Search Results (205)

This study analyzes long-term deterioration patterns in 1378 Maryland steel bridges using annual Bridge Health Index (BHI) records from 1995–2021. Missing observations were addressed through linear interpolation combined with forward/backward filling, after which feature-wise z-score standardization was applied to ensure comparability across annual trajectories. Euclidean K-means clustering (k-means++ initialization, 10 restarts) was implemented to identify deterioration archetypes, with K = 6 selected using the elbow method and the silhouette coefficient. Cluster-internal stability was evaluated using bridge-level Root Mean Squared Error (RMSE), and uncertainty in median deterioration profiles was quantified using 2000-iteration percentile-based bootstrap confidence intervals. To interpret structural and contextual drivers within each group, Principal Component Analysis (PCA) was performed on screened and standardized geometric, structural, and traffic-related attributes. Results revealed strong imbalance in cluster membership (757, 503, 35, 33, 44, and 6 bridges), reflecting substantial diversity in long-term BHI behavior. Cluster-median RMSE values ranged from 2.69 to 22.66, while wide confidence bands in smaller clusters highlighted elevated uncertainty due to limited sample size. PCA indicated that span length, deck width, truck percentage, and projected future ADT were the most influential differentiators of deteriorating clusters, while stable clusters were distinguished by consistently high BHI component values and limited geometric complexity. Missing rehabilitation records prevented definitive attribution of U-shaped or recovering trajectories to specific intervention events. Overall, this study establishes a scalable, statistically supported framework for deterioration-trajectory profiling and provides actionable insight for proactive inspection scheduling, rehabilitation prioritization, and long-term asset management planning for state-level bridge networks. Full article

Wisdom in negotiation is increasingly vital in managing conflicts, yet its behavioural expression remains underexplored. This study explores the behavioural signatures of individuals nominated as wise negotiators within an organisational context. There were 48 participants recruited as wise negotiators from a larger pool of 313 participants. There were three manipulations used: archetypes (personality), reciprocity style, and emotionality, resulting in a 4X3X2 design (24 conditions). Participants were also asked to fill out various wisdom related questionnaires. Each participant had to go through 24 conditions separately before playing an investment game each time. For the analysis purpose, three-way repeated ANOVA and three-way repeated ANCOVA were used. The results revealed that there was a difference in how wise negotiators negotiate differently with different archetypes (p < 0.01), reciprocity (p < 0.01) and emotional situations (p < 0.01). Additionally, there were also interaction effects of archetypes, reciprocity and emotional situations (p < 0.05). Notably, when wisdom variables were statistically controlled, these differences became nonsignificant. A supplementary 2 × 2 design explored gender interactions, showing that outcomes differed by opponents’ gender but not by the gender of the wise negotiator. This finding highlights the role of wisdom traits in strategic negotiation and has implications for training and selection in a high-stakes negotiation context. Full article

The construction phase of buildings contributes significantly to greenhouse gas (GHG) emissions, yet mitigation strategies within the contractor’s scope—particularly those affecting transport, on-site energy use, and waste—remain underexplored in life cycle assessments (LCAs). This study develops a modelling framework to evaluate 20 mitigation strategies targeting modules A4 and A5 of the LCA, using a generalised business-as-usual (BAU) scenario derived from 15 representative archetypes based on 279 built projects and weighted by national construction statistics. Monte Carlo simulations are applied to capture variability in material composition and component distribution, and marginal abatement cost analysis is used to assess cost-effectiveness. The results show that transport-related strategies offer the highest mitigation potential under Danish conditions with minimal or negative costs, while waste strategies provide moderate reductions and often result in net savings. Energy strategies, though impactful in percentage terms, tend to have lower absolute reductions and higher costs. The applicability of strategies varies across building sizes, with economies of scale influencing feasibility. The modelling framework offers a structured basis for comparing mitigation actions by climate benefit and cost-efficiency, supporting strategic planning for low-carbon construction, while recognising that practical implementation depends on project-specific and organisational factors. Full article

Accurate classification of indoor scenes remains a challenging problem in computer vision, particularly when datasets contain diverse room types and varying levels of contamination. We propose a novel method, CLIP-Guided Clustering, which introduces archetype-based similarity as a semantic feature space. Instead of directly using raw image embeddings, we compute similarity scores between each image and predefined textual archetypes (e.g., “clean room,” “cluttered room with dry debris,” “moldy bathroom,” “room with workers”). These scores form low-dimensional semantic vectors that enable interpretable clustering via K-Means. To evaluate clustering robustness, we systematically explored UMAP parameter configurations (n_neighbors, min_dist) and identified the optimal setting (n_neighbors = 5, min_dist = 0.0) with the highest silhouette score (0.631). This objective analysis confirms that archetype-based representations improve separability compared with conventional visual embeddings. In addition, we developed a hybrid segmentation pipeline combining the Segment Anything Model (SAM), DeepLabV3, and pre-processing techniques to accurately extract floor regions even in low-quality or cluttered images. Together, these methods provide a principled framework for semantic classification and segmentation of residential environments. Beyond application-specific domains, our results demonstrate that combining vision–language models with segmentation networks offers a generalizable strategy for interpretable and robust scene understanding. Full article

