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Traditionally, the binary search method is used to collect the denoising data in the array pixilated CdZnTe nuclear detector. Due to the high dispersion of the data itself, the acquisition efficiency is low and the acquisition result has a large error. A denoising data acquisition algorithm for array pixilated CdZnTe nuclear detector is proposed. The detector principle and system noise type are analyzed. The buffer half-full storage algorithm and multi-thread control method are used to collect the noise data of array pixilated CdZnTe nuclear detector. The experimental data show that the proposed algorithm can effectively collect the denoising data of the array pixilated CdZnTe nuclear detector, and the acquisition error rate is only 0.25, the acquisition speed growth rate is up to 96%, with high acquisition accuracy and efficiency.

To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

The current information retrieval research on industrial clusters has low precision, low recall ratio, obvious delay and high energy consumption. Thus, in this paper, a information retrieval algorithm based on vector space for industrial clusters is proposed. By optimizing the unlawful labels in the database network, dividing the web pages of the industrial cluster information database and calculating the keyword scores of the relevant information of the industrial cluster corresponding to a web page, a set of well-divided database pages is obtained, and the purification of the industrial cluster information database is realized. According to the purification of industrial cluster information database, RFD algorithm is used to extract the page data features of purified industrial cluster information database. The extracted results are substituted into the information retrieval, and the vectors composed of retrieval units are used to describe the information of various types of industrial clusters and each retrieval. The matching results of information retrieval are obtained by calculating the correlation between the information of industrial clusters and the query, and the information retrieval of industrial clusters is completed. Experimental results show that the algorithm has high precision and recall ratio, short retrieval time and low energy consumption.

The traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps.

In this article, we investigate some new results of Neutrosophic multiplication module E (shortly Ne(E)). We additionally introduce some light points about some concepts in which have relationship with Neutrosophic multiplication module. We prove that if E is Neutrosophic Artinian multiplication module and Neutrosophic Jacobson radical of E is a Neutrosophic small submodule of Ne(E), then Ne(E) is a Neutrosophic cyclic module. Finally, we show that if E is a Neutrosophic divisible module over Neutrosophic integral domain, then E is a Neutrosophic multiplication module if and only if E is a Neutrosophic cyclic module. [ABSTRACT FROM AUTHOR]

Aim/Purpose This research aims to analyze the effect of human resource management on humanitarian operations performance, using humanitarian organizations in Malaysia as a case. Background Humanitarian organizations need to develop and continue effective on-the-job human resource management, such as training and development and managing employee performance to enhance the performance of their humanitarian operations. Methodology The sampling technique that was conducted is probability sampling. In particular, the technique is called stratified sampling. This technique is chosen because it is involving the division of a population into a smaller group, called "strata". The questionnaire survey was distributed to humanitarian organizations in Malaysia to collect research data, and PLS-SEM analysis was conducted to validate the conceptual model. Contribution This research focuses on the effect of human resource management on humanitarian operations performance in humanitarian organizations with consistent training to ensure successful humanitarian operations. Findings The results of PLS-SEM analysis confirmed that Training and Employee Development, Recruitment and Employee Selection, and Communicative Management Style are significantly correlated with humanitarian operations performance, giving 75.7% variations which means that these human resource management are critical factors for increasing humanitarian operations performance in Malaysian humanitarian organizations. Recommendations for Practitioners This research will enhance humanitarian operations performance for humanitarian organizations, in-line policies outlined under the Malaysia National Security Council Directive No. 20, and benefit the field of disaster management. Recommendations for Researchers This research can be used by the authorized individual involved in humanitarian operations to satisfy the needs of the victims, which ultimately contributes to the performance of these humanitarian organizations. Impact on Society This research highlighted the human resource management that is vital for humanitarian organizations, which will increase humanitarian operations performance in an organization. Future Research This study is conducted in the context of humanitarian organizations in Malaysia. It is unclear whether the key findings of this study can be generalized. Therefore, it is suggested that, in future research, the current research model should be extended to include different countries for validation. [ABSTRACT FROM AUTHOR]

