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Immersive HCI for Intangible Cultural Heritage in Tourism Contexts: A Narrative Review of Design and Evaluation
No full textImmersive technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and multisensory interaction are increasingly deployed to support the transmission and presentation of intangible cultural heritage (ICH), particularly within tourism and heritage interpretation contexts. In cultural tourism, ICH is often encountered through museums, heritage sites, festivals, and digitally mediated experiences rather than through sustained community-based transmission, raising important challenges for interaction design, accessibility, and cultural representation. This study presents a narrative review of immersive human–computer interaction (HCI) research in the context ICH, with a particular focus on tourism-facing applications. An initial dataset of 145 records was identified through a structured search of major academic databases from their inception to 2024. Following staged screening based on relevance, publication type, and temporal criteria, 97 empirical or technical studies published after 2020 were included in the final analysis. The review synthesises how immersive technologies are applied across seven ICH domains and examines their deployment in key tourism-related settings, including museum interpretation, heritage sites, and sustainable cultural tourism experiences. The findings reveal persistent tensions between technological innovation, cultural authenticity, and user engagement, challenges that are especially pronounced in tourism context. The review also maps the dominant methodological approaches, including user-centred design, participatory frameworks, and mixed-method strategies. By integrating structured screening with narrative synthesis, the review highlights fragmentation in the field, uneven methodological rigour, and gaps in both cultural adaptability and long-term sustainability, and outlines future directions for culturally responsive and inclusive immersive HCI research in ICH tourism
Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks
No full textCollectives are often able to process information in a distributed fashion, surpassing each individual member’s processing capacity. In fission-fusion dynamics, where group members come together and split from others often, sharing complementary information about uniquely known foraging areas could allow a group to track a heterogenous foraging environment better than any group member on its own. We analyse the partial overlaps between individual spider monkey core ranges, which we assume represent the knowledge of an individual during a given season. Sets of individuals with complementary overlaps are identified, showing a balance between redundantly and uniquely known portions, and we use simplicial complexes to represent these higher-order interactions. The structures of the simplicial complexes show holes in various dimensions, revealing complementarity in the foraging information that is being shared. We propose that the complex spatial networks arising from fission-fusion dynamics allow for adaptive, collective processing of foraging information in dynamic environments
Investigating the voltaic efficiency of 3D-printed macro-patterned electrodes for hydrogen evolution reactions in water electrolysis
No full textConventional electrodes of water electrolysis face limitations in mass transport and bubble detachment, hindering sustainable hydrogen production. This study investigates the enhancement of hydrogen evolution reaction (HER) efficiency in water electrolysis using 3D-printed macro-patterned 17-4 PH-grade stainless steel electrodes. Leveraging additive manufacturing, stainless steel-based electrodes were fabricated via 3D printing, debinding and sintering, featuring three distinct macro-patterns namely small and large semi-spherical dimples, as well as pyramidal pits. Electrochemical testing using chronoamperometry and efficiency calculations, using KOH electrolyte in a H-cell setup, revealed that patterned electrodes significantly outperformed their flat counterparts. Results show up to a 6.5-percentage point higher voltaic efficiency, and visual observation revealed enhanced bubble detachment. Scanning Electron Micrography (SEM) imaging confirmed inherent microporosity from 3D printing, increasing active surface area. The pyramidal-pit electrode initiated HER at lower voltages, while dimpled designs achieved higher peak current densities. The experimentally measured current densities showed good agreement with the Butler–Volmer model with electrode surface bubble coverage considered. An empirical model developed, shows a strong correlation between the cell’s normalised voltaic efficiency, the non-dimensional current density and the non-dimensional surface area, highlighting the critical role of surface geometry in the efficiency of electrolysis cells. Gold coating reduced ohmic losses but did not consistently improve hydrogen yield. These results add to the growing experimental evidence that 3D-printed macro-patterns are beneficial, and in this case, enabled by an innovative metal additive manufacturing process. HER voltaic efficiency is boosted by at least 5 percentage points for a flat electrode of the same form factor through optimised bubble management and surface area. The study hence underlines the importance of patterned electrodes for industrial green hydrogen production with attendant tangible economic and sustainability benefits
