Qualitative Modelling Approach Research Articles

Fishermen Community Participation in Tiku Awareness Tourism Group on Sustainable Tourism Development.

Implementing sustainable tourism development within tourist villages has emerged as a growing trend in the tourism industry, catering to the contemporary needs of tourists and serving as a valuable economic resource. It is also regarded as a gauge of the successful execution of sustainable tourism within local communities, exemplifying their active involvement in harnessing the tourism potential of their regions. This article seeks to chronicle the execution of sustainable tourism development in coastal areas, focusing on village-based tourism due to its profound impact on local economies and the transformations in people's ways of life. To illustrate this, a case study was conducted in South Tiku Village, Tanjung Mutiara District, Agam Regency. The research employed a descriptive qualitative modeling approach and in-depth interviews involving village stakeholders, the Awareness Tourism Group (Kelompok Sadar Wisata), and the South Tiku Tourism Village community. As a result, it became evident that the residents of Tiku Tourist Village have effectively harnessed their village's potential through sustainable tourism development. This is substantiated by the notable increase in income among the South Tiku Tourist Village community, primarily attributable to tourism-related activities. Furthermore, this transformation has seen former fishermen transition into tourism entrepreneurs, reshaping their livelihoods.

Identifying and explaining resilience in ecological networks.

Resilient ecological systems are more likely to persist and function in the Anthropocene. Current methods for estimating an ecosystem's resilience rely on accurately parameterized ecosystem models, which is a significant empirical challenge. In this paper, we adapt tools from biochemical kinetics to identify ecological networks that exhibit 'structural resilience', a strong form of resilience that is solely a property of the network structure and is independent of model parameters. We undertake an exhaustive search for structural resilience across all three-species ecological networks, under a generalized Lotka-Volterra modelling framework. Out of 20,000 possible network structures, approximately 2% display structural resilience. The properties of these networks provide important insights into the mechanisms that could promote resilience in ecosystems, provide new theoretical avenues for qualitative modelling approaches and provide a foundation for identifying robust forms of ecological resilience in large, realistic ecological networks.

Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals. However, traditional diagnostic approaches often overlook the complex spatial dynamics of the atria. This review explores the emerging role of three-dimensional (3D) atrial geometry in the evaluation and management of AF. Advancements in imaging technologies and computational modeling have enabled detailed reconstructions of atrial anatomy, providing insights into the pathophysiology of AF that were previously unattainable. We examine current methodologies for interpreting 3D atrial data, including qualitative, basic quantitative, global quantitative, and statistical shape modeling approaches. We discuss their integration into clinical practice, highlighting potential benefits such as personalized treatment strategies, improved outcome prediction, and informed treatment approaches. Additionally, we discuss the challenges and limitations associated with current approaches, including technical constraints and variable interpretations, and propose future directions for research and clinical applications. This comprehensive review underscores the transformative potential of leveraging 3D atrial geometry in the evaluation and management of AF, advocating for its broader adoption in clinical practice.

A fix that may fail: A qualitative model to explore potential rebound effects of digital textile microfactories

AbstractFast fashion principles and mass production within globalised supply chains are pivotal in driving the economic success of the fashion industry, but they also contribute significantly to its severe environmental impacts, which must be mitigated. Digital textile microfactories are innovative small‐scale fully digitalised design and manufacturing systems that present a promising technological solution. These offer the companies that adopt them environmental and economic advantages. However, their widespread adoption in the industry may have unintended consequences. This study hypothesises a rebound effect within the industry and proposes a qualitative modelling approach based on causal loop diagrams. We present a model for identifying rebound mechanisms that may accelerate purchases, along with a mitigation strategy emphasising the consideration of customer behaviour. The contribution of this paper is twofold: Firstly, it provides a starting point for detailed quantitative studies, and secondly, it offers insights for policy‐making towards a sustainable transition in the industry.

