Search Results (3,257)

Generative artificial intelligence is increasingly embedded in architectural practice and education, yet its role often remains confined to image production or optimization tasks. This study situates generative AI within a broader design ecology. It examines how structured human–AI interaction can support environmentally oriented architectural thinking in design education. The article presents an international design workshop as a research setting in which architecture students engaged with AI through a multi-agent workflow. This workflow combined large language models, diffusion-based image generation, 2D–3D translation tools, parametric modeling, and clay-based 3D printing. Central to the methodology is the concept of prompt choreographies. These are deliberate dialogs between human and AI agents, based on a language of prompts and AI-generated outcomes. Through this process, the design concept moves toward a final architectural proposal. The workshop addressed complex ecological challenges emerging from interactions among Earth’s spheres. These were conceived as environmental interfaces defined by behavioral continuity rather than typological form. Using qualitative, design-based evaluation criteria focused on environmental, spatial, and material aspects, the study identifies recurring patterns of human–AI collaboration. The findings indicate that generative AI supports architectural ideation most effectively when embedded in structured workflows that emphasize curatorial decision-making and reduce generative overproduction. While limited to a workshop-based educational context, the research offers transferable methodological insights for architectural pedagogy and conceptual practice. It proposes a process-oriented framework for designing with generative AI and outlines an emerging form of architectural literacy and multi-agent collaboration that warrants further empirical validation. Full article

Query–service matching in customer service systems faces a critical challenge of accurately aligning user queries expressed in colloquial language with formally defined services while balancing business objectives. Traditional keyword-based and embedding approaches fail to capture complex semantic nuances and cannot provide interpretable explanations. We address this problem by proposing a novel reasoning-enhanced framework that leverages large language models (LLMs) for structured multi-criteria evaluation. Our key innovation is a reasoning-first scoring architecture where the model generates detailed explanations before numerical scores, reducing score variance by 18% through conditional mutual information. We introduce a controlled stochastic perturbation mechanism with theoretically derived optimal parameters that balance diversity and relevance, alongside a knowledge distillation pipeline enabling 960× model compression (480B→0.5B parameters) while retaining 94% performance. Rigorous theoretical analysis establishes Pareto optimality guarantees for multi-criteria evaluation, information-theoretic entropy reduction bounds, and PAC learning guarantees for distillation. Experimental validation on real-world telecommunications data demonstrates 89% Precision@1 (15.3% improvement over baselines), 23% diversity enhancement, and 96× latency reduction, with deployment cost decreasing 1200× compared to direct LLM inference. This work bridges the gap between LLM capabilities and production deployment requirements through principled mathematical foundations and practical system design. Full article

The Spanish Public University System (SUPE) assigns student placements through a multi-step application process governed by legal criteria. Analyzing how students move between different degree programs during this process is crucial for universities to optimize and plan their academic offerings. This paper analyzes a decade of student pre-registration data (2014–2023) to track evolving preferences and mobility between degrees. We model this process as a directed graph, mapping student traffic and studying the formation of directional communities within the degree network. A significant challenge is the weakly connected and poorly conditioned nature of these graphs, which impedes standard community detection algorithms. Extending prior work that relied on manually set thresholds for pruning edges, we propose a novel adaptive pruning algorithm that requires no manual intervention. Applying this method to annual data improves community detection performance and reveals gradual shifts in student preferences and demand, particularly in response to new degrees. These insights provide a valuable decision-making tool for higher education institutions, helping them refine their degree offerings in response to evolving trends. Full article

Integrating health-oriented physical activity into community-scale walking environments is a key strategy for promoting healthy aging within sustainable urban development. However, community walking environments are often planned and managed without systematic evaluation frameworks to determine whether daily walking conditions effectively support health-oriented physical activity. To address this gap, this study proposes a planning-oriented health effectiveness assessment framework that translates exercise prescription principles into spatial, functional, and managerial performance indicators. Based on the Frequency, Intensity, Time, Type, Volume, and Progression (FITT-VP) exercise prescription framework, a multi-method approach was adopted. Evaluation indicators were identified through a structured literature review and refined using the Delphi method. User perception differences were incorporated using the Kano model, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to quantitatively evaluate and rank the health effectiveness of community walking environments. The framework was empirically tested through a case study of Binshui communities in the Jimei District of Xiamen, China. The outcomes imply that priority indicators include progression route planning integrity, interval training feasibility, multifunctional training area match, monthly maintenance frequency, nighttime illumination uniformity. Community walking environments can function as effective everyday planning instruments for promoting physical activity among aging populations when exercise science principles are systematically embedded into urban design and management. By operationalizing exercise prescription principles as planning performance criteria, this study advances sustainable urban planning research and provides an evidence-based assessment tool for age-friendly neighborhood regeneration and community health governance. Full article

