Linguistic Variables Research Articles

Decoding Abilities in Early Elementary School Bosnian Children: Towards a Better Understanding of Reading

<p style="text-align: justify;">Acquiring mastery in reading is considered one of the primary academic skills during the early years of elementary school. Additionally, attaining proficiency in reading during the early grades of elementary school serves as a strong predictor for future academic achievements and overall success in life. This cross-sectional study aimed to examine the effect of several linguistic and cognitive variables on decoding abilities in the Bosnian language. The sample consisted of 153 children in the 2nd to 5th grades of elementary school. To assess decoding abilities, we employed tasks involving reading real words and non-words (pseudowords). The key predictors for both tasks included phonological awareness, rapid automatized naming of letters, working memory, and processing speed. Both models accounted for approximately 64% of the variance in the scores, signifying a substantial advancement compared to existing models of reading in the Bosnian language. The results of this study may contribute to the development of targeted and effective reading interventions in early elementary education.</p>

Strategic Analysis for Advancing Smart Agriculture with the Analytic SWOT/PESTLE Framework: A Case for Turkey

In the contemporary discourse, smart agriculture (SA) stands out as a potent driver for sustainable economic growth. The challenges of navigating SA transition are notably intricate in developing nations. To effectively embark on this transformative journey, strategic approaches are imperative, necessitating a thorough examination of the prevailing agricultural ecosystem. This study seeks to formulate strategies that advance Turkey’s agricultural sector. The primary research questions focus on optimizing the benefits of SA by aligning strengths and opportunities with diverse socio-economic and environmental factors, while also exploring effective strategies to mitigate the impact of weaknesses and threats within the agricultural landscape. To achieve this objective, the utilization of the 2-Tuple linguistic (2TL) model integrated DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology in conjunction with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PESTLE (Political, Economic, Social, Technological, Legal, Environmental) analyses is proposed. The integration of linguistic variables enhances the capacity to delve deeper into system analysis, aligning more closely with human cognitive processes. The research commences with SWOT and PESTLE analyses applied to Turkey’s agricultural sector. Subsequently, the 2TL-DEMATEL approach is employed to investigate interrelationships among analysis components. This inquiry aims to establish causal relations, facilitating the derivation of relevant strategies. The case study centers on Turkey, a developing country, with outcomes indicating that the highest-priority strategies revolve around addressing ‘environmental threats’ and ‘economic weaknesses’. The subsequent evaluation encompasses eight dimensions, resulting in the generation of fifteen distinct strategies, a process facilitated by collaboration with field experts. Importantly, both the results and strategies undergo rigorous validation, drawing upon insights from the recent literature and field experts. Significantly, these findings align seamlessly with the Sustainable Development Goals (SDGs), substantiating the study’s broader significance in fostering a sustainable future for Turkey.

Fuzzy Controller Implemented for Movement of a Tendon-Driven 3D Robotic Lumbar Spine Mechanism.

Notable efforts have been devoted to the development of biomechanical models of the spine, so the development of a motion system to control the spine becomes expressively relevant. This paper presents a fuzzy controller to manipulate the movement of a 3D robotic mechanism of the lumbar spine, which is driven by tendons. The controller was implemented in Matlab/Simulink R2023a software, MathWorks (Brazil), considering mathematical modeling based on the Lagrangian methodology for simulating the behavior of the lumbar spine dynamic movement. The fuzzy controller was implemented to perform movements of two joints of the 3D robotic mechanism, which consists of five vertebrae grouped into two sets, G1 and G2. The mechanism's movements are carried out by four servomotors which are driven by readings from two sensors. For control, the linguistic variables of position, velocity and acceleration were used as controller inputs and the torque variables were used for the controller output. The experimental tests were carried out by running the fuzzy controller directly on the 3D physical model (external to the simulation environment) to represent flexion and extension movements analogous to human movements.

