Applied Sciences

Museum and archive storage facilities worldwide face persistent difficulties in maintaining stable microclimatic conditions. This study presents a Smart Archival Box, a modular hardware–software system for preventive conservation, together with a model-based evaluation of its microclimate control. The numerical domain has a nominal volume of 0.125 m³ (0.5 m × 0.5 m × 0.5 m) and combines a 1 kg ArtSorb cassette, four TEC1-12706 thermoelectric modules, particulate/activated-carbon filtration, distributed monitoring and RFID-based access control. Temperature is regulated by a single proportional–integral–derivative (PID) controller with ambient-load feedforward, back-calculation anti-windup, hot-side derating and a dew-point safety limiter. Three scenarios—hybrid (H), active (A) and passive (P)—were evaluated over 10,173 one-second intervals (10,174 records; 2.8258 h) while the prescribed ambient temperature rose from 16 to 32 °C, and ambient relative humidity fell from 67% to 27%. H and A remained within the broader 16–20 °C preservation band for 100% of the run and within the tighter 17.5–18.5 °C control band for 79.7% and 80.5%, respectively, finishing at 18.53 °C and 18.56 °C. P reached 21.20 °C and remained within the tighter band for 47.7% of the run. Calculated electrical consumption was 0.05177 kWh for H, 0.05156 kWh for A and 0.00565 kWh for P. Relative humidity remained within 45–55% in all scenarios. The filtered H and A scenarios reduced mean PM2.5 and total volatile organic compound (TVOC) exposure by 98.4% and 94.4%, respectively, relative to the prescribed ambient profiles. The graphs for document prototype construction and functional bench observations and model results that have not yet been validated by enclosure tests are presented in Results Section. The close H/A performance over this short disturbance does not establish an advantage of ArtSorb and requires repeated humidity-disturbance experiments.

Public health emergencies place acute pressure on medical supply systems. This study develops Med-iDR-MARL, a hierarchical multi-agent reinforcement learning (MARL) framework for regulating hospital requests before allocation. Regional incentives influence hospital responses for deferrable (DD) and reducible (RD) demand, while a local scheduler retains non-deferrable (ND) demand and applies hospital-level service bounds. Across 20 simulated 30-day windows, the framework reduced DD/RD requests by 37.71% with mean incentive expenditure of CNY 274,844.23, compared with 31.03% and CNY 350,038.43 for Rule-Adaptive. Independent hospital-agent deep Q-network learning achieved greater reduction, lower expenditure, and better pooled reduction per unit expenditure. The proposed framework also had the highest peak-to-average ratio despite lower absolute peaks than No-DR. In a separate five-seed ablation, removing the minimum-service guard increased the hospital-day violation rate from zero to 58.95%. Local scheduler diagnostics found feasible solutions in 132,000 tested states but material optimality gaps and no consistent runtime advantage over vectorized exact enumeration. The results identify conditional efficiency and service trade-offs. The Shanghai COVID-19 series supplies an external risk trajectory; hospital behavior, clinical outcomes, and operational logistics remain unvalidated.

Background: Wearable electrocardiogram (ECG) devices capable of recording a single-lead signal—typically Lead I—have proliferated rapidly, enabling long-term ambulatory cardiac monitoring outside clinical settings. However, the extent to which Lead I-derived heart rate variability (HRV) metrics are concordant with those obtained from the clinical gold standards—12-lead ECG and 3-channel Holter monitoring—remains incompletely characterised. Methods: We analysed RR-interval series extracted from (i) the PTB-XL large-scale publicly available 12-lead ECG dataset (n = 18,869 patients; 100 Hz, 10 s recordings) and (ii) ambulatory 3-channel Holter ECG recordings (Suzuken Pico303) from four participants (two elderly adults and two young adults). Pan–Tompkins R-wave detection was applied uniformly. Time-domain (SDNN, RMSSD, and pNN50), Poincaré (SD1 and SD2), and Bland–Altman agreement metrics were computed for Lead I versus Lead II comparisons. Results: Analysis of 300 PTB-XL records (a contiguous pilot subset: records 00040–00350) stratified by diagnostic superclass revealed strong Lead I–Lead II RRI concordance for normal ECGs (NORM: r = 0.844, ICC(2,1) = 0.841, mean bias = −1.4 ms, 95% LoA [−52.4, +49.7] ms; n = 179 records) and progressively lower agreement in pathological categories (MI: r = 0.659, ICC = 0.663; CD: r = 0.433, ICC = 0.484; HYP: r = 0.556, ICC = 0.574; STTC: r = 0.635, ICC = 0.634). Holter Pico303 recordings demonstrated significant inter-individual variability; SDNN ranged from 37.0 ms (elderly male) to 80.8 ms (elderly female) and 80.4 ms (young male), while RMSSD ranged from 27.0 to 68.0 ms. R-wave amplitude was systematically higher in Lead II (0.734 ± 0.413 mV) than Lead I (0.667 ± 0.339 mV) across the 300-record cohort (overall mean). Conclusions: Single-lead (Lead I) ECG provides HRV information comparable to Lead II for time-domain metrics, supporting the utility of wearable single-lead ECG for RRI estimation and time-domain HRV monitoring; however, systematic differences in R-wave morphology warrant lead-specific calibration of automated detection algorithms.