Dual Set Research Articles

Modeling impedance boundary conditions and acoustic barriers using the immersed boundary method: The three-dimensional case.

One of the main challenges in time domain wave-based acoustics is the accurate simulation of both boundary conditions and barriers capable of reflecting and transmitting energy. Such scattering structures are generally of irregular geometry and characterised in terms of frequency-dependent reflectances and transittances. Conditions for numerical stability can be difficult to obtain in either case. Immersed boundary methods, which are heavily used in computational fluid dynamics applications, replace boundaries by discrete driving terms, avoiding volumetric meshing and staircasing approaches altogether. The main contribution of this article is a unified numerical treatment of both impedance boundary conditions and barriers capable of transmitting energy and suitable for use in the setting of wave-based acoustics. It is framed in terms of the immersed boundary method within a finite difference time domain scheme, using a dual set of matched discrete driving terms in both the conservation of mass and momentum equations that can be tuned against a desired reflectance or transmittance. Numerical results in three dimensions are presented, illustrating non-porous barriers and impedance boundary conditions, and highlight important features such as spurious leakage through an immersed boundary. A brief demonstration of conditions for numerical stability of the immersed boundary method in this context is provided in an appendix.

Complex dual hesitant fuzzy TODIM method and their application in Russia–Ukraine war’s impact on global economy

The primary objective of this research is to examine how the Russia–Ukraine war impacted the global economy. To achieve this goal, we propose operational laws, a score and accuracy function, and a divergence measure for complex dual hesitant fuzzy sets. Furthermore, we investigate the challenges associated with multi-criteria decision-making (MCR) using aggregation operators and the TODIM method with complex dual hesitant fuzzy (CDHF) information. Drawing inspiration from arithmetic aggregation operations, we introduce several aggregation operators for complex dual hesitant fuzzy information, including the complex dual hesitant fuzzy weighted average (CDHFWA) operator, complex dual hesitant fuzzy ordered weighted average (CDHFOWA) operator, and complex dual hesitant fuzzy hybrid average (CDHFHA) operator. We thoroughly analyze the unique characteristics of these proposed operators and use them in conjunction with the TODIM method to develop practical approaches for solving complex dual hesitant fuzzy multi-criteria decision-making problems. To validate our approach and demonstrate its applicability, we provide a practical example of how the Russia–Ukraine war affected the global economy. Our study confirms the effectiveness of our approach and highlights how it can be used to solve real-world problems.

Some results related to Hurwitz stability of combinatorial polynomials

Many important problems are closely related to the zeros of certain polynomials derived from combinatorial objects. The aim of this paper is to observe some results and applications for the Hurwitz stability of polynomials in combinatorics and study other related problems.We first present a criterion for the Hurwitz stability of the Turán expressions of recursive polynomials. In particular, it implies the q-log-convexity or q-log-concavity of the original polynomials. We also give a criterion for the Hurwitz stability of recursive polynomials and prove that the Hurwitz stability of any palindromic polynomial implies its semi-γ-positivity, which illustrates that the original polynomial with odd degree is unimodal. In particular, we get that the semi-γ-positivity of polynomials implies their parity-unimodality and the Hurwitz stability of polynomials implies their parity-log-concavity. Those results generalize the connections between real-rootedness, γ-positivity, log-concavity and unimodality to Hurwitz stability, semi-γ-positivity, parity-log-concavity and parity-unimodality (unimodality). As applications of these criteria, we derive some Hurwitz stability results occurred in the literature in a unified manner. In addition, we obtain the Hurwitz stability of Turán expressions for alternating run polynomials of types A and B and the Hurwitz stability for alternating run polynomials defined on a dual set of Stirling permutations.Finally, we study a class of recursive palindromic polynomials and derive many nice properties including Hurwitz stability, semi-γ-positivity, non-γ-positivity, unimodality, strong q-log-convexity, the Jacobi continued fraction expansion and the relation with derivative polynomials. In particular, these properties of the alternating descents polynomials of types A and B can be implied in a unified approach.