The structure of variable assessment of the feasibility of organizing urban passenger transport priority traffic

Problem. An important source of increasing the efficiency of urban transport systems is the development of high-speed connections, which in practice can be achieved by allocating special traffic lanes for urban passenger transport. However, at the stage of justifying such a decision, it is necessary to comprehensively assess the expected positive results and all possible risks, including the complication of traffic conditions. Goal. The purpose of the work is to establish a procedure for determining a rational variant of organizing priority movement of urban passenger transport from the standpoint of evaluating the improvement of the route efficiency and the emergence of critical risks of functioning of the street and road network elements. Methodology. As a basis for establishing the principles of evaluating the options for organization of high-speed urban passenger transport, an analysis of their impact on improving the quality of public transport services, capital costs for the implementation of such measures and possible negative consequences for the street and road network was used. Results. The central part of evaluating the variant of organizing the priority traffic of urban passenger transport is determination of the value of the permissible risk. As a risk, an assessment of the degree of organization of high-speed connections was used, which from the point of view of consumers provides the possibility of obtaining a positive result in relation to travel time, reducing the number of rolling stock on the route and ensuring a suitable level of capacity of the street-road network. Qualitative assessment of the parameters of the risk system is carried out using linguistic variables that can be represented in the form of basic term-sets. For risk factors, such assessments are presented in three variants: low, partial and full compliance with the requirements of the organization of high-speed communication. Originality. The analysis of the systematicity, completeness and interconnectedness of the conditions for the formation of management decisions in the field of choosing an option for the organization of priority urban passenger transport made it possible to form the principles of using a risk system, whose unity is ensured by taking into account the interrelationships of the time parameters of traffic on the route and the conditions of traffic complications on the network. The formation of a fuzzy product includes the description of its components, that is, the prerequisites and the conclusion, whose combination determines the fuzzy product. Input components of products are divided into two groups: route parameters and road network parameters. Practical value. According to the results of experimental calculations for route No. 24 “District 602 – Academika Barabashova metro station” (Kharkiv) established that the implementation of special traffic lanes for urban passenger transport is appropriate only on a separate section of the route. This is due to the fact that on this section of the road network, a pleasant level of risk of traffic complications will be ensured, and a 37% reduction in journey time will lessen the need for rolling stock on the route to 6 units.

Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c≥0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.

Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c = 0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.

Bilingualism in the Clinical Diagnosis of Mild Cognitive Impairment and Dementia

AbstractBackgroundNeuropsychological assessments determine the presence of cognitive impairment in Alzheimer’s Disease and Related Dementias (ADRD). These tests are susceptible to the influence of cultural and linguistic variables. Bilingualism is a potential contributor to cognitive reserve in elderly individuals. Positive effects of bilingualism have been observed in executive function tasks, while negative effects have been reported on verbal fluency tests. Maintaining two active languages may modify cognitive and brain reserve among bilinguals, but evidence is inconsistent and contradictory. We investigated the relationship between bilingualism and neuropsychological test performance in aging participants from the 1Florida ADRC using a longitudinal design.MethodsWe studied 238 Latino older adults (age 71.79 ± 8.01 years old, 65% female). 158 Latino participants were Spanish‐English bilinguals; 80 were Spanish monolinguals. Bilingualism was assessed by a proficiency questionnaire. Six cognitive composites were developed (Figure 1). We investigated cognitive test change scores between language group, visit (2 visits: M = 14.61 months; SD = 3.52), and diagnosis (normal, MCI, dementia) using 2×2×3 repeated measures ANCOVAs (age and level of education). Cognitive scores were correlated with frontal and temporal brain volumes in bilinguals and monolinguals separately.ResultsMain effects of the diagnostic group and visit were significant for all composites (ps < .001), with the dementia group showing the lowest scores. No significant main effect of Bilingualism was observed for any of the composites. An interaction between visit and bilingualism was seen for the Trail Making Test B‐ A. Three‐way interactions were found between diagnostic groups, visit, and bilingualism for the Verbal composite and the Logical Memory delay subtest (part of the Memory composite). A more significant decline in the monolingual groups was observed, more so in the dementia group. Significant correlations were found between most composites in both language groups, being more related to the temporal than frontal lobe areas, using MRI volumetric measures.ConclusionBilingualism seems to interact with cognitive decline in aging. It is essential to consider the individual’s level of bilingualism to obtain an accurate assessment of cognitive impairment. The interaction of bilingualism with other sociodemographic and cognitive variables will be discussed.

