Linguistic Variables Research Articles

If Not For-If It Weren’t/Wasn’t For Counterfactual Constructions: A Multivariate Extension of Collostructional Analysis

Abstract The present paper investigates if not for-if it weren’t/wasn’t for constructions in American English based on the analysis of more than 2,100 constructions extracted from The Corpus of Contemporary American English (coca). In analyzing these constructions, we focus on significant interdependencies between the slots of the protasis (i.e. animacy of the referent of the np) and apodosis (i.e. semantics of the verb lemma) and how these cross-clausal associations interact with other linguistic variables, such as the polarity of the apodosis. We apply a new multivariate extension of collostructional analysis that combines distinctive and (co-varying) collexeme analysis via a hierarchical configural frequency analysis. This allows the analyst to identify associations not just of one slot to a construction or one slot to one other slot in one construction, but to include other features to identify (i) which constructions are preferred by which fillers in, now, one or more slots of one construction and (ii) which constructions are preferred by which (combinations) of one or more fillers.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

Response Coordination Model for Real Time Decision Making in Coastal Search and Rescue Operation Using Fuzzy Logic Technique

Coastal search and rescue (SAR) operations are complex, involving dynamic and uncertain conditions that demand real-time, effective decision-making. This paper aimed to analyze response coordination for real- time decision-making in coastal SAR operations using the fuzzy logic technique. The main aims of this study were to identify the key parameters and linguistic variables critical for effective decision-making in SAR operations and finally to design a fuzzy logic model tailored to the dynamic and uncertain conditions inherent in coastal SAR operations. The proposed fuzzy logic model demonstrated improved responsiveness and adaptability to changing conditions, offering a more robust framework for decision-making in SAR operations. However, this study contributes to enhancing the efficiency and effectiveness of real time decision making in SAR operations in coastal environments, with broader implications for maritime safety.

German Noun Plurals in Simultaneous Bilingual vs. Successive Bilingual vs. Monolingual Kindergarten Children: The Role of Linguistic and Extralinguistic Variables

(1) Background: The complex phenomenon of German noun plural inflection is investigated in three groups of German-speaking kindergarten children: (a) monolinguals (1L1), (b) simultaneous bilinguals (2L1) also acquiring Croatian, and (c) successive bilinguals (L2) acquiring Turkish as L1. Predictions of the usage-based schema model and of Natural Morphology concerning different linguistic variables are used to explore their impact on plural acquisition in the three groups of children. (2) Methods: A longitudinal study (from mean age 3;1 to 4;8) is conducted using two procedures (a formal plural test and spontaneous recordings in kindergarten), and the data are analyzed using generalized linear (mixed-effects) regression models in R. (3) Results: All children produce more errors in the metalinguistically challenging test compared to spontaneous speech, with L2 children being particularly disadvantaged. Socioeconomic status (henceforth SES) and teachers’ plural type frequency are most relevant for 1L1 children, and kindergarten exposure is more relevant for L2 children, while the linguistic variables are more important for 2L1 children. (4) Conclusions: The main predictions of the schema model and of Natural Morphology are largely confirmed. All of the linguistic variables investigated show significant effects in some analyses, but morphotactic transparency turns out to be the most relevant variable for all three groups of children.

Fuzzy Rule-based Comparison of Alternative Jet Fuels

In today's aviation alternative jet fuels play an increasingly important role. Their application incurs new engineering and environmental protection challenges. The key properties of their feasibility of integration are from technological point of view aging behavior and applicability as well as from environmental protection point of view the carbon footprint. During the comparison process experts can evaluate the above characteristics with linguistic variables. The application of linguistic variables always results in some degree of uncertainty – by their subjectivities. Fuzzy calculation methods are used to mathematically describe these uncertainties. The purpose of this study as a pilot project is to gain experience in developing a methodology of a multi-level fuzzy rule-based jet fuel qualification process.

