Drawing Inferences Research Articles

Approaches and Strategies for Character Education for Santri at the AI ​​Riyadloh Islamic Boarding School, Semarang Regency

The objective of this research is to identify optimal methods for suitably imparting character education. The data-gathering methods employed in this research encompass observation, interviews, and documentation. Triangulation serves as a method to assess the accuracy and reliability of the acquired data. Data analysis was conducted via data reduction methodologies, data visualization, and inference drawing. The research findings indicate that character education at the API Al Riyadloh Islamic Boarding School employs two distinct approaches: a teacher-centered approach and a student-centered approach. In a teacher-centered method, the instructor assumes an active position as an authoritative figure who is revered and obeyed by pupils based on the teachings found in the texts being taught. Conversely, a student-centered approach promotes the active participation of students in structured nighttime learning activities. In addition to that, the API Al Riyadloh Islamic Boarding School employs six ways to cultivate the students' character. The tactics encompass habituation, cultivation, acquiring knowledge of positive attributes, experiencing and cherishing positivity, engaging in virtuous activities, emulating positive influences from the environment, and seeking repentance. These tactics aim to cultivate students' familiarity with kindness, comprehension of moral principles, emotional connection to and appreciation for kindness, and active engagement in virtuous deeds

Implementation of Curriculum Management in Increasing the Relevance and Effectiveness of Learning at Madrasah Aliyah

This research aims to enhance the pertinence and efficacy of Learning by implementing curriculum management. This study employs a descriptive qualitative methodology with a case study approach. The objective of this research is to comprehensively and thoroughly describe the implementation of curriculum management in enhancing the relevance and effectiveness of learning at Madrasah Aliyah Nurul Jadid Paiton Probolinggo. The data collection methods employed in this study encompassed observation, interviews, and documentation analysis. The research informants consisted of principals, curriculum departments, teachers, and fifteen students who served as responders. The data analysis procedures employed with Miles and Huberman data analysis involve the processes of data reduction, data presentation, and drawing inferences. The study findings indicated that the implementation of Curriculum Management in Madrasah Aliyah Nurul Jadid educational institutions comprises various components, namely Content and skills, evidence-based interventions, instructional settings, Classroom Management and Construction Procedures, Evaluation of progress, Effective binding learning when there are several components, management of learning applications, communicative processes, responses from students, and learning activities.

A study of a survival data using kernel estimates of hazard rate and aging intensity functions

Analyzing survival (life-testing) data and drawing inferences about them is a part of engineering and health sciences. So far, various statistical tools, e.g., survival (reliability) function (s f ), probability density function (pd f ), and hazard rate function (HR) were available among decision-making scientists to handle time-to-event data (complete or censored). But because functions (pd f ) estimators were interval (window) based, they mostly gave qualitative ideas having pictorial representation resembling step functions, ordinate remain constant when abscissa vary over an interval, thereby giving incomplete information. However, it can be sorted out with the use of kernel estimates of the above mentioned functions, resulting into smooth estimators. Moreover, the metric based on aging intensity function (AI) gives an alternative way of studying lifetime or clinical datasets as it is a quantitative measure (not interval-based), thereby depicting a broader view of a given data. In our study, we primarily focus on AI and HR functions estimated using four different kernels. We apply them to a case study of patients with primary malignant tumors of sternum (cf. Daniel and Cross, 2014) with the right-censored data. Our result shows that kernel estimates of HR and AI functions for patients with high grade tumor (HGT) are higher than for patients with low grade tumor (LGT), as expected. Thus, the study opens up a new direction for applying AI and HR functions in health sciences and engineering studies.

