Background: Environmental ionizing radiation plays a major role in human radiation exposure. Natural background radiation originates from the Earth’s crust and outer space. The terrestrial component consists of primordial radioisotopes (40K, 238U, 235U, 232Th), as well as elements of the uranium and thorium decay chains, of which, from a dosimetric perspective, 222Rn and its decay products carry the greatest weight. These account for more than half of the crustal component. Cosmic radiation consists of gamma rays, X-rays, and particles such as protons, electrons, neutrons, mesons, and hyperons. Methods: The main objective of our research is to determine the background radiation levels characteristic of various regions of Transylvania. In addition, we aim to determine the effects of local environmental, diurnal, and seasonal changes on radiation levels. Results: A statistically significant difference was observed between the values of the individual regions (p < 0.001). The values we measured ranged from 0.051 µSv/h to 0.285 µSv/h. Based on the results, we can conclude that there is a statistically significant difference (p < 0.05) between the median value dropped after ventilation (0.159 µSv/h), compared to the one before ventilation (0.164 µSv/h). According to our database, radiation levels are significantly (p < 0.01) higher during the fall and winter months. Conclusions: On this basis, Transylvania can be regarded as a safe and livable region in terms of natural background radiation levels. Nevertheless, it is important to maintain a healthy lifestyle, ventilate homes frequently, and spend as much time as possible outdoors in nature.

Accurate emergency triage is critical for patient safety and efficient resource allocation. Large language models (LLMs) have shown promise in clinical reasoning tasks; however, their predictions may be inconsistent when not grounded in structured clinical knowledge. This study proposes TRAG (Triage Retrieval-Augmented Generation), a domain-specific framework that integrates rule-based knowledge retrieval with LLM reasoning to support Emergency Severity Index (ESI) prediction. TRAG retrieves triage rules encoded as if–then logic from an ESI knowledge base and incorporates them into the model prompt to guide ESI prediction. The framework evaluated multiple LLMs under different retrieval settings (Top-5, Top-10, Top-15) and compared with zero-shot baselines on 100 curated triage cases. Performance was assessed using accuracy, precision, recall, F1-score, and Quadratic Weighted Kappa (QWK). Results show that retrieval-augmented prompting improves classification performance, particularly for lower-performing models. For example, GPT-3.5 accuracy increased from 0.45 to 0.68 and QWK from 0.67 to 0.82 under the Top-15 setting. Improvements were also observed in reducing under-triage in several configurations, while higher-performing models demonstrated more modest and configuration-dependent gains. These findings suggest that integrating structured clinical rules within a retrieval-augmented framework can enhance the consistency and reliability of LLM-based triage prediction. The proposed TRAG framework highlights the potential of combining structured clinical knowledge with generative models to support safer and more interpretable decision-making in emergency care.

In mining TBM excavation, the mineralogical heterogeneity of rock significantly impacts tunneling efficiency and rock-breaking performance. The cutting process of tunnel boring machine (TBM) double-disc cutters is significantly influenced by the combined effects of mineral composition differences and cutter spacing parameters. In this study, a heterogeneous granite model was constructed using the finite–discrete element method (FDEM), with quartz content fixed at 30%. Different mineral compositions were generated by adjusting the proportions of feldspar and mica, and a double-disc cutter–rock contact model was employed with various cutter spacings to perform numerical cutting simulations. Cutter work was calculated by integrating the force–displacement curves, rock-breaking efficiency was evaluated by the specific energy (SE) defined as the energy consumed per unit rock chip area, and fracture types, as well as fragmentation volumes, were identified. The results show that the total input energy ranged from 2.2 to 3.4 mJ, reaching a peak at medium cutter spacings of 50–70 mm; as feldspar content increased, the overall energy level rose significantly. Rock-breaking efficiency was relatively high at medium cutter spacings, and the favorable spacing range shifted from approximately 50 mm to 60 mm when feldspar content increased from 40–50% to 60%. Excessively small spacing led to a higher proportion of crushing and repeated damage, while overly large spacing weakened crack interactions, both of which reduced efficiency. Overall, cutter spacing mainly controlled the crack interaction patterns, whereas the feldspar–mica ratio dominated energy utilization. These findings suggest that, in practical TBM excavation, cutter spacing should be reasonably optimized according to the mineral composition of the surrounding rock to avoid energy waste caused by extreme spacing and to achieve a balance between efficiency and energy consumption.

