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Objective To investigate the status quo of hospital readiness of pregnant women with gestational diabetes mellitus (GDM) and analyze its main influencing factors. Methods Objective sampling method was used to select 118 pregnant women with GDM in a grade A hospital as the research object. The general information questionnaire, Readiness for Hospital Discharge Study-New Mother Form(RHDS-NMFS), Quality of Discharge Teaching Scale New Mother Form(OB-QDTS) were performed to collected data and the related factors affecting the status quo of readiness for hospital discharge were analyzed. Results The score of RHDS-NMFS of GDM women was (118. 16±18. 36) points, which was at the medium level. There were statistically significant differences in RHDS-NMFS scores of GDM women with education, other pregnancy complications or complications, mode of postpartum confinement, understanding of gestational diabetes, and whether they usually monitored blood glucose by themselves (P＜0. 05). The total score of RHDS-NMFS was positively correlated with the total score of OB-QDTS (P＜0. 05). Education level, understanding of gestational diabetes mellitus, self-monitoring of blood glucose and quality of discharge guidance were the main influencing factors of discharge preparation for GDM parturients (P＜0. 05). Conclusion Medical staff can start from the main factors affecting the discharge readiness of GDM parturients, strengthen the explanation of disease knowledge, encourage parturients to self-measure blood sugar, improve the quality of discharge guidance, and thus improve their discharge readiness. (目的 调查妊娠期糖尿病(GDM)产妇出院准备度的现状, 分析其主要影响因素。方法 采用目的抽样法选取某三甲医院的118例GDM产妇作为调查对象, 选取一般资料调查表、产妇出院准备度量表(RHDS-NMFS)、产妇出院指导质量量表(OB-QDTS)进行相关调查, 分析影响出院准备度水平的相关因素。结果 GDM产妇RHDS-NMFS量表得分为(118. 16±18. 36)分, 处于中等水平。文化程度、有无其他妊娠合并症或并发症、“坐月子”方式、对GDM的了解程度、平常是否自己监测血糖的GDM产妇出院准备度得分差异有统计学意义(P＜0. 05); RHDS-NMFS量表总分与OB-QDTS量表总分呈正相关(P＜0. 05); 文化程度、对GDM的了解程度、平常是否自己监测血糖和出院指导质量是影响GDM产妇出院准备的主要因素(P＜0. 05)。结论 医护人员可从影响GDM产妇出院准备度的主要因素着手, 加强疾病知识的讲解, 鼓励产妇自测血糖, 提高出院指导质量, 从而提高其出院准备水平。)

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing. [ABSTRACT FROM AUTHOR]

The AC charging pile is the main energy supply facility for household electric vehicles, which uses a vehicle mounted charger to charge the power battery. The current standard of the State Grid Corporation of China clearly stipulates the function of the AC charging pile and does not take into account the impact of the harmonics of the vehicle mounted charger on the power grid. Therefore, in view of the deficiency that AC charging piles cannot suppress the current harmonics of the vehicle mounted charger, application of the active power filtering technology to the design of AC charging piles is proposed to form a new type of AC charging pile with better functions. In the experimental prototype that was built, for vehicle mounted chargers with two load characteristics, the composite control method of traditional PI control and repetitive control is adopted, where the new AC charging pile effectively suppresses the harmonics of the vehicle-mounted charger. Experiments show that the AC charging pile using active power filtering technology cannot only improve the power quality of the grid side but also reduce the impact of harmonics on the power metering and billing system, ensuring the stability of the charging communication system. [ABSTRACT FROM AUTHOR]