Qualitative Model Research Articles

Earthquakes Three-Stage Early Warning and Short-Term Prediction

Among the anomalous geophysical phenomena observed preceding earthquakes, specific attention has been given to VLF/LF, ULF (very low frequency/low frequency, ultra-low frequency) electromagnetic (EM) emissions, recorded before earthquakes, because of an interesting correlation between them and seismic activity. Numerous scientific papers have been published on this topic, providing convincing evidence of these processes observed using ground-based and satellite ground-based observations during the preparation for the earthquake. These phenomena are detectable both at laboratory and geological scales. The authors of this paper have used an avalanche-like unstable geological model of fault formation and a model of the generation of electromagnetic emissions detected before the earthquakes to prove the prediction capabilities of VLF/LF EM emissions. The first gives a clear imagination of earthquake preparation in all stages in the focal area from the beginning of the micro-cracks appearing to the final formation of fault. Another one, based on electrodynamics, explains the EM emissions origination process and offers a formula that analytically connects the observed frequency of EM radiation with the linear size of the emitted body (fault). It is worth emphasizing the synthesis and rather harmonious relation of the mentioned models. Based on the above models conducted studies clearly show that the description of a qualitative avalanche-unstable geological model of fault formation using VLF/LF EM radiation data made it possible to quantitatively characterize the full cycle of preparation and occurrence of earthquake process. Namely, in the case of VLF/LF emissions monitoring, the beginning of the “avalanche process” of ruptures is considered the first stage of early warning of an incoming earthquake. The so-called EM emissions “silence” period is assessed as the second stage of early warning. The third early warning or an alarm about an incoming earthquake may be announced immediately at the moment of the first anomaly appearance subsequently the “silence” period. This article offers the method of earthquake three-step early warning and short-term (hourly) prediction.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by near-Infrared Spectroscopy with Chemometrics.

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions. This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions. The models were produced under different conditions to distinguish the origins distinctly. Ten pre-processing methods have been used to pre-process the original spectra (OS) and to select the most optimal spectral pre-processing method. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables. The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate. The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua. The utilization of near-infrared spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating quality assurance measures.

Organizational Effects of the Hubris Syndrome and the Ways of Solution

Hubris syndrome is defined as a managerial disease caused by the power possessed by people in managerial positions, and gives rise to positive and negative outcomes in organizations. For this purpose, the banking sector was chosen as the research area due to the fact that banking sector is one of the areas where the hubris syndrome can most commonly be seen due to the known characteristics of the sector such as tough working conditions and working goals. Data were obtained via interviews carried out with 12 managers working in the field of banking with a semi-structured interview form using the phenomenology experience research pattern, which is one of the qualitative research models. Maxqda 2022 data analysis software was used for data analysis. Study results according to data received from managers revealed that the characteristics of people suffering from hubris syndrome are seeing oneself as perfect and superior, believing that anything can be done with authority, the utopical goals given to lower-level employees and disregarding opinions of others. The reasons for the emergence of the syndrome are the problems in the institutional structure and functioning, bullying and exclusion experienced in the past and the lack of clarity about the authority and responsibilities of people within the organization. When the organizational impacts of Hubris syndrome are taken into account, two types of impacts are observed, such as the impacts on the organization and the impacts on the employees. People with the hubris syndrome harm the organization, prevent the emergence of new ideas and decrease the motivation of employees.

Is the Productivity of Faculty Members Sustainable? The Perspective of Faculty Members

In the global economic landscape, a nation’s ability to secure a prominent position is intricately linked to its capacity for knowledge augmentation and technological innovation. This correlation underscores the pivotal role of universities and academicians, whose importance directly reflects a country’s level of development. The effectual execution of educational, research, and innovative pursuits within university settings necessitates the presence of qualified, productive, and dedicated academicians. As key contributors to academic, social, and humanitarian spheres, academicians bear responsibilities spanning research, teaching, societal enlightenment, and problem solving within higher education. The scholarly productivity of researchers is commonly gauged through metrics involving scientific publications and academic engagements. Notably, in many countries, advancements and appointments within academic institutions are contingent on the demonstrated productivity of academicians. Aligning with this global trend, Turkey accordingly structures its promotion and appointment procedures within academia. This study aims to ascertain faculty members’ perspectives regarding academic productivity after their associate professors’ and professors’ appointments in Turkey. Furthermore, we endeavor to identify the underlying factors contributing to this decline and increase academic productivity. Our findings emphasize the intricate interplay of various factors influencing academic productivity and sustainability. These include the significance of institutional support, economic stability, intrinsic motivation, and challenges such as bureaucratic processes and gender-related issues, which collectively shape the academic landscape and faculty members’ ability to produce impactful research over time. The research study group comprised 20 faculty members working at a public University in Turkey in the 2022–2023 academic year. This study was prepared using a qualitative research model. This study used the interview technique, one of the data collection techniques used in process-based evaluation applications. Furthermore, the obtained data were analyzed using the content analysis technique used in qualitative research methods.