In this paper, we define the notion of Q-neutrosophic normal soft groups and discuss several related structural characteristics and properties. Additionally, we discuss the relation between Q-neutrosophic normal soft groups and normal soft groups. Furthermore, we define the concept of Q-neutrosophic soft cosets and discuss several relevant attributes. [ABSTRACT FROM AUTHOR]

As an extension of neutrosophic soft sets, Q-neutrosophic soft sets were established to deal with twodimensional indeterminate data. Different hybrid models of fuzzy sets were utilized to different algebraic structures, for example groups, rings, fields and lie-algebras. A field is an essential algebraic structure, which is widely used in algebra and several domains of mathematics. The motivation of the current work is to extend the thought of Q-neutrosophic soft sets to fields. In this paper, we define the notion of Q-neutrosophic soft fields. Structural characteristics of it are investigated. Moreover, the concepts of homomorphic image and pre-image of Q-neutrosophic soft fields are discussed. Finally, the Cartesian product of Q-neutrosophic soft fields is defined and some related properties are discussed. [ABSTRACT FROM AUTHOR]

The neutrosophic cubic averaging and Einstein averaging aggregation operators are presented and applied to the air pollution model of the city of Peshawar, Pakistan. Neutrosophic cubic set (NCS) is a more generalized version of the neutrosophic set (NS) and an interval neutrosophic set (INS). It is in a better position to express consistent, indeterminant and incomplete information, thus it is able to be applied to aggregate the air pollution model. Aggregation operators have a key role in science and engineering problems. Firstly, the neutrosophic cubic weighted averaging (NCWA) operator, neutrosophic cubic ordered weighted averaging (NCOWA) operator, neutrosophic cubic hybrid aggregation (NCHA) operator, neutrosophic cubic Einstine weighted averaging (NCEWA) operator, neutrosophic cubic Einstine ordered weighted averaging (NCEOWA) operator and neutrosophic cubic Einstine hybrid aggregation (NCEHA) operator are defined. Secondly, these operators are applied to the air pollution model of particulate matter with the size of less than 10 micron (PM10) in Peshawar. Subsequently, the results are compared with the World Health Organization (WHO) standards using score/accuracy function. The pollution of PM10 is found to be very much higher than WHO standards. Hence, strong measures are required to control air pollution. [ABSTRACT FROM AUTHOR]

To tackle the real life problems we come across, in various fields like computer sciences, medical sciences, social sciences and engineering works where we are facing many ambiguities and imprecisions. Here we bring an idea of neutrosophic bipolar fuzzy decision making where hybridized multi-attributes are involved, which is a very helpful tool to tackle the ambiguities and imprecisions. We present the neutrosophic bipolar fuzzy transformation techniques. The different types of attributes are transformed into unified neutrosophic bipolar fuzzy values. It includes the group decision making mode based on hybrid decision making problems with exact values, interval values and linguistic variables. Calculations of weights by decision makers, composition of aggregated weighted neutrosophic bipolar fuzzy decision matrices, determination of entropy weights, finding positive ideal solution(PIS),and negative ideal solution(NIS), calculation of grey relational coefficient ,calculation of degree of weighted grey relational coefficient of each alternative, determination of relative relational degree of each alternative from the positive ideal solution (PIS) and negative ideal solution (NIS) and ranking of the alternatives are the concepts which are introduced in the case of neutrosophic bipolar fuzzy hybrid multi-attribute group decision making. Eventually, we apply these concepts and techniques upon hybrid multi-attributes decision making problem of selecting the best medicine to cure some particular diseases and develop an algorithm for neutrosophic bipolar fuzzy hybrid multi-attribute group decision making. [ABSTRACT FROM AUTHOR]