“It Makes Me More Aware of What I Should be Looking for”: Using Holistic Assessment to Foster Strength-Based Understanding of Students’ Biliteracy Development

Deficit mindsets of multilingual writers must be upended. Yet, little research has been done to explore how to prepare bilingual teachers with strength-based orientations, especially in approaches to assessment. Using a holistic biliteracy lens, this qualitative case study explored how Transliteracy observation shaped teachers’ understanding of biliterate writing development in K-5 dual language settings. Findings showed that teachers learned that writing is multifaceted and observable, influenced by oral language, and reflective of one linguistic repertoire. Participants gained a deeper understanding of biliteracy development and their students’ capabilities, supporting an asset-based orientation that challenged monolingual norms. Implications for teacher preparation are shared.

Probabilistic dual hesitant fuzzy MAGDM method based on generalized extended power average operator and its application to online teaching platform supplier selection

Probabilistic dual hesitation fuzzy set (PDHFS) is a new tool used to enhance fuzzy and imprecise information in a changing and complex decision-making environment. Compared with the existing fuzzy sets, PDHFS is more suitable for identifying uncertain evaluation data in complex and realistic decision-making situations. The extended power average (EPA) and generalized power average (GPA) operators provide more information for the information aggregation process, reflecting the mutual support among all aggregation values. To solve the evaluation problems related to decision analysis involving extremely complex information, especially in the case of extreme data in the consideration of decision problems. In this paper, the generalized extended power average (GEPA) operator is proposed in the PDHF environment. This research has the following four important contributions. First, we propose a probability splitting algorithm of normalizing PDHFE, and give some new basic operations, score function, distance measures and aggregation operators. Second, to better integrate the advantages of EPA and GPA operators, we developed the GEPA operator, weighted form (WGEPA) and ordered form (GEPOWA). Meanwhile, by adjusting the parameter in three types of operators, we can find that many existing operators are special forms of the three types of operators. Third, extended the three types of operators to the PDHF environment, the PDHFGEPA, PDHFWGEPA, PDHFGEPOWA operators and some special aggregation operators are obtained, and studied their valuable properties in detail. Finally, taking the application of PDHFWGEPA operator as an example, a new multi-attribute group decision-making (MAGDM) technique is developed and used to solve the problem of online teaching platform supplier selection (OTPSS). Through parameter sensitivity analysis, the flexibility and stability of MAGDM technique are illustrated, and compared with some existing decision-making methods, the effectiveness of MAGDM method is proved. In addition, this technique also provides some theoretical references for dealing with other complex MAGDM problems, and provides a new operator for the extension of GEPA operator in other fuzzy environments. The biggest advantage of several operators is that they can pay full attention to the special role of extreme data in decision-making.

A modified combined active-set Newton method for solving phase-field fracture into the monolithic limit

In this work, we examine a numerical phase-field fracture framework in which the crack irreversibility constraint is treated with a primal–dual active set method and a linearization is used in the degradation function to enhance the numerical stability. The first goal is to carefully derive from a complementarity system our primal–dual active set formulation, which has been used in the literature in numerous studies, but for phase-field fracture without its detailed mathematical derivation yet. Based on the latter, we formulate a modified combined active-set Newton approach that significantly reduces the computational cost in comparison to comparable prior algorithms for quasi-monolithic settings. For many practical problems, Newton converges fast, but active set needs many iterations, for which three different efficiency improvements are suggested in this paper. Afterwards, we design an iteration on the linearization in order to iterate the problem to the monolithic limit. Our new algorithms are implemented in the programming framework pfm-cracks from Heister and Wick (2020). In the numerical examples, we conduct performance studies and investigate efficiency enhancements. The main emphasis is on the cost complexity by keeping the accuracy of numerical solutions and goal functionals. Our algorithmic suggestions are substantiated with the help of several benchmarks in two and three spatial dimensions. Therein, predictor–corrector adaptivity and parallel performance studies are explored as well.

