Search Results (1,380)

Background/Objectives: Temporal attention and temporal expectation are two key mechanisms that facilitate perception by prioritizing information at specific moments and by leveraging temporal predictability, respectively. While their behavioral interaction is established, the underlying neural mechanisms remain poorly understood. Building on functional magnetic resonance imaging (fMRI) evidence linking temporal attention to parietal cortex activity and the role of alpha oscillations in temporal prediction, we investigated whether the right posterior parietal cortex (rPPC) may be involved in integrating these two processes. Methods: Experiment 1 used a behavioral paradigm to dissociate temporal expectation from attention across 600 ms and 1400 ms intervals. Experiment 2 retained only the 600 ms interval, combining behavioral assessments with electroencephalography (EEG), recording following transcranial alternating current stimulation (tACS) applied to the rPPC to probe neural mechanisms. Results: Experiment 1 showed an attention/expectation interaction exclusively at 600 ms: enhanced expectation improved response times under attended, not unattended, conditions. Experiment 2 replicated these behavioral and event-related potential (ERP) findings. Temporal attention modulated N1 amplitude: in attended conditions, the N1 was significantly more negative under high versus low expectation, while no difference was observed in unattended contexts. Anodal tACS over the rPPC reduced this N1 amplitude difference between high and low attentional expectation conditions to non-significance. Restricting analyses to attended conditions, paired-samples t-tests revealed that alpha-band power differed between high and low expectation under sham tACS, but this difference was absent under anodal tACS, which also attenuated the corresponding behavioral attention/expectation interaction effects. Conclusions: These findings provide suggestive evidence that the rPPC may be key to integrating temporal attention and expectation, occurring in early processing stages and specific to brief intervals. Full article

Background: Pain management for preterm infants has emerged as a key intervention aimed at enhancing their developmental trajectories. However, little is known regarding the response and recovery of the neonatal brain following procedural pain. This study examined the temporal dynamics of electroencephalography (EEG) power in preterm infants during and up to 30 min after procedural pain. Methods: fifty-seven datasets were collected from preterm infants (mean gestational age 32.5 ± 3.3 weeks). We computed Time–Frequency analysis for EEG power and EEG power ratio relative to baseline across eight EEG channels in the low delta (1–2 Hz), high delta (2–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), and beta (16–20 Hz) during the procedure, immediately after, and at intervals up to 30 min post-procedure. Results: EEG power increased significantly in all channels and frequency bands during the procedure compared to baseline (p < 0.05), declined immediately after but remained above baseline (p < 0.05), and recovered to near-baseline levels by four minutes post-procedure (p > 0.05), except for alpha and beta power at C3 and C4, which were lower than baseline (p < 0.05). The EEG power ratio at the frontal, occipital, and temporal showed the greatest power changes in the beta. The C3 and C4 exhibited the most prominent relative changes in the low delta. Conclusion: the preterm brain exhibits widespread responses to procedural pain and recovers gradually, not returning to the resting state for at least four minutes after a painful procedure. These results underscore the potential benefit of quantifying the time-integral of EEG power, rather than its peak intensity, when developing a biosensor for procedural pain using neonatal EEG. Full article

Traditional lie detection technologies, such as the polygraph and event-related potential (ERP)-based approaches, often face limitations in real-world applicability due to their sensitivity to psychological states and the complex, nonlinear nature of electroencephalogram (EEG) signals. In this study, we propose SparseMambaNet, a novel neural architecture that integrates the recently developed Bi-Mamba model with a Sparsely Activated Mixture of Experts (MoE) structure to effectively model the intricate spatio-temporal dynamics of EEG data. By leveraging the near-linear computational complexity of Mamba and the bidirectional contextual modeling of Bi-Mamba, the proposed framework efficiently processes long EEG sequences while maximizing representational power through the selective activation of expert networks tailored to diverse input characteristics. Experiments were conducted with 46 healthy subjects using a simulated criminal scenario based on the Comparison Question Technique (CQT) with monetary incentives to induce realistic psychological tension. We extracted nine statistical and neural complexity features, including Hjorth parameters, Sample Entropy, and Spectral Entropy. The results demonstrated that Sample entropy and Hjorth parameters achieved exceptional classification performance, recording F1 scores of 0.9963 and 0.9935, respectively. Statistical analyses further revealed that the post-response “answer” interval provided significantly higher discriminative power compared to the “question” interval. Furthermore, channel-level analysis identified core neural loci for deception in the frontal and fronto-central regions, specifically at channels E54 and E63. These findings suggest that SparseMambaNet offers a highly efficient and precise solution for EEG-based lie detection, providing a robust foundation for the development of personalized brain–computer interface (BCI) systems in forensic and clinical settings. Full article