A design of computational fuzzy set-based semantics for extracting linguistic summaries

Linguistic summarization is to extract a set of summary sentences in the form of natural language, so-called linguistic summaries, from numeric data. The extracted linguistic summaries should be compact and diverse, and have a validity measure greater than a given threshold, so genetic algorithms are applied to extract such linguistic summaries. Besides, the interpretability of linguistic summary content is considered in recent studies in such a way that enlarged hedge algebras are applied to generate multi-semantic structures for linguistic words of linguistic variables ensuring the interpretability of the content of the linguistic summaries. However, the membership function of computational fuzzy-set-based semantics of linguistic words is usually in the shape of trapezoid. In this paper, a membership function of the form S function is applied to improve the quality of extracted linguistic summaries. Besides, the applied algorithm is parallelized to reduce running time. The experimental results with the creep dataset have demonstrated the effectiveness of the proposed method

Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Are numerical scores important for grant assessment? A cross-sectional study

Background: In the evaluation of research proposals, reviewers are often required to provide their opinions using various forms of quantitative and qualitative criteria. In 2020, the European Commission removed, for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) funding scheme, the numerical scores from the individual evaluations but retained them in the consensus report. This study aimed to assess whether there were any differences in reviewer comments’ linguistic characteristics after the numerical scoring was removed, compared to comments from 2019 when numerical scoring was still present. Methods: This was an observational study and the data were collected for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) evaluation reports from the calls of 2019 and 2020, for both individual and consensus comments and numerical scores about the quality of the research proposal on three evaluation criteria: Excellence, Impact and Implementation. All comments were analyzed using the Linguistic Inquiry and Word Count (LIWC) program. Results: For both years, the comments for proposal’s strengths were written in a style that reflects objectivity, clout, and positive affect, while in weaknesses cold and objective style dominated, and that pattern remained stable across proposal status and research domains. Linguistic variables explained a very small proportion of the variance of the differences between 2019 and 2020 (McFadden R2=0.03). Conclusions: Removing the numerical scores was not associated with the differences in linguistic characteristics of the reviewer comments. Future studies should adopt a qualitative approach to assess whether there are conceptual changes in the content of the comments.

Low literacy skills in adults can be largely explained by basic linguistic and domain-general predictors.

Despite having sufficient formal education, a large group of people cannot complete everyday tasks like reading, writing, or making basic calculations. Regarding reading, millions of people are not able to understand more complex texts despite the ability to read simple words or sentences; they have low literacy skills. Even though this problem has been known for decades, the causes and predictors of their poor reading comprehension skills are not fully explored. Socioeconomic, sociodemographic, and reading-related (i.e., linguistic) factors, especially of English-speaking participants and thus users of an opaque orthography, were often assessed. The goal of this study was to examine which linguistic, domain-general, or numerical factors predict substandard complex text reading as the core symptom of low literacy skills in adulthood. To this end, we assessed a group of German-speaking participants-users of a transparent orthography-who are at risk for complex text reading deficits. The results indicated that linguistic variables (reduced word/pseudoword reading, weaker oral semantic and grammatical comprehension), working memory, and age predicted lower performance in text comprehension. This model explained 73% of the total variance, indicating that most of the deficits in complex text reading can be explained by a group of basic underlying linguistic and domain-general factors. We conclude that interventions for adults with low literacy skills and others at risk for complex text reading deficits should address word/pseudoword reading and focus on both written and oral comprehension.

Fuzzy Logic Harmony in Water: Mamdani Inference System Applied to Evaluate Pristine Pond Water Quality

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system’s procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation.

Reducing the Effect of Uncertainty in Mamdani–Type Fuzzy Inference System Using Qubits

The success of fuzzy logic in effectively capturing subjective knowledge which are uncertain or ambiguous in nature lies on the fact that fuzzy sets assigns a degree of membership to each linguistic variable of a crisp value. As a condition the degree of membership values to all linguistic variables should add up to one. This abstract condition is neither probabilistic nor realistic in quantum mechanics. Probabilistic is in a sense that the normalization condition of quantum mechanics isn’t satisfied. Realistic is in a sense that the condition isn’t physically proven experimentally. In this paper, a novel quantum Mamdani-type fuzzy inference system is proposed. The proposed system consists of quantum fuzzification, quantum fuzzy inference and quantum defuzzification steps. Instead of assigning degree of membership values for linguistic variables, the proposed inference engine assigns a quantum state for a crisp value. Hence, a crisp input is fuzzified into superposition of linguistic variables represented by qubits. The proposed quantum Mamdani-type fuzzy inference system is tested to captures expert knowledge more realistically than the Mamdani-type fuzzy inference system, is thus less vulnerable to effects of uncertainties.