A hybrid approach based on dual hesitant q-rung orthopair fuzzy Frank power partitioned Heronian mean aggregation operators for estimating sustainable urban transport solutions

Transportation systems are a key part of sustainable development, and they need to be carefully evaluated to show that they have a strong impact on the target area’s social, environmental, and economic sustainability. For this reason, involving the developed decision support systems helps to shed light on the users’ demand and provide unblemished policy decisions considering the existing situation. The “q-rung orthopair fuzzy set (q-ROFS)” is a generalization of the “intuitionistic fuzzy sets (IFSs)” and “Pythagorean fuzzy sets (PFSs)” that expresses vague and uncertain data more efficiently. In the interim, the notion of “dual hesitant q-rung orthopair fuzzy set (DHq-ROFS)” is presented to account for human hesitancy, which may be more applicable to genuine “multicriteria group decision-making (MCGDM)” situations. The main goal of this study is to address MCGDM problems using Heronian mean (HM) and DHq-ROF data. The first step is to introduce the Frank t-norm and t-conorm-based DHq-ROF HM (DHq-ROFFHM) operator. DHq-ROFFHM’s features are next described in depth. In addition, the DHq-ROF Frank weighted HM (DHq-ROFFWHM) operator is presented, which takes into account different degrees of liking for input arguments. The DHq-ROF Frank weighted power partitioned HM model is then used to come up with a way to solve models in MCGDM problems where individual arguments are grouped together and have relationships with each other. A final example shows how the established model can be implemented and how well it works.

Sustainable supplier selection by using dual probabilistic linguistic EDAS and ITARA method

With the development of globalization, companies from all over the world are now more closely connected, and they all play different roles in the industry in which they are located. There are more and more companies in a complete supply chain, which can greatly influence the stability of the supply chain, presents certain challenges. Therefore, choosing suppliers with sustainable development capabilities, especially in the event of interruption, can ensure the stability of the entire supply chain, thereby enhancing the company’s image and competitive advantage in a large-scale competition. The sustainable supplier selection is a classical multiple attribute group decision making (MAGDM) issues. In this study, the dual probabilistic linguistic EDAS (DPL-EDAS) method is built based on the traditional EDAS method and dual probabilistic linguistic term sets (DPLTSs). Firstly, the DPLTSs is introduced. Then, combine the traditional EDAS method with DPLTSs information, the DPL-EDAS method is established and the computing steps for MAGDM are built. Finally, there are a numerical case involving sustainable supplier selection and some comparisons in this paper. The comparisons are used to illustrate advantages of DPL-EDAS method.

Green Supplier Selection in the Construction Industry Using a Novel Fuzzy Decision-Making Approach

Concern for the environment among individuals, stakeholders, and governments has steadily increased during the last few decades. Strict environmental regulations and policies have prompted enterprises to develop positive improvements such as green supply chain selection practices. Selecting a suitable supplier is essential for the competitiveness and profitability of an enterprise due to environmental and economic impacts. Managers face challenges as a result of uncertainty in supply chain management. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties for green supplier selection in the construction industry. In this paper, a new approach using the best-worst method (BWM) and the superiority and inferiority ranking (SIR) is applied to assist managers in the appropriate supplier selection, in which the assessment is carried out with probabilistic dual hesitant fuzzy sets (PDHFS). The BWM with PDHFSs is used to generate the weights for criteria and experts. Also, a consistency index based on an input-based consistency ratio (CR) is introduced to solve the problem of poor consistency and make the computing process simpler. Moreover, the extended SIR method with the PDHFS is established using the criteria weights obtained by the BWM-PDHFS. A numerical study is employed to show the reliability and validity of the proposed approach, and comparisons are made to highlight its superiority.