The development of urban green infrastructures (UGI) is considered among the main nature-based solutions for climate mitigation in cities; however, the role of soils in the carbon (C) balance of UGI ecosystems remains largely overlooked. Urban green spaces are typically dominated by constructed Technosols, created by adding organic materials on top of former natural or agricultural subsoils. The combined effects of land-use history and current UGI management result in a high spatial variation of soil organic carbon (SOC) stocks and soil CO₂ emissions. Our study aimed to explore this variation for the case of Wageningen University campus. Developed on a former agricultural land, the campus area includes green spaces dominated by trees, shrubs, lawns, and herbs, with well-documented management practices for each vegetation type. Across the campus area (~32 ha), a random stratified topsoil sampling (n = 90) was conducted to map the spatial variation of topsoil (0–10 cm) SOC stocks. At the key sites (n = 8), representing different vegetation types and time of development (old, intermediate, and recent), SOC profile distribution was analyzed including SOC fractionation in surface and subsequent horizons, as well as the dynamics in soil CO₂ emissions, temperature, and moisture. Topsoil SOC contents on campus ranged from 1.1 to 5.5% (95% confidence interval). On average, SOC stocks under trees and shrubs were 10–15% higher than those under lawns and herbs. The highest CO₂ emissions were observed from soil under lawns and coincided with a high proportion of labile SOC fraction. Temporal dynamics in soil CO₂ emissions were mainly driven by soil temperature, with the strongest relation (R² = 0.71–0.88) observed for lawns. Extrapolating this relationship to the calendar year and across the campus area using high-resolution remote sensing data on surface temperatures resulted in a map of the CO₂ emissions/SOC stocks ratio, used as a spatial proxy for C turnover. Areas dominated by recent and intermediate lawns emerged as hotspots of rapid C turnover, highlighting important differences in the role of various UGI types in the C balance of urban green spaces. Full article

Investigating how climatic and hydrological conditions in ecological resource-enriched zones of marginal seas respond to external forcing, particularly during past greenhouse climates, holds considerable significance for understanding current environmental and resource challenges driven by global warming. In marginal seas, climatic hydrological states, including salinity, redox conditions, and productivity, are key environmental parameters controlling organic matter production, preservation, and ultimately the formation of high-quality shale. Herein, high-resolution cyclostratigraphic and multi-proxy geochemical analyses were conducted on a continuous core from the upper part of Member 4 of the Eocene Shahejie Formation (Es4cu) in Well NY1, Dongying Sag, Bohai Bay Basin. Based on these data, a refined astronomical timescale was accordingly established for the studied interval. By integrating sedimentological observations with multiple proxy indicators, including elemental geochemistry (e.g., Sr/Ba and Ca/Al ratios), organic geochemistry, and mineralogical data, the evolution of climate and paleo-water mass conditions during the study period was reconstructed. Spectral analyses revealed prominent astronomical periodicities in paleosalinity, productivity, and redox proxies, indicating that sedimentation was modulated by cyclic changes in eccentricity, obliquity, and precession. It was hereby proposed that orbital forcing governed periodic shifts in basin hydrology by regulating the intensity and seasonality of the East Asian monsoon. Intervals of enhanced summer monsoon associated with high eccentricity and obliquity were typically accompanied by increased sediment supply and intensified chemical weathering. Increased precipitation and runoff raised the lake level while promoting stronger connectivity with the ocean. In contrast, during weak seasonal monsoon intervals linked to eccentricity minima, basin conditions shifted from humid to arid, characterized by reduced precipitation, lower lake level, decreased sediment supply, and a concomitant decline in proxies for water salinity. The present results demonstrated orbital forcing as a primary external driver of cyclical changes in conditions favorable for resource formation in the Eocene lacustrine strata of the Bohai Bay Basin. Overall, this study yields critical paleoclimate evidence and a mechanistic framework for predicting the spatial-temporal distribution of high-quality shale under comparable astronomical-climate boundary conditions. Full article