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

Modelling the behaviour in socio-technical systems for policy assessment - A comparison of modelling approaches using the example of Sponge City concept implementation

How are policies affecting the social behaviour of actors and thus the dynamics of the corresponding socio-technical systems? This question is of central relevance to understand what policies might be effective for sustainable transitions and transformation where social changes are immanent. Exploratory modelling approaches such as agent-based modelling (ABM) are suitable and established to analyse how individual, especially heterogeneous behaviour emerges to affect social systems. In contrast to this micro-level perspective of understanding how social behaviour influences system behaviour, System Dynamics models (SDM) focus on the macro-level perspective by analysing how system interactions drive complex system behaviour. Qualitative modelling approaches such as Causal Loop Diagrams belonging to the Systems Thinking Modelling (STM) toolbox remain descriptive but need much less effort for model development. In this article, we compare how the different approaches ABM, SDM and STM can model social behaviour in socio-technical systems and what different kind of insights can be gained to assess the efficacy of policies. It discusses the advantages and limitations of the different approaches for their application in behavioural public policy modelling for sustainability transition. Therefore, three exemplary models (STM, SDM, ABM) are created for the same topic of implementation dynamics of adaptation measures to foster rainwater retention (Sponge City concept) in a multi-residential quarter. As a result, the risk of green gentrification is identified. Three hypothetical policies are implemented to illustrate the differences between the three modelling approaches for their use in policy assessment. Based on that, we discuss how the three modelling approaches conceptualise behaviour in general.

Addressing uncertainty in Participatory Integrated Assessment: qualitative modeling approach for risk estimation

Participatory Integrated Assessment (PIA) has become a vital tool for decision-making for sustainable development, but it faces significant challenges due to the inherent uncertainty of socio-ecological systems. Uncertainty arises from multiple sources, such as incomplete data, knowledge gaps, and unpredictable events, which can lead to inadequate risk estimations and potentially undermine the effectiveness of environmental planning efforts. To address these challenges, this study proposes a qualitative modeling approach for risk estimation in PIA. The approach employs Decision Making under Deep Uncertainty (DMDU) to combine qualitative insights and information from stakeholders with available quantitative data. It allows for the exploration of alternative future states of the world and the identification of robust scenarios that promote sustainable development. The effectiveness of the proposed approach is demonstrated through the Ecological Ordinance of Yucatán, Mexico, a policy-making tool for multi-sectoral environmental planning. The study shows how qualitative DMDU can identify critical uncertainties and provide insights into regional management strategies. It also emphasizes the importance of stakeholder engagement and transparency in the decision-making process. Overall, this study presents a promising approach for addressing multiple forms of uncertainty in PIA and improving ecological risk estimation for decision-making in complex socio-ecological systems.

Macro impacts of plastic pollution in Ghana

Abstract In recent decades, the proliferation of single-use plastic products has significantly contributed to a surge in plastic pollution on a global scale. Researchers have extensively investigated the impacts of plastic pollution across various regions, yet a comprehensive holistic and location-based understanding of these impacts in the West African context is lacking. This study addresses this gap through a systemic assessment of the impacts of plastic pollution, in West Africa, particularly Ghana. Employing a qualitative system dynamics causal modelling approach, this study establishes linkages between pollution effects at the macro level, constructing a hierarchical outline of both high- and low-level impacts. The significance of this research lies in the application of pertinent systems thinking techniques to comprehend the broader impacts of plastic pollution. The outcomes of this study will inform the development of effective policies aimed at preventing or mitigating plastic pollution in Ghana, and potentially the wider West African context.

Retracted: Impact of Social Media Usage on Civic Engagement towards Societal Problems: Qualitative Modelling Approach

Retracted: Impact of Social Media Usage on Civic Engagement towards Societal Problems: Qualitative Modelling Approach

On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models.

Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential equations were built. These models were relevant for studying dynamical phenomena and still are. However, they face important limitations in data-poor situations. As a response, several formal and non-formal qualitative dynamical modeling approaches were independently developed to overcome some limitations of the existing methods. Qualitative approaches allow studying qualitative dynamics as relevant abstractions of those provided by quantitative models (e.g., response to press perturbations). Each modeling framework can be viewed as a different assemblage of properties (e.g., determinism, stochasticity or synchronous update of variable values) designed to satisfy some scientific objectives. Based on four stated objectives commonly found in complex environmental sciences ((1) grasping qualitative dynamics, (2) making as few assumptions as possible about parameter values, (3) being explanatory and (4) being predictive), our objectives were guided by the wish to model complex and multidisciplinary issues commonly found in ecosystem modeling. We then discussed the relevance of existing modeling approaches and proposed the ecological discrete-event networks (EDEN) modeling framework for this purpose. The EDEN models propose a qualitative, discrete-event, partially synchronous and possibilistic view of ecosystem dynamics. We discussed each of these properties through ecological examples and existing analysis techniques for such models and showed how relevant they are for environmental science studies.