A Proposed Two-stage Bayesian Modeling for Calibration of Epidemiological Forecasts with Applications on Dengue Cases in Butuan City

This paper presents a comparative analysis of modeling approaches for a high-frequency time series. The moving average of 567 morbidity weeks of dengue cases in Butuan City, a Highly Urbanized City (HUC), was used as the response variable, with the aggregated rainfall, mean maximum temperature, mean minimum temperature, and mean relative humidity as the set of regressors. The last 24 morbidity weeks were set to be used for validation of predictive accuracy. Some pieces of literature support the robustness of the use of Bayesian methods in drawing inferences, modeling, and predicting epidemiological data. Hence, candidate Bayesian Econometric models were applied following appropriate assumptions. The applicability of Bayesian Vector Autoregression (BVAR) for variable selection and lag inclusion purposes was empirically supported. The BVAR results show that the dependent variable was mostly sensitive only to the variabilities in both the (a) direct effects and (b) lags of the cases themselves, and rainfall. The generated lags as included regressors were used in a separate model using the Bayesian Metropolis-Hastings (BMH) Algorithm. For comparison, a Frequentist Vector Autoregression (FVAR) Model as the baseline model, and BMH Algorithm were applied, too. Predictions comparison shows that the variable and lag selection process of BVAR combined with the BMH Algorithm (BVAR-BMH) simulation resulted in promising gains in predictive accuracy against straightforwardly using FVAR, BVAR, or BMH algorithm for the original set of variables. The promising gains in predictive accuracy may be used in anticipatory actions for dengue epidemiological surveillance for the specified HUC, or other locations.

Data Pre-processing Issues in Medical Data Classification

With digitalization of data and the rise of World Wide Web, access to information has been very easy and affordable. Especially the Web and the Internet have boosted research activities by facilitating access to large, publicly available medical datasets under open access scheme. These developments have resulted in explosive amounts of data being generated varying in volume, variety and velocity thus referred to as big data. Availability of such medical big data has catalyzed the research in medical predictive analytics. However, the true value of such data can be derived only after subjecting it to careful processing and analysis before drawing inferences from it. Publicly available medical datasets have noise in the form of missing values, outliers and data inconsistencies, that may affect the results or outcomes negatively. Pre-processing of such data is essential to eliminate noisy elements and refine the data to be suitable for further analysis and processing. This paper signifies the need for data pre-processing and explains the data pre-processing pipeline with various underlying stages constituting it. It also presents a comparative analysis of various data pre-processing techniques for handling missing values and outliers in a dataset..

Recurrent diphtheria outbreaks in Nigeria: A review of the underlying factors and remedies.

The introduction of the diphtheria-tetanus-pertussis (DTP) vaccine into childhood immunization programs resulted in its widespread elimination in high-income countries. However, Nigeria is currently experiencing an outbreak. The primary cause of diphtheria outbreaks and its high mortality rates in Nigeria was waning herd immunity due to low DTP coverage and a lack of diphtheria antitoxin (DAT), respectively. However, the underlying causes of Nigeria's low DTP coverage and DAT supply remain unknown. Relevant studies and reports included in our review were obtained by a search through Google Scholar, PubMed, and organization websites using the terms "Diphtheria-Pertussis-Tetanus vaccine OR Diphtheria antitoxin and Nigeria OR Diphtheria Outbreak." All articles considering diphtheria outbreaks, DTP vaccine, and DAT supply in Nigeria were considered without time restriction due to the paucity of data. We used the narrative synthesis approach to critically appraise, analyze, and draw inferences from the selected articles. The main causes of low DTP coverage are insufficient supply, an inefficient cold chain system, and low uptake due to poor health literacy and negative sociocultural and religious beliefs, whereas the key barriers to DAT availability are insufficient production by pharmaceutical industries because of low demand and priority. The underlying causes of Nigeria's low DTP coverage and DAT supply are multifactorial. Both short-term and long-term measures are needed to control this outbreak and prevent future occurrences.

