Search Results (36)

This study addresses the inverse design of carbon fibre-reinforced polymer laminated plates with prescribed natural frequencies. The problem is difficult because stacking sequences are discrete, the design space is large, and multiple layups may produce nearly identical frequency spectra. This study does not seek to introduce a new tandem-network architecture. Rather, it adapts the established tandem inverse-design strategy to the discrete and non-unique vibration design of carbon fibre-reinforced polymer laminated plates. In the proposed framework, a trainable inverse network is coupled to a pre-trained forward frequency surrogate, allowing the inverse model to be optimised through frequency reconstruction instead of direct ply-angle supervision. A dataset of 50,000 symmetric CFRP laminates is generated using Classical Laminate Theory and a Rayleigh–Ritz vibration solver, covering four boundary conditions and a range of plate geometries. The forward model achieves R² values above 0.99 and mean absolute percentage errors below 3% for the first five natural frequencies. Compared with a genetic algorithm, the proposed inverse model provides stacking sequences about 7000 times faster while producing multiple feasible designs for each target. Permutation sensitivity analysis shows that plate geometry has the strongest influence on the frequency response, followed by boundary condition and ply orientation. Four engineering cases confirm the method’s usefulness for vibration isolation, frequency-gap control, and multi-mode frequency prescription. The principal contribution is the integration of multi-boundary-condition vibration modelling, discrete stacking-sequence inverse design, response-based treatment of non-uniqueness, speed/diversity benchmarking, and sensitivity-based physical interpretation within a single composite-laminate design framework. Full article

Healthy building concepts are increasingly recognized as important for improving occupant health and well-being, yet empirical evidence on their understanding and implementation in sub-Saharan African contexts remains limited. This study provides an exploratory assessment of construction professionals’ awareness and self-reported application of healthy building concepts in Gabon. Using a structured questionnaire survey of 45 construction professionals, including architects, engineers, and contractors, the study examines sources of awareness, patterns of application across project stages, and health-related dimensions prioritized in practice. The results indicate high levels of conceptual awareness within the surveyed group, but uneven and context-dependent application. Implementation is strongly concentrated at the design stage, while continuity during construction and operation remains limited. Professionals tend to prioritize tangible and measurable dimensions such as lighting, materials, air quality, and thermal comfort, whereas psychosocial and community-related aspects receive less attention. Based on these empirical patterns, the study proposes an empirically informed and context-sensitive framework structured around six strategic pillars to support the gradual integration of healthy construction practices in Gabon. Rather than offering a prescriptive model, the framework serves as an analytical reference to inform future research, professional capacity building, and policy dialog. Given the exploratory nature of the study and its reliance on self-reported data, the findings should be interpreted as indicative rather than generalizable. Full article

Building on Gutman’s (1987) argument that architectural practice should reflect the nature of the problem, this article explores four eras of architectural practice: the Patronage Model, the Clientage Model, the Transitional Models, and Future Models. Each era is examined in relation to six “Questions of Praxis”: (1) What is the nature of the problem?, (2) What is the nature of the intervention?, (3) What knowledge is valued?, (4) What is the stance toward the problem?, (5) What is the continuity in the relationship?, and (6) What is the prioritization of professional obligations? Through a comparative analysis of questions 2–5—the analytic core of action-taking—alongside four drivers of change in today’s volatile, uncertain, complex, ambiguous world, yields 16 possible futures for architects. Further synthesis identifies five primary roles for architects of the future: systems-thinking designer (embracing complexity), steward (building trust amid volatility), facilitator (reducing ambiguity through shared meaning), curator (making sense of uncertainty), and strategic forecaster (transforming volatility into preparedness). These roles embody a care-based approach—prioritizing ongoing relationships over episodic interventions, collective capacity-building over expert prescriptions, and adaptive readiness over static solutions. This reflects the positioning of architecture as a public good, focused on strengthening social, ecological, and systemic foundations so communities not only withstand disruption but also adapt, learn, and thrive through it. Full article

