Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Assessing target optical camouflage effects using brain functional networks:A feasibility study

Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

Effectiveness and safety of traditional Chinese medicine decoction for diabetic gastroparesis:A network meta-analysis

BACKGROUND Diabetic gastroparesis(DGP)is a prevalent complication of diabetes that impairs people's quality of life and places a significant financial burden on them.The gastrointestinal symptoms of DGP patients can be improved by several Traditional Chinese Medicine(TCM)decoctions that have been shown to be effective in treating the disease.There are still many unanswered questions regarding the identification of appropriate therapeutic agents for the treatment of DGP in clinical practice.AIM To analyze the efficacy of several TCM decoctions in the treatment of DGP using Bayesian network meta-analysis for reference.METHODS PubMed,EMBASE,Cochrane Library,Web of Science,China National Knowledge Infrastructure,The China Biology Medicine DVD,Wanfang,and CQVIP were searched from inception to September 17,2022,to collect randomized controlled trials(RCTs)about TCM decoctions for DGP.Clinical effects and symptom scores were the primary outcomes.Additionally,we assessed motilin(MOT),somatostatin(SS),gastrin(GAS),gastric emptying rate,gastric emptying time,and adverse drug events as secondary outcomes.RESULTS A total of 67 eligible RCTs involving 4790 DGP patients and 7 TCM decoctions were included.The results of network meta-analysis(NMA)and surface under the cumulative ranking curve showed that with western medicine(WM)as a common control,the Banxia Xiexin Decoction(BXXD)+WM was most effective in clinical effects and enhancing early satiety scores;the Simo decoction(SMD)+WM was most effective in improving nausea and vomiting scores and anorexia scores,bloating scores;the Chaishao Liujunzi Decoction(CSLJD)was most effective in MOT,the Zhishi Xiaopi Decoction(ZSXPD)was most effective in SS and upgrading emptying rate;the Jianpi Xiaozhi Decoction was most effective in GAS;the CSLJD+WM was most effective in improving gastric emptying time.CONCLUSION These NMA results suggest that the BXXD+WM and SMD+WM may be one of the potential optimal treatments.Due to various limitations,further large-sample,double-blind,multi-center randomized RCTs are needed.

Effectiveness and safety of traditional Chinese medicine for diabetic retinopathy:A systematic review and network meta-analysis of randomized clinical trials

BACKGROUND Diabetic retinopathy(DR)is currently recognized as one of the most serious diabetic microangiopathies and a major cause of adult blindness.Commonly used clinical approaches include etiological control,microvascular improvement,and surgical intervention,but they are ineffective and have many side effects.Oral Chinese medicine(OCM)has been used for thousands of years to treat DR and is still widely used today,but it is unclear which OCM is more effective for DR.AIM To estimate relative effectiveness and safety profiles for different classes of OCMs for DR,and provide rankings of the available OCMs.METHODS The search time frame was from the creation of the database to January 2023.RevMan 5.3 and Stata 14.0 software were used to perform the systematic review and Network meta-analyses(NMA).RESULTS A total of 107 studies and 9710 patients were included,including 4767 cases in the test group and 4973 cases in the control group.Based on previous studies and clinical reports,and combined with the recommendations of Chinese guidelines for the prevention and treatment of DR,9 OCMs were finally included in this study,namely Compound Xueshuantong Capsules,Qiming Granules,Compound Danshen Dripping Pills,Hexue Mingmu Tablets(HXMM),Qiju Dihuang Pills(QJDH),Shuangdan Mingmu Capsules(SDMM),Danggui Buxue Decoction(DGBX),Xuefu Zhuyu Decoction and Buyang Huanwu Decoction.When these nine OCMs were analyzed in combination with conventional western medicine treatment(CT)compared with CT alone,the NMA results showed that HXMM+CT has better intervention effect on the overall efficacy of DR patients,HXMM+CT has better effect on improving patients'visual acuity,SDMM+CT has better effect on inhibiting vascular endothelial growth factor,DGBX+CT has better effect on reducing fundus hemorrhage area,HXMM+CT has better effect on reducing fasting blood glucose,and QJDH+CT has better effect on reducing glycated hemoglobin.When there are not enough clinical indicators for reference,SDMM+CT or HXMM+CT treatments can be chosen because they are effective for more indicators and demonstrate multidimensional efficacy.CONCLUSION This study provides evidence that combining OCMs with CT leads to better outcomes in all aspects of DR compared to using CT alone.Based on the findings,we highly recommend the use of SDMM or HXMM for the treatment of DR.These two OCMs have demonstrated outstanding efficacy across multiple indicators.