Implementation of Activity Based Costing Method in the Yellow Noodle Industry

This study aims to determine the application of the activity based costing method on yellow noodle industry. The activity based costing method is aimed at presenting accurate of cost of products for management purposes, by carefully measuring the consumption of resources in every activity used to obtain maximum profit. This study uses a qualitative model with an evaluation approach, where the interactive model used is three procedures; data reduction, data presentation, and drawing conclusions or verification. Findings, in this study there are 2 inefficient production process activities, namely the activity of the yellow noodle drying process where the allocation of labor costs uses fixed costs and the process of weighing the yellow noodles which uses the same labors as the cost per unit is different. Based on the application of the activity based costing method, the two inefficient activities are converted into variable costs per unit and allocated in the parent process activity. The impact of allocation changes is an increase in profit per unit, from Rp. 6,475 per pack of yellow noodles to Rp. 7,167 per pack of yellow noodles.

A qualitative risk assessment model for water reuse: Risks related to agricultural irrigation in Brazil

Urbanization and industrialization are increasing extreme weather events, causing water quantity and quality reduction. Global water scarcity impacts 32.5 % of the urban population and is growing. Brazil has also witnessed water scarcity, notably in the southeast (2014–2015) and south (2019–2020), with reservoirs dropping below 20 % capacity. Water reuse is vital for mitigating scarcity, though it presents risks due to contaminants. Risk analysis studies are crucial for evaluating contamination sources, pathways, and exposure scenarios in water reuse practices. Various methodologies, including quantitative, semi-quantitative, and qualitative analyses, can be employed. Given the uncertainty and diverse factors, qualitative methods are recommended for non-potable water reuse risk analysis. This work presents a qualitative risk analysis methodology that allows to evaluate non-potable water reuse categories. It assesses factors affecting human health and the environment, considering exposure scenarios, characteristics of the receptors, and sources of reused water. The risk analysis of water reuse was carried out focusing on agricultural reuse, considering as alternatives the irrigation of soybean and sugarcane crops. By reviewing literature, the probability of occurrence and the magnitude of impact of the risk factors were identified and rated, using an increasing relative numeric scale. This process resulted in an overall risk value for comparing agricultural irrigation alternatives. The obtained results indicate a promising risk analysis model that can be adjusted and applied to various water reuse modalities and key factors. This adaptable risk analysis model is mainly related to water treatment methods, prompting the proposal of risk control measures.

Teachers’ Views and Strategies for Improving Children’s Critical Thinking Skills

The study aimed to investigate preschool teachers' perceptions regarding the enhancement of children's critical thinking skills, as well as their employed strategies for fostering such skills. The study was designed as a case study, a qualitative model. The sample group consisted of 30 preschool teachers from Turkey. Data were collected using online interviews and examined using descriptive and content analysis. According to the preschool teachers, the study findings showed that Turkish-language and science-math activities are the activities that contribute most to improving children’s critical thinking skills. Furthermore, the preschool teachers stated that they mainly used the strategy of asking open-ended questions during book reading activities and least used the strategy of promoting the interaction/collaboration among students to improve children’s critical thinking skills.

Bearing the weight: A qualitative study on expert views on integrating ethics in autonomous vehicles

AVs are increasingly becoming present on public roads, the ethics of which have captured the publics attention. This study represents the first ever investigation into the opinion of AV development experts on the ethics of programming AVs in collision scenarios. The study makes use of a qualitative research model. A sample of 24 Swiss experts were interviewed using semi-structured interviews. The majority of experts were directly involved in the development of AVs and represented both research, private, and public sectors as well as national regulatory bodies. Three core themes emerged from the data. 1) Experts were keenly aware of the ethical challenges involved in AVs on public roads, including in collision situations. 2) Experts felt a moral responsibility for how AVs are programmed and developed. However, experts’ moral framework was not well developed with many experts stating that their own personal moral framework should not be used to program motion planning. 3) Experts saw a need for an ethical framework to guide the development of AVs. The paper concludes that it is clear experts are keenly aware of the ethics involved in AVs on public roads, and in particular collision scenarios. They feel a sense of responsibility for how AVs are programmed but do not feel ethically well-equipped themselves. Experts would, therefore, appreciate an ethical framework that would assist the development of these algorithms. This framework may be driven by either industry or government but should be clear and simple. We recommend countries like Switzerland develop such a framework.

