Hesitancy Degree Research Articles

Expanding community-academic collaborations to improve COVID-19 vaccination: A rural-focused COVID-19 vaccination campaign

Background: Rural populations in the USA demonstrate a high degree of vaccine hesitancy, particularly surrounding COVID-19 vaccines. Objectives: Following the successful implementation of a COVID-19 vaccination campaign with a rural-based health system in New York state, the goal of this project was to develop a large community–academic partnership with multiple rural-based community organisations to implement an additional COVID-19 vaccination campaign tailored to rural audiences. Methods: Campaign materials were created and refined with several collaborators across multiple rural-based hospitals, health centres and community organisations. Campaign effectiveness was examined through an analysis of New York state vaccination data. Lessons learned: This project shows that larger community–academic partnerships involving multiple organisations can be achieved and utilised to address complex public health issues such as vaccine hesitancy. Conclusions: Larger and longer-term community–academic partnerships can be leveraged to combat a wide range of public health issues and enable a personalised focus on specific communities.

Novel Linguistic Intuitionistic Fuzzy Score and Accuracy functions for Linguistic Intuitionistic Fuzzy TOPSIS Method

Abstract: This work proposes various Linguistic Intuitionistic Fuzzy Score and Accuracy functions (LIF-Score and LIFAccuracy), including linguistic degree, membership, non-membership, and hesitancy degree, for Linguistic Intuitionistic Fuzzy Sets (LIFSs) in Multiple Attribute Group Decision Making (MAGDM) problems. The proposed functions are integrated into the Linguistic Intuitionistic Fuzzy-Technique of Order Preference by Similarity to Ideal Solution (LIF-TOPSIS) MAGDM approach to rank the alternatives, and a numerical example demonstrating their use in ranking alternatives is shown in the paper.

An efficient decision-making model for evaluating irrigation systems under uncertainty: Toward integrated approaches to sustainability

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods.

MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications

Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuitionistic fuzzy c means clustering. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. The clustering algorithm has membership and non membership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. Furthermore, the algorithm is extended for calculating membership and updating prototypes by minimizing the new objective function of weighted LINEX intuitionistic fuzzy c-means. Finally, the developed algorithms are illustrated through conducting experiments on random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.

Short-term load forecasting: cascade intuitionistic fuzzy time series—univariate and bivariate models

AbstractShort-term load forecasting (STLF) is essential for developing reliable and sustainable economic and operational strategies for power systems. This study presents a forecasting model combining cascade forward neural network (CFNN) and intuitionistic fuzzy time series (IFTS) models for STLF. The proposed cascading intuitionistic fuzzy time series forecasting model (C-IFTS-FM) offers the advantage of CFNN using the links of both linear and nonlinear to model fuzzy relations between inputs and outputs. Moreover, it offers a more reliable and realistic approach to uncertainty, taking notice of also the degree of hesitation. C-IFTS-FM works in univariate structure when it uses only hourly load data, and in bivariate structure when it uses hourly load data and hourly temperature time series together. The conversion of time series into IFTS is realized with intuitionistic fuzzy c-means (IFCM). Thus, the membership and non-membership values for each data point are produced. In modelling process, membership and non-membership values, in addition to actual lagged observations, are used as input of the CFNNs. The effectiveness of C-IFTS-FM on test sets for both structures was discussed comparatively via different error criteria, in addition, the convergence time was examined, and also the fit of forecasts and observations was presented with different illustrations. Among different combinations of hyperparameters, in the best case, approximately 86% better accuracy is achieved than the best of the others, while even in the case of the worst of hyperparameters combination, the accuracy was improved by over 20% for the PSJM data sets. For HEXING, CHENGNAN, and EUNITE data sets, these progress rates reached approximately 90% in the best case.

Impact of the COVID-19 pandemic on self-paid vaccination intentions for children: a cross-sectional study in China

ObjectivesWhile it is widely accepted that COVID-19 has disrupted routine vaccination globally, the long-term impact of COVID-19 on parental vaccination intentions is uncertain. This study aims to estimate whether COVID-19...

