Qualitative Modelling Approach Research Articles

Bringing systems thinking into the classroom

ABSTRACT Systems thinking is the ability to reason about biological systems in terms of their characteristics and can assist students in developing a coherent understanding of biology. Literature reports about several recommendations regarding teaching systems thinking, while it seems that systems thinking has not reached classroom practice. The main aim of this study was to identify design guidelines to implement systems thinking in upper-secondary biologyeducation. Based on the recommendations of literature and experience a teacher team developed, tested and evaluated two lessons in two upper-secondary biology classes (15–16 years old students, n = 26, n = 19) using Lesson Study. Lesson one focused on the application of seven system characteristics: boundary, components, interactions, input & output, feedback, dynamics, and hierarchy. Lesson two focused on the improvement of students’ understanding of the characteristics feedback and dynamics by using a qualitative modelling approach. Based on classroom observations, student products and interviews, the results suggest that a first step is made: most students are able to name and apply the seven characteristics. It seems important to pay attention to the: (1) introduction of the seven characteristics; (2) application of the characteristics in a wide variety of contexts; (3) individual characteristics; (4) explicit use of system language.

QUALITATIVE MODELLING IN EMBODIMENT DESIGN - INVESTIGATING THE CONTACT AND CHANNEL APPROACH THROUGH ANALYSIS OF PROJECTS

AbstractPurposeful qualitative modelling of embodiment function relations is a challenge in embodiment design. This contribution investigates the applicability and usefulness of the Contact and Channel Approach as a qualitative modelling approach in a survey study. From 23 development and research projects, advantages and challenges regarding applicability and usefulness are identified. A further result is that many different models are used additionally to the Contact and Channel Approach. Based on the findings, research potential for optimization and development of links to other models emerges.

Applying a GIS based DRASTIC Model in the Assessment of Risk to Groundwater Resources Within the Underlying Aquifers in Part of Lagos State, Nigeria

Lagos, Nigeria is one of the megacities in Africa with an estimated annual growth rate of 8%, and its socio-economic growth has continually encouraged influx of people into the city. Hence, this has increased pressure on the underlying water resources with implications for public health and ecological related issues. In this study, anthropogenic induced risk posed to water resources within the underlying aquifers in part of Lagos State, Nigeria, was evaluated using qualitative DRASTIC modelling approach. Geo-hydrological data were acquired for site characterization and setting up of the model. The geology indicated multi-layered aquifer horizons, with extensive lateral lithological variations. The topography ranges between 1.5 and 60 m above mean sea level (amsl). The depth to groundwater is shallow, typically less than 1 m in the south, and a maximum depth of 20 m below ground level, in the north. A vulnerability map was developed from the cumulated sum of the DRASTIC index values. Anthropogenic activities within the study area were super-imposed on the vulnerability map to generate a risk map. The results show that 1% of the study area is designated as High-Vulnerability-High-Risk area. Also, High-Vulnerability-Medium-Risk and Medium-Vulnerability-High-Risk constitute 3% each of the area, while 16% and 25% were designated as Medium-Vulnerability-Medium-Risk and Low-Vulnerability-Medium-Risk, respectively. Although, based on the DRASTIC input parameters, approximately 75% of the area is designated as high and medium vulnerability, however absence of risk activities suggests no risk is posed to the underlying groundwater resource in approximately 52% of this delineated subarea.

Development of a model to assess the impact of lean practices on firm performance in SMEs

The purpose of this paper is to develop a model that assesses the impact of lean manufacturing practices on performance enhancement in Indian SMEs. In this present work, empirical study did in small and medium enterprises of Northern India and case studies have been used for drawing out the learning issues and their inferences for developing a model using qualitative modelling approach. Qualitative modelling which consists of options field methodology (OFM), options profile methodology (OPM), analytical Hierarchy process (AHP) and fuzzy set theory (FST) methods are applied for synthesising the learning issues into options and further prioritising the performance parameters. Four options profiles mainly considered were process improvements based approach (PBA), people oriented approach (POA), top management leadership (TML) and mixed approach (MA). From all the approaches, MA has come out to be preferred for effective implementing the lean manufacturing practices and enhancing the overall performance of the organisation.