Despite unprecedented political endorsement, nature-based solutions (NbS) consistently fail to achieve the systemic transformation required for climate and biodiversity crises. This implementation deadlock stems from a profound triple strategic gap: a translational evidence gap between fragmented science and actionable design, a strategic design gap in misaligned institutions, and a fundamental theoretical integration gap disconnecting ecological principles from socio-economic solutions. This study forges and validates the symbiosis framework—an interdisciplinary blueprint designed to bridge this triple gap. Employing design science research, we: (1) synthesize ecological theory with institutional economics to distill three core design principles—functional reciprocity, nested modular network architecture, and strategic leverage and foundational support; (2) translate these into a conceptual model and strategic implementation blueprint; and (3) validate the framework through comparative analysis of global NbS case studies. The resulting framework provides a novel translational logic, moving beyond critique to offer a prescriptive design tool. It enables practitioners to diagnose systemic failures and design interventions that emulate ecological intelligence while applying institutional design principles: cultivating reciprocal partnerships, structuring resilient networks through polycentric governance, and strategically targeting catalytic leverage points and foundational assets. We conclude that scaling NbS requires a paradigm shift from managing isolated symptoms to architecting symbiotic systems. The symbiosis framework provides the essential interdisciplinary blueprint for this shift. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

The highly competitive nature of the online food delivery (OFD) market faces a serious retention problem, with acquiring new users typically being much more expensive than retaining existing users. Traditional prediction methods that rely primarily upon static transactional metrics such as recency and frequency are often unable to capture the psychological ‘disconfirmation’ which occurs prior to churn. To fill this gap, this study proposes a framework based on Expectation-Confirmation Theory (ECT). Unsupervised K-Means clustering was employed to classify a simulated and filtered dataset with 1500 customer records containing behaviour, geography, etc. This framework also couples sentiment analysis from BERT, allowing it to identify psychological “silent” attrition. Heterogeneous cohorts, which exhibit different psychological antecedents (utilitarian versus hedonic), were identified. The empirical results of our analyses demonstrated that Random Forest Classifiers with segment-specific features outperform baseline transactional models (F1 = 0.76) with an F1 Score of 0.89. The visual analytic interface developed provides a holistic view of the consumption process than traditional prediction models, including prescriptive, automated segment-based mitigation strategies. Our findings contradict the assumption that the “frequency–loyalty” model applies to all users. High-frequency discretionary users are found to be elastic in terms of retention and will experience significant churn. By utilising the automated action log, managers can plan targeted, highly efficient retention strategies rather than blanket discounting approaches. Full article

The increasingly frequent and severe natural disasters have posed significant challenges to the resilience of power systems worldwide, creating an urgent need to investigate the security issues associated with these extreme events and to develop effective risk mitigation strategies. Meanwhile, as one of the leading topics in current research, artificial intelligence (AI) has demonstrated outstanding performance across various domains, such as AI-driven smart grids and smart cities. In particular, its efficiency in processing big data and solving complex computational problems has made AI a powerful tool for supporting decision-making in complex scenarios. This article presents a focused overview of power system resilience against natural disasters, highlighting recent advancements in AI-based approaches aimed at enhancing system security and response capabilities. It begins by introducing various types of natural disasters and their corresponding impacts on power systems. Then, a systematic overview of AI applications in power systems under disaster scenarios is provided, with a classification based on the task categories, i.e., predictive, descriptive and prescriptive tasks. Following this, this article analyzes current research trends and finds a growing shift from knowledge-based models towards data-driven models. Furthermore, this paper discusses the major challenges in this research field, including data processing, data management, and data analytics; the challenges introduced by large language models in power systems; and the limitations related to AI model interpretability and generalization capability. Finally, this article outlines several potential future research directions. Full article

