Search Results (4,027)

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

As virtual reality technologies evolve toward widespread adoption in education, industry, and social communication, their increasing complexity exposes new and often overlooked security challenges. Immersive environments collect continuous multimodal data, including motion tracking, gaze, voice, and biometric indicators that extend far beyond traditional computing attack surfaces. This paper synthesizes recent research (2023–2025) on cybersecurity, privacy, and behavioral safety in virtual reality (VR) systems, identifies the main vulnerabilities, and proposes a unified defense architecture: the three-layer VR Security Framework (TVR-Sec). Through comparative review and conceptual integration of 31 peer-reviewed studies, three interdependent protection domains emerged: (1) System Integrity, securing hardware, firmware, and network communications against spoofing and malware; (2) User Privacy, ensuring the ethical management of biometric and behavioral data through federated learning and consent-based control; and (3) Socio-Behavioral Safety, addressing harassment, manipulation, and psychological exploitation in shared virtual spaces. The framework situates VR security as a multidimensional adaptive process that combines technical hardening with human-centered defense and ethical design. By aligning cyber–human protections through an AI-driven monitoring and policy engine, TVR-Sec advances a holistic paradigm for securing future immersive ecosystems. Full article

Co-infection with human T-cell lymphotropic virus types 1 and 2 (HTLV-1/2) and HIV is not routinely screened for, yet it may significantly influence clinical progression, mortality, and quality of life in affected individuals. This study aimed to estimate the prevalence of HTLV-1/2 co-infection among adults living with HIV and to identify associated epidemiological factors in the Peruvian Amazon. A cross-sectional study was conducted including patients receiving antiretroviral therapy through the multidisciplinary TARGA program in Iquitos, Peru, during the second quarter of 2013. Screening for HTLV-1/2 antibodies was performed using enzyme-linked immunosorbent assay, with reactive samples confirmed by Line Immunoassay. Demographic and behavioral variables were collected, and prevalence odds ratios with 95% confidence intervals were estimated using logistic regression models. Among the 284 patients included, 28 were co-infected with HIV and HTLV-1/2, resulting in a prevalence of 10% with a 95% confidence interval of 6.5 to 14.1. In multivariable analysis, age over 35 years and having more than 10 lifetime sexual partners were independently associated with co-infection, with prevalence odds ratios of 12.4 and 3.6, respectively. HTLV-1/2 co-infection was highly prevalent among people living with HIV in the Peruvian Amazon, and the main risk factors identified suggest that cumulative exposure and sexual behavior play a significant role in the joint transmission of both retroviruses, supporting the need to consider systematic HTLV screening in endemic settings. Full article

Bisphenol-A (BPA) and butylparaben (BP) are recognized as emerging contaminants due to their extensive use in plastics and personal care products, posing significant risks to ecosystems and human health. Understanding their transport behavior is vital for predicting environmental fate and designing mitigation measures. This study quantifies the diffusion coefficients of BPA and BP under infinite dilution conditions to simulate realistic environmental scenarios. Laboratory experiments employed a UV-Visible spectrophotometer to monitor concentration changes over time at four initial BP concentrations (0.0005–0.0025 M) and at temperatures between 294.85 K and 304.15 K. Experimental data show that BP concentrations at lower initial values (0.0005 M and 0.00075 M) remained constant, indicating minimal diffusion. Theoretical estimations using the Stokes–Einstein equation yielded diffusion coefficients at 299.38 K of 1.51 × 10⁻¹³ m²/s for BP and 8.47 × 10⁻¹⁴ m²/s for BPA. The Wilke–Chang equation estimated higher values: 1.21 × 10⁻¹⁰ m²/s for BP and 1.18 × 10⁻¹⁰ m²/s for BPA at the same temperature. Results confirm that temperature increases enhance diffusion, while molecular size differences cause BP to diffuse faster than BPA. The robust experimental dataset produced here supports the refinement of predictive models for contaminant mobility. These insights are critical for risk assessment and for developing targeted strategies to minimize the persistence and spread of endocrine-disrupting chemicals in aquatic and terrestrial systems. Full article

