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This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.

Background: Many complications may occur after placement of the ventriculoperitoneal shunt (VP shunt) for hydrocephalus, and delayed shunt insufficiency (DSI) is among the most common. It is often caused by abdominal adhesions, which increases the difficulty of diagnosis. This study aimed to explore the clinical value of dynamic plain abdominal radiography (DPAR) as a simple diagnostic method for patients with DSI due to terminal adhesion of the peritoneal shunt after VP surgery. Methods: A total of 30 patients with high suspicion of DSI due to abdominal adhesions after VP surgery were included. DPAR was used for prospective assessment. The interval between the first and second PAR was 4–6 h before surgery. If two plain abdominal radiography at different times indicated that the end of the shunt tube in the abdominal segment was fixed, it was diagnosed as DSI due to adhesion of the shunt tube at the abdominal end. The peritoneal end of the shunt tube was surgically repositioned. Postoperative DPAR was repeated to evaluate the distance of the shunt outlet within the abdominal segment. Results: All cases showed clinical symptoms or imaging findings of shunt insufficiency. The diagnostic accuracy of DPAR was 96.67% (29/30). The end of the shunt tube in the abdominal segment of the preoperative group was fixed with abdominal plain film twice with a mean difference of 1.74 ± 1.18 cm. The mean postoperative change in the position of the end of the shunt tube in the abdominal section was 9.36 ± 2.64 cm, showing a significant difference compared with the preoperative group (P < 0.001). The mean postoperative EVANs index (0.37 ± 0.08) was significantly lower than the preoperative (0.42 ± 0.08) (P = 0.007), Glasgow coma scale score (12.8 ± 2.69) was higher than the mean preoperative score (11.36 ± 2.43) (P = 0.013). Conclusion: DPAR is a simple and effective method for the diagnosis of shunt insufficiency caused by delayed abdominal end adhesion after VP shunt. [ABSTRACT FROM AUTHOR]

External ventricular drainage (EVD) is a common procedure in neurosurgical practice. Presently, the three methods used most often include direct EVD (dEVD), long-tunneled external ventricular drains (LTEVDs), and EVD via the Ommaya reservoir (EVDvOR). But they possess drawbacks such as limited duration of retention, vulnerability to iatrogenic secondary infections, and challenges in regulating drainage flow. This study aimed to explore the use of a modified ventriculoperitoneal shunt (mVPS)—the abdominal end of the VPS device was placed externally—as a means of temporary EVD to address the aforementioned limitations. This retrospective cohort study, included 120 cases requiring EVD. dEVD was performed for 31 cases, EVDvOR for 54 cases (including 8 cases with previously performed dEVD), and mVPS for 35 cases (including 6 cases with previously performed EVDvOR). The one-time success rate (no need for further other EVD intervention) for dEVD, EVDvOR, and mVPS were 70.97%, 88.89%, and 91.42%, dEVD vs EVDvOR (P < 0.05), dEVD vs mVPS (P < 0.05), EVDvOR vs mVPS (P > 0.05). Puncture needle displacement or detachment was observed in nearly all cases of EVDvOR, while no such complications have been observed with mVPS. Apart from this complication, the incidence of postoperative complications was 35.48%, 14.81%, and 8.5%, dEVD vs EVDvOR (P < 0.05), dEVD vs mVPS (P < 0.05), EVDvOR vs mVPS (P > 0.05). Mean postoperative retention for EVD was 14.68 ± 9.50 days, 25.96 ± 15.14 days, and 82.43 ± 64.45 days, respectively (P < 0.001). In conclusion, mVPS significantly extends the duration of EVD, which is particularly beneficial for patients requiring long-term EVD. [ABSTRACT FROM AUTHOR]

Urban last-mile express delivery in China encounters several challenges. This paper presents the establishment of a sharing logistics center aimed at enhancing the overall efficiency of urban last-mile express delivery while optimizing the utilization of essential resources. The successful implementation of shared delivery within sharing logistics center necessitates the creation of a robust collaborative mechanism. Recognizing that cooperative benefit allocation is dynamically influenced by factors such as resource input, operational efficiency, risk management, and other cost-related considerations, this study introduces a multi-weight H-Shapley value method for benefit allocation. By conducting empirical analyses of urban last-mile express delivery in Beijing within a sharing logistics service framework, our findings reveal that the revised benefit allocation model better aligns with the interests of participating entities and positively correlates with their contributions. Analyzing the impact of delivery volume and express operational costs changes, it is found that when the delivery volume and express operational costs of the sharing logistics center change, the benefits of participating enterprises move in the same direction. The benefit allocation model established in this study enriches the existing body of research in the field of shared delivery and offers valuable insights for benefit allocation issues that necessitate consideration of the dynamic effects of multiple parameter variations. [ABSTRACT FROM AUTHOR]