there a need for fuzzy logic?

“Is there a need for fuzzy logic?” is an issue which is associated with a long history of spirited discussions and debate. There are many misconceptions about fuzzy logic. Fuzzy logic is not fuzzy. Basically, fuzzy logic is a precise logic of imprecision and approximate reasoning. More specifically, fuzzy logic may be viewed as an attempt at formalization/mechanization of two remarkable human capabilities. First, the capability to converse, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, conflicting information, partiality of truth and partiality of possibility - in short, in an environment of imperfect information. And second, the capability to perform a wide variety of physical and mental tasks without any measurements and any computations [L.A. Zadeh, From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions, IEEE Transactions on Circuits and Systems 45 (1999) 105-119; L.A. Zadeh, A new direction in AI - toward a computational theory of perceptions, AI Magazine 22 (1) (2001) 73-84]. In fact, one of the principal contributions of fuzzy logic - a contribution which is widely unrecognized - is its high power of precisiation.Fuzzy logic is much more than a logical system. It has many facets. The principal facets are: logical, fuzzy-set-theoretic, epistemic and relational. Most of the practical applications of fuzzy logic are associated with its relational facet.In this paper, fuzzy logic is viewed in a nonstandard perspective. In this perspective, the cornerstones of fuzzy logic - and its principal distinguishing features - are: graduation, granulation, precisiation and the concept of a generalized constraint.A concept which has a position of centrality in the nontraditional view of fuzzy logic is that of precisiation. Informally, precisiation is an operation which transforms an object, p, into an object, p^∗, which in some specified sense is defined more precisely than p. The object of precisiation and the result of precisiation are referred to as precisiend and precisiand, respectively. In fuzzy logic, a differentiation is made between two meanings of precision - precision of value, v-precision, and precision of meaning, m-precision. Furthermore, in the case of m-precisiation a differentiation is made between mh-precisiation, which is human-oriented (nonmathematical), and mm-precisiation, which is machine-oriented (mathematical). A dictionary definition is a form of mh-precisiation, with the definiens and definiendum playing the roles of precisiend and precisiand, respectively. Cointension is a qualitative measure of the proximity of meanings of the precisiend and precisiand. A precisiand is cointensive if its meaning is close to the meaning of the precisiend.A concept which plays a key role in the nontraditional view of fuzzy logic is that of a generalized constraint. If X is a variable then a generalized constraint on X, GC(X), is expressed as X isr R, where R is the constraining relation and r is an indexical variable which defines the modality of the constraint, that is, its semantics. The primary constraints are: possibilistic, (r = blank), probabilistic (r = p) and veristic (r = v). The standard constraints are: bivalent possibilistic, probabilistic and bivalent veristic. In large measure, science is based on standard constraints.Generalized constraints may be combined, qualified, projected, propagated and counterpropagated. The set of all generalized constraints, together with the rules which govern generation of generalized constraints, is referred to as the generalized constraint language, GCL. The standard constraint language, SCL, is a subset of GCL.In fuzzy logic, propositions, predicates and other semantic entities are precisiated through translation into GCL. Equivalently, a semantic entity, p, may be precisiated by representing its meaning as a generalized constraint.By construction, fuzzy logic has a much higher level of generality than bivalent logic. It is the generality of fuzzy logic that underlies much of what fuzzy logic has to offer. Among the important contributions of fuzzy logic are the following:

1.

FL-generalization. Any bivalent-logic-based theory, T, may be FL-generalized, and hence upgraded, through addition to T of concepts and techniques drawn from fuzzy logic. Examples: fuzzy control, fuzzy linear programming, fuzzy probability theory and fuzzy topology.

2.

Linguistic variables and fuzzy if-then rules. The formalism of linguistic variables and fuzzy if-then rules is, in effect, a powerful modeling language which is widely used in applications of fuzzy logic. Basically, the formalism serves as a means of summarization and information compression through the use of granulation.

3.

Cointensive precisiation. Fuzzy logic has a high power of cointensive precisiation. This power is needed for a formulation of cointensive definitions of scientific concepts and cointensive formalization of human-centric fields such as economics, linguistics, law, conflict resolution, psychology and medicine.

4.

NL-Computation (computing with words). Fuzzy logic serves as a basis for NL-Computation, that is, computation with information described in natural language. NL-Computation is of direct relevance to mechanization of natural language understanding and computation with imprecise probabilities. More generally, NL-Computation is needed for dealing with second-order uncertainty, that is, uncertainty about uncertainty, or uncertainty² for short.

In summary, progression from bivalent logic to fuzzy logic is a significant positive step in the evolution of science. In large measure, the real-world is a fuzzy world. To deal with fuzzy reality what is needed is fuzzy logic. In coming years, fuzzy logic is likely to grow in visibility, importance and acceptance.     