Accumulation of tetracycline (TC) in aquatic environments poses a significant threat to human health and ecosystems, driving the need for efficient removal technologies. Two-dimensional metal–organic frameworks (2D MOFs) are promising adsorbents due to their tunable structures and abundant active sites. In this work, three 2D MOFs, M₃(HHTP)₂ (M = Cu, Ni, Co), were synthesized via a solvothermal method. Among them, Cu₃(HHTP)₂ exhibited superior TC adsorption with a maximum capacity of 302.84 mg/g. The adsorption process, best described by the Langmuir isotherm and pseudo-second-order kinetic models, indicates chemisorption. Mechanistic investigations reveal that the high-activity coordination sites formed by Cu²⁺ due to Jahn–Teller distortion enable strong coordination with TC. This is identified as the key factor governing the differential adsorption performance among the three MOFs. Simultaneously, the surface functional groups facilitate hydrogen bonding, and the advantageous pore structure of the material itself, together forming a synergistic adsorption. This work not only elucidates the microscopic mechanism behind the efficient adsorption of TC by Cu₃(HHTP)₂ but also, through comparative analysis of isostructural MOFs, confirms the decisive role of metal center electronic structure in modulating the adsorption behavior of 2D MOFs. The insights gained from this study may serve as a reference for the design of 2D high-performance adsorbents. Full article

Epileptogenesis is commonly defined by the emergence of spontaneous seizures after an initial insult; however, convergent experimental and clinical evidence indicates that the underlying disease process begins well before seizures become clinically detectable. During this pre-seizure phase, persistent molecular cascades remodel synaptic plasticity, circuit architecture, and glial–immune signaling. These processes are associated with trait-like alterations in cognition, affect, and behavior. Despite their clinical relevance, these neurobehavioral signatures remain poorly integrated into molecular models of epileptogenesis and are rarely considered as translational biomarkers of disease progression. This review synthesizes evidence linking core epileptogenic molecular cascades—maladaptive synaptic plasticity, glial–immune signaling, oxidative–metabolic stress, and activity-dependent gene regulation—to reproducible alterations in executive control, cognitive flexibility, emotional regulation, and motivational–social behavior. We outline an integrative framework in which these phenotypes are conceptualized as system-level readouts of progressive network reconfiguration rather than nonspecific “comorbidities” or mere consequences of recurrent seizures. Within this perspective, neurobehavioral markers can complement electrophysiological and molecular measures by capturing disease-relevant changes during windows when anti-epileptogenic interventions would be most effective. To increase mechanistic specificity, we provide representative pathway and gene-level anchors across epileptogenesis stages, a structured molecular-to-neurobehavioral mapping, and an operational biomarker panel specifying confounders and minimal controls. These anchors are included to ground the framework in experimentally documented molecular nodes with stage-dependent relevance; examples are representative rather than exhaustive, and evidence strength is indicated as preclinical mechanistic versus associative human observations. Finally, we discuss methodological requirements for biomarker validity (specificity, temporal anchoring, and cross-model consistency) and outline how integrating molecular and neurobehavioral trajectories may refine target discovery and improve the translation of anti-epileptogenic strategies. Conceptualizing epileptogenesis as a progressive disease process with measurable pre-seizure neurobehavioral signatures may broaden biomarker strategies beyond seizure occurrence and support the development of disease-modifying interventions. Full article