Dynamics of technological change: nuclear energy and electric vehicles in France

Technological change is shaped by a confluence of processes that are governed by socio-political, economic, and regulatory factors within a region. In this paper, the authors present a theoretical framework to describe the dynamics of technology change. Based on a qualitative modelling approach, the authors employ concepts from literature on innovation, technology adoption, and technological change, and map reinforcing and retarding cycles of processes of innovation. The way these processes (or functions) are interconnected and combined hinders or enables the overall success of technology diffusion and shapes the long-term outcome for the new technology. The authors develop the framework with examples of energy systems in UK and UAE, and apply the model to examine two cases of nuclear power and electric vehicles in France. They highlight the role as well as limits of policy in initiating and sustaining reinforcing cycles for new technology diffusion and can provide a basis for forming expectations about emerging technologies.

A Complex Systems Investigation of Group Work Dynamics in L2 Interactive Tasks

AbstractWorking with Korean university‐level learners of English, this study provides a detailed analytical comparison of 2 task work groups that were video‐recorded, with 1 group scoring very high and the other relatively low based on the results of a Group Work Dynamic (GWD) measuring instrument. Adopting a complexity theory (CT) perspective and utilizing what Dörnyei (2014) has termed a retrodictive qualitative modeling (RQM) approach to research, the study sought to identify how various elements interacted to influence the emerging GWD patterns. Based on different layers of data from the GWD measuring instrument, affect‐related state questionnaires, interviews, and extracts from the transcribed interaction, the signature dynamics for a strong and weak GWD related to a combination of (a) positive and negative GWD behaviors, (b) affect‐related states in the form of motivation, emotional state, and perceived difficulty, (c) critical moments in the interaction, and (d) the system's initial conditions, which were characterized by specific task characteristics and the learners’ affective states just prior to beginning the task. Acting as key control parameters in the shaping of GWD outcomes were task conditions associated with the use of imagination, planning time, humorous content, difficulty level, and multiple task outcomes.

Assessing cumulative socio-ecological impacts of offshore wind farm development in the Bay of Seine (English Channel)

As part of the energy transition, the French government is planning the construction of Offshore Wind Farms (OWFs) in Normandy. These OWFs will be integrated into an ecosystem already facing multiple anthropogenic disturbances. A holistic view of cumulated impacts (OWF construction, global warming and fisheries) were developed on the Courseulles-sur-Mer’ ecosystem through the use of a qualitative mathematical modelling approach. This modelling approach provides the mean to consider alternative hypotheses about how the ecosystem structure and function affects its dynamics. Alternative models were constructed to address the different hypotheses regarding the behaviour of top predator (whether the top predators will be scared away by the OWF or attracted by the reef effect), impacts of global warming and changes in fisheries activities. Key findings from these analyses are that the OWF construction could lead to an increase in benthos species and fish benthos feeders whatever the perturbation scenario, while the predicted response of top predators was ambiguous across all perturbation scenario. Qualitative modelling results can play a vital role in decision making by improving long term planning for the marine environment but also as a tool for communication with the public and so contribute to a better acceptability of the Marine Renewable Energy (MRE) project.

Accelerating Transition Dynamics in City Regions: A Qualitative Modeling Perspective

In this article, we take stock of the findings from conceptual and empirical work on the role of transition initiatives for accelerating transitions as input for modeling acceleration dynamics. We applied the qualitative modeling approach of causal loop diagrams to capture the dynamics of a single transition initiative evolving within its regional context. In doing so, we aim to address two key challenges in transition modeling, namely conceptualization, and the framing of empirical insights obtained for various case study regions in a consistent modeling framework. Our results show that through this systematic approach one can translate conceptual and qualitative empirical work into a transition model design. Moreover, the causal loop diagrams can be used as discussion tools to support dialogue among researchers and stakeholders, and may support a comparison of transition dynamics across case-study regions. We reflect on main limitations related to empirical model validation (lack of data) and to model structure (high level of aggregation), and describe next steps for moving from a qualitative single transition initiative to a quantitative multiple transition initiatives model.