Neutrosophic set theory was initiated as a method to handle indeterminate uncertain data. It is identified via three independent memberships represent truth T, indeterminate I and falsity F membership degrees of an element. As a generalization of neutrosophic set theory, Q-neutrosophic set theory was established as a new hybrid model that keeps the features of Q-fuzzy soft sets which handle two-dimensional information and the features of neutrosophic soft sets in dealing with uncertainty. Different extensions of fuzzy sets have been already implemented to several algebraic structures, such as groups, symmetric groups, rings and lie algebras. Group theory is one of the most essential algebraic structures in the field of algebra. The inspiration of the current work is to broaden the idea of Q-neutrosophic soft set to group theory. In this paper the concept of Q-neutrosophic soft groups is presented. Numerous properties and basic attributes are examined. We characterize the thought of Q-level soft sets of a Q-neutrosophic soft set, which is a bridge between Q-neutrosophic soft groups and soft groups. The concept of Q-neutrosophic soft homomorphism is defined and homomorphic image and preimage of a Q-neutrosophic soft groups are investigated. Furthermore, the cartesian product of Q-neutrosophic soft groups is proposed and some relevant properties are explored. [ABSTRACT FROM AUTHOR]

Smarandache introduced the concept of neutrosophic set which is the genralistion of fuzzy set, intuitionistic fuzzy set and a better mathematical tool to handle incomplete, inconsistance and vague information. In this work, we propose the concept of multi Q-single valued neutrosophic soft expert set and its basic operations such as, union, intersection, complement, And and OR. Further, we construct an algorithm for decision-making method on multi Q-SVNSES. Finally, an example is provided to show the practicality and effectiveness of the proposed algorithm which consist of indeterminate and inconsistent information. At the end, a comparison has been made through an example with the existing theory. [ABSTRACT FROM AUTHOR]

Molodtsov introduced the concept of the soft set as a general mathematical tool for dealing with uncertainty. We intend to further this foray into fuzzy sets and soft sets by introducing the concepts of Q-cubic set and a Q-cubic soft multiset and their basic operations, namely complement, union, intersection, OR and AND operations along with illustrative examples. Basic theorems on the operations of Q-cubic soft multi set are also given. [ABSTRACT FROM AUTHOR]

We introduce the concept of complex multi-fuzzy soft expert set (CMFSES) for which the range of its membership functions are rep- resented in terms of complex numbers. CMFSES has the ability to realize more range of values while handling uncertainty of data that is captured by the amplitude terms and phase terms of the complex numbers, simultaneously. It also has a mechanism to incorporate the adequate parameterization capabilities and the opinions of all experts regarding the validity of the information at hand in one mode, thus making it quite appropriate for use in decision-making problems, whereas the time factor plays a key role in determining the final decision. Some operations related to this new concept have been defined, such as complement, union and intersection, AND and OR operations. We also investigate structural properties of these operations based on this concept. An algorithm is developed in complex multi-fuzzy expert soft set setting for a decision making method. Finally an illustrative example is employed to show that it can be successfully applied to problems that contain uncertainties. In addition, a comparison between CMFSES and other existing methods is made to reveal the dominance of our proposed method. [ABSTRACT FROM AUTHOR]