The rapid evolution of smart building technologies has transformed the hotel industry, necessitating structured methodologies for evaluating building intelligence. This research, dedicated to engineering problems, proposes an integrated decision-making model that combines fuzzy Shannon entropy and fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) to assess the intelligence level of buildings within the hospitality sector. The model systematically determines the relative importance of intelligence criteria, including engineering, environmental, economic, social and cultural, technological, and energy conservation criteria. By leveraging fuzzy Shannon entropy, the framework objectively assigns weights to criteria based on information distribution, minimizing subjective biases in evaluation. Fuzzy MOORA is then applied to rank alternative intelligent buildings in hotels, ensuring an accurate comparative assessment. The proposed model is tested on real-world hotel data, demonstrating its effectiveness in identifying optimal intelligent building configurations. The results of applying fuzzy Shannon entropy reveal that human comfort, the emission of greenhouse gases (pollution), and system integration are the most important sub-criteria. Finally, by applying the importance of the criteria in the fuzzy MOORA model, the intelligence levels of hotels are evaluated. The results show that the Parsian Kowsar, Piroozy and Sepahan Hotels are the best hotels based on the intelligent building criteria. Full article

The digital transformation of the textile industry poses unique challenges due to its labor-intensive processes, complex global supply chains, and coexistence of traditional methods and emerging technologies. Despite the urgency of this transition, existing digital maturity models lack sector-specific frameworks and often fail to integrate multi-criteria decision-making (MCDM) methodologies for quantitative performance assessment. This study addresses these gaps by proposing a novel digital maturity model tailored specifically to the textile sector. The research employs an integrated decision-making framework using the Method Based on the Removal Effects of Criteria (MEREC) to determine objective criterion weights and the Operational Competitiveness Rating Analysis (OCRA) method to rank firm-level digital maturity performance. The findings indicate that Strategy is the most influential dimension, whereas Technology receives the lowest weight. At the sub-criterion level, Management Support, Market Analysis, and Vision and Strategic Awareness are the most critical factors, while Technology Usage Competency is less influential. The performance evaluation shows that Company A3 achieves the highest level of digital maturity, whereas Company A2 ranks lowest. The robustness of the proposed framework is comprehensively validated through a scenario-based sensitivity analysis and a comparative evaluation using the Additive Ratio Assessment System (ARAS) method. Overall, the results suggest that successful digital transformation in the textile sector depends primarily on strategic vision and managerial support rather than on technological infrastructure alone. Full article

The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, and multi-criteria optimization of KPIs, rather than as a direct virtual replica of a specific physical production system. This paper proposes a hybrid simulation–prediction model based on a digital twin framework for optimization of KPIs in sustainable digital printing, with particular emphasis on overall equipment effectiveness (OEE). Due to the limited availability of structured industrial data, the model is developed using a synthetically generated dataset constructed in accordance with industry-reported operating ranges and technically realistic digital printing process variables. Random Forest and XGBoost algorithms are applied to model nonlinear relationships between process parameters and KPIs, including material waste, energy consumption, machine downtime, and OEE. Based on these predictive models, a constrained multi-objective optimization procedure is performed to identify Pareto-efficient configurations that reduce material waste and energy consumption while maintaining acceptable downtime and OEE levels. The results characterize structural trade-offs among environmental and operational KPIs within a formally defined decision space. Full article

