Search

Abstract National innovation capability (NIC) is an important manifestation of economic development quality and international competitiveness. Previous studies focus mainly on the static impact of factors in the national innovation system on NIC in a given year. Research is still lacking on whether these factors have continuing, stable impacts on NIC in different economies. To fill this literature gap, this study uses fuzzy set qualitative comparative analysis (fsQCA) to conduct cross-economy and cross-year comparisons and discusses how the five elements of the NIS jointly affected the NIC of different economies from 2011 to 2022. The results indicate that the necessity consistency of infrastructure for NIC shows a temporal trend. There are four paths to generating high NIC in high-income economies, but only two such paths for upper middle-income economies. The path combining human capital and research, market sophistication, and business sophistication can drive both types of economies to generate high NIC. The research results have improved the transparency of innovation paths and provided a reference for different economies to achieve sustainable development.

Enzyme-assisted ultrasonic extraction was employed to extract polysaccharides from white lilacs. The effects of various experimental conditions on the yield of polysaccharides from white lilacs were systematically analyzed using the response surface methodology (RSM). The crude extract was purified through DEAE-52 cellulose column and Sephadex G-100 gel column chromatography. High-performance gel permeation chromatography (HPGPC), ion chromatography (IC), ultraviolet spectrum analysis, Fourier infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were utilized for characterization of the polysaccharide structure and its antioxidant activity in vitro was investigated. The results demonstrated that the optimal extraction process for polysaccharides from white lilacs involved ultrasonic power 150 W, ultrasonic time 40 min, ultrasonic temperature 40 ℃, enzyme dosage 2.2%, solid-liquid ratio 1:40 g/mL. Under these conditions, the yield of polysaccharides was 3.03%±0.09%. The polysaccharides were purified using a DEAE-52 cellulose column, and the main component SP-c was collected. Subsequently, SP-c-1 was obtained through a Sephadex G-100 gel column. The monosaccharide composition and molar ratio of SP-c-1 were galacturonic acid, arabinose, galactose, rhamnose, glucose, glucuronic acid, xylose=18.39:11.13:8.96:2.61:1:0.83:0.57, respectively. The weight-average molecular weight (Mw) of SP-c-1 was 14069 Da, and the numerical mean molecular weight (Mn) was 13637 Da. SP-c-1 had the characteristic absorption peak of polysaccharide and contained D-grape pyranose configuration. The half-inhibitory concentration (IC50) of SP-c-1 on DPPH and ABTS+ free radicals was 0.87 and 1.355 mg/mL, respectively. Overall results indicate that SP-c-1 exhibits significant antioxidant activity.

Abstract The data‐driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro‐electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large‐scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large‐scale MEA customization through cascading, and reduce the bandwidth of large‐scale MEA through spike compression. Firstly, based on in‐pixel spike detection, a row‐based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large‐scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation‐based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large‐scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.

During the operation of the drill string, it displays a degree of flexibility. Simultaneously, its dynamic properties, influenced by complex stress conditions, manifest nonlinearity and uncertainty. A comprehensive investigation into the dynamics of flexible drill strings is imperative for deep well drilling. This paper presents a model that simulates random interactions between a flexible drill string and the borehole wall, simplifying the actual drill string model using analogous principles. Dynamic simulation software is utilized for analysis, and an indoor experimental setup has been established. The results reveal that with a constant weight on bit (WOB), higher drill string rotational speeds correlate with increased susceptibility to buckling deformation. Additionally, the critical time for deformation onset exhibits a nearly linear relationship with rotational speed. Maintaining a constant rotational speed, an increase in WOB enhances the likelihood of buckling deformation. The experimental findings suggest a correlation between the drill string’s rotation frequency and the WOB.

To establish an environmentally friendly and efficient method for extracting polyphenols from Syringa oblata Lindl. flowers and study the antioxidant activity of its polyphenols, this study used ultrasound-assisted deep eutectic solvents (UADESs) to extract polyphenols from Syringa oblata Lindl. flowers. Firstly, the choline chloride-malice acid, at a molar ratio of 1:1.5, and on the basis of the results of single-factor experiments, the extraction temperature, ultrasonic time, solid-liquid ratio and ultrasonic power, were selected as the key factors for optimization, and the extraction amount of polyphenols was used as the response value, and the Box-Behnken response surface method was used to optimize, and the scavenging ability of Syringa oblata Lindl. polyphenols on DPPH radicals and hydroxyl radicals was studied. The results showed that the optimized extraction parameters were extraction temperature of 60 ℃, ultrasonic time of 60 min, solid-liquid ratio 1:30 g/mL and ultrasonic power of 300 W, the polyphenol content could reach 52.19±0.13 mg/g, significantly higher than that of traditional extraction (60% ethanol, methanol) (P