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

High-precision temperature prediction is crucial for dealing with extreme weather events under the background of global warming. However, due to the limitations of computing resources, numerical weather prediction models are difficult to directly provide high spatio-temporal resolution data that meets the specific application requirements of a certain region. This problem is particularly prominent in areas with complex terrain. The use of remote sensing data, especially high-resolution terrain data, provides key information for understanding and simulating the interaction between land and atmosphere in complex terrain, making the integration of remote sensing and NWP outputs to achieve high-precision meteorological element downscaling a core challenge. Aiming at the challenge of temperature scaling in complex terrain areas of Southwest China, this paper proposes a novel deep learning model—Terrain Adaptive U-Net Transformer (TAUT). This model takes the encoder–decoder structure of U-Net as the skeleton, deeply integrates the global attention mechanism of Swin Transformer and the local spatiotemporal feature extraction ability of three-dimensional convolution, and innovatively introduces the multi-branch terrain adaptive module (MBTA). The adaptive integration of terrain remote sensing data with various meteorological data, such as temperature fields and wind fields, has been achieved. Eventually, in the complex terrain area of Southwest China, a spatio-temporal high-resolution downscaling of 2 m temperature was realized (from 0.1° in space to 0.01°, and from 3 h intervals to 1 h intervals in time). The experimental results show that within the 48 h downscaling window period, the TAUT model outperforms the comparison models such as bilinear interpolation, SRCNN, U-Net, and EDVR in all evaluation metrics (MAE, RMSE, COR, ACC, PSNR, SSIM). The systematic ablation experiment verified the independent contributions and synergistic effects of the Swin Transformer module, the 3D convolution module, and the MBTA module in improving the performance of each model. In addition, the regional terrain verification shows that this model demonstrates good adaptability and stability under different terrain types (mountains, plateaus, basins). Especially in cases of high-temperature extreme weather, it can more precisely restore the temperature distribution details and spatial textures affected by the terrain, verifying the significant impact of terrain remote sensing data on the accuracy of temperature downscaling. The core contribution of this study lies in the successful construction of a hybrid architecture that can jointly leverage the local feature extraction advantages of CNN and the global context modeling capabilities of Transformer, and effectively integrate key terrain remote sensing data through dedicated modules. The TAUT model offers an effective deep learning solution for precise temperature prediction in complex terrain areas and also provides a referential framework for the integration of remote sensing data and numerical model data in deep learning models. Full article

This study investigates how temporal fluency cues shape human ratings of L2 English pronunciation in Korean learners, using a large read-speech corpus annotated with five-point pronunciation scores. We focus on two timing-derived measures—articulation rate (AR) and mean silence duration (SilMean)—and examine whether these cues predict (i) articulation-accuracy ratings and (ii) prosody/fluency ratings. To account for dependencies in corpus data and to control for key learner- and task-level covariates, we fitted cumulative link mixed models with random intercepts for speakers and scripts, including proficiency band (ability), age, gender, and test type as fixed effects. Across models, faster articulation and shorter silent intervals were associated with higher articulation ratings, and a combined model including both AR and SilMean provided the best fit (lowest AIC). Temporal cues were even more strongly associated with prosody ratings, supporting construct alignment between timing measures and the prosody dimension of the rubric. Marginal predicted probabilities illustrate how the likelihood of receiving high ratings (score ≥ 4) increases with AR across proficiency and linguistic-complexity strata (with SilMean held constant), and how long silent intervals reduce these probabilities when AR is held constant. These findings indicate that temporal organization provides robust information about perceived pronunciation quality in read L2 speech and underscore the importance of construct-aware modeling when developing AI-based scoring and feedback systems trained on human-labeled data. Full article

Computational musicology and music information retrieval research on Korean Pansori requires reliable analysis of vocal energy and tempo variation across rhythmic patterns known as jangdan. In this work, a jangdan is treated as a downbeat period: analogous to downbeats in Western music, it denotes both a rhythmic pattern type and the temporal span between two consecutive downbeats. Under this formulation, jangdan tracking is equivalent to downbeat tracking, allowing conventional downbeat-tracking methods to be directly applied to Pansori. Downbeat tracking in Pansori is challenging due to expressive rhythmic cycles, flexible tempi, and sparse accompaniment, which limit the generalization of systems trained on Western music. This paper proposes a rhythm-pattern-aware downbeat (i.e., jangdan) tracking framework based on offline and online Temporal Convolutional Networks (TCNs) and RoFormer-based models. A jangdan-aware Dynamic Bayesian Network (DBN) constrains minimum and maximum downbeat intervals using prior rhythmic knowledge. Using 22.4 h of annotated Pansori recordings, the proposed approach consistently outperforms general-purpose downbeat trackers across all jangdan patterns, with the offline RoFormer and tuned DBN achieving the strongest results. The improved jangdan inference enables detailed analysis of vocal energy and tempo variation. An A-weighted, beat-level vocal energy labeling method reveals characteristic energy contours aligned with specific jangdan cycles, while tempo analysis shows how performers modulate pacing in relation to rhythmic structure. These results demonstrate that identifying jangdan as a downbeat analog and incorporating rhythm-pattern-aware decoding substantially improves downbeat reliability and enables fine-grained analysis of temporal expressivity in Korean Pansori. Full article

