Multidisciplinary Engineering Science Open

This review aims to synthesize the evolution of multi-objective optimization frameworks that embed sustainability constraints, tracing the conceptual and methodological transition from Pareto-front optimization toward boundary-aware design paradigms aligned with planetary sustainability limits. This qualitative systematic review employed a structured literature-based design focusing on peer-reviewed studies published between 2013 and 2025 across engineering, optimization, and sustainability domains. Fourteen eligible articles were selected through database searches in Scopus, Web of Science, and ScienceDirect, using inclusion criteria centered on multi-objective design incorporating environmental, economic, and social sustainability dimensions. Data collection was limited to document analysis, and data analysis followed qualitative thematic synthesis using NVivo 14 software. Open, axial, and selective coding were applied to extract conceptual patterns from the literature. The coding process continued until theoretical saturation was reached, yielding four overarching themes: evolution of sustainability-constrained optimization, modeling of sustainability constraints, computational and analytical methodologies, and sustainability assessment within planetary boundaries. Results indicate that sustainability-constrained multi-objective optimization is transforming engineering design by embedding life-cycle, ecological, and socio-economic dimensions into the optimization process. Studies increasingly integrate environmental thresholds and planetary boundary indicators as explicit constraints rather than post-analysis metrics. Computational advances, including surrogate modeling, hybrid multi-fidelity frameworks, and AI-assisted Pareto analysis, enable tractable exploration of complex sustainability trade-offs. Furthermore, the alignment of optimization outcomes with planetary boundary frameworks introduces a normative anchor for absolute sustainability assessment. However, challenges persist regarding data uncertainty, inter-scale consistency, and the translation of global ecological limits into local design decisions. The synthesis underscores a paradigm shift from efficiency-oriented optimization to ecologically bounded design, where feasible solutions are defined by the biosphere’s limits. Integrating planetary boundaries within multi-objective frameworks offers a transformative pathway for reconciling engineering innovation with global sustainability imperatives.

This review aims to synthesize methodological and conceptual advances in surrogate-assisted global optimization (SAGO) for computationally expensive engineering systems, highlighting the evolution from deterministic trust-region frameworks to probabilistic Bayesian optimization approaches. A qualitative systematic review design was employed using content analysis of peer-reviewed literature. Twenty articles published between 2010 and 2025 were selected through purposive sampling after comprehensive database searches in Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Only studies addressing surrogate-assisted strategies for expensive or multi-fidelity optimization were included. Data collection relied exclusively on literature review, and theoretical saturation was achieved after analyzing 20 studies. The qualitative coding and thematic synthesis were conducted using Nvivo 14 software, following open, axial, and selective coding procedures to extract major conceptual themes related to surrogate frameworks, optimization strategies, and robustness mechanisms. Three overarching themes emerged: (1) Evolution of Surrogate Modeling Frameworks—the transition from polynomial and RBF surrogates to probabilistic Kriging, multi-fidelity, and deep learning-based surrogates such as physics-informed neural networks; (2) Global Optimization Strategies and Trust-Region Adaptation—the convergence of deterministic trust-region methods with Bayesian acquisition-based algorithms that integrate uncertainty-aware exploration and exploitation; and (3) Robustness, Generalization, and Application Integration—the expansion of surrogate-assisted methods into real-world workflows emphasizing uncertainty quantification, transfer learning, and digital twin integration. Together, these themes reveal a paradigm shift toward scalable, adaptive, and hybrid optimization systems that unify physics-based modeling with data-driven intelligence. Surrogate-assisted optimization has evolved from local curve-fitting into a data-efficient, uncertainty-aware framework fundamental to modern engineering design. The field now converges toward hybrid, physics-informed, and AI-integrated paradigms that enable robust, automated decision-making in computationally intensive environments.

This study aimed to synthesize and critically evaluate the role of differentiable physics frameworks in advancing multidisciplinary design optimization (MDO), focusing on implicit gradient computation, adjoint-based sensitivity analysis, and robustness of optimization performance under distributional shifts. A qualitative review design was adopted to examine sixteen peer-reviewed articles published between 2015 and 2025 that addressed differentiable physics, adjoint methods, and robust optimization in MDO contexts. Data collection relied exclusively on systematic literature review procedures across Scopus, Web of Science, IEEE Xplore, and ScienceDirect databases. Studies were selected through purposive sampling until theoretical saturation was achieved. Data were analyzed thematically using Nvivo 14 software through open, axial, and selective coding stages. Emergent concepts were organized into four major themes: differentiable physics foundations, adjoint-based optimization, robustness under distribution shift, and future integration challenges. The synthesis revealed that implicit differentiation and adjoint-based gradient computation form the computational backbone of differentiable physics in MDO, enabling scalable and memory-efficient sensitivity analysis across coupled physical domains. However, computational efficiency, gradient stability, and numerical conditioning remain significant challenges that limit generalization across problem types. The findings also indicate that while differentiable frameworks have achieved theoretical maturity, their robustness under distributional shift—such as environmental or boundary condition changes—remains underexplored. Integration with uncertainty quantification, Bayesian robustness, and domain adaptation is emerging as a promising solution. Additionally, the analysis underscored the lack of standardized benchmarks and reproducibility protocols, which constrains cross-study validation. Differentiable physics represents a paradigm shift in engineering optimization by bridging first-principles simulation and gradient-based learning. Yet, realizing its full potential requires methodological advancements in implicit solvers, cross-domain adjoint coupling, and robustness-aware design. Future work should emphasize scalable algorithms, reproducible benchmarking, and integration with real-world uncertainty modeling to foster reliable and interpretable differentiable MDO systems.

