This paper presents a detailed study of the influence of yttrium (Y) concentration on thermally stimulated luminescence (TSL) of LYSO:Ce crystals and establishes the relationship between electron traps and scintillati...
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This paper presents a detailed study of the influence of yttrium (Y) concentration on thermally stimulated luminescence (TSL) of LYSO:Ce crystals and establishes the relationship between electron traps and scintillation properties. With the increase of Y concentration, the glow curve shifts toward higher temperatures and the trap depth increases gradually. Our results revealed two groups of electron traps with one shallow (E-t approximate to 0.10-0.26 eV) and one deep (E-t approximate to 1.00-1.21 eV). Our TSL results suggest that the amount of shallow traps varied with Y content has a positive correlation with energy resolution, while the amount of deep traps is inversely proportional to light output. In order to analyze the variation trend of trap depths and their mechanism in detail, we calculated the band gap with the PBE functional and VUV excitation spectra. The results show that the increase of band gap possibly leads to the increase of deep electron trap depth.
In recent years, vehicular communications have attached great interests in both academy and industry for its potential of promoting safety and autonomous driving. Unlike classical communication scenarios, in vehicular...
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In recent years, vehicular communications have attached great interests in both academy and industry for its potential of promoting safety and autonomous driving. Unlike classical communication scenarios, in vehicular communications the optimal resource allocation must be accomplished in a real-time manner, in order to maximally reduce the response delay. This presents a substantial challenge for current machine learning based intelligent resource optimization methods which may be sample inefficient, especially when the problem space becomes extremely huge. In this study, we develop a fast reinforcement learning (RL) framework for the real-time resource optimization of vehicular communications, whereby the transmitting power and the accessing frequency channels need to be jointly allocated. The main concept of our new method is that it incorporates a sample efficient structured exploration mechanism in the action space, which firstly ignores the local exploitation but focuses on a randomized global exploration. Thus, our exploration-first method, in contrast to classical exploitation-first RL, can reconstruct the coarse-grained global landscape of a huge Q-table from only the few samples. This learned prior knowledge would remarkably accelerate the convergence of subsequent incremental learning process, by concentrating on the identified attentional subspace of the Q-table. As demonstrated by numerical results, our new method would reduce the time complexity or the response delay by around 10 folds. As such, our fast RL method would have the great potential to such challenging optimization problems whereby the acquisition of massive training samples is time demanding, which hence provides the great promise to the emerging vehicular networks.
During high-rate discharge, power batteries generate a considerable amount of heat. If this thermal energy is not dissipated effectively, the resulting rapid temperature rise can significantly impact the operational l...
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During high-rate discharge, power batteries generate a considerable amount of heat. If this thermal energy is not dissipated effectively, the resulting rapid temperature rise can significantly impact the operational lifespan of the battery. Consequently, building a thermal control system that can keep the battery temperature status in an acceptable range and increase the homogeneity is vital. To this purpose, this study proposes an ADRC method that combines a BP neural network and an INFO algorithm to regulate the temperature environment suitable for the battery cluster's operation. The findings confirm the capability of the optimization algorithm to effectively reduce the battery's temperature fluctuation under different operating conditions. After optimization, the total temperature variation decreases by over 24%. In addition, INFO-ADRC technology meets the current requirements of the electric vehicle industry by keeping peak temperature deviations within 1 degrees C within the battery clusters.
Currently, the scientific basis for establishing soil environmental criteria is lacking. In order to establish reasonable soil environmental criteria values suitable for soils with different properties, this study sel...