This paper proposes an innovative methodological framework for integrating Cultural Ecosystem Services (CES) into landscape planning, with the aim of enhancing the conservation and adaptive management of rural historical landscapes. Grounded in the principles of the European Landscape Convention and the recent Nature Restoration Law, the study advocates for a shift from prescriptive and sectoral approaches toward performance-based and ecosystem-oriented models. The research focuses on the Marche Region (Italy), where the historical landscape shaped by the mezzadria (sharecropping) system provides a representative case for testing the proposed methodology. Six spatial layers have been selected as ecosystem-based indicators to identify new potential landscape CES’ hotspots as agricultural landscape high-value areas, and to redefine protection and management strategies. The analysis integrates historical, ecological, and cultural dimensions to construct a spatially explicit value matrix, supporting the definition of differentiated management zones. Results reveal the persistence of high landscape and ecosystem values in mid- and upper-hill areas, contrasted by the progressive loss of structural and functional diversity in lowland and peri-urban contexts. The findings highlight the need for more adaptive and flexible planning models, capable of incorporating nature-based actions, climate-smart agriculture, and performance-oriented evaluation criteria. The proposed approach demonstrates potential for replicability and policy integration, providing a decision-support framework to align landscape planning with rural development strategies and climate adaptation objectives. Despite limitations related to data availability and model simplification, the methodology contributes to the ongoing paradigm shift toward dynamic, evidence-based, and transdisciplinary landscape governance across Mediterranean regions. Full article

Healthcare workforce planning continues to face entrenched challenges arising from the complex adaptive nature of health systems and the ongoing misalignment between workforce capabilities and system purpose. This paper introduces a conceptual meta-system framework grounded in systems and complexity thinking, positioning patient-centred care as the core system purpose and the guiding principle for workforce design. Drawing on the vortex model to visualise the nested layers of a health system—from individual care to national policy—the framework integrates interdependent domains, including system-level workforce needs. By synthesising global examples and varied planning strategies, the paper critiques the limitations of traditional linear forecasting and advocates for whole-system, needs-based approaches that embed dynamic feedback and stakeholder collaboration. It underscores the importance of strong partnerships between education and practice and highlights the role of adaptive leadership in aligning workforce planning with organisational purpose. Rather than offering prescriptive solutions, the framework serves as a catalyst for critical reflection, encouraging policymakers and healthcare leaders to tailor workforce strategies to their specific contexts. Ultimately, this conceptual approach seeks to enhance system resilience, improve health outcomes, and reduce future care demands through genuine alignment between workforce planning and the evolving needs of patients and health systems. Full article

Making time-critical decisions with serious consequences is a daily aspect of work environments. To support the process of finding optimal actions, data-driven approaches are increasingly being used. The most advanced form of data-driven analytics is prescriptive analytics, which prescribes actionable recommendations for users. However, the produced recommendations rely on complex models and optimization techniques that are difficult to understand or justify to non-expert users. Currently, there is a lack of platforms that offer easy integration of domain-specific prescriptive analytics workflows into production environments. In particular, there is no centralized environment and standardized approach for implementing such prescriptive workflows. To address these challenges, large language models (LLMs) can be leveraged to improve interpretability by translating complex recommendations into clear, context-specific explanations, enabling non-experts to grasp the rationale behind the suggested actions. Nevertheless, we acknowledge the inherent black-box nature of LLMs, which may introduce limitations in transparency. To mitigate these limitations and to provide interpretable recommendations based on real user knowledge, a knowledge graph is integrated. In this paper, we present and validate a prescriptive analytics platform that integrates ontology-based graph retrieval-augmented generation (GraphRAG) to enhance decision making by delivering actionable and context-aware recommendations. For this purpose, a knowledge graph is created through a fully automated workflow based on an ontology, which serves as the backbone of the prescriptive platform. Data sources for the knowledge graph are standardized and classified according to the ontology by employing a zero-shot classifier. For user-friendly presentation, we critically examine the usability of GraphRAG in prescriptive analytics platforms. We validate our prescriptive platform in a customer clinic with industry experts in our IoT-Factory, a dedicated research environment. Full article

This study examines non-standard comparative and superlative adjective forms in Ukrainian and English, emphasizing their evaluative meanings and grammatical deviations. While prescriptive grammar dictates conventional comparison patterns, modern discourse—particularly in advertising, informal communication, and literary texts—exhibits an increasing prevalence of innovative comparative structures. Using a corpus-based approach, this research identifies patterns of positive and negative evaluative meanings, revealing that positive evaluations dominate non-standard comparatives in both languages, particularly in advertising (English: 78.5%, Ukrainian: 80.2%). However, English exhibits a higher tolerance for grammatical flexibility, while Ukrainian maintains a more restricted use, primarily in commercial and expressive discourse. The findings highlight the pragmatic and evaluative functions of such constructions, including hyperbolic emphasis, rhetorical contrast, and branding strategies. These insights contribute to research on comparative grammar, sentiment analysis, and natural language processing, particularly in modeling evaluative structures in computational linguistics. Full article

Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation. Full article

Very few studies, all employing surveys, have investigated the perceptions of community pharmacists regarding antimicrobial stewardship (AMS). A qualitative inquiry exploring factors affecting community pharmacists’ participation in AMS may assist in the implementation of AMS in the primary care setting. This study aimed to explore the perceived barriers and enablers of community pharmacists’ participation in AMS. One-on-one semi-structured telephone interviews were conducted with a sample of community pharmacists from across Australia. Interviews were transcribed verbatim and analysed using the Framework Analysis method. Twenty community pharmacists (70% female), representing urban, regional, and remote areas of Australia participated in the study. Pharmacists identified a discord between clinical needs of patients and practice policies as the primary source of excessive prescribing and dispensing of antibiotics. The fragmented nature of the primary healthcare system in Australia was seen as limiting information exchange between community pharmacists and general practitioners about antibiotic use, that was encouraging inappropriate and, at times, unsupervised use of antibiotics. The existing community pharmacy funding model in Australia, where individual pharmacists do not benefit from any financial incentives associated with clinical interventions, was also discouraging their participation in AMS. Pharmacists suggested restricting default antibiotic repeat supplies, reducing legal validity of antibiotic prescriptions to less than the current 12 months, and adopting a treatment duration-based approach to antibiotic prescribing instead of the ‘quantity-based’ approach, where the quantity prescribed is linked to the available pack size of the antibiotic. Structural changes in the way antibiotics are prescribed, dispensed, and funded in the Australian primary care setting are urgently needed to discourage their misuse by the public. Modifications to the current funding model for pharmacist-led cognitive services are needed to motivate pharmacists to participate in AMS initiatives. Full article

Background: Although forest therapy services in South Korea have demonstrated mental and physical effects, there is no established system for forest therapy prescriptions. To this end, it is necessary to devise a systematic model for the introduction of forest therapy prescriptions by linking the existing forest therapy infrastructure and medical services. Therefore, this study aimed to derive the components and application plans needed to devise a forest therapy prescription model for the spread of medical-linked forest therapy services and to secure a forest therapy prescription infrastructure. Methods: To this end, Korean and foreign cases of prescription models and healthcare service provision systems were analyzed to derive the necessary components for prescription models. Subsequently, a Focus Group Interview (FGI) was conducted with eight experts in the fields of forest therapy and welfare, psychiatry, and health and nursing, and opinions were derived regarding the conception and empirical application of the forest therapy prescription model through content analysis. Results: As a result of the study, five components (clear role-sharing and a collaboration system, a continuous system, customized service provision, various technologies and content, and a database-based prescription system) were derived from cases of prescription models and healthcare service provision systems according to field. Furthermore, the FGI identified three primary topics: stakeholders’ scope and role, procedures and effectiveness, and additional considerations. Each was categorized into eight sub-categories relevant to the design of the forest therapy prescription model. Conclusions: These results can be used as basic data for devising a systematic Korean forest therapy prescription model in which forest therapy and medical services are linked, providing a foundation for personalized forest therapy prescriptions to be implemented. Full article

In performance-based design and assessment, there are prescriptive limits based not only on element-based performance evaluation but also on comparing story drifts with limit values. The process of determining performance levels at the element level involves obtaining the required data through numerous calculation steps, followed by evaluation, which makes it a time-consuming process. The iterative nature of this process emphasizes the importance of selecting the structural system, element dimensions, and target performance levels during the preliminary design stage to ensure they are consistent with the final analysis results. For this purpose, the determination of story drifts, which is widely accepted in the literature, is a critical aspect of performance evaluation studies, particularly for high-rise buildings, within the framework of deformation-based calculation assumptions. The continuum model is a practical approach for the approximate analysis of high-rise buildings including moment-resisting frames and shear wall-frame systems. In the continuum model, discrete buildings are simplified such that their overall behavior is described through the contributions of flexural and shear stiffnesses at the story levels. In this study, the aim is to enhance the Miranda and Taghavi (2005) model, which is classified among the approximate methods in the literature for determining story drifts and is developed within the framework of continuum model approaches. Full article