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The purpose of this paper is to make a scientific contribution to web information retrieval (IR).

A multiagent system for web IR is proposed based on new technologies: Hierarchical Small‐Worlds (HSW) and Possibilistic Networks (PN). This system is based on a possibilistic qualitative approach which extends the quantitative one.

The paper finds that the relevance of the order of documents changes while passing from a profile to another. Even if the selected terms tend to select the relevant document, these terms are not the most frequent of the document. This criterion shows the asset of the qualitative approach of the SARIPOD system in the selection of relevant documents. The insertion of the factors of preference between query terms in the calculations of the possibility and the necessity consists in increasing the scores of possibilistic relevance of the documents containing these terms with an aim of penalizing the scores of relevance of the documents not containing them. The penalization and the increase in the scores are proportional to the capacity of the terms to discriminate between the documents of the collection.

It is planned to extend the tests of the SARIPOD system to other grammatical categories, like refining the approach for the substantives by considering for example, the verbal occurrences in names definitions, etc. Also, it is planned to carry out finer measurements of the performances of SARIPOD system by extending the tests with other types of web documents.

The system can be useful to help research students find their relevant scientific papers. It must be located in the document server of any research laboratory.

The paper presents SARIPOD, a new qualitative possibilistic model for web IR using multiagent system.

The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.

The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.

The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.

The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

Researchers are now designing a new generation of machines that can reason, make judgements, learn from experience ‐ intelligent machines. Already this technology is being incorporated into a new generation of machines and manufacturing processes. Explores the use of fuzzy logic, neural networks and genetic algorithms in the design of intelligent machines and explains their basic structure. Gives examples of how this technology can be incorporated into applications such as the design of robot end effectors, CNC machine tools and autonomous guided vehicles. Discusses the development of the intelligent machine and the impact that these will have on manufacturing in the future.

Most people agree that preparing for the future is necessary to excel, but doing it effectively is difficult. All risk analyses offer some foresight, but tools based on classic probabilistic calculus open for deception through apparent accuracy in some situations because ambiguity and fuzziness is largely ignored. We believe this is particularly a problem in strategic settings as it may lead to less informed decision making. We also believe that strategic risk analysis can hardly be performed well without matching risk management actions to the organization’s characteristics. We therefore present a new approach towards strategic risk analysis that remedies the two aforementioned problems. The purpose is to analyze strategic risks in a meaningful and practical way, yet capable of handling the broader scope of strategic risk. The approach is illustrated by a case.

To present the hypergraph as a systems model that is more versatile than are the single‐purpose models known from literature.

Parting from a previously known approach, pertinent hypergraph structures are specified, three new propositions are proved and the role of system elements and relations is analyzed.

Unlike a simple graph and a simple logical analysis, hypergraphs make it possible to apply a single theoretical approach to modelling a variety of systems. A worked‐out example illustrates the procedure.

Hypergraphs as a research tool appear sparsely in scientific literature. This paper fills the gap by presenting the hypergraph‐based systems model as being more versatile than are the models known from literature. It also highlights the effect of system elements and relations on the make‐up of the system in point.

Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is subjective and fuzzy. The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals.

On the one hand, fuzzy extensions of Allen temporal relations are investigated and, on the other hand, extended temporal relations to define the positions of two fuzzy time intervals are introduced. Then, a database system, called Fuzzy Temporal Information Management and Exploitation (Fuzz-TIME), is developed for the purpose of processing fuzzy temporal queries.

To evaluate the proposal, the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose. Some demonstrative scenarios from history domain are proposed and discussed.

The authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system. However, thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.

The tool developed (Fuzz-TIME) can have many practical applications where time information has to be dealt with. In particular, in several real-world applications like history, medicine, criminal and financial domains, where time is often perceived or expressed in an imprecise/fuzzy manner.

The social implications of this work can be expected, more particularly, in two domains: in the museum to manage, exploit and analysis the piece of information related to archives and historic data; and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases.

This paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.

This paper presents a comprehensive review of the available literature on applications of the rough set theory. Concepts of the rough set theory are discussed for approximation, dependence and reduction of attributes, decision tables and decision rules. The applications of rough sets are discussed in pattern recognition, information processing, business and finance, industry, environment engineering, medical diagnosis and medical data analysis, system fault diagnosis and monitoring and intelligent control systems. Development trends and future efforts are outlined. An extensive list of references is also provided to encourage interested readers to pursue further investigations.

This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered.

At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models.

The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models.

This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

The purpose of this paper is to derive an economic order quantity (EOQ) for an inventory control problem where the inventory carrying cost and the order cost are uncertain, represented by fuzzy numbers. The fuzzy numbers used herein are most general so far, represented by adaptive trapezoidal fuzzy numbers. This paper attempts to use the most general form of fuzziness to represent the uncertainty of the parameters in the inventory model.

The fuzzy EOQ formula derivation is analytical. Given the inventory cost C_c and the order cost C_o as fuzzy numbers and the demand, a crisp number and instant replenishment of inventory, a fuzzy EOQ is derived. This is done by using the possibilistic mean and the possibilistic variance of the fuzzy total inventory cost. Then for practical implementation, this quantity is defuzzyfied using the middle of the maxima (MOM) of the fuzzy EOQ, in order to get the crisp value of the EOQ that minimizes the (fuzzy) total inventory cost.

The fuzzy EOQ model derived herein is the most general fuzzy model. It is then converted to a crisp optimal order quantity and a crisp order cycle. The model assumptions cover the uncertainties in estimating the order cost and the inventory carrying cost. However, the results that can be extended in case of the shortage in inventory stock are allowed.

Inventories by their nature are the basic part of consideration in any production, supply chain, warehousing and retail policies. The inventories consume a large part of budget, space, overheads and maintenance. Even though the problem considered in this paper is limited to single period and single item inventories, it can be extended to multiple items and multi‐period inventories. The paper gives an illustrative example and its solution at the end.

EOQ is the most fundamental concept in making inventory policies. However, in inventory literature, covering the risk of uncertainty in the various cost estimations such as carrying and order or shortage costs, is more recent and is not well developed.

The purpose of this paper is to develop the model of telemetry data processing flow (TDPF) for TDPF development and the TDPF run-time infrastructure to improve the spacecraft health monitoring capability.

This research tries to develop the TDPF by flow-based programming (FBP) method and the component-based telemetry data processing software.

The result from the case study is positive, thus reflecting the appropriateness of the suggested method.

Application of the proposed TDPF model and the component-based telemetry data processing software may result in improved development efficiency and less development costs.

This paper provides an effective way to develop TDPF without recompiling the software. It greatly facilitates the TDPF development that hopefully will save the TDPF development cost.