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Players' museum authoring game experience based on art expertise level: Case study of Occupy White Walls
No full textThis study explores differences in the virtual museum authoring game Occupy White Walls based on players' art expertise levels. We identified key distinctions between novice and expert players through thematic analysis of behavior observation, think-aloud, and semi-structured interviews with 12 players. Four themes emerged: authoring focus, authoring autonomy, asset recommendations, and asset manipulation. Experts spent more time on exhibition planning, emphasizing storytelling and autonomy in displaying artwork. In contrast, novices focused more on constructing virtual museum spaces, facing challenges in planning and curation while investing significant effort in spatial design. These differences highlight the role of art expertise in shaping gameplay experiences. The study underscores the need for expertise-sensitive game design in virtual museum-authoring environments. By analyzing player interactions in OWW, we propose recommendations for adaptive design strategies to foster engagement and personalization, ensuring a more inclusive and rewarding experience for players of varying expertise levels.
Integrated earth observation satellite scheduling with relay-assisted downlink
No full textEfficient scheduling of Earth observation satellites is crucial for maximizing observational coverage and ensuring timely data transmission within limited operational periods. The integration of data relay satellites significantly enhances communication capabilities for low Earth orbit observation satellites, effectively addressing limitations posed by conventional direct-downlink methods. This study presents an optimization-based approach for integrated satellite scheduling, explicitly incorporating observation tasks and a hybrid downlink strategy that combines direct and relay-assisted downlink paths. The proposed formulation systematically considers essential mission constraints such as task sequencing, data consistency requirements, and specific operational time windows to identify optimal scheduling solutions. Simulation results demonstrate that the integrated optimization model with data relay capabilities quantitatively increases the number of successfully observed and transmitted targets by a maximum of 76.3 % compared to conventional non-integrated approaches, and by a maximum of 335.4% compared to approaches without data relay capabilities under exact optimization conditions. These results highlight the effectiveness of the proposed method for satellite mission planning.
Causal unsupervised semantic segmentation
No full textUnsupervised semantic segmentation aims to achieve high-quality semantic grouping without human annotations. With the advent of self-supervised learning, various frameworks utilize pre-trained features for the unsupervised prediction. However, a significant challenge in this unsupervised setup is determining the appropriate level of granularity required for segmenting concepts. To address it, we propose a novel framework, CAusal Unsupervised Semantic sEgmentation (CAUSE), which leverages insights from causal inference. Specifically, we bridge an intervention-oriented approach to define two-step unsupervised prediction: (i) constructing a discretized concept clusterbook as a mediator, representing concept prototypes, (ii) concept-wise self-supervised learning for pixel-level grouping using an explicit link from the mediator. Through extensive experiments, we corroborate the effectiveness of CAUSE and achieve state-of-the-art in unsupervised semantic segmentation.
Meta-heuristic-based design of high-order stable digital filters using pole-zero placement
No full textThis study presents a meta-heuristic optimization approach for digital IIR filter design that addresses fundamental limitations of conventional coefficient-based methods. Rather than optimizing filter coefficients directly, the proposed method identifies optimal locations of zeros, poles, and gain in the z-plane for a given frequency response. This pole-zero formulation provides an intuitive framework for managing filter characteristics, particularly stability constraints. The fitness function simultaneously optimizes magnitude and phase responses, enabling frequency response shaping for a wide range of applications. Extensive simulations across four complex design scenarios-including low-order filter, low-pass filters, curved frequency responses, and stabilized inverse systems-demonstrate the algorithm's superior performance compared to related work for high-order implementations. Results show that the proposed approach maintains strong exploration capability even in high-dimensional optimization landscapes while guaranteeing stable filter realizations. This methodology provides engineers with a flexible and reliable tool for prototyping digital filters that accommodate specific operational requirements beyond conventional filter designs.
