Dual Set Research Articles

Research on social ecological evaluation of the spatial form of old urban blocks based on dual probabilistic linguistic term sets

Under the rapid process of urbanization, many early renovated urban villages have also encountered many problems. Due to the rapid development of urban construction and the continuous changes in spatial functions, early renovated urban villages have already encountered problems such as unreasonable commercial distribution, lack of parking spaces, reduced commercial vitality, and commercial activities crowding out affecting the normal lives of villagers. There is a serious contradiction between the need for development and the quality of life of villagers. Due to the fixed nature of architectural space, only by fully understanding the essential morphological characteristics of the space can we find the optimal solutions for different space usage functions, and obtain the matching of the optimal solutions in the existing space requirements. The social ecological evaluation of the spatial form of old urban blocks is a multi-attribute group decision making (MAGDM). Recently, the grey relational analysis (GRA) and CRITIC method has been used to cope with MAGDM issues. The dual probabilistic linguistic term sets (DPLTSs) are used as a tool for characterizing uncertain information during the social ecological evaluation of the spatial form of old urban blocks. In this manuscript, the dual probabilistic linguistic GRA (DPL-GRA) method is built to solve the MAGDM under DPLTSs. The CRITIC method is used to obtain the attributes weights. In the end, a numerical case study for social ecological evaluation of the spatial form of old urban blocks is given to validate the proposed method.

Projection-based TOPSIS method to MAGDM in dual probabilistic linguistic information environment

The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods.

A novel group decision-making method for interval-valued q-rung dual hesitant fuzzy information using extended power average operator and Frank operations

This paper advances the field of multi-attribute group decision making (MAGDM) by proposing a novel framework based on interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs). IVq-RDHFSs, which surpass most existing fuzzy sets, effectively represent complex fuzzy information by describing membership and non-membership degrees through interval value sets. However, prior MAGDM methods based on IVq-RDHFSs have been limited by the functions of operation rules and aggregation operators (AOs). This limitation is addressed through the construction of a new MAGDM framework, leveraging the robust Frank t-norm and t-conorm (FTT) operation and the extended power average (EPA) operator. The proposed framework features the interval-valued q-rung dual hesitant fuzzy Frank weighted extended power average (IVq-RDHFFWEPA) operator to obtain comprehensive evaluation values. The paper also introduces novel techniques for determining the weights of decision-makers and attributes. Practical applications of the proposed method are demonstrated through the assessment of desalination technology selection and rural green eco-tourism projects. Sensitivity and comparison analyses validate the superior functionality, accuracy, and flexibility of this method compared to many state-of-the-art methods. The contributions of this paper are two-fold: it develops efficient measurement techniques for IVq-RDHFSs, such as distance and weight calculation, and it introduces a comprehensive MAGDM method by integrating FTT and EPA under IVq-RDHFSs, which improves the efficiency of solving decision-making problems.

Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods

Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives, and develop two approaches for solving such problems. The first approach is based on the minimization of the norm of the gradient in the dual problem and then the reconstruction of an approximate primal solution. Recently, Grapiglia and Nesterov [Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] showed lower complexity bounds for the problem of minimizing the gradient norm of the function with Lipschitz pth order derivatives. Still, the question of optimal or near-optimal methods remained open as the algorithms presented in [Grapiglia and Nesterov, Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] achieve suboptimal bounds only. We close this gap by proposing two near-optimal (up to logarithmic factors) methods with complexity bounds O ~ ( ε − 2 ( p + 1 ) / ( 3 p + 1 ) ) and O ~ ( ε − 2 / ( 3 p + 1 ) ) with respect to the initial objective residual and the distance between the starting point and solution, respectively. We then apply these results (having independent interest) to our primal–dual setting. As the second approach, we propose a direct accelerated primal–dual tensor method for convex problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives. For this algorithm, we prove O ~ ( ε − 1 / ( p + 1 ) ) complexity in terms of the duality gap and the residual in the constraints. We illustrate the practical performance of the proposed algorithms in experiments on logistic regression, entropy-regularized optimal transport problem, and the minimal mutual information problem.

An enhanced adaptive differential evolution algorithm with dual performance evaluation metrics for numerical optimization

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer.