Pythagorean Fuzzy Set for Enhancement of Low Contrast Mammogram Images

ABSTRACTBreast masses are often one of the primary signs of breast cancer, and precise segmentation of these masses is essential for accurate diagnosis and treatment planning. Diagnosis may be complex depending on the size and visibility of the mass. When the mass is not visible clearly, precise segmentation becomes very difficult and in that case enhancement is essential. Inadequate compression, patient movement, or paddle/breast movement during the exposure process might cause hazy mammogram images. Without enhancement, accurate segmentation and detection cannot be done. As there exists uncertainties in different regions, reducing uncertainty is still a main problem and so fuzzy methods may deal these uncertainties in a better way. Though there are many fuzzy and advanced fuzzy methods, we consider Pythagorean fuzzy set as one of the fuzzy sets that may be powerful to deal with uncertainty. This research proposes a new Pythagorean fuzzy methodology for mammography image enhancement. The image is first transformed into a fuzzy image, and the nonmembership function is then calculated using a newly created Pythagorean fuzzy generator. Membership function of Pythagorean fuzzy image is computed from nonmembership function. The plot between the membership value and the hesitation degree is used to calculate a constant term in the membership function. Next, an enhanced image is obtained by applying fuzzy intensification operator to the Pythagorean fuzzy image. The proposed method is compared qualitatively and quantitatively with those of non‐fuzzy, intuitionistic fuzzy, Type 2 fuzzy, and Pythagorean fuzzy methods, it is found that the suggested method outperforms the other methods. To show the usefulness of the proposed enhanced method, segmentation is carried out on the enhanced images.

A new intuitionistic fuzzy scheme of data envelopment analysis for evaluating rural comprehensive health service centers

In this study, the classical CCR-IO DEA model (an input-oriented form of the DEA model of Charnes, Cooper, and Rhodes [24] is extended to an intuitionistic fuzzy environment. For this aim, the trapezoidal intuitionistic fuzzy numbers are used to show the values of inputs and outputs. An intuitionistic fuzzy number is characterized by membership, non-membership, and hesitancy degrees. Therefore, such type of fuzzy numbers is more complete and contains more information to reflect a fuzzy type of uncertainty comparing to the conventional fuzzy values. In order to solve the proposed intuitionistic fuzzy CCR-IO model, for the first time a possibility-based approach is developed for the intuitionistic fuzzy DEA models. In this approach for the first time the ME measure of fuzzy events is extended to intuitionistic fuzzy DEA models, therefore, some new definitions and propositions are presented and clarified. This measure is more flexible and effective comparing to the other measures such as credibility, necessity, and possibility measures. A case study from the health care sector of Iran is considered to evaluate the proposed solution approach numerically. The case study is solved under different scenarios, a full sensitivity analysis is performed, and finally some useful managerial insights are presented. In addition, the results of the proposed intuitionistic CCR-IO model are compared to the classical models of the literature.

Novel complex fuzzy distance measures with hesitance values and their applications in complex decision-making problems

A complex fuzzy distance measure (CFDMs) plays a significant role in applications involving complex or high-dimensional data where traditional distance measures may not adequately capture the nuances of the data relationships. The significance of CFDMs lies in their ability to handle uncertainty, imprecision, and complexity in various domains. Numerous researchers introduced different concepts of CFDMs, yet these CFDMs fails to convey any information regarding the hesitancy degree associated with an element. The main objective of this paper is to introduce some new distance measures based on complex fuzzy sets, called complex fuzzy hesitance distance measure and complex fuzzy Euclidean Hesitance distance measure, which is the generalization of complex fuzzy normalized Hamming distance measure and complex fuzzy Euclidean distance measure. Some new operations and primay results are discussed in the environment of proposed CFDMs and complex fuzzy operations. Moreover, we discussed the applications of the proposed CFDMs in addressing decision-making problems. We introduced a new decision-making algorithm that integrates CFDMs into decision-making processes, providing a robust methodology for handling real-world complexities. Further, the comparative study of the proposed CFDMs is discussed with some existing CFDMs.

INT-FUP: Intuitionistic Fuzzy Pooling

Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.

Psychological hesitation in performing touch among Paralympic players in the Salma Hussein Al-Jumaili Championship

The goal of the study is to measure the degree of psychological hesitancy experienced by the Olympic fencers competing in the Salma Hussein Al-Jumaili Championship during touch performances. The study made the assumption that psychological reluctance differs between male and female players. To fit the nature of the research, the researchers adopted the descriptive technique in the comparison procedure. The research community was identified as players participating in the Salma Hussein Al-Jamili championship, they represent the population and the research sample only. Forms that do not meet the conditions were excluded from them, so their number reached (70) students. They were chosen intentionally and the tools used in the research were the psychological hesitation scale. The researcher used the appropriate statistical treatment and the researchers concluded that there is psychological hesitation among the Paralympic fencing players in a manner close to the hypothesized mean. There is more psychological hesitation for female players than for male players, and this is what the results showed. Therefore, the researchers recommend that coaches pay attention to the psychological preparation of Paralympic fencing players in general and fencing in particular in order to gain control and self-confidence in order to get rid of psychological hesitation. Pay attention to increasing the confidence of Paralympic fencing players and their ability to make decisions. To prevent psychological problems and hinder their performance.