Identification of dual kinase inhibitors of CK2 and GSK3β: combined qualitative and quantitative pharmacophore modeling approach

PTEN, a tumor suppressor protein, gets deactivated by casein kinase 2 (CK2) and glycogen synthase kinase 3β (GSK3β), which are the major causes of PI3K/AKT-driven tumors. To surmount this problem, the multi-target inhibitor strategy may be of great significance. The goal of this study was to design dual-target inhibitors of CK2 and GSK3β using a combination of pharmacophore modeling and molecular docking studies. The common feature-based (qualitative) and 3DQSAR-based (quantitative) pharmacophore models were generated and validated. The best pharmacophore models (Pharm18 and Hypo1) comprised two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic features. The models were used to screen various chemical database and top mapped compounds from each database were selected. They were processed for Lipinski filter, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, and docking studies. We have obtained six hits with comparable dock score to the reported inhibitors. We have concluded Hit15 as a competent candidate based on its docking and Density Functional Theory (DFT) calculations. It showed 140.73 and 130.79 dock score in CK2 and GSK3β, respectively. The electronic property of Hit 15 showed the lowest energy gap (0.021) compared to other hits and active ligands which suggest its higher reactivity. In conclusion, this study may assist in the development of new potent dual kinase inhibitors of CK2 and GSK3β. Also, the overture effort of combined qualitative and quantitative modeling for the development of multi-target inhibitors may support the future endeavors.

Using qualitative models to define sustainable management for the commons in data poor conditions

Nearly 50 years after Hardin’s “tragedy of the commons” we have not yet found predictive tools to guide us towards sustainable management of common-pool resources (CPR). We often have a good understanding of the qualitative relationships between the principal actors in socioecological systems (SESs), but classical quantitative approaches require a tremendous amount of data to understand the drivers of SESs sustainability. Here we show that qualitative modelling approaches can provide important governance insights for SESs that are understood but not quantified.We used Loop Analysis to test the outcomes of different management regimes on a simple nature-based tourism SES described by economic, social and environmental variables. We tested the sustainability of different management scenarios on this system and we identified the necessary conditions to achieve it.We found that management regimes where property rights and responsibilities are shared between different stakeholders are more likely to be successful. However, the system is generally highly unstable and it is important to tune each strategy to each particular situation.The conditions for sustainability found across the different systems tested were: a low reinvestment rate of tourist revenues into new infrastructures and a low growth rate of the environment. Management strategies based on maximum sustainable yield, which keep the environment far from its carrying capacity, have less chance to be sustainable.Qualitative models of SESs are powerful diagnostic tools; they can help identifying variables that play an important role in determining socioecological sustainability in data-poor circumstances and guide the design of efficient data collection programmes. Our results highlight the importance of careful planning when designing management strategies for nature-based tourism. The application of one-size-fits-all solutions to every situation is likely to lead to the failure of the commons; however tourism-based SESs can be sustainable if management strategies are tuned to each particular case.

A qualitative multi-criteria modelling approach to the assessment of electric energy production technologies in Slovenia

A methodological approach to the strategic evaluation of electric energy production technologies in Slovenia is presented. The aim of this work is to make a transparent and reproducible identification of reliable, rational, and environmentally sound production of electric energy in Slovenia by 2050. The approach is based on a qualitative multi-criteria modelling method DEX and consists of three stages: (1) assessment of individual technologies for electricity production, (2) assessment of mixtures of technologies and (3) evaluation of scenarios of shutting-down existing old power plants and constructing the new ones until 2050. Technology alternatives include both conventional and renewable energy sources: coal fired, gas fired, biomass fired, oil fired, nuclear, hydro, wind, and photovoltaic. The results indicate that only mixtures of nuclear, hydro, and gas fired technologies can meet expected energy needs in a sufficiently reliable and rational way.

Mechanisms driving Antarctic microbial community responses to ocean acidification: a network modelling approach