Protection Coordination of Microgrids Using Dual Setting Directional Overcurrent Relays Based on Optimal Operating Characteristics and Curve Breakpoints

Protection Coordination of Microgrids Using Dual Setting Directional Overcurrent Relays Based on Optimal Operating Characteristics and Curve Breakpoints

An examination of cloud computing adoption decisions: Rational choice or cognitive bias?

The objective of this study is to challenge the prevalent assumption that rational choice is the sole driver behind decisions related to Software as a Service (SaaS) adoption, and to demonstrate the impact of status quo bias, manifested as inertia, on the hesitancy to adopt SaaS. To validate our premise, we compared the effects of rational choice and status quo bias on SaaS adoption decisions made by decision-makers in both adopting and non-adopting firms, expanding upon prior research that primarily focused on adopting organizations. By analyzing a dual set of 157 cloud-adopting and 24 non-adopting organizations, our results show that inertia affects the decisions of non-adopters but not previous adopters. Furthermore, we found that previous adopters are more likely to consider the benefits of cloud computing, while non-adopters are more influenced by loss aversion in their cloud adoption decisions. Counter to expectations however, is the finding that previous adopters did not consider the risks of cloud computing adoption. This study adds to the understanding of cloud computing adoption by highlighting the impact of status quo bias on decision-making and providing deeper insights into the complexities of the cloud adoption process.

Extended DPL-VIKOR Method for Risk Assessment of Technological Innovation Using Dual Probabilistic Linguistic Information

The main objective of the present study is to evaluate the multicriteria group decision-making problem in risk management for technological innovation projects (TIPs) under dual-probabilistic linguistic information. The suggested approach is based on an enhanced dual probabilistic linguistic-vise kriterijumska optimizacija kompromisno resenje (DPL-VIKOR) technique to assess risk management in TIP employing probabilistic linguistic information. The conventional VIKOR approach is incapable of dealing with the complexity and difficulty of risk assessment in TIP. Therefore, we incorporated the information included in the dual probabilistic linguistic term set. In DPL-VIKOR, we investigated the relationship between the positive ideal solution (PIS) of the alternative and the negative ideal solution (NIS). To deal with the multicriteria group decision-making problem of risk assessment in TIP, we proposed the extended DPL-VIKOR approach rather than the traditional VIKOR method. We compared the findings to those of several decision-making problem techniques and examined the effectiveness and reliability, as well as advantages of the proposed approach. From the comparison and sensitivity analysis, we conclude that the proposed method is more reliable and effective for evaluating the best alternative in risk management problems for TIP.

An Improved Normal Compliance method for dynamic hyperelastic problems with energy conservation property

The purpose of this work is to present an improved energy conservation method for hyperelastodynamic contact problems based on specific normal compliance conditions. In order to determine this Improved Normal Compliance (INC) law, we use a Moreau–Yosida α-regularization to approximate the unilateral contact law. Then, based on the work of Hauret and Le Tallec (2006), we propose in the discrete framework a specific approach allowing to respect the energy conservation of the system in adequacy with the continuous case. This strategy (INC) is characterized by a conserving behavior for frictionless impacts and admissible dissipation for friction phenomena while limiting penetration. Then, we detail the numerical treatment within the framework of the semi-smooth Newton method and primal–dual active set strategy for the normal compliance conditions with friction. We finally provide some numerical experiments to bring into light the energy conservation and the efficiency of the INC method by comparing with different classical methods from the literature through representative contact problems.