To investigate the efficient method of total flavonoids from Syringa oblata leaves, this paper firstly used cellulase-assisted ultrasound method to extract flavonoids from four Syringa oblata leaves. The effects of cellulase dosage, pH, ultrasound time, ultrasound power, liquid-material ratio, temperature and ethanol volume fraction on the extraction rate of total flavonoids from Syringa pubescens Turcz. leaves were analyzed by single-factor experiments, and the best extraction process was obtained by applying response surface methodology optimization. The scavenging ability of Syringa oblata leaves flavonoids on DPPH and OH radicals was also examined in vitro as an antioxidant. The results showed that, the optimum extraction process was enzyme addition 2%, pH5.5, sonication time 40 min, sonication power 400 W, ethanol volume fraction 63%, temperature 56 ℃, liquid to material ratio 30:1 (mL/g), and under these conditions, the Syringa oblata flavonoids content was 32.21±0.16 mg/g, which was in proximity to the predicted value of 32.57 mg/g from the regression model. The results of antioxidant activity showed that the scavenging ability of the four Syringa oblata leaf flavonoids on DPPH was Syringa pubescens Turcz.>Syringa reticulata var. mamds huricq.>Syringa oblata var. affinis lingdelsh.>Syringa oblata Linal. The scavenging ability on OH was Syringa pubescens Turcz.>Syringa reticulata var. mamds huricq.> Syringa oblata Linal.>Syringa oblata var. affinis lingdelsh. The cellulase-assisted ultrasonication method can efficiently extract flavonoids from Syringa oblata leaves, and the flavonoids of Syringa oblata leaves have good antioxidant properties and have certain potential for development.

BackgroundPostoperative cognitive dysfunction (POCD) is a postoperative complication that often occurs in the elderly. This systematic review and meta-analysis aimed to compare intravenous anesthetics (propofol) with inhalation anesthetics (sevoflurane) regarding the occurrence of POCD in the elderly who underwent non-cardiac surgery.MethodsThe investigators searched for published articles from the PubMed, Embase, Web of Science, Scopus, Cochrane, and Clinicalkey databases. Clinical studies comparing the incidence of POCD in elderly patients undergoing intravenous or inhalation anesthesia in general were selected. Primary outcomes included the occurrence of POCD at 1, 3, and 7 days. The secondary outcomes were the patient’s plasma S-100β protein levels (pg*mL−1) and delayed neurocognitive recovery incidence 5–7 days after surgery.ResultsFifteen studies including 3,817 patients were enrolled in the systematic review. Ten studies involving 1,829 patients were enrolled in the meta-analysis. The results demonstrate that there was no difference between the intravenous and inhalation groups in the incidence of POCD within 1–7 days (95% CI 0.73–1.26, p = 0.77) and the occurrence of delayed neurocognitive recovery 5–7 days after surgery (95% CI −353.15 to −295.44, p = 0.28). Plasma S-100β protein levels in the intravenous anesthesia group were lower than those in the inhalation group (95% CI 0.48–1.24, p

Most turbine designs are based on the continuous rotating turbine structure. In this paper, a novel hydraulic balanced turbine (HBT) with a symmetrical blade structure that can be used in a hydro-environment is proposed. The turbine, which uses the hydraulic impact of the nozzle jet and balanced turbine blade design in stability control, could be used to control stability in certain applications, e.g. in drilling engineering. This is a completely new way of thinking about turbine design. Because of the special blade design of the HBT, when the nozzle deviates from the balance position, the HBT will be restored to the balance position under the action of external moment. The follow-up performance of the HBT is directly related to the rotational speed of the nozzle. The theoretical and numerical solutions in this paper have good consistency. Modern computational resources allow computational fluid dynamics to be an integral part of turbine design. The simulation results proved that this innovative design is feasible and has potential application prospects. The HBT realizes the ‘soft’ connection between the controlled stable platform and the actuator in the vertical drilling tool, which can significantly improve the control precision of mechanical vertical drilling.

