Optimization of biodiesel injection parameters based on support vector machine

For the running diesel engine, spray-atomization, mixed-combustion, and thermal-power conversion processes are inseparable, which causes difficulty to investigate atomization effect separately. This study was conducted to improve the atomization efficiency of the soybean fatty acid methyl ester (SFAME) in engine, to achieve the minimum effective specific fuel consumption in specific engine working conditions, the different injection parameters combination were explored on the influence of effective specific fuel consumption at elevated fule temperature. The effective specific fuel consumption prediction model was established based on support vector machine (SVM). With small samples, the intrinsic functional relationship was determined and the best injection parameters were validated under seven different experimental conditions. The study results have shown that the engine's spray-thermal-power conversion process could be simulated accurately by using SVM. It will be more favorable to improve application effect of biodiesel in the engine to select the fuel temperature as injection parameters which influence atomization effect. Furthermore, using enumeration-verification methods to simulate the parameters might save a lot of resources as compared to the similar experiments

Missing value estimation for microarray data by Bayesian principal component analysis and iterative local least squares

Missing values are prevalent in microarray data, they course negative influence on downstream microarray analyses, and thus they should be estimated from known values. We propose a BPCA-iLLS method, which is an integration of two commonly used missing value estimation methods-Bayesian principal component analysis (BPCA) and local least squares (LLS). The inferior row-average procedure in LLS is replaced with BPCA, and the least squares method is put into an iterative framework. Comparative result shows that the proposed method has obtained the highest estimation accuracy across all missing rates on different types of testing datasets

Effect of the Moso Bamboo <i>Pyllostachys edulis</i> (Carrière) J.Houz. on Soil Phosphorus Bioavailability in a Broadleaf Forest (Jiangxi Province, China)

Moso bamboo (Phyllostachys edulis (Carrière) J.Houz.) is a fast-growing species that commonly invades neighboring broadleaf forests and has been widely reported in subtropical forest ecosystems. However, little is known about the effect on soil phosphorus (P) bioavailability and its potential influence factor during the P. edulis expansion. Here, the four soil P bioavailable fractions (i.e., CaCl2-P, Citrate-P, Enzyme-P, and HCl-P), acid phosphatase activity, iron and aluminum oxides (Fed and Ald), and soil total P pool at depths of 0–10 cm, 10–20 cm, and 20–40 cm were measured in three expanding interfaces (a broadleaf forest, a mixed bamboo–broadleaf forest, and a pure P. edulis forest) in subtropical forests of southern China. Regardless of soil depths, the CaCl2-P content was significantly lower in the mixed bamboo–broadleaf forest than the other two forest types, with contents ranging from 0.09 to 0.16 mg/kg, whereas the HCl-P content was significantly lower in the broadleaf forest, with contents ranging from 3.42 to 14.33 mg/kg, and the Enzyme-P content and acid phosphatase activity were notably lower in P. edulis forest with contents of 0.17–0.52 mg/kg and 68.66–74.80 μmol MUF released g−1 min−1, respectively. Moreover, the soil total P pool was enhanced in the mixed bamboo–broadleaf forest in 0–10 cm depth compared to broadleaf and P. edulis forests, with increases of 27.40% and 31.02%, respectively. The redundancy analysis showed that soil pH plays an important role in regulating soil P bioavailability during the P. edulis expansion (p P. edulis into broadleaf forests has resulted in soil P bioavailability and storage capacity. The results of this study suggest that when P. edulis invades broadleaf forests, it could affect the soil P bioavailability by elevating soil pH, which in turn drives and facilitates the completion of the expansion. This is important for understanding P cycling during the P. edulis forest expansion in subtropical regions

Macroscopic expressions of molecular adiabatic compressibility of methyl and ethyl caprate under high pressure and high temperature