Sustainable Bioavtur: A System Dynamics Approach to Addressing the Aviation Industry’s Environmental Impact

The global energy crisis, predominantly affecting fossil fuel sources, poses significant challenges to numerous sectors worldwide, leading to escalating fuel prices and rising costs of living and production. The aviation industry, in particular, faces severe repercussions as domestic avtur production fails to meet the soaring demand, jeopardizing economic profitability and energy security. To tackle these issues, this study employs a qualitative modeling approach using system dynamics to investigate the interconnections among social, economic, technological, and environmental components within the biofuels development system. The research highlights the urgency of implementing several measures, including land conversion restrictions, regulation of palm oil trade schemes, and technological advancements in bioavtur production in Indonesia, to mitigate potential adverse consequences. The findings underscore the aviation industry’s contribution to greenhouse gas emissions and emphasize the need for sustainable biovehicles. This study provides valuable insights for policymakers, scientists, and professionals in the agricultural environment, ecology, and resources field, offering strategies to address the aviation sector’s environmental impact while ensuring sustainable energy alternatives.

Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware.

Spiking neuron models simulate neuronal activities and allow us to analyze and reproduce the information processing of the nervous system. However, ionic-conductance models, which can faithfully reproduce neuronal activities, require a huge computational cost, while integral-firing models, which are computationally inexpensive, have some difficulties in reproducing neuronal activities. Here we propose a Piecewise Quadratic Neuron (PQN) model based on a qualitative modeling approach that aims to reproduce only the key dynamics behind neuronal activities. We demonstrate that PQN models can accurately reproduce the responses of ionic-conductance models of major neuronal classes to stimulus inputs of various magnitudes. In addition, the PQN model is designed to support the efficient implementation on digital arithmetic circuits for use as silicon neurons, and we confirm that the PQN model consumes much fewer circuit resources than the ionic-conductance models. This model intends to serve as a tool for building a large-scale closer-to-biology spiking neural network.

Resolving ecosystem complexity in ecological risk assessment for mine site rehabilitation.

Ecological Risk Assessments (ERAs) are important tools for supporting evidence-based decision making. However, most ERA frameworks rarely consider complex ecological feedbacks, which limit their capacity to evaluate risks at community and ecosystem levels of organisation. We used qualitative mathematical modelling to add additional perspectives to previously conducted ERAs for the rehabilitation of the Ranger uranium mine (Northern Territory, Australia) and support an assessment of the cumulative risks from the mine site. Using expert elicitation workshops, separate qualitative models and scenarios were developed for aquatic and terrestrial systems. The models developed in the workshops were used to construct Bayes Nets that predicted whole-of-ecosystem outcomes after components were perturbed. The terrestrial model considered the effect of fire and weeds on established native vegetation that will be important for the successful rehabilitation of Ranger. It predicted that a combined intervention that suppresses both weeds and fire intensity gave similar response predictions as for weed control alone, except for lower levels of certainty to tall grasses and fire intensity in models with immature trees or tall grasses. However, this had ambiguous predictions for short grasses and forbs, and tall grasses in models representing mature vegetation. The aquatic model considered the effects of magnesium (Mg), a key solute in current and predicted mine runoff and groundwater egress, which is known to adversely affect many aquatic species. The aquatic models provided support that attached algae and phytoplankton assemblages are the key trophic base for food webs. It predicted that shifts in phytoplankton abundance arising from increase in Mg to receiving waters, may result in cascading effects through the food-chain. The qualitative modelling approach was flexible and capable of modelling both gradual (i.e. decadal) processes in the mine-site restoration and the comparatively more rapid (seasonal) processes of the aquatic ecosystem. The modelling also provides a useful decision tool for identifying important ecosystem sub-systems for further research efforts.

Improved forecasting of coffee leaf rust by qualitative modeling: Design and expert validation of the ExpeRoya model