Teachers' Opinions on Second Grade Language Skills of Students Who Learned Initial Literacy Through Distance Education

This study examines teachers' opinions on how the language skills in the second grade (listening, speaking, reading, writing) of students who learned initial literacy in the first grade of elementary school (2020-2021) through distance education due to the pandemic were affected. This study was conducted using a descriptive qualitative research model. The study group comprised 18 teachers who conducted first-grade initial literacy teaching via distance education in 2020-2021 academic year and worked in the second grade during 2021-2022 academic year. Each teacher had instructed first grade at least three times in their career. A semi-structured interview form served as the data collection tool, and thematic analysis was employed for data analysis. The data yielded four themes: listening skills, speaking skills, reading skills, and writing skills, with corresponding sub-themes identified and interpreted. Participants reported that the pandemic led to temporary school closures and a shift from face-to-face to distance learning for first-grade literacy. Consequently, students missed the opportunity to develop listening, speaking, reading, and writing skills through direct interaction in the classroom. They stated the limitations and conditions of distance education were cited as reasons for this shortfall. As a result, they stated that there are inadequacies in students' listening, speaking, reading and writing skills, and that these inadequacies have a negative impact on their listening skills in the second grade. This resulted in inadequacies in students' listening, speaking, reading, and writing skills, negatively impacting their second-grade. It was reported that there were lack of gains in the achievements of this group compared to the students of previous years, and that these were due to the fact that the initial literacy teaching was carried out through distance education.

The Implementation of Project Based Learning to Teach Speaking

Learning model is one of the important tools to transfer material by teachers to students in the classroom. Until this 21st century, many learning models have been created and developed which aim to facilitate learning in the classroom to be well organized and achieve the learning objectives themselves. This research aims to analyze the implementation of Project Based Learning (PJBL) learning model in teaching speaking. This research was conducted qualitatively with Milles, Huberman, and Saldana (2014) qualitative description model. Data collection in this study used observation, interview, and document collection methods, while the research participant of this study was one of the language classes that used the PJBL learning model in teaching speaking and three language teachers. The researcher chose a sample of one class IX at SMP Negeri 11 Semarang as the material and place of research. The validation of the instrument used by the researcher has been validated by educational expert with doctoral qualifications. The research findings showed that the language classes in SMP Negeri 11 Semarang had implemented the PJBL learning model in accordance with the instrument that became the research reference in the form of characteristics and steps of the PJBL learning process. However, the researcher also found problems in the learning process that can be handled collaboratively by students with teacher guidance.

Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using Osprey Optimization Algorithm (OOA) and a quantitative prediction model combining Continuous Projection Algorithm (SPA) with Partial Least Squares Algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than that of the PSO-optimized ELM model. Among all the models of qualitative analysis, SG-LLE-OOA-ELM had the best prediction effect, with mean square error (MSE) of 0.00834, coefficient of determination (R2) of 0.99841, root mean square error (RMSE) of 0.09134, and mean absolute error (MAE) of 0.07038. Among the models of quantitative analysis, the best effect is SG-SPA-PLS model with R2 of 0.99951.

An integrated structural equation modeling and fuzzy qualitative comparative analysis model for examining green procurement adoption drivers

This research examines the factors that influence the implementation of green procurement in small and medium enterprises (SMEs), namely top management commitment, regulations, costs, pressure from consumers, and transformational leadership as moderation. The research employs a quantitative approach with 206 samples from the Indonesian SME industry. It utilizes Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). The data set includes factors such as top management commitment, regulations, costs, consumer pressure, and transformational leadership as moderation variables. The results show that management commitment, regulations, costs, and customer pressure influence green procurement. The role of the moderating variable shows that transformational leadership only strengthens the influence of costs on green procurement and regulation on green procurement. Meanwhile, the influence of top management commitment to green procurement and customer pressure on green procurement shows that transformational leadership does not strengthen these variables. The research’s novelty lies in combining SEM and FsQCA to analyze the causal relationships among variables influencing green procurement in SMEs. It highlights the importance of understanding and effectively managing these relationships for organizations adopting green procurement practices.