Dual Probabilistic Linguistic Full Consistency Additive Ratio Assessment Model for Medical Equipment Supplier Selection

In comparison to probabilistic linguistic term sets (PLTSs), dual probabilistic linguistic term sets (DPLTSs) reveal the complexity and uncertainty of this realistic world more accurately. The power average operator can mitigate the consequences of some decision-makers’ strong biases while evaluating data. Additionally, the Dombi operators during the phase of aggregation are very adaptable with general parameter. Inspired by these topics, we propose some dual probabilistic linguistic Dombi power aggregation operators to aggregate the decision-making expert’s preferences and various elegant properties of these proposed dual probabilistic linguistic (DPL)-aggregating operators. Further, we establish an integrated method with the combination of proposed DPL-aggregation operators, full consistency method (FUCOM) and additive ratio assessment (ARAS) model called the “DPL-FUCOM-ARAS” methodology for solving multi-criteria decision analysis problems under dual probabilistic linguistic environment. In this methodology, the FUCOM is applied to determine criteria weight, while the ARAS model is presented to prioritize the options from dual probabilistic linguistic perspective. We present a case study on the selection of a medical equipment supplier to highlight the applicability and show the usefulness of the proposed DPL-FUCOM-ARAS methodology. Further, we discuss the sensitivity investigation and comparative discussion to check the stability and robustness of the proposed DPL-FUCOM-ARAS methodology.

Integrated DWT-DHT Feature Set for ABC Optimized SVM-Based PQ Classifier

The power quality (PQ) literature contains abundant methodologies for the classification of PQ disturbances that show the significance of the problem and the necessity to design a reliable method that will accurately and quickly identify the type of disturbance. However, most of the available methods are not suitable for real-time applications as they are sensitive to noise, require more memory, computationally complex, and are also time-consuming. This work suggests an approach using Artificial Bee Colony Algorithm (ABC) for concurrent feature and classifier parameters selection to reduce execution time and enhance classification accuracy. An integrated feature set consisting of features extracted using two signal processing techniques namely Discrete Wavelet Transform (DWT) and Discrete Hilbert Transform (DHT) is proposed for clear discrimination of disturbances in contrast to the conventional single feature extraction tool commonly employed. To authenticate the effectiveness of the proposed dual set features, this method has been tested on both simulated and real-time PQ data and is found to perform better than methods using a single signal processing tool.

Verifiable Privacy-Preserving Outsourced Frequent Itemset Mining on Vertically Partitioned Databases

In the data era, to simultaneously relieve the heavy computational burden of mining data information from data owners and protecting data privacy, privacy-preserving frequent itemset mining (PPFIM) is presented and has attracted much attention. In PPFIM, data owners and miners outsource the complex task of data mining to the cloud server, which supports strong storage and computing power, and the cloud server cannot extract additional data privacy other than that which is shown by data owners or miners. However, most existing solutions assume that cloud servers will honestly perform the mining process and return the correct results, whereas cloud services are usually provided by a charging third party that may in practice return incorrect results due to computation errors, malicious or criminal activities, etc. To solve this problem, in this paper, we present a verifiable PPFIM protocol on vertically partitioned databases to support the verifiability of the integrity of the mining results, where data owners can authorize the cloud server to perform federated mining on vertically partitioned databases without leaking data information and detect dishonest behaviors in the cloud server from the returned results. We adopt a dual cloud setting to enable data owners to be offline after uploading their encrypted databases to the cloud server, which further relieves the burden on data owners. We implement our protocol and give a detailed analysis in terms of verification accuracy, which shows that the dishonest behaviors of the cloud server can be detected with a probability close to 1 and a sacrifice of only a 1% increase in database size.

The fatal flaw in most intent to return literature

ABSTRACT Most intent to return studies measure ‘intent’ using a single aspirational metric and not through the use of a time bound and probabilistic approach suggested in the core literature on this topic. As a result, studies tend to vastly over-exaggerate the likelihood of repeat visitation. This empirical study tests intent to return using a single metric and then a dual set of metrics and concludes that ‘intent’ may be overstated by up to 90%.

