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[...]SHOREmap requires a much larger sample size; the NGM studies the genes belongs to the recessive homozygous mutant phenotype; MutMap mainly identifies the single gene‐controlled quality traits; QTL‐seq constructs only two pools showing extreme opposite trait values for a given phenotype in a segregating progeny and maps 1–2 major genes for target trait; GWAS is applicable to natural population with a large sample size and thus its cost is high, and it is also difficult to detect the rare mutations and minor effective genes. [...]we compared the SNP‐index between every two Pool‐seq to map the genomic candidate regions. For the genomic candidate regions with overlapping physical positions on the same chromosome, the intersection regions were selected as the final genomic candidate regions. [...]the regions showing a significant association with anthocyanin biosynthesis‐related genes in rice pericarp are shown in Figure c. Three genomic candidate regions were adjacent to or contained the cloned genes of anthocyanin biosynthesis (Figure c). The number of SNPs in the genomic region nearby Rd was greatly reduced (Figure e). [...]the false‐positive result may be resulted from a decrease in nucleotide polymorphism within this genomic region.

The pool fire-triggered domino events in chemical and process industries result in multi-fold damages with the worst severity. Consequence analysis of multiple pool fire (MPF) through Quantitative Risk Assessment (QRA) is highly essential to save the workers and machinery beforehand. This research aims to develop a computational methodology for predicting the safety zone and fire dynamics of MPF in various meteorological environments using ANSYS Fluent. The present paper predicts the fire dynamics of heptane stored in four tanks within a square dike of 2.7 m × 2.7 m. The simulations are performed using the unsteady flamelet-generated manifold (FGM) and discrete ordinate (DO) radiation model within the framework of the large eddy simulation (LES) turbulence model. The Moss-Brookes soot model is deployed for capturing the soot formation. The hexahedral mesh with local refinement is utilized to resolve the flame characteristics. To attain the optimal mesh, an extensive grid-independence study is conducted. The experimented still-air condition along with the worst-case scenario is modelled to evaluate the safety zone using flame surface irradiation. The proposed computational fluid dynamics (CFD) methodology is validated using the predicted temperature profile, O 2 and CO 2 mass fraction within 2.2% error of experimental findings. The safety distance is predicted using the radiative heat flux values of CFD models. The validated CFD model consisting of FGM approach with LES turbulence model can be a robust methodology to determine the safety perimeter of an industrial MPF during crosswind situations, with the aim of averting human casualties and property damage in advance.

In many real-world systems, the entry rate of particles into a lane is affected by the occupancy of nearby pools. For instance, in biological networks, the concentration of molecules on the side of a membrane affects the entry of particles through the membrane. To understand the behaviour of such networks, we develop a network model of ribosome flow models (RFMs) having multiple pools where each RFM captures the dynamics of particle flow in a lane and competes for the finite resources present at the nearby pool. We study a ribosome flow model network with two pools (RFMNTP) and show that the network always admits a steady state. We then analyse the behaviour of the RFMNTP with respect to modifying the transition rate through a theoretical framework. Simulations of the RFMNTP demonstrate a counterintuitive result. For example, increasing any of the transition rates in the presence of a slow site in an RFM can increase the output rate of some RFMs and decrease the output rate of the other RFMs simultaneously. This suggests that the role of local sharing of particles incorporated is non-trivial. Finally, we illustrate how these results can provide insights into studying a network with multiple pools.

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

The emissivity of the double pool fires (DPF) is helpful in radiative heat transfer calculations, fundamental research, and numerical simulations. In this work, emissivity measurements are performed on 0.1 m gasoline circular DPF using infrared camera. Along with emissivity measurements, the mass burning rate (MBR), flame merging probability, and height are also measured. The results show that MBR, flame merging probability, height, and emissivity increase with a decrease in non-dimensional separation distance (S/D). The variation of emissivity along the flame height is also studied and realised that it decreases with the increase in flame height.

Recent studies have introduced the metric GWPbio, an indicator of the potential global warming impact of CO2 emissions from biofuels. When a time horizon of 100 years was applied, the studies found the GWPbio of bioenergy from slow‐growing forests to be significantly lower than the traditionally calculated GWP of CO2 from fossil fuels. This result means that bioenergy is an attractive energy source from a climate mitigation perspective. The present paper provides an improved method for quantifying GWPbio. The method is based on a model of a forest stand that includes basic dynamics and interactions of the forest's multiple carbon pools, including harvest residues, other dead organic matter, and soil carbon. Moreover, the baseline scenario (with no harvest) takes into account that a mature stand will usually continue to capture carbon if not harvested. With these methodological adjustments, the resulting GWPbio estimates are found to be two to three times as high as the estimates of GWPbio found in other studies, and also significantly higher than the GWP of fossil CO2, when a 100‐year time horizon is applied. Hence, the climate impact per unit of CO2 emitted seems to be even higher for the combustion of slow‐growing biomass than for the combustion of fossil carbon in a 100‐year time frame.

Flame merging is deemed to enhance the burning intensity and make the fire more destructive. This paper presents an experimental study on merging behaviors of two same rectangular heptane pool fires with long sides parallel. The pan aspect ratio was set 2–4 and the spacing was changed. The burning rate and flame height were measured. As the spacing decreases, the flame shape was divided into five regions, i.e., (I) no interaction, (II) tilt but non-merging, (III) intermittent merging, (IV) upper flames fully merging but lower flames separated and (V) flames merging from the pan base. The results showed that both the burning rate and flame height increase within the stages I–IV and then decrease in stage V. A normalized parameter ψ is introduced to characterize the air entrainment restriction. A unified correlation between burning rate and ψ is then developed. Connecting with the theoretical force analysis, the criteria of merging from the base and beginning merging are determined as ψ = 0.33 and ψ = 0.61. Then a piecewise correlation of the merging flame height is established. The proposed correlations for burning rate and flame height are verified using present and literature data and their scope of application is further expanded into square pool fires.