Why Carbon Nanotubes Improve Aqueous Nanofluid Thermal Conductivity: A Qualitative Model Critical Review

Media thermal conductivity is important in various heat-transfer processes. Many conventional fluid conductors suffered low conductivity and environmental issues. Therefore, research was active in finding out alternative systems, mostly relying on aqueous liquids that are low-cost and ecofriendly. After the emergence of carbon nanotubes (CNTs), with their many special structural, electrical and thermal properties, they have been examined for many applications, including heat-transfer processes. Adding CNTs to water yields CNT aqueous nanofluids that have been widely investigated as heat-transfer media. The literature shows that CNT addition improves water thermal conductivity and other water properties, such as viscosity, surface tension, freezing point and boiling point. The literature also shows that nanofluid thermal conductivity improvement is affected by CNT type and concentration, in addition to other factors such as surfactant addition. All these subjects were widely described in literature, focusing on experimental, modelling and theoretical accounts. Despite the wide literature, there exist inconsistencies and discrepancies between reports that need to be justified. In addition to technical papers, many reviews were published on various aspects of the subject including experimental results and mathematical modeling. However, the very basic question here is as follows: Why does adding CNT to water affect its thermal conductivity? In spite of the wide published literature, this issue was not targeted in a simple qualitative approach. This review provides a clear understanding of how CNTs improve thermal conductivity of aqueous nanofluids. A qualitative model is presented to explain mechanisms behind improvement as presented in the literature. CNT type effects are discussed with other factors such as aspect ratio, Reynold number, dispersion quality, composition, temperature and additives. CNT functionalization is described. Relations to estimate nanofluid thermal conductivity are discussed. The model will help specialists to tailor CNT aqueous nanofluid characteristics as desired by varying types and concentrations of CNT and surfactant, and other factors.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

High-precision discrimination of maize silage based on olfactory visualization technology integrated with chemometrics analysis

Rapid, reliable and non-destructive detection of the quality of maize silage is essential to high-efficiency animal husbandry and food safety. In this study, the colorimetric sensor array (CSA) integrated with chemometric methods is innovatively proposed for qualitative discrimination of maize silage. First, 12 color-sensitive dyes were selected to fabricate colorimetric sensor arrays to be used as artificial olfactory sensors for obtaining odor fingerprints of maize silage. Machine vision algorithms were utilized to extract the color features, and principal component analysis was applied to reduce the dimensionality of the obtained data. Finally, the PCA results were input variables to develop different qualitative discrimination models. These models involve support vector machines (SVM), extreme learning machine (ELM), and random forest (RF). The analysis results show the 100% correct identification rate for independent samples. The general results sufficiently reveal that olfactory visualization technology integrated with chemometrics analysis has promising applications for high-precision discrimination of maize silage.

Detection the quality of pumpkin seeds based on terahertz coupled with convolutional neural network

AbstractPumpkin seeds are nutritious and have some medicinal value. However, the mold and sprouting are produced during the storage of pumpkin seeds. Food safety and quality problems may be caused if they are not removed in time for processing. The traditional testing methods are cumbersome to operate, complex, and destructive in sample preparation. Therefore, terahertz time‐domain spectroscopy (THz‐TDS) technology was proposed to achieve the detection of the internal quality of pumpkin seeds. Firstly, samples of pumpkin seeds of different qualities were crafted, and they were moldy for 3 days, moldy for 6 days, sprouted and moldy, sprouted and normal pumpkin seeds, respectively. Then, the pumpkin seeds of different qualities were dried, ground, and pressed, and their spectral data were collected. The terahertz spectra of the five types of samples were significantly different. The support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) qualitative discriminant models were established with the raw absorbance spectral data, the preprocessed absorbance spectral data, and the preprocessed and band‐screened absorbance spectral data, respectively, where the CNN model based on the raw spectral data has the highest classification accuracy of 96%. The CNN models do not require advance spectral data processing, simplifying the spectral analysis process. And it achieves best classification results in the accuracy of detection compared to traditional chemometric models. The CNN combined with THz‐TDS method has great potential for application in the detection of agricultural products. It provides a new detection method for the field of quality detection of agricultural products.