Techno-economic Evaluation of the Effects of Decarbonisation and Waste Heat Utilisation on Green Hydrogen Produced from Microalgae Steam Gasification
No full textA comprehensive techno-economic evaluation of the application of steam gasification to different microalgae for hydrogen production was performed. The production of hydrogen-rich syngas from three microalgal feedstocks (namely S. Acuminatus; S. Almeriensis and a co-culture of the two microalgae) was firstly considered and then separated to produce hydrogen. The flue gases were decarbonised using Calcium Looping Combustion (CaLC) process. Since decarbonisation comes with a significant energy penalty, waste heat utilization (WHU) was applied to the CaLC for combined heat and power generation. S. Acuminatus produced the highest amount of syngas and correspondingly the greatest amount of hydrogen (157.3 kg hr−1). The yields of the produced decarbonised hydrogen of the three microalgae ranged from 13.4 to 16.5%, higher than published values ranging from 8.0 to 12.0%. Since S. Acuminatus showed the most favourable technical performance, an economic analysis was performed, resulting in levelised costs of hydrogen (LCOH) ranging from 2.46 to 3.87 $/kg. Optimising the operation conditions of CaLC process resulted in an overall decarbonisation of 97% with final carbon emission intensities less than 0.5 kg CO2/kg H2, less than reported intensities for green hydrogen production. Implementing CaLC process resulted in energy penalties ranging from 10.36 to 11.24%. Nevertheless, applying WHU resulted in overall surplus in energy generation and overall plant efficiency. Finally, CO2 abatement costs were estimated and found to be comparable to published values. Overall, this research proves that steam gasification of microalgae combined with CaLC technology is a feasible option for green hydrogen production
Multi-Modal Multi-Stage Multi-Task Learning for Occlusion-Aware Facial Landmark Localisation
No full textThermal facial imaging enables non-contact measurements of face heat patterns that are valuable for healthcare and affective computing, but common occluders (glasses, masks, scarves) and the single-channel, texture-poor nature of thermal frames make robust landmark localisation and visibility estimation challenging. We propose M3MSTL, a multi-modal, multi-stage, multi-task framework for occlusion-aware landmarking on thermal faces. M3MSTL pairs a ResNet-50 backbone with two lightweight heads: a compact fully connected landmark regressor and a Vision Transformer occlusion classifier that explicitly fuses per-landmark temperature cues. A three-stage curriculum (mask-based backbone pretraining, head specialisation with a frozen trunk, and final joint fine-tuning) stabilises optimisation and improves generalisation from limited thermal data. On the TFD68 dataset, M3MSTL substantially improves both visibility and localisation: the occlusion accuracy reaches 91.8% (baseline 89.7%), the mean NME reaches 0.246 (baseline 0.382), the ROC–AUC reaches 0.974, and the AP is 0.966. Paired statistical tests confirm that these gains are significant. Our approach aims to improve the reliability of temperature-based biometric and clinical measurements in the presence of realistic occluders
This is our rhythm: academic becoming and realignment in deaf space
No full textDeaf scholars have long worked at the margins of academic institutions not designed for them. Designated deaf academic spaces—where deaf ways of knowing, teaching, and communicating are centered—remain rare. This study explores what becomes possible when such a space exists, presenting Dr Deaf as a case study. Drawing on interviews with participants and teachers, we show how deaf epistemologies and pedagogies are enacted through cross-stage responsibility and academic becoming through re-alignment of deaf participants and teachers. We also identify a distinct deaf rhythm that emerges in this space. At the same time, we recognize that these practices are not experienced or valued equally by all participants and teachers: needs, priorities, and ways of engaging differ, and Dr Deaf’s approaches may not resonate for all. Yet its values offer a flexible framework for imagining and sustaining other deaf academic and broader educational spaces
Short hierarchically hyperbolic groups II: Quotients and the Hopf property for Artin groups
No full textWe prove that most Artin groups of large and hyperbolic type are Hopfian, meaning that every self-epimorphism is an isomorphism. The class covered by our result is generic, in the sense of Goldsborough-Vaskou. Moreover, assuming the residual finiteness of certain hyperbolic groups with an explicit presentation, we get that all large and hyperbolic type Artin groups are residually finite. We also show that “most” quotients of the five-holed sphere mapping class group are hierarchically hyperbolic, up to taking powers of the normal generators of the kernels.The main tool we use to prove both results is a Dehn-filling-like procedure for short hierarchically hyperbolic groups (these also include e.g. non-geometric 3-manifolds, and triangle- and square-free RAAGs)