To address the limitations of traditional energy management strategies in fuel cell hybrid power systems—specifically their difficulty in simultaneously accommodating dynamic driving condition adaptability, hydrogen fuel economy, and energy storage system stability—this study proposes a power distribution optimization strategy based on Deep Deterministic Policy Gradient (DDPG). The strategy targets a hybrid powertrain architecture dominated by a fuel cell (FC) and assisted by a lithium battery and a supercapacitor. By constructing a multi-dimensional state space that integrates vehicle speed, acceleration, the state of charge (SOC) of the energy storage system, and load power demand, a multi-objective reward function encompassing hydrogen consumption, SOC deviation, system efficiency, and power fluctuation is designed to achieve dynamic power allocation in a continuous action space. Simulation studies are conducted under three typical driving cycles—WLTP, CLTC-P, and UDDS—with comparative evaluations against the conventional Equivalent Consumption Minimization Strategy (ECMS) and Deep Q-Network (DQN)-based strategies. The results demonstrate that the DDPG-based strategy reduces hydrogen consumption to 607.1 g/100 km, 580.2 g/100 km, and 560.0 g/100 km under the three driving cycles, respectively, achieving a maximum reduction of 28% compared with ECMS. The average system efficiency increases to 64–66%, representing an improvement of 38.9%, while the operating proportion of the fuel cell within the high-efficiency region (40–80% load) increases by 15%. In addition, the strategy exploits the high-frequency response capability of the supercapacitor to smooth instantaneous power fluctuations, effectively reducing the inefficient start–stop events of the fuel cell. Although the SOC fluctuation range of the lithium battery increases by 32.5% compared with ECMS, a dynamic balance between energy efficiency and battery lifespan can be achieved through optimized weighting of the SOC deviation penalty term in the reward function. Overall, this study provides a solution with both theoretical significance and engineering feasibility for global energy optimization of fuel cell–energy storage systems under complex driving conditions.

An automated air-conditioner cleaning system was developed as a retrofit solution for conventional split-type units to reduce residual moisture in the evaporator section and suppress post-shutdown microbial accumulation. The system was integrated with an 18,000 BTU h−1 air-conditioner and implemented using an Arduino-based closed-loop control platform with temperature and relative humidity monitoring. After shutdown, the indoor fan was operated under low-, medium-, or high-speed conditions to remove retained moisture from the cooling coil. System performance was evaluated in an 18 m3 test room through measurements of electrical consumption, operating cost, relative humidity, and microbial contamination in room air and on the evaporator coil before and after system installation. Low-speed operation showed the lowest current demand, power consumption, and electricity cost, with corresponding values of 0.36 ± 0.01 A, 79.2 ± 0.8 W, and 0.47 THB per 150 min. Post-shutdown humidity reduction was achieved under all tested conditions, while the high-speed mode provided the fastest drying response, reducing relative humidity to approximately 60% within 120 min. In the room air, the greatest reduction in airborne fungi after shutdown was observed at low speed, whereas the greatest reduction in airborne bacteria was observed at medium speed. On the evaporator coil, the strongest bacterial suppression was obtained at low speed, where the bacterial count after 24 h decreased from 633.33 ± 34.27 CFUs before installation to below the detection limit after installation. These results indicate that the proposed system reduced moisture retention and microbial contamination with minimal energy consumption.

In panel-furniture manufacturing, dynamic scheduling is challenging because upstream motherboard release and parallel CUT-machine assignment are tightly coupled with downstream multibranch processing, finite-capacity buffers, and machine disturbances. This study investigates an event-driven scheduling problem in a multibranch panel-furniture workshop and aims to minimize the cumulative tardiness of all parts. To capture the structural dependencies of the shop floor, we construct a heterogeneous graph to represent the real-time production state. In this graph, candidate motherboards, parallel CUT machines, and downstream branch-level production units form different node types. The representation explicitly encodes motherboard–branch coupling features and machine–branch inflow relations, while aggregated branch-state features summarize detailed process-order information within each downstream branch. Based on this structured state representation, we develop a proximal policy optimization (PPO)-based actor–critic framework to learn adaptive motherboard release and CUT-assignment policies. We compare the proposed method with three heuristic rules and two PPO-based learning baselines under different machine-availability levels, buffer-capacity settings, and dataset scales. The results show that the proposed method achieves the best overall tardiness performance across the tested baselines and converges faster and more stably than the other PPO-based methods under different dataset scales and workshop conditions. These findings demonstrate the effectiveness of combining heterogeneous graph modeling with reinforcement learning for dynamic scheduling in multibranch panel-furniture production systems.