A Learner-Centred International Perspective on the Advantages of Distance Thesis

Purpose: The research aims to specify attitude towards DT as well as advantages/strengths/opportunities of DT based on views of international learners in the times of the coronavirus or similar necessary conditions. Design/Methodology/Approach: Qualitative data and qualitative data analysis techniques were need to reach the purpose of the research. Therefore, the study which was conducted to determine the views of international learners about advantages/strengths/opportunities of DT was designed by the qualitative model. Totally 14 international learners participated the research from different countries. An online form was administered to international learners in Turkey to collect data for the research questions. Qualitative descriptive analysis was used to analyze the data. Findings: The findings consist of two subjects as the attitude towards distance thesis and the advantages of distance thesis. Attitudes of international learners towards DT are very positive that a great majority of the them confirm idea of DT (%85.70) and most of them (%78.57) want to apply DT in case of necessity like coronavirus. The advantages of DT are determined as follows: Flexibility, time saving, cost saving, access information in digital world, chance for working learners, uninterrupted supervision, communication with online meeting, working with digital information-applications-tools-technologies, learning diversity, getting a degree from university at a distance, collaboration with peers and experts from different institutions, support for modernization of universities, contribution to internationalization, achievement of self-management skill, not to being in the crowd. Highlights: In the literature, no study was found that addressing advantages of distance thesis for graduate programs. In line with the opinions of international learners, the advantages of distance thesis were determined in the research. It has been revealed that international learners have a positive attitude towards distance thesis and they want to apply for distance thesis in times of pandemic or similar necessary conditions. This article can inspire new researches, new projects on distance thesis and the determination of the distance thesis framework in our country.

Commitment to Sport Changes Psychological Needs: A Study on Individuals Who Regularly Engage in Fitness

It is as important to be able to continue sport as it is to start sport. In some sports branches, not practicing sports may cause some psychological problems. For this reason, this study aims to explain what changes individuals who train 3 days a week for at least 12 months in bodybuilding centers feel when they disrupt their training program and to show the relationship between their psychological needs in sports and their commitment to sports. The sample group of the study, in which quantitative and qualitative research model was used, consisted of 249 people (66 female, 183 male) who participated in sports for body development. Personal information form, open-ended questionnaire, sport psychological needs scale and sport commitment scale were used for the research. Content analysis was used in the qualitative data analysis of the study. Linear regression and Pearson correlation analysis were used for quantitative data analysis. The results of the qualitative data study show that the participants have positive emotions when they exercise and negative emotions when they do not exercise. The examination of quantitative data revealed that there was a positive weak relationship between psychological needs and sport addiction of the research group. In the study, it was concluded that the participant group enjoyed life, felt more talented, and were happy when they practiced sports, while when they did not spend time on sports, they felt a lack of time in their lives and experienced a sense of dissatisfaction. Keywords: Fitness, Commitment to sport, psychological needs in sport, chronic exercise  

The simulation experiment description markup language (SED-ML): language specification for level 1 version 5.

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Assessment of contamination dispersion in a porous aquifer environment using explicit and implicit methods in the MODFLOW–MODPATH model

This study presents a meticulously edited article that assesses the uncertainty associated with contamination dispersion in a porous aquifer environment. The article employs a conceptual model incorporating finite difference flow modeling and particle tracking methods grounded in particle movement theory within groundwater flow. The groundwater flow model utilizes an automatic calibration technique, PES, and independent verification, to minimize statistical discrepancies in the optimized parameters. The statistical summary of the model output reveals the average pollution concentration in the study area, specifically the landfill aquifer in northern Iran. The results indicate that the initial contamination value at the boundaries is zero, whereas 91,802 units are discharged into the sea. Moreover, approximately 3,317,290,003 pollution units have been extracted from pumping wells along the contamination plume over 10 years. Furthermore, the drainage network releases 1,778,680,001 pollution units, resulting in a net outflow of 5,082,740,007 units from the same drainage network. Additionally, 9,912,180,003 units were discharged from the leaky boundaries. It should be noted that these statistics can be reversed depending on the specified period. The difference between the input and output pollution values in the study area indicates a net accumulation of 8192 units. To analyze the sensitivity of contamination dispersion in the porous aquifer environment and the variations in coastal drainage patterns obtained from the finite difference simulation, the explicit and implicit methods, as well as the random walk particle tracking method, were investigated in a portion of the basin area using the South Side Implicit method. One of the main objectives of this sensitivity analysis is to evaluate the results of the uncalibrated qualitative model and examine the particle dispersion in the adjacent area of the contaminated landfill region to assess the error of the proposed model by developing a limited test. The calculated error or computational difference between the explicit and implicit methods and the applet model is considered negligible. For example, the calculated error between the explicit and backward methods and the applet model ranges from 0.086 to 0.372 units for a concentration interval of 0.512 units and 0.680 units for a time interval 0.537. Therefore, the computational differences are not significant. Furthermore, based on theoretical sensitivity analysis of contamination dispersion, the laboratory model demonstrates that the flow direction in this modeling is from west to east, as expected.