The objective of this review is to synthesize current advances in physics-informed neural network architectures for medical imaging reconstruction, evaluate trustworthiness and interpretability considerations, and examine pathways toward clinical translation. A qualitative literature review was conducted on 16 selected high-impact articles focused on AI-assisted image reconstruction, physics-informed neural networks, and trustworthiness in medical imaging. Articles were sourced from peer-reviewed journals, screened for relevance, and analyzed using NVivo 14 software. Thematic coding was applied to identify key concepts, subthemes, and overarching categories until theoretical saturation was reached, enabling a systematic synthesis of architectures, trust metrics, and translational considerations. Five major themes emerged: (1) physics-informed neural networks enhance reconstruction fidelity and generalizability by integrating physical priors and forward models; (2) hybrid deep learning architectures combining physics and data-driven components demonstrate superior performance in undersampled or noisy imaging; (3) trustworthiness features—interpretability, uncertainty quantification, robustness, fairness, and human-in-the-loop mechanisms—are critical for clinical adoption; (4) translation to clinical practice remains limited, with few studies addressing multicenter validation, workflow integration, regulatory compliance, and safety; and (5) future research directions include federated and privacy-preserving learning, physics–AI co-design, standardized benchmarking, and improved human–AI interaction. The synthesis indicates that while technical innovations are promising, systemic challenges in trust, usability, and regulatory readiness persist. Physics-informed neural networks represent a significant advancement in AI-assisted medical imaging reconstruction, offering improved fidelity and interpretability. However, adoption in clinical settings requires concerted efforts to embed trustworthiness, validate across diverse datasets, align with regulatory standards, and integrate with clinical workflows. The review provides a roadmap for researchers, clinicians, and regulators to navigate the integration of physics-informed AI reconstruction into practice safely and effectively.

This review aims to synthesize current advancements in organs-on-chips and microphysiological systems, focusing on their applications in disease modeling, analytical readouts, and regulatory and translational adoption. A qualitative literature review was conducted on 19 peer-reviewed articles selected through purposive sampling from Scopus, PubMed, and Web of Science. The review included studies addressing organ-on-chip design, disease modeling, biosensor integration, multi-organ systems, and regulatory considerations. Data were analyzed using thematic synthesis in NVivo 14, employing open, axial, and selective coding to identify key concepts and themes, with theoretical saturation achieved after analysis of the 17th article. The analysis identified four main themes. First, technological foundations highlighted microfabrication, biomaterials, dynamic perfusion, co-culture systems, and integrated biosensors as essential for replicating organ-level physiology. Second, disease modeling and therapeutic testing demonstrated that OoCs accurately recapitulate organ-specific pathophysiology, support multi-organ crosstalk, enable predictive drug screening, and facilitate personalized medicine through patient-derived cells. Third, analytical readouts and computational integration emphasized the role of multi-parametric sensors, omics profiling, and AI-driven computational modeling in enhancing mechanistic understanding, reproducibility, and predictive capability. Fourth, regulatory and translational dimensions showed growing acceptance by agencies such as the FDA and EMA, the necessity for standardization and validation, ethical considerations in cell sourcing, and increasing industrial adoption, although challenges remain in scalability, cost, and harmonization of protocols. Organs-on-chips and microphysiological systems represent a transformative approach in biomedical research, offering human-relevant models that enhance disease understanding, therapeutic evaluation, and regulatory assessment. While technological and translational challenges persist, these platforms provide a predictive, ethical, and scalable alternative to conventional preclinical models, supporting the advancement of personalized medicine and drug development.