Conceptualisation and Contextualisation of Mixed-Methods Research: A Review

The purpose of this study was to investigate the conceptualisation and contextualisation of mixed-methods research within the research methodology paradigm to ascertain the categorisation of mixed methods as a type of research, research design or research approach. The methodology adopted for this paper is qualitative in nature and involves the analysis of documents and contents. The content of journals and books was analysed to review and draw inferences relating to the themes set out for investigation in the study. Thematic analysis was used for the analysis of data for the study. The study revealed that mixed-methods research was situated within the context of types of research, research design and research approach as evident in the result of the systematic review of the articles selected for the study. Findings from the study further revealed that the conceptualisation of mixed methods within the context type of research and research approach was more than that of a research design. This study is novel and has contributed new knowledge in the aspect of research methodology with specific emphasis and definitive conceptualisation, contextualisation, and categorisation of mixed methods as a type of research, research design or research approach. Just like qualitative and quantitative research, there is a need for definitive conceptualisation, contextualisation, and categorisation of mixed-methods research. Meanwhile, this article is a synthesis for further discourse on mixed-methods research to establish a definitive conclusion on mixed-methods research conceptualisation.

Bringing Children into the Sociology of Consumption: A Symbolic Consumption Perspective

This article explores children’s consumption practices from a sociology of consumption perspective. The aim is to discover whether South African children, in their consumption of clothing, engage in symbolic consumption and to identify the ways in which they do so. Existing studies on children and symbolic consumption have largely excluded African children, hence this study represents an attempt to include their voices in the conceptualisation of childhood consumer behaviour. Data were collected through a survey of 192 children aged 10–14, using a questionnaire developed from the literature on symbolic consumption. The article presents the design and implementation of a quantitative empirical study into children’s engagement in symbolic consumption. Exploratory factor analysis (EFA) was used to tease out the factors representing the ways in which children engage in symbolic consumption, while confirmatory factor analysis was used to verify the factor structure and test whether the data fit the model. The EFA revealed four factors that represent the ways in which children engage in symbolic consumption: identity, affiliation, perception, and image. The findings reveal that children use their clothing to create their individual identities, fit in with social groups, draw inferences about others through their clothing, and as a yardstick to gauge social image. This research furthers the enquiry into children as consumers while addressing the paucity of research related to African child consumers. The results of the study have implications for marketing practitioners, while also providing directions for future research.

Why Pakistan Forges Ahead and India Lags Behind? Analysis of Sports Equipment Exports in Global Context

Focusing on India and Pakistan, this article examines their relative performance in sports equipment exports during the 2003–2020 periods. Utilizing secondary trade data available from UN Comtrade, it analyses their performance in sports equipment exports (HS-92) at the six-digit level, mainly to ascertain disparities in export performance at commodity level. Besides drawing inferences about global sports equipment exports (since 1990), it examines sports equipment export performance by tracing the pattern of export composition, export concentration and export growth, along with inferring the incidence of export instability, comparative advantage and trade balance. Indices like Herfindahl–Hirschman index (and its standardized version), Hall–Tideman index, entropy concentration index, Cappock instability index, Cuddy-Della Valle index, Balassa’s measure of comparative advantage and trade balance index are estimated to draw inferences. An analytical exercise indicates that India’s global share has not only declined over time, but it has also been lagging in exporting various types of sports equipment—its comparative advantage has waned over time, whereas such is not the case with Pakistan, which has recorded relatively better performance. A comparative perspective on the nature of the policy framework in both nations is drawn. Moreover, the article examines commodity-wise global dominance of India and Pakistan (in comparison with China) to trace commodities for which these countries have been gaining significance in global markets. Finally, the article urges the Indian State to devote serious attention to strengthening sports goods manufacturing in India, as India is currently facing a trade deficit in the global trade of sports equipment.

Adaptation of HLA testing to characterize the cynomolgus macaque MHC polymorphisms and alloantibody signatures.