Digital Twin Modeling for Acanthamoeba Keratitis: From Empirical Therapy to Predictive Ophthalmology
No full textAcanthamoeba keratitis is a rare, vision-threatening corneal infection that remains difficult to diagnose and treat, with therapy often extending for many months. Despite recent advances, the management of Acanthamoeba keratitis still depends largely on empirical regimens combining biguanides, diamidines, and azoles. Outcomes vary widely, reflecting differences in pathogen virulence, drug penetration, host response, and timing of diagnosis. It is proposed that digital-twin technology offers a powerful new framework for studying and managing this disease. Digital twin is a data-driven computational approach that creates continuously updating virtual replicas of biological systems. By integrating multimodal clinical, imaging, and molecular data, digital twins could simulate corneal infection dynamics, drug diffusion, and cyst reactivation, providing clinicians with predictive insight rather than retrospective interpretation. Here, it is discussed how digital-twin models could be constructed for Acanthamoeba keratitis, challenges to implementation, and implications for precision ophthalmology
Exploring spatiotemporal heterogeneity and nonlinear effects in electric vehicle crash risk prediction: A hybrid modeling approach
No full textElectric vehicle (EV)-related risk and uncertainty pose critical challenges for urban traffic management. Fine-grained crash risk prediction at 1 km × 1 km and hour-of-day resolution remains difficult due to rapidly evolving, strongly spatiotemporally heterogeneous crash patterns. Crash risk research spans risk measurement, prediction modeling, and factor selection, with a move toward interpretable nonlinear hybrid methods, yet temporal dynamics and local heterogeneity remain insufficiently modeled. This study addresses these limitations by first constructing a Spatio-Temporal Adaptive Network Kernel Density Estimation (ST-ANKDE) method that combines network-constrained proximity, cyclic time weighting, severity weighting, and adaptive bandwidths, and then developing a Multiscale Geographically and Temporally Weighted Regression–Extreme Gradient Boosting (MGTWR-XGBoost) method to learn local heterogeneity and nonlinear effects. To capture the influence of preceding periods and adjacent grids, we introduce temporal and spatial weighted crash risk variables (T-AccRisk and S-AccRisk). These are analyzed alongside road-network density, built-environment variables, socioeconomic variables, and EV-specific infrastructure variables. An empirical case study on 14,818 EV crashes shows that ST-ANKDE effectively captures crash risk dynamics, with a mean value of 6.57, and reveals pronounced spatiotemporal heterogeneity. The results show that MGTWR-XGBoost, enhanced by S-AccRisk and T-AccRisk to capture spatiotemporal dependence, achieves MAE = 1.54 and RMSE = 2.06 and outperforms standalone machine learning and other hybrid methods; road-network density, built-environment features, population density, and EV infrastructure coefficients exhibit significant spatiotemporal heterogeneity. Moreover, SHapley Additive exPlanations (SHAP) further analyzes nonlinear effects. These findings enable grid-level early warning, priority targeting of high-risk periods/locations, and data-driven deployment of enforcement and infrastructure for EV safety management
Techno-economic evaluation of emission control configurations for AMP/PZ-based post-combustion CO₂ capture
No full textMinimizing the environmental impacts of amine-based post-combustion carbon capture technologies is essential for regulatory compliance and public acceptance. Experimental campaigns at RWE's CO2 capture pilot plant in Niederaussem using CESAR1 demonstrated that combining existing emission abatement technologies can substantially reduce the concentration of amines and degradation products in the CO2-depleted flue gas to below the detection limit of an infrared spectrometer. The campaign confirmed that a proprietary dry bed technology (OEASE aerozone™) or a second water wash can reduce AMP and PZ emissions to below 1 mg/Nm3. However, an acid or chemically active wash downstream of the water wash is necessary to reduce NH3 emissions to very low levels (<2 mg/Nm3). Combining a dry bed with an acid wash significantly reduces both the required acid solution and the resulting acid waste. The techno-economic analysis indicates that implementing emission mitigation technologies provides substantial environmental benefits for the CESAR1 process, with only a marginal increase (<1 €/tCO₂) in both the carbon capture cost (CCC) and CO₂ avoidance cost (CAC). The additional capital cost associated with extra column packing is offset by operational cost savings from reduced solvent losses. For stringent emission permits targeting NH₃, a configuration combining a dry bed upstream of the water wash followed by an acid wash achieves the best balance between emission control and cost efficiency. This configuration results in a Levelized Cost of Electricity (LCOE) of 143 €/MWh, a CCC of 45.4 €/tCO₂, and a CAC of 88.4 €/tCO₂.</p
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