Retracted: Correlation Coefficient and Entropy Measures Based on Complex Dual Type-2 Hesitant Fuzzy Sets and Their Applications

Retracted: Correlation Coefficient and Entropy Measures Based on Complex Dual Type-2 Hesitant Fuzzy Sets and Their Applications

Energy recovery from a system with double magnet. Analytical approach

The main goal of the research presented in this paper is to find an analytical solution for an electromagnetic energy harvester with double magnet. A double magnet configuration is defined as a structure in which two magnets, either attracting or repelling, are positioned at a constant distance from each other. Analytical dependencies that govern the shape of electromechanical coupling coefficient curves for various double magnet configurations are provided. In the subsequent step of the analysis, resonance curves for its vibrations and the corresponding recovered energy were determined for the selected dual magnet settings using the harmonic balance method. These characteristics enabled us to ascertain the optimal resistance and estimate the maximum electrical power that can be harvested from the vibrations of the double magnets.

Exploring Transformational Leadership Practices among School Leaders in Korean Schools

This study explored transformational leadership practices among school leaders in Korean Schools specifically in Baguio City, Philippines. Utilizing a combined-method approach, the respondent pool consisted of 20 school heads and 108 teachers from three Korean schools in Baguio City and La Trinidad, carefully selected through purposive sampling. The data collection process encompassed dual sets of questionnaires and interviews tailored for both school heads and teachers. The gathered data was processed using frequency counts and percentages, weighted mean, F-test, and Pearson correlation which formed the basis for the discussion and interpretation of data in the results and discussion section of the paper. Findings revealed consistent engagement in highly practiced transformational behaviors, with a notable emphasis on clear communication, continuous support, coaching, and mentorship. Education level and tenure of service emerged as key factors, indicating a significant effect on leadership practices and their impact on teachers. However, challenges such as resource constraints pose hurdles that need addressing. Recommendations include recognition programs for academic accomplishments, fostering intergenerational collaboration, and implementing personalized coaching and mentorship initiatives to enhance leadership effectiveness. Strategies to overcome resource constraints involve exploring partnerships and optimizing resource utilization for sustainable transformational leadership practices. The Korean schools may implement suggested strategies, promoting the effectiveness of school head transformational leadership practices and ultimately contributing to a thriving educational environment.

A multi-attribute group decision making method based on novel distance measures and regret theory under probabilistic dual hesitant fuzzy sets

As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior.

Corporate reputation, cost of capital and the moderating role of economic development: international evidence

PurposeThe purpose of the study is to examine the impact of corporate reputation (hereafter CR) and the degree of economic development on firms’ cost of capital remains unresolved. This study addresses these issues.Design/methodology/approachUsing a global sample across 20 countries, the study investigates the discrete and joint effects of CR and jurisdictional economic development on the cost of equity (COE) and cost of debt (COD) capital. The analysis encompasses a dual data set, comprising 1,308 observations for COE and 1,223 observations for COD, allowing for a comprehensive exploration of these dynamics.FindingsThe findings indicate that CR leads to a reduction in the cost of capital for reputable firms. Nevertheless, the extent of this decrease varies per type of capital and firm’s reputation level and is contingent upon the economic development level within the firm’s jurisdiction. Particularly noteworthy is the moderating effect of economic development on CR, which shows that COE capital tends to be lower for reputable firms operating in economically developed jurisdictions. Albeit, this is not the case for COD capital for reputable firms in similarly developed jurisdictions.Practical implicationsThis study illustrates that effective CR management, aimed at reducing the cost of capital, necessitates a combination of the firm’s unique competitive advantage and the economic development context of its jurisdiction to truly achieve its intended goal.Originality/valueTo the best of the authors’ knowledge, this is the first global study to explore the impact of CR on both COE and COD capital. Furthermore, this study is primarily towards understanding the moderating role of economic development in the relationship between CR and cost of capital.