This review aims to synthesize current evidence on wearable and implantable bioelectronic systems designed for closed-loop therapeutics, highlighting their architectures, interface designs, adaptive control mechanisms, and translational considerations. A qualitative literature review was conducted using 18 peer-reviewed studies selected from Scopus, PubMed, Web of Science, and IEEE Xplore, covering the period 2016–2025. Articles were included if they addressed wearable or implantable bioelectronics for adaptive therapeutic applications. Data were analyzed through thematic synthesis using Nvivo 14, with open, axial, and selective coding to identify main themes, subthemes, and key concepts. Theoretical saturation was reached at the 18th article, ensuring comprehensive coverage of technological, clinical, and ethical dimensions. Four major themes emerged: (1) smart bioelectronic architectures, including flexible, stretchable, and biocompatible materials integrated with miniaturized circuits and modular designs; (2) wearable and implantable interface engineering, featuring skin-integrated electronics, neural and muscular implants, biofluidic integration, and wireless communication networks; (3) closed-loop therapeutic mechanisms, encompassing biosignal acquisition, adaptive feedback algorithms, multimodal data fusion, and patient-specific actuation strategies; and (4) translational, ethical, and regulatory considerations, addressing clinical validation, data privacy, algorithmic transparency, accessibility, and sustainability. Collectively, these findings demonstrate that personalized bioelectronics enable real-time monitoring, autonomous adaptation, and individualized therapeutic interventions, representing a shift from conventional open-loop devices to intelligent, patient-centered healthcare systems. Personalized bioelectronics for closed-loop therapeutics represent a transformative frontier in healthcare, integrating advanced materials, adaptive control systems, and ethical governance to provide dynamic, patient-specific interventions. These systems have the potential to improve clinical outcomes, enhance patient quality of life, and support the development of sustainable, responsive healthcare ecosystems.

This review aims to critically synthesize the life-cycle assessment (LCA) literature on negative-emissions technologies (NETs) to evaluate how system boundaries, co-benefits, and trade-offs have been operationalized across diverse pathways. A qualitative literature review was conducted using fifteen peer-reviewed studies selected from major scientific databases, including Scopus and Web of Science. The analysis focused exclusively on LCAs of NETs, employing theoretical saturation to ensure conceptual completeness. Data were extracted and coded using NVivo 14 software, with open, axial, and selective coding applied to identify key themes related to system boundary definition, environmental and socioeconomic co-benefits, trade-offs, and comparative assessment across NETs such as bioenergy with carbon capture and storage (BECCS), direct air capture (DAC), enhanced weathering, and biochar systems. The review revealed significant methodological heterogeneity in LCA of NETs, particularly in system boundary selection, functional units, temporal treatment of carbon storage, and inclusion of indirect effects. Co-benefits such as improved soil fertility, biodiversity enhancement, and air quality improvement were often reported alongside trade-offs including land-use competition, water demand, and energy intensity. Comparative analyses across NET pathways indicated that technology-specific impacts vary substantially, with hybrid and integrated systems offering potential synergies but remaining underrepresented in existing studies. Thematic synthesis highlighted the need for transparent boundary definition, inclusion of socioeconomic dimensions, and sensitivity analyses to improve credibility and comparability. NET LCAs exhibit substantial variability and uncertainty, yet provide critical insights into environmental trade-offs and co-benefits. Standardized methodological frameworks, transparent reporting, and integration of social and ecological impacts are essential to guide policy and technology deployment decisions. Harmonized approaches will facilitate robust comparisons, inform climate mitigation strategies, and support sustainable scaling of NETs to achieve net-negative emissions targets.

This review aims to synthesize qualitative evidence on the design, environmental performance, and socio-institutional dimensions of sponge cities and blue-green infrastructures to evaluate their effectiveness in urban water management. A qualitative literature review was conducted, focusing on sixteen peer-reviewed studies published between 2015 and 2025. Articles were selected based on relevance to sponge city and blue-green infrastructure implementation, ecological and hydrological performance, and socio-economic and governance aspects. Data were analyzed using NVivo 14 software following thematic content analysis, with open, axial, and selective coding applied to identify key themes, subthemes, and concepts. Theoretical saturation was achieved after analyzing all sixteen articles, ensuring comprehensive coverage of design principles, environmental performance, and institutional dimensions. Analysis revealed three main themes. First, design and planning principles emphasize permeable surfaces, wetlands, bio-swales, distributed drainage, and multifunctional land use, which collectively enhance urban resilience and water retention. Second, environmental and hydrological performance demonstrates significant reductions in runoff, flood peaks, and pollutant loads, alongside improvements in water quality, biodiversity, and microclimate regulation. Third, socio-economic and institutional factors—including public participation, policy coordination, financing mechanisms, and governance frameworks—substantially influence the adoption, maintenance, and long-term sustainability of nature-based systems. The studies indicate that while ecological and hydrological benefits are robust, performance outcomes vary across climatic, spatial, and socio-political contexts, and co-benefits such as public health, recreational value, and aesthetic enhancement remain under-measured. Sponge cities and blue-green infrastructures represent effective and multifunctional approaches for urban water management, offering hydrological, ecological, and social benefits. Their successful implementation requires integrated planning, participatory governance, and context-sensitive adaptation strategies. Future research should focus on long-term performance monitoring, quantitative evaluation of co-benefits, and mechanisms for scaling nature-based solutions in diverse urban environments.