Trust Improvement Consensus Model Considering Unreliability Degree of Opinions with Hesitant Fuzzy Sets

Trust is a crucial element in the consensus decision-making process, as it significantly impacts a group’s capacity to achieve consensus. Nevertheless, low trust by experts may result in detrimental decision-making behaviors that decrease group consensus degree. Meanwhile, it is important to note that experts’ opinions are not always reliable, and ignoring the unreliability degree of opinions may have an impact on the result of consensus decision-making. To deal with these issues, this paper proposes a trust improvement consensus model considering unreliability degree of opinions. First, considering that the discreteness of membership degree has a much smaller impact on the hesitancy degree in hesitant fuzzy elements than the number of membership degrees in extant research, an improved approach is suggested to quantify the unreliability degree of opinions. Second, a trust improvement method is proposed based on the score function for trust propagation path and the trust propagation operator considering unreliability degree of opinions. Third, a method for adjusting the opinions of experts is suggested, considering unreliability degree of opinions. Finally, the feasibility, effectiveness, and advantages of the proposed trust improvement consensus model are verified through a case study on the selection of cascade utilization alternatives for power lithium-ion batteries, as well as simulation and comparative analysis. The results show that: (1) The improved method exhibits a significant impact of the discreteness of membership degree on the hesitancy degree; (2) Trust improvement can facilitate group consensus, and the score function of trust propagation path can enhance trust degree among experts more effectively; (3) The unreliability degree of opinions not only affects the degree of trust improvement but also affects the ranking of alternatives. By taking into account the unreliability degree of opinions throughout the process of adjusting opinions, it is possible to promote group consensus and decrease the unreliability degree of opinions.

Human Papillomavirus Vaccination Hesitancy, Screening Disparities, and Associated Factors Among Sexual and Gender Minority Women: A Systematic Review of Global Literature [ID 2683547

INTRODUCTION: Human papillomavirus (HPV) ranks as the most prevalent sexually transmitted infection globally, with a significant association with cervical cancer in cervix-having individuals. Understanding the epidemiological patterns of HPV vaccination rates and behaviors within the context of sexual and gender minority (SGM) women is essential in identifying factors hindering the initiation of HPV vaccination and screening. METHODS: A systematic review was conducted by searching five databases (PubMed, CINAHL, Embase, Web of Sciences, MedLine). Eligibility criteria included the following: reported HPV vaccination or screening outcomes, conducted among SGM women, data collected since 2010. All studies were independently screened and synthesized following PRISMA guidelines. Of 44 items, 13 studies met the criteria. RESULTS: Sexual and gender minority women reported substantial HPV vaccine hesitancy and decreased screening practices compared to their non-SGM peers, with the degree of vaccine hesitancy and delayed screening varying among bisexual, lesbian, and transgender individuals. Individual factors associated with HPV vaccine hesitancy and decreased screening include socioeconomic status, vaccination awareness, and SGM identity. Meanwhile, interpersonal and structural factors include community biases, family support, lack of provider LGBTQ+ health education, and structural racism. CONCLUSION: Sexual and gender minority women experience a substantial health care disparity surrounding HPV vaccination hesitancy and screening status. Results from this study substantiate the need for the standardization of health care practice, individual patient vaccine counseling, and the expansion of sexual and gender minority health training at all tiers of medical education. Furthermore, additional disaggregated research is needed to understand the unique health circumstances of bisexual, lesbian, and transgender individuals, as the current research landscape fails to capture the community’s health needs.

What can writing-process data add to the assessment of spelling difficulties?

