arXiv subjects

Apala Chakrabarti

Publications and source records attributed to Apala Chakrabarti.

4 recordsLinked to original sources

arXiv2026

DREAMS: Modelling Support for Research into Engineering and Artistic Design

Design Research Methodology (DRM) supports systematic design research through representations such as Reference Models and Impact Models. However, the practical construction and maintenance of these models often remains manual, requiring repeated redrawing, layout adjustment, and separate handling of assumptions, references, and supporting evidence. This can make DRM modelling time-consuming, visually cluttered, and difficult to revise as models increase in complexity. This paper presents DREAMS, an early-stage prototype modelling environment developed to support the creation and maintenance of DRM Reference Models and Impact Models. The tool enables users to construct typed causal models using DRM-relevant elements, define signed causal relationships, and attach assumptions, experiential inputs, and references directly to causal links. It also provides layout support and search functions to improve readability, modifiability, and retrieval of supporting information. A preliminary comparative evaluation with four DRM users was conducted against manual modelling practice. The results indicate reductions in model creation time, revision time, repositioning effort, edge crossings, and evidence retrieval time when using DREAMS. These findings are interpreted as early evidence of practical potential rather than full validation. The contribution of the paper lies in identifying requirements for DRM-aligned modelling support, presenting the design and implementation of DREAMS, and demonstrating its potential to reduce modelling effort and improve traceability in DRM-based research.

cs.SE↗

arXiv2025

A Framework for Assessing Sustainability Conflicts in the Design of Medical Devices

Medical devices improve healthcare outcomes but often involve sustainability conflicts across environmental, economic, and social pillars. Existing approaches typically prioritize one or two pillars and lack a unified framework to assess cross-domain conflicts. This paper presents a structured framework to identify and quantify sustainability conflicts in medical device design. It integrates life cycle analysis, cause-effect mapping, and multi-criteria decision analysis to evaluate the impact of design choices across all three pillars. A case study of an oxygen concentrator illustrates the framework's application and generates a composite sustainability score based on identified trade-offs. The framework supports informed and data-driven design decisions while meeting regulatory and ethical requirements. This work addresses a key gap in sustainable medical device development by offering a repeatable and quantifiable approach to conflict assessment and resolution.

physics.soc-ph↗

arXiv2025

An AI-Based Framework for Assessing Sustainability Conflicts in Medical Device Development

Designing sustainable medical devices requires balancing environmental, economic, and social demands, yet trade-offs across these pillars are difficult to identify using manual assessment alone. Current methods depend heavily on expert judgment, lack standardisation, and struggle to integrate diverse lifecycle data, which leads to overlooked conflicts and inconsistent evaluations. This paper introduces an AI-driven framework that automates conflict detection. Machine learning and natural language processing are used to extract trade-offs from design decisions, while Multi-Criteria Decision Analysis (MCDA) quantifies their magnitude through a composite sustainability score. The approach improves consistency, reduces subjective bias, and supports early design decisions. The results demonstrate how AI-assisted analysis provides scalable, data-driven support for sustainability evaluation in medical device development.

physics.soc-ph↗

arXiv2025

An Artificial Intelligence Framework for Conflict Mapping and Resolution for Sustainability of Systems

Early design decisions strongly influence environmental, economic and social outcomes, yet sustainability assessment tools rarely reveal trade-offs among these three pillars. This study presents a framework for Conflict Mapping and Resolution for Sustainability of Systems (CONFARM). CONFARM consists of four steps: lifecycle documentation, cause-effect mapping, conflict database construction and multi-criteria scoring. A conflict is recorded when a single decision produces positive and negative effects across pillars. Each effect is evaluated using impact magnitude and pillar weight to generate a sustainability ratio. CONFARM may be applied manually or through automated extraction using natural-language processing and large language models. The method is demonstrated in three sectors representing different data structures and system scales: agriculture (rice and corn), fashion (slow and fast fashion) and energy (nuclear and natural gas). Each system was analysed at increasing conflict densities. Results consistently showed that sustainability scores converged as more conflicts were mapped, indicating stable evaluation across methods. Slow fashion and nuclear systems exhibited relatively higher sustainability performance, while fast fashion and natural gas systems showed lower performance. CONFARM improves early-stage decision support by making trade-offs explicit and enabling comparative evaluation. It offers a structured approach for cleaner production and scalable sustainability assessment across domains.

physics.soc-ph↗