Questions and Answers for 12 May posters.xlsx

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1	QUESTIONS AND ANSWERS for the Case-Study Poster Presentations (both for manual modeliing and AI-assisted modeling)
2			(the posters are posted on the course website: https://kermitcooperation.wixsite.com/platform/case-studies-from-2026)
3	Please avoid asking "yes" or "no" questions if possible. Please write your name at the begining of each question & answer.
4	Topic
5	AI Assisted Modeling for Coastal Wetlands Preservation - Christian Montoro-Paredes
6	Question:	Liana - How can AI-assisted modeling improve decision-making in coastal wetlands preservation?
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8		Answer:	AI-assited modelling improved the decision-making because it provided a wider perspective than the water resources' which is the area of my expertise. Therefore, it provided a good insight of other system factors that I ignored initially, a part from the water resources. It also helped with the classification of the functional facies.
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11	Question:	Rod - Good "system structural analysis. The MCE results do not separate drip and flood irrigation in the table of variations for scenario 1 & 2. As I understtod, the change to drip irrigation was one of the main concerns for the wetlands water balance. Why did you choose to consider changes the TOTAL irrigation as the table suggests?
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13		Answer:	That is true. Good point. Thank you. With that table, I want to show a summary of the criteria to define the different scenarios. In that sense, I want to highlight the increasing relevance of irrigation when precipitation become more scarce due to, for instance, climate change. Then within the scenarios, the utility from irrigation is always higher for the flood irrigation compared with the drip irrigation meaning that flood irrigation provides more recharge to the aquifer so it can compensate better the effect of a precipitation decrease.
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27	Cognitive-Structural Analysis of WordFormationin IT Terminology - Veronika Dmytruk, Anatolii Dmytruk, Beata Kushka
28	Question:	Liana - In your opinion, do the incorporation of professional jargon from IT, HR, and other fields contribute to the development of the Ukrainian language or, conversely, threaten it?
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30		Answer:	A highly relevant issue for linguists: the balance between internationalization and the preservation of national linguistic identity in the context of increasing Anglicization.
31			For IT specialists, the use of Anglicisms is often practically unavoidable.
32			However, for professionals outside highly technical domains, the excessive use of English jargon often reflects cultural imitation rather than genuine communicative necessity. In many cases, Ukrainian already possesses adequate lexical equivalents that preserve semantic transparency and national linguistic identity. Thus, instead of using popular Anglicisms such as
33	Question:	Zoriana - Which English borrowings can be substituted by native Ukrainian equivalents?
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36			Thus, instead of using popular Anglicisms such as deadline, meeting, feedback, or HR, speakers may use Ukrainian equivalents like кінцевий термін, зустріч, відгук, and кадрова служба.
37			Similar concerns apply to the frequent and often unnecessary use of words such as manager, startup, performance, and upgrade, even when understandable native alternatives already exist.
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49	Formation of an Enviornmentally Conscious Mindset in the University Community - Zoriana Dvulit & Liana Maznyk
50	Question:	Rod - How were the indicators of the multicriteria analysis matrix obtained?
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52		Answer:	The indicators of the multicriteria analysis matrix used to evaluate the formation of an environmentally conscious mindset (Y) were obtained through a systematic expert-based approach. 1. Expert Cross-Impact Assessment: The initial data for the matrix was gathered using an Expert Cross-Impact Matrix, where specialists in the field evaluated the "tightness of connection" or influence between the seven key variables (X1–X7). These interactions were rated on a scale ranging from 0 (no connection) to 3 (strong direct connection). 2. Normalization of Weights (Wi): The raw expert scores were then normalized into relative weights (Wi). This process ensures that the inherent structural impact of each variable is objectively derived, with the total sum of influence for each row in the matrix equaling 1.00 (or 100%). 3. Determination of Utility (Ui): The practical effectiveness of these indicators, referred to as Utility (Ui), is considered "elastic". These values were obtained by assessing how each variable performs under different organizational constraints and campus architectures, such as the "Traditional Model" versus the "Integrated Smart Campus". 4. Estimation of Residuals (ϵ): The "friction" or barriers that reduce the effectiveness of the variables (residuals ϵ) were estimated via proxy indicators, such as barrier surveys. These surveys identify specific institutional, behavioral, and infrastructure risks that prevent the variables from achieving their expected impact on the mindset (Y). 5. Mathematical Integration: Finally, the total utility of the system is calculated using the formula ∑(Wi⋅Ui), which combines the objectively derived structural weights with the practical performance indicators to determine the most effective adaptation scenario.