The molecular compressibility, which is a macroscopic quantity to reveal the microcompressibility by additivity of molecular constitutions, is considered as a fixed value for specific organic liquids. In this study, we introduced two calculated expressions of molecular adiabatic compressibility to demonstrate its pressure and temperature dependency. The first one was developed from Wada's constant expression based on experimental data of density and sound velocity. Secondly, by introducing the 2D fitting expressions and their partial derivative of pressure and temperature, molecular compressibility dependency was analyzed further, and a 3D fitting expression was obtained from the calculated data of the first one. The third was derived with introducing the pressure and temperature correction factors based on analogy to Lennard-Jones potential function and energy equipartition theorem. In wide range of temperatures (293<T/K<393) and pressures (0.1<P/MPa<210), which represent the typical values used in dynamic injection process for diesel engines, the calculated results consistency of three formulas demonstrated their effectiveness with the maximum 0.5384% OARD; meanwhile, the dependency on pressure and temperature of molecular compressibility was certified. © 2014 Fuxi Shi et al

Identification and Characterization of a Phosphate-Solubilizing Bacterium and Its Growth-Promoting Effect on Moso Bamboo Seedlings

Phosphate-solubilizing bacteria (PSB) offer an eco-friendly approach to boost plant growth in soils low or deficient in phosphorus (P). In this study, we isolated 97 PSB strains from the soil around moso bamboo roots in Jiangxi Province, China. The RW37 strain was identified as Enterobacter soli through its physical characteristics and genetic sequencing. Our experiments revealed that RW37 could dissolve phosphate at levels exceeding 400 mg L−1 across a wide range of environmental conditions, including temperature (25–35 °C), pH levels (3.5–7.2), salinities (0–2.0%), and volumes of medium (1/5–3/5 of flask volume), showcasing its adaptability. Furthermore, RW37 showed remarkable phosphate-solubilizing abilities at various pH levels using different phosphate sources, with the highest capacity observed in a medium containing CaHPO4. This study also found a negative correlation between P-solubilizing capacity and fermentation broth pH, indicating that RW37 likely secretes organic acids to dissolve phosphate sources. Pot experiments demonstrated that applying RW37 significantly improved the plant height, biomass, root growth, and P uptake of moso bamboo seedlings in red soil. Our results highlight the potential of RW37 as an eco-friendly biofertilizer for subtropical bamboo forests

The Changes in Soil Microbial Communities and Assembly Processes along Vegetation Succession in a Subtropical Forest

Soil microbes are the primary drivers of the material cycling of the forest ecosystem, and understanding how microbial structure and composition change across succession assists in clarifying the mechanisms behind succession dynamics. However, the response of soil microbial communities and assembly processes to succession is poorly understood in subtropical forests. Thus, through the “space instead of time” and high throughput sequencing method, the dynamics of the soil bacterial and fungal communities and assembly process along the succession were studied, where five succession stages, including Abandoned lands (AL), Deciduous broad-leaved forests (DB), Coniferous forests (CF), Coniferous broad-leaved mixed forests (CB), and Evergreen broad-leaved forests (EB), were selected in a subtropical forest on the western slope of Wuyi Mountain, southern China. The results demonstrated that succession significantly decreased soil bacterial α-diversity but had little effect on fungal α-diversity. The composition of soil bacterial and fungal communities shifted along with the succession stages. LEfSe analysis showed the transition from initial succession microbial communities dominated by Firmicutes, Bacteroidota, Ascomycota, and Chytridiomycota to terminal succession communities dominated by Actinobacteriota and Basidiomycota. Distance-based redundancy analysis (db-RDA) revealed that soil total organic carbon (TOC) was the main factor explaining variability in the structure of soil bacterial communities, and multiple soil environmental factors such as the TOC, soil total nitrogen (TN), C:N ratio, and pH co-regulated the structure of fungi. The null models illustrated that deterministic processes were dominant in the soil bacterial communities, while the stochastic processes contributed significantly to the soil fungal communities during succession. Collectively, our results suggest that different patterns are displayed by the soil bacterial and fungal communities during the succession. These findings enhance our comprehension of the processes that drive the formation and maintenance of soil microbial diversity throughout forest succession

Classification and Identification of Apple Leaf Diseases and Insect Pests Based on Improved ResNet-50 Model

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50’s image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model’s generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model’s effective feature extraction capabilities