Prion diseases are caused by self-propagating and transmissible alternative conformations of certain proteins, which induce neurotoxicity and lead to transmissible spongiform encephalopathy (TSE) in mammalian. Prions were also found in fungi, and in particular, the yeast Saccharomyces cerevisiae. Manganese (Mn) is an essential nutrient and plays crucial roles in central nervous system. However, high concentration of manganese is regarded as an environmental neuronal stressor which would induce striatal neurotoxicity. Long-term exposure to high concentration of manganese would increase the proportion of the infectiously pathogenic isoform (PrPSc) of prion protein. Additionally, increase of manganese levels was found to be age-related in human brain. Here, we studied the effect of manganese on prion using budding yeast prion [URE3] as model organism. We found the exposure to manganese can enhance the de novo generation and propagation of yeast prion [URE3], as well as the expression levels of chaperones Hsp104p and Hsp70p, in a dose-dependent manner.

Comprehensive room acoustic characterization requires resolving reflection behavior across time, frequency, and space. The recently proposed eigenbeam–vMF-based analyzer provides a framework for this by modeling the reflection field as a time–frequency-dependent directional power distribution, estimated via spatial correlation of eigenbeams (ambisonics) and parameterized using von Mises–Fisher clustering. This formulation enables a unified and interpretable description of anisotropic early reflections, their transition into diffuse reverberation, and frequency-dependent acoustic behavior. Prior work showed that the analyzer reliably captures these features using higher-order ambisonics from a 32-channel spherical microphone array (SMA) and that constraining the same array to the first order still led to retaining the dominant features. This paper investigates whether this capability extends to first-order microphone arrays with sparser spatial sampling for more economical and practical deployment. A comparative study is conducted in a recording studio with variable wall panels (wood and felt), evaluating a four-channel first-order array against a 32-channel SMA. The results reveal distinct acoustic differences between panel settings, which are consistent across both arrays. While the SMA captures finer spatial detail and prolonged anisotropic reflections more effectively, the first-order array demonstrates potential for preliminary room acoustic assessments by identifying room mode frequencies, dominant reflection directions, and highly reflective surfaces.

Subsurface rolling contact fatigue (RCF) failure is one of the primary failure modes in properly installed and lubricated rolling bearings. Its actual service life often exhibits significant scatter, posing a formidable challenge to the reliable life prediction and operational safety of bearings. This study establishes a macro-meso-coupled rolling contact fatigue model that accounts for crystalline anisotropy and grain topological structures. This model utilizes Voronoi tessellations and random Euler angles to construct a polycrystalline mesoscopic model, which is subsequently integrated with a macroscopic Hertzian contact finite element analysis to simulate the roller bearing loading cycles and determine the localized stress responses within the material. The results indicate that variations in macroscopic structural and operating parameters primarily affect the overall stress level of the subsurface RCF failure. The relative fatigue life of the bearing exhibits an exceptionally high sensitivity to changes in macroscopic and operating parameters. Specifically, an increase in radial load leads to an exponential decrease in relative life, with the Weibull slope ranging between 1.001 and 1.129, which is broadly consistent with the classical Lundberg–Palmgren experimental value of 1.125. Conversely, the heterogeneity of the mesoscopic crystalline structure strongly influences the statistical variance of localized extreme stresses. The scatter in bearing fatigue life demonstrates a much more pronounced sensitivity to mesostructural alterations; as the grain size increases from 10 μm to 40 μm, the Weibull slope drops from 1.041 to 0.784. This study provides an analytical basis for the reliable life prediction of rolling bearings.

To address severe strata pressure induced by large end-area hanging spans and poor caving of thick, hard roofs in western coal mines, this study takes the 1302 working face of Zhujiamao Coal Mine as a case study. A multiscale mechanical model is developed to describe the progressive evolution of a stratified hard roof from a continuous beam to a cantilever beam and finally to an arched triangular hanging roof. Limit criteria for the maximum hanging length under bending and shear failure are derived, indicating that bending governs end-area roof instability. The theoretical results show good agreement with field observations and numerical simulations, providing guidance for liquid nitrogen fracturing target selection. Coupled FLAC3D-3DEC simulations reveal the staged deformation of overlying strata and clarify the spatial correspondence between the “O-X” fracture pattern and the arched triangular hanging roof. Based on these findings, a collaborative weakening strategy integrating directional drilling, hydraulic pre-cracking, and deep liquid nitrogen fracturing is proposed. Field observations and comparative tests confirm that this method effectively forms a three-dimensional fracture network, reduces roof stiffness and strength, shortens the caving interval, lowers peak shield resistance, and promotes stable caving of the end-area hanging roof.