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Irrigation and fertilization are indispensable links in the jujube planting industry in southern Xinjiang, China. Regulating the relationship between fertilization and irrigation can effectively reduce costs and improve economic efficiency. A 2-year water and fertilizer optimization coupling test was conducted to determine the optimal water and nutrient supply scheme. The three-factor randomized block experiment included water (W), nitrogen (N), and phosphorus (P). According to the principal component analysis of each index, each treatment’s comprehensive score was obtained. Using yield and economic regression models, the theoretical value and yield value of the optimal economic benefit are inferred. When W, N, and P were applied together, the fruit quality and yield of each treatment significantly differed, and the vitamin C, soluble sugar, and sugar-acid ratio increased significantly with an increase in N fertilizer. However, the titratable acid decreased. An increase in irrigation and nitrogen application significantly increased fruit yield. The comprehensive score was the highest in the N4P3W2 treatment, which improved fruit quality, and the lowest in the N3P3W2 treatment. When the amounts of N, P, and W were 275.56 kg hm−2, 413. 66 kg hm−2, and 7278.19 m3 hm−2, respectively, the theoretical economic benefit was the best. The N4P3W2 treatment is the optimal treatment.

Considering the pear in the arid region as the research object, single-factor testing and water–fertilizer coupling testing were conducted. The response of pear tree growth to water, nitrogen, and phosphorus was explored and provided a theoretical basis for efficient water and fertilizer management. Among them, the single-factor test set water, nitrogen, and phosphorus as the three factors, and five levels were set. Screening out W3, W4, N3, N4, P3, and P4 promoted plant nutrient uptake and fruit quality. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4, W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The yield, single fruit weight, and primary fruit rate were the highest under T3 treatment. The gray correlation degree analysis of fruit quality showed that the T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that the T3 treatment had the highest fruit quality. The yield model showed that irrigation with 6510.06 m3 hm−2, nitrogen fertilizer with 337.5 kg N hm−2, and phosphate fertilizer with 262.5 kg P hm−2 had the best yield. A detailed investigation of pear tree growth and fruit quality showed that the T3 treatment had the best fruit growth and development performance, and the pear fruit quality was the best.

Scientific fertilization is an important technical means of achieving high and stable peanut yields. Using soil testing and formula fertilization, the “3414” optimal regression design was used and included 14 nitrogen (N), phosphorus (P), and potassium (K) fertilization treatments. Ternary quadratic functions of the fertilizer effect were established according to three-season field experiments and the regression analysis of fertilizer–yield function was performed to explore the optimal fertilizer application mode and ratio for peanuts under mulched drip irrigation (MDI), and a suitable fertilizer application system was established. The ternary quadratic equation relating peanut yield (y) and the fertilizer application rates of N (N), P (P2O5), and K (K2O) was obtained after fitting, i.e., y = 2912.528 + 21.432N + 16.324P + 6.181K − 0.051N2 − 0.109P2 − 0.061K2 + 0.017NP + 0.023NK + 0.086PK, and significance analysis and typicality assessment were performed. The model R 2 was 0.9709, both values are extremely significant (p < 0.01), which indicates that the obtained ternary quadratic fertilizer effect function is typical and could be used for statistical purposes and fertilization recommendations. Three quadratic fertilizer effect functions were obtained. Among them, the equation for K is extremely significant, and the equations of N and P are significant. According to the assumption that the marginal yield is zero and the marginal profit is zero, the fertilizer application rate with the maximum yield, the fertilizer application rate with the best economic benefits, and the corresponding yields were obtained. The optimal fertilizer application rate predicted by the ternary quadratic fertilizer effect function was relatively high, so the three quadratic fertilizer effect functions were used for prediction. Under the test conditions, the recommended fertilizer application rates for peanuts under MDI are 256.6 kg N per ha, 164.2 kg P2O5 per ha, and 213.2 kg K2O per ha, the recommended fertilization ratio is 1:0.64:0.83, and the recommended ratio under formula fertilization is 23:15:19. The study has developed a data-based decision support system for Xinjiang drip-irrigated peanut, which assists farmers and agricultural managers in making more scientific and precise fertilization decisions based on the specific growth requirements of the crops and soil conditions. This evidence-based methodology enhances the precision of agricultural management, which is conducive to increasing crop yields while reducing resource wastage and environmental impact. However, multipoint and multiyear experiments are still needed to ensure that the findings are adaptable to the diverse soil conditions and fluctuating climate patterns that may be encountered in practice.

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation–distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes. [ABSTRACT FROM AUTHOR]

Neonatal sepsis continues to be a leading and most important cause of mortality among low birth weight (LBW) preterm infants.

In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky–Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R2 of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves. [ABSTRACT FROM AUTHOR]

Poultry manure, which contains abundant water‐soluble organic matter (WSOM), is commonly applied in plastic greenhouse vegetable production. There is little research on whether WSOM stimulates nitrous oxide (N2O) emission after poultry manure application. A 30 day incubation experiment and a field experiment are conducted to investigate the response of the N2O flux after poultry manure or WSOM inputs. The cumulative N2O emissions from soil amended with WSOM in the incubation experiment are two to four times than those from unamended soil. Similarly, in the field experiment, the N2O emissions significantly increased by 225% (to 1.13 kg N ha−1) after poultry manure and irrigation application compared to a control during summer fallow season, accounting for ≈20% of the annual N2O emissions from plastic greenhouse vegetable production. In conclusion, WSOM in poultry manure increases N2O emissions from plastic greenhouse soil during its initial application; moreover, N2O emissions during summer fallow season should be included in the N2O emission inventory of plastic greenhouse vegetable production. Measures for controlling N2O emissions such as avoiding nitrate accumulation and improving water and poultry manure management should be adopted in plastic greenhouse vegetable production. [ABSTRACT FROM AUTHOR]