Traditionally, the binary search method is used to collect the denoising data in the array pixilated CdZnTe nuclear detector. Due to the high dispersion of the data itself, the acquisition efficiency is low and the acquisition result has a large error. A denoising data acquisition algorithm for array pixilated CdZnTe nuclear detector is proposed. The detector principle and system noise type are analyzed. The buffer half-full storage algorithm and multithread control method are used to collect the noise data of array pixilated CdZnTe nuclear detector. The experimental data show that the proposed algorithm can effectively collect the denoising data of the array pixilated CdZnTe nuclear detector, and the acquisition error rate is only 0.25, the acquisition speed growth rate is up to 96%, with high acquisition accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The current information retrieval research on industrial clusters has low precision, low recall ratio, obvious delay and high energy consumption. Thus, in this paper, a information retrieval algorithm based on vector space for industrial clusters is proposed. By optimizing the unlawful labels in the database network, dividing the web pages of the industrial cluster information database and calculating the keyword scores of the relevant information of the industrial cluster corresponding to a web page, a set of well-divided database pages is obtained, and the purification of the industrial cluster information database is realized. According to the purification of industrial cluster information database, RFD algorithm is used to extract the page data features of purified industrial cluster information database. The extracted results are substituted into the information retrieval, and the vectors composed of retrieval units are used to describe the information of various types of industrial clusters and each retrieval. The matching results of information retrieval are obtained by calculating the correlation between the information of industrial clusters and the query, and the information retrieval of industrial clusters is completed. Experimental results show that the algorithm has high precision and recall ratio, short retrieval time and low energy consumption. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the active queue management algorithm can not explicitly control the queue length and the relationship between throughput and delay, a fuzzy information control algorithm based on Active Queue Management for digital substation communication network congestion is proposed. After packet grouping is entered into the router cache queue, the packets that first enter the cache area of the router are preprocessed, and the data packet is processed fairly by using the geometric distribution function. The combination of Smith predictive control and adaptive fuzzy control is used to compensate the network delay of packets, eliminate the negative impact of time delay on active queue jitter and delay jitter, and control congestion according to fuzzy rules intelligently. The experimental results showed that the proposed algorithm can maintain smaller queue oscillations, especially when the network conditions change. It can effectively eliminate the impact of time delay on queue jitter and delay jitter, and improve the overall performance of the network. [ABSTRACT FROM AUTHOR]

In the past, intelligent system often realized reasoning operation by interpolation method for one-dimensional sparse rule base, and could not analyze fuzzy reasoning of multi-dimensional sparse rule condition, which greatly improved the error and volatility of reasoning results. Therefore, a multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is proposed. Through the CMAC neural network, the influence weight of each variable is extracted. CMAC neural network is applied to train weights of multi-dimensional variables in multiple multi-dimensional fuzzy reasoning rules, and local correction weights are made, so that the weights of each modification are very few. After fast learning, the influence weights of the multi-dimensional variables on the reasoning result are obtained. A multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is applied to input the given neighboring rules into CMAC neural network, and the weights of the variables in the neighboring rules are obtained. According to the linear interpolation and the sequence of interpolation cardinal numbers, the influence weights of the variables in the observation value are determined. According to the linear interpolation reasoning method, a new fuzzy rule is constructed. Based on the approximation between the new fuzzy rules and the observed values, the similarity between the predicted values and the new fuzzy rules is constructed. The result of fuzzy inference is obtained according to the similarity. The experimental results show that the proposed algorithm has high reasoning precision and stability, and the practical application effect is good. [ABSTRACT FROM AUTHOR]

The key algorithm of the traditional system is aimed at the minimum of a certain factor, but does not consider the uncertain conditions and various modes of transportation, and the result of the scheduling is not excellent. To this end, a new fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed. Based on the GPS module, a fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed, and the overall structure of the system is given. The scheduling optimization problem of freight transport lines is described, and the volume of demand, the total volume of delivery and the remaining number of vehicles are made fuzzy processing. The goal is to minimize the total time of the advance or tardiness of the transportation and the total cost, so that the fuzzy scheduling model of transportation path is built. According to the principle of ant colony algorithm, the built multi-objective model will be transformed into a single objective model, and combined with the objective function, the index heuristic information and the performance of ant colony algorithm are set, and the optimal solution of that the deviation is minimum with the ideal solution is calculated by using ant colony algorithm, so as to achieve the multi-objective transportation path scheduling. The experimental results show that the total transportation distance of the designed system is short, the total cost is low, and the goods can be delivered in time. [ABSTRACT FROM AUTHOR]