Purpose Our objective was to build a mechanism-based pharmacodynamic model for the time course of neutropenia in cancer patients following paclitaxel treatment with a tocopherol-based Cremophor-free formulation (Tocosol Paclitaxel®) and Cremophor® EL-formulated paclitaxel (Taxol®). Methods A randomized two-way crossover trial was performed with 35 adult patients who received 175 mg/m² paclitaxel as either 15 min (Tocosol Paclitaxel) or 3 h (Taxol) intravenous infusions. Paclitaxel concentrations were measured by LC-MS/MS. NONMEM VI was used for population pharmacodynamics. Results The cytotoxic effect on neutrophils was described by four mechanism-based models predicated on known properties of paclitaxel that used unbound concentrations in the central, deep peripheral or an intracellular compartment as forcing functions. Tocosol Paclitaxel was estimated to release 9.8% of the dose directly into the deep peripheral compartment (DPC). All models provided reasonable fitting of neutropenic effects. The model with the best predictive performance assumed that this dose fraction was released into 22.5% of the DPC which included the site of toxicity. The second-order cytotoxic rate constant was 0.00211 mL/ng per hour (variability: 52% CV). The relative exposure at the site of toxicity was 2.21 ± 0.41 times (average ± SD) larger for Tocosol Paclitaxel compared to Taxol. Lifespan was 11.0 days for progenitor cells, 1.95 days for maturating cells, and 4.38 days for neutrophils. Total drug exposure in blood explained half of the variance in nadir to baseline neutrophil count ratio. Conclusions The relative exposure of unbound paclitaxel at the site of toxicity was twice as large for Tocosol Paclitaxel compared to Taxol. The proposed mechanism-based models explained the extent and time course of neutropenia jointly for both formulations.

Mangroves sequester large quantities of carbon (C) that become significant sources of greenhouse gases when disturbed through land-use change. Thus, they are of great value to incorporate into climate change adaptation and mitigation strategies. In response, a global network of mangrove plots was established to provide policy-relevant ecological data relating to interactions of mangrove C stocks with climatic, tidal, plant community, and geomorphic factors. Mangroves from 190 sites were sampled across five continents encompassing large biological, physical, and climatic gradients using consistent methodologies for the quantification of total ecosystem C stocks (TECS). Carbon stock data were collected along with vegetation, physical, and climatic data to explore potential predictive relationships. There was a 28-fold range in TECS (79–2,208 Mg C/ha) with a mean of 856 ± 32 Mg C/ha. Belowground C comprised an average 85% of the TECS. Mean soil depth was 216 cm, ranging from 22 to >300 cm, with 68 sites (35%) exceeding a depth of 300 cm. TECS were weakly correlated with metrics of forest structure, suggesting that aboveground forest structure alone cannot accurately predict TECS. Similarly, precipitation was not a strong predictor of TECS. Reasonable estimates of TECS were derived via multiple regression analysis using precipitation, soil depth, tree mass, and latitude (𝑅² = 0.54) as variables. Soil carbon to a 1 m depth averaged 44% of the TECS. Limiting analyses of soil C stocks to the top 1 m of soils result in large underestimates of TECS as well as in the greenhouse gas emissions that would arise from their conversion to other land uses. The current IPCC Tier 1 default TECS value for mangroves is 511 Mg C/ha, which is only 60% of our calculated global mean. This study improves current assessments of mangrove C stocks providing a foundation necessary for C valuation related to climate change mitigation. We estimate mangroves globally store about 11.7 Pg C: an aboveground carbon stock of 1.6 Pg C and a belowground carbon stock of 10.2 Pg C). The differences in the estimates of total ecosystem carbon stocks based on climate, salinity, forest structure, geomorphology, or geopolitical boundaries are not as much of an influence as the choice of soil depth included in the estimate. Choosing to limit soils to a 1 m depth resulted in estimates of <5 Pg whereas those that included the soil profile >1 m depth resulted in global carbon stock estimates that exceeded 11.2 Pg C.

Urban dynamic ride-pooling faces significant challenges in achieving efficient real-time order matching and path planning, primarily due to the complex spatio-temporal coupling of passenger demand and traffic conditions. Traditional algorithms often struggle to dynamically integrate these features and adapt to multi-objective optimization under real-world constraints. To address these limitations, this study proposes a novel dual-optimization framework that synergizes a Spatio-Temporal Graph Neural Network (ST-GNN) with a multi-objective path planning algorithm. Our approach begins by constructing a demand-adaptive urban spatial structure using Voronoi polygons. A spatio-temporal graph is then built upon this structure, where a graph neural network model, incorporating multi-head attention and Transformer mechanisms, is employed to learn node embeddings that capture complex urban dynamics. These embeddings inform the matching of suitable ride-pooling pairs and guide an improved Dijkstra algorithm to generate optimal paths that co-optimize travel distance, passenger detour, and carbon emissions while strictly adhering to passenger time windows. Validated on a large-scale real-world dataset from Chengdu (Didi Chuxing), our method achieves a matching success rate of 86.6%, reduces carbon emissions by 0.34 kg CO2 per order on average, and maintains a low average detour rate of 0.1202. The results demonstrate that the proposed model enhances spatio-temporal collaboration in complex scenarios and offers a practical and efficient solution for the intelligent upgrade of shared mobility systems, contributing to optimized urban traffic resources and low-carbon travel practices.