Design and development of the intelligent voice recognition‐based cognitive assessment WeChat mini‐program

AbstractBackgroundEarly screening of mild cognitive impairment (MCI) is crucial for the elderly to access formal diagnosis and intervention of Alzheimer’s Disease (AD) and related dementias (ADRD) at early stages. Owing to the rapid development of artificial intelligence (AI) and voice recognition technology, the detection method using speech patterns as digital biomarkers collected by digital consumer devices can accommodate a huge demand for early and large‐scale detection. We here reported the design and development of the Shanghai Cognitive Screening (SCS) mini‐program, the first intelligent voice recognition‐based cognitive assessment WeChat mini‐program in China.MethodThe SCS mini‐program was developed based on the cognitive paradigm and classic pencil‐and‐paper neuropsychological assessments (e.g. Montreal cognitive assessment basic [MoCA‐B]). Through Automatic Speech Recognition (ASR), users speak to complete tasks including Naming, Immediate Recall, Delayed Recall, Symbol Digit Modalities Test (SDMT), Delayed RecallII, and Recognition (Figure 1). The acoustic, phonetics, and linguistic speech pattern variables would also be extracted and processed as digital biomarkers. The SCS intelligent prediction model enrolls speech pattern digital biomarkers, individual task scores, and time use variables with age, gender, and education level as linking variables, which enables to grade the user’s global cognitive function and individual cognitive domains (Figure 2).ResultAfter completing all tasks in the SCS, users can immediately read the comprehensive results which are divided into 4 categories corresponding to 4 grading intervals: “healthy” (cognitively normal), “low risk” (suspected SCD), “medium‐high risk” (suspected MCI), and “high risk” (suspected AD). We also use a radar chart with scores of cognitive domains (i.e., memory, attention, visual perception, language, and executive functions), a memory trend graph (i.e., immediate and delayed recalls), an attention persistence graph, and related advice in the automatically generated report (Figure 3).ConclusionThe SCS mini‐program is more efficient and economical compared to traditional paper‐based cognitive assessments. It is suitable for clinical cognitive assessment, remote self‐rating, and community screening for MCI and dementias among Chinese older adults and other ADRD high‐risk groups. Clinical trials are yet to be conducted to validate the accuracy and diagnostic value of the SCS WeChat mini‐program.

Articulatory Insights into the L2 Acquisition of English-/l/ Allophony.

In many English varieties, /l/ is produced differently in onsets and codas. Compared with "light" syllable-initial realizations, "dark" syllable-final variants involve reduced tongue tip-alveolar ridge contact and a raised/retracted tongue dorsum. We investigate whether native French and Spanish speakers whose L1 lacks such positionally conditioned variation can acquire English-/l/ allophony, testing the hypotheses that (1) the allophonic pattern will be acquired by both groups but (2) learners will differ from native speakers in their phonetic implementation, particularly in codas; and (3) French-speaking learners will outperform their Spanish-speaking counterparts. The production of syllable-initial and -final /l/ (singletons and clusters) in words read in isolation and a carrier sentence by 4 French- and 3 Spanish-speaking learners as well as three native English speakers was analyzed via electropalatography and acoustic analysis. While some learners produced distinct onset and coda variants and all learners had moved away to some extent from their L1 production, they differed from the native speakers in certain ways. Moreover, between- and within-group variability was observed including greater target-like anterior and posterior contact reduction in codas in the L1 French versus L1 Spanish group and generally higher F2 values in both learner groups compared with their native speaker peers. A comparison of the learners' L1 and L2 production revealed L1-based patterns of positional reduction of the tongue tip and dorsum gestures. We conclude by addressing the contributions of EPG to our understanding of L2 speech and highlight avenues for future research including the study of both linguistic and speaker variables.

Quantitative Perspectives on the Geolinguistic Structures of Dialect Morphology in Austria

Abstract The traditional focus of (German) dialectology has been on spatial patterns observed in phonology. However, dialectometric approaches have generated interest in exploring geographical structures in other linguistic subsystems, including morphology. Building on this research, this study investigates geolinguistic patterns of dialect morphology in contemporary Austrian dialects using aggregative and non-aggregative measures, such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and Cluster Analysis (CA). Data are based on a comprehensive dialect survey with 163 speakers from 40 locations across Austria, examining 120 linguistic variables related to inflection. The results show a clear geographic structuring of morphological variation, which significantly differs from traditional dialect classifications. The analysis of the linguistic basis of the geographical patterns suggests that these differences might be ascribed to methodological factors and language change. Methodologically, it is also demonstrated that the findings of aggregative and non-aggregative methods are complementary, not contradictory. Ultimately, the study underscores the importance of integrating diverse methods to gain a comprehensive understanding of geographical patterns in dialect variation.

Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

The intelligent logistic system (ILS) has benefited Industry 4.0. ILS assures clean, on-time manufacturing. Due to its unique architectures, qualities, and sensory aspects, the robot logistic system (RLS) is sought after as an ILS in Industry 4.0. In case of COVID-19, multiple nations routinely used RLS as ILS to cleanse areas, check patients, and monitor crowds on highways. Research documents (RDs) show that prior researchers attempted to build the static robot logistic system (RLS) performance mapping index; however, most indexes measured only anatomy performance of static RLS. Thus, few RDs are edited previously in the context of MRLS. It is sensed that few RDs examined MRLS-linked performance mapping indexes, including only regular subjective (S) or objective (O) designs, excluding mixed S-O architectures. Most RDs constantly execute the linguistic variables related to fuzzy, grey, rough, ambiguous, and intuitionistic sets/scales to tackle the uncertainty connected with MRLS designs. The authors prioritize those as RGs. The authors proposed (1) an MRLS performance mapping index with respect to technical, cost, and value O-S architectures for recruiting MRLS, (2) linear information to assign ratings in a range of min-max values choosing from 1 to 100% without executing the linguistic scale, and (3) Holistic Managerial Models (HMM-1 and HMM-2) to handle subjective ratings and significance, assigned by Ex against evaluated O-S architectures under linear scale (1–100%). To prove the concept, RLS performance mapping is shown. Only MRLS recruitment and selection are covered. The effort helps CIM, FMS, and WCMS create sustainable, cleaner operations and achieve future goals.

Design and Development of an Intelligent Decision Support System for HR Recruitment

In the current era of globalized business environments, the effective management of human resources (HRM) plays a critical role in attracting and retaining skilled employees as companies expand into international markets. This study aims to tackle the challenges encountered in the HR recruitment process by designing and developing an intelligent decision support system based on the Mamdani fuzzy inference system. The proposed system harnesses the capabilities of fuzzy logic and advanced analytics to enhance the efficiency, accuracy, and efficacy of candidate selection. By automating tasks, the system streamlines the recruitment process and enhances candidate-job matching through the utilization of linguistic variables such as Technical Skillset, Experience, Certifications, Qualification, and Projects completed. The Mamdani fuzzy inference system facilitates adaptable decision-making based on imprecise or uncertain inputs, capturing expert knowledge and domain-specific heuristics. This approach optimizes the candidate ranking process, contributing to the successful acquisition of talented individuals and fostering organizational growth. By adopting this technology-driven HRM approach, organizations can surmount the limitations of manual methods, elevate their recruitment processes, and make more well-informed hiring decisions. The proposed Mamdani fuzzy inference system presents a robust and efficient solution that capitalizes on the potential of information technology to augment human resource management practices and gain a competitive edge in talent acquisition.

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.

First person singular subject pronoun expression of young Spanish speakers from Quito, Ecuador

Abstract This variationist study analyzes the first-person subject pronoun expression (SPE) of speakers from Quito, Ecuador. To date, this morphosyntactic variable has not been explored in this Andean variety of Spanish. The data consists of 20 sociolinguistic interviews. Results reveal an SPE rate of 17%, comparable to other Andean Spanish varieties. As per Rbrul’s quantitative analysis, the predictors that promote the presence of the ‘yo’ in this variety of Spanish are co-referential Priming, switch reference, ambiguous TMA endings and main clauses. In addition, following Orozco and Hurtado (2021), lexical effects of the verb were observed by analyzing the verb lemma. This predictor revealed similarly opposing tendencies between verbs in the same lexical category. This study adds to the growing body of SPE research by examining which linguistic variables influence the use of ‘yo’ in this Ecuadorian Andean Spanish (EAS) variety and comparing these results to those of other Andean Spanish varieties.