Nonhuman primates are the closest animal models to humans with respect to genetics and physiology. Consequently, a critical component of immunogenetics research relies on drawing inferences from the cynomolgus macaque to inform human trials. Despite the conserved organization of the Major Histocompatibility Complex (MHC) between cynomolgus macaques and humans, MHC genotyping of cynomolgus macaques is challenging due to high rates of copy number variants, duplications, and rearrangements, particularly at the MHC class I loci. Furthermore, the limited availability of commercial reagents specific to cynomolgus macaques that can be used to characterize anti-MHC class I and class II antibody (Ab) specificities in cynomolgus macaques presents a major bottleneck in translational research. Here we successfully characterized cynomolgus macaque Mafa class I and class II serologic specificities in 86 animals originating from various geographical regions using the complement dependent cytotoxicity (CDC) assay with human HLA class I and class II monoclonal antibody (mAb) typing trays. Further, we successfully induced and characterized anti-Mafa class I and class II alloantibody specificity using HLA single antigen bead assays. We also subsequently tracked the alloAb burden in the animals during treatment with anti-B lymphocyte stimulator (BLyS) treatment. Altogether, these methods can be easily used in translational research to serotype MHC class I and class II specificity in macaques, characterize their alloAb specificity, and evaluate the efficacy of novel therapeutic modalities in depleting circulating alloAbs in these animals.

Catalysts of Economic Welfare in Africa: A Cross-Sectional Autoregressive Distributed Lag Approach

Abstract This study provides empirical perspectives on the catalysts of economic welfare in Africa, drawing inference from macroeconomic and non-macroeconomic factors. Leveraging a sample of a balanced panel dataset of 35 countries across Africa, this study provides novel applications of the cross-sectional autoregressive distributed lag methodology to economic welfare analysis in Africa. Issues of cross-sectional dependence and slope homogeneity were accounted for whilst establishing causal relationships between economic welfare proxied by the Human Development Index and macroeconomic and non-macroeconomic drivers of welfare. Based on cross-sectional autoregressive distributed lag estimation results, a 1% increase in economic growth was shown to account for a 0.233 percent and 0.253 percent increase in economic welfare in the long run and short run respectively. In addition, technology accounted for a 1.81 percent increase in economic welfare in the long run. The outcome of the Dumitrescu–Hurlin causality test demonstrated causality between trade openness, government effectiveness, economic growth, and economic welfare.

Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods

Methodology of using acoustic emissions for enhancing rodent behavioral analysis

Rodent models of behavior used in the fields of neuroscience and psychology generate a wealth of multimodal data. For instance, as a rodent moves and behaves in its environment, muscle contractions apply subtle forces to any surface the animal contacts. These forces generate acoustic waves that propagate through the waveguide as Lamb and shear horizontal (SH) waves and contain information about the rodent’s physiology, behavior, and underlying psychological state. If the information in these waves were to be tapped, it would provide a novel, non-invasive way to study rodent behavior. This article lays the foundations for using guided ultrasonic waves generated by a mouse’s movement on an aluminum plate for detecting behavior and drawing inferences about acoustic startle responses. The experimental setup involves piezoelectric sensors capturing the waves generated by the rodent’s movement, which are then stored as discrete acoustic emission (AE) hits using an amplitude threshold-based data acquisition system. This method of data acquisition ensures that data collection occurs only when the animal moves or behaves, and each movement/behavior is represented by values of features within the generated wavepackets (AE hits). Through open field tests with C57BL/6J mice, utilizing piezoelectric sensors and the DAQ system, it was observed that every movement/behavior of the animal generated Lamb wavepackets within the frequency range of 20 kHz to 100 kHz. Furthermore, rearing behavior in the animals also led to the generation of SH wavepackets in the frequency range of 75 kHz to 230 kHz. This criterion was subsequently employed to detect rearing behavior. In the acoustic startle response test, where the animals’ responses to intense sound pulse were recorded, AE hits’ features proved useful in quantifying the response. These experimental findings validate the proposed technology’s practicality and demonstrate its capability to enhance studies of rodent behavior.