As global decarbonization accelerates, the environmental and economic viability of hydrogen production largely depends on the evolving electricity supply mix. This study focused on alkaline water electrolysis (AWE) to identify the key factors affecting the competitiveness of green hydrogen. In this study, the temporal dynamics of grid transformation in Germany and the EU over a 20-year period (2025–2045) were addressed by developing a multi-objective optimization framework that integrates environmental impact analysis with machine-learning surrogate models to evaluate trade-offs between the carbon footprint and energy cost per kilogram of hydrogen. Grid-mix scenarios were generated via constrained Latin Hypercube Sampling under policy constraints, including coal phase-out and ≥80% renewables, screened for Pareto optimality, and clustered into distinct archetypes. The results indicated that cost-effective, low-carbon hydrogen production can be achieved through balanced portfolios that emphasize hydropower, biomass, and solar energy. Scenarios that minimize energy costs alone tend to breach environmental targets, whereas ultra-low-emission paths incur steep energy cost penalties. A representative scenario for 2034 (GWP = 24.57 kg CO₂-eq/kg H₂; Energy Cost = 9.47 €/kg H₂) demonstrated a realistic synergy between policy constraints, cost, and environmental impact. Full article

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning. Full article

Waterfront wilderness landscapes in mountainous cities, such as Chongqing, play a vital role in sustaining urban biodiversity and human well-being amid steep topography and hydrological variations that create unique habitats. However, public recognition of their ecological values and potential ecological–aesthetic conflicts remain underexplored. This study investigated biodiversity features and public preferences in Chongqing’s central urban waterfront wilderness through field surveys of 218 quadrats for biodiversity assessment (e.g., Shannon–Wiener and Simpson indices, cluster analysis identifying 12 typical communities) and two questionnaire surveys (N = 260 and 306) evaluating spatial features and plant attributes, with correlation and regression analyses examining relationships between ecological indices and preference scores. Results recorded 116 plant species from 41 families, dominated by herbaceous plants (77.6%), with herbaceous, shrub-herbaceous, and tree-herbaceous communities prevalent. No significant correlations existed between objective diversity indices and preference scores; instead, structure (β = 0.444, p < 0.001) and color (β = 0.447, p < 0.001) drove preferences (explaining 96.7% variance), favoring accessible mid-successional shrub-herbaceous structures over dense, low-diversity evergreen types. These findings reveal ecological–aesthetic conflicts in mountainous settings where aesthetic dominance limits biodiversity recognition. Implications include user-centered zoning: restrict access in low-preference steep areas with buffers for conservation, while enhancing high-preference flat zones via selective pruning and native colorful species introduction, supplemented by educational signage. This research provides a mountainous city archetype, enriching global urban wilderness studies and informing sustainable management in rapidly urbanizing regions. Full article

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector. Full article

South Africa’s overlapping crises, namely ecological overshoot, energy insecurity, unemployment, and inequality, are not isolated challenges but systemic outcomes of a political economy dependent on growth. This article advances a degrowth by design framework that positions systems thinking as the primary driver of transformative change. By embedding Meadows’ leverage points within canonical archetypes such as Limits to Growth, Shifting the Burden, Success to the Successful, and Tragedy of the Commons the analysis demonstrates how reinforcing and balancing feedbacks perpetuate overshoot and social inequity and how targeted leverage strategies can reorient systems toward sufficiency, equity, and ecological repair. The framework integrates decolonial ethics, Ubuntu-informed relational dignity, pluriversal design perspectives, and legislative anchors such as South Africa’s Climate Change Act and Just Energy Transition. While the contribution is primarily conceptual, it is strengthened by illustrative vignettes, descriptive statistics, and the proposal of measurable indicators including material footprint per capita and energy intensity of wellbeing. Acknowledging the limitations of qualitative mapping and partial empirical application, the article outlines a research agenda centred on empirical validation, comparative municipal case studies, participatory action research, and open indicator repositories. The unique contribution lies in reframing degrowth as a diagnostic and prescriptive leverage strategy that is both contextually grounded and transferable. Rooted in South Africa yet relevant across the Global South, the degrowth compass functions as a normative and analytical benchmark to guide contested transitions toward just and ecologically restorative futures. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