Near-field thermophotovoltaic heat exchanger for harvesting extremely high-density thermal energy
No full textThermophotovoltaic systems are solid-state heat engines that convert heat from various sources, such as solar radiation and combustion gases, into electricity. Through spectral control of thermal radiation, efficiencies up to 44% have been demonstrated from a single-junction thermophotovoltaic cell. Thermophotovoltaic systems benefit from harnessing near-field radiation through photon tunneling across nanogaps, enabling compact utilization of high-density thermal energy. To further enhance output from a fixed footprint, higher thermal energy density is required, necessitating extension of the power generation area beyond the heat input area. However, existing system geometries for such extension enlarge the footprint and intensify electrical resistive loss, particularly in near-field thermophotovoltaic systems. Here, a near-field thermophotovoltaic heat exchanger is proposed, which achieves efficient area extension by compactly stacking thermophotovoltaic units, enabling a power generation area tens of times larger than the footprint while minimizing electrical resistive loss. Through system optimization, the near-field thermophotovoltaic heat exchanger generates 228.5 W of electrical power from a 1 cm2 footprint with a 1600 K heat source, i.e., an order of magnitude higher output than conventional near-field thermophotovoltaic systems, with an efficiency of 36.8%. A far-field thermophotovoltaic heat exchanger designed without nanogaps also achieves threefold higher power output. The near-field thermophotovoltaic heat exchanger achieves a mass-specific power of up to 140.3 kW/kg, significantly surpassing competing systems, while being scalable by increasing the number of thermophotovoltaic units. This strategy offers a compact and scalable pathway to advance thermophotovoltaic technology and can be extended to solid-state energy converters such as thermophotonic, thermoradiative, and thermoelectric engines.
Microkinetic insights into the impact of coking in dry reforming of methane
No full textCoking remains one of the most critical challenges in dry reforming of methane (DRM), causing catalyst deactivation and severe performance loss. While microkinetic modeling (MKM) can capture reaction dynamics at the elementary-step level, existing DRM models lack the ability to represent the evolving nature of coke formation and its mechanistic impact on the reaction network. This study introduces a novel coke-inclusive MKM that explicitly incorporates coke formation pathways and is experimentally validated against DRM data. To interpret the complex, time-dependent behavior of coking, we develop a novel phase-based framework that systematically segments coke accumulation into distinct temporal regimes, each characterized by unique rates and patterns of carbon buildup. Phase-specific mechanistic analysis reveals a gradual shift in the dominant reaction pathways as coking progresses. Early-stage coke formation involves a broad set of surface reactions, opening multiple opportunities for targeted intervention, whereas later stages show a concentration of coking influence in a few critical reactions, such as methane decomposition and CO2 adsorption. To enhance practicality, a reduced-order coke-inclusive MKM is constructed, retaining essential kinetic features while greatly improving computational efficiency. This integrated modeling strategy - the first to combine a coke-inclusive MKM with phase-based analysis - provides a powerful bridge between detailed reaction mechanisms and application-focused catalyst and reactor design, offering new tools to improve catalyst durability and advance the sustainability of DRM systems.
The portfolio effect in the power sector of economies prioritizing renewable energy
No full textThis paper applies modern portfolio theory to analyze the power sector portfolio effect in economies with a significant share of renewable energy. This study contributes to the literature by adopting a socio-economic perspective instead of relying solely on financial analysis, while also incorporating up-to-date data on renewable technology capabilities and costs. It explores the optimal combination of electricity-generating assets by considering both cost and risk using the levelized cost of electricity. The study compares current and future power generation mixes, showing that a high share of renewable energy can enhance energy security by reducing cost fluctuations. It suggests that addressing nuclear safety concerns could accelerate the adoption of less cost-competitive renewable technologies. The research emphasizes that efficient power generation mixes require consideration of both cost and risk, and recommends a carefully phased investment approach to renewable energy technologies for countries where renewables are less cost-competitive than fossil fuels.