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. Full article

This study assessed the accuracy of the EXERGEN TAT-5000 temporal scanner (T_EXERGEN) (EXERGEN, Watertown, MA, USA) for estimating core body temperature (T_CORE) during rest, progressive cycling exercise, and post-exercise recovery in a hot environment. Fourteen healthy adults (7 men and 7 women) completed a laboratory protocol consisting of 10 min of rest, 60 min of cycling, and 25 min of recovery at an ambient temperature of 32 °C and a relative humidity of 60%. Gastrointestinal temperature (T_Gi), measured via telemetry capsules, served as the criterion method. A total of 5376 paired measurements were analyzed. Throughout the protocol, T_EXERGEN systematically underestimated T_CORE compared with T_Gi, with mean biases between −0.35 °C and −1.15 °C. The overall 95% confidence intervals ranged from ±0.91 to ±1.43 °C, demonstrating poor precision. Limits of agreement were wide (from −2.00 to 0.87 °C), and concordance correlation coefficients (CCC) indicated poor agreement (CCC < 0.90 in all conditions). The underestimation was more pronounced during exercise and recovery, when T_CORE remained high according to T_Gi but decreased according to T_EXERGEN. These results indicate that T_EXERGEN does not monitor T_CORE accurately under heat stress or during rapid metabolic changes. Therefore, the use of this device is not recommended during and after exercises under environmental heat stress. Full article

Hollowness of the cucumber fruit, caused by carpel separation during growth, severely impacts fruit quality. Several Sikkim cucumber accessions originating from the India–Pakistan region exhibit pronounced internal cavities. We previously identified the QTL mfh2.1 as a key contributor to this phenotype. In this study, we investigated the genetic and physiological basis of fruit hollowness in the Sikkim cucumber line WI7120 through an integrative analysis combining histological staining, HPLC for hormonal profiling, and fine mapping using a large F2 segregation population. Comparative analysis between the hollow-fruited WI7120 and the non-hollow line 9930 revealed distinct growth dynamics: WI7120 displayed accelerated radial expansion and aberrant cell patterning at carpel junctions. Histological examination using paraffin sectioning uncovered disorganized endocarp cell arrangements in WI7120 occurring as early as pre-anthesis (0 days post-pollination), with enlarged suture cells that likely facilitate tissue separation during fruit enlargement. Hormonal assays indicated elevated levels of gibberellin (GA) and zeatin (ZT), along with reduced indole-butyric acid (IBA) in WI7120, suggesting that a hormonal imbalance and mechanical stress contribute to compromised cell adhesion. By screening ~2000 F₂ individuals with SSR and InDel markers, we refined the mfh2.1 locus to a 50.92 kb interval on chromosome 2, pinpointing CsRPT4Bb—encoding a 26S proteasome subunit—as the candidate gene. A non-synonymous SNP (I135V) in CsRPT4Bb was associated with tissue-specific expression patterns during cavity formation, implicating proteasome-mediated cellular remodeling in carpel cohesion. Spatial-temporal expression analysis further revealed upregulation of CsRPT4Bb in the WI7120 exocarp during fruit expansion, potentially influencing cell wall dynamics. This study demonstrates a coordinated interplay among genetic, hormonal, and mechanical factors underlying cucumber fruit hollowness, offering new avenues for breeding cultivars with improved fruit integrity and postharvest quality. Full article

In a situation where cities are increasingly being developed vertically and complexly, a novel approach for analyzing vertical urban functional patterns is proposed. For this purpose, a multi-dimensional GDC (Geo Data Cube) consisting of spatial and temporal data x, y, z, t, and f dimensions containing layer information was created. At this time, the size of the GDC cell (interval in x, y, z dimensions) is calculated by cell point data using the three-dimensional (3D) Moran’s I index value calculated with the 3D Diversity Factor (DF) based on information entropy proposed to reduce the uncertainty of information for each cell. In other words, the cell with the smallest index value was chosen to minimize the influence of Modifiable Areal Unit Problem (MAUP) that occurs when mapping. The 3D land use index (3D LUI) is calculated as a linearly weighted sum of the spatial accessibility of uses between cells (3D KDF) and the enrichment of uses (3D EF), taking into account the first law of geography. Finally, the 3D LUI value for each use was calculated for each cell of the GDC, and the use with the highest value was determined as the urban function of the cell. As a result of applying this to Seocho-gu, Seoul, Republic of Korea (ROK) in June 2024 and visually evaluating it using the street view provided by Kakao Map, it was confirmed that commercial and residential functions were vertically separated in buildings with residential–commercial complexes or shops on the ground floor. It was also confirmed that such characteristics did not appear in the two-dimensional (2D) urban functional patter analysis. Full article