This study investigates the dynamics of information diffusion and uncertainty evolution in online public opinion systems under human-made disasters. A variant of the SIR model considering individual psychological involvement and group herd behavior is proposed. The theoretical analysis derives the propagation equilibrium points and the propagation threshold and further examines the stability of the system. The results indicate that the transmission rate, immunity rate, and herd behavior coefficient are key parameters influencing the dynamics of public opinion propagation. The simulation results validate the theoretical findings and provide a visualization of the sensitivity of the key parameters. Finally, an empirical case study is conducted to verify the effectiveness and applicability of the proposed model. The results indicate that controlling contact rate, reducing herd behavior, and lowering psychological involvement can effectively suppress opinion diffusion, with herd behavior and psychological involvement exerting a greater influence than contact rate on spreaders of the public opinion system. Consequently, mitigating public emotional resonance and herd effects constitutes an effective strategy for managing public opinion in human-made disasters, but reducing herd behavior makes the system relatively more uncertain compared with other scenarios. Finally, managerial implications for public opinion governance in human-made disasters are proposed. The findings enrich the theoretical system of information evolution modeling for complex social systems based on entropy and information theory, offer practical guidance for governments in developing scientific public opinion management strategies, and realize the transformation of public opinion systems from high-entropy disorder to low-entropy order. Full article

Background/Objectives: Dynamic remodeling of the zona pellucida (ZP) is a fundamental biochemical and structural process during human preimplantation development; however, its quantitative characterization and clinical relevance remain incompletely defined. The objective of this study was to evaluate dynamic ZP thinning as a functional marker of embryo developmental competence and to examine its relationship with follicular fluid (FF) biomarkers and clinical pregnancy. Methods: This prospective observational study included 47 IVF cycles performed at a single center, yielding 64 transferred blastocysts with complete time-lapse data. ZP thickness was measured from fertilization to 120 h post-fertilization using time-lapse imaging. Two quantitative parameters were derived: the relative thinning ratio (Δrel) and the linear thinning rate (slope). FF concentrations of growth differentiation factor 9 (GDF-9), hyaluronic acid (HA), and syndecan-4 (Syn4) were quantified by ELISA. Embryo-level associations with spontaneous blastocyst hatching were assessed using logistic regression and multivariate analyses, while patient-level models evaluated predictors of clinical pregnancy. Results: Embryos that underwent spontaneous hatching exhibited significantly greater Δrel than non-hatching embryos (p < 0.001). Δrel remained the strongest predictor of hatching in multivariable models (AUC = 0.91). Among FF biomarkers, only GDF-9 showed a positive association with spontaneous hatching. At the patient level, higher Δrel values of transferred embryos were associated with clinical pregnancy (OR 3.65, p = 0.009), whereas FF biomarkers and assisted hatching showed no significant association. Conclusions: Dynamic ZP thinning quantified by Δrel represents a promising indicator of embryo developmental competence. The concordance between embryo-level hatching behavior and patient-level clinical pregnancy suggests potential clinical relevance of ZP dynamics as an integrative embryological marker, warranting validation in larger cohorts. Full article

The deployment of green hydrogen production is increasingly considered a strategic opportunity for energy-exporting countries. However, beyond technological and environmental aspects, large-scale industrial projects may generate complex and uncertain social and economic impacts at the regional level. This study investigates the potential social implications of introducing a green hydrogen production plant in the Department of Paysandú, Uruguay, using a System Dynamics modeling approach. It proposes an initial system model designed to establish a foundational Modeling and Simulation framework. The model explicitly represents feedback mechanisms linking public finance, education, labor competencies, productivity, and social behavior impact, allowing the exploration of long-term socio-economic trajectories under alternative institutional and policy conditions. It is used as an exploratory decision-support tool to assess conditional pathways, trade-offs, and risks. Results indicate that positive social outcomes, such as human capital accumulation and regional income growth, are possible but not automatic; they depend critically on governance capacity, fiscal sustainability, labor market coordination, and social acceptance, and may be attenuated or delayed under adverse scenarios. While this framework provides a strategic engineering lens on the social dimension, it represents a first step toward a comprehensive decision-making tool. The study analyzes a complex system by integrating energy, production, economic, social, and environmental aspects from strategic engineering lens and contributes to the literature by integrating social dimension and institutional constraints into a Modeling and Simulation framework applied to green hydrogen industrialization, offering insights into policy design under uncertainty in emerging energy-export contexts. Full article