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article

Introduction: Balancing hypnosis and antinociception during general anesthesia remains challenging, as traditional clinical and hemodynamic signs incompletely reflect cortical and nociceptive processing. Electroencephalogram (EEG)-derived indices such as qCON (hypnosis) and qNOX (nociception probability) (Quantium Medical, Barcelona, Spain), as well as their predecessors IoC1 (Index of consciousness) and IoC2 (Angel-6000 A multi-parameter Anesthesia Monitor, Shenzen Weihao Kang Medical Technology Co., Ltd., Shenzen, Guangdong, China), have been developed to provide a dual assessment of anesthetic state. Their clinical role, technical limitations, and impact on drug titration, however, remain incompletely defined. Methods: A structured narrative review was conducted based on studies investigating IoC/qCON and qNOX in the context of anesthetic depth or nociception monitoring. Studies were grouped into three thematic domains: (1) validation against clinical or EEG standards, (2) use in guiding anesthetic or opioid administration, and (3) technical characteristics, including signal delay and pharmacodynamic modeling implications. Results: Sixteen studies met inclusion criteria. Eight validation studies demonstrated that IoC/qCON correlates strongly with clinical sedation scales and established EEG-derived indices such as BIS and entropy. Five interventional studies evaluating drug titration found limited impact of qCON-guided hypnosis control on anesthetic consumption but more consistent effects of qNOX/IoC2 guidance on opioid dosing and intraoperative stability. Three technical investigations showed that qCON exhibits processing delays on the order of tens of seconds that can be accounted for by incorporating monitor lag into pharmacodynamic analyses. Conclusions: qCON and qNOX provide complementary EEG-based indices of hypnosis and cortical nociceptive responsiveness. Evidence supports their validity as indicators of anesthetic brain state but highlights technical limitations, such as processing delay and susceptibility to physiologic factors. Their optimal clinical use lies in multimodal monitoring strategies that integrate EEG besides classic clinical and monitoring parameters. Full article

A key limitation of conventional early-stage oil tanker structural design is that the accidental limit state performance is rarely included as an explicit design objective, even though major topology and arrangement decisions are taken before detailed nonlinear analyses become feasible. This paper proposes a crashworthiness-driven structural design methodology for the concept design phase (CDP), in which crashworthiness is introduced as an explicit safety-related performance measure through surrogate modeling and used within a multi-objective optimization framework. Crashworthiness is represented by the internal energy absorption of a double-hull side structure under collision, which is obtained from a limited set of high-fidelity nonlinear simulations and approximated by response surface surrogate models to enable computationally efficient design-space exploration. The optimization framework considers structural weight and crashworthiness while enforcing rule-based adequacy constraints consistent with current classification practice, and it can be extended to additional safety-related measures. Application to an Aframax tanker case study demonstrates that Pareto-optimal solutions can be generated that improve the collision energy dissipation capability without disproportionate increases in structural weight at a stage where topology changes are still practical. The results confirm that crashworthiness-oriented criteria can be embedded within CDP design workflows in a manner compatible with established industrial practice. Full article

Background/Objectives: Hepatoblastoma, the most common pediatric primary liver cancer, is no longer regarded as a conventional malignancy but rather as a tumor emerging from disrupted hepatic developmental processes. Although improvements in chemotherapy, surgical techniques, and liver transplantation have markedly enhanced survival, therapeutic decision-making is still primarily guided by anatomical criteria and insufficiently reflects the biological heterogeneity that contributes to variable treatment response and disease recurrence. This narrative review integrates recent advances in molecular biology, tumor stemness, microenvironmental interactions, and translational research models in pediatric hepatoblastoma. We critically examine how developmental signaling pathways, cellular plasticity, and immune–vascular context shape tumor behavior and therapeutic vulnerability, with a focus on emerging targeted, anti-angiogenic, immune, and epigenetic strategies. Results: Hepatoblastoma is characterized by aberrant activation of key developmental pathways, including Wnt/β-catenin, Hippo–YAP, IGF, and mTOR signaling, which cooperate to sustain proliferation, stem-like phenotypes, and treatment resistance. Tumor heterogeneity is further reinforced by cancer stem cell populations and a predominantly immune-cold microenvironment. While innovative therapeutic approaches show promise, their clinical impact has been limited by biological complexity and insufficient integration into current treatment algorithms. Liquid biopsy biomarkers, advanced translational models, and multi-omics approaches offer new opportunities for biologically informed risk stratification and therapy adaptation. Conclusions: Future progress in pediatric hepatoblastoma will require a paradigm shift from purely clinicopathological management toward an integrated molecular and surgical framework. Incorporating biological stratification into therapeutic decision-making may enable personalized treatment, rational therapy de-escalation, and improved outcomes for high-risk disease. This review highlights the foundations and future directions for precision medicine in hepatoblastoma. Full article