Contemporary philosophers and epistemologists as well as scholars of Islamic studies have not failed to notice some striking similarities between aspects of the Islamic notion of the “fiṭra” (humanity’s archetypal nature) articulated by the medieval Hanbalī traditionalist jurist and theologian Ibn Taymiyya (d.728/1328) and the account of the sensus divinitatis (an innate, direct perception of God) espoused by the reformed philosopher Alvin Plantinga (1932–present). This article systematically compares both these notions and more by first situating them within the antecedent historical factors and developments leading up to their emergence in their respective intellectual milieu, the theological anthropology espoused by both thinkers and the religious epistemology of each respective thinker. The article will also discuss salient differences between each doctrine and their broader parent epistemologies and will examine major objections raised against them. The comparative study reveals not only a rich source of Islamic religious epistemology to be mined by diligent researchers but the exciting application of philosophical analysis to the thought of Ibn Taymiyya. Finally, the article argues that Ibn Taymiyya’s account of the fiṭra faces some problematic epistemological conundrums, one of which will be explored in detail. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

This study aims to examine the corporate values of IBEX35-listed companies and compare them with the sustainability governance information in their Non-Financial Statements (NFSs) to identify cultural patterns indicating high sustainability governance maturity (HSGM). The study uses the Cultural Fit Assessment Method (CFAM©) based on the Competing Values Framework (CVF) and six cultural archetypes (People, Goals, Digital, Innovation, Norms, ESG). It also incorporates sector-specific indicators related to sustainability governance from the IV Comparative Report of the NFS of IBEX35-listed companies published in 2021 by Ernst and Young (E&Y). A fuzzy-set qualitative comparative analysis (fsQCA) was conducted to identify patterns of corporate culture that explain the high maturity levels of sustainability governance. The results reveal two sector-level paths to HSGM in which Digital emerges as a core presence condition and ESG appears as a core absence condition (~ESG). ESG does not emerge as a necessary condition; instead, HSGM arises configurationally. The first combination encompasses the presence of people, goals, and digital cultures, coupled with the absence of innovation, norms, and ESG archetypes, resulting in an HSGM model. The other alternative to obtaining HSGM is through a cultural combination of the absence of people, goals, and ESG cultures alongside a strong emphasis on digital, innovation, and norms archetypes. This study offers a unique approach to assessing the maturity of sustainability governance based on corporate culture. Identifying patterns of corporate culture that indicate high maturity levels of sustainability governance offers practical guidance on how organizations can enhance their sustainability practices. Full article

The transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit operators’ macro-behaviour in the Italian Ancillary Services market (MSD). Focusing on the Northern zone (NORD) during the pivotal period of 2022–2024, a stability-driven k-means clustering framework is applied to a dataset of capacity-normalized features from the day-ahead market (MGP), intraday market (MI), and MSD. The number of clusters is determined using the Gap Statistic with a 1-SE criterion and validated with bootstrap stability (Adjusted Rand Index), resulting in a robust and reproducible 13-group taxonomy. The use of up-to-date data (2022–2024) enabled a unique investigation into post-2021 market phenomena, including the effects of geopolitical events and extreme price volatility. The findings reveal clear operator-coherent archetypes ranging from units that mainly trade in the day-ahead market to specialists that monetize flexibility in the MSD. The analysis further highlights the dominance of thermoelectric and dispatchable hydro technologies in providing ancillary services, while illustrating varying degrees of responsiveness to price signals. The proposed taxonomy offers regulators and policymakers a practical tool to identify inefficiencies, monitor concentration risks, and inform future market design and policy decisions. Full article

Aggregate masonry buildings in historic urban centers constitute tangible testimony of collective identity and historical continuity. They encompass both simple terraced configurations and more intricate clusters, which are inherently vulnerable to earthquake-induced damage, due to their typological features and the transformations that occurred in the course of time. Strategies aimed at the protection and valorization of such typical architectural heritage should be based on the recognition of their peculiarities, so that the intangible values embedded within the historic fabric can be preserved. A simplified approach able to identify the effect of facade layout on the vulnerability of terraced buildings was validated on a historical center struck by the Central Italy earthquake. It is based on the evaluation of vulnerability factors derived by the application of a multi-level procedure on a large scale, which integrates data on typological and structural aspects, as well as on the condition state and previous interventions. In the center in question, the evidence of prevalent shear damage in the continuous frontage of the buildings facing the main street suggested the in-depth analysis of the facade’s characteristics, and its relationship with the main direction of the seismic swarm. Starting from a preliminary abacus of twelve vulnerability factors, 16 archetypes of facades at increasing vulnerability defined by a combination of the most significant geometrical features of building aggregates were identified. These virtual models encompass typical features that can be found in similar buildings in different contexts, thus enabling preventive actions based on parametric assessment. Full article