Effectiveness and safety of seven oral Chinese patent medicines as adjuvant therapy for cancer-related anemia: A systematic review and network meta-analysis of randomized controlled trials

Objective:To evaluate the effectiveness and safety of seven oral Chinese patent medicines(CPMs)as adjuvant therapy for cancer-related anemia(CRA)by network meta-analysis(NMA).Methods:A literature search to obtain randomized controlled trials(RCTs)of seven oral CPMs in the adjuvant treatment of CRA was conducted in multiple databases from the inception to April 2022.The RevMan5.3 and R 4.1.1 software were used for NMA.Results:We ultimately included 29 RCTs with 2140 patients.Traditional meta-analysis showed that Fufang E'jiao syrup(FFEJS),Shengxuebao mixture(SXBM),Shengxuening tablets(SXNT),Jianpi Shengxue granules(JPSXG),and Yixuesheng capsule(YXSC)combined with basic Western treatment(BWT)could improve the hemoglobin(HGB)level.JPSXG combined with BWT could improve the red blood cell(RBC).FFEJS combined with BWT improved the Karnofsky performance status(KPS).NMA showed that FFEJS,JPSXG,SXBM,and SXNT plus BWT improved HGB better than Shengxue tablets(SXT)plus BWT,with top three ranking results being JPSXG plus BWT>SXNT plus BWT>FFEJS plus BWT.FFEJS plus BWT,JPSXG plus BWT,SXBM plus BWT,SXNT plus BWT,and SXT plus BWT improved RBC better than BWT,with top three ranking results being SXNT plus BWT>JPSXG plus BWT>FFEJS plus BWT.In terms of the KPS score,compared with SXT plus BWT,FFEJS,JPSXG,SXBM,SXNT,and Yizhong Shengxue capsule(YZSXC)plus BWT had higher KPS,with top three ranking results being SXBM plus BWT>JPSXG plus BWT>FFEJS plus BWT.Conclusions:Our NMA demonstrated that seven oral CPMs used as adjuvant treatment of CRA had a definite clinical effect.JPSXG not only increases the levels of HGB and RBC to enhance the clinical effect but also improves patients'quality of life.More accurate conclusions need to be verified by more high-quality RCTs.

Quality assessment of the Topo-Iberia CGPS stations and data quality's effects on postfit ionosphere-free phase residuals

This paper analyzed GPS data from the Topo-Iberia network spanning almost 12 years(2008-2020).The data quality information for all 26 Topo-Iberia stations is provided for the first time,complementing the Spanish Geological Survey’s storage work.Data analyses based on quality indicators obtained using TEQC have been carried out.The guidelines and data quality information from the IGS stations have been considered as the quality references,with the stations ALJI,EPCU,and TIOU standing out as the worst stations,while on the contrary,FUEN,PALM,PILA,and TRIA meet the quality requirements to become an IGS station.The relationship between the GPS data quality and their GAMIT-and Gipsy X-derived postfit ionosphere-free phase residuals has also been investigated,and the results reveal an inversely proportional relationship.It has been found that the stations showing an increase in elevation of the horizon line,also show an increase in cycle slips and multipath,are among the poorest quality stations,and among those with the highest postfit RMS of phase residuals.Moreover,the evolution of the vegetation around the antenna should be considered as it could cause a progressive loss of quality,which is not complying with the IGS standards.The quality assessment shows that the Topo-Iberia stations are appropriate for geodetic purposes,but permanent monitoring would be necessary to avoid the least possible loss of data and quality.In addition,a method to characterize the GNSS data quality is proposed.

Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks

Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.

Exploring the mechanism of active ingredients of Smilacis Glabrae Rhizoma in treating migraine headache through network pharmacology and animal experiments