Spelling difficulties are commonly associated primarily with spelling errors. However, it is not uncommon for spelling challenges to transform the whole writing process into a formidable struggle. This paper delves into the exploration of whether and to what extent analyses of children’s writing processes can enhance our understanding of their difficulties, potentially contributing to the assessment of spelling challenges. We focused particularly on the degree of hesitation within words and the ability to detect and correct spelling errors among children with and without reading and spelling difficulties, as well as how these processes impact the quality and lexical diversity of their texts. Additionally, we sought to contribute to disentangling the influence of spelling and decoding abilities on these processes. A cohort of 47 children, aged 10–13, participated in the study, comprising 16 typically developing children, 16 with predominantly spelling difficulties, and 15 with both reading and spelling difficulties. Our analysis encompassed their spelling performance in both standardized tests and task-oriented writing samples, as well as an examination of their pausing and revision behaviour. As expected, we found robust correlations between the children’s spelling test scores and the proportions of spelling errors in their texts. Furthermore, our findings indicated that children encountering spelling difficulties exhibited a reduced ability to detect and correct errors compared to their peers without such challenges. Additionally, they displayed a slightly higher tendency to experience word-internal interruptions, aligning with prior research. The children who also had reading difficulties produced fewer words and processed words more slowly compared to children in both the other groups. Intriguingly, process data did not reliably predict text characteristics, suggesting that dysfluent writing may not significantly detriment the overall quality of the text, contrary to our initial expectations based on prevailing writing development models. Nevertheless, the study revealed considerable individual variation, with some participants demonstrating a high degree of struggling and dysfluency, resulting in poorer text outcomes, but also others whose struggling processes led to better outcomes. We posit that the crucial aspect lies in identifying these individuals within a classroom context and gaining insights into their processes to provide them with appropriate, formative feedback and adequate writing tools to facilitate their writing.

Multi-attribute three-way decision making based on interval-valued intuitionistic fuzzy sets

In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model.

A novel approach to hazard evaluation for MASS operation based on a hesitant fuzzy linguistic term set

Expert judgment has been widely utilized in ship risk assessment. The traditional method is believed to be capable of processing only designated linguistic variables, which fails to capture the degree of expert hesitation in assessing a complex system. A novel model, the bi-independent hesitant fuzzy linguistic term set (BHFLTS), based on the hesitant fuzzy linguistic term set (HFLTS), is proposed to incorporate this hesitation. This paper also identifies ten navigational hazards to maritime autonomous surface ship (MASS) operation based on a literature review. In addition, the BHFLTS approach combined with the technique for order preference by similarity to an ideal solution (TOPSIS) is utilized for hazard assessment and ranking, and with this method, “Collision avoidance decision algorithm malfunction”, “Ship situation awareness system failure” and “Command transmission system failure” are rated as the top three navigational hazards that threaten MASS operation.

Evaluation of optimal waste lithium-ion battery recycling technology driven by multiple factors

As energy and environmental issues become increasingly prominent, the process of global carbon emission reduction is now underway. In the transportation sector, the application of electric vehicles is an effective way to achieve energy saving and emission reduction. Lithium-ion battery, an important component of electric vehicles, has received widespread attention for its advantages including small size, high energy density and smooth discharge voltage. However, their potential safety and pollution issues are becoming prominent and must be properly handled. Moreover, the technical route and future direction of LIB recycling are still unclear at this stage. Herein, this paper evaluates different waste lithium-ion battery recycling technologies in a multi-criteria decision framework to determine the best technology. A criteria system driven by multiple factors is established, including environmental impact (C1), technical risk (C2), comprehensive resource utilization (C3), resource consumption (C4) and economic performance (C5). Subsequently, since the optimal technology is novel and difficult to select, the hesitant fuzzy linguistic term sets are used to express the degree of hesitation in expert decision making. Finally, from the aspects of the choice of sorting method, the Tomada de decisao interativa multicriterio method based on the expectation effect is used to evaluate the optimal waste lithium-ion battery recycling technology, and the validity of the framework is illustrated by data analysis.

Partisan divide and Racial/Ethnic disparities in COVID-19 vaccine hesitancy among Parents/Guardians and vaccine uptake among children in the U.S.

ObjectivesWe undertook an observational study to assess the impact of state-level partisanship and parents’/guardians’ race/ethnicity on their degree of COVID-19 vaccine hesitancy. Material and MethodsWe observed a pooled cross-section of 59,280 U.S. adults residing with children in the same household between June 29 and November 14, 2022. Using household-weighted logistic regression models, we evaluated the association between partisanship, race/ethnicity, and vaccine hesitancy, while controlling for other social determinants of COVID-19 vaccine hesitancy. Results and ConclusionsWe found that children were less likely to receive a COVID-19 vaccine if they resided in Republican as compared to Democratic states, with the difference in probability greatest among those households where parents/guardians identified as White. We also found that children were less likely to receive a COVID-19 vaccine if their parents/guardians identified as White as compared to any other race/ethnicity, with the differences in probability greatest among households in Republican states.

Spherical fuzzy sets based integrated DEMATEL, ANP, VIKOR approach and its application for renewable energy selection in Turkey

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs’ preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings.

ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection

Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.