With the escalating demand for forest-derived ecological products, quantifying forest ecological product production capacity (EPC) has become essential for precise ecological governance. Addressing the methodological gaps and complexity in current assessments, this study develops a transferable Forest EPC theoretical framework integrated across four dimensions: environmental background, vegetation status, human pressure, and human investment. Using Henan Province as a case study, we established a multi-criteria evaluation model to characterize the spatial drivers and supply potential of Forest EPC. Our findings reveal that the provincial forest EPC stands at a moderate level (0.341). The spatial distribution is highly heterogeneous: the “medium” EPC grade dominates the landscape (36.69%), whereas “high-level” areas are critically scarce (3.76%). Notably, forest EPC exhibits a strong spatial gradient, with high-performance clusters in the southern and western highlands contrasting with lower values in the northern plains. The identification of significant spatial autocorrelation (Global Moran’s I = 0.71) highlights the necessity of regional collaborative management. This study provides a methodological reference that is adaptable to diverse regional contexts through the recalibration of local indicators and weights, offering a scientific benchmark for optimizing the spatial layout of ecological product supply. Full article

Background: Stereotactic brain biopsy is the gold standard for the pathological diagnosis of malignant brain tumors. However, conventional procedures rely heavily on manual path planning and unintuitive navigation, which significantly increase the risk of severe complications and impose an additional cognitive burden on surgeons. Methods: We propose an augmented reality-based navigation system that synergizes multi-objective path planning with hybrid registration. Preoperatively, the system utilizes a constrained multi-objective optimization (MOO) model derived from clinical criteria to automatically calculate and visualize optimal biopsy paths within a three-dimensional anatomical environment. Intraoperatively, the system performs rapid initial alignment using quick response (QR) codes, followed by precise refinement through anatomical landmarks. This process ultimately enables the highly accurate, real-time overlay of the surgical path and anatomical models onto the patient’s operative field. Results: An expert study across four common brain tumor locations demonstrated that the MOO model significantly outperformed manual methods in satisfying safety criteria. The hybrid registration reduced the mean fiducial registration error (FRE) from 4.19 ± 1.11 mm to 2.37 ± 0.91 mm (p < 0.001), with a mean target registration error (TRE) of 2.34 ± 0.71 mm and a mean clinical setup time of 2.63 ± 0.36 min. Conclusions: This system assists stereotactic brain biopsy through automated path planning and immersive augmented reality-based guidance, highlighting its potential to support surgical workflow consistency and procedural safety. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

In the high Andean regions of Peru, above 3500 m a.s.l., selecting a roofing system requires balancing budgetary constraints, technical performance, and environmental impacts under severe frost and demanding climatic conditions. This study compared several roofing alternatives from a comprehensive perspective to determine the most suitable solution by simultaneously considering economic, environmental, and social criteria. For this purpose, the Integrated Value Model for Sustainable Assessment (MIVES)—a multi-criteria decision-making methodology—was employed to evaluate five systems: traditional ichu thatched roof (ITR), ceramic tile (CT-II), corrugated galvanized steel with insulation (CGS-II), fiber cement with insulation (FC-II), and sandwich panel with an insulating core (PIR-SP). The model was implemented using a requirements tree with 11 indicators and its stability was assessed through a sensitivity analysis involving five weighting configurations. The overall sustainability indices ranked ITR first (0.697), primarily due to its low carbon footprint and favorable thermal performance. It was followed by CT-II (0.632), due to its superior landscape integration; CGS-II (0.602), owing to its cost-effectiveness; FC-II (0.586), for its balanced environmental profile; and finally, PIR-SP (0.504), which excelled in industrial efficiency and construction speed despite a higher environmental impact. In summary, the results indicated that vernacular solutions minimized environmental impacts and optimized local resources, whereas industrialized options were preferable when durability and assembly times were prioritized. The sensitivity analysis, with variations below 5%, supported the model’s consistency as a decision-support tool and its potential to guide policies for sustainable social housing in high-mountain contexts. Full article