PROJECT BASED LEARNING METHOD: TEACHING AND LEARNING BAHASA IN D-CLASS OF 2021 NURSING STUDY PROGRAM AT UNIVERSITAS BINA SEHAT PPNI MOJOKERTO

The application of project-based learning in the Indonesian Class D Class 2021 Study Program Nursing at Universitas Bina Sehat PPNI Mojokerto is described in this study. Project assignments focus on creating community service initiatives and gathering research based on the outcomes of service projects completed by students at their schools. The project organizes volunteer work for the community and gathers academic papers to help students develop their four language skills listening, speaking, reading, and writing so they are proficient in using Indonesian both orally and in writing. This study makes use of qualitative research techniques, which provide descriptive information via interviews, observations of people's behavior, or written or spoken statements from participants. Observation and documentation are two data collection methods. Both observation and documentation are methods of data acquisition. The researcher's analysis of the data is as follows: (1) Data compression In this process, the data are chosen based on their level of relevance and relationship to the data group. They are then gathered into one unit and coded. (2) presenting data through gathering pertinent data, and (3) drawing inferences to understand the significance of the sporadic symptoms. Based on the findings and discussion, it can be said that students successfully applied the project-based learning approach to language learning when they planned and carried out community service projects. They were also able to construct coherent and methodical scientific articles on the outcomes of their community service projects.

Moral Messages in the Film Cinta Subuh by Ali Farighi: A Semiotic Analysis of Roland Barthes

Film is one of the mass communication media that is currently favored by teenagers. This media is considered capable of being an alternative to preaching to the general public. One of them is Cinta Subuh’s film. This film is a medium for da’wah because, in this film, we can learn many moral messages. This study aimed to discuss moral values and understand the meaning of the moral message shown in the film "Cinta Subuh by Ali Farighi". This research is a descriptive qualitative research using a semiotic analysis model by Roland Barthes which is carried out in three stages, namely denotation, connotation, and myth. The data collection technique was carried out by directly analyzing the contents of the film Cinta Subuh to find out the description of the characteristics of the content and draw inferences from the content featured in the film. The results of the research contain moral values that are categorized as: 1) Morals to Allah such as devotion to Allah, love for Allah, sincerity, khauf and king', gratitude, muraqobah, repentance, benevolence, and trust. 2) Morals towards oneself, such as patience, faithfulness, humility, gratitude, istiqamah, iffah, forgiveness, and trust. 3) Morals towards fellow human beings, such as maintaining good relationships, telling the truth, not belittling others, being kind, and loving. 4) Morals towards the environment, such as the prohibition of causing damage to the earth, the prohibition of damaging plants and animals, the prohibition of polluting seawater, maintaining environmental safety, maintaining physical hygiene, and others.

Artificial intelligence algorithm comparison and ranking for weight prediction in sheep

In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Artificial Intelligence, with its capacity to handle big data is rapidly becoming popular. In addition, it can also handle non-linear, noisy data and is not limited by the conditions required for conventional data analysis. This study was therefore undertaken to compare the most popular machine learning (ML) algorithms and rank them as per their ability to make predictions on sheep farm data spanning 11 years. Data was cleaned and prepared was done before analysis. Winsorization was done for outlier removal. Principal component analysis (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA (wherein only PCA was used), PCA+ FS (both techniques used for dimensionality reduction), and FS (only feature selection used) bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991, 0.991, 0.991, 0.99, 0.99, 0.984, 0.984, 0.957, 0.949, 0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus help in drawing inferences and making futuristic predictions on farms for their economic prosperity, performance improvement and subsequently food security.

Profound barriers to value management adoption on construction projects in a developing nation