Urban environments face increasing thermal stress from climate change and the Urban Heat Island effect, with significant implications for livability, public health, and energy sustainability. Outdoor thermal comfort is defined as the state in which conditions are perceived as acceptable, depends on interactions among meteorological, morphological, physiological, and behavioral factors. This review synthesizes the application of machine learning (ML) to outdoor thermal comfort assessment into a practice-oriented taxonomy. Research spans diverse climates and urban forms, using inputs across environmental and human domains. Supervised learning dominates. Regression approaches (linear regression, support vector regression, random forest, gradient boosting) and classification algorithms (decision trees, support vector machines, K-nearest neighbors, Naïve Bayes, random forest classifiers) are widely used to predict thermal indices such as the Physiological Equivalent Temperature and Universal Thermal Climate Index, or to classify subjective responses including thermal sensation, comfort, and acceptability. Unsupervised learning (clustering, principal component analysis) supports identification of microclimatic zones and perceptual clusters, while deep learning (multilayer perceptrons, convolutional and recurrent neural networks, generative adversarial networks) achieves superior accuracy for complex, high-dimensional, and spatiotemporal data. Algorithms such as random forests, support vector machines, and gradient boosting consistently show strong performance for both indices and subjective responses when integrating multi-domain inputs. Semi-supervised and reinforcement learning remain underexplored but offer promise for leveraging large-scale sensor data and enabling adaptive, real-time comfort management. The review concludes with a roadmap emphasizing explainable artificial intelligence, scalable surrogate modeling, and integration with simulation-based optimization and parametric design tools. Full article

In autonomous driving, motion sickness (MS) arises from physical or visual stimuli, or a combination of both. However, objective quantification of MS level (MSL) remains limited beyond questionnaire-based assessments. Using multimodal human signals (physiological and behavioral) collected in an autonomous driving simulator, this study addresses the association between these signals and MSL, across these MS types, by (i) screening and curating a decade of human-signal MS studies (HS-Set) to establish a data-driven foundation for selecting target sensor domains and features, (ii) constructing a dataset with subjective measures of MSL (fast motion sickness scale and simulator sickness questionnaire (SSQ)), alongside human signals (electroencephalogram (EEG), photoplethysmogram (PPG), electrodermal activity (EDA), skin temperature, and head/eye movement), (iii) conducting a correlation analysis between MSL and the identified features from HS-Set, and (iv) quantifying multivariable contributions at the feature and sensor domains through an explainable boosting machine (EBM). Key correlations include head amplitude/energy (pitch/surge) with SSQ total/oculomotor, eye entropy with nausea/oculomotor (positive), and EDA with nausea (negative). The EBM-based contribution analysis highlights EEG connectivity and head kinematics as dominant contributors; excluding EEG, the interpretability of single-domain models remains limited. Additionally, a combination of Head, PPG, and EDA domains retains over 80% of the full model’s interpretability. Full article

Virtual Reality (VR) has emerged as a powerful medium for immersive human–computer interaction, where users’ emotional and experiential states play a pivotal role in shaping engagement and perception. However, existing affective computing approaches often model emotion recognition and immersion estimation as independent problems, overlooking their intrinsic coupling and the structured relationships underlying multimodal physiological signals. In this work, we propose a modality-aware multi-task learning framework that jointly models emotion recognition and immersion estimation from a graph-structured and symmetry-aware interaction perspective. Specifically, heterogeneous physiological and behavioral modalities—including eye-tracking, electrocardiogram (ECG), and galvanic skin response (GSR)—are treated as relational components with structurally symmetric encoding and fusion mechanisms, while their cross-modality dependencies are adaptively aggregated to preserve interaction symmetry at the representation level and introduce controlled asymmetry at the task-optimization level through weighted multi-task learning, without introducing explicit graph neural network architectures. To support reproducible evaluation, the VREED dataset is further extended with quantitative immersion annotations derived from presence-related self-reports via weighted aggregation and factor analysis. Extensive experiments demonstrate that the proposed framework consistently outperforms recurrent, convolutional, and Transformer-based baselines. Compared with the strongest Transformer baseline, the proposed framework yields consistent relative performance gains of approximately 3–7% for emotion recognition metrics and reduces immersion estimation errors by nearly 9%. Beyond empirical improvements, this study provides a structured interpretation of multimodal affective modeling that highlights symmetry, coupling, and controlled symmetry breaking in multi-task learning, offering a principled foundation for adaptive VR systems, emotion-driven personalization, and dynamic user experience optimization. Full article