The Linguistic Landscape of Los Angeles Chinatowns

This paper presents a multi-level analysis of the signage in Los Angeles (LA) Chinatowns. Through a combined method of ethnographic observations, interviews and textual and visual analysis of language orthographies, the processes in which signage is designed, perceived, and interpreted are examined. A discussion of the range of interpretive readings of Chinese orthography and romanization is presented, with a focus on the relationship between the linguistic landscape and people’s language perception, and the changing social indexicalities of different linguistic variables displayed in LA Chinatowns. The paper concludes that the linguistic landscape can be taken as a form of informal language input which impacts Chinese immigrants’ language perception. Moreover, linguistic variables displayed on public signage not only contribute to the construction of the sociolinguistic context but also to the immigrants’ identity transformation.

The Linguistic Landscape of Los Angeles Chinatowns

This paper presents a multi-level analysis of the signage in Los Angeles (LA) Chinatowns. Through a combined method of ethnographic observations, interviews and textual and visual analysis of language orthographies, the processes in which signage is designed, perceived, and interpreted are examined. A discussion of the range of interpretive readings of Chinese orthography and romanization is presented, with a focus on the relationship between the linguistic landscape and people’s language perception, and the changing social indexicalities of different linguistic variables displayed in LA Chinatowns. The paper concludes that the linguistic landscape can be taken as a form of informal language input which impacts Chinese immigrants’ language perception. Moreover, linguistic variables displayed on public signage not only contribute to the construction of the sociolinguistic context but also to the immigrants’ identity transformation.

‘Fuck’ and emoticons

This quantitative study investigates gender variation online by focusing on two ‘gendered’ linguistic variables: the swear word fuck, which has often been associated with men, and emoticons, which have been shown to be used more frequently by women than men. It is based on a nineteen-million-word gender-diverse corpus, which includes comments posted in English on the community website Reddit by 372 cisgender women, 372 cisgender men, 100 transgender women, 100 transgender men and 100 nonbinary individuals (all according to self-identification). The statistical analyses take both the gender and age of the Redditors into account. Results show that in most cases transgender men do not align with cisgender men; that transgender women mostly align with cisgender women in their use of fuck, but the former produce more emoticons than the latter, and that the use of the variables did not differ between nonbinary individuals assigned male versus female at birth.

The Application of Integrated Neutrosophic CRITIC and Preference Selection Index Methods for Multi Criteria Decision Making Over the Implementation of An Integrated Fish Farming System

This paper presents the application of integrated Neutrosophic CRITIC (The Criteria Importance Through Inter-criteria Correlation) and PSI (Preference Selection Index) methods using by the linguistic variables are converted to Neutrosophic set, which is an effective decision making model in a real life environment. This integrated methods is used to rank the 5 alternatives (Labeo rohita , Cirrhina mrigala , Catfishes, Catla catla, Murrels or Snakeheads) and 6 main criteria( Azolla with Duck Cum-Fish Culture, Paddy -Cum -Fish Culture, Duck- Cum -Fish farming, Fish - Cum -Poultry farming, Fish- cum Pig Culture, Cattle -Fish integrated farming system) for the growth of different varieties of fish by using Neutrosophic Environment.

Factors associated with nonword repetition skills in 4-to 6-year-old deaf and hard-of-hearing and typically hearing children