The pendulum model is a classic model in physics and has important theoretical and practical significance. In the actual application process of engineering, the characteristic that the simple pendulum has a tendency to always be stable towards the low side of the gravitational potential energy is often used to design the corresponding deformation mechanism as a sensing or control component. In this paper, a deformation structure of a simple pendulum based on this characteristic is analyzed, and a nonlinear dynamic model of the simple pendulum under variable damping is established according to the actual force analysis. Due to the complexity of its analytical solution, this paper adopts the method of numerical analysis to establish a simulation analysis model to analyze the motion pattern of the pendulum when the relative speed and direction of the pendulum and the damper are different and the damping force of the pendulum is different.

BackgroundAlthough many efforts have been devoted to identify biomarkers to predict the responsiveness of immune checkpoint inhibitors, including expression of programmed death-ligand 1 (PD-L1) and major histocompatibility complex (MHC) I, microsatellite instability (MSI), mismatch repair (MMR) defect, tumor mutation burden (TMB), tertiary lymphoid structures (TLSs), and several transcriptional signatures, the sensitivity of these indicators remains to be further improved.Materials and methodsHere, we integrated T-cell spatial distribution and intratumor transcriptional signals in predicting the response to immune checkpoint therapy in MMR-deficient tumors including tumors of Lynch syndrome (LS).ResultsIn both cohorts, MMR-deficient tumors displayed personalized tumor immune signatures, including inflamed, immune excluded, and immune desert, which were not only individual-specific but also organ-specific. Furthermore, the immune desert tumor exhibited a more malignant phenotype characterized by low differentiation adenocarcinoma, larger tumor sizes, and higher metastasis rate. Moreover, the tumor immune signatures associated with distinct populations of infiltrating immune cells were comparable to TLSs and more sensitive than transcriptional signature gene expression profiles (GEPs) in immunotherapy prediction. Surprisingly, the tumor immune signatures might arise from the somatic mutations. Notably, patients with MMR deficiency had benefited from the typing of immune signatures and later immune checkpoint inhibition.ConclusionOur findings suggest that compared to PD-L1 expression, MMR, TMB, and GEPs, characterization of the tumor immune signatures in MMR-deficient tumors improves the efficiency of predicting the responsiveness of immune checkpoint inhibition.

The Weilasituo-bairendaba district is located at the eastern end of the Central Asian Orogenic Belt, which is an important component of the Cu-Pb-Zn polymetallic metallogenic belt on the Western slope of the Greater Xing’an Range in Inner Mongolia, China. The known Cu-Zn deposits such as the Weilasituo Cu-Zn deposit and the Bairendaba Ag-Pb-Zn deposit are the same tectonic-magmatic product. The district’s structure framework consists of the NE-trending regional faults, while the secondary faults provide channels and space for mineralization. The ore-bearing rocks are either Baoyintu Group gneisses or quartz diorites. The typical Cu-Zn deposits exhibit obvious Cu, Pb, Zn geochemical anomaly as well as obvious magnetic anomaly. The district-scale two-dimensional (2D) mineral prospectivity modeling has been reported. Nowadays, three-dimensional (3D) mineral prospectivity modeling is necessary and urgent. Integrated deposit geology and accumulated exploration data, the above four exploration criteria (regional fault, secondary fault, geochemical anomaly and magnetic susceptibility) are used for 3D mineral prospectivity modeling. Filtering (upward continuation, low pass filtering, two-dimensional empirical mode decomposition), magnetic inversion and 3D modeling techniques were used to construct geological models. Excellent machine learning algorithms such as random forest (RF) and XGBoost are applied. The two machine learning methods confirm each other to improve the accuracy of 3D mineral prospectivity modeling. In this paper, repeated random sampling and Bayesian optimization are combined to construct and tune models. This joint method can avoid the contingency caused by random sampling of negative samples, and can also realize automatic optimization of hyperparameters. The optimal models (RF28 and XGBoost11) were selected among thirty repeated training models for mineral prospectivity modeling. The obtained areas under the ROC curves of RF28 and XGBoost11 were 0.987 and 0.986, respectively. The prediction-area (P-A) plot and C-A fractal were used to delineate targets and grade targets. The targets were divided into Ⅰ-level targets and Ⅱ-level targets. The I- and II-targets are not only highly consistent with the known Cu-Zn deposits, but also exhibit obvious ore-forming geological features. The 3D targets are beneficial for Cu-Zn exploration in the Weilasituo-bairendaba district.