The notion of classical soft multisets is extended to neutrosophic vague soft multisets by applying the theory of soft multisets to neutrosophic vague sets to be more effective and useful. We also define its basic operations, namely complement, subset, union, intersection along with illustrative examples, and study some related properties with supporting proofs. Lastly, this notion is applied to a decision making problem and its effectiveness is demonstrated using an illustrative example. [ABSTRACT FROM AUTHOR]

Q-neutrosophic soft sets are essentially neutrosophic soft sets characterized by three independent two-dimensional membership functions which stand for uncertainty, indeterminacy and falsity. Thus, it can be applied to two-dimensional imprecise, indeterminate and inconsistent data which appear in most real life problems. Relations are a suitable tool for describing correspondences between objects. In this study we introduce and discuss Q-neutrosophic soft relations, which can be discussed as a generalization of fuzzy soft relations, intuitionistic fuzzy soft relations, and neutrosophic soft relations. Q-neutrosophic soft relation is a sub Q-neutrosophic soft set of the Cartesian product of the Q-neutrosophic soft sets, in other words Q-neutrosophic soft relation is Q-neutrosophic soft sets in a Cartesian product of universes. We also present the notions of inverse, composition of Q-neutrosophic soft relations and functions along with some related theorems and properties. Reflexivity, symmetry, transitivity as well as equivalence relations and equivalence classes of Q-neutrosophic soft relations are also defined. Some properties of these concepts are presented and supported by real life examples. Finally, an algorithm to solve decision making problems using Q-neutrosophic soft relations is developed and verified by an example to show the efficiency of this method. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present and to validate a research model that includes economic sustainability, social sustainability, environmental sustainability and corporate reputation in the context of public-sector organizations in Pakistan. The methodological approach is survey-based, using partial least squares structural equation modelling (PLS-SEM) to assess the research model. The proposed hypotheses were tested based on a sample of 425 respondents from public-sector organizations in Pakistan. The findings of the study indicate that there is a positive relationship between economic sustainability, social sustainability, environmental sustainability and corporate reputation. Therefore, it is inferred from the results that different aspects of sustainability can create and enhance the reputation of public-sector organizations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel soft computing technique, called the complex neutrosophic soft expert relation (CNSER), to evaluate the degree of interaction between two hybrid models called complex neutrosophic soft expert sets (CNSESs). CNSESs are used to represent two-dimensional data that are imprecise, uncertain, incomplete and indeterminate. Moreover, it has a mechanism to incorporate the parameter set and the opinions of all experts in one model, thus making it highly suitable for use in decision-making problems where the time factor plays a key role in determining the final decision. The complex neutrosophic soft expert set and complex neutrosophic soft expert relation are both defined. Utilizing the properties of CNSER introduced, an empirical study is conducted on the relationship between the variability of the currency exchange rate and Malaysian exports and the time frame (phase) of the interaction between these two variables. This study is supported further by an algorithm to determine the type and the degree of this relationship. A comparison between different existing relations and CNSER to show the ascendancy of our proposed CNSER is provided. Then, the notion of the inverse, complement and composition of CNSERs along with some related theorems and properties are introduced. Finally, we define the symmetry, transitivity and reflexivity of CNSERs, as well as the equivalence relation and equivalence classes on CNSESs. Some interesting properties are also obtained. [ABSTRACT FROM AUTHOR]

This paper presents a novel complex neutrosophic soft expert set (CNSES) concept. The range of values of CNSES is extended to the unit circle in the complex plane by adding an additional term called the phase term which describes CNSES's elements in terms of the time aspect. CNSES is a hybrid structure of soft sets and single-valued neutrosophic sets (SVNSs) defined in a complex setting where the experts' opinions are included, thus making it highly suitable for use in decision-making problems that involve uncertain and indeterminate data where the time factor plays a key role in determining the final decision. Based on this new concept we define some concepts related to this notion as well as some basic operations namely the complement, union, intersection, AND and OR. The basic properties and relevant laws pertaining to this concept such as the De Morgan's laws are also verified. Lastly, we propose an algorithm to solve complex neutrosophic soft expert decision-making problem by converting it from the complex state to the real state and subsequently provided the detailed decision steps. This study is supported by the comparison with other existing methods. [ABSTRACT FROM AUTHOR]

The traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of complex multi-fuzzy sets (CMk FSs) as a generalization of the concept of multi-fuzzy sets by adding the phase term to the definition of multi-fuzzy sets. In other words, we extend the range of multi-membership function from the interval [0,1] to unit circle in the complex plane. The novelty of CMk FSs lies in the ability of complex multi-membership functions to achieve more range of values while handling uncertainty of data that is periodic in nature. The basic operations on CMk FSs, namely complement, union, intersection, product and Cartesian product are studied along with accompanying examples. Properties of these operations are derived. Finally, we introduce the intuitive definition of the distance measure between two complex multi-fuzzy sets which are used to define d-equalities of complex multi-fuzzy sets. [ABSTRACT FROM AUTHOR]

Coronary artery disease affects millions of people all over the world including a major portion in Egypt every year. Although much progress has been done in medical science, early detection of this disease is still a challenge for prevention. In this paper we, will extend the concept of fuzzy soft set theory so as to develop a knowledge-based system in medicine and devise a prediction system named fuzzy soft expert system consisting of four main components. These are a fuzzification which translates inputs into fuzzy values, fuzzification of data sets to obtain fuzzy soft sets, a new fuzzy soft set by normal parameter reduction of fuzzy soft set and an algorithm to produce the resultant output. The fuzzy soft expert system developed is then used to predict for coronary artery disease using systolic blood pressure, low-density lipoprotein cholesterol, maximum heart rate, blood sugar, old peak and age of patients. A preliminary study is conducted on nine male patients undergoing treatment in the Cardiac Unit of the Faculty of Medicine, Assiut University, Egypt. It is found that the fuzzy soft expert system developed is able is to help the expert doctor to decide whether a patient needs to be given drug therapy or intervention. [ABSTRACT FROM AUTHOR]

The aim of this paper is to correct the assertions (3) and (4) of Theorem 3.23 proposed by Zhang and Shu [Journal of Intelligent and Fuzzy Systems 27 (2014) 2115-2125]. A counterexample illustrates the flaw of the assertions.We introduce the notion of generalized possibility multi-fuzzy soft sets and use it to correct the flawin those assertions. Finally we introduce two new definitions and five theorems to improve the work of Zhang and Shu, along with two examples. [ABSTRACT FROM AUTHOR]

In this article we use the concept of a multi Q-fuzzy set for defining a multi Q-fuzzy soft interval set and propose properties and corresponding theorems. An adjustable approach is presented by means of average probability. Finally, a novel concept called soft interval cut-sets is proposed for dealing with decision making problems based on multi Q-fuzzy soft interval set, followed by a few illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper, we recall the concept of vague soft expert set theory and its operations. We define the AND and OR operations of vague soft expert set and some properties. We prove the De Morgan's law on vague soft expert set. We then provide an illustrative example of vague soft expert set and its application in decision making. [ABSTRACT FROM AUTHOR]

Our previous method that is PMZSS1 has a rate of convergence of at least eight. The aim of repeating the steps in PMZSS1 is to yield a better rate of convergence. The resulting method is called the repeated midpoint zoro PRMZSS1 where its rate of convergence is at least 7r + 1 with r ≥ 1. The proof of this result is detailed in the convergence analysis of PRMZSS1. Numerical results and comparison with the existing procedures of PZSS1 and PMZSS1 are included to confirm our theoretical results, where the rate of convergence of PZSS1 and PMZSS1 are four and eight respectively. [ABSTRACT FROM AUTHOR]