Aquatic pollution threatens biodiversity, disrupts ecological balance, and poses risks to communities dependent on freshwater resources. Aquaculture ponds are especially susceptible, as contaminants directly influence both ecosystem stability and the safety of fish for human consumption. With the rapid growth of pond-based aquaculture, accurate modeling of pollutant dynamics is essential. This study analyzes pollution in a system of n interconnected ponds, assuming a clean water source, constant volume, and steady pollutant inflow and outflow. A previous model based on ordinary differential equations is solved using matrices, eigenvalues, eigenvectors, and generalized eigenvectors. A generalized fractional model is then developed employing the Caputo–Liouville derivative. Unlike classical models, fractional models account for memory effects and anomalous diffusion, providing a more realistic description of pollutant behavior. Analytical solutions are derived to track pollutant variation across ponds, and a comparison of the two formulations is presented. The results enhance understanding of pollution transport in aquaculture systems and offer insights for sustainable water quality management in fish farming. Full article

Affiliated Open Spaces (AOS) constitute vital public assets within high-density vertical cities. However, prevailing scholarship remains largely confined to two-dimensional horizontal perspectives, overlooking the quantitative impact of vertical built environment characteristics on spatial distribution and human behavior. Focusing on four high-density districts in Guangzhou typified by distinct three-dimensional morphologies, this study integrates field surveys, 3D geospatial data acquisition, and 621 valid questionnaires to empirically analyze the impact of 3D spatial features on user behavior and the mediating role of accessibility. Utilizing the ArcGIS 3D Analyst for vertical accessibility measurement and Partial Least Squares Structural Equation Modeling (PLS-SEM) for path analysis, the study tests the hypothesized relationships using multi-source data. The results indicate that (1) a user’s vertical location exerts a significant negative impact on both accessibility and human behavior; (2) building density and building functional diversity indirectly promote user engagement primarily by significantly enhancing accessibility, thereby confirming accessibility as a critical mediator; and (3) significant spatial heterogeneity exists, revealing distinct correlation patterns across varying built environments. This research elucidates the pivotal constraint of “vertical location” and validates the mediating efficacy of accessibility, offering empirical insights for human-centric vertical urban planning. Full article

Traditional Agent-based Models (ABMs) often struggle to capture the nuance of adaptive human decision-making during complex crises due to their reliance on static, predefined rules. Large Language Models (LLMs) offer a transformative solution by acting as cognitive engines that empower agents with human-like common-sense reasoning. In this paper, we introduce an LLM-driven Multi-Agent Simulation framework to investigate coupled epidemic–economic dynamics, incorporating a Perception-Deliberation-Action (PDA) loop. Agents, acting as heterogeneous cognitive entities, utilize Chain-of-Thought processes to autonomously balance health risks against economic necessities. This approach endogenously generates adaptive behaviors without explicit scripting. Extensive experiment results across diverse LLM backends confirm the framework’s robustness, revealing divergent socio-economic trajectories under distinct macroscopic conditions and effectively quantifying the trade-offs between public health and economic stability. This approach establishes a high-fidelity computational laboratory for investigating complex scenarios under distinct macroscopic conditions, effectively bridging the gap between micro-level cognition and macro-level societal outcomes. Full article