Background: Evidence guiding secondary repair of persistent full-thickness macular holes (FTMHs) remains limited and heterogeneous. Temporal arcuate relaxing retinotomy has been described as a salvage maneuver intended to increase temporal retinal compliance, yet functional safety data are scarce. We report consecutive real-world outcomes of temporal arcuate relaxing retinotomy for persistent FTMHs after failed standard repair(s). Methods: Retrospective consecutive case series of patients with persistent FTMH after ≥1 pars plana vitrectomy (PPV) with internal limiting membrane (ILM) peeling, treated with repeat PPV and temporal arcuate relaxing retinotomy. Outcomes included OCT (Optical Coherence Tomography)-confirmed closure after gas absorption and best-corrected visual acuity (BCVA, logMAR), ellipsoid zone (EZ) status, retinotomy-site morphology on OCT/fundus autofluorescence (FAF), and safety/functional outcomes (systematic scotoma symptom inquiry; Humphrey visual field testing when feasible). Exact binomial 95% confidence intervals (CI) were calculated for proportions. Results: Nine eyes (median age 70 years; range 55–76) underwent temporal arcuate relaxing retinotomy for persistent FTMH. Minimum linear diameter ranged 412–1037 µm (median 613 µm). OCT-confirmed closure was achieved in 7/9 eyes (77.8%; 95% CI 40.0–97.2) at a mean follow-up of 5.9 months (range 2–12). BCVA improved in 8/9 eyes (88.9%; 95% CI 51.8–99.7); mean BCVA improved from 1.26 ± 0.51 logMAR pre-operatively to 0.61 ± 0.18 logMAR at last follow-up (mean change −0.64 logMAR; Wilcoxon signed-rank test p = 0.011). As a sensitivity analysis, the paired t-test yielded p = 0.008. Humphrey visual fields were obtained in 6/9 eyes; one patient reported a new paracentral nasal scotoma, which was subjectively well tolerated. Conclusions: In this small consecutive series, temporal arcuate relaxing retinotomy was associated with a 78% closure rate and mean BCVA improvement in eyes with persistent FTMH after failed standard repair(s), with limited symptomatic scotoma reporting in those assessed. Given the retrospective design, small cohort, and incomplete standardized functional testing, larger comparative studies with uniform functional endpoints (microperimetry, RNFL/GCL metrics, and systematic perimetry) are needed to define patient selection, reproducibility, and relative performance versus contemporary salvage options. Full article

This study investigates peak daylight exposure in zenithally lit museum rooms at 51° latitude through an experimental campaign using a 1:20 physical mock-up of a 12 × 12 × 6 m exhibition gallery space. Nine configurations of shading and light-transmitting elements (CSaLTE) were tested under real clear-sky conditions between June and October. To ensure a valid comparative analysis, indoor vertical illuminance (E_v) was measured at 15 min intervals and subsequently interpolated and normalised to a unified equinox-day solar geometry (06:00–18:00). This hybrid empirical-computational methodology allows for a direct performance comparison across different geometric arrangements regardless of their specific measurement dates. The results demonstrate that while traditional annual metrics are the standard, short-term illuminance peaks pose a severe and underexplored threat to conservation safety. Even the most light-attenuating diffusing-roof configurations produced short-term illuminance peaks and cumulative clear-sky exposures that are comparable in magnitude to commonly cited annual limits for highly light-sensitive materials, with several configurations recording extreme spikes surpassing the sensor’s 20,000 lx saturation limit. Stable, low-illuminance distributions were observed only in selected diffusing-roof arrangements (M05–M07), whereas direct-glazing systems (M01–M04) produced unsafe exposure patterns with high temporal variability and poor visual adaptation conditions. The study concludes that passive roof geometries alone are insufficient to ensure conservation-level safety without additional active filtering or adaptive control strategies, providing an experimentally grounded framework for designing zenithal daylighting systems in museum environments. The results are intended for relative peak-risk comparison under controlled clear-sky conditions rather than direct generalisation to whole-room annual conservation safety. Full article