Molodtsov's soft set theory is a newly emerging mathematical tool for handling uncertainty. However, classical multiparameterized soft sets are not appropriate for imprecise and Q-fuzzy parameters. This work aims to extend the classical multiparameterized soft sets to interval-valued Q-fuzzy multiparameterized soft (IVQFMP -soft) sets by giving an importance degree for each element in the multiparameterized sets. The equality, subset, complement, union, intersection, Λ and ∨-product operations are defined for the IVQFMP-soft sets. Finally, the proposed IVQFMP-soft set is applied to a decision-making problem, and its effectiveness is demonstrated through a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we first introduce the concept of neutrosophic vague soft expert sets (NVSESs for short) which combines neutrosophic vague sets and soft expert sets to be more effective and useful. We also define its basic operations, namely complement, union, intersection, AND and OR along with illustrative examples, and study some related properties with supporting proofs. Lastly, this concept is applied to a decision making problem and its effectiveness is demonstrated using a hypothetical example. [ABSTRACT FROM AUTHOR]

The order of convergence of the existing interval zoro symmetric singlestep procedure is at least 4. The point version shares the same order of convergence. The point version of the interval zoro symmetric singlestep procedure, aptly called point zoro symmetric single-step procedure, is modified by repeating the two forward and one backward steps r times. This modified procedure is named as point repeated zoro symmetric single-step procedure. It is shown that this procedure converges at the rate of at least 3r + 1 where r ≥ 1. This procedure and that of the point zoro symmetric single-step procedure are identical when r = 1. Numerical results show that the proposed point repeated zoro symmetric single-step procedure possesses higher rate of convergence than does the point zoro symmetric single-step procedure . [ABSTRACT FROM AUTHOR]

Molodtsov introduced the concept of soft set as a general mathematical tool for dealing with uncertainty. In thiswork, we first defined a multi Q-fuzzy soft expert set and its basic operations, namely complement, union, intersection, OR and AND. We then construct a decision-making method on multi Q-fuzzy soft expert set. We finally provide an example to show that the method can be successfully applied to problems which contain uncertainties. [ABSTRACT FROM AUTHOR]

The objective in the p-median problem is to minimize the total demand-weighted distance between facilities and demand points. Two different assignment techniques are considered. These techniques are the classical assignment technique and the assignment through urgencies technique. A fuzzy genetic algorithm, crossover operator and crossover rate are used to compare the behavior and efficiency of these methods. The performances of these techniques are assessed by using the benchmark problems currently available in open literature. It is found that the assignment through urgencies is much superior to the classical assignment method. [ABSTRACT FROM AUTHOR]

A weighted multi-criteria model is built to maximize the distribution of students into academic programs of a department. The optimal distribution of students to these programs is determined by taking into account the constraints inherent in each academic program while complying with the limits of space capacity, the constraints of financial allocation, the number of instructors and affirmative action quotas. Each constraint has a weight attached to it that must be met. This model is applied to the School of Mathematical Sciences, Universiti Kebangsaan Malaysia, which consists of three academic programs. The successful application demonstrates the ability of the model to provide optimal distribution in compliance with the student intake requirement and constraints of each academic program in the department. [ABSTRACT FROM AUTHOR]

Molodtsov introduced the theory of soft sets as a generalized tool for modelling complex systems involving uncertain or not clearly defined objects. In this paper the concepts of vague soft set relations are introduced as a sub-vague soft set of the Cartesian product of the vague soft sets and related concepts such as equivalent vague soft set relation, partition, composition and function along with some properties and illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper, we give the definitions of a multi Q-fuzzy set and a multi Q-fuzzy parameterized soft (MkQFP-soft) set and their operations, namely complement, quality, subset, union, and intersection operations. We then propose a decision making method based on MkQFP-soft set theory. We finally give an example which shows that the method can be successfully applied to problems that contain uncertainties. [ABSTRACT FROM AUTHOR]