Multi-Criteria Decision Making Model For Hotel Selection Problem Under Complex Dual Hesitant Fuzzy Information

ABSTRACT The notion of the “complex dual hesitant fuzzy set (CDHFS)” is the combination of the “dual hesitant fuzzy set (DHFS)” and the “complex fuzzy set (CFS).” It is characterized by two degrees, namely the membership and nonmembership, in the form of a finite subset on a unit disc in the complex plane. CDHFS is useful for dealing with real-world problems involving uncertain or hard-to-predict information. Also, to approximate smoothly, the Einstein operators are well-known aggregation operators, while prioritized operators are effective tools for prioritization among criteria. Therefore, the goal of this study is to develop some prioritized aggregation operators under the CDHFS environment; namely the complex dual hesitant fuzzy prioritized averaging (CDHFPA) operator, the complex dual hesitant fuzzy prioritized geometric (CDHFPG) operator, the complex dual hesitant fuzzy Einstein prioritized averaging (CDHFEPA) operator, and complex dual hesitant fuzzy Einstein prioritized geometric (CDHFEPG) operator. Some properties of the proposed operators are investigated in detail. In addition, a multi-criteria decision-making (MCDM) method based on the proposed operators with the complex dual hesitant fuzzy setting is developed. Moreover, a numerical example is given for the application and effectiveness of the developed MCDM approach. A comparison study is also done with existing methods to show that the proposed MCDM method is better and more reliable. The study finds that if the expert’s preference is used to choose the right aggregation operators, the decision maker will have access to a wide range of compromise solutions.

The large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets.

We proposed a novel decision-making method, the large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets, to address the challenge of large-scale group multi-attribute decision-making in fuzzy environments. This method concurrently accounted for the membership and non-membership degrees of decision-making experts in fuzzy environments and the corresponding probabilistic value to quantify expert decision information. Furthermore, it applied to complex scenarios involving groups of 20 or more decision-making experts. We delineated five major steps of the method, elaborating on the specific models and algorithms used in each phase. We began by constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights. The following steps involved classifying large-scale decision-making expert groups and selecting the optimal classification scheme based on effectiveness assessment criteria. A global consensus degree threshold was established, followed by implementing a consensus-reaching model to synchronize opinions within the same class of expert groups. Decision information was integrated within and between classes using an information integration model, leading to a comprehensive decision matrix. Decision outcomes for the objects were then determined through a ranking method. The method's effectiveness and superiority were validated through a case study on urban emergency capability assessment, and its advantages were further emphasized in comparative analyses with other methods.

Dual soft condensed sets

The work here was based on some topological concepts and studied within dual soft set theory where three types of condensed were identified within these sets and their topological effects were highlighted.

Deep Attentional Guided Image Filtering.

Guided filter is a fundamental tool in computer vision and computer graphics, which aims to transfer structure information from the guide image to the target image. Most existing methods construct filter kernels from the guidance itself without considering the mutual dependency between the guidance and the target. However, since there typically exist significantly different edges in two images, simply transferring all structural information from the guide to the target would result in various artifacts. To cope with this problem, we propose an effective framework named deep attentional guided image filtering, the filtering process of which can fully integrate the complementary information contained in both images. Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target and then adaptively combine them by modeling the pixelwise dependency between the two images. Meanwhile, we propose a multiscale guided image filtering module to progressively generate the filtering result with the constructed kernels in a coarse-to-fine manner. Correspondingly, a multiscale fusion strategy is introduced to reuse the intermediate results in the coarse-to-fine process. Extensive experiments show that the proposed framework compares favorably with the state-of-the-art methods in a wide range of guided image filtering applications, such as guided super-resolution (SR), cross-modality restoration, and semantic segmentation. Moreover, our scheme achieved the first place in the real depth map SR challenge held in ACM ICMR 2021. The codes can be found at https://github.com/zhwzhong/DAGF.

How primary trainee teachers’ intersectionality exacerbates issues of wellbeing

ABSTRACT This research, conducted in the north-west of England, examined what primary trainee teachers believe affects their wellbeing, while studying on a 1-year post-graduate course. The research adopted a phenomenological case study using semi-structured self-directed interviews. Our thematic analysis illuminates that trainee teachers’ intersectionality (being both a student and a primary trainee teacher) places them in a vulnerable position, whereby their dual set status exacerbates any wellbeing issues. These issues include financial anxiety; difficulties managing workloads; poor relationships with school staff and perceived negative school cultures. As many of these issues are systemic, trainee teachers are often powerless to ameliorate these problems, and therefore this piece of relevance to policy makers and ITE curriculum creators.