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55	Question:	Marharyta - How did you determine the set of variables for the model?
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57		Answer:	The set of variables for the model was determined through a rigorous systemic modeling approach that maps the university as an integrated ecosystem. The selection process involved the following key steps: 1. Ecosystem Mapping and Sectoral Division. The university environment was analyzed to define exactly where institutional control ends and individual behavior begins. This led to the division of the system into three functional sectors: 1.1. Sector 1 (Institutional). Identified drivers like Administrative Policy (X1) and External Incentives (X6) that set the strategic direction. 1.2. Sector 2 (Specific Methods). Selected operational tools such as Curriculum Integration (X2), Campus Infrastructure (X3), Social Communication (X4), and Student-Led Initiatives (X5) that directly influence the community. 1.3. Sector 3 (Results). Focused on the target outcome, Individual Values and Motivation (Y), and the Monitoring & Feedback (X7) system required for closed-loop adaptation. 2. Defining the Target Outcome (Y). The core objective was identified as shifting "baseline environmental beliefs and readiness for behavioral change," which became the dependent variable Y. 3. Expert Cross-Impact Assessment. To validate these variables, an Expert Cross-Impact Matrix was employed. Specialists evaluated the "tightness of connection" between these factors (e.g., how strongly Policy affects Infrastructure), ensuring the chosen variables had significant structural impact within the model. 4. Identification of Strategic Levers. A Cause vs. Effect analysis was conducted to distinguish between "Drivers" (causes like X1 and X6) and "Outcomes" (effects like X5 and Y), ensuring the model captured the entire chain of influence from leadership to individual mindset. 5. Incorporation of Systemic Friction (ϵ). Residuals were included to account for "unmeasured or hidden factors" and "behavioral barriers" (such as "green fatigue" or information asymmetry) that reduce the effectiveness of the primary variables. This combination of top-down structural analysis and expert-driven impact evaluation resulted in the final set of seven independent variables (X1–X7) and one target variable (Y) that form the functional equation: Y=f(X1,…,X7)+ϵ.
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71	Effectiveness of NBS for the restoration of soil contaminated by explosive remnants of war - Iryna Protsenko, Yevheniya Volchk
72	Question:	Liana - How can Nature-Based Solutions (NBS) contribute to the effective restoration of soils contaminated by explosive remnants of war while ensuring long-term environmental sustainability?
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77	Question:	(Christian)	Your factors and the evaluation are really interesting. I wonder if this evaluation can be applied to any type of contaminant or you assume that is suitable for a particular one, for instance, heavy metals.
78			In a similar way, do you refer to any particular kind of NB solution?
79			I find interesting a MCE evaluation between different sorts of contaminants and NB solutions. Do you think it could be possible?
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83	Question:	Rod - Your utility diagrams help us understand how you have reasoned. A dot for each scenario on the utility curves would make this even easier. The only utility curve that I wonder about it the first one for landscape. Why would a flat landscape be more favorable for NbS than a slope or hill, conisdering that some grandwater movement is usually beneficial for soil processes?
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94	Strategic Pivot Modeling and System Analysis for GreenTwin AI - Pasichnyk Maksym, Vladlena Borysova, Mykhailo Vavrychuk, Anna Zhelykhivska, Anastasiia Yatseiko
95	Question:	Liana - What technical infrastructure and digital tools are required to ensure the effective implementation and scalability of the GreenTwin AI system?
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100	Question:	Zoriana - How sensitive is the AI optimizer to changes in environmental conditions, such as energy costs or climate parameters?