CONTEXTCoffee leaf rust (CLR) epidemics on Coffea arabica have led to severe socio-economic crises in Latin America starting in 2008. Until now, the scattered nature of scientific and empirical knowledge of the highly complex CLR-coffee pathosystem has been an obstacle to the development of CLR forecasting models. OBJECTIVETo help prevent new severe epidemics, we built ExpeRoya, a qualitative model, based on a review of the scientific literature and expert opinion, to forecast the risk of a monthly increase in the incidence of CLR at plot and landscape levels. METHODSWe adopted the IPSIM (Injury Profile SIMulator) framework, a qualitative and aggregative modeling approach that describes the effects of the cropping system and the plot environment on injuries, thereby making it possible to incorporate scattered knowledge on the system and all its complexity in a simplified way. Involving experts makes this approach powerful and robust because it builds on empirical knowledge based on a very large number of field observations. We argue that broad expert knowledge provides more accurate information on the manifold interactions in the system than existing quantitative models can. The structure of ExpeRoya was discussed with coffee sector experts in 19 workshops and validated in an online survey with 17 CLR experts. RESULTS AND CONCLUSIONSExpeRoya successfully integrates in a simple way 229 multiple interactions that exist within the CLR-coffee pathosystem based on only 12 input variables easily acquired in the field: one incidence monitoring variable; two meteorological variables (temperature and rainfall), four crop management variables (management of shade cover, fungicide application, nutrition and pruning of coffee trees) and five coffee tree characteristics (dates of flowering, beginning and end of harvest, fruit load and cultivar genetic resistance). Coffee institutes in Honduras and Nicaragua now use ExpeRoya, hosted by the platform Pergamino (https://www.redpergamino.net/app-experoya), to assist them in preparing their monthly CLR warning bulletins for growers. ExpeRoya is an improved forecasting model of CLR by fully incorporating the main biophysical factors affecting CLR at the plot and landscape levels. SIGNIFICANCEExpeRoya is both a framework and a proof of concept that improves both forecasting and the comprehensive modeling of CLR. ExpeRoya is a powerful yet user-friendly model designed for all actors of the coffee sector, particularly smallholder farmers and extension agents. ExpeRoya is adaptable: users can modify the model according to advances in knowledge and/or their own expertise of the system. ExpeRoya can help prevent future socio-economic crises.

Retracted] Impact of Social Media Usage on Civic Engagement towards Societal Problems: Qualitative Modelling Approach

Civic engagement participation in solving the social problems gained momentum among social media users. It is probed via literature survey that many research studies conducted in understanding the civic behaviour and personality development; therefore, research documents were found still limited in the area of factors and their constructive factors (hypotheses) identification towards intensifying the online civic engagement for solving and reforming the society. Therefore, the purpose of the research is directed to build a qualitative research model of crucial factors by using the social capital theory, developing constructive factors (hypotheses) based on the research model, and testing constructive factors, which could intensify the civic engagement of online social media user’s (Facebook, Twitter, etc.) towards solving the social problems. To fulfil aforesaid agendas, the authors employed the qualitative survey method, which consisted of a simulation of an online survey against H1–H8 hypotheses of the research model. The survey data was collected by exploring the Google Form under the five‐point Likert scale as a data collection tool. Participated students were prioritized from a metro city of India on a purposive mode. Collected usable samples of 341 students were considered for descriptive analysis such as reliability, validity, content, and construct of factors of the research model. Next, confirmatory factor analysis was performed to measure of fitness of factors of the research model. Finally, structural equation modelling (path analysis) carried out to assess the constructive factors (hypotheses) of research model. The summary of the results stated that usage of social media, trust, civic skills, and social responsibility are found as global significant factors, which affected an individual’s civic engagement in solving the social problems. This study aids the practitioners and academicians to audit and evaluate the advanced factors and their constructive factors in future, which affect the young social media users’ civic engagement in solving the social problems.

Risk assessment approach to regulatory compliance challenges in aging facilities: A case study of Harlypet Oil and Gas Nigeria Limited Facilities


 
 
 Compliance with new regulations in old plants remains a recurring challenge because of negative outcome of incidents. This challenge stems from uncertainties in the facilities’ integrity, owing to inadequacy of existing integrity-validating technologies. Process facilities deteriorate through cyclic operations, while encroachments from expanding population characteristically raise the risk-levels, leading to need for higher grade materials to meet operational expansions. Retroactive compliance becomes a nightmare with every new regulation without a robust cost-to-benefit assurance. This paper discusses two-phased qualitative and quantitative risk modelling approach through systematic field-data-gathering, hazards identification and analysis by a twelve-man risk management engineers. The methodology successfully computed a “health-check" of the facility’s compliance to new regulations, 17 high-risk-hazards were extracted from 42 potential hazards and successfully established varied individual risk levels ranging from 4.07E-06 to 1.64E-04/year. Also, risks ranged from 1.00E-04 to 5.00E-05/year of tolerable risks to the environment, society, and business were recorded across the facility while, 22 risk-mitigation actions were recommended.
 