МЕТОД АВТОМАТИЗОВАНОЇ КЛАСИФІКАЦІЇ НАДЗВИЧАЙНИХ СИТУАЦІЙ ІЗ СУДНОМ В АКВАТОРІЇ МОРСЬКОГО ПОРТУ

The aim of the study is to develop a method that allows to improve the accuracy of classification of emergencies with a ship in the seaport area. To achieve the research objective, an improved method for automated classification of ship emergencies in the seaport area under conditions of deterministic uncertainty is proposed. The method consists of methods for formalising and processing knowledge on determining the classes of ship emergencies. The method of knowledge formalisation is based on the formation of a set of factors influencing the classification of ship emergencies in the seaport water area based on fuzzy binary relations of non-strict preference. The method of knowledge processing is based on the formation of productive rules for classifying emergencies with a ship in the seaport area according to the predicted or current values of the desired factors in a fuzzy formulation. The determination of the membership functions of several fuzzy variables to linguistic variables is based on the processing of expert data represented by a matrix of binary relations of the values of the membership function of the elements of the domain of determining linguistic variables. A comparative assessment of the classes of recognisable emergencies with a vessel in the seaport water area involves solving a multi-criteria optimisation problem using the hierarchy analysis method. As a mathematical model for determining the classes of emergency situations with a vessel in the seaport waters, the article substantiates a logical-linguistic productive hierarchical model. The process of determining the classes of emergency is described with the help of an algebraic model, which is closest to the linguistic description. In case of inexpediency of synthesis of products, it is proposed to use the method of fuzzy identification to reduce the number of product rules. The proposed method allows to improve the accuracy of classification of emergencies with a ship in the seaport area under conditions of deterministic uncertainty. Keywords: classification, maritime port, hierarchy analysis, fuzzy identification, logicallinguistic production hierarchical model, vessel, class of emergency situations, fuzzy variable, linguistic variable

Readiness level assessment for lean Six Sigma implementation in the healthcare sector

PurposeThe purpose of this study is to identify the critical readiness factors (CRFs) that mainly affect the implementation of Lean Six Sigma (LSS) in an organization and their interactions, and to develop a model that allows the management to assess the Lean Implementation Readiness Level.Design/methodology/approachThe methodology is separated into two main parts: the literature review and the assessment model development. In the literature review, the main CRFs and their interactions for LSS implementation in Scopus Data Base were identified. The second part of the methodology is the model development. It was built on a stepwise framework that considers the relations among the CRFs and their importance. Moreover, it was used fuzzy-based linguistic variables given by the experts working in the company to consider the actual performance rating of each CRF. The model has been validated in the healthcare sector in nine hospitals.FindingsFrom the model application, it is possible to note that the most frequent level among the nine hospitals interviewed is “Average Ready”. Also, the most extreme level of readiness occurred ones while the most extreme level of not readiness never occurred. Results show that in 78% of the cases, there would have been a high probability of implementation failure. Also, it was possible to identify for each hospital if the CRFs are good, if they are weak and need to change or if another factor needs to be improved before it and what this factor is.Originality/valueThis work proposes a new methodology that eliminates the negative aspects and limitations of the total interpretive structural modeling methodology and the fuzzy logic approach currently applied to evaluate the LSS readiness of a company. The present methodology lies in the fact that it provides a solution not only by defining the weak CRFs but also by giving an indication of priority as it identifies the weak antecedent factors that inhibit the preparedness of the depending factors.

Boosting Biodiesel Production from Dairy-Washed Scum Oil Using Beetle Antennae Search Algorithm and Fuzzy Modelling

The major goal of this study was to develop a robust fuzzy model to mimic the generation of biodiesel from the transesterification of dairy-washed milk scum (DWMS) oil. Four process parameters were considered: the molar ratio of methanol to oil, the concentration of KOH, the reaction temperature, and the reaction time. The proposed technique was divided into two steps: fuzzy modelling and optimum parameter identification. The capability of fuzzy tools to capture and make use of linguistic variables and fuzzy sets is one of their main benefits. This means that fuzzy logic allows for the representation and manipulation of values that fall across a continuum rather than merely relying on crisp values or binary categories. When dealing with non-linear relationships, this is especially helpful since it gives a more accurate and nuanced depiction of the underlying data. As a result, an accurate fuzzy model was initially built based on collected data to simulate the biodiesel production in terms of the molar ratio of methanol to oil, the concentration of KOH, the temperature of the reaction, and the reaction duration. In the second phase, the beetle antennae search (BAS) algorithm was applied to identify the optimal values of the process parameters to boost the production of biodiesel. The BAS algorithm draws inspiration from beetle behavior, particularly how they navigate using their antennae. It employs a swarm-intelligence method by deploying virtual beetles that swarm over the problem area in search of the best solution. One of its main features is the BAS algorithm’s capacity to balance exploration and exploitation. This is accomplished through the algorithm’s adaptable step-size mechanism during the search phase. As a result, the algorithm can first investigate a large portion of the problem space before gradually moving closer to the ideal answer. Compared with ANOVA, and thanks to fuzzy, the RMSE decreased from 7 using ANOVA to 0.73 using fuzzy (a decrease of 89%). The predicted R2 increased from 0.8934 using ANOVA to 0.9614 using fuzzy (an increase of 7.6). Also, the optimisation results confirmed the superiority of the BAS algorithm. Biodiesel production increased from 92% to 98.16%.