Background:To explore the potential mechanism of action of the active ingredients of Smilacis Glabrae Rhizoma(SGR)in the treatment of migraine using network pharmacology and in vivo experiments.Methods:Through the search of Traditional Chinese Medicines Systems Pharmacology Database and Analysis Platform,Genecards,Drugbank and other databases,we obtained active ingredients,targets of SGR and related disease targets of migraine,and took the intersection for protein-protein interactions analysis.After constructing the network diagram,network topology analysis was performed to derive the core targets and key active ingredients,and Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.Finally,molecular docking was performed and validated by in vivo experiments.In vivo experiments,18 male BALB/c mice were selected,and the SGR group was fed with SGR drinking tablet concentrate,and nitroglycerin injection was used to construct a mouse model of migraine.Enzyme-linked immunosorbent assay test was used to detect the levels of TNF-α,IL-1β,IL-6,and AKT1 in plasma.Results:The results showed that the core targets of SGR for the treatment of migraine were TNF-α,IL-1β,IL-6,and AKT1.These core targets and key active ingredients had better binding ability.Compared with the blank group,the number of head scratching in the model group increased.Compared with the model group,there was a significant reduction of the number of head scratching in the SGR group.In comparison with the blank group,the protein level in the plasma in the model group was markedly higher.Compared with the model group,the protein level in the SGR group was significantly lower.Conclusion:SGR has the characteristics of improving migraine based on multi-targets,multi-components and multi-pathways,and the mechanism of action may be related to the inhibition of the release of inflammatory factors,neuron protection,and interference with apoptosis and other processes.

Action Mechanism of Components from Gardenia jasminoides on Eye Skin Based on Network Pharmacology

[Objectives]To explore the pharmacological effects of Gardenia jasminoides and its potential benefits on eye skin.[Methods]TCMSP and SymMap databases were used to screen the active components and corresponding targets of G.jasminoides.Human eye skin-related targets were screened,and the active component-target network and protein-protein interaction(PPI)network were established.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were performed.[Results]Twenty-six active compounds were screened out from G.jasminoides,and 277 targets were obtained.From the Gencards database,26652 disease targets were retrieved and 205 related gene targets were screened.The active component-action target network of G.jasminoides constructed by Cytoscape software revealed the potential of G.jasminoides to play a role in multiple biological pathways.In addition,PPI-network analysis,GO function analysis and KEGG pathway enrichment analysis revealed that the active components of G.jasminoides mainly regulate the biological processes such as inflammatory response,oxidative stress and apoptosis,involving MAPK,NF-κB and other important signaling pathways.[Conclusions]This study provides a theoretical basis for the eye skin protection of G.jasminoides and an important clue for future drug development.

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.

Effect of Chromium on CCT Diagrams of Novel Air-Cooled Bainite Steels Analyzed by Neural Network

The quantitative effects of chromium content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels were analyzed using artificial neural network models. The results showed that the chromium may retard the high and medium-temperature martensite transformation.

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.

Effects of Reduced Frequency on Network Configuration and Synchronization Transition

Synchronization of networked phase oscillators depends essentially on the correlation between the topological structure of the graph and the dynamical property of the elements. We propose the concept of ＇reduced frequency＇, a measure which can quantify natural frequencies of each pair of oscillators. Then we introduce an evolving network whose linking rules are controlled by its own dynamical property. The simulation results indicate that when the linking probability positively correlates with the reduced frequency, the network undergoes a first-order phase transition. Meanwhile, we discuss the circumstance under which an explosive synchronization can be ignited. The numerical results show that the peculiar butterfly shape correlation between frequencies and degrees of the nodes contributes to an explosive synchronization transition.

Coseismic effects recorded by Fujian subsurface fluid network and its meaning to earthquake prediction

Based on analyses of the spatio-temporal evolutionary characteristics of teleseismic response recorded by Fujian subsurface fluid network and in combination with earthquakes happened in Fujian province during the same period, this paper points out that the step-like rising of water level after distant earthquakes may include some regional stress field information, and the area where water level step-like rises could be the position that the stress concentrated on and where the future earthquakes would occur. If combined with other impending precursors, the location of the events may be predicted to a certain degree.

A Quantitative and Network Approach to Alignment Effects in L2 Continuation Tasks

The present study employed a quantitative and network approach to detect alignment effects in second language(L2) continuation tasks designed on the xu-argument(Wang, 2016). The materials used in this study were 6 sub-corpora consisting of two selected input stories and two groups of L2 written production based on two continuation tasks. During continuation, the participants were required to continue in English a story with its ending removed, with one group reading and continuing the Chinese version and the other group the English version, and then switching their roles in the two tasks. Results show that the alignment effect differs across the two versions of continuation. Specifically, compared with the Chinese-version continuation, L2 learners produced more use of unigrams and bigrams similar to the input story in terms of lexical items, frequency and ranking correlations in the English-version task;on the other hand, the English-version continuation can facilitate generating linguistic networks that are much closer to the native English networks. Moreover, this research corroborates that written production in L2 continuation tasks can be influenced by input content.