A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems

Providing efficient workload management is an important issue for a large-scale heterogeneous distributed computing environment where a set of periodic applications is executed. The considered shipboard distributed system is expected to operate in an environment where the input workload is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The tasks consist of multiple strings, each made up of an ordered sequence of applications. There is a quality of service (QoS) minimum throughput constraint that must be satisfied for each application in a string, and a maximum utilization constraint that must be satisfied on each of the hardware resources in the system. The challenge, therefore, is to efficiently and robustly manage both computation and communication resources in this unpredictable environment to achieve high performance while satisfying the imposed constraints. This work addresses the problem of finding a robust initial allocation of resources to strings of applications that is able to absorb some level of unknown input workload increase without rescheduling. The proposed hybrid two-stage method of finding a near-optimal allocation of resources incorporates two specially designed mapping techniques: (1) the Permutation Space Genitor-Based heuristic, and (2) the follow-up Branch-and-Bound heuristic based on an Integer Linear Programming (ILP) problem formulation. The performance of the proposed resource allocation method is evaluated under different simulation scenarios and compared to an iteratively computed upper bound.     

Classical plasticity and viscoplasticity models reformulated: theoretical basis and numerical implementation

The well-known phenomenological model of small strain rate-independent plasticity is reformulated in this paper. The main difference from the classical expositions concerns the absence of the plastic strain from the list of state variables. We show that with the proposed choice of state variables, including the total and the elastic strains and strain-like variables which control hardening, we recover all the ingredients of the classical model from a minimum number of hypotheses: instantaneous elastic response and the principle of maximum plastic dissipation. We also show that using a regularized, penalty-like form of the principle of maximum plastic dissipation, we can recover the classical viscoplasticity model. As opposed to the previous schemes used for the finite element implementation of this model (e.g. B-bar method), we propose an approach in which the basic set of equations need not be modified. The operator split method is used to simplify the details of the numerical implementation concerning both the computation of state variables and the incompatible mode based finite element approximations. The latter proves to be indispensable for accommodating the near-incompressible deformation patterns arising in the classical plasticity. An extensive set of numerical simulations is used to illustrate the proposed formulation. © 1998 John Wiley & Sons, Ltd.     

Biosourced vanillin Schiff base platform monomers as substitutes for DGEBA in thermoset epoxy

Diglycidyl ether Schiff base monomers were prepared from vanillin and various diamines. FT-IR, ¹H NMR, ¹³C NMR, and mass spectroscopy were used to determine their structure. Cured thermoset epoxies made with them were compared to commercial epoxy in terms of mechanical properties. Tensile strengths ranged from 35.1 to 60.4 MPa, Young's modulus from 3.9 to 6.9 GPa, similar to the commercial product. The glass transition ranged from 80 to 117°C, the phase transition T_α from 80 to 121°C and the storage modulus from 2 to 3.5 GPa. Thermogravimetric analysis showed that the vanillin-based epoxies were less heat resistant but had higher residual mass (20-30% wt/wt). Hydrolysis, hydrophobicity and degradation were also monitored to evaluate their potential for coating applications.     

Effect of microstructure of fluorinated acrylic coatings on UV degradation testing

This paper presents research results on the relationships between the microstructure and the performance/weatherability of fluoropolymer/acrylic coatings. We studied fluoropolymer/acrylic blends of identical composition, prepared as films using three different methods: 2-stage emulsion polymerization followed by latex film formation; cold-blending (physically mixing) acrylic and fluoropolymer latex dispersions followed by latex film formation; and solution casting using an organic solvent. We investigated the effects of the mixing method, and the level of acrylic in the blend on the microstructure/morphology and on the durability-related physical properties of the fluoropolymer/acrylic films. Small angle neutron scattering was performed to determine the microstructure/morphology of fluoropolymer-rich micro-domains in the coatings prepared using these three methods. The physical properties tested included the glass transition temperature, the crystallinity fraction, and the tensile strength. The mass loss rates observed during UV exposure testing correlate with the final microstructures of the films.     

A Distributed Artificial Network Solving Complex and Multiple Causal Associations

Causal reasoning (known also as abduction) is a hard task that cognitive agents perform reliably and quickly. A particular class of causal reasoning that raises several difficulties is the cancellation class. Cancellation occurs when a set of causes (hypotheses) cancel each other's explanation with respect to a given effect (observation). For example, a cloudy sky may suggest a rainy weather; whereas a shiny sky may suggest the absence of rain. In the current paper, we extend a recent neural model to handle cancellation interactions. We conduct a sensitivity analysis of this proposal on ad hoc problems put at extreme cases. Finally, we test the model on a large database and propose objective criteria to quantitatively evaluate its performance. Simulation results are very satisfactory and should encourage research.