 

Trophic mediation and ecosystem stability: An assessment using qualitative network models

AbstractNontrophic interactions can contribute to negative and positive feedbacks within a community, thus affecting likelihood of regime shifts; however, assessing the nature and importance of these effects in a network remains challenging, especially for pelagic ecosystems. Here, we present a qualitative modeling approach for assessing the importance of different effects and resultant feedbacks for community stability, using a Southern Ocean example. A potentially important positive feedback in the Southern Ocean ecosystem involves production of a chemical cue, dimethyl sulfide (DMS), by some phytoplankton. Production of DMS can promote phytoplankton growth by attracting predators of phytoplankton‐grazers, and nutrients released as feces from those predators help fertilize the water column. We explored how uncertainties in the nature of this feedback affect community stability in a set of small, community models. We found that stability varied substantially depending on how the community was modeled, but that the interactions most important for determining stability were consistent across all models. Model stability was sensitive to the strength of phytoplankton competition, controls on phytoplankton, DMS production and release, and predator attraction to DMS, suggesting that the community could be destabilized by perturbation affecting these interactions. Incorporating DMS‐mediated feedbacks into a larger Southern Ocean network had a moderate impact on stability characteristics and altered the trophic level at which the system would be most vulnerable to perturbation.

Linking Fishing Behavior and Ecosystem Dynamics Using Social and Ecological Network Models

One goal of ecosystem-based management is studying an ecosystem and its people, the socio-ecological system, in a qualitative and quantitative modeling approach that can provide management agencies with possible outcomes of their actions using scenario forecasting. Ecosystem-based fisheries management strives to use the socio-ecological system approach, including direct and indirect impacts on multiple species including the behavioral responses of fishers after a regulatory change (a gillnet ban). Here, we link fisher behavioral networks with a mass-balanced food-web ECOPATH network model of an estuarine ecosystem and its commercial fisheries for an analysis of fishing impacts after a gillnet ban on multiple species using ECOSIM. We modeled fisher behavioral networks using reported catches of species from individual fishers along with the gear fished to create nodes in a gear/species affiliation network. Individual fishers with common gear/species use are indicative of common fishing behavior. When such fishers have high network centrality and are engaged in multiple gear/species fisheries, they can transition to other gear/species fisheries along “switching pathways” when facing a regulatory change. We used an index of joint gear participation to identify likely gear switching pathways, and we predicted changes in fishing effort after a gill net ban. We simulated the gill net ban in ECOSIM under two scenarios of fishing effort: Scenario 1, gill net fishing effort of 0%; Scenario 2, gill net fishing effort of 0% with increased effort in the alternative gear fisheries using the predicted switching pathways for the affiliation network. Scenario 1 predicted an increase in flounder (Paralichthys spp.) biomass over a decade. Under Scenario 2, fishers targeting flounders were predicted to switch from gill nets to pound nets. Scenario 2 predicted a 7% decline in flounder biomass over ten years, rather than an increase in flounders. The gillnet ban with increased effort due to switching is predicted to have the opposite effect on the conservation goal, which was to increase flounder stocks. Fishery management that incorporates a socio-ecological approach modeling both fisher behaviors and multi-species ecosystem responses can reveal single-species responses that are in the opposite direction of the anticipated management goals.

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits.

Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

Predicting the ecosystem-wide impacts of eradication with limited information using a qualitative modelling approach.

Conservation-motivated eradications may cause unexpected perverse effects, and these undesirable consequences can be difficult to predict due to the paucity of information on species interactions. A probabilistic qualitative approach, which does not require extensive model parameterization, is becoming increasingly accepted and applied to conservation scenarios when information is limited. However, recent work has criticized this approach on philosophical grounds and proposed an alternative non-probabilistic Boolean analysis method, which circumvents the philosophical difficulties. There is a need for exploring the ability of this novel approach for informing conservation decisions. To do so, we applied the first real-world test of the non-probabilistic Boolean approach using a case study of management of Felis catus (feral cat) and Rattus rattus (black rat) on Christmas Island. We also applied the probabilistic approach as a contrast. Our modeling results showed that the probabilistic approach generated ambiguous outcomes, making it impractical to draw management recommendations. In contrast, the non-probabilistic Boolean approach revealed interpretable rules governing species responses, suggesting that while cat management alone is a risky strategy, the risk of negative effects of cat management on native species can be reduced by the addition of rat management. Thus, given limited resources, in combination with cat management it is prudent to prioritize rat management efforts in the habitats of potentially impacted native species of high concern and value. We conclude that the Boolean approach can be very useful when little information is available to model an ecological system and that it provides a way of identifying the potential risks and benefits of management strategies, enabling better informed conservation decision-making in the face of limited knowledge.