Bipolar neutrosophic WINGS for green technology innovation

Green technology innovation is a crucial assurance of achieving sustainable economic and environmental development, so improving the capability of green technology innovation is an urgent problem. In order to provide a more objective and accurate tool for identifying the most important impact factor of green technology innovation, this study innovatively proposes a new method by combining the bipolar neutrosophic sets with Weighted Influence Nonlinear Gauge System (WINGS) method. Furthermore, this paper intends to provide recommendations in improving green technology innovation capability. We invite five experts to evaluate fifteen factors influencing green technology innovation using the bipolar neutrosophic linguistic variables. Then, the proposed bipolar neutrosophic set WINGS (Bipolar NS-WINGS) method is applied to measure the influence of each impact factor of green technology innovation. Finally, we divide all the factors into cause group and effect group. Moreover, the network relation map is constructed to visualize the interrelationships between all impact factors. The Bipolar NS-WINGS suggests that Science and Technology Innovation Environment (Ω7) is the most important factor of green technology innovation. The result also indicates that R&D Investment (Ω8) is the most influential factor in which it has impacted many other factors. It is obvious that the integrated method not only enriches the research in the field of decision theory, which has not combined the bipolar-NS and WINGS method for analyzing relationships of factors, but also contributes to the improvement of green technology innovation capabilities.

An integrated method for complex heterogeneous multi-attribute group decision-making and application to photovoltaic power station site selection

This paper abstracts the complex heterogeneous multi-attribute group decision-making (CHMAGDM) characterized by two-layer decision-makers (DMs), individual attribute sets, complex relationships among attributes and heterogeneous evaluation information. DMs are composed by internal decision-makers (IDMs) and external decision-makers (EDMs). The evaluation information is represented by linguistic variables (LVs), intervals and real numbers. To handle such CHMAGDM, this paper puts forward an integrated method of prospect theory, DEMATEL (decision-making trial and evaluation laboratory) and QUALIFLEX (qualitative flexible). Firstly, a novel reference point determining approach is proposed based on three special indicators. A linguistic variable inverse prospect value function is designed and introduced to the transformation process from LVs to clouds to reduce the impacts of DMs’ psychological behaviors on decision-making. Secondly, a cloud-based DEMATEL approach is proposed to determine the attribute weights, where LVs are employed to judge the relationships among attributes. A new linguistic scale function is initiated and introduced to another transformation process from clouds to LVs. A representative indicator of cloud is designed for facilitating the normalization of direct relation matrix. Moreover, a linear programming model is built to calculate the attribute weights. Thirdly, an extended QUALIFLEX approach is developed to obtain the optimal ranking and EDM weights, where adoption coefficient is provided by IDMs. Another linear programming model is constructed to calculate the optimal collective concordance/discordance index of each permutation. The optimal ranking is generated by the maximum collective concordance/discordance index. Finally, a photovoltaic power station site selection example validates the proposed method. The advantages are demonstrated with sensitivity and comparison analyses.

A quantitative fuzzy-valued intersection matrix for obtaining fuzzy relationships between vague spatial objects

Topological relation between geographical objects is one of the key components of the Geographical Information System (GIS). Considering the objects to be exact with sharp boundaries, four intersection, and nine intersection matrices initiated the study of calculating the topological relations. Due to the extensive existence of vague spatial phenomenons, the study of uncertainty object modeling has grown rapidly. Parallelly, the relationship calculating tools have been upgraded to handle vague concepts. The fuzzy 9-intersection matrix and Egg-Yolk methods are popular tools. Although the geographical objects are uncertain, surprisingly, most of these tools generate certain relations between them. The qualitative nature of these tools overlooks the essence of their uncertainty. We propose a quantitative fuzzy-valued 9-intersection matrix to overcome these drawbacks to obtain a fuzzy relationship between uncertain geographical objects. Then two new similarity computation techniques are introduced to calculate the membership grade between the proposed matrix and the known crisp matrices. These similarity computations allow two spatial objects to have partial membership against the eight established topological relations. The quantitative calculations indicate the strength of the relationship. The superiority of the proposed model is exhibited through comparative studies. Further, certain linguistic variables are linked to the evaluated membership grades to generate an immediate association with the known crisp relations. Examples are provided to demonstrate the applicability and efficacy of this association.