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Currently, the scientific basis for establishing soil environmental criteria is lacking. In order to establish reasonable soil environmental criteria values suitable for soils with different properties, this study selected soils from 16 different sites to determine the toxicity threshold of Zn based on toxicity tests of barley root elongation. In addition, leaching treatments were set up in seven soils with different properties to eliminate the influence of the accompanying anions (Cl-) on the determination of the Zn toxicity threshold. The results indicated that the toxicity thresholds of different soils vary greatly. The EC10 and EC50 ranges of barley root elongation in 16 kinds of non-leached soils were 18.5 mg kg- 1 to 1618.7 mg kg- 1 and 277.9 mg kg- 1 to 3179.8 mg kg- 1, respectively. The hormesis effect appeared in the dose response of Zn, and relative barley root elongation reached more than 150%. Leaching significantly reduced the Zn toxicity in acidic soils. The variation ranges of the leaching factor (LF) in the seven soils were LF10 = 1.1-9.3, LF50 = 1.0-3.2. The LF prediction model indicated that pH explained 81.4% of the LF variation (p < 0.01). The soil pH, cation exchange capacity (CEC), and conductivity (EC) explained 97.8% of the EC50 variation in the leached soil (p < 0.01). The results provide reference values for Zn environmental criteria.
BackgroundWith the development of the economy, the number of obese patients has been increasing annually worldwide. The proportion of asthma patients associated with obesity is also gradually rising. However, the path...
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BackgroundWith the development of the economy, the number of obese patients has been increasing annually worldwide. The proportion of asthma patients associated with obesity is also gradually rising. However, the pathogenesis of obesity-related asthma remains incompletely understood, and conventional pharmacological treatments generally show limited *** study aims to explore the causal relationship between obesity and allergic asthma, elucidate the pathogenesis of obesity-related asthma, and identify the plasma proteins involved in its development, providing new insights for clinical *** this study, we employed a two-step approach for mediation Mendelian randomization (MR) analysis, utilizing stringent selection criteria to identify instrumental variables (IVs). This approach was used to assess the causal impact of obesity on allergic asthma and to validate the plasma proteins identified as mediating factors. We further explored the functions and enriched pathways of the mediating proteins using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, we conducted drug-targeted MR analysis to evaluate the potential of each mediator plasma proteins as a drug target gene. If significant heterogeneity remained among the IVs, we applied the weighted median method as the primary analytical tool. Otherwise, we utilized the inverse variance weighted (IVW) method as the main analytical approach. Additionally, we conducted various sensitivity analyses and statistical tests to further illustrate the robustness of the observed *** research findings indicate a causal relationship between obesity and allergic asthma. Plasma proteins such as TPST1, ROR1, and DAPK1 mediate this relationship, with TPST1 accounting for over 10% of the mediation effect. GO and KEGG analyses show that the genes corresponding to these mediator proteins are primarily enriched in pathways related to responses to st
Cloud seeding models are essential for understanding seeding mechanisms, yet their reliability remains insufficiently verified due to limited cases with confirmed seeding effects. On 19 March 2017, significant seeding...
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Cloud seeding models are essential for understanding seeding mechanisms, yet their reliability remains insufficiently verified due to limited cases with confirmed seeding effects. On 19 March 2017, significant seeding signals were observed by multiple instruments following airborne cloud seeding over a stratiform cloud system with abundant supercooled water in northern China. This study performed an ensemble simulation of the case using two cloud microphysics schemes and three silver iodide (AgI) nucleation parameterizations, successfully replicating the vertical structure and evolution of the seeding-induced cloud. The simulated seeding impact area, precipitation intensity, and changes in raindrop spectra closely aligned with observations. Results indicate that cloud seeding increased ice crystal amounts primarily through the deposition nucleation of AgI particles, activated the auto-conversion of ice crystals to snow, enhanced snow deposition and riming processes, and ultimately increased surface precipitation through enhanced snow melting. An ensemble of simulation was conducted to simulate a cloud seeding case with clear and distinct seeding signals in China All the simulations successfully replicate the structure and evolution of seeding-induced cloud, surface rainfall and raindrop spectra Seeding enhanced ice deposition, ice-to-snow conversion, snow deposition and riming processes, ultimately increasing precipitation Cloud seeding models are used to estimate seeding effects and study seeding mechanisms, but their reliability has not been well validated against observations. This research evaluated the performance of a cloud seeding model using data from a case where the seeding effect was unambiguously confirmed by observations. The ensemble simulation results demonstrate that the model successfully replicates the evolution of the observed seeding-induced radar echoes, changes in surface precipitation and raindrop spectra. The microphysical responses to cloud
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