The immediate consequences operator has been a widely studied and used operator for defining the semantics of a logic program. For instance, it has been considered in the fuzzy case for handling datasets with imperfect, imprecise or vague information. The natural generalization of this operator to the mentioned fuzzy framework is based on the supremum operator, which preserves the strict nature of the universal quantifier. As a consequence, errors in the data, which are usual in the uncertainty environment of the considered dataset, can cause loss of information. This is the main reason why this paper makes different generalizations of this operator by using weighted aggregation operators and introducing interesting results.

The purpose of this study was to evaluate the environmental risks of Alborz Dam on Babolrood River in Babol city, using MCDM methods. First, the environment of the study area was identified, then a list of identified environmental risks was prepared in the construction and exploitation phase, and in the form of a questionnaire was provided to environmental experts familiar with the Alborz dam basin. Environmental risks were rated by experts according to the Likert scoring range based on the three indicators of occurrence intensity, probability of occurrence and risk significance, then environmental risks ranking was performed using OWA method. The most important risks of Alborz dam were obtained during the construction phase: when the decision maker is risk aversion, respectively: Destruction of forest within the dam with a score of 0.048, Displacement of reservoir residents with a score of 0.047, and, When the decision maker is risky, respectively: Destruction of forest within the dam with a score of 0.051, Displacement of reservoir residents with a score of 0.050. Also, The most important risks of Alborz Dam were obtained during the operation phase: When the decision maker is risk averse, respectively: Thermal stratification of dam reservoir with a score of 0.053, Reduction of self-purification of Babelrood River with a score of 0.051, and, When the decision maker is risky, respectively: Thermal stratification of dam reservoir with a score of 0.057, Landslide with a score of 0.056.

Control charts are powerful tools of statistical process control (SPC) used for monitoring processes. Control charts are not only used to detect changes in process, but also enable the quality engineers to prepare the system for an appropriate response before production of the faulty products. The main objective of most researches on control charts is developing a favorable and cost-effective design as well as most appropriate sampling methods. In most of the researches that have been done in this field, the control charts are designed based on simple random sampling (SRS). However, there are many charts in the literature of statistical process control that are not based on SRS and use alternative methods such as ranked set sampling (RSS) or generalized methods. The RSS method is an alternative to SRS when taking actual measurement of the quality characteristic is costly or production of the faulty products is expensive. In this paper, in order to improve the performance of mixed EWMA-CUSUM (MEC) and mixed CUSUM-EWMA (MCE) control charts in estimating the population mean, ranked set sampling method rather than common simple random sampling method is used to design these mixed control charts. The performances of the proposed MEC-RSS and MCE-RSS control charts are evaluated through simulation studies using the average run length (ARL) criterion. Moreover, the performances of the proposed control charts are compared with those of CUSUM-SRS, CUSUM-RSS, EWMA-SRS, EWMA-RSS, MEC-SRS, and MCE-SRS control charts. The results revealed that RSS method improves control charts performance compared to SRS method because it provides an efficient estimation of the mean and variance of the population. In addition, using the ranked set sampling scheme makes the proposed mixed control charts more sensitive to detecting the small and moderate shifts in the process mean than the other corresponding classical control charts. Moreover, based on the same sampling methods, the performances of mixed control charts are better than those of the single CUSUM and EWMA control charts.

In this paper, the problem of distributed high-dimensional nonlinear target tracking over radar networks is investigated. Since radar performances are different in range and measurement functions, utilizing radar networks, data fusion, and data processing increase the accuracy of tracking. Among all data fusion methods for radar networks, distributed manner gains the most reliability and less computation burden with high tracking accuracy. First, nonlinear radar measurements are processed by a local cubature Kalman filter in each agent. Secondly, all processed data over all agents reach an agreement by utilizing a weighted average consensus filter. The proposed algorithm using the consensus on information method decreases the tracking error and computation burden and guarantees the boundedness for any number of consensus steps. Finally, the efficiency of the proposed algorithm and superiority to traditional methods is demonstrated by performing a numerical simulation of tracking a maneuvering target.

Decision-making models such as Analytical Hierarchy Process (AHP), Weighted Decision Matrix (WDM), Pugh Matrix and the likes have been able to assist in the decision process considering the objectives of each evaluation criteria in the alternatives. However, these models need to consider the qualitative and subjective nature of the design features. In order to reduce the unbalanced scale of judgment and the uncertainty associated with the crisp information in the decision process, fuzzified and hybridized models are necessary. Existing hybridized decision models applied for machine concept selection deploy several design features and sub-features at the conceptual product design, which thus make the decision making process to be tedious. In light of this, this article presents a hybridized decision-making model, which harness the comparative strength and computational integrity of fuzzy pairwise comparison matrix and fuzzy weighted average, to numerically analyse a reasonable amount of machine design features, thereby making decision making process less tedious. Design for reconfigurability and functionality which are peculiar to reconfigurable machines was introduced using a Reconfigurable Assembly Fixture (RAF) as a case study while other design features related to design concept evaluation were grouped under design for X. The result of the hybridized model shows that, concept three is the optimal design from four sets of designs. This is compared to previous publication using the RAF design concepts with different design features and sub-features. The comparison indicates that there is a close range in the final values of the designs due to the inclusion of several sub-features in the decision process which were not used in the previous study.

This study proposed a dual ExponentiallyWeighted Moving Average (EWMA) statistics based nonparametric sign control chart to monitor the process deviation from the targeted value. The performance of the proposed chart is compared with the existing nonparametric version of the EWMA sign and Cumulative Sum (CUSUM) mean control charts by using out-of-control average run length for various shifts in the process. The simulation study showed the superiority of the suggested chart over the existing counterparts. A real dataset is also considered for the application of the proposed chart.

In this paper, a new nonparametric control chart based on two EWMA statistics is proposed for monitoring process deviation from the target value. The performance comparison of the proposed chart with the existing nonparametric EWMA sign control chart and a nonparametric CUSUM mean control chart are made by using out-of-control average run length. The simulation study showed the superiority of the suggested chart over the four other parametric and nonparametric charts. An empirical illustration is also provided for performance evaluation of the proposed chart. By comparing the proposed chart with the existing charts, it is concluded that the proposed chart is more sensitive in detecting a small shift in the process.