Rising atmospheric CO2 concentrations and the subsequent changes to ocean chemistry may have pronounced effects on marine microbial communities, particularly for the cold Southern Ocean. Changes to the microbial community in this region could affect the way nutrients are cycled, impact the efficiency of carbon drawdown, and cause shifts in food supply to higher trophic levels. Increased CO2 could affect the bioavailability of iron to phytoplankton. Fertilisation experiments show that iron can influence phytoplankton community composition, favouring large phytoplankton species in iron-replete conditions. The potential interactive effects of CO2 and iron bioavailability are currently poorly understood but are likely to be important in determining CO2-induced changes to the microbial community. We employ a qualitative network modelling approach to evaluate alternative hypotheses regarding the effects of elevated CO2 on Antarctic microbial communities in incubation experiments. We used a sequential approach to model development and testing, where we first formulated a base model for microbial community interactions, and then sequentially added direct and indirect effects of elevated CO2 on particular groups. We found that model simulations were most consistent with observations from incubation experiments when we assumed an indirect effect of CO2 on phytoplankton. In particular, when we assumed a negative effect of elevated CO2 on the uptake of iron by large phytoplankton, as a result of a decrease in iron bioavailability. Our findings show that qualitative network models can be used to test hypotheses relating to results from experimental studies, and help identify key processes to target in future studies.

Destination loyalty modeling of the global tourism

This study examines the antecedents of destination loyalty and its relation to destination image, consumer travel experience, and destination satisfaction in the tourism context. This study raises important questions concerning how destination image, consumer travel experience, and destination satisfaction affect destination loyalty. This research attempts to identify three key antecedents of loyalty in the tourism context. The study empirically tests predicted relationships by using personal interview data from 475 foreign tourists. The conceptual model investigates the relevant relationships among the research constructs by using fuzzy-set Qualitative Comparative Analysis (fsQCA) and structural equation modeling (SEM) approach. Findings from the research sample support the argument that destination image, consumer travel experience, destination satisfaction are the key determinants of destination loyalty. Furthermore, destination image and consumer travel experience influence destination satisfaction. The study also discusses theoretical and managerial implications of research findings for marketing the tourism products globally.

The uncertain impact of climate change on forest ecosystems – How qualitative modelling can guide future research for quantitative model development

Climate change could significantly alter forest productivity and climax states. Hence modelling productivity under climate change will need to account for many alternative ecosystem states. We apply qualitative modelling to identify the most likely ecosystem representations for a well-researched Tasmanian forest. Its main ecosystem is a tiered forest with rainforest, wet sclerophyll and myrtaceae components. Interactions between these components are uncertain, especially under additional pressures from climate change. Qualitative modelling is a structured method to analyse these uncertainties. We identify the most appropriate models and research efforts for model development. Further, we identify research needs for interactions between root pathogens and forest components, with research on some impacts of system components on fire being ruled out. The qualitative modelling approach applied here was useful in identifying research priorities for modelling complex ecosystems, even under uncertain system understanding or deficiencies in quantitative data.

Simulating mesoscale coastal evolution for decadal coastal management: A new framework integrating multiple, complementary modelling approaches

Coastal and shoreline management increasingly needs to consider morphological change occurring at decadal to centennial timescales, especially that related to climate change and sea-level rise. This requires the development of morphological models operating at a mesoscale, defined by time and length scales of the order 101 to 102years and 101 to 102km. So-called ‘reduced complexity’ models that represent critical processes at scales not much smaller than the primary scale of interest, and are regulated by capturing the critical feedbacks that govern landform behaviour, are proving effective as a means of exploring emergent coastal behaviour at a landscape scale. Such models tend to be computationally efficient and are thus easily applied within a probabilistic framework. At the same time, reductionist models, built upon a more detailed description of hydrodynamic and sediment transport processes, are capable of application at increasingly broad spatial and temporal scales. More qualitative modelling approaches are also emerging that can guide the development and deployment of quantitative models, and these can be supplemented by varied data-driven modelling approaches that can achieve new explanatory insights from observational datasets. Such disparate approaches have hitherto been pursued largely in isolation by mutually exclusive modelling communities. Brought together, they have the potential to facilitate a step change in our ability to simulate the evolution of coastal morphology at scales that are most relevant to managing erosion and flood risk. Here, we advocate and outline a new integrated modelling framework that deploys coupled mesoscale reduced complexity models, reductionist coastal area models, data-driven approaches, and qualitative conceptual models. Integration of these heterogeneous approaches gives rise to model compositions that can potentially resolve decadal- to centennial-scale behaviour of diverse coupled open coast, estuary and inner shelf settings. This vision is illustrated through an idealised composition of models for a ~70km stretch of the Suffolk coast, eastern England. A key advantage of model linking is that it allows a wide range of real-world situations to be simulated from a small set of model components. However, this process involves more than just the development of software that allows for flexible model coupling. The compatibility of radically different modelling assumptions remains to be carefully assessed and testing as well as evaluating uncertainties of models in composition are areas that require further attention.