Hybridizations of Archimedean copula and generalized MSM operators and their applications in interactive decision-making with q-rung probabilistic dual hesitant fuzzy environment

The q-rung probabilistic dual hesitant fuzzy sets (qRPDHFSs), which outperform dual hesitant fuzzy sets, probabilistic dual hesitant fuzzy sets, and probabilistic dual hesitant Pythagorean fuzzy sets, are used in this research to develop an interactive group decision-making approach. We first suggest the Archimedean Copula-based operations on q-rung probabilistic dual hesitant fuzzy (qRPDHF) components and investigate their key features before constructing the approach. We then create some new aggregation operators (AOs) in light of these operations, including the qRPDHF generalized Maclaurin symmetric mean (MSM) operator, qRPDHF geometric generalized MSM operator, qRPDHF weighted generalized MSM operator, and qRPDHF weighted generalized geometric generalized MSM operator. These aggregation operators are better than current operators on qRPDHF because they can take into account the interactions between a large number of criteria and probability distributions. The evaluation findings are distorted since the present methodologies do not take expert involvement into account in order to achieve the required consistency level. We employ the idea of interaction, consistency, resemblance, and consensus-building among the decision-makers in our method to get around this. We create an optimization model based on the cross-entropy of the qRPDHF components to estimate the weights of the criterion. We provide contextual research on the choice of open-source software LMS in order to demonstrate the relevance of the recommended AOs. Likewise, we ran a sensitivity test on the weights of the criterion to make sure that our model is consistent. The comparison investigation has demonstrated that the suggested approach can overcome the challenges of previous works.

Artificial Neural Network Dual Hesitant Fermatean Fuzzy Implementation in Transportation of COVID-19 Vaccine

This article, in order to address impreciseness, initiated the notion of dual hesitant fermatean fuzzy sets (DHFFSs), as a generalization of the combination of dual hesitant fuzzy set (DHFS), dual hesitant Pythagorean fuzzy set (DHPFS) and Fermatean fuzzy set (FFS). The authors defined the fundamental set of operations for DHFFS. Additionally, the authors have also proposed two ranking functions and an accuracy function for the ordering of this novel set. In order to facilitate the pragmatic implementation of DHFFS in optimization, the authors formulated three types of transportation problem with dual hesitant Fermatean fuzzy (DHFF) parameters. To optimize the DHFF-TP, an algorithm was proposed with the help of one of the proposed ranking functions. Artificial neural network is also applied to the transportation problems in DHFF environment. A numerical example based on the transportation of COVID-19 vaccine with DHFF cost has also been carried out to validate out to validate our technique. In this article the proposed fuzzy system is included into this model to control uncertainties in Time, Quality, Cost, Reliability, and Availability induced in the transportation interruptions. The paper illustrates implication of the proposed fuzzy based model using a simulation in the context of a transportation process. The proposed model seeks to satisfy customers' demands for high-quality products and services, on a timely manner, at lowest cost possible. These characteristics are essential during times of disturbance, such as the COVID-19 pandemic.

Prioritizing healthcare distance simulation educators’ technological competence: descriptive analysis of qualitative interviews

Since the beginning of the pandemic, the community spread of COVID-19 and the unavailability of clinical instructional sites led to heavy dependence on distance simulation to continue health professions education. The challenges faced by educators, combined with the lack of established parameters to prepare simulationists for distance simulation, prompted scholars to find a solution to fill this gap. In the absence of practical guidelines or parameters for healthcare educators to use emerging simulation technologies, this study explored the technological competencies of an ideal distance simulation educator at the basic and advanced level in light of the proposed Distance Simulation Educator Guidelines v3.0 (DSEG). This qualitative descriptive study used deductive content analysis of interviews with distance simulation experts using the DSEG as a codebook for deductive analysis. Nine experts with a diverse healthcare professions background and distance simulation experience were interviewed. The identified main categories included a dual set of competencies, technological and non-technological. The non-technological competencies included professional values and applied principles of simulation and learning. Eight generic categories (competencies of the DSEG) emerged for basic- and advanced-level educators. Although several key findings were expected, this descriptive analysis study prioritized the competencies of basic- and advanced-level distance simulation educators. It also provided insight into how the DSEG could be applied in the real world. Further research is recommended for these preliminary findings.