Clinical Effect of Lobectomy Under Single-Hole Thoracoscope in Minimally Invasive Surgical Treatment of Non-Small Cell Lung Cancer

Aims: To explore the effect of single-hole thoracoscopic lobectomy in the treatment of non-small cell lung cancer (NSCLC). Methods: A total of 56 patients with NSCLC from the Third People's Hospital of Jinan during May 2020 to May 2022 were selected as the study subjects, which were divided into control group and observation group according to the difference of treatment methods. Three holes thoracoscopic lobectomy was performed in the control group, and single-hole thoracoscopic lobectomy was conducted in the observation group. The operation time, intraoperative bleeding, total number of lymph node dissection, total drainage volume of thoracic duct 3 days after operation, extubation time of thoracic duct, postoperative hospital stays, postoperative pain score (day 1, 3, 7), and postoperative complication rate were compared between the two groups. Results: Compared with the control group, the operation time, postoperative decannulation time and postoperative hospital stay of patients in the observation group were significantly shortened (P<0.05), the amount of intraoperative bleeding and the total drainage volume of thoracic duct 3 days after operation were significantly reduced (P<0.05), and the postoperative pain score and the incidence of postoperative complications were significantly improved (P<0.05). Conclusion: Single-hole thoracoscopic lobectomy is effective and safe in the treatment of non-small cell lung cancer, which is worthy of clinical promotion

Using cooperation to improve the experience of web services consumers

Web Services (WS) are one of the most promising approaches for building loosely coupled systems. However, due to the heterogeneous and dynamic nature of the WS environment, ensuring good QoS is still non-trivial. While WS tend to scale better than tightly coupled systems, they introduce a larger communication overhead and are more susceptible to server/resource latency. Traditionally this problem has been addressed by relying on negotiated Service Level Agreement to ensure the required QoS, or the development of elaborate compensation handlers to minimize the impact of undesirable latency. This research focuses on the use of cooperation between consumers and providers as an effective means of optimizing resource utilization and consumer experiences. It introduces a novel cooperative approach to implement the cooperation between consumers and providers

Boundary Hamiltonian theory for gapped topological phases on an open surface

In this paper we propose a Hamiltonian approach to gapped topological phases on an open surface with boundary. Our setting is an extension of the Levin-Wen model to a 2d graph on the open surface, whose boundary is part of the graph. We systematically construct a series of boundary Hamiltonians such that each of them, when combined with the usual Levin-Wen bulk Hamiltonian, gives rise to a gapped energy spectrum which is topologically protected; and the corresponding wave functions are robust under changes of the underlying graph that maintain the spatial topology of the system. We derive explicit ground-state wavefunctions of the system and show that the boundary types are classified by Morita-equivalent Frobenius algebras. We also construct boundary quasiparticle creation, measuring and hopping operators. These operators allow us to characterize the boundary quasiparticles by bimodules of Frobenius algebras. Our approach also offers a concrete set of tools for computations. We illustrate our approach by a few examples.Comment: 21 pages;references correcte

Understanding the Drivers’ Continuous Intention of Online Car Booking Service

Based upon commitment theory, this study explores the effect of organizational commitment on drivers’ continuous intention to provide online car booking service. We further investigate the antecedent factors of the drivers’ organizational commitment. Online survey is utilized to collect data from the drivers who are providing service current from various companies in China. The results show that affective commitment and normative commitment serve as the crucial determinants to affect drivers’ continuous intention. Besides, social interaction ties with company, with customers, drivers’ rewards, as well as their sense of self-value cultivate their organizational commitment perception. We then propose our theoretical and practical implications according to the findings of this study

Highly-Accurate Electricity Load Estimation via Knowledge Aggregation

Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates in a deregulated environment. Traditional forecasting models fail to incorporate external knowledge while modern data-driven ignore the interpretation of the model, and the load series can be influenced by many complex factors making it difficult to cope with the highly unstable and nonlinear power load series. To address the forecasting problem, we propose a more accurate district level load prediction model Based on domain knowledge and the idea of decomposition and ensemble. Its main idea is three-fold: a) According to the non-stationary characteristics of load time series with obvious cyclicality and periodicity, decompose into series with actual economic meaning and then carry out load analysis and forecast. 2) Kernel Principal Component Analysis(KPCA) is applied to extract the principal components of the weather and calendar rule feature sets to realize data dimensionality reduction. 3) Give full play to the advantages of various models based on the domain knowledge and propose a hybrid model(XASXG) based on Autoregressive Integrated Moving Average model(ARIMA), support vector regression(SVR) and Extreme gradient boosting model(XGBoost). With such designs, it accurately forecasts the electricity demand in spite of their highly unstable characteristic. We compared our method with nine benchmark methods, including classical statistical models as well as state-of-the-art models based on machine learning, on the real time series of monthly electricity demand in four Chinese cities. The empirical study shows that the proposed hybrid model is superior to all competitors in terms of accuracy and prediction bias

Nitrogen-Rich Perylene Nanosheet Enhanced Bismaleimide Resin

The low toughness of bismaleimide resin (BMI) hinders its application in the aerospace field. In order to improve the strength and toughness of BMI resin simultaneously, this study proposes to introduce perylene-dicyandiamide (P-DCD) nanosheets with an ultra-s rigid conjugated planar structure into the polymer matrix of bismaleimide resin through hydrogen bonding and cross-linking to construct modified composites. The research results showed that the modified cured composites exhibited excellent mechanical properties, with a significant increase in impact strength of 135.8%, flexural strength and flexural modulus of 87.1% and 44.6%, respectively. The thermal properties of the resin were maintained before and after modification, with the glass transition temperature (Tg) of 284.0 ˚C and decomposition temperature > 520 ˚C. Meanwhile, the strengthening and toughening mechanism of the bismaleimide-based system modified by additive P-DCD were also explored. The results showed that the functional group of dicyandiamide in nanosheets and the hydrogen bonding effect in P-DCD synergically increased the cross-linking network and compatibility between P-DCD and the matrix resin