A qualitative modelling approach for the representation of trajectories: application to the analysis of flight patterns

Over the past few years, a series of computational and semantic frameworks have been developed to model and represent the spatial and temporal properties of moving entities. Despite the interest of these contributions, it is recognized that there is still a need for a qualitative reasoning support at the abstract and formal levels. The research presented in this paper introduces a qualitative approach for representing and manipulating moving entities. The model combines topological relations with qualitative distances over a spatial and temporal framework. Several basic movement configurations over dynamic entities are identified as well as movement transitions. The whole approach is illustrated in the context of the analysis of flight patterns.

Exploring the population-level impact of MenB vaccination via modeling: Potential for serogroup replacement

Various meningococcal conjugate vaccines exist against serogroups A, C, W and Y. A new protein-based vaccine targeting serogroup B (MenB) is also now available. The potential of such vaccines to drive serogroup replacement is considered a possible public health concern when implementing nationwide routine immunization programmes. The aim of this work was to investigate if and how serogroup replacement may occur following widespread vaccination with a MenB vaccine that may protect against carriage. To that end, we built a dynamic transmission model with age and serogroup stratification, focusing on European settings where most invasive meningococcal disease (IMD) cases are caused by serogroups B and C. For illustration purposes, the model was employed in 2 such settings: UK (England and Wales) and Czech Republic. Preliminary model-based projections suggest that, under strong serogroup competition for colonization, vaccine-induced serogroup replacement may occur even with a relatively low vaccine efficacy against serogroup B carriage (e.g., 20%), with potential subsequent increase in serogroup C IMD. The magnitude and speed of the model-projected serogroup C IMD increase depend on the MenB vaccination strategy, vaccine efficacy against carriage and the extent of any potential cross-protection against other serogroups. These analyses are neither exhaustive nor definitive, and focused on simulating potential population-level trends in IMD post-vaccination, under certain assumptions. Due to present inherent limitations and uncertainties, this study has limited quantitative value and is best regarded as an explorative qualitative modeling approach, to complement and challenge the current status quo, and suggest areas where collecting additional data may be essential.

In silico prediction of cellular permeability of diverse chemicals using qualitative and quantitative SAR modeling approaches

The permeability of the molecules through the cultured Caco-2 cells is an established in vitro method for the assessment of the absorption of oral drugs. The computational approach for predicting the cellular permeability of molecules may potentiate the screening of new drugs. In this study, gradient boosted tree (GBT) approach based qualitative and quantitative structure–activity relationship (SAR) models have been established for binary classification (moderate–poor and highly permeable) and permeability prediction of molecules using the Caco-2 cell dataset. The structural diversity of the chemicals and nonlinear structure in the considered data were tested by the similarity index and Brock–Dechert–Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in QSAR literature. In complete data, the qualitative SAR model rendered classification accuracy of 99.26%, while the quantitative SAR model yielded a correlation (R2) of 0.917 between the measured and predicted permeability values with the mean squared error (MSE) of 0.08. The results suggest for the appropriateness of the developed SAR models to reliably predict the cellular permeability of diverse chemicals in Caco-2 cells and can be useful tools for initial screening of molecules in the drug development process.

Relating food web structure to resilience, keystone status and uncertainty in ecological responses

Food webs often include substructures that occur in much higher numbers than would be expected in random networks. These are referred to in the literature as network motifs. Here we explore how feedbacks within the most common food web motifs influence their responses to external factors such as environmental change or harvesting of species. We have used qualitative modelling approaches to demonstrate that simple three‐ and four‐member motifs that include a closed feedback loop can exhibit a diverse range of qualitative responses, some of which are non‐intuitive. The same outcomes are demonstrated to be emergent behaviors of larger food webs represented in complex ecosystem models. The sensitivity of ecological responses to small differences in food web motifs underlines the broader importance of structural uncertainty in ecological models. It is also argued that the resilience of species and their keystone status may be strongly influenced by their ecological role within particular motifs. Examples are provided from fisheries off southeastern Australian, where the slow recovery rates of some species have not previously been explained.

Learning Boolean logic models of signaling networks with ASP

Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.