ASSOCIATION OF THE PRESENCE OF BCR-ABL1 GENE REARRANGEMENTS AND MYELOID ABERRANT ANTIGENS IN PRECURSOR B-ACUTE LYMPHOBLASTIC LEUKEMIA (PRE-B-ALL) PATIENTS

Objective: To determine the association of the presence of BCR-ABL1 gene rearrangements and myeloid aberrant antigens in precursor B-ALL patients. Material and Methods: Both males and females, of all age groups, diagnosed with precursor B-ALL on flow cytometry were included in the study. BCR-ABL1 gene rearrangement was identified by performing Fluorescence in situ hybridization (FISH) using Vysis LSI BCR/ABL, Dual color, Dual fusion translocation probe set. The results were noted and compared with the results of flow cytometry. Results: Male patients were 36 (60%) and female patients were 24 (40%) out of total 60 patients. Median age was 22 years (range 1-73). BCR-ABL1 fusion gene was identified in 47 (78%) patients while 13 (22%) patients were negative for BCR-ABL1. Aberrant myeloid antigens were expressed in 24 (40%) patients and all of these patients were BCR-ABL1 positive. None of BCR-ABL1 negative patients expressed aberrant antigens. There was statistically significant association between BCR-ABL1 positivity and CD117 expression (P<0.05). Conclusion: Keywords: BCR-ABL1, Pre B-ALL, Myeloid aberrant antigens, Flow cytometry, FISH.

Modeling single-molecule stretching experiments using statistical thermodynamics.

Single-molecule stretching experiments are widely utilized within the fields of physics and chemistry to characterize the mechanics of individual bonds or molecules, as well as chemical reactions. Analytic relations describing these experiments are valuable, and these relations can be obtained through the statistical thermodynamics of idealized model systems representing the experiments. Since the specific thermodynamic ensembles manifested by the experiments affect the outcome, primarily for small molecules, the stretching device must be included in the idealized model system. Though the model for the stretched molecule might be exactly solvable, including the device in the model often prevents analytic solutions. In the limit of large or small device stiffness, the isometric or isotensional ensembles can provide effective approximations, but the device effects are missing. Here a dual set of asymptotically correct statistical thermodynamic theories are applied to develop accurate approximations for the full model system that includes both the molecule and the device. The asymptotic theories are first demonstrated to be accurate using the freely jointed chain model and then using molecular dynamics calculations of a single polyethylene chain.

Fuzzy reliability framework under hesitant and dual hesitant fuzzy sets to air conditioning system

Fuzzy reliability framework under hesitant and dual hesitant fuzzy sets to air conditioning system

Distance and similarity measures of Hesitant bi-fuzzy set and its applications in renewable energy systems

The concept of Hesitant bi-fuzzy set (HBFS) is an extension of dual hesitant fuzzy set, which play an important role in minimizing uncertainty in an efficient way. Distance and Similarity measures are an important tool for illustrating and integrating the differences between elements and its class and have a great importance in decision making, medical diagnosis and pattern recognition. To enhance and expand the applicability of HBFS, in this study, we have defined a series of distance and similarity measures of HBFS in both continuous and discrete environments. Since the information in day-to-day life may be biased, therefore, to show the importance of each factor/criterion weighted measures are also defined. Due to global warming and climate change, the utilization of renewable energy systems not only plays a significant role but also decreases greenhouse gas emissions. Choosing the right energy system affects each nation's economic, social, and environmental development. To show the efficiency of proposed measures, these measures are implemented in renewable energy systems. An algorithm for hesitant bi-fuzzy TOPSIS has also been developed to demonstrate the application of these measures in decision- making. Finally, a comparative study is done in pattern recognition problem, to show the supremacy and efficiency of the proposed measures.

An Enhanced Interactive and Multi-criteria Decision-Making (TODIM) Method with Probabilistic Dual Hesitant Fuzzy Sets for Risk Evaluation of Arctic Geopolitics

An Enhanced Interactive and Multi-criteria Decision-Making (TODIM) Method with Probabilistic Dual Hesitant Fuzzy Sets for Risk Evaluation of Arctic Geopolitics