Smart home healthcare using artificial intelligence of things: Emergency prediction and prevention for cerebrovascular disease patients
No full textCerebrovascular disease (CeVD) is a major cause of mortality and disability, necessitating continuous monitoring for timely intervention. Home environments are effective for contactless health monitoring, as CeVD risk factors can be continuously tracked without obtrusion. This study leveraged smart home data and artificial intelligence (AI) to predict CeVD emergencies early. The dataset included individual health conditions and sequential life-logs, such as physical activity, sleep, and thermal environment, from 1130 CeVD patients, including 130 emergencies. We achieved an area under the precision-recall curve (AUPRC) of 0.94 with 28 days of data, corresponding to an accuracy of 98.2 %, precision of 97.1 %, recall of 87.2 %, and an F1-score of 91.9 %, using the CrossNet architecture, which integrates static and time-series data while handling missing values by cross-modal imputation. We further identified emergency prevention strategies, including: increasing both low and high active time by 1 and 1.5 h/day, while decreasing inactive time for patients aged 85+; limiting high active time while increasing low active time for patients aged under 85; maintaining 7 h of sleep for cardiovascular patients (8 h if cardiovascular); minimizing sleep fragmentation for patients aged 85+ and with diabetes; and in general, cold indoor temperatures increase the emergency risk, while hot indoor temperatures are risky in cold weather. These findings highlight the potential of smart home monitoring based on the artificial intelligence of things (AIoT) to predict emergencies and identify prevention strategies. This study provides insights into scalable, contactless, AI-enabled home healthcare solutions for continuous management of CeVD.
An integrated framework for reliability analysis and design optimization using input, simulation, and experimental data: Confidence-based design optimization under aleatory and epistemic uncertainty
No full textEngineering systems are inherently influenced by aleatory variability, and quantifying this uncertainty from data and understanding how it propagates to system responses remain significant challenges in reliability analysis and design optimization. In simulation-based reliability analysis, three types of interrelated epistemic uncertainty may arise when only limited data are available: (i) input model uncertainty in the statistical characterization of observable random parameters, (ii) surrogate model uncertainty in Gaussian process (GP) emulators of simulations, and (iii) calibration uncertainty introduced when inversely inferring unobservable random parameters and model discrepancy from experimental data. Although each type of uncertainty has been widely studied, no research has combined all three in a single framework and propagated their effects to the uncertainty in reliability. We propose an integrated framework that quantifies both aleatory and epistemic uncertainty from three complementary data sources - observations for the input model, simulation data for surrogate modeling, and experimental data for model calibration - and propagates their effects through reliability analysis, thereby enabling estimation of the resulting confidence level given limited data. Furthermore, it supports confidence-based design optimization (CBDO) to minimize the objective while achieving a reliable and conservative optimum under epistemic uncertainty. The framework's effectiveness is demonstrated through mathematical examples and an application to a thermoelectric generator (TEG) system.
Advanced non-hierarchical co-Kriging using latent map multi-output Gaussian process
No full textTo achieve the accuracy of high-fidelity models at a reduced computational cost, multi-fidelity modeling techniques have been developed to incorporate low-fidelity data into surrogate model construction. Among them, non-hierarchical multi-fidelity methods have gained attention due to their ability to construct multi-fidelity models without a prescribed hierarchy among multiple low-fidelity outputs. However, current non-hierarchical multi-fidelity methods face significant challenges in capturing complex correlations among low-fidelity sources and between high-fidelity and low-fidelity datasets; linear combination models often neglect inter-source dependencies, and latent variable-based approaches such as the latent map Gaussian process are limited by fixed low-dimensional latent spaces and the absence of discrepancy modeling for the high-fidelity response. These limitations hinder both accuracy and robustness, particularly in data-scarce settings. To address these issues, this paper proposes an advanced non-hierarchical multi-fidelity framework based on a latent map multi-output Gaussian process. The proposed method models low-fidelity correlations via multi-output Gaussian processes and captures relationships between high- and low-fidelity through co-Kriging, including an explicit discrepancy term. In latent map multi-output Gaussian process, a decomposition-based optimization scheme is introduced to estimate higher-dimensional latent coordinates, enhancing both model flexibility and robustness. Furthermore, low-fidelity weights are estimated from inter-fidelity correlations derived from the latent map multi-output Gaussian process, rather than being determined by conventional tuning criteria. Numerical and engineering examples demonstrate that the proposed method achieves superior accuracy and stability compared to existing non-hierarchical multi-fidelity approaches.