Outcomes of prophylactic lauromacrogol injection versus non-injection in patients with endogenous cesarean scar pregnancy treated by hysteroscopic surgery: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the efficacy of hysteroscopic surgery for endogenous cesarean scar pregnancy (CSP) and the value of prophylactic ultrasound-guided local injection of lauromacrogol. METHODS: This retrospective study included 131 patients diagnosed with endogenous CSP who underwent hysteroscopic surgery at the Hangzhou Fuyang Women and Children Hospital between January 2018 and May 2022. Lauromacrogol (10-20 mL) was administered within 24 h preoperatively using an ultrasound-guided vaginal injection to 78 patients (L group) versus not administered to 53 patients (non-L group). Their clinical data and outcomes were analyzed. RESULTS: Mean gestational age, gestational mass size, and uterine scar thickness and median preoperative blood ß-human chorionic gonadotropin levels of the non-L versus L groups were 46.26 versus 45.01 days, 2.05 versus 2.39 cm, 0.35 versus 0.32 cm, and 19850.0 versus 26790.0 U/L, respectively (P > 0.05 for each). The non-L and L groups had similar success rates (98.1% vs. 98.7%, P = 1.0). Complications related to lauromacrogol administration, including abdominal pain, massive bleeding, and bradycardia, were experienced by 46.2% (36/78; P < 0.001) of L group patients. The non-L had a significantly shorter mean hospital stay (4.85 ± 1.12 vs 5.44 ± 1.08 days) and lower total cost (6148.75 ± 1028.71 vs 9016.61 ± 1181.19) (P < 0.01). CONCLUSIONS: Hysteroscopic surgery is effective and safe for patients with endogenous CSP. Prophylactic lauromacrogol injection increases the incidence of complications and costs. Direct hysteroscopic surgery can reduce pain and financial burden in patients with endogenous CSP and save medical resources for other patients.

A new method for axis adjustment of the hydro-generator unit using machine learning.

The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new method for axis adjustment of hydro-generator unit based on an improved grey prediction model and swarms intelligence optimization neural network. First of all, it proposes a sequence acceleration translation and mean value transformation method, which is used to pre-process the axis net total swing sequence that exhibits oscillating fluctuations. It uses e1 and e2 factor transformation to establish an improved axis net total swing gray prediction model. Then, the advanced flamingo search algorithm is used to search the maximum value of the sine function of the net total pendulum of the axis, and the axis adjustment orientation is obtained. This method solves the problem that GM(1, 1) can only be predicted by monotone sequence in the past and the problem that the search algorithm is easy to fall into local optimum, effectively improves the calculation efficiency of axis and shorts the search time. Simulation examples show that the proposed method can significantly improve accuracy of axis adjustment. This method greatly improves the efficiency of azimuth search for axis adjustment.

Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision making.

IN the trend of energy revolution, power data becomes one of the key elements of the power grid. And an advance power system with "electric power + computing power" as the core has become an inevitable choice. However, the traditional search approach based on directory query is commonly used for power grid operation data in domestic and international. The approach fails to effectively meet the user's need for fast, accurate and personalized retrieval of useful information from the vast amount of power grid data. It seriously affects the real-time availability of data and the efficiency of business-critical analytical decisions. For this reason, an intelligent retrieval approach for power grid operation data based on improved SimHash and multi-attribute decision making is proposed in this paper. This method elaborates the properties of SimHash and multi-attribute decision making algorithms. And an intelligent parallel retrieval algorithm MR-ST based on MapReduce model is designed. Finally, real time grid operation data from multiple sources are analyzed on the cloud platform for example. The experimental results show the effectiveness and precision of the method. Compared with traditional methods, the search accuracy rate, search completion rate and search time are significantly improved. Experiments show that the method can be applied to intelligent retrieval of power grid operation data.

Short-Term Power Prediction of a Photovoltaic Power Station Based on the SSA-CEEMDAN-FCN Model.

Photovoltaic power generation is greatly affected by weather factors. To improve the prediction accuracy of photovoltaic power generation, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN) is proposed to preprocess the power sequence. Then, the full convolutional network (FCN) model optimized based on the sparrow search algorithm (SSA) is used to predict the short-term photovoltaic power. SSA can more reasonably determine the parameters of FCN and improve the prediction performance of FCN. Therefore, the FCN model optimized by the SSA algorithm is used to establish prediction models for subsequences and predict each subsequence, respectively. Finally, the predicted value of each subsequence is superimposed. Taking the actual data of a photovoltaic power station in Jiangsu province of China as an example, by comparing some different common prediction models, it is proved that the proposed method is reasonable and feasible.

EMBRYONIC FLOWER2B, coming from a stable QTL, represses the floral transition in cotton.

The EMBRYONIC FLOWER 2 (EMF2) gene encodes a VEFS (VRN2-EMF2-FIS2-Su(z)12) domain protein involved in plant growth and development. Herein, genome-wide characterization of the VEFS-box gene family in Gossypium raimondii, G. arboreum, G. barbadense, and G. hirsutum was performed with a total of 3, 3, 6, and 6 homologous sequences respectively identified in the four species. The gene structure, protein motifs, and gene expression were further investigated. Based on our previous research on multiple stable quantitative trait loci for early maturity, GhEMF2B on chromosome D03 was selected as a candidate gene for further study. Quantitative real-time PCR analysis indicated that GhEMF2B was upregulated in the apical buds of late-maturing cultivars at the fourth and fifth true-leaf stages compared to that of early-maturing cultivars. Virus-induced gene silencing of GhEMF2B in cotton seedlings repressed expression by 50%-70%, which led to earlier floral bud development, young curled leaves, and abnormal petal formation. Further analysis demonstrated that the silencing of GhEMF2B enhanced the expression levels of the positive floral regulators AGAMOUS-LIKE 6 (GhAGL6), FLOWERING LOCUS T (GhFT), and APETALA 1 (GhAP1). Thus, it can be inferred that GhEMF2B plays important roles in the floral transition and development of cotton.

Improved feature-selection method considering the imbalance problem in text categorization.

The filtering feature-selection algorithm is a kind of important approach to dimensionality reduction in the field of the text categorization. Most of filtering feature-selection algorithms evaluate the significance of a feature for category based on balanced dataset and do not consider the imbalance factor of dataset. In this paper, a new scheme was proposed, which can weaken the adverse effect caused by the imbalance factor in the corpus. We evaluated the improved versions of nine well-known feature-selection methods (Information Gain, Chi statistic, Document Frequency, Orthogonal Centroid Feature Selection, DIA association factor, Comprehensive Measurement Feature Selection, Deviation from Poisson Feature Selection, improved Gini index, and Mutual Information) using naïve Bayes and support vector machines on three benchmark document collections (20-Newsgroups, Reuters-21578, and WebKB). The experimental results show that the improved scheme can significantly enhance the performance of the feature-selection methods.