In the production process of oil and gas wells, the migration of solid particles, such as mud and sand, often leads to screen pipe clogging, resulting in production reduction or even shutdown. Controlled plasma pulse plugging removal technology is a new physical method for unplugging. Plasma, generated by the electrical explosion of the wire, then triggers a shock wave, which can remove the plugging. In this paper, the performance of controllable plasma pulse plugging removal technology is evaluated by simulating the plugging state of the downhole screen through laboratory experiments. The test results of screen plate unplugging effect evaluation fully show that the plasma pulse decongestion technology can carry out effective decongestion, can effectively reduce the pressure behind the clogging zone and increase the flow rate of the clogging zone, and is suitable for different screen plate blockage states such as mild, moderate and severe. Thus, this technology has a good application prospect.

Abstract Randomness lays the foundation for information security. Quantum random number generation based on various quantum principles has been proposed to provide true randomness in the last two decades. We integrate four different types of quantum random number generators on the Alibaba Cloud servers to enhance cybersecurity. Post-processing modules are integrated into the quantum platform to extract true random numbers. We employ improved authentication protocols where original pseudo-random numbers are replaced with quantum ones. Users from the Alibaba Cloud, such as Ant Financial and Smart Access Gateway, request random numbers from the quantum platform for various cryptographic tasks. For cloud services demanding the highest security, such as Alipay at Ant Financial, we combine the random numbers from four quantum devices by XOR the outputs to enhance practical security. The quantum platform has been continuously run for more than a year.

Abstract Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

Entrepreneurship competitions are an important way to implement entrepreneurship education in universities and the main way for many students improve their entrepreneurial competence. To clarify the mechanism of the role of entrepreneurship competition on the entrepreneurial competence of university students, based on data from a sample of 170,764 university students from 31 provinces in China, this study constructs a moderated mediation model that focuses on the mediating role of entrepreneurial spirit (ES) in entrepreneurial competition (ECompetition) and entrepreneurial competence (ECompetence) and the moderating role of entrepreneurial practice (EP). The results showed that ECompetition found to have a significant positive predictive effect on ECompetence. ES plays a mediating role in the relationship between ECompetition and competence. The direct predictive effect of ECompetition on ECompetence and the mediating effect of ES on their relationship is moderated by EP. The results provide a new perspective on the impact of entrepreneurship competition on college students’ entrepreneurial competence and expands the experiential learning theory in entrepreneurship education.

At present, research in the field of college students' entrepreneurship has proliferated, but these studies tend to analyze the net benefits of various factors on entrepreneurial activities, which are affected by the configuration effects of multiple factors; hence, it remains unclear whether entrepreneurial education can make graduates more efficient to started their own companies. To fill this gap in the literature, drawing on general systems theory and using fuzzy-set qualitative comparative analysis (fsQCA), we take 1,87,914 undergraduate and junior college students from 1,231 colleges and universities in China as a sample to explore the relationships among the five conditions in the entrepreneurship education environment and cognitive level (i.e., the quality of staff, subject curriculum, entrepreneurial competition, intentions, and opportunity identifications) and entrepreneurial activities. The fsQCA results show that none of these factors are sufficient for entrepreneurial activity. In contrast, three combinations of the five conditions (i.e., co-creation type, competition-oriented environment, and entrepreneurship education that fits under the guidance of entrepreneurial intention) can produce high entrepreneurial activity, as well as substitution and complementarity among the various elements within the configuration. These results show that the combined effect of the five conditions is more conducive to the entrepreneurial activities of college students. Finally, after a discussion of the study's findings, theoretical, and practical contributions are analyzed with regard to the field of entrepreneurship in Chinese colleges, and alternative options indicate that college students are more likely to become entrepreneurs in the future.

Research shows that entrepreneurial activities significantly promote economic development, which enhances the importance of the innovative entrepreneurial potential of college students. This study analyzes the effect of entrepreneurship education on entrepreneurial intention from the perspective of planned behavior theory. By examining the significant role of entrepreneurship education at colleges and universities on economic and social development, we established a conceptual model. To understand the relationship between entrepreneurship education and entrepreneurial intention, the hypotheses propose the intermediary role of entrepreneurial ability, and the study provides evidence from China the relationship between entrepreneurship education and entrepreneurial intention. Improving entrepreneurial intention and encouraging college students to establish businesses through entrepreneurship education in universities is crucial. This study proposes a hypothetical model of the relationship between entrepreneurial competence and entrepreneurial intention in entrepreneurship education at universities. Using a questionnaire survey of college students with practical experience in the Yangtze River Delta of China, the bootstrap method in the SPSS macro program process software verifies the hypotheses. The results show that entrepreneurial teaching, business plan competition, and entrepreneurial practice support positively affect entrepreneurial competence. In addition, entrepreneurial competence plays an intermediary role in the relationship between entrepreneurial teaching, business plan competition, entrepreneurship practice support, and entrepreneurial intention. Entrepreneurship education improves the ability to establish a business in the present and in entrepreneurial activities in the future. Entrepreneurial competence obtained through entrepreneurship education continuously affects entrepreneurial intention.

Mechanical automatic vertical drilling tools (MAVDT) have gradually gained attention as a drilling tool that can achieve active correction in harsh working environments such as high temperature and high pressure. The gravity sensing mechanism can sense the deviation and convert the deviation signal into the control signal to drive the actuator to correct the deviation. It is the core component of the mechanical automatic vertical drilling tool. This paper analyzes the force on the gravity sensing mechanism based on the structural analysis of the mechanical automatic vertical drilling tool. Then, the general dynamic equation of the gravity sensing mechanism is established based on D’Alembert principle. The critical response position where the acceleration value is zero is used as the research object to complete the preliminary design and analysis of the sensing mechanism. Through analysis, it can be found that there is a conflict between the response accuracy and control stability of the gravity sensing mechanism. High response accuracy often means poor control stability. For the gravity sensing mechanism with definite structural parameters, there is a limit value of the allowable friction coefficient. When the friction coefficient of the thrust bearing exceeds the limit value, the gravity sensing mechanism cannot achieve the inclination response no matter how big the inclination angle and deflection angle are. The friction coefficient between the disc valves and the force between the disc valves of the gravity sensing mechanism have a linear effect on the performance of the mechanism, and the smaller the deviation angle, the greater the influence coefficient of the force or the friction coefficient between the disc valves on the length of the gravity sensing mechanism. During the process of dynamic swing, the dynamic stable position of the gravity sensing mechanism is related to the relative relationship between the restoring force of the mechanism and the friction damping. To be precise, it is related to the potential energy zero point and the speed zero point during the gravity sensing mechanism swing process before it reaches the dynamic stable position.

Amore efficient new channel coding scheme was required for NRby the service features and capability indices of 5G.Due to its excellent performance,Polar code was specified as the control channel coding scheme for 5G enhancedmobile broadband(eMBB)scenario.An overview of Polar code was presented and themain existing research issues of the standardization for new radio(NR)technology oriented Polar code,including code construction,sequence design,rate-matching and channel interleaving,etc,were elaborated.Then some specific analysis and comparisons of the Polar code schemes which were proposed by 3GPPmember institutes in RAN1 conferences recently were presented.Finally,a summary of the standardization works of Polar code was given on the basis of the above discussions.

Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.

Objective: To investigate the analgesic effect of thoracic paravertebral block (TPVB) in patients undergoing thoracoscopic lobectomy under general anesthesia (GA). Methods: Clinical records of 82 patients who underwent thoracoscopic lobectomy under GA from October 2021 to October 2022 in the General Hospital of Southern Theater Command were retrospectively analyzed. The patients were divided into two groups according to the method of anesthesia used: general anesthesia group (Group-G, n=37), and TPVB plus GA group (Group-T, n=45). The analgesic effect, mean arterial pressure (MAP), heart rate (HR) and the rate of adverse events in both groups were compared. Results: Visual analogue scale (VAS) scores of patients in Group-T at 12h, 24h and 48h after the operation were significantly lower compared to Group-G (P<0.05). MAP and HR the time of tracheal intubation induction (T1), single lung ventilation (T2), skin incision (T3), operation completion (T4), and 20 minutes after the extubation (T5) were lower in both groups compared to T0, and were significantly higher in Group-T compared to Group-G (P<0.05). The rate of adverse events in Group-T was 6.67%, significantly lower compared to Group-G (24.32%) (P<0.05). Conclusions: TPVB combined with GA can improve the analgesic effect, improve MAP and HR during the operation, and reduce the incidence of adverse events in patients undergoing thoracoscopic lobectomy. [ABSTRACT FROM AUTHOR]