Purpose The adoption of value management (VM) has been on the rise in the construction industry, especially in the past decade. Identifying the main barriers to VM application for ameliorating poor project outcomes in the construction industry is a critical step to overcoming its implementation. Therefore, this study aims to examine the barriers to the adoption of VM in the Nigerian construction industry (NCI) and identify the critical ones for action by construction stakeholders. Design/methodology/approach This study used two rounds of Delphi surveys with 15 VM experts to identify the challenges to VM adoption. The Delphi team’s data from the two surveys were analysed using descriptive statistics. The mean-mode approach was used to identify ten major barriers to VM adoption based on the consensus reached during the second round of the Delphi poll. Following that, an interrater agreement analysis and a significant level analysis were performed. Findings Of the 17 identified barriers hindering VM adoption on construction projects in the NCI, 10 were found to be profound after statistical analyses of the data. The significant barriers include communication gap between the client and other consultants; delay in design preparation because of VM services; the pretext that VM is being practiced in the construction industry; and the wrong perception of VM by construction professionals, among others. The findings of the analysis suggested that the lack of VM adoption in the NCI was because of inadequate knowledge of VM techniques among construction professionals. Therefore, it is advised that VM training be provided for concerned stakeholders to address the lack of VM expertise among Nigerian construction professionals. Practical implications This study investigated the barriers to VM adoption on construction projects in the NCI with the opinions of a few construction professionals who can be regarded as VM experts. As against obtaining large survey responses from construction professionals who may not be knowledgeable in the VM process, the limited VM experts’ responses provide a valid basis for drawing inferences and making recommendations to address the paucity of VM applications in the NCI. Originality/value Through a Delphi survey, the critical barriers to the adoption of VM were identified, along with recommendations to help construction stakeholders overcome the challenges and enjoy the benefits of VM.

ANALISIS PEMAHAMAN KERAGAMAN SUKU BANGSA DI NEGERIKU MELALUI MEDIA PETA PADA SISWA KELAS IV SD NEGERI 14 TANJUNG BATU

The purpose of this study was to analyze the level of understanding of ethnic diversity in my country through the media of maps in grade IV students at SD Negeri 14 Tanjung Batu. The main object or informant in this study was the teacher at SD Negeri 14 Tanjung Batu while the students were the key informants. The research method used in this study is a qualitative descriptive method. Data collection techniques include interviews, tests and documentation, while data analysis techniques include data reduction, data display, and conclusion drawing. The results of the research on understanding IPS, interpreting, exemplifying, classifying, drawing inferences, dissecting and explaining that it can be seen that in the very good category there are 12 students, in the good category there are 8 students and in the sufficient category there are 3 students so that the level of understanding of Social Sciences with an average value 78 is included in the good category. From the results of the data analysis above, it can be concluded that the level of understanding of the subject matter of ethnic diversity in my country through the media of maps in grade IV SD Negeri 14 Tanjung Batu is good.

Nurturing the soul at work: unveiling the impact of spiritual leadership, interpersonal justice and voice behavior on employee intention to stay

PurposeThis study investigates how the spiritual leadership style of a manager affects employees' intention to stay with the organization, taking into account the post-pandemic workplace and the expected economic downturn. Furthermore, this study aims to assess how employee voice behavior mediates the linkages between the spiritual leadership style and intention to stay and how this mediation is influenced by perceived interpersonal justice. Grounded on the self-determination theory of intrinsic motivation and social-exchange theory, this study seeks to advance the theoretical understanding of spiritual leadership and its associated outcomes.Design/methodology/approachThe descriptive study included 379 frontline employees in India's tourism and hospitality sector. Responses were collected from selected employees using the snowball sampling method and met strict inclusion criteria. Self-reporting questionnaires were used to collect data from the participants. Confirmatory factor analysis was conducted using IBM AMOS 21.0, and hypothesis testing and drawing inferences were carried out using path analytic procedures with PROCESS Macro 3.0.FindingsConsistent with the hypotheses presented in this paper, this study demonstrated a statistically significant indirect impact of spiritual leadership on employees' intention to stay with the organization, through indirect effects of employee voice behavior. Additionally, the conditional indirect effects of spiritual leadership on employees' intention to stay, mediated by voice behavior, were contingent upon the level of interpersonal justice as a moderator. Specifically, these effects were significant when the levels of interpersonal justice were low but not when they were high.Originality/valueThis study makes significant strides in developing and testing a pioneering model that examines the association between spiritual leadership and employees’ intention to stay with the organization. This research explores explicitly how this relationship is influenced by perceived interpersonal justice and employee voice behavior. The results of this study emphasize the criticality of cultivating a culture that inspires constructive criticism and elucidates its potential advantages, effectively bridging a gap in the existing scholarly literature.

Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

AbstractSome types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets. Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results.