ABSTRACT Children identified as deaf and hard of hearing (DHH) exhibit an increased risk of speech and language difficulties. Nonword repetition (NWR) is a potential tool for identifying language difficulties in children with limited experience with the target language. In this study, we explored associations between auditory, linguistic, demographic and cognitive factors and NWR performance in DHH children and their typically hearing (TH) peers. We also examined the effect of the group on NWR outcomes when different factors were statistically controlled for. Our study included 68 4- to 6-year-old children: 18 with bilateral hearing aids (BiHAs), 18 with bilateral cochlear implants (BiCIs) and 32 with TH. The participants completed the Finnish Nonword Repetition Test comprising 24 test items with varying syllable lengths. The assessment methods also included tests of phonological, lexical, language comprehension and sentence repetition skills, along with a measure of nonverbal intelligence. The results showed that none of the auditory, linguistic, demographic and cognitive factors examined in the present study were correlated with NWR performance in the BiHA group, while significant correlations between NWR skills and linguistic abilities were observed in the BiCI group. The DHH children showed extraordinary difficulties in NWR compared to their TH peers, and these group differences remained after controlling for linguistic and demographic variables. The NWR task may be a potential clinical tool for identifying DHH children at risk for poor language outcomes and in need of speech and language intervention.

Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations

Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. Employing ANFIS offers several advantages: it enhances clarity regarding the correspondence between output and input values and rules, improves system interpretability, and facilitates the representation of linguistic variables and rules, thereby encouraging domain experts' involvement in enhancing the system's performance as needed. This paper illustrates the utility of ANFIS in adjusting the detected particulate matter (PM) concentration and compares its effectiveness with other established machine-learning techniques, including linear regression, decision trees, random forest, SVR and a multilayer perceptron (MLP). These methods are chosen as benchmarks owing to their established effectiveness in calibration procedures.We propose certain preprocessing steps for detecting and rectifying anomalies, alongside introducing two distinct data-splitting methodologies. Additionally, a discussion about feature selection is presented to elucidate the impact of specific features on performance enhancement. The efficacy of ANFIS in refining PM data is demonstrated through a comparative assessment, where it outperforms all the established machine-learning techniques. Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. The paper also explores various ANFIS configurations, including two distinct optimization algorithms, and investigates the impact of the number and type of membership functions on the fuzzy system's performance. Our study highlights the potential of the Adaptive Neuro-Fuzzy Inference System as a versatile and effective tool for addressing real-world challenges in environmental sensing.

Generalized ATOVIC System Based on Triangular Fuzzy Numbers for Pattern Classification

Diseases such as diabete and cancer must be detected and treated precociouse. They are generally detected using recognition and classification systems. The latter must be choosen according their accuracies and other measures. In addition, some difficulties and ambiguities can exist related with features representing data sets such as their variant types (eg., integer, nominal, etc.) or overlapping between them. Also, data can be imprecise or incomplete and can influence negatively classification results. Fuzzy sets and their generalizations, resolve problems of data uncertainty by modeling them with linguistic variables. The latter are presented with membership functions having different shapes. Triangular fuzzy numbers which represent tringular functions are very rarely used in classification systems. The cause can be that triangular shape does not preserve in case of some calculations. This research proposes to generalize the classification system "‘Amended Fused TOPSIS-VIKOR for classification"’ (ATOVIC) with triangular fuzzy numbers and to apply it to some UCI date sets. The objective is to unveil the impact of triangular fuzzy numbers in classification. The results of generalized fuzzy ATOVIC are compared to those of its crisp version and those of some existing classification systems using fuzzy sets. Results of ATOVIC generalized with tringular fuzzy numbers are promising. Especially its results on breast cancer Wisconsin and IRIS data sets are higher than Mamdani and Segano fuzzy inference systems using triangular fuzzy numbers from literature.

Evaluation of Vital Parameters Influencing Oxygen Demand with Pythagorean Neutrosophic-Fuzzy Cognitive Map

Objectives: The primary goal is to provide a novel idea known as the Pythagorean neutrosophic fuzzy cognitive map. A multi-objective goal programming issue with a set of objectives and constraints is solved using PNCM. Methods: Linguistic variables are transformed into crisp values using a score function and the Pythagorean neutrosophic quantification scale. The linear combinations of crisp values and factors yield the goal and constraint equations. Findings: To enhance decision-making in emergency situations like COVID-19, the implications of the novel PNCM concept were utilized to examine the variables influencing oxygen demand. Novelty: Fuzzy cognitive mapping combined with Pythagorean Neutrosophic can improve the modelling and analysis of real-world issues with imprecise data. Keywords: Pythagorean Neutrosophic set (PNs), Fuzzy Cognitive Map (FCM), Goal programming, Oxygen Demand, Multi­objective Optimisation

Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.