Unraveling the efficacy network: A network meta-analysis of adjuvant external beam radiation therapy methods after hepatectomy

BACKGROUND Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis.Postoperative adjuvant external radiation therapy(RT)has been shown to effectively prevent recurrence after liver cancer resection.However,there are multiple RT techniques available,and the differ-ential effects of these techniques in preventing postoperative liver cancer re-currence require further investigation.AIM To assess the advantages and disadvantages of various adjuvant external RT methods after liver resection based on overall survival(OS)and disease-free survival(DFS)and to determine the optimal strategy.METHODS This study involved network meta-analyses and followed the PRISMA guidelines.The data of qualified studies published before July 10,2023,were collected from PubMed,Embase,the Web of Science,and the Cochrane Library.We included relevant studies on postoperative external beam RT after liver resection that had OS and DFS as the primary endpoints.The magnitudes of the effects were determined using risk ratios with 95%confidential intervals.The results were analyzed using R software and STATA software.RESULTS A total of 12 studies,including 1265 patients with hepatocellular carcinoma(HCC)after liver resection,were included in this study.There was no significant heterogeneity in the direct paired comparisons,and there were no significant differences in the inclusion or exclusion criteria,intervention measures,or outcome indicators,meeting the assumptions of heterogeneity and transitivity.OS analysis revealed that patients who underwent stereotactic body radiotherapy(SBRT)after resection had longer OS than those who underwent intensity modulated radiotherapy(IMRT)or 3-dimensional conformal RT(3D-CRT).DFS analysis revealed that patients who underwent 3D-CRT after resection had the longest DFS.Patients who underwent IMRT after resection had longer OS than those who underwent 3D-CRT and longer DFS than those who underwent SBRT.CONCLUSION HCC patients who undergo liver cancer resection must consider distinct advantages and disadvantages when choosing between SBRT and 3D-CRT.IMRT,a RT technique that is associated with longer OS than 3D-CRT and longer DFS than SBRT,may be a preferred option.

Behavior Analysis of Bank into E-Commerce under Network Externalities

With the advent of Internet financial innovation,many commercial banks quietly have started to enter into the Ecommercial in order to prevent oligarchs from eroding financial market.From the perspective of industrial division,this paper reveals the nature of a phenomenon that E-commercial enterprises and banks have stepped into each other's field,which E-commerce of banks can give full play to network effects.Then it uses game theory to analyze the motions of banks to involve into E-commerce and the short-term competitive equilibrium of large incumbent Ecommercial enterprises as well.For individual rationality,the dominant strategy of banks and E-commercial enterprises is(enter,counterattack).Considering network externalities,it constructs a competing model on banks and incumbent E-commercial enterprises and simulates competitive trends and balanced results of their behaviors,which illustrates that banks can obtain network effect after choosing E-commerce strategy.

The Mechanism Behind Urban Population Growth and Shrinkage from the Perspective of Urban Network Externalities

Urban shrinkage is a global phenomenon,and it will coexist with urban growth for many years.At the same time,the network connection between cities continuously improved due to the construction of the transportation and information networks.However,the relationship between urban network externalities and urban population growth/shrinkage remains unclear.Therefore,based on high-speed railway(HSR)flow data,a spatial econometric model is used to explore the mechanism behind urban population growth and shrinkage from the perspective of network externalities in China.The results indicate that:1)the urban network experiences a certain clubbing effect.Growing cities that are strongly connected are concentrated along China’s main railway lines and the southeastern coastal areas,while shrinking cities that are weakly connected are distributed at the periphery of the network.2)Moreover,the network externality disregards spatial distance and together with the agglomeration externality influences the growth and shrinking of cities.3)Urban economic development still promotes the development of Chinese cities.However,the improvement of the urban economy has a negative cross-regional spillover effect on neighboring cities due to urban competition.4)Lastly,Local spillovers of urban network externalities are positive,while cross-regional ones are negative.Consequently,the government needs to promote the construction of multi-dimensional network connections between cities to promote cities’sustainable development.This study reveals the relationship between urban network externalities and urban development,enriches the theories of network externalities and urban growth/shrinkage,and provides a reference for regional coordinated development.

Circular Formation Flight Control for Unmanned Aerial Vehicles With Directed Network and External Disturbance

This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles(UAVs)to perform coordinated circular tracking around a set of circles on a target sphere.Different from the previous results limited in bidirectional networks and disturbance-free motions,this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound.Distinguishing from the design of a common Lyapunov fiunction for bidirectional cases,we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input.Then the whole control system is regarded as a cascade connection of these two subsystems,which is proved to be stable by input-tostate stability(ISS)theory.For the purpose of encountering the external disturbance,the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere(say,the circular tracking control algorithm)with the help of the switching function.Meanwhile,the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere(say,the formation keeping control law)for formation keeping.The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.

Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network

Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time.Identifying the causes of external corrosion and taking specific maintenance measures is essential.In this work,a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network.A fault tree model is first employed to identify the causes of external corrosion.The Bayesian network for risk analysis is determined accordingly.The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk.The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model.Because of the limitations of historical data,some of the parameters in the Bayesian network are obtained from a probabilistic estimation model,which combines expert experience and fuzzy set theory.Finally,a case study is carried out to verify the feasibility of the maintenance decision model.This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion.

Pinning Synchronization Between Two General Fractional Complex Dynamical Networks With External Disturbances

In this paper, the pinning synchronization between two fractional complex dynamical networks with nonlinear coupling, time delays and external disturbances is investigated. A Lyapunov-like theorem for the fractional system with time delays is obtained. A class of novel controllers is designed for the pinning synchronization of fractional complex networks with disturbances. By using this technique, fractional calculus theory and linear matrix inequalities, all nodes of the fractional complex networks reach complete synchronization. In the above framework, the coupling-configuration matrix and the innercoupling matrix are not necessarily symmetric. All involved numerical simulations verify the effectiveness of the proposed scheme. © 2017 Chinese Association of Automation.

High-speed Railway External Power Supply Reliability Evaluation of Bayesian Network

Reliability evaluation is important in high speed railway external power supply design, based on probability reasoning bayesian network applied in high-speed railway external power supply reliability evaluation, establish the minimum cut and the minimum path of bayesian network model, quantitative calculation external power supply system in each element posterior probability, and the example analysis verified the feasibility and correctness of the above method. Using bayesian network bidirection reasoning technology, quantitative calculation the posterior probability of each element in external power supply system, realized the identification of weak link in external power supply. The research methods and the results of the study can be used in the scheme optimization design of high speed railway external power supply.

Innovation in Chinese Agribusinesses： Internal Resources and External Networks

The existing literature on innovation concentrates mostly on large industrial firms and high-technology industries, whereas, little attention has been given to agribusiness. Empirical evidence regarding the driving forces behind innovation in agribusinesses in developing countries, China in particular is scarce. This paper helps fill that void. It develops a framework in which innovation results from synergies between internal resources and external networks. This paper applies and tests the framework using 2003-2005 data from a panel survey of 32 leading agribusiness firms in Shandong Province, China. The empirical results indicate the importance of internal resources, external networks and the synergies between them. We find that R＆D expenditures and the number of technical employees are internal resources that are both important to product innovation. Surprisingly, management quality is negatively related to the possession of a unique technology and new products as a proportion of all products. It is possible that management quality is associated with more formalization and rigidity in decision-making, hindering creativity and lengthening the new product development cycle. In order to develop innovative products, our results suggest that investing in R＆D and hiring more technical staff may be more effective approaches than spending on managerial talent.

Network meta-analysis of the effectiveness of TCM external treatment in the treatment of mycoplasmal pneumonia

Objective: To compare the efficacy and safety of different TCM external treatment combined with azithromycin in the treatment of Mycoplasma pneumoniae pneumonia. Methods: The keywords and free words were combined to search the literatures of clinical randomized or quasi-randomized controlled trials on the effects of TCM external treatment combined with azithromycin in the treatment of children with MPP in CNKI, VIP, CBM, Wan Fang Date, PubMed, Sciencedirect and Google Academic Database. The search time limit is from August 2019. After two independent reviewers selected the literature, extracted data and literature quality evaluation according to the inclusion and exclusion criteria, the ADDI software, RevMan5.3 and stata14.0 software were used for mesh meta-analysis. Results: A total of 18 randomized controlled literatures were included, involving 1536 children with MPP, 6 Chinese medicine external treatment methods (ultra-short wave, acupoint application, cupping, enema, Chinese medicine patch, massage). The results of mesh meta-analysis showed that treatment In terms of MPP efficiency, ultrashort wave, acupoint application, cupping, enema, Chinese medicine patch, massage combined with azithromycin treatment is more effective than azithromycin alone, and the probability distribution shows that the Chinese medicine patch combined with azithromycin treatment is the best solution (P= 0.34). In the reduction of adverse reactions, because the ultrashort wave and traditional Chinese medicine patch literature did not mention the non-performing rate, this study only analyzed the other four external treatment methods, acupoint application, cupping, enema, massage combined with azithromycin is better than single With azithromycin, the probability distribution showed that the probability of massage combined with azithromycin was the best (P=0.67). Conclusion: In terms of efficiency and reduction of adverse reactions, each Chinese medicine external treatment combined with azithromycin has an advantage over single azithromycin. Probability distribution shows that in the treatment of MPP efficiency, Chinese medicine patch + Achi > ultrashort wave + AZM > enema + AZM > cupping + AZM > massage + Aqi, Chinese medicine patch + azithromycin program is the optimal program;In terms of reducing adverse reactions, massage + AZM > enema + AZM > acupressure application + AZM > cupping +AZM.

Decision and Coordination of an E-commerce Supply Chain Considering Returns and Network Externalities

Considering the large number of returns in online sales and the network externalities of e-platforms,we develop a decentralized model and a centralized model to explore the impacts of returns and network externalities on e-commerce supply chain(ECSC)decisions.We show that in the decentralized model,the service level,price,market demand,and ECSC members’profits increase with the network externality strength.However,the service level and price increase,while the market demand and ECSC members’profits decrease with the product return rate.The centralized model is the optimal operating mode when it is properly coordinated.We design the“commission and return cost-sharing”contract to optimize ECSC,in which the proportion of the e-platform’s sharing of the return handling cost is exactly equal to the proportion of the system profit after coordination.Based on the decentralized model,we develop two extended models in which we endogenize the impacts of the service level and return rate on the network externality strength.Through comparisons between the extended and decentralized models,we show that high-quality service can improve ECSC’s profitability,while a high return rate hurts its economic performance.

Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input(NARX) algorithm

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the "thermal damping effect" will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input(NARX) algorithm was used as a novel method to predict the dry-bulb temperature(Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network(ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical "damping coefficient" for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Tdat the bottom of intake shafts.

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.

Reduced-Order GPIO Based Dynamic Event-Triggered Tracking Control of a Networked One-DOF Link Manipulator Without Velocity Measurement

In networked robot manipulators that deeply integrate control, communication and computation, the controller design needs to take into consideration the limited or costly system resources and the presence of disturbances/uncertainties. To cope with these requirements, this paper proposes a novel dynamic event-triggered robust tracking control method for a onedegree of freedom(DOF) link manipulator with external disturbance and system uncertainties via a reduced-order generalized proportional-integral observer(GPIO). By only using the sampled-data position signal, a new sampled-data robust output feedback tracking controller is proposed based on a reduced-order GPIO to attenuate the undesirable influence of the external disturbance and the system uncertainties. To save the communication resources, we propose a discrete-time dynamic event-triggering mechanism(DETM), where the estimates and the control signal are transmitted and computed only when the proposed discrete-time DETM is violated. It is shown that with the proposed control method, both tracking control properties and communication properties can be significantly improved. Finally, simulation results are shown to demonstrate the feasibility and efficacy of the proposed control approach.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

Bursting synchronization in clustered neuronal networks

Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network, Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.

Stability analysis of delayed cellular neural networks with and without noise perturbation

The stability of a class of delayed cellular neural networks （DCNN） with or without noise perturbation is studied. After presenting a simple and easily checkable condition for the global exponential stability of a deterministic system, we further investigate the case with noise perturbation. When DCNN is perturbed by external noise, the system is globally stable. An important fact is that, when the system is perturbed by internal noise, it is globally exponentially stable only if the total noise strength is within a certain bound. This is significant since the stochastic resonance phenomena have been found to exist in many nonlinear systems.

A Sentence Retrieval Generation Network Guided Video Captioning

Currently,the video captioning models based on an encoder-decoder mainly rely on a single video input source.The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning,which is not conducive to the accurate descrip-tion and understanding of video content.To address this issue,a novel video captioning method guided by a sentence retrieval generation network(ED-SRG)is proposed in this paper.First,a ResNeXt network model,an efficient convolutional network for online video understanding(ECO)model,and a long short-term memory(LSTM)network model are integrated to construct an encoder-decoder,which is utilized to extract the 2D features,3D features,and object features of video data respectively.These features are decoded to generate textual sentences that conform to video content for sentence retrieval.Then,a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual sentences.The candidate sentences are screened out through similarity measurement.Finally,a novel GPT-2 network model is constructed based on GPT-2 network structure.The model introduces a designed random selector to randomly select predicted words with a high probability in the corpus,which is used to guide and generate textual sentences that are more in line with human natural language expressions.The proposed method in this paper is compared with several existing works by experiments.The results show that the indicators BLEU-4,CIDEr,ROUGE_L,and METEOR are improved by 3.1%,1.3%,0.3%,and 1.5%on a public dataset MSVD and 1.3%,0.5%,0.2%,1.9%on a public dataset MSR-VTT respectively.It can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.

成年国民阅读意愿的动因解析及“全民阅读”活动启示

提高成年国民阅读意愿是全民阅读活动的根本目的。文章在理论刻画阅读意愿生成逻辑的基础上,运用机器学习方法实证分析了我国成年国民阅读意愿的系统动因和核心要素,由此形成全民阅读活动提质增效的管理启示。研究发现:(1)系统动因分析显示“预期绩效、预期努力、享乐动机”等内部动因贡献度是“社会影响、促进条件”等外部动因贡献度的1.5倍;(2)核心要素分析显示“享乐动机”的敏感度为0.245,位居所有影响因子之首;(3)对低阅读意愿组的“用户画像”结果显示其受教育程度和经济条件与阅读意愿密切相关,就其内部动因而言,该群体对阅读“预期绩效”